diff --git a/.github/workflows/gha_workflow_llama_stack_tests.yml b/.github/workflows/gha_workflow_llama_stack_tests.yml new file mode 100644 index 000000000..89e5edf71 --- /dev/null +++ b/.github/workflows/gha_workflow_llama_stack_tests.yml @@ -0,0 +1,355 @@ +name: "Run Llama-stack Tests" + +on: + #### Temporarily disable PR runs until tests run as intended within mainline. + #TODO Add this back. + #pull_request_target: + # types: ["opened"] + # branches: + # - 'main' + # paths: + # - 'llama_stack/**/*.py' + # - 'tests/**/*.py' + + workflow_dispatch: + inputs: + runner: + description: 'GHA Runner Scale Set label to run workflow on.' + required: true + default: "llama-stack-gha-runner-gpu" + + checkout_reference: + description: "The branch, tag, or SHA to checkout" + required: true + default: "main" + + debug: + description: 'Run debugging steps?' + required: false + default: "true" + + sleep_time: + description: '[DEBUG] sleep time for debugging' + required: true + default: "0" + + provider_id: + description: 'ID of your provider' + required: true + default: "meta_reference" + + model_id: + description: 'Shorthand name for target model ID (llama_3b or llama_8b)' + required: true + default: "llama_3b" + + model_override_3b: + description: 'Specify shorthand model for ' + required: false + default: "Llama3.2-3B-Instruct" + + model_override_8b: + description: 'Specify shorthand model for ' + required: false + default: "Llama3.1-8B-Instruct" + +env: + # ID used for each test's provider config + PROVIDER_ID: "${{ inputs.provider_id || 'meta_reference' }}" + + # Path to model checkpoints within EFS volume + MODEL_CHECKPOINT_DIR: "/data/llama" + + # Path to directory to run tests from + TESTS_PATH: "${{ github.workspace }}/llama_stack/providers/tests" + + # Keep track of a list of model IDs that are valid to use within pytest fixture marks + AVAILABLE_MODEL_IDs: "llama_3b llama_8b" + + # Shorthand name for model ID, used in pytest fixture marks + MODEL_ID: "${{ inputs.model_id || 'llama_3b' }}" + + # Override the `llama_3b` / `llama_8b' models, else use the default. + LLAMA_3B_OVERRIDE: "${{ inputs.model_override_3b || 'Llama3.2-3B-Instruct' }}" + LLAMA_8B_OVERRIDE: "${{ inputs.model_override_8b || 'Llama3.1-8B-Instruct' }}" + + # Defines which directories in TESTS_PATH to exclude from the test loop + EXCLUDED_DIRS: "__pycache__" + + # Defines the output xml reports generated after a test is run + REPORTS_GEN: "" + +jobs: + execute_workflow: + name: Execute workload on Self-Hosted GPU k8s runner + permissions: + pull-requests: write + defaults: + run: + shell: bash + runs-on: ${{ inputs.runner != '' && inputs.runner || 'llama-stack-gha-runner-gpu' }} + if: always() + steps: + + ############################## + #### INITIAL DEBUG CHECKS #### + ############################## + - name: "[DEBUG] Check content of the EFS mount" + id: debug_efs_volume + continue-on-error: true + if: inputs.debug == 'true' + run: | + echo "========= Content of the EFS mount =============" + ls -la ${{ env.MODEL_CHECKPOINT_DIR }} + + - name: "[DEBUG] Get runner container OS information" + id: debug_os_info + if: ${{ inputs.debug == 'true' }} + run: | + cat /etc/os-release + + - name: "[DEBUG] Print environment variables" + id: debug_env_vars + if: ${{ inputs.debug == 'true' }} + run: | + echo "PROVIDER_ID = ${PROVIDER_ID}" + echo "MODEL_CHECKPOINT_DIR = ${MODEL_CHECKPOINT_DIR}" + echo "AVAILABLE_MODEL_IDs = ${AVAILABLE_MODEL_IDs}" + echo "MODEL_ID = ${MODEL_ID}" + echo "LLAMA_3B_OVERRIDE = ${LLAMA_3B_OVERRIDE}" + echo "LLAMA_8B_OVERRIDE = ${LLAMA_8B_OVERRIDE}" + echo "EXCLUDED_DIRS = ${EXCLUDED_DIRS}" + echo "REPORTS_GEN = ${REPORTS_GEN}" + + ############################ + #### MODEL INPUT CHECKS #### + ############################ + + - name: "Check if env.model_id is valid" + id: check_model_id + run: | + if [[ " ${AVAILABLE_MODEL_IDs[@]} " =~ " ${MODEL_ID} " ]]; then + echo "Model ID '${MODEL_ID}' is valid." + else + echo "Model ID '${MODEL_ID}' is invalid. Terminating workflow." + exit 1 + fi + + ####################### + #### CODE CHECKOUT #### + ####################### + - name: "Checkout 'meta-llama/llama-stack' repository" + id: checkout_repo + uses: actions/checkout@v4 + with: + ref: ${{ inputs.branch }} + + - name: "[DEBUG] Content of the repository after checkout" + id: debug_content_after_checkout + if: ${{ inputs.debug == 'true' }} + run: | + ls -la ${GITHUB_WORKSPACE} + + ########################################################## + #### OPTIONAL SLEEP DEBUG #### + # # + # Use to "exec" into the test k8s POD and run tests # + # manually to identify what dependencies are being used. # + # # + ########################################################## + - name: "[DEBUG] sleep" + id: debug_sleep + if: ${{ inputs.debug == 'true' && inputs.sleep_time != '' }} + run: | + sleep ${{ inputs.sleep_time }} + + ############################ + #### UPDATE SYSTEM PATH #### + ############################ + - name: "Update path: execute" + id: path_update_exec + run: | + # .local/bin is needed for certain libraries installed below to be recognized + # when calling their executable to install sub-dependencies + mkdir -p ${HOME}/.local/bin + echo "${HOME}/.local/bin" >> "$GITHUB_PATH" + + ##################################### + #### UPDATE CHECKPOINT DIRECTORY #### + ##################################### + - name: "Update checkpoint directory" + id: checkpoint_update + run: | + echo "Checkpoint directory: ${MODEL_CHECKPOINT_DIR}/$LLAMA_3B_OVERRIDE" + if [ "${MODEL_ID}" = "llama_3b" ] && [ -d "${MODEL_CHECKPOINT_DIR}/${LLAMA_3B_OVERRIDE}" ]; then + echo "MODEL_CHECKPOINT_DIR=${MODEL_CHECKPOINT_DIR}/${LLAMA_3B_OVERRIDE}" >> "$GITHUB_ENV" + elif [ "${MODEL_ID}" = "llama_8b" ] && [ -d "${MODEL_CHECKPOINT_DIR}/${LLAMA_8B_OVERRIDE}" ]; then + echo "MODEL_CHECKPOINT_DIR=${MODEL_CHECKPOINT_DIR}/${LLAMA_8B_OVERRIDE}" >> "$GITHUB_ENV" + else + echo "MODEL_ID & LLAMA_*B_OVERRIDE are not a valid pairing. Terminating workflow." + exit 1 + fi + + - name: "[DEBUG] Checkpoint update check" + id: debug_checkpoint_update + if: ${{ inputs.debug == 'true' }} + run: | + echo "MODEL_CHECKPOINT_DIR (after update) = ${MODEL_CHECKPOINT_DIR}" + + ################################## + #### DEPENDENCY INSTALLATIONS #### + ################################## + - name: "Installing 'apt' required packages" + id: install_apt + run: | + echo "[STEP] Installing 'apt' required packages" + sudo apt update -y + sudo apt install -y python3 python3-pip npm wget + + - name: "Installing packages with 'curl'" + id: install_curl + run: | + curl -fsSL https://ollama.com/install.sh | sh + + - name: "Installing packages with 'wget'" + id: install_wget + run: | + wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh + chmod +x Miniconda3-latest-Linux-x86_64.sh + ./Miniconda3-latest-Linux-x86_64.sh -b install -c pytorch -c nvidia faiss-gpu=1.9.0 + # Add miniconda3 bin to system path + echo "${HOME}/miniconda3/bin" >> "$GITHUB_PATH" + + - name: "Installing packages with 'npm'" + id: install_npm_generic + run: | + sudo npm install -g junit-merge + + - name: "Installing pip dependencies" + id: install_pip_generic + run: | + echo "[STEP] Installing 'llama-stack' models" + pip install -U pip setuptools + pip install -r requirements.txt + pip install -e . + pip install -U \ + torch torchvision \ + pytest pytest_asyncio \ + fairscale lm-format-enforcer \ + zmq chardet pypdf \ + pandas sentence_transformers together \ + aiosqlite + - name: "Installing packages with conda" + id: install_conda_generic + run: | + conda install -q -c pytorch -c nvidia faiss-gpu=1.9.0 + + ############################################################# + #### TESTING TO BE DONE FOR BOTH PRS AND MANUAL DISPATCH #### + ############################################################# + - name: "Run Tests: Loop" + id: run_tests_loop + working-directory: "${{ github.workspace }}" + run: | + pattern="" + for dir in llama_stack/providers/tests/*; do + if [ -d "$dir" ]; then + dir_name=$(basename "$dir") + if [[ ! " $EXCLUDED_DIRS " =~ " $dir_name " ]]; then + for file in "$dir"/test_*.py; do + test_name=$(basename "$file") + new_file="result-${dir_name}-${test_name}.xml" + if torchrun $(which pytest) -s -v ${TESTS_PATH}/${dir_name}/${test_name} -m "${PROVIDER_ID} and ${MODEL_ID}" \ + --junitxml="${{ github.workspace }}/${new_file}"; then + echo "Ran test: ${test_name}" + else + echo "Did NOT run test: ${test_name}" + fi + pattern+="${new_file} " + done + fi + fi + done + echo "REPORTS_GEN=$pattern" >> "$GITHUB_ENV" + + - name: "Test Summary: Merge" + id: test_summary_merge + working-directory: "${{ github.workspace }}" + run: | + echo "Merging the following test result files: ${REPORTS_GEN}" + # Defaults to merging them into 'merged-test-results.xml' + junit-merge ${{ env.REPORTS_GEN }} + + ############################################ + #### AUTOMATIC TESTING ON PULL REQUESTS #### + ############################################ + + #### Run tests #### + + - name: "PR - Run Tests" + id: pr_run_tests + working-directory: "${{ github.workspace }}" + if: github.event_name == 'pull_request_target' + run: | + echo "[STEP] Running PyTest tests at 'GITHUB_WORKSPACE' path: ${GITHUB_WORKSPACE} | path: ${{ github.workspace }}" + # (Optional) Add more tests here. + + # Merge test results with 'merged-test-results.xml' from above. + # junit-merge merged-test-results.xml + + #### Create test summary #### + + - name: "PR - Test Summary" + id: pr_test_summary_create + if: github.event_name == 'pull_request_target' + uses: test-summary/action@v2 + with: + paths: "${{ github.workspace }}/merged-test-results.xml" + output: test-summary.md + + - name: "PR - Upload Test Summary" + id: pr_test_summary_upload + if: github.event_name == 'pull_request_target' + uses: actions/upload-artifact@v3 + with: + name: test-summary + path: test-summary.md + + #### Update PR request #### + + - name: "PR - Update comment" + id: pr_update_comment + if: github.event_name == 'pull_request_target' + uses: thollander/actions-comment-pull-request@v2 + with: + filePath: test-summary.md + + ######################## + #### MANUAL TESTING #### + ######################## + + #### Run tests #### + + - name: "Manual - Run Tests: Prep" + id: manual_run_tests + working-directory: "${{ github.workspace }}" + if: github.event_name == 'workflow_dispatch' + run: | + echo "[STEP] Running PyTest tests at 'GITHUB_WORKSPACE' path: ${{ github.workspace }}" + + #TODO Use this when collection errors are resolved + # pytest -s -v -m "${PROVIDER_ID} and ${MODEL_ID}" --junitxml="${{ github.workspace }}/merged-test-results.xml" + + # (Optional) Add more tests here. + + # Merge test results with 'merged-test-results.xml' from above. + # junit-merge merged-test-results.xml + + #### Create test summary #### + + - name: "Manual - Test Summary" + id: manual_test_summary + if: always() && github.event_name == 'workflow_dispatch' + uses: test-summary/action@v2 + with: + paths: "${{ github.workspace }}/merged-test-results.xml" diff --git a/README.md b/README.md index 82af89e50..147e2d379 100644 --- a/README.md +++ b/README.md @@ -80,6 +80,7 @@ Additionally, we have designed every element of the Stack such that APIs as well | **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | | :----: | :----: | :----: | :----: | :----: | :----: | :----: | | Meta Reference | Single Node | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | +| Cerebras | Hosted | | :heavy_check_mark: | | | | | Fireworks | Hosted | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | | | AWS Bedrock | Hosted | | :heavy_check_mark: | | :heavy_check_mark: | | | Together | Hosted | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | | @@ -94,12 +95,13 @@ Additionally, we have designed every element of the Stack such that APIs as well | **Distribution** | **Llama Stack Docker** | Start This Distribution | |:----------------: |:------------------------------------------: |:-----------------------: | -| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-gpu.html) | -| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) | -| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/ollama.html) | -| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/tgi.html) | -| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/together.html) | -| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/remote_hosted_distro/fireworks.html) | +| Meta Reference | [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) | +| Meta Reference Quantized | [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-quantized-gpu.html) | +| Cerebras | [llamastack/distribution-cerebras](https://hub.docker.com/repository/docker/llamastack/distribution-cerebras/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/self_hosted_distro/cerebras.html) | +| Ollama | [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/ollama.html) | +| TGI | [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/tgi.html) | +| Together | [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/together.html) | +| Fireworks | [llamastack/distribution-fireworks](https://hub.docker.com/repository/docker/llamastack/distribution-fireworks/general) | [Guide](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/fireworks.html) | ## Installation @@ -112,7 +114,8 @@ You have two ways to install this repository: ``` 2. **Install from source**: - If you prefer to install from the source code, follow these steps: + If you prefer to install from the source code, make sure you have [conda installed](https://docs.conda.io/projects/conda/en/stable). + Then, follow these steps: ```bash mkdir -p ~/local cd ~/local @@ -129,7 +132,7 @@ You have two ways to install this repository: Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details. -* [CLI reference](https://llama-stack.readthedocs.io/en/latest/cli_reference/index.html) +* [CLI reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html) * Guide using `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. * [Getting Started](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) * Quick guide to start a Llama Stack server. @@ -137,7 +140,7 @@ Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest * The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack). * A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples. * [Contributing](CONTRIBUTING.md) - * [Adding a new API Provider](https://llama-stack.readthedocs.io/en/latest/api_providers/new_api_provider.html) to walk-through how to add a new API provider. + * [Adding a new API Provider](https://llama-stack.readthedocs.io/en/latest/contributing/new_api_provider.html) to walk-through how to add a new API provider. ## Llama Stack Client SDKs diff --git a/distributions/cerebras/build.yaml b/distributions/cerebras/build.yaml new file mode 120000 index 000000000..bccbbcf60 --- /dev/null +++ b/distributions/cerebras/build.yaml @@ -0,0 +1 @@ +../../llama_stack/templates/cerebras/build.yaml \ No newline at end of file diff --git a/distributions/cerebras/compose.yaml b/distributions/cerebras/compose.yaml new file mode 100644 index 000000000..f2e9a6f42 --- /dev/null +++ b/distributions/cerebras/compose.yaml @@ -0,0 +1,16 @@ +services: + llamastack: + image: llamastack/distribution-cerebras + network_mode: "host" + volumes: + - ~/.llama:/root/.llama + - ./run.yaml:/root/llamastack-run-cerebras.yaml + ports: + - "5000:5000" + entrypoint: bash -c "python -m llama_stack.distribution.server.server --yaml_config /root/llamastack-run-cerebras.yaml" + deploy: + restart_policy: + condition: on-failure + delay: 3s + max_attempts: 5 + window: 60s diff --git a/distributions/cerebras/run.yaml b/distributions/cerebras/run.yaml new file mode 120000 index 000000000..9f9d20b4b --- /dev/null +++ b/distributions/cerebras/run.yaml @@ -0,0 +1 @@ +../../llama_stack/templates/cerebras/run.yaml \ No newline at end of file diff --git a/distributions/dependencies.json b/distributions/dependencies.json index 36426e862..a2393cdea 100644 --- a/distributions/dependencies.json +++ b/distributions/dependencies.json @@ -2,9 +2,11 @@ "hf-serverless": [ "aiohttp", "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -13,6 +15,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -29,9 +34,11 @@ ], "together": [ "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -39,6 +46,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -56,9 +66,11 @@ ], "vllm-gpu": [ "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -66,6 +78,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -94,6 +109,8 @@ "nltk", "numpy", "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -110,9 +127,11 @@ ], "fireworks": [ "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -121,6 +140,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -138,9 +160,11 @@ "tgi": [ "aiohttp", "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -149,6 +173,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -165,10 +192,12 @@ ], "bedrock": [ "aiosqlite", + "autoevals", "blobfile", "boto3", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -176,6 +205,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -193,9 +225,11 @@ "meta-reference-gpu": [ "accelerate", "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "fairscale", "faiss-cpu", "fastapi", @@ -205,6 +239,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -225,9 +262,11 @@ "meta-reference-quantized-gpu": [ "accelerate", "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "fairscale", "faiss-cpu", "fastapi", @@ -238,6 +277,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -256,12 +298,42 @@ "sentence-transformers --no-deps", "torch --index-url https://download.pytorch.org/whl/cpu" ], + "cerebras": [ + "aiosqlite", + "blobfile", + "cerebras_cloud_sdk", + "chardet", + "faiss-cpu", + "fastapi", + "fire", + "httpx", + "matplotlib", + "nltk", + "numpy", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", + "pandas", + "pillow", + "psycopg2-binary", + "pypdf", + "redis", + "scikit-learn", + "scipy", + "sentencepiece", + "tqdm", + "transformers", + "uvicorn", + "sentence-transformers --no-deps", + "torch --index-url https://download.pytorch.org/whl/cpu" + ], "ollama": [ "aiohttp", "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -270,6 +342,9 @@ "nltk", "numpy", "ollama", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", @@ -287,9 +362,11 @@ "hf-endpoint": [ "aiohttp", "aiosqlite", + "autoevals", "blobfile", "chardet", "chromadb-client", + "datasets", "faiss-cpu", "fastapi", "fire", @@ -298,6 +375,9 @@ "matplotlib", "nltk", "numpy", + "openai", + "opentelemetry-exporter-otlp-proto-http", + "opentelemetry-sdk", "pandas", "pillow", "psycopg2-binary", diff --git a/docs/requirements.txt b/docs/requirements.txt index c182f41c4..d455cf6b5 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -9,3 +9,4 @@ sphinx-tabs sphinx-design sphinxcontrib-openapi sphinxcontrib-redoc +sphinxcontrib-mermaid diff --git a/docs/resources/llama-stack-spec.html b/docs/resources/llama-stack-spec.html index 090253804..14e311cfc 100644 --- a/docs/resources/llama-stack-spec.html +++ b/docs/resources/llama-stack-spec.html @@ -21,7 +21,7 @@ "info": { "title": "Llama Stack Specification", "version": "alpha", - "description": "This is the specification of the Llama Stack that provides\n a set of endpoints and their corresponding interfaces that are tailored to\n best leverage Llama Models. Generated at 2024-11-22 17:23:55.034164" + "description": "This is the specification of the Llama Stack that provides\n a set of endpoints and their corresponding interfaces that are tailored to\n best leverage Llama Models." }, "servers": [ { @@ -29,6 +29,39 @@ } ], "paths": { + "/alpha/datasetio/append-rows": { + "post": { + "responses": { + "200": { + "description": "OK" + } + }, + "tags": [ + "DatasetIO" + ], + "parameters": [ + { + "name": "X-LlamaStack-ProviderData", + "in": "header", + "description": "JSON-encoded provider data which will be made available to the adapter servicing the API", + "required": false, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/AppendRowsRequest" + } + } + }, + "required": true + } + } + }, "/alpha/batch-inference/chat-completion": { "post": { "responses": { @@ -1026,15 +1059,15 @@ ] } }, - "/alpha/telemetry/get-trace": { - "get": { + "/alpha/telemetry/get-span-tree": { + "post": { "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { - "$ref": "#/components/schemas/Trace" + "$ref": "#/components/schemas/SpanWithChildren" } } } @@ -1045,13 +1078,21 @@ ], "parameters": [ { - "name": "trace_id", + "name": "span_id", "in": "query", "required": true, "schema": { "type": "string" } }, + { + "name": "max_depth", + "in": "query", + "required": false, + "schema": { + "type": "integer" + } + }, { "name": "X-LlamaStack-ProviderData", "in": "header", @@ -1061,7 +1102,17 @@ "type": "string" } } - ] + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/GetSpanTreeRequest" + } + } + }, + "required": true + } } }, "/alpha/post-training/job/artifacts": { @@ -1778,6 +1829,86 @@ } } }, + "/alpha/telemetry/query-spans": { + "post": { + "responses": { + "200": { + "description": "OK", + "content": { + "application/jsonl": { + "schema": { + "$ref": "#/components/schemas/Span" + } + } + } + } + }, + "tags": [ + "Telemetry" + ], + "parameters": [ + { + "name": "X-LlamaStack-ProviderData", + "in": "header", + "description": "JSON-encoded provider data which will be made available to the adapter servicing the API", + "required": false, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QuerySpansRequest" + } + } + }, + "required": true + } + } + }, + "/alpha/telemetry/query-traces": { + "post": { + "responses": { + "200": { + "description": "OK", + "content": { + "application/jsonl": { + "schema": { + "$ref": "#/components/schemas/Trace" + } + } + } + } + }, + "tags": [ + "Telemetry" + ], + "parameters": [ + { + "name": "X-LlamaStack-ProviderData", + "in": "header", + "description": "JSON-encoded provider data which will be made available to the adapter servicing the API", + "required": false, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/QueryTracesRequest" + } + } + }, + "required": true + } + } + }, "/alpha/datasets/register": { "post": { "responses": { @@ -2066,6 +2197,39 @@ } } }, + "/alpha/telemetry/save-spans-to-dataset": { + "post": { + "responses": { + "200": { + "description": "OK" + } + }, + "tags": [ + "Telemetry" + ], + "parameters": [ + { + "name": "X-LlamaStack-ProviderData", + "in": "header", + "description": "JSON-encoded provider data which will be made available to the adapter servicing the API", + "required": false, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/SaveSpansToDatasetRequest" + } + } + }, + "required": true + } + } + }, "/alpha/scoring/score": { "post": { "responses": { @@ -2226,6 +2390,39 @@ } } }, + "/alpha/datasets/unregister": { + "post": { + "responses": { + "200": { + "description": "OK" + } + }, + "tags": [ + "Datasets" + ], + "parameters": [ + { + "name": "X-LlamaStack-ProviderData", + "in": "header", + "description": "JSON-encoded provider data which will be made available to the adapter servicing the API", + "required": false, + "schema": { + "type": "string" + } + } + ], + "requestBody": { + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/UnregisterDatasetRequest" + } + } + }, + "required": true + } + } + }, "/alpha/memory-banks/unregister": { "post": { "responses": { @@ -2296,6 +2493,47 @@ "jsonSchemaDialect": "https://json-schema.org/draft/2020-12/schema", "components": { "schemas": { + "AppendRowsRequest": { + "type": "object", + "properties": { + "dataset_id": { + "type": "string" + }, + "rows": { + "type": "array", + "items": { + "type": "object", + "additionalProperties": { + "oneOf": [ + { + "type": "null" + }, + { + "type": "boolean" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "array" + }, + { + "type": "object" + } + ] + } + } + } + }, + "additionalProperties": false, + "required": [ + "dataset_id", + "rows" + ] + }, "BuiltinTool": { "type": "string", "enum": [ @@ -4130,14 +4368,11 @@ "step_id": { "type": "string" }, - "model_response_text_delta": { + "text_delta": { "type": "string" }, "tool_call_delta": { "$ref": "#/components/schemas/ToolCallDelta" - }, - "tool_response_text_delta": { - "type": "string" } }, "additionalProperties": false, @@ -5845,13 +6080,38 @@ ], "title": "A safety shield resource that can be used to check content" }, - "Trace": { + "GetSpanTreeRequest": { "type": "object", "properties": { + "attributes_to_return": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "additionalProperties": false + }, + "SpanStatus": { + "type": "string", + "enum": [ + "ok", + "error" + ] + }, + "SpanWithChildren": { + "type": "object", + "properties": { + "span_id": { + "type": "string" + }, "trace_id": { "type": "string" }, - "root_span_id": { + "parent_span_id": { + "type": "string" + }, + "name": { "type": "string" }, "start_time": { @@ -5861,13 +6121,49 @@ "end_time": { "type": "string", "format": "date-time" + }, + "attributes": { + "type": "object", + "additionalProperties": { + "oneOf": [ + { + "type": "null" + }, + { + "type": "boolean" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "array" + }, + { + "type": "object" + } + ] + } + }, + "children": { + "type": "array", + "items": { + "$ref": "#/components/schemas/SpanWithChildren" + } + }, + "status": { + "$ref": "#/components/schemas/SpanStatus" } }, "additionalProperties": false, "required": [ + "span_id", "trace_id", - "root_span_id", - "start_time" + "name", + "start_time", + "children" ] }, "Checkpoint": { @@ -6280,13 +6576,6 @@ "name" ] }, - "SpanStatus": { - "type": "string", - "enum": [ - "ok", - "error" - ] - }, "StructuredLogEvent": { "type": "object", "properties": { @@ -6425,11 +6714,15 @@ "$ref": "#/components/schemas/StructuredLogEvent" } ] + }, + "ttl_seconds": { + "type": "integer" } }, "additionalProperties": false, "required": [ - "event" + "event", + "ttl_seconds" ] }, "DPOAlignmentConfig": { @@ -6739,6 +7032,185 @@ "scores" ] }, + "QueryCondition": { + "type": "object", + "properties": { + "key": { + "type": "string" + }, + "op": { + "$ref": "#/components/schemas/QueryConditionOp" + }, + "value": { + "oneOf": [ + { + "type": "null" + }, + { + "type": "boolean" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "array" + }, + { + "type": "object" + } + ] + } + }, + "additionalProperties": false, + "required": [ + "key", + "op", + "value" + ] + }, + "QueryConditionOp": { + "type": "string", + "enum": [ + "eq", + "ne", + "gt", + "lt" + ] + }, + "QuerySpansRequest": { + "type": "object", + "properties": { + "attribute_filters": { + "type": "array", + "items": { + "$ref": "#/components/schemas/QueryCondition" + } + }, + "attributes_to_return": { + "type": "array", + "items": { + "type": "string" + } + }, + "max_depth": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "attribute_filters", + "attributes_to_return" + ] + }, + "Span": { + "type": "object", + "properties": { + "span_id": { + "type": "string" + }, + "trace_id": { + "type": "string" + }, + "parent_span_id": { + "type": "string" + }, + "name": { + "type": "string" + }, + "start_time": { + "type": "string", + "format": "date-time" + }, + "end_time": { + "type": "string", + "format": "date-time" + }, + "attributes": { + "type": "object", + "additionalProperties": { + "oneOf": [ + { + "type": "null" + }, + { + "type": "boolean" + }, + { + "type": "number" + }, + { + "type": "string" + }, + { + "type": "array" + }, + { + "type": "object" + } + ] + } + } + }, + "additionalProperties": false, + "required": [ + "span_id", + "trace_id", + "name", + "start_time" + ] + }, + "QueryTracesRequest": { + "type": "object", + "properties": { + "attribute_filters": { + "type": "array", + "items": { + "$ref": "#/components/schemas/QueryCondition" + } + }, + "limit": { + "type": "integer" + }, + "offset": { + "type": "integer" + }, + "order_by": { + "type": "array", + "items": { + "type": "string" + } + } + }, + "additionalProperties": false + }, + "Trace": { + "type": "object", + "properties": { + "trace_id": { + "type": "string" + }, + "root_span_id": { + "type": "string" + }, + "start_time": { + "type": "string", + "format": "date-time" + }, + "end_time": { + "type": "string", + "format": "date-time" + } + }, + "additionalProperties": false, + "required": [ + "trace_id", + "root_span_id", + "start_time" + ] + }, "RegisterDatasetRequest": { "type": "object", "properties": { @@ -7455,6 +7927,35 @@ }, "additionalProperties": false }, + "SaveSpansToDatasetRequest": { + "type": "object", + "properties": { + "attribute_filters": { + "type": "array", + "items": { + "$ref": "#/components/schemas/QueryCondition" + } + }, + "attributes_to_save": { + "type": "array", + "items": { + "type": "string" + } + }, + "dataset_id": { + "type": "string" + }, + "max_depth": { + "type": "integer" + } + }, + "additionalProperties": false, + "required": [ + "attribute_filters", + "attributes_to_save", + "dataset_id" + ] + }, "ScoreRequest": { "type": "object", "properties": { @@ -7894,6 +8395,18 @@ ], "title": "Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold." }, + "UnregisterDatasetRequest": { + "type": "object", + "properties": { + "dataset_id": { + "type": "string" + } + }, + "additionalProperties": false, + "required": [ + "dataset_id" + ] + }, "UnregisterMemoryBankRequest": { "type": "object", "properties": { @@ -7982,6 +8495,10 @@ "name": "AppEvalTaskConfig", "description": "" }, + { + "name": "AppendRowsRequest", + "description": "" + }, { "name": "Attachment", "description": "" @@ -8137,6 +8654,10 @@ "name": "GetAgentsSessionRequest", "description": "" }, + { + "name": "GetSpanTreeRequest", + "description": "" + }, { "name": "GraphMemoryBank", "description": "" @@ -8291,6 +8812,14 @@ "name": "QLoraFinetuningConfig", "description": "" }, + { + "name": "QueryCondition", + "description": "" + }, + { + "name": "QueryConditionOp", + "description": "" + }, { "name": "QueryDocumentsRequest", "description": "" @@ -8299,6 +8828,14 @@ "name": "QueryDocumentsResponse", "description": "" }, + { + "name": "QuerySpansRequest", + "description": "" + }, + { + "name": "QueryTracesRequest", + "description": "" + }, { "name": "RLHFAlgorithm", "description": "" @@ -8370,6 +8907,10 @@ "name": "SamplingStrategy", "description": "" }, + { + "name": "SaveSpansToDatasetRequest", + "description": "" + }, { "name": "ScoreBatchRequest", "description": "" @@ -8419,6 +8960,10 @@ { "name": "Shields" }, + { + "name": "Span", + "description": "" + }, { "name": "SpanEndPayload", "description": "" @@ -8431,6 +8976,10 @@ "name": "SpanStatus", "description": "" }, + { + "name": "SpanWithChildren", + "description": "" + }, { "name": "StopReason", "description": "" @@ -8521,6 +9070,10 @@ "name": "URL", "description": "" }, + { + "name": "UnregisterDatasetRequest", + "description": "" + }, { "name": "UnregisterMemoryBankRequest", "description": "" @@ -8594,6 +9147,7 @@ "AgentTurnResponseTurnCompletePayload", "AgentTurnResponseTurnStartPayload", "AppEvalTaskConfig", + "AppendRowsRequest", "Attachment", "BatchChatCompletionRequest", "BatchChatCompletionResponse", @@ -8629,6 +9183,7 @@ "FinetuningAlgorithm", "FunctionCallToolDefinition", "GetAgentsSessionRequest", + "GetSpanTreeRequest", "GraphMemoryBank", "GraphMemoryBankParams", "HealthInfo", @@ -8663,8 +9218,12 @@ "PreferenceOptimizeRequest", "ProviderInfo", "QLoraFinetuningConfig", + "QueryCondition", + "QueryConditionOp", "QueryDocumentsRequest", "QueryDocumentsResponse", + "QuerySpansRequest", + "QueryTracesRequest", "RLHFAlgorithm", "RegexParserScoringFnParams", "RegisterDatasetRequest", @@ -8682,6 +9241,7 @@ "SafetyViolation", "SamplingParams", "SamplingStrategy", + "SaveSpansToDatasetRequest", "ScoreBatchRequest", "ScoreBatchResponse", "ScoreRequest", @@ -8692,9 +9252,11 @@ "Session", "Shield", "ShieldCallStep", + "Span", "SpanEndPayload", "SpanStartPayload", "SpanStatus", + "SpanWithChildren", "StopReason", "StructuredLogEvent", "SupervisedFineTuneRequest", @@ -8716,6 +9278,7 @@ "TrainingConfig", "Turn", "URL", + "UnregisterDatasetRequest", "UnregisterMemoryBankRequest", "UnregisterModelRequest", "UnstructuredLogEvent", diff --git a/docs/resources/llama-stack-spec.yaml b/docs/resources/llama-stack-spec.yaml index 8ffd9fdef..86fcae23d 100644 --- a/docs/resources/llama-stack-spec.yaml +++ b/docs/resources/llama-stack-spec.yaml @@ -132,8 +132,6 @@ components: const: step_progress default: step_progress type: string - model_response_text_delta: - type: string step_id: type: string step_type: @@ -143,10 +141,10 @@ components: - shield_call - memory_retrieval type: string + text_delta: + type: string tool_call_delta: $ref: '#/components/schemas/ToolCallDelta' - tool_response_text_delta: - type: string required: - event_type - step_type @@ -242,6 +240,27 @@ components: - eval_candidate - scoring_params type: object + AppendRowsRequest: + additionalProperties: false + properties: + dataset_id: + type: string + rows: + items: + additionalProperties: + oneOf: + - type: 'null' + - type: boolean + - type: number + - type: string + - type: array + - type: object + type: object + type: array + required: + - dataset_id + - rows + type: object Attachment: additionalProperties: false properties: @@ -1059,6 +1078,14 @@ components: type: string type: array type: object + GetSpanTreeRequest: + additionalProperties: false + properties: + attributes_to_return: + items: + type: string + type: array + type: object GraphMemoryBank: additionalProperties: false properties: @@ -1277,8 +1304,11 @@ components: - $ref: '#/components/schemas/UnstructuredLogEvent' - $ref: '#/components/schemas/MetricEvent' - $ref: '#/components/schemas/StructuredLogEvent' + ttl_seconds: + type: integer required: - event + - ttl_seconds type: object LogSeverity: enum: @@ -1825,6 +1855,33 @@ components: - rank - alpha type: object + QueryCondition: + additionalProperties: false + properties: + key: + type: string + op: + $ref: '#/components/schemas/QueryConditionOp' + value: + oneOf: + - type: 'null' + - type: boolean + - type: number + - type: string + - type: array + - type: object + required: + - key + - op + - value + type: object + QueryConditionOp: + enum: + - eq + - ne + - gt + - lt + type: string QueryDocumentsRequest: additionalProperties: false properties: @@ -1887,6 +1944,39 @@ components: - chunks - scores type: object + QuerySpansRequest: + additionalProperties: false + properties: + attribute_filters: + items: + $ref: '#/components/schemas/QueryCondition' + type: array + attributes_to_return: + items: + type: string + type: array + max_depth: + type: integer + required: + - attribute_filters + - attributes_to_return + type: object + QueryTracesRequest: + additionalProperties: false + properties: + attribute_filters: + items: + $ref: '#/components/schemas/QueryCondition' + type: array + limit: + type: integer + offset: + type: integer + order_by: + items: + type: string + type: array + type: object RLHFAlgorithm: enum: - dpo @@ -2392,6 +2482,26 @@ components: - top_p - top_k type: string + SaveSpansToDatasetRequest: + additionalProperties: false + properties: + attribute_filters: + items: + $ref: '#/components/schemas/QueryCondition' + type: array + attributes_to_save: + items: + type: string + type: array + dataset_id: + type: string + max_depth: + type: integer + required: + - attribute_filters + - attributes_to_save + - dataset_id + type: object ScoreBatchRequest: additionalProperties: false properties: @@ -2731,6 +2841,39 @@ components: - step_id - step_type type: object + Span: + additionalProperties: false + properties: + attributes: + additionalProperties: + oneOf: + - type: 'null' + - type: boolean + - type: number + - type: string + - type: array + - type: object + type: object + end_time: + format: date-time + type: string + name: + type: string + parent_span_id: + type: string + span_id: + type: string + start_time: + format: date-time + type: string + trace_id: + type: string + required: + - span_id + - trace_id + - name + - start_time + type: object SpanEndPayload: additionalProperties: false properties: @@ -2764,6 +2907,46 @@ components: - ok - error type: string + SpanWithChildren: + additionalProperties: false + properties: + attributes: + additionalProperties: + oneOf: + - type: 'null' + - type: boolean + - type: number + - type: string + - type: array + - type: object + type: object + children: + items: + $ref: '#/components/schemas/SpanWithChildren' + type: array + end_time: + format: date-time + type: string + name: + type: string + parent_span_id: + type: string + span_id: + type: string + start_time: + format: date-time + type: string + status: + $ref: '#/components/schemas/SpanStatus' + trace_id: + type: string + required: + - span_id + - trace_id + - name + - start_time + - children + type: object StopReason: enum: - end_of_turn @@ -3237,6 +3420,14 @@ components: format: uri pattern: ^(https?://|file://|data:) type: string + UnregisterDatasetRequest: + additionalProperties: false + properties: + dataset_id: + type: string + required: + - dataset_id + type: object UnregisterMemoryBankRequest: additionalProperties: false properties: @@ -3400,7 +3591,7 @@ components: info: description: "This is the specification of the Llama Stack that provides\n \ \ a set of endpoints and their corresponding interfaces that are tailored\ - \ to\n best leverage Llama Models. Generated at 2024-11-22 17:23:55.034164" + \ to\n best leverage Llama Models." title: Llama Stack Specification version: alpha jsonSchemaDialect: https://json-schema.org/draft/2020-12/schema @@ -3684,6 +3875,27 @@ paths: description: OK tags: - BatchInference (Coming Soon) + /alpha/datasetio/append-rows: + post: + parameters: + - description: JSON-encoded provider data which will be made available to the + adapter servicing the API + in: header + name: X-LlamaStack-ProviderData + required: false + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/AppendRowsRequest' + required: true + responses: + '200': + description: OK + tags: + - DatasetIO /alpha/datasetio/get-rows-paginated: get: parameters: @@ -3789,6 +4001,27 @@ paths: description: OK tags: - Datasets + /alpha/datasets/unregister: + post: + parameters: + - description: JSON-encoded provider data which will be made available to the + adapter servicing the API + in: header + name: X-LlamaStack-ProviderData + required: false + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/UnregisterDatasetRequest' + required: true + responses: + '200': + description: OK + tags: + - Datasets /alpha/eval-tasks/get: get: parameters: @@ -4756,14 +4989,19 @@ paths: description: OK tags: - SyntheticDataGeneration (Coming Soon) - /alpha/telemetry/get-trace: - get: + /alpha/telemetry/get-span-tree: + post: parameters: - in: query - name: trace_id + name: span_id required: true schema: type: string + - in: query + name: max_depth + required: false + schema: + type: integer - description: JSON-encoded provider data which will be made available to the adapter servicing the API in: header @@ -4771,12 +5009,18 @@ paths: required: false schema: type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/GetSpanTreeRequest' + required: true responses: '200': content: application/json: schema: - $ref: '#/components/schemas/Trace' + $ref: '#/components/schemas/SpanWithChildren' description: OK tags: - Telemetry @@ -4801,6 +5045,77 @@ paths: description: OK tags: - Telemetry + /alpha/telemetry/query-spans: + post: + parameters: + - description: JSON-encoded provider data which will be made available to the + adapter servicing the API + in: header + name: X-LlamaStack-ProviderData + required: false + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/QuerySpansRequest' + required: true + responses: + '200': + content: + application/jsonl: + schema: + $ref: '#/components/schemas/Span' + description: OK + tags: + - Telemetry + /alpha/telemetry/query-traces: + post: + parameters: + - description: JSON-encoded provider data which will be made available to the + adapter servicing the API + in: header + name: X-LlamaStack-ProviderData + required: false + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/QueryTracesRequest' + required: true + responses: + '200': + content: + application/jsonl: + schema: + $ref: '#/components/schemas/Trace' + description: OK + tags: + - Telemetry + /alpha/telemetry/save-spans-to-dataset: + post: + parameters: + - description: JSON-encoded provider data which will be made available to the + adapter servicing the API + in: header + name: X-LlamaStack-ProviderData + required: false + schema: + type: string + requestBody: + content: + application/json: + schema: + $ref: '#/components/schemas/SaveSpansToDatasetRequest' + required: true + responses: + '200': + description: OK + tags: + - Telemetry security: - Default: [] servers: @@ -4849,6 +5164,9 @@ tags: - description: name: AppEvalTaskConfig +- description: + name: AppendRowsRequest - description: name: Attachment - description: name: GetAgentsSessionRequest +- description: + name: GetSpanTreeRequest - description: name: GraphMemoryBank @@ -5076,12 +5397,23 @@ tags: - description: name: QLoraFinetuningConfig +- description: + name: QueryCondition +- description: + name: QueryConditionOp - description: name: QueryDocumentsRequest - description: name: QueryDocumentsResponse +- description: + name: QuerySpansRequest +- description: + name: QueryTracesRequest - description: name: RLHFAlgorithm - description: name: SamplingStrategy +- description: + name: SaveSpansToDatasetRequest - description: name: ScoreBatchRequest @@ -5161,6 +5496,8 @@ tags: - description: name: ShieldCallStep - name: Shields +- description: + name: Span - description: name: SpanEndPayload - description: name: SpanStatus +- description: + name: SpanWithChildren - description: name: StopReason - description: name: URL +- description: + name: UnregisterDatasetRequest - description: name: UnregisterMemoryBankRequest @@ -5294,6 +5637,7 @@ x-tagGroups: - AgentTurnResponseTurnCompletePayload - AgentTurnResponseTurnStartPayload - AppEvalTaskConfig + - AppendRowsRequest - Attachment - BatchChatCompletionRequest - BatchChatCompletionResponse @@ -5329,6 +5673,7 @@ x-tagGroups: - FinetuningAlgorithm - FunctionCallToolDefinition - GetAgentsSessionRequest + - GetSpanTreeRequest - GraphMemoryBank - GraphMemoryBankParams - HealthInfo @@ -5363,8 +5708,12 @@ x-tagGroups: - PreferenceOptimizeRequest - ProviderInfo - QLoraFinetuningConfig + - QueryCondition + - QueryConditionOp - QueryDocumentsRequest - QueryDocumentsResponse + - QuerySpansRequest + - QueryTracesRequest - RLHFAlgorithm - RegexParserScoringFnParams - RegisterDatasetRequest @@ -5382,6 +5731,7 @@ x-tagGroups: - SafetyViolation - SamplingParams - SamplingStrategy + - SaveSpansToDatasetRequest - ScoreBatchRequest - ScoreBatchResponse - ScoreRequest @@ -5392,9 +5742,11 @@ x-tagGroups: - Session - Shield - ShieldCallStep + - Span - SpanEndPayload - SpanStartPayload - SpanStatus + - SpanWithChildren - StopReason - StructuredLogEvent - SupervisedFineTuneRequest @@ -5416,6 +5768,7 @@ x-tagGroups: - TrainingConfig - Turn - URL + - UnregisterDatasetRequest - UnregisterMemoryBankRequest - UnregisterModelRequest - UnstructuredLogEvent diff --git a/docs/source/building_applications/index.md b/docs/source/building_applications/index.md index 6d2f9e3ac..6e2062204 100644 --- a/docs/source/building_applications/index.md +++ b/docs/source/building_applications/index.md @@ -1,15 +1,418 @@ -# Building Applications +# Building AI Applications -```{admonition} Work in Progress -:class: warning +Llama Stack provides all the building blocks needed to create sophisticated AI applications. This guide will walk you through how to use these components effectively. -## What can you do with the Stack? +## Basic Inference -- Agents - - what is a turn? session? - - inference - - memory / RAG; pre-ingesting content or attaching content in a turn - - how does tool calling work - - can you do evaluation? +The foundation of any AI application is the ability to interact with LLM models. Llama Stack provides a simple interface for both completion and chat-based inference: +```python +from llama_stack_client import LlamaStackClient + +client = LlamaStackClient(base_url="http://localhost:5001") + +# List available models +models = client.models.list() + +# Simple chat completion +response = client.inference.chat_completion( + model_id="Llama3.2-3B-Instruct", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Write a haiku about coding"} + ] +) +print(response.completion_message.content) +``` + +## Adding Memory & RAG + +Memory enables your applications to reference and recall information from previous interactions or external documents. Llama Stack's memory system is built around the concept of Memory Banks: + +1. **Vector Memory Banks**: For semantic search and retrieval +2. **Key-Value Memory Banks**: For structured data storage +3. **Keyword Memory Banks**: For basic text search +4. **Graph Memory Banks**: For relationship-based retrieval + +Here's how to set up a vector memory bank for RAG: + +```python +# Register a memory bank +bank_id = "my_documents" +response = client.memory_banks.register( + memory_bank_id=bank_id, + params={ + "memory_bank_type": "vector", + "embedding_model": "all-MiniLM-L6-v2", + "chunk_size_in_tokens": 512 + } +) + +# Insert documents +documents = [ + { + "document_id": "doc1", + "content": "Your document text here", + "mime_type": "text/plain" + } +] +client.memory.insert(bank_id, documents) + +# Query documents +results = client.memory.query( + bank_id=bank_id, + query="What do you know about...", +) +``` + +## Implementing Safety Guardrails + +Safety is a critical component of any AI application. Llama Stack provides a Shield system that can be applied at multiple touchpoints: + +```python +# Register a safety shield +shield_id = "content_safety" +client.shields.register( + shield_id=shield_id, + provider_shield_id="llama-guard-basic" +) + +# Run content through shield +response = client.safety.run_shield( + shield_id=shield_id, + messages=[{"role": "user", "content": "User message here"}] +) + +if response.violation: + print(f"Safety violation detected: {response.violation.user_message}") +``` + +## Building Agents + +Agents are the heart of complex AI applications. They combine inference, memory, safety, and tool usage into coherent workflows. At its core, an agent follows a sophisticated execution loop that enables multi-step reasoning, tool usage, and safety checks. + +### The Agent Execution Loop + +Each agent turn follows these key steps: + +1. **Initial Safety Check**: The user's input is first screened through configured safety shields + +2. **Context Retrieval**: + - If RAG is enabled, the agent queries relevant documents from memory banks + - For new documents, they are first inserted into the memory bank + - Retrieved context is augmented to the user's prompt + +3. **Inference Loop**: The agent enters its main execution loop: + - The LLM receives the augmented prompt (with context and/or previous tool outputs) + - The LLM generates a response, potentially with tool calls + - If tool calls are present: + - Tool inputs are safety-checked + - Tools are executed (e.g., web search, code execution) + - Tool responses are fed back to the LLM for synthesis + - The loop continues until: + - The LLM provides a final response without tool calls + - Maximum iterations are reached + - Token limit is exceeded + +4. **Final Safety Check**: The agent's final response is screened through safety shields + +```{mermaid} +sequenceDiagram + participant U as User + participant E as Executor + participant M as Memory Bank + participant L as LLM + participant T as Tools + participant S as Safety Shield + + Note over U,S: Agent Turn Start + U->>S: 1. Submit Prompt + activate S + S->>E: Input Safety Check + deactivate S + + E->>M: 2.1 Query Context + M-->>E: 2.2 Retrieved Documents + + loop Inference Loop + E->>L: 3.1 Augment with Context + L-->>E: 3.2 Response (with/without tool calls) + + alt Has Tool Calls + E->>S: Check Tool Input + S->>T: 4.1 Execute Tool + T-->>E: 4.2 Tool Response + E->>L: 5.1 Tool Response + L-->>E: 5.2 Synthesized Response + end + + opt Stop Conditions + Note over E: Break if: + Note over E: - No tool calls + Note over E: - Max iterations reached + Note over E: - Token limit exceeded + end + end + + E->>S: Output Safety Check + S->>U: 6. Final Response +``` + +Each step in this process can be monitored and controlled through configurations. Here's an example that demonstrates monitoring the agent's execution: + +```python +from llama_stack_client.lib.agents.event_logger import EventLogger + +agent_config = AgentConfig( + model="Llama3.2-3B-Instruct", + instructions="You are a helpful assistant", + # Enable both RAG and tool usage + tools=[ + { + "type": "memory", + "memory_bank_configs": [{ + "type": "vector", + "bank_id": "my_docs" + }], + "max_tokens_in_context": 4096 + }, + { + "type": "code_interpreter", + "enable_inline_code_execution": True + } + ], + # Configure safety + input_shields=["content_safety"], + output_shields=["content_safety"], + # Control the inference loop + max_infer_iters=5, + sampling_params={ + "temperature": 0.7, + "max_tokens": 2048 + } +) + +agent = Agent(client, agent_config) +session_id = agent.create_session("monitored_session") + +# Stream the agent's execution steps +response = agent.create_turn( + messages=[{"role": "user", "content": "Analyze this code and run it"}], + attachments=[{ + "content": "https://raw.githubusercontent.com/example/code.py", + "mime_type": "text/plain" + }], + session_id=session_id +) + +# Monitor each step of execution +for log in EventLogger().log(response): + if log.event.step_type == "memory_retrieval": + print("Retrieved context:", log.event.retrieved_context) + elif log.event.step_type == "inference": + print("LLM output:", log.event.model_response) + elif log.event.step_type == "tool_execution": + print("Tool call:", log.event.tool_call) + print("Tool response:", log.event.tool_response) + elif log.event.step_type == "shield_call": + if log.event.violation: + print("Safety violation:", log.event.violation) +``` + +This example shows how an agent can: Llama Stack provides a high-level agent framework: + +```python +from llama_stack_client.lib.agents.agent import Agent +from llama_stack_client.types.agent_create_params import AgentConfig + +# Configure an agent +agent_config = AgentConfig( + model="Llama3.2-3B-Instruct", + instructions="You are a helpful assistant", + tools=[ + { + "type": "memory", + "memory_bank_configs": [], + "query_generator_config": { + "type": "default", + "sep": " " + } + } + ], + input_shields=["content_safety"], + output_shields=["content_safety"], + enable_session_persistence=True +) + +# Create an agent +agent = Agent(client, agent_config) +session_id = agent.create_session("my_session") + +# Run agent turns +response = agent.create_turn( + messages=[{"role": "user", "content": "Your question here"}], + session_id=session_id +) +``` + +### Adding Tools to Agents + +Agents can be enhanced with various tools: + +1. **Search**: Web search capabilities through providers like Brave +2. **Code Interpreter**: Execute code snippets +3. **RAG**: Memory and document retrieval +4. **Function Calling**: Custom function execution +5. **WolframAlpha**: Mathematical computations +6. **Photogen**: Image generation + +Example of configuring an agent with tools: + +```python +agent_config = AgentConfig( + model="Llama3.2-3B-Instruct", + tools=[ + { + "type": "brave_search", + "api_key": "YOUR_API_KEY", + "engine": "brave" + }, + { + "type": "code_interpreter", + "enable_inline_code_execution": True + } + ], + tool_choice="auto", + tool_prompt_format="json" +) +``` + +## Building RAG-Enhanced Agents + +One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example: + +```python +from llama_stack_client.types import Attachment + +# Create attachments from documents +attachments = [ + Attachment( + content="https://raw.githubusercontent.com/example/doc.rst", + mime_type="text/plain" + ) +] + +# Configure agent with memory +agent_config = AgentConfig( + model="Llama3.2-3B-Instruct", + instructions="You are a helpful assistant", + tools=[{ + "type": "memory", + "memory_bank_configs": [], + "query_generator_config": {"type": "default", "sep": " "}, + "max_tokens_in_context": 4096, + "max_chunks": 10 + }], + enable_session_persistence=True +) + +agent = Agent(client, agent_config) +session_id = agent.create_session("rag_session") + +# Initial document ingestion +response = agent.create_turn( + messages=[{ + "role": "user", + "content": "I am providing some documents for reference." + }], + attachments=attachments, + session_id=session_id +) + +# Query with RAG +response = agent.create_turn( + messages=[{ + "role": "user", + "content": "What are the key topics in the documents?" + }], + session_id=session_id +) +``` + +## Testing & Evaluation + +Llama Stack provides built-in tools for evaluating your applications: + +1. **Benchmarking**: Test against standard datasets +2. **Application Evaluation**: Score your application's outputs +3. **Custom Metrics**: Define your own evaluation criteria + +Here's how to set up basic evaluation: + +```python +# Create an evaluation task +response = client.eval_tasks.register( + eval_task_id="my_eval", + dataset_id="my_dataset", + scoring_functions=["accuracy", "relevance"] +) + +# Run evaluation +job = client.eval.run_eval( + task_id="my_eval", + task_config={ + "type": "app", + "eval_candidate": { + "type": "agent", + "config": agent_config + } + } +) + +# Get results +result = client.eval.job_result( + task_id="my_eval", + job_id=job.job_id +) +``` + +## Debugging & Monitoring + +Llama Stack includes comprehensive telemetry for debugging and monitoring your applications: + +1. **Tracing**: Track request flows across components +2. **Metrics**: Measure performance and usage +3. **Logging**: Debug issues and track behavior + +The telemetry system supports multiple output formats: + +- OpenTelemetry for visualization in tools like Jaeger +- SQLite for local storage and querying +- Console output for development + +Example of querying traces: + +```python +# Query traces for a session +traces = client.telemetry.query_traces( + attribute_filters=[{ + "key": "session_id", + "op": "eq", + "value": session_id + }] +) + +# Get detailed span information +span_tree = client.telemetry.get_span_tree( + span_id=traces[0].root_span_id +) +``` + +For details on how to use the telemetry system to debug your applications, export traces to a dataset, and run evaluations, see the [Telemetry](telemetry) section. + +```{toctree} +:hidden: +:maxdepth: 3 + +telemetry ``` diff --git a/docs/source/building_applications/telemetry.md b/docs/source/building_applications/telemetry.md new file mode 100644 index 000000000..6c8067035 --- /dev/null +++ b/docs/source/building_applications/telemetry.md @@ -0,0 +1,242 @@ +# Telemetry +```{note} +The telemetry system is currently experimental and subject to change. We welcome feedback and contributions to help improve it. +``` + + + +The Llama Stack telemetry system provides comprehensive tracing, metrics, and logging capabilities. It supports multiple sink types including OpenTelemetry, SQLite, and Console output. + +## Key Concepts + +### Events +The telemetry system supports three main types of events: + +- **Unstructured Log Events**: Free-form log messages with severity levels +```python +unstructured_log_event = UnstructuredLogEvent( + message="This is a log message", + severity=LogSeverity.INFO +) +``` +- **Metric Events**: Numerical measurements with units +```python +metric_event = MetricEvent( + metric="my_metric", + value=10, + unit="count" +) +``` +- **Structured Log Events**: System events like span start/end. Extensible to add more structured log types. +```python +structured_log_event = SpanStartPayload( + name="my_span", + parent_span_id="parent_span_id" +) +``` + +### Spans and Traces +- **Spans**: Represent operations with timing and hierarchical relationships +- **Traces**: Collection of related spans forming a complete request flow + +### Sinks +- **OpenTelemetry**: Send events to an OpenTelemetry Collector. This is useful for visualizing traces in a tool like Jaeger. +- **SQLite**: Store events in a local SQLite database. This is needed if you want to query the events later through the Llama Stack API. +- **Console**: Print events to the console. + +## APIs + +The telemetry API is designed to be flexible for different user flows like debugging/visualization in UI, monitoring, and saving traces to datasets. +The telemetry system exposes the following HTTP endpoints: + +### Log Event +```http +POST /telemetry/log-event +``` +Logs a telemetry event (unstructured log, metric, or structured log) with optional TTL. + +### Query Traces +```http +POST /telemetry/query-traces +``` +Retrieves traces based on filters with pagination support. Parameters: +- `attribute_filters`: List of conditions to filter traces +- `limit`: Maximum number of traces to return (default: 100) +- `offset`: Number of traces to skip (default: 0) +- `order_by`: List of fields to sort by + +### Get Span Tree +```http +POST /telemetry/get-span-tree +``` +Retrieves a hierarchical view of spans starting from a specific span. Parameters: +- `span_id`: ID of the root span to retrieve +- `attributes_to_return`: Optional list of specific attributes to include +- `max_depth`: Optional maximum depth of the span tree to return + +### Query Spans +```http +POST /telemetry/query-spans +``` +Retrieves spans matching specified filters and returns selected attributes. Parameters: +- `attribute_filters`: List of conditions to filter traces +- `attributes_to_return`: List of specific attributes to include in results +- `max_depth`: Optional maximum depth of spans to traverse (default: no limit) + +Returns a flattened list of spans with requested attributes. + +### Save Spans to Dataset +This is useful for saving traces to a dataset for running evaluations. For example, you can save the input/output of each span that is part of an agent session/turn to a dataset and then run an eval task on it. See example in [Example: Save Spans to Dataset](#example-save-spans-to-dataset). +```http +POST /telemetry/save-spans-to-dataset +``` +Queries spans and saves their attributes to a dataset. Parameters: +- `attribute_filters`: List of conditions to filter traces +- `attributes_to_save`: List of span attributes to save to the dataset +- `dataset_id`: ID of the dataset to save to +- `max_depth`: Optional maximum depth of spans to traverse (default: no limit) + +## Providers + +### Meta-Reference Provider +Currently, only the meta-reference provider is implemented. It can be configured to send events to three sink types: +1) OpenTelemetry Collector +2) SQLite +3) Console + +## Configuration + +Here's an example that sends telemetry signals to all three sink types. Your configuration might use only one. +```yaml + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + sinks: ['console', 'sqlite', 'otel'] + otel_endpoint: "http://localhost:4318/v1/traces" + sqlite_db_path: "/path/to/telemetry.db" +``` + +## Jaeger to visualize traces + +The `otel` sink works with any service compatible with the OpenTelemetry collector. Let's use Jaeger to visualize this data. + +Start a Jaeger instance with the OTLP HTTP endpoint at 4318 and the Jaeger UI at 16686 using the following command: + +```bash +$ docker run --rm --name jaeger \ + -p 16686:16686 -p 4318:4318 \ + jaegertracing/jaeger:2.1.0 +``` + +Once the Jaeger instance is running, you can visualize traces by navigating to http://localhost:16686/. + +## Querying Traces Stored in SQLIte + +The `sqlite` sink allows you to query traces without an external system. Here are some example queries: + +Querying Traces for a agent session +The client SDK is not updated to support the new telemetry API. It will be updated soon. You can manually query traces using the following curl command: + +``` bash + curl -X POST 'http://localhost:5000/alpha/telemetry/query-traces' \ +-H 'Content-Type: application/json' \ +-d '{ + "attribute_filters": [ + { + "key": "session_id", + "op": "eq", + "value": "dd667b87-ca4b-4d30-9265-5a0de318fc65" }], + "limit": 100, + "offset": 0, + "order_by": ["start_time"] + + [ + { + "trace_id": "6902f54b83b4b48be18a6f422b13e16f", + "root_span_id": "5f37b85543afc15a", + "start_time": "2024-12-04T08:08:30.501587", + "end_time": "2024-12-04T08:08:36.026463" + }, + ........ +] +}' + +``` + +Querying spans for a specifc root span id + +``` bash +curl -X POST 'http://localhost:5000/alpha/telemetry/get-span-tree' \ +-H 'Content-Type: application/json' \ +-d '{ "span_id" : "6cceb4b48a156913", "max_depth": 2 }' + +{ + "span_id": "6cceb4b48a156913", + "trace_id": "dafa796f6aaf925f511c04cd7c67fdda", + "parent_span_id": "892a66d726c7f990", + "name": "retrieve_rag_context", + "start_time": "2024-12-04T09:28:21.781995", + "end_time": "2024-12-04T09:28:21.913352", + "attributes": { + "input": [ + "{\"role\":\"system\",\"content\":\"You are a helpful assistant\"}", + "{\"role\":\"user\",\"content\":\"What are the top 5 topics that were explained in the documentation? Only list succinct bullet points.\",\"context\":null}" + ] + }, + "children": [ + { + "span_id": "1a2df181854064a8", + "trace_id": "dafa796f6aaf925f511c04cd7c67fdda", + "parent_span_id": "6cceb4b48a156913", + "name": "MemoryRouter.query_documents", + "start_time": "2024-12-04T09:28:21.787620", + "end_time": "2024-12-04T09:28:21.906512", + "attributes": { + "input": null + }, + "children": [], + "status": "ok" + } + ], + "status": "ok" +} + +``` + +## Example: Save Spans to Dataset +Save all spans for a specific agent session to a dataset. +``` bash +curl -X POST 'http://localhost:5000/alpha/telemetry/save-spans-to-dataset' \ +-H 'Content-Type: application/json' \ +-d '{ + "attribute_filters": [ + { + "key": "session_id", + "op": "eq", + "value": "dd667b87-ca4b-4d30-9265-5a0de318fc65" + } + ], + "attributes_to_save": ["input", "output"], + "dataset_id": "my_dataset", + "max_depth": 10 +}' +``` + +Save all spans for a specific agent turn to a dataset. +```bash +curl -X POST 'http://localhost:5000/alpha/telemetry/save-spans-to-dataset' \ +-H 'Content-Type: application/json' \ +-d '{ + "attribute_filters": [ + { + "key": "turn_id", + "op": "eq", + "value": "123e4567-e89b-12d3-a456-426614174000" + } + ], + "attributes_to_save": ["input", "output"], + "dataset_id": "my_dataset", + "max_depth": 10 +}' +``` diff --git a/docs/source/conf.py b/docs/source/conf.py index b657cddff..2a9e3d17c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -28,6 +28,7 @@ extensions = [ "sphinx_tabs.tabs", "sphinx_design", "sphinxcontrib.redoc", + "sphinxcontrib.mermaid", ] myst_enable_extensions = ["colon_fence"] @@ -47,6 +48,7 @@ exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] myst_enable_extensions = [ "amsmath", "attrs_inline", + "attrs_block", "colon_fence", "deflist", "dollarmath", @@ -65,6 +67,7 @@ myst_substitutions = { "docker_hub": "https://hub.docker.com/repository/docker/llamastack", } + # Copy button settings copybutton_prompt_text = "$ " # for bash prompts copybutton_prompt_is_regexp = True diff --git a/docs/source/contributing/new_api_provider.md b/docs/source/contributing/new_api_provider.md index 9fea31d87..e0a35e946 100644 --- a/docs/source/contributing/new_api_provider.md +++ b/docs/source/contributing/new_api_provider.md @@ -8,7 +8,7 @@ This guide contains references to walk you through adding a new API provider. - {repopath}`Remote Providers::llama_stack/providers/remote` - {repopath}`Inline Providers::llama_stack/providers/inline` -3. [Build a Llama Stack distribution](https://llama-stack.readthedocs.io/en/latest/distribution_dev/building_distro.html) with your API provider. +3. [Build a Llama Stack distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) with your API provider. 4. Test your code! ## Testing your newly added API providers diff --git a/docs/source/distributions/building_distro.md b/docs/source/distributions/building_distro.md index a45d07ebf..67d39159c 100644 --- a/docs/source/distributions/building_distro.md +++ b/docs/source/distributions/building_distro.md @@ -66,121 +66,247 @@ llama stack build --list-templates ``` ``` -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| Template Name | Providers | Description | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| hf-serverless | { | Like local, but use Hugging Face Inference API (serverless) for running LLM | -| | "inference": "remote::hf::serverless", | inference. | -| | "memory": "meta-reference", | See https://hf.co/docs/api-inference. | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| together | { | Use Together.ai for running LLM inference | -| | "inference": "remote::together", | | -| | "memory": [ | | -| | "meta-reference", | | -| | "remote::weaviate" | | -| | ], | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| fireworks | { | Use Fireworks.ai for running LLM inference | -| | "inference": "remote::fireworks", | | -| | "memory": [ | | -| | "meta-reference", | | -| | "remote::weaviate", | | -| | "remote::chromadb", | | -| | "remote::pgvector" | | -| | ], | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| databricks | { | Use Databricks for running LLM inference | -| | "inference": "remote::databricks", | | -| | "memory": "meta-reference", | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| vllm | { | Like local, but use vLLM for running LLM inference | -| | "inference": "vllm", | | -| | "memory": "meta-reference", | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| tgi | { | Use TGI for running LLM inference | -| | "inference": "remote::tgi", | | -| | "memory": [ | | -| | "meta-reference", | | -| | "remote::chromadb", | | -| | "remote::pgvector" | | -| | ], | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| bedrock | { | Use Amazon Bedrock APIs. | -| | "inference": "remote::bedrock", | | -| | "memory": "meta-reference", | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| meta-reference-gpu | { | Use code from `llama_stack` itself to serve all llama stack APIs | -| | "inference": "meta-reference", | | -| | "memory": [ | | -| | "meta-reference", | | -| | "remote::chromadb", | | -| | "remote::pgvector" | | -| | ], | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| meta-reference-quantized-gpu | { | Use code from `llama_stack` itself to serve all llama stack APIs | -| | "inference": "meta-reference-quantized", | | -| | "memory": [ | | -| | "meta-reference", | | -| | "remote::chromadb", | | -| | "remote::pgvector" | | -| | ], | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| ollama | { | Use ollama for running LLM inference | -| | "inference": "remote::ollama", | | -| | "memory": [ | | -| | "meta-reference", | | -| | "remote::chromadb", | | -| | "remote::pgvector" | | -| | ], | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ -| hf-endpoint | { | Like local, but use Hugging Face Inference Endpoints for running LLM inference. | -| | "inference": "remote::hf::endpoint", | See https://hf.co/docs/api-endpoints. | -| | "memory": "meta-reference", | | -| | "safety": "meta-reference", | | -| | "agents": "meta-reference", | | -| | "telemetry": "meta-reference" | | -| | } | | -+------------------------------+--------------------------------------------+----------------------------------------------------------------------------------+ ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| Template Name | Providers | Description | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| tgi | { | Use (an external) TGI server for running LLM inference | +| | "inference": [ | | +| | "remote::tgi" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| remote-vllm | { | Use (an external) vLLM server for running LLM inference | +| | "inference": [ | | +| | "remote::vllm" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| vllm-gpu | { | Use a built-in vLLM engine for running LLM inference | +| | "inference": [ | | +| | "inline::vllm" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| meta-reference-quantized-gpu | { | Use Meta Reference with fp8, int4 quantization for running LLM inference | +| | "inference": [ | | +| | "inline::meta-reference-quantized" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| meta-reference-gpu | { | Use Meta Reference for running LLM inference | +| | "inference": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| hf-serverless | { | Use (an external) Hugging Face Inference Endpoint for running LLM inference | +| | "inference": [ | | +| | "remote::hf::serverless" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| together | { | Use Together.AI for running LLM inference | +| | "inference": [ | | +| | "remote::together" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| ollama | { | Use (an external) Ollama server for running LLM inference | +| | "inference": [ | | +| | "remote::ollama" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| bedrock | { | Use AWS Bedrock for running LLM inference and safety | +| | "inference": [ | | +| | "remote::bedrock" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "remote::bedrock" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| hf-endpoint | { | Use (an external) Hugging Face Inference Endpoint for running LLM inference | +| | "inference": [ | | +| | "remote::hf::endpoint" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| fireworks | { | Use Fireworks.AI for running LLM inference | +| | "inference": [ | | +| | "remote::fireworks" | | +| | ], | | +| | "memory": [ | | +| | "inline::faiss", | | +| | "remote::chromadb", | | +| | "remote::pgvector" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ +| cerebras | { | Use Cerebras for running LLM inference | +| | "inference": [ | | +| | "remote::cerebras" | | +| | ], | | +| | "safety": [ | | +| | "inline::llama-guard" | | +| | ], | | +| | "memory": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "agents": [ | | +| | "inline::meta-reference" | | +| | ], | | +| | "telemetry": [ | | +| | "inline::meta-reference" | | +| | ] | | +| | } | | ++------------------------------+----------------------------------------+-----------------------------------------------------------------------------+ ``` You may then pick a template to build your distribution with providers fitted to your liking. diff --git a/docs/source/distributions/configuration.md b/docs/source/distributions/configuration.md index abf7d16ed..6fee67936 100644 --- a/docs/source/distributions/configuration.md +++ b/docs/source/distributions/configuration.md @@ -81,6 +81,8 @@ A few things to note: - The configuration dictionary is provider-specific. Notice that configuration can reference environment variables (with default values), which are expanded at runtime. When you run a stack server (via docker or via `llama stack run`), you can specify `--env OLLAMA_URL=http://my-server:11434` to override the default value. ## Resources +``` + Finally, let's look at the `models` section: ```yaml models: diff --git a/docs/source/distributions/importing_as_library.md b/docs/source/distributions/importing_as_library.md index 815660fd4..7e15062df 100644 --- a/docs/source/distributions/importing_as_library.md +++ b/docs/source/distributions/importing_as_library.md @@ -21,7 +21,7 @@ print(response) ```python response = await client.inference.chat_completion( messages=[UserMessage(content="What is the capital of France?", role="user")], - model="Llama3.1-8B-Instruct", + model_id="Llama3.1-8B-Instruct", stream=False, ) print("\nChat completion response:") diff --git a/docs/source/distributions/index.md b/docs/source/distributions/index.md index b61e9b28f..d361cad2f 100644 --- a/docs/source/distributions/index.md +++ b/docs/source/distributions/index.md @@ -35,6 +35,6 @@ If so, we suggest: - **Do you want to run Llama Stack inference on your iOS / Android device** If so, we suggest: - [iOS SDK](ondevice_distro/ios_sdk) - - Android (coming soon) + - [Android](ondevice_distro/android_sdk) You can also build your own [custom distribution](building_distro). diff --git a/docs/source/distributions/ondevice_distro/android_sdk.md b/docs/source/distributions/ondevice_distro/android_sdk.md new file mode 100644 index 000000000..47af8967b --- /dev/null +++ b/docs/source/distributions/ondevice_distro/android_sdk.md @@ -0,0 +1,247 @@ +# Llama Stack Client Kotlin API Library + +We are excited to share a guide for a Kotlin Library that brings front the benefits of Llama Stack to your Android device. This library is a set of SDKs that provide a simple and effective way to integrate AI capabilities into your Android app whether it is local (on-device) or remote inference. + +Features: +- Local Inferencing: Run Llama models purely on-device with real-time processing. We currently utilize ExecuTorch as the local inference distributor and may support others in the future. + - [ExecuTorch](https://github.com/pytorch/executorch/tree/main) is a complete end-to-end solution within the PyTorch framework for inferencing capabilities on-device with high portability and seamless performance. +- Remote Inferencing: Perform inferencing tasks remotely with Llama models hosted on a remote connection (or serverless localhost). +- Simple Integration: With easy-to-use APIs, a developer can quickly integrate Llama Stack in their Android app. The difference with local vs remote inferencing is also minimal. + +Latest Release Notes: [v0.0.54.1](https://github.com/meta-llama/llama-stack-client-kotlin/releases/tag/v0.0.54.1) + +## Android Demo App +Check out our demo app to see how to integrate Llama Stack into your Android app: [Android Demo App](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app) + +The key files in the app are `LlamaStackLocalInference.kt`, `LlamaStackRemoteInference.kts`, and `MainActivity.java`. With encompassed business logic, the app shows how to use Llama Stack for both the environments. + +## Quick Start + +### Add Dependencies +#### Kotlin Library +Add the following dependency in your `build.gradle.kts` file: +``` +dependencies { + implementation("com.llama.llamastack:llama-stack-client-kotlin:0.0.54.1") +} +``` +This will download jar files in your gradle cache in a directory like `~/.gradle/caches/modules-2/files-2.1/com.llama.llamastack/` + +If you plan on doing remote inferencing this is sufficient to get started. + +#### Dependency for Local + +For local inferencing, it is required to include the ExecuTorch library into your app. + +Include the ExecuTorch library by: +1. Download the `download-prebuilt-et-lib.sh` script file from the [llama-stack-client-kotlin-client-local](https://github.com/meta-llama/llama-stack-client-kotlin/blob/release/0.0.54.1/llama-stack-client-kotlin-client-local/download-prebuilt-et-lib.sh) directory to your local machine. +2. Move the script to the top level of your Android app where the app directory resides: +

+ +

+ +3. Run `sh download-prebuilt-et-lib.sh` to create an `app/libs` directory and download the `executorch.aar` in that path. This generates an ExecuTorch library for the XNNPACK delegate with commit: [0a12e33](https://github.com/pytorch/executorch/commit/0a12e33d22a3d44d1aa2af5f0d0673d45b962553). +4. Add the `executorch.aar` dependency in your `build.gradle.kts` file: +``` +dependencies { + ... + implementation(files("libs/executorch.aar")) + ... +} +``` + +## Llama Stack APIs in Your Android App +Breaking down the demo app, this section will show the core pieces that are used to initialize and run inference with Llama Stack using the Kotlin library. + +### Setup Remote Inferencing +Start a Llama Stack server on localhost. Here is an example of how you can do this using the firework.ai distribution: +``` +conda create -n stack-fireworks python=3.10 +conda activate stack-fireworks +pip install llama-stack=0.0.54 +llama stack build --template fireworks --image-type conda +export FIREWORKS_API_KEY= +llama stack run /Users//.llama/distributions/llamastack-fireworks/fireworks-run.yaml --port=5050 +``` + +Other inference providers: [Table](https://llama-stack.readthedocs.io/en/latest/index.html#supported-llama-stack-implementations) + +How to set remote localhost in Demo App: [Settings](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app#settings) + +### Initialize the Client +A client serves as the primary interface for interacting with a specific inference type and its associated parameters. Only after client is initialized then you can configure and start inferences. + + + + + + + + + + +
Local InferenceRemote Inference
+ +``` +client = LlamaStackClientLocalClient + .builder() + .modelPath(modelPath) + .tokenizerPath(tokenizerPath) + .temperature(temperature) + .build() +``` + + +``` +// remoteURL is a string like "http://localhost:5050" +client = LlamaStackClientOkHttpClient + .builder() + .baseUrl(remoteURL) + .build() +``` +
+ + +### Run Inference +With the Kotlin Library managing all the major operational logic, there are minimal to no changes when running simple chat inference for local or remote: + +``` +val result = client!!.inference().chatCompletion( + InferenceChatCompletionParams.builder() + .modelId(modelName) + .putAdditionalQueryParam("seq_len", sequenceLength.toString()) + .messages(listOfMessages) + .build() + ) + +// response contains string with response from model +var response = result.asChatCompletionResponse().completionMessage().content().string(); +``` + +### Setup Tool Calling + +Android demo app for more details: [Tool Calling](https://github.com/meta-llama/llama-stack-apps/tree/main/examples/android_app#tool-calling) + +## Advanced Users + +The purpose of this section is to share more details with users that would like to dive deeper into the Llama Stack Kotlin Library. Whether you’re interested in contributing to the open source library, debugging or just want to learn more, this section is for you! + +### Prerequisite + +You must complete the following steps: +1. Clone the repo (`git clone https://github.com/meta-llama/llama-stack-client-kotlin.git -b release/0.0.54.1`) +2. Port the appropriate ExecuTorch libraries over into your Llama Stack Kotlin library environment. +``` +cd llama-stack-client-kotlin-client-local +sh download-prebuilt-et-lib.sh --unzip +``` + +Now you will notice that the `jni/` , `libs/`, and `AndroidManifest.xml` files from the `executorch.aar` file are present in the local module. This way the local client module will be able to realize the ExecuTorch SDK. + +### Building for Development/Debugging +If you’d like to contribute to the Kotlin library via development, debug, or add play around with the library with various print statements, run the following command in your terminal under the llama-stack-client-kotlin directory. + +``` +sh build-libs.sh +``` + +Output: .jar files located in the build-jars directory + +Copy the .jar files over to the lib directory in your Android app. At the same time make sure to remove the llama-stack-client-kotlin dependency within your build.gradle.kts file in your app (or if you are using the demo app) to avoid having multiple llama stack client dependencies. + +### Additional Options for Local Inferencing +Currently we provide additional properties support with local inferencing. In order to get the tokens/sec metric for each inference call, add the following code in your Android app after you run your chatCompletion inference function. The Reference app has this implementation as well: +``` +var tps = (result.asChatCompletionResponse()._additionalProperties()["tps"] as JsonNumber).value as Float +``` +We will be adding more properties in the future. + +### Additional Options for Remote Inferencing + +#### Network options + +##### Retries + +Requests that experience certain errors are automatically retried 2 times by default, with a short exponential backoff. Connection errors (for example, due to a network connectivity problem), 408 Request Timeout, 409 Conflict, 429 Rate Limit, and >=500 Internal errors will all be retried by default. +You can provide a `maxRetries` on the client builder to configure this: + +```kotlin +val client = LlamaStackClientOkHttpClient.builder() + .fromEnv() + .maxRetries(4) + .build() +``` + +##### Timeouts + +Requests time out after 1 minute by default. You can configure this on the client builder: + +```kotlin +val client = LlamaStackClientOkHttpClient.builder() + .fromEnv() + .timeout(Duration.ofSeconds(30)) + .build() +``` + +##### Proxies + +Requests can be routed through a proxy. You can configure this on the client builder: + +```kotlin +val client = LlamaStackClientOkHttpClient.builder() + .fromEnv() + .proxy(new Proxy( + Type.HTTP, + new InetSocketAddress("proxy.com", 8080) + )) + .build() +``` + +##### Environments + +Requests are made to the production environment by default. You can connect to other environments, like `sandbox`, via the client builder: + +```kotlin +val client = LlamaStackClientOkHttpClient.builder() + .fromEnv() + .sandbox() + .build() +``` + +### Error Handling +This library throws exceptions in a single hierarchy for easy handling: + +- **`LlamaStackClientException`** - Base exception for all exceptions + + - **`LlamaStackClientServiceException`** - HTTP errors with a well-formed response body we were able to parse. The exception message and the `.debuggingRequestId()` will be set by the server. + + | 400 | BadRequestException | + | ------ | ----------------------------- | + | 401 | AuthenticationException | + | 403 | PermissionDeniedException | + | 404 | NotFoundException | + | 422 | UnprocessableEntityException | + | 429 | RateLimitException | + | 5xx | InternalServerException | + | others | UnexpectedStatusCodeException | + + - **`LlamaStackClientIoException`** - I/O networking errors + - **`LlamaStackClientInvalidDataException`** - any other exceptions on the client side, e.g.: + - We failed to serialize the request body + - We failed to parse the response body (has access to response code and body) + +## Reporting Issues +If you encountered any bugs or issues following this guide please file a bug/issue on our [Github issue tracker](https://github.com/meta-llama/llama-stack-client-kotlin/issues). + +## Known Issues +We're aware of the following issues and are working to resolve them: +1. Streaming response is a work-in-progress for local and remote inference +2. Due to #1, agents are not supported at the time. LS agents only work in streaming mode +3. Changing to another model is a work in progress for local and remote platforms + +## Thanks +We'd like to extend our thanks to the ExecuTorch team for providing their support as we integrated ExecuTorch as one of the local inference distributors for Llama Stack. Checkout [ExecuTorch Github repo](https://github.com/pytorch/executorch/tree/main) for more information. + +--- + +The API interface is generated using the OpenAPI standard with [Stainless](https://www.stainlessapi.com/). diff --git a/docs/source/distributions/self_hosted_distro/bedrock.md b/docs/source/distributions/self_hosted_distro/bedrock.md index e0a5d80d0..ae03c89da 100644 --- a/docs/source/distributions/self_hosted_distro/bedrock.md +++ b/docs/source/distributions/self_hosted_distro/bedrock.md @@ -1,6 +1,3 @@ ---- -orphan: true ---- # Bedrock Distribution ```{toctree} @@ -15,9 +12,12 @@ The `llamastack/distribution-bedrock` distribution consists of the following pro | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `remote::bedrock` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `remote::bedrock` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | diff --git a/docs/source/distributions/self_hosted_distro/cerebras.md b/docs/source/distributions/self_hosted_distro/cerebras.md new file mode 100644 index 000000000..08b35809a --- /dev/null +++ b/docs/source/distributions/self_hosted_distro/cerebras.md @@ -0,0 +1,61 @@ +# Cerebras Distribution + +The `llamastack/distribution-cerebras` distribution consists of the following provider configurations. + +| API | Provider(s) | +|-----|-------------| +| agents | `inline::meta-reference` | +| inference | `remote::cerebras` | +| memory | `inline::meta-reference` | +| safety | `inline::llama-guard` | +| telemetry | `inline::meta-reference` | + + +### Environment Variables + +The following environment variables can be configured: + +- `LLAMASTACK_PORT`: Port for the Llama Stack distribution server (default: `5001`) +- `CEREBRAS_API_KEY`: Cerebras API Key (default: ``) + +### Models + +The following models are available by default: + +- `meta-llama/Llama-3.1-8B-Instruct (llama3.1-8b)` +- `meta-llama/Llama-3.1-70B-Instruct (llama3.1-70b)` + + +### Prerequisite: API Keys + +Make sure you have access to a Cerebras API Key. You can get one by visiting [cloud.cerebras.ai](https://cloud.cerebras.ai/). + + +## Running Llama Stack with Cerebras + +You can do this via Conda (build code) or Docker which has a pre-built image. + +### Via Docker + +This method allows you to get started quickly without having to build the distribution code. + +```bash +LLAMA_STACK_PORT=5001 +docker run \ + -it \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ./run.yaml:/root/my-run.yaml \ + llamastack/distribution-cerebras \ + --yaml-config /root/my-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY +``` + +### Via Conda + +```bash +llama stack build --template cerebras --image-type conda +llama stack run ./run.yaml \ + --port 5001 \ + --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY +``` diff --git a/docs/source/distributions/self_hosted_distro/fireworks.md b/docs/source/distributions/self_hosted_distro/fireworks.md index e54302c2e..06a12cb1d 100644 --- a/docs/source/distributions/self_hosted_distro/fireworks.md +++ b/docs/source/distributions/self_hosted_distro/fireworks.md @@ -15,9 +15,12 @@ The `llamastack/distribution-fireworks` distribution consists of the following p | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `remote::fireworks` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | diff --git a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md b/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md index 084e90dfb..d46039318 100644 --- a/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md +++ b/docs/source/distributions/self_hosted_distro/meta-reference-gpu.md @@ -15,9 +15,12 @@ The `llamastack/distribution-meta-reference-gpu` distribution consists of the fo | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `inline::meta-reference` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | @@ -36,7 +39,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ ls ~/.llama/checkpoints @@ -57,6 +60,7 @@ LLAMA_STACK_PORT=5001 docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-meta-reference-gpu \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct @@ -68,6 +72,7 @@ If you are using Llama Stack Safety / Shield APIs, use: docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-meta-reference-gpu \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ diff --git a/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md b/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md index 0c679788c..837be744a 100644 --- a/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md +++ b/docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md @@ -15,9 +15,12 @@ The `llamastack/distribution-meta-reference-quantized-gpu` distribution consists | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `inline::meta-reference-quantized` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | @@ -36,7 +39,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ ls ~/.llama/checkpoints @@ -57,6 +60,7 @@ LLAMA_STACK_PORT=5001 docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-meta-reference-quantized-gpu \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct @@ -68,6 +72,7 @@ If you are using Llama Stack Safety / Shield APIs, use: docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-meta-reference-quantized-gpu \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ diff --git a/docs/source/distributions/self_hosted_distro/ollama.md b/docs/source/distributions/self_hosted_distro/ollama.md index 0eb245483..c915a7ac3 100644 --- a/docs/source/distributions/self_hosted_distro/ollama.md +++ b/docs/source/distributions/self_hosted_distro/ollama.md @@ -15,9 +15,12 @@ The `llamastack/distribution-ollama` distribution consists of the following prov | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `remote::ollama` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | @@ -118,9 +121,9 @@ llama stack run ./run-with-safety.yaml \ ### (Optional) Update Model Serving Configuration -> [!NOTE] -> Please check the [OLLAMA_SUPPORTED_MODELS](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers.remote/inference/ollama/ollama.py) for the supported Ollama models. - +```{note} +Please check the [model_aliases](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/ollama.py#L45) for the supported Ollama models. +``` To serve a new model with `ollama` ```bash diff --git a/docs/source/distributions/self_hosted_distro/tgi.md b/docs/source/distributions/self_hosted_distro/tgi.md index 59485226e..84b91da38 100644 --- a/docs/source/distributions/self_hosted_distro/tgi.md +++ b/docs/source/distributions/self_hosted_distro/tgi.md @@ -16,9 +16,12 @@ The `llamastack/distribution-tgi` distribution consists of the following provide | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `remote::tgi` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | diff --git a/docs/source/distributions/self_hosted_distro/together.md b/docs/source/distributions/self_hosted_distro/together.md index 5cfc9e805..c458fdb5f 100644 --- a/docs/source/distributions/self_hosted_distro/together.md +++ b/docs/source/distributions/self_hosted_distro/together.md @@ -15,9 +15,12 @@ The `llamastack/distribution-together` distribution consists of the following pr | API | Provider(s) | |-----|-------------| | agents | `inline::meta-reference` | +| datasetio | `remote::huggingface`, `inline::localfs` | +| eval | `inline::meta-reference` | | inference | `remote::together` | | memory | `inline::faiss`, `remote::chromadb`, `remote::pgvector` | | safety | `inline::llama-guard` | +| scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` | | telemetry | `inline::meta-reference` | diff --git a/docs/source/getting_started/index.md b/docs/source/getting_started/index.md index e6365208f..c6227db99 100644 --- a/docs/source/getting_started/index.md +++ b/docs/source/getting_started/index.md @@ -19,16 +19,17 @@ export LLAMA_STACK_PORT=5001 ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m ``` -By default, Ollama keeps the model loaded in memory for 5 minutes which can be too short. We set the `--keepalive` flag to 60 minutes to enspagents/agenure the model remains loaded for sometime. +By default, Ollama keeps the model loaded in memory for 5 minutes which can be too short. We set the `--keepalive` flag to 60 minutes to ensure the model remains loaded for sometime. ### 2. Start the Llama Stack server Llama Stack is based on a client-server architecture. It consists of a server which can be configured very flexibly so you can mix-and-match various providers for its individual API components -- beyond Inference, these include Memory, Agents, Telemetry, Evals and so forth. +To get started quickly, we provide various Docker images for the server component that work with different inference providers out of the box. For this guide, we will use `llamastack/distribution-ollama` as the Docker image. + ```bash -docker run \ - -it \ +docker run -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ -v ~/.llama:/root/.llama \ llamastack/distribution-ollama \ @@ -42,8 +43,7 @@ Configuration for this is available at `distributions/ollama/run.yaml`. ### 3. Use the Llama Stack client SDK -You can interact with the Llama Stack server using the `llama-stack-client` CLI or via the Python SDK. - +You can interact with the Llama Stack server using various client SDKs. We will use the Python SDK which you can install using: ```bash pip install llama-stack-client ``` @@ -62,7 +62,7 @@ llama-stack-client --endpoint http://localhost:$LLAMA_STACK_PORT models list You can test basic Llama inference completion using the CLI too. ```bash llama-stack-client --endpoint http://localhost:$LLAMA_STACK_PORT \ - inference chat_completion \ + inference chat-completion \ --message "hello, what model are you?" ``` @@ -118,11 +118,11 @@ async def run_main(): model=os.environ["INFERENCE_MODEL"], instructions="You are a helpful assistant", tools=[{"type": "memory"}], # enable Memory aka RAG + enable_session_persistence=True, ) agent = Agent(client, agent_config) session_id = agent.create_session("test-session") - print(f"Created session_id={session_id} for Agent({agent.agent_id})") user_prompts = [ ( "I am attaching documentation for Torchtune. Help me answer questions I will ask next.", @@ -139,7 +139,7 @@ async def run_main(): attachments=attachments, session_id=session_id, ) - async for log in EventLogger().log(response): + for log in EventLogger().log(response): log.print() @@ -153,3 +153,10 @@ if __name__ == "__main__": - Learn how to [Build Llama Stacks](../distributions/index.md) - See [References](../references/index.md) for more details about the llama CLI and Python SDK - For example applications and more detailed tutorials, visit our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repository. + + +## Thinking out aloud here in terms of what to write in the docs + +- how to get a llama stack server running +- what are all the different client sdks +- what are the components of building agents diff --git a/docs/source/index.md b/docs/source/index.md index dc2776b85..5d7499a04 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -13,38 +13,32 @@ Our goal is to provide pre-packaged implementations which can be operated in a v The Stack APIs are rapidly improving but still a work-in-progress. We invite feedback as well as direct contributions. ``` -## Philosophy +## Quick Links -### Service-oriented design +- New to Llama Stack? Start with the [Introduction](introduction/index) to understand our motivation and vision. +- Ready to build? Check out the [Quick Start](getting_started/index) to get started. +- Need specific providers? Browse [Distributions](distributions/index) to see all the options available. +- Want to contribute? See the [Contributing](contributing/index) guide. -Unlike other frameworks, Llama Stack is built with a service-oriented, REST API-first approach. Such a design not only allows for seamless transitions from a local to remote deployments, but also forces the design to be more declarative. We believe this restriction can result in a much simpler, robust developer experience. This will necessarily trade-off against expressivity however if we get the APIs right, it can lead to a very powerful platform. +## Available SDKs -### Composability - -We expect the set of APIs we design to be composable. An Agent abstractly depends on { Inference, Memory, Safety } APIs but does not care about the actual implementation details. Safety itself may require model inference and hence can depend on the Inference API. - -### Turnkey one-stop solutions - -We expect to provide turnkey solutions for popular deployment scenarios. It should be easy to deploy a Llama Stack server on AWS or on a private data center. Either of these should allow a developer to get started with powerful agentic apps, model evaluations or fine-tuning services in a matter of minutes. They should all result in the same uniform observability and developer experience. - -### Focus on Llama models - -As a Meta initiated project, we have started by explicitly focusing on Meta's Llama series of models. Supporting the broad set of open models is no easy task and we want to start with models we understand best. - -### Supporting the Ecosystem - -There is a vibrant ecosystem of Providers which provide efficient inference or scalable vector stores or powerful observability solutions. We want to make sure it is easy for developers to pick and choose the best implementations for their use cases. We also want to make sure it is easy for new Providers to onboard and participate in the ecosystem. - -Additionally, we have designed every element of the Stack such that APIs as well as Resources (like Models) can be federated. +We have a number of client-side SDKs available for different languages. +| **Language** | **Client SDK** | **Package** | +| :----: | :----: | :----: | +| Python | [llama-stack-client-python](https://github.com/meta-llama/llama-stack-client-python) | [![PyPI version](https://img.shields.io/pypi/v/llama_stack_client.svg)](https://pypi.org/project/llama_stack_client/) +| Swift | [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift) | [![Swift Package Index](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fmeta-llama%2Fllama-stack-client-swift%2Fbadge%3Ftype%3Dswift-versions)](https://swiftpackageindex.com/meta-llama/llama-stack-client-swift) +| Node | [llama-stack-client-node](https://github.com/meta-llama/llama-stack-client-node) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) +| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) ## Supported Llama Stack Implementations -Llama Stack already has a number of "adapters" available for some popular Inference and Memory (Vector Store) providers. For other APIs (particularly Safety and Agents), we provide *reference implementations* you can use to get started. We expect this list to grow over time. We are slowly onboarding more providers to the ecosystem as we get more confidence in the APIs. +A number of "adapters" are available for some popular Inference and Memory (Vector Store) providers. For other APIs (particularly Safety and Agents), we provide *reference implementations* you can use to get started. We expect this list to grow over time. We are slowly onboarding more providers to the ecosystem as we get more confidence in the APIs. | **API Provider** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | | :----: | :----: | :----: | :----: | :----: | :----: | :----: | | Meta Reference | Single Node | Y | Y | Y | Y | Y | +| Cerebras | Single Node | | Y | | | | | Fireworks | Hosted | Y | Y | Y | | | | AWS Bedrock | Hosted | | Y | | Y | | | Together | Hosted | Y | Y | | Y | | @@ -54,29 +48,13 @@ Llama Stack already has a number of "adapters" available for some popular Infere | Chroma | Single Node | | | Y | | | | Postgres | Single Node | | | Y | | | | PyTorch ExecuTorch | On-device iOS | Y | Y | | | - -## Dive In - -- Look at [Quick Start](getting_started/index) section to get started with Llama Stack. -- Learn more about [Llama Stack Concepts](concepts/index) to understand how different components fit together. -- Check out [Zero to Hero](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) guide to learn in details about how to build your first agent. -- See how you can use [Llama Stack Distributions](distributions/index) to get started with popular inference and other service providers. - -We also provide a number of Client side SDKs to make it easier to connect to Llama Stack server in your preferred language. - -| **Language** | **Client SDK** | **Package** | -| :----: | :----: | :----: | -| Python | [llama-stack-client-python](https://github.com/meta-llama/llama-stack-client-python) | [![PyPI version](https://img.shields.io/pypi/v/llama_stack_client.svg)](https://pypi.org/project/llama_stack_client/) -| Swift | [llama-stack-client-swift](https://github.com/meta-llama/llama-stack-client-swift) | [![Swift Package Index](https://img.shields.io/endpoint?url=https%3A%2F%2Fswiftpackageindex.com%2Fapi%2Fpackages%2Fmeta-llama%2Fllama-stack-client-swift%2Fbadge%3Ftype%3Dswift-versions)](https://swiftpackageindex.com/meta-llama/llama-stack-client-swift) -| Node | [llama-stack-client-node](https://github.com/meta-llama/llama-stack-client-node) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) -| Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) - -You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo. +| PyTorch ExecuTorch | On-device Android | | Y | | | ```{toctree} :hidden: :maxdepth: 3 +introduction/index getting_started/index concepts/index distributions/index diff --git a/docs/source/introduction/index.md b/docs/source/introduction/index.md new file mode 100644 index 000000000..9c2a70341 --- /dev/null +++ b/docs/source/introduction/index.md @@ -0,0 +1,95 @@ +# Why Llama Stack? + +Building production AI applications today requires solving multiple challenges: + +**Infrastructure Complexity** +- Running large language models efficiently requires specialized infrastructure. +- Different deployment scenarios (local development, cloud, edge) need different solutions. +- Moving from development to production often requires significant rework. + +**Essential Capabilities** +- Safety guardrails and content filtering are necessary in an enterprise setting. +- Just model inference is not enough - Knowledge retrieval and RAG capabilities are required. +- Nearly any application needs composable multi-step workflows. +- Finally, without monitoring, observability and evaluation, you end up operating in the dark. + +**Lack of Flexibility and Choice** +- Directly integrating with multiple providers creates tight coupling. +- Different providers have different APIs and abstractions. +- Changing providers requires significant code changes. + + +### The Vision: A Universal Stack + + +```{image} ../../_static/llama-stack.png +:alt: Llama Stack +:width: 400px +``` + +Llama Stack defines and standardizes the core building blocks needed to bring generative AI applications to market. These building blocks are presented as interoperable APIs with a broad set of Service Providers providing their implementations. + +#### Service-oriented Design +Unlike other frameworks, Llama Stack is built with a service-oriented, REST API-first approach. Such a design not only allows for seamless transitions from local to remote deployments but also forces the design to be more declarative. This restriction can result in a much simpler, robust developer experience. The same code works across different environments: + +- Local development with CPU-only setups +- Self-hosted with GPU acceleration +- Cloud-hosted on providers like AWS, Fireworks, Together +- On-device for iOS and Android + + +#### Composability +The APIs we design are composable. An Agent abstractly depends on { Inference, Memory, Safety } APIs but does not care about the actual implementation details. Safety itself may require model inference and hence can depend on the Inference API. + +#### Turnkey Solutions + +We provide turnkey solutions for popular deployment scenarios. It should be easy to deploy a Llama Stack server on AWS or in a private data center. Either of these should allow a developer to get started with powerful agentic apps, model evaluations, or fine-tuning services in minutes. + +We have built-in support for critical needs: + +- Safety guardrails and content filtering +- Comprehensive evaluation capabilities +- Full observability and monitoring +- Provider federation and fallback + +#### Focus on Llama Models +As a Meta-initiated project, we explicitly focus on Meta's Llama series of models. Supporting the broad set of open models is no easy task and we want to start with models we understand best. + +#### Supporting the Ecosystem +There is a vibrant ecosystem of Providers which provide efficient inference or scalable vector stores or powerful observability solutions. We want to make sure it is easy for developers to pick and choose the best implementations for their use cases. We also want to make sure it is easy for new Providers to onboard and participate in the ecosystem. + +Additionally, we have designed every element of the Stack such that APIs as well as Resources (like Models) can be federated. + +#### Rich Provider Ecosystem + +```{list-table} +:header-rows: 1 + +* - Provider + - Local + - Self-hosted + - Cloud +* - Inference + - Ollama + - vLLM, TGI + - Fireworks, Together, AWS +* - Memory + - FAISS + - Chroma, pgvector + - Weaviate +* - Safety + - Llama Guard + - - + - AWS Bedrock +``` + + +### Unified API Layer + +Llama Stack provides a consistent interface for: + +- **Inference**: Run LLM models efficiently +- **Safety**: Apply content filtering and safety policies +- **Memory**: Store and retrieve knowledge for RAG +- **Agents**: Build multi-step workflows +- **Evaluation**: Test and improve application quality diff --git a/docs/source/references/llama_stack_client_cli_reference.md b/docs/source/references/llama_stack_client_cli_reference.md index d3835e488..b35aa189d 100644 --- a/docs/source/references/llama_stack_client_cli_reference.md +++ b/docs/source/references/llama_stack_client_cli_reference.md @@ -27,8 +27,6 @@ $ llama-stack-client configure Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5000 ``` -## Provider Commands - ### `llama-stack-client providers list` ```bash $ llama-stack-client providers list @@ -119,8 +117,25 @@ $ llama-stack-client memory_banks list +--------------+----------------+--------+-------------------+------------------------+--------------------------+ ``` -## Shield Management +### `llama-stack-client memory_banks register` +```bash +$ llama-stack-client memory_banks register --type [--provider-id ] [--provider-memory-bank-id ] [--chunk-size ] [--embedding-model ] [--overlap-size ] +``` +Options: +- `--type`: Required. Type of memory bank. Choices: "vector", "keyvalue", "keyword", "graph" +- `--provider-id`: Optional. Provider ID for the memory bank +- `--provider-memory-bank-id`: Optional. Provider's memory bank ID +- `--chunk-size`: Optional. Chunk size in tokens (for vector type). Default: 512 +- `--embedding-model`: Optional. Embedding model (for vector type). Default: "all-MiniLM-L6-v2" +- `--overlap-size`: Optional. Overlap size in tokens (for vector type). Default: 64 + +### `llama-stack-client memory_banks unregister` +```bash +$ llama-stack-client memory_banks unregister +``` + +## Shield Management ### `llama-stack-client shields list` ```bash $ llama-stack-client shields list @@ -134,16 +149,51 @@ $ llama-stack-client shields list +--------------+----------+----------------+-------------+ ``` -## Evaluation Tasks +### `llama-stack-client shields register` +```bash +$ llama-stack-client shields register --shield-id [--provider-id ] [--provider-shield-id ] [--params ] +``` + +Options: +- `--shield-id`: Required. ID of the shield +- `--provider-id`: Optional. Provider ID for the shield +- `--provider-shield-id`: Optional. Provider's shield ID +- `--params`: Optional. JSON configuration parameters for the shield + +## Eval Task Management ### `llama-stack-client eval_tasks list` ```bash -$ llama-stack-client eval run_benchmark --num-examples 10 --output-dir ./ --eval-task-config ~/eval_task_config.json +$ llama-stack-client eval_tasks list ``` -where `eval_task_config.json` is the path to the eval task config file in JSON format. An example eval_task_config +### `llama-stack-client eval_tasks register` +```bash +$ llama-stack-client eval_tasks register --eval-task-id --dataset-id --scoring-functions [ ...] [--provider-id ] [--provider-eval-task-id ] [--metadata ] ``` -$ cat ~/eval_task_config.json + +Options: +- `--eval-task-id`: Required. ID of the eval task +- `--dataset-id`: Required. ID of the dataset to evaluate +- `--scoring-functions`: Required. One or more scoring functions to use for evaluation +- `--provider-id`: Optional. Provider ID for the eval task +- `--provider-eval-task-id`: Optional. Provider's eval task ID +- `--metadata`: Optional. Metadata for the eval task in JSON format + +## Eval execution +### `llama-stack-client eval run-benchmark` +```bash +$ llama-stack-client eval run-benchmark [ ...] --eval-task-config --output-dir [--num-examples ] [--visualize] +``` + +Options: +- `--eval-task-config`: Required. Path to the eval task config file in JSON format +- `--output-dir`: Required. Path to the directory where evaluation results will be saved +- `--num-examples`: Optional. Number of examples to evaluate (useful for debugging) +- `--visualize`: Optional flag. If set, visualizes evaluation results after completion + +Example eval_task_config.json: +```json { "type": "benchmark", "eval_candidate": { @@ -160,3 +210,14 @@ $ cat ~/eval_task_config.json } } ``` + +### `llama-stack-client eval run-scoring` +```bash +$ llama-stack-client eval run-scoring --eval-task-config --output-dir [--num-examples ] [--visualize] +``` + +Options: +- `--eval-task-config`: Required. Path to the eval task config file in JSON format +- `--output-dir`: Required. Path to the directory where scoring results will be saved +- `--num-examples`: Optional. Number of examples to evaluate (useful for debugging) +- `--visualize`: Optional flag. If set, visualizes scoring results after completion diff --git a/docs/to_situate/developer_cookbook.md b/docs/to_situate/developer_cookbook.md index 152035e9f..56ebd7a76 100644 --- a/docs/to_situate/developer_cookbook.md +++ b/docs/to_situate/developer_cookbook.md @@ -13,13 +13,13 @@ Based on your developer needs, below are references to guides to help you get st * Developer Need: I want to start a local Llama Stack server with my GPU using meta-reference implementations. * Effort: 5min * Guide: - - Please see our [meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/meta-reference-gpu.html) on starting up a meta-reference Llama Stack server. + - Please see our [meta-reference-gpu](https://llama-stack.readthedocs.io/en/latest/distributions/self_hosted_distro/meta-reference-gpu.html) on starting up a meta-reference Llama Stack server. ### Llama Stack Server with Remote Providers * Developer need: I want a Llama Stack distribution with a remote provider. * Effort: 10min * Guide - - Please see our [Distributions Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/distributions/index.html) on starting up distributions with remote providers. + - Please see our [Distributions Guide](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions) on starting up distributions with remote providers. ### On-Device (iOS) Llama Stack @@ -38,4 +38,4 @@ Based on your developer needs, below are references to guides to help you get st * Developer Need: I want to add a new API provider to Llama Stack. * Effort: 3hr * Guide - - Please see our [Adding a New API Provider](https://llama-stack.readthedocs.io/en/latest/api_providers/new_api_provider.html) guide for adding a new API provider. + - Please see our [Adding a New API Provider](https://llama-stack.readthedocs.io/en/latest/contributing/new_api_provider.html) guide for adding a new API provider. diff --git a/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb b/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb index 7225f0741..bdfd3520f 100644 --- a/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb +++ b/docs/zero_to_hero_guide/01_Local_Cloud_Inference101.ipynb @@ -231,7 +231,7 @@ "source": [ "Thanks for checking out this notebook! \n", "\n", - "The next one will be a guide on [Prompt Engineering](./01_Prompt_Engineering101.ipynb), please continue learning!" + "The next one will be a guide on [Prompt Engineering](./02_Prompt_Engineering101.ipynb), please continue learning!" ] } ], diff --git a/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb b/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb index c66192d81..c1c8a5aa9 100644 --- a/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb +++ b/docs/zero_to_hero_guide/02_Prompt_Engineering101.ipynb @@ -276,7 +276,7 @@ "source": [ "Thanks for checking out this notebook! \n", "\n", - "The next one will be a guide on how to chat with images, continue to the notebook [here](./02_Image_Chat101.ipynb). Happy learning!" + "The next one will be a guide on how to chat with images, continue to the notebook [here](./03_Image_Chat101.ipynb). Happy learning!" ] } ], diff --git a/docs/zero_to_hero_guide/03_Image_Chat101.ipynb b/docs/zero_to_hero_guide/03_Image_Chat101.ipynb index 93042f3fc..02c32191f 100644 --- a/docs/zero_to_hero_guide/03_Image_Chat101.ipynb +++ b/docs/zero_to_hero_guide/03_Image_Chat101.ipynb @@ -175,7 +175,7 @@ "source": [ "Thanks for checking out this notebook! \n", "\n", - "The next one in the series will teach you one of the favorite applications of Large Language Models: [Tool Calling](./03_Tool_Calling101.ipynb). Enjoy!" + "The next one in the series will teach you one of the favorite applications of Large Language Models: [Tool Calling](./04_Tool_Calling101.ipynb). Enjoy!" ] } ], diff --git a/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb b/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb index 9719ad31e..4f0d2e887 100644 --- a/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb +++ b/docs/zero_to_hero_guide/04_Tool_Calling101.ipynb @@ -286,6 +286,9 @@ " input_shields = [] if disable_safety else [\"llama_guard\"]\n", " output_shields = [] if disable_safety else [\"llama_guard\"]\n", "\n", + " # Initialize custom tool (ensure `WebSearchTool` is defined earlier in the notebook)\n", + " webSearchTool = WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)\n", + " \n", " # Define the agent configuration, including the model and tool setup\n", " agent_config = AgentConfig(\n", " model=MODEL_NAME,\n", @@ -296,18 +299,7 @@ " \"top_p\": 0.9,\n", " },\n", " tools=[\n", - " {\n", - " \"function_name\": \"web_search\", # Name of the tool being integrated\n", - " \"description\": \"Search the web for a given query\",\n", - " \"parameters\": {\n", - " \"query\": {\n", - " \"param_type\": \"str\",\n", - " \"description\": \"The query to search for\",\n", - " \"required\": True,\n", - " }\n", - " },\n", - " \"type\": \"function_call\",\n", - " },\n", + " webSearchTool.get_tool_definition()\n", " ],\n", " tool_choice=\"auto\",\n", " tool_prompt_format=\"python_list\",\n", @@ -316,11 +308,8 @@ " enable_session_persistence=False,\n", " )\n", "\n", - " # Initialize custom tools (ensure `WebSearchTool` is defined earlier in the notebook)\n", - " custom_tools = [WebSearchTool(api_key=BRAVE_SEARCH_API_KEY)]\n", - "\n", " # Create an agent instance with the client and configuration\n", - " agent = Agent(client, agent_config, custom_tools)\n", + " agent = Agent(client, agent_config, [webSearchTool])\n", "\n", " # Create a session for interaction and print the session ID\n", " session_id = agent.create_session(\"test-session\")\n", diff --git a/docs/zero_to_hero_guide/05_Memory101.ipynb b/docs/zero_to_hero_guide/05_Memory101.ipynb index e7e64d8fa..21678fd55 100644 --- a/docs/zero_to_hero_guide/05_Memory101.ipynb +++ b/docs/zero_to_hero_guide/05_Memory101.ipynb @@ -373,7 +373,7 @@ "source": [ "Awesome, now we can embed all our notes with Llama-stack and ask it about the meaning of life :)\n", "\n", - "Next up, we will learn about the safety features and how to use them: [notebook link](./05_Safety101.ipynb)" + "Next up, we will learn about the safety features and how to use them: [notebook link](./06_Safety101.ipynb)." ] } ], diff --git a/docs/zero_to_hero_guide/06_Safety101.ipynb b/docs/zero_to_hero_guide/06_Safety101.ipynb index bf37e83ea..6b5bd53bf 100644 --- a/docs/zero_to_hero_guide/06_Safety101.ipynb +++ b/docs/zero_to_hero_guide/06_Safety101.ipynb @@ -107,7 +107,7 @@ "source": [ "Thanks for leaning about the Safety API of Llama-Stack. \n", "\n", - "Finally, we learn about the Agents API, [here](./06_Agents101.ipynb)" + "Finally, we learn about the Agents API, [here](./07_Agents101.ipynb)." ] } ], diff --git a/docs/zero_to_hero_guide/README.md b/docs/zero_to_hero_guide/README.md index 449e40430..68c012164 100644 --- a/docs/zero_to_hero_guide/README.md +++ b/docs/zero_to_hero_guide/README.md @@ -1,37 +1,21 @@ # Llama Stack: from Zero to Hero -Llama-Stack allows you to configure your distribution from various providers, allowing you to focus on going from zero to production super fast. +Llama Stack defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Providers providing their implementations. These building blocks are assembled into Distributions which are easy for developers to get from zero to production. -This guide will walk you through how to build a local distribution, using Ollama as an inference provider. +This guide will walk you through an end-to-end workflow with Llama Stack with Ollama as the inference provider and ChromaDB as the memory provider. Please note the steps for configuring your provider and distribution will vary a little depending on the services you use. However, the user experience will remain universal - this is the power of Llama-Stack. -We also have a set of notebooks walking you through how to use Llama-Stack APIs: +If you're looking for more specific topics, we have a [Zero to Hero Guide](#next-steps) that covers everything from Tool Calling to Agents in detail. Feel free to skip to the end to explore the advanced topics you're interested in. -- Inference -- Prompt Engineering -- Chatting with Images -- Tool Calling -- Memory API for RAG -- Safety API -- Agentic API - -Below, we will learn how to get started with Ollama as an inference provider, please note the steps for configuring your provider will vary a little depending on the service. However, the user experience will remain universal-this is the power of Llama-Stack. - -Prototype locally using Ollama, deploy to the cloud with your favorite provider or own deployment. Use any API from any provider while focussing on development. - -# Ollama Quickstart Guide - -This guide will walk you through setting up an end-to-end workflow with Llama Stack with ollama, enabling you to perform text generation using the `Llama3.2-3B-Instruct` model. Follow these steps to get started quickly. - -If you're looking for more specific topics like tool calling or agent setup, we have a [Zero to Hero Guide](#next-steps) that covers everything from Tool Calling to Agents in detail. Feel free to skip to the end to explore the advanced topics you're interested in. - -> If you'd prefer not to set up a local server, explore our notebook on [tool calling with the Together API](Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb). This guide will show you how to leverage Together.ai's Llama Stack Server API, allowing you to get started with Llama Stack without the need for a locally built and running server. +> If you'd prefer not to set up a local server, explore our notebook on [tool calling with the Together API](Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb). This notebook will show you how to leverage together.ai's Llama Stack Server API, allowing you to get started with Llama Stack without the need for a locally built and running server. ## Table of Contents -1. [Setup ollama](#setup-ollama) +1. [Setup and run ollama](#setup-ollama) 2. [Install Dependencies and Set Up Environment](#install-dependencies-and-set-up-environment) 3. [Build, Configure, and Run Llama Stack](#build-configure-and-run-llama-stack) -4. [Run Ollama Model](#run-ollama-model) -5. [Next Steps](#next-steps) +4. [Test with llama-stack-client CLI](#test-with-llama-stack-client-cli) +5. [Test with curl](#test-with-curl) +6. [Test with Python](#test-with-python) +7. [Next Steps](#next-steps) --- @@ -39,107 +23,137 @@ If you're looking for more specific topics like tool calling or agent setup, we 1. **Download Ollama App**: - Go to [https://ollama.com/download](https://ollama.com/download). - - Download and unzip `Ollama-darwin.zip`. + - Follow instructions based on the OS you are on. For example, if you are on a Mac, download and unzip `Ollama-darwin.zip`. - Run the `Ollama` application. 1. **Download the Ollama CLI**: - - Ensure you have the `ollama` command line tool by downloading and installing it from the same website. + Ensure you have the `ollama` command line tool by downloading and installing it from the same website. 1. **Start ollama server**: - - Open the terminal and run: - ``` - ollama serve - ``` - + Open the terminal and run: + ``` + ollama serve + ``` 1. **Run the model**: - - Open the terminal and run: - ```bash - ollama run llama3.2:3b-instruct-fp16 - ``` - **Note**: The supported models for llama stack for now is listed in [here](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/ollama.py#L43) - + Open the terminal and run: + ```bash + ollama run llama3.2:3b-instruct-fp16 --keepalive -1m + ``` + **Note**: + - The supported models for llama stack for now is listed in [here](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/ollama.py#L43) + - `keepalive -1m` is used so that ollama continues to keep the model in memory indefinitely. Otherwise, ollama frees up memory and you would have to run `ollama run` again. --- ## Install Dependencies and Set Up Environment 1. **Create a Conda Environment**: - - Create a new Conda environment with Python 3.10: - ```bash - conda create -n ollama python=3.10 - ``` - - Activate the environment: - ```bash - conda activate ollama - ``` + Create a new Conda environment with Python 3.10: + ```bash + conda create -n ollama python=3.10 + ``` + Activate the environment: + ```bash + conda activate ollama + ``` 2. **Install ChromaDB**: - - Install `chromadb` using `pip`: - ```bash - pip install chromadb - ``` + Install `chromadb` using `pip`: + ```bash + pip install chromadb + ``` 3. **Run ChromaDB**: - - Start the ChromaDB server: - ```bash - chroma run --host localhost --port 8000 --path ./my_chroma_data - ``` + Start the ChromaDB server: + ```bash + chroma run --host localhost --port 8000 --path ./my_chroma_data + ``` 4. **Install Llama Stack**: - - Open a new terminal and install `llama-stack`: - ```bash - conda activate hack - pip install llama-stack==0.0.53 - ``` + Open a new terminal and install `llama-stack`: + ```bash + conda activate ollama + pip install llama-stack==0.0.55 + ``` --- ## Build, Configure, and Run Llama Stack 1. **Build the Llama Stack**: - - Build the Llama Stack using the `ollama` template: - ```bash - llama stack build --template ollama --image-type conda - ``` - -After this step, you will see the console output: - -``` -Build Successful! Next steps: + Build the Llama Stack using the `ollama` template: + ```bash + llama stack build --template ollama --image-type conda + ``` + **Expected Output:** + ``` + ... + Build Successful! Next steps: 1. Set the environment variables: LLAMASTACK_PORT, OLLAMA_URL, INFERENCE_MODEL, SAFETY_MODEL - 2. `llama stack run /Users/username/.llama/distributions/llamastack-ollama/ollama-run.yaml` -``` + 2. `llama stack run /Users//.llama/distributions/llamastack-ollama/ollama-run.yaml + ``` -2. **Set the ENV variables by exporting them to the terminal**: -```bash -export OLLAMA_URL="http://localhost:11434" -export LLAMA_STACK_PORT=5001 -export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" -export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B" -``` +3. **Set the ENV variables by exporting them to the terminal**: + ```bash + export OLLAMA_URL="http://localhost:11434" + export LLAMA_STACK_PORT=5051 + export INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" + export SAFETY_MODEL="meta-llama/Llama-Guard-3-1B" + ``` 3. **Run the Llama Stack**: - - Run the stack with command shared by the API from earlier: - ```bash - llama stack run ollama \ - --port $LLAMA_STACK_PORT \ - --env INFERENCE_MODEL=$INFERENCE_MODEL \ - --env SAFETY_MODEL=$SAFETY_MODEL \ - --env OLLAMA_URL=http://localhost:11434 - ``` - -Note: Everytime you run a new model with `ollama run`, you will need to restart the llama stack. Otherwise it won't see the new model + Run the stack with command shared by the API from earlier: + ```bash + llama stack run ollama \ + --port $LLAMA_STACK_PORT \ + --env INFERENCE_MODEL=$INFERENCE_MODEL \ + --env SAFETY_MODEL=$SAFETY_MODEL \ + --env OLLAMA_URL=$OLLAMA_URL + ``` + Note: Everytime you run a new model with `ollama run`, you will need to restart the llama stack. Otherwise it won't see the new model. The server will start and listen on `http://localhost:5051`. --- +## Test with `llama-stack-client` CLI +After setting up the server, open a new terminal window and install the llama-stack-client package. -## Testing with `curl` +1. Install the llama-stack-client package + ```bash + conda activate ollama + pip install llama-stack-client + ``` +2. Configure the CLI to point to the llama-stack server. + ```bash + llama-stack-client configure --endpoint http://localhost:5051 + ``` + **Expected Output:** + ```bash + Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:5051 + ``` +3. Test the CLI by running inference: + ```bash + llama-stack-client inference chat-completion --message "Write me a 2-sentence poem about the moon" + ``` + **Expected Output:** + ```bash + ChatCompletionResponse( + completion_message=CompletionMessage( + content='Here is a 2-sentence poem about the moon:\n\nSilver crescent shining bright in the night,\nA beacon of wonder, full of gentle light.', + role='assistant', + stop_reason='end_of_turn', + tool_calls=[] + ), + logprobs=None + ) + ``` + +## Test with `curl` After setting up the server, open a new terminal window and verify it's working by sending a `POST` request using `curl`: ```bash -curl http://localhost:5051/inference/chat_completion \ +curl http://localhost:$LLAMA_STACK_PORT/inference/chat_completion \ -H "Content-Type: application/json" \ -d '{ "model": "Llama3.2-3B-Instruct", @@ -168,15 +182,16 @@ You can check the available models with the command `llama-stack-client models l --- -## Testing with Python +## Test with Python You can also interact with the Llama Stack server using a simple Python script. Below is an example: -### 1. Active Conda Environment and Install Required Python Packages +### 1. Activate Conda Environment and Install Required Python Packages The `llama-stack-client` library offers a robust and efficient python methods for interacting with the Llama Stack server. ```bash -conda activate your-llama-stack-conda-env +conda activate ollama +pip install llama-stack-client ``` Note, the client library gets installed by default if you install the server library @@ -188,6 +203,8 @@ touch test_llama_stack.py ### 3. Create a Chat Completion Request in Python +In `test_llama_stack.py`, write the following code: + ```python from llama_stack_client import LlamaStackClient @@ -227,15 +244,15 @@ This command initializes the model to interact with your local Llama Stack insta ## Next Steps **Explore Other Guides**: Dive deeper into specific topics by following these guides: -- [Understanding Distribution](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html#decide-your-inference-provider) +- [Understanding Distribution](https://llama-stack.readthedocs.io/en/latest/concepts/index.html#distributions) - [Inference 101](00_Inference101.ipynb) -- [Local and Cloud Model Toggling 101](00_Local_Cloud_Inference101.ipynb) -- [Prompt Engineering](01_Prompt_Engineering101.ipynb) -- [Chat with Image - LlamaStack Vision API](02_Image_Chat101.ipynb) -- [Tool Calling: How to and Details](03_Tool_Calling101.ipynb) -- [Memory API: Show Simple In-Memory Retrieval](04_Memory101.ipynb) -- [Using Safety API in Conversation](05_Safety101.ipynb) -- [Agents API: Explain Components](06_Agents101.ipynb) +- [Local and Cloud Model Toggling 101](01_Local_Cloud_Inference101.ipynb) +- [Prompt Engineering](02_Prompt_Engineering101.ipynb) +- [Chat with Image - LlamaStack Vision API](03_Image_Chat101.ipynb) +- [Tool Calling: How to and Details](04_Tool_Calling101.ipynb) +- [Memory API: Show Simple In-Memory Retrieval](05_Memory101.ipynb) +- [Using Safety API in Conversation](06_Safety101.ipynb) +- [Agents API: Explain Components](07_Agents101.ipynb) **Explore Client SDKs**: Utilize our client SDKs for various languages to integrate Llama Stack into your applications: @@ -244,7 +261,7 @@ This command initializes the model to interact with your local Llama Stack insta - [Swift SDK](https://github.com/meta-llama/llama-stack-client-swift) - [Kotlin SDK](https://github.com/meta-llama/llama-stack-client-kotlin) -**Advanced Configuration**: Learn how to customize your Llama Stack distribution by referring to the [Building a Llama Stack Distribution](https://llama-stack.readthedocs.io/en/latest/distributions/index.html#building-your-own-distribution) guide. +**Advanced Configuration**: Learn how to customize your Llama Stack distribution by referring to the [Building a Llama Stack Distribution](https://llama-stack.readthedocs.io/en/latest/distributions/building_distro.html) guide. **Explore Example Apps**: Check out [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) for example applications built using Llama Stack. diff --git a/docs/zero_to_hero_guide/Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb b/docs/zero_to_hero_guide/Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb index e9bff5f33..b21f3d64c 100644 --- a/docs/zero_to_hero_guide/Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb +++ b/docs/zero_to_hero_guide/Tool_Calling101_Using_Together's_Llama_Stack_Server.ipynb @@ -71,7 +71,8 @@ } ], "source": [ - "!pip install llama-stack-client" + "!pip install llama-stack-client==0.0.50\n", + "!pip install -U httpx==0.27.2 # https://github.com/meta-llama/llama-stack-apps/issues/131" ] }, { @@ -355,6 +356,9 @@ "async def create_weather_agent(client: LlamaStackClient) -> Agent:\n", " \"\"\"Create an agent with weather tool capability.\"\"\"\n", "\n", + " # Create the agent with the tool\n", + " weather_tool = WeatherTool()\n", + " \n", " agent_config = AgentConfig(\n", " model=LLAMA31_8B_INSTRUCT,\n", " #model=model_name,\n", @@ -369,23 +373,7 @@ " \"top_p\": 0.9,\n", " },\n", " tools=[\n", - " {\n", - " \"function_name\": \"get_weather\",\n", - " \"description\": \"Get weather information for a location\",\n", - " \"parameters\": {\n", - " \"location\": {\n", - " \"param_type\": \"str\",\n", - " \"description\": \"City or location name\",\n", - " \"required\": True,\n", - " },\n", - " \"date\": {\n", - " \"param_type\": \"str\",\n", - " \"description\": \"Optional date (YYYY-MM-DD)\",\n", - " \"required\": False,\n", - " },\n", - " },\n", - " \"type\": \"function_call\",\n", - " }\n", + " weather_tool.get_tool_definition()\n", " ],\n", " tool_choice=\"auto\",\n", " tool_prompt_format=\"json\",\n", @@ -394,8 +382,6 @@ " enable_session_persistence=True\n", " )\n", "\n", - " # Create the agent with the tool\n", - " weather_tool = WeatherTool()\n", " agent = Agent(\n", " client=client,\n", " agent_config=agent_config,\n", @@ -470,5 +456,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/llama_stack/__init__.py b/llama_stack/__init__.py index 756f351d8..34b866692 100644 --- a/llama_stack/__init__.py +++ b/llama_stack/__init__.py @@ -3,3 +3,5 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +# +# from .distribution.library_client import LlamaStackAsLibraryClient, AsyncLlamaStackAsLibraryClient diff --git a/llama_stack/apis/agents/agents.py b/llama_stack/apis/agents/agents.py index 25de35497..575f336af 100644 --- a/llama_stack/apis/agents/agents.py +++ b/llama_stack/apis/agents/agents.py @@ -23,6 +23,7 @@ from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel, ConfigDict, Field from typing_extensions import Annotated +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol from llama_models.llama3.api.datatypes import * # noqa: F403 from llama_stack.apis.common.deployment_types import * # noqa: F403 from llama_stack.apis.inference import * # noqa: F403 @@ -339,9 +340,8 @@ class AgentTurnResponseStepProgressPayload(BaseModel): step_type: StepType step_id: str - model_response_text_delta: Optional[str] = None + text_delta: Optional[str] = None tool_call_delta: Optional[ToolCallDelta] = None - tool_response_text_delta: Optional[str] = None @json_schema_type @@ -418,6 +418,7 @@ class AgentStepResponse(BaseModel): @runtime_checkable +@trace_protocol class Agents(Protocol): @webmethod(route="/agents/create") async def create_agent( diff --git a/llama_stack/apis/agents/event_logger.py b/llama_stack/apis/agents/event_logger.py index 25931b821..4c379999e 100644 --- a/llama_stack/apis/agents/event_logger.py +++ b/llama_stack/apis/agents/event_logger.py @@ -121,7 +121,7 @@ class EventLogger: else: yield event, LogEvent( role=None, - content=event.payload.model_response_text_delta, + content=event.payload.text_delta, end="", color="yellow", ) @@ -171,12 +171,14 @@ class EventLogger: and event_type == EventType.step_complete.value ): details = event.payload.step_details - content = interleaved_text_media_as_str(details.inserted_context) - content = content[:200] + "..." if len(content) > 200 else content + inserted_context = interleaved_text_media_as_str( + details.inserted_context + ) + content = f"fetched {len(inserted_context)} bytes from {details.memory_bank_ids}" yield event, LogEvent( role=step_type, - content=f"Retrieved context from banks: {details.memory_bank_ids}.\n====\n{content}\n>", + content=content, color="cyan", ) diff --git a/llama_stack/apis/datasetio/datasetio.py b/llama_stack/apis/datasetio/datasetio.py index c5052877a..22acc3211 100644 --- a/llama_stack/apis/datasetio/datasetio.py +++ b/llama_stack/apis/datasetio/datasetio.py @@ -37,3 +37,8 @@ class DatasetIO(Protocol): page_token: Optional[str] = None, filter_condition: Optional[str] = None, ) -> PaginatedRowsResult: ... + + @webmethod(route="/datasetio/append-rows", method="POST") + async def append_rows( + self, dataset_id: str, rows: List[Dict[str, Any]] + ) -> None: ... diff --git a/llama_stack/apis/datasets/client.py b/llama_stack/apis/datasets/client.py index 9e5891e74..c379a49fb 100644 --- a/llama_stack/apis/datasets/client.py +++ b/llama_stack/apis/datasets/client.py @@ -78,6 +78,21 @@ class DatasetsClient(Datasets): return [DatasetDefWithProvider(**x) for x in response.json()] + async def unregister_dataset( + self, + dataset_id: str, + ) -> None: + async with httpx.AsyncClient() as client: + response = await client.delete( + f"{self.base_url}/datasets/unregister", + params={ + "dataset_id": dataset_id, + }, + headers={"Content-Type": "application/json"}, + timeout=60, + ) + response.raise_for_status() + async def run_main(host: str, port: int): client = DatasetsClient(f"http://{host}:{port}") diff --git a/llama_stack/apis/datasets/datasets.py b/llama_stack/apis/datasets/datasets.py index 2ab958782..e1ac4af21 100644 --- a/llama_stack/apis/datasets/datasets.py +++ b/llama_stack/apis/datasets/datasets.py @@ -64,3 +64,9 @@ class Datasets(Protocol): @webmethod(route="/datasets/list", method="GET") async def list_datasets(self) -> List[Dataset]: ... + + @webmethod(route="/datasets/unregister", method="POST") + async def unregister_dataset( + self, + dataset_id: str, + ) -> None: ... diff --git a/llama_stack/apis/inference/inference.py b/llama_stack/apis/inference/inference.py index 5aadd97c7..233cd1b50 100644 --- a/llama_stack/apis/inference/inference.py +++ b/llama_stack/apis/inference/inference.py @@ -21,6 +21,8 @@ from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel, Field from typing_extensions import Annotated +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol + from llama_models.llama3.api.datatypes import * # noqa: F403 from llama_stack.apis.models import * # noqa: F403 @@ -220,6 +222,7 @@ class ModelStore(Protocol): @runtime_checkable +@trace_protocol class Inference(Protocol): model_store: ModelStore diff --git a/llama_stack/apis/memory/memory.py b/llama_stack/apis/memory/memory.py index 48b6e2241..2f3a94956 100644 --- a/llama_stack/apis/memory/memory.py +++ b/llama_stack/apis/memory/memory.py @@ -16,6 +16,7 @@ from pydantic import BaseModel, Field from llama_models.llama3.api.datatypes import * # noqa: F403 from llama_stack.apis.memory_banks import * # noqa: F403 +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol @json_schema_type @@ -43,6 +44,7 @@ class MemoryBankStore(Protocol): @runtime_checkable +@trace_protocol class Memory(Protocol): memory_bank_store: MemoryBankStore diff --git a/llama_stack/apis/memory_banks/memory_banks.py b/llama_stack/apis/memory_banks/memory_banks.py index 1b16af330..a17e8e48d 100644 --- a/llama_stack/apis/memory_banks/memory_banks.py +++ b/llama_stack/apis/memory_banks/memory_banks.py @@ -20,6 +20,7 @@ from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel, Field from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol @json_schema_type @@ -129,6 +130,7 @@ class MemoryBankInput(BaseModel): @runtime_checkable +@trace_protocol class MemoryBanks(Protocol): @webmethod(route="/memory-banks/list", method="GET") async def list_memory_banks(self) -> List[MemoryBank]: ... diff --git a/llama_stack/apis/models/models.py b/llama_stack/apis/models/models.py index cbd6265e2..cb9cb1117 100644 --- a/llama_stack/apis/models/models.py +++ b/llama_stack/apis/models/models.py @@ -10,6 +10,7 @@ from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel, ConfigDict, Field from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol class CommonModelFields(BaseModel): @@ -43,6 +44,7 @@ class ModelInput(CommonModelFields): @runtime_checkable +@trace_protocol class Models(Protocol): @webmethod(route="/models/list", method="GET") async def list_models(self) -> List[Model]: ... diff --git a/llama_stack/apis/safety/client.py b/llama_stack/apis/safety/client.py index d7d4bc981..a9396c70c 100644 --- a/llama_stack/apis/safety/client.py +++ b/llama_stack/apis/safety/client.py @@ -17,6 +17,8 @@ from llama_models.llama3.api.datatypes import * # noqa: F403 from pydantic import BaseModel from termcolor import cprint +from llama_stack.apis.version import LLAMA_STACK_API_VERSION + from llama_stack.distribution.datatypes import RemoteProviderConfig from llama_stack.apis.safety import * # noqa: F403 @@ -45,7 +47,7 @@ class SafetyClient(Safety): ) -> RunShieldResponse: async with httpx.AsyncClient() as client: response = await client.post( - f"{self.base_url}/safety/run_shield", + f"{self.base_url}/{LLAMA_STACK_API_VERSION}/safety/run-shield", json=dict( shield_id=shield_id, messages=[encodable_dict(m) for m in messages], @@ -91,7 +93,7 @@ async def run_main(host: str, port: int, image_path: str = None): ]: cprint(f"User>{message.content}", "green") response = await client.run_shield( - shield_id="llama_guard", + shield_id="meta-llama/Llama-Guard-3-1B", messages=[message], ) print(response) diff --git a/llama_stack/apis/safety/safety.py b/llama_stack/apis/safety/safety.py index 724f8dc96..26ae45ae7 100644 --- a/llama_stack/apis/safety/safety.py +++ b/llama_stack/apis/safety/safety.py @@ -10,6 +10,8 @@ from typing import Any, Dict, List, Protocol, runtime_checkable from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol + from llama_models.llama3.api.datatypes import * # noqa: F403 from llama_stack.apis.shields import * # noqa: F403 @@ -43,6 +45,7 @@ class ShieldStore(Protocol): @runtime_checkable +@trace_protocol class Safety(Protocol): shield_store: ShieldStore diff --git a/llama_stack/apis/shields/shields.py b/llama_stack/apis/shields/shields.py index 5ee444f68..8d4d5f9fd 100644 --- a/llama_stack/apis/shields/shields.py +++ b/llama_stack/apis/shields/shields.py @@ -10,6 +10,7 @@ from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel from llama_stack.apis.resource import Resource, ResourceType +from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol class CommonShieldFields(BaseModel): @@ -38,6 +39,7 @@ class ShieldInput(CommonShieldFields): @runtime_checkable +@trace_protocol class Shields(Protocol): @webmethod(route="/shields/list", method="GET") async def list_shields(self) -> List[Shield]: ... diff --git a/llama_stack/apis/telemetry/telemetry.py b/llama_stack/apis/telemetry/telemetry.py index 31f64733b..12ec5f1d9 100644 --- a/llama_stack/apis/telemetry/telemetry.py +++ b/llama_stack/apis/telemetry/telemetry.py @@ -6,12 +6,24 @@ from datetime import datetime from enum import Enum -from typing import Any, Dict, Literal, Optional, Protocol, runtime_checkable, Union +from typing import ( + Any, + Dict, + List, + Literal, + Optional, + Protocol, + runtime_checkable, + Union, +) from llama_models.schema_utils import json_schema_type, webmethod from pydantic import BaseModel, Field from typing_extensions import Annotated +# Add this constant near the top of the file, after the imports +DEFAULT_TTL_DAYS = 7 + @json_schema_type class SpanStatus(Enum): @@ -29,6 +41,11 @@ class Span(BaseModel): end_time: Optional[datetime] = None attributes: Optional[Dict[str, Any]] = Field(default_factory=dict) + def set_attribute(self, key: str, value: Any): + if self.attributes is None: + self.attributes = {} + self.attributes[key] = value + @json_schema_type class Trace(BaseModel): @@ -123,10 +140,73 @@ Event = Annotated[ ] +@json_schema_type +class EvalTrace(BaseModel): + session_id: str + step: str + input: str + output: str + expected_output: str + + +@json_schema_type +class SpanWithChildren(Span): + children: List["SpanWithChildren"] = Field(default_factory=list) + status: Optional[SpanStatus] = None + + +@json_schema_type +class QueryConditionOp(Enum): + EQ = "eq" + NE = "ne" + GT = "gt" + LT = "lt" + + +@json_schema_type +class QueryCondition(BaseModel): + key: str + op: QueryConditionOp + value: Any + + @runtime_checkable class Telemetry(Protocol): @webmethod(route="/telemetry/log-event") - async def log_event(self, event: Event) -> None: ... + async def log_event( + self, event: Event, ttl_seconds: int = DEFAULT_TTL_DAYS * 86400 + ) -> None: ... - @webmethod(route="/telemetry/get-trace", method="GET") - async def get_trace(self, trace_id: str) -> Trace: ... + @webmethod(route="/telemetry/query-traces", method="POST") + async def query_traces( + self, + attribute_filters: Optional[List[QueryCondition]] = None, + limit: Optional[int] = 100, + offset: Optional[int] = 0, + order_by: Optional[List[str]] = None, + ) -> List[Trace]: ... + + @webmethod(route="/telemetry/get-span-tree", method="POST") + async def get_span_tree( + self, + span_id: str, + attributes_to_return: Optional[List[str]] = None, + max_depth: Optional[int] = None, + ) -> SpanWithChildren: ... + + @webmethod(route="/telemetry/query-spans", method="POST") + async def query_spans( + self, + attribute_filters: List[QueryCondition], + attributes_to_return: List[str], + max_depth: Optional[int] = None, + ) -> List[Span]: ... + + @webmethod(route="/telemetry/save-spans-to-dataset", method="POST") + async def save_spans_to_dataset( + self, + attribute_filters: List[QueryCondition], + attributes_to_save: List[str], + dataset_id: str, + max_depth: Optional[int] = None, + ) -> None: ... diff --git a/llama_stack/cli/stack/build.py b/llama_stack/cli/stack/build.py index 00d62bd73..3bd061424 100644 --- a/llama_stack/cli/stack/build.py +++ b/llama_stack/cli/stack/build.py @@ -73,7 +73,7 @@ class StackBuild(Subcommand): "--image-type", type=str, help="Image Type to use for the build. This can be either conda or docker. If not specified, will use the image type from the template config.", - choices=["conda", "docker"], + choices=["conda", "docker", "venv"], default="conda", ) @@ -124,8 +124,8 @@ class StackBuild(Subcommand): image_type = prompt( "> Enter the image type you want your Llama Stack to be built as (docker or conda): ", validator=Validator.from_callable( - lambda x: x in ["docker", "conda"], - error_message="Invalid image type, please enter conda or docker", + lambda x: x in ["docker", "conda", "venv"], + error_message="Invalid image type, please enter conda or docker or venv", ), default="conda", ) @@ -261,7 +261,6 @@ class StackBuild(Subcommand): ) -> None: import json import os - import re import yaml from termcolor import cprint @@ -291,20 +290,8 @@ class StackBuild(Subcommand): run_config_file = build_dir / f"{build_config.name}-run.yaml" shutil.copy(template_path, run_config_file) - with open(template_path, "r") as f: - yaml_content = f.read() - # Find all ${env.VARIABLE} patterns - env_vars = set(re.findall(r"\${env\.([A-Za-z0-9_]+)}", yaml_content)) - cprint("Build Successful! Next steps: ", color="green") - cprint( - f" 1. Set the environment variables: {list(env_vars)}", - color="green", - ) - cprint( - f" 2. Run: `llama stack run {template_name}`", - color="green", - ) + cprint("Build Successful!", color="green") else: self._generate_run_config(build_config, build_dir) diff --git a/llama_stack/distribution/build.py b/llama_stack/distribution/build.py index fb4b6a161..bdda0349f 100644 --- a/llama_stack/distribution/build.py +++ b/llama_stack/distribution/build.py @@ -10,6 +10,7 @@ from typing import List import pkg_resources from pydantic import BaseModel +from termcolor import cprint from llama_stack.distribution.utils.exec import run_with_pty @@ -37,6 +38,7 @@ SERVER_DEPENDENCIES = [ class ImageType(Enum): docker = "docker" conda = "conda" + venv = "venv" class ApiInput(BaseModel): @@ -45,7 +47,7 @@ class ApiInput(BaseModel): def get_provider_dependencies( - config_providers: Dict[str, List[Provider]] + config_providers: Dict[str, List[Provider]], ) -> tuple[list[str], list[str]]: """Get normal and special dependencies from provider configuration.""" all_providers = get_provider_registry() @@ -90,11 +92,12 @@ def get_provider_dependencies( def print_pip_install_help(providers: Dict[str, List[Provider]]): normal_deps, special_deps = get_provider_dependencies(providers) - print( - f"Please install needed dependencies using the following commands:\n\n\tpip install {' '.join(normal_deps)}" + cprint( + f"Please install needed dependencies using the following commands:\n\npip install {' '.join(normal_deps)}", + "yellow", ) for special_dep in special_deps: - log.info(f"\tpip install {special_dep}") + cprint(f"pip install {special_dep}", "yellow") print() @@ -118,7 +121,7 @@ def build_image(build_config: BuildConfig, build_file_path: Path): str(BUILDS_BASE_DIR / ImageType.docker.value), " ".join(normal_deps), ] - else: + elif build_config.image_type == ImageType.conda.value: script = pkg_resources.resource_filename( "llama_stack", "distribution/build_conda_env.sh" ) @@ -128,6 +131,16 @@ def build_image(build_config: BuildConfig, build_file_path: Path): str(build_file_path), " ".join(normal_deps), ] + elif build_config.image_type == ImageType.venv.value: + script = pkg_resources.resource_filename( + "llama_stack", "distribution/build_venv.sh" + ) + args = [ + script, + build_config.name, + str(build_file_path), + " ".join(normal_deps), + ] if special_deps: args.append("#".join(special_deps)) diff --git a/llama_stack/distribution/build_venv.sh b/llama_stack/distribution/build_venv.sh new file mode 100755 index 000000000..8136e3120 --- /dev/null +++ b/llama_stack/distribution/build_venv.sh @@ -0,0 +1,105 @@ +#!/bin/bash + +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +# TODO: combine this with build_conda_env.sh since it is almost identical +# the only difference is that we don't do any conda-specific setup + +LLAMA_MODELS_DIR=${LLAMA_MODELS_DIR:-} +LLAMA_STACK_DIR=${LLAMA_STACK_DIR:-} +TEST_PYPI_VERSION=${TEST_PYPI_VERSION:-} + +if [ -n "$LLAMA_STACK_DIR" ]; then + echo "Using llama-stack-dir=$LLAMA_STACK_DIR" +fi +if [ -n "$LLAMA_MODELS_DIR" ]; then + echo "Using llama-models-dir=$LLAMA_MODELS_DIR" +fi + +if [ "$#" -lt 3 ]; then + echo "Usage: $0 []" >&2 + echo "Example: $0 mybuild ./my-stack-build.yaml 'numpy pandas scipy'" >&2 + exit 1 +fi + +special_pip_deps="$4" + +set -euo pipefail + +build_name="$1" +env_name="llamastack-$build_name" +build_file_path="$2" +pip_dependencies="$3" + +# Define color codes +RED='\033[0;31m' +GREEN='\033[0;32m' +NC='\033[0m' # No Color + +# this is set if we actually create a new conda in which case we need to clean up +ENVNAME="" + +SCRIPT_DIR=$(dirname "$(readlink -f "$0")") +source "$SCRIPT_DIR/common.sh" + +run() { + local env_name="$1" + local pip_dependencies="$2" + local special_pip_deps="$3" + + if [ -n "$TEST_PYPI_VERSION" ]; then + # these packages are damaged in test-pypi, so install them first + pip install fastapi libcst + pip install --extra-index-url https://test.pypi.org/simple/ \ + llama-models==$TEST_PYPI_VERSION llama-stack==$TEST_PYPI_VERSION \ + $pip_dependencies + if [ -n "$special_pip_deps" ]; then + IFS='#' read -ra parts <<<"$special_pip_deps" + for part in "${parts[@]}"; do + echo "$part" + pip install $part + done + fi + else + # Re-installing llama-stack in the new conda environment + if [ -n "$LLAMA_STACK_DIR" ]; then + if [ ! -d "$LLAMA_STACK_DIR" ]; then + printf "${RED}Warning: LLAMA_STACK_DIR is set but directory does not exist: $LLAMA_STACK_DIR${NC}\n" >&2 + exit 1 + fi + + printf "Installing from LLAMA_STACK_DIR: $LLAMA_STACK_DIR\n" + pip install --no-cache-dir -e "$LLAMA_STACK_DIR" + else + pip install --no-cache-dir llama-stack + fi + + if [ -n "$LLAMA_MODELS_DIR" ]; then + if [ ! -d "$LLAMA_MODELS_DIR" ]; then + printf "${RED}Warning: LLAMA_MODELS_DIR is set but directory does not exist: $LLAMA_MODELS_DIR${NC}\n" >&2 + exit 1 + fi + + printf "Installing from LLAMA_MODELS_DIR: $LLAMA_MODELS_DIR\n" + pip uninstall -y llama-models + pip install --no-cache-dir -e "$LLAMA_MODELS_DIR" + fi + + # Install pip dependencies + printf "Installing pip dependencies\n" + pip install $pip_dependencies + if [ -n "$special_pip_deps" ]; then + IFS='#' read -ra parts <<<"$special_pip_deps" + for part in "${parts[@]}"; do + echo "$part" + pip install $part + done + fi + fi +} + +run "$env_name" "$pip_dependencies" "$special_pip_deps" diff --git a/llama_stack/distribution/datatypes.py b/llama_stack/distribution/datatypes.py index c2bff4eed..1159372d4 100644 --- a/llama_stack/distribution/datatypes.py +++ b/llama_stack/distribution/datatypes.py @@ -165,5 +165,5 @@ class BuildConfig(BaseModel): ) image_type: str = Field( default="conda", - description="Type of package to build (conda | container)", + description="Type of package to build (conda | docker | venv)", ) diff --git a/llama_stack/distribution/library_client.py b/llama_stack/distribution/library_client.py new file mode 100644 index 000000000..8766f7a72 --- /dev/null +++ b/llama_stack/distribution/library_client.py @@ -0,0 +1,313 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import asyncio +import inspect +import json +import os +import queue +import threading +from concurrent.futures import ThreadPoolExecutor +from enum import Enum +from pathlib import Path +from typing import Any, Generator, get_args, get_origin, Optional, Type, TypeVar, Union + +import yaml +from llama_stack_client import AsyncLlamaStackClient, LlamaStackClient, NOT_GIVEN +from pydantic import BaseModel, TypeAdapter +from rich.console import Console + +from termcolor import cprint + +from llama_stack.distribution.build import print_pip_install_help +from llama_stack.distribution.configure import parse_and_maybe_upgrade_config +from llama_stack.distribution.resolver import ProviderRegistry +from llama_stack.distribution.server.endpoints import get_all_api_endpoints +from llama_stack.distribution.stack import ( + construct_stack, + get_stack_run_config_from_template, + replace_env_vars, +) + +T = TypeVar("T") + + +def in_notebook(): + try: + from IPython import get_ipython + + if "IPKernelApp" not in get_ipython().config: # pragma: no cover + return False + except ImportError: + return False + except AttributeError: + return False + return True + + +def stream_across_asyncio_run_boundary( + async_gen_maker, + pool_executor: ThreadPoolExecutor, +) -> Generator[T, None, None]: + result_queue = queue.Queue() + stop_event = threading.Event() + + async def consumer(): + # make sure we make the generator in the event loop context + gen = await async_gen_maker() + try: + async for item in gen: + result_queue.put(item) + except Exception as e: + print(f"Error in generator {e}") + result_queue.put(e) + except asyncio.CancelledError: + return + finally: + result_queue.put(StopIteration) + stop_event.set() + + def run_async(): + # Run our own loop to avoid double async generator cleanup which is done + # by asyncio.run() + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + try: + task = loop.create_task(consumer()) + loop.run_until_complete(task) + finally: + # Handle pending tasks like a generator's athrow() + pending = asyncio.all_tasks(loop) + if pending: + loop.run_until_complete( + asyncio.gather(*pending, return_exceptions=True) + ) + loop.close() + + future = pool_executor.submit(run_async) + + try: + # yield results as they come in + while not stop_event.is_set() or not result_queue.empty(): + try: + item = result_queue.get(timeout=0.1) + if item is StopIteration: + break + if isinstance(item, Exception): + raise item + yield item + except queue.Empty: + continue + finally: + future.result() + + +def convert_pydantic_to_json_value(value: Any, cast_to: Type) -> dict: + if isinstance(value, Enum): + return value.value + elif isinstance(value, list): + return [convert_pydantic_to_json_value(item, cast_to) for item in value] + elif isinstance(value, dict): + return {k: convert_pydantic_to_json_value(v, cast_to) for k, v in value.items()} + elif isinstance(value, BaseModel): + # This is quite hacky and we should figure out how to use stuff from + # generated client-sdk code (using ApiResponse.parse() essentially) + value_dict = json.loads(value.model_dump_json()) + + origin = get_origin(cast_to) + if origin is Union: + args = get_args(cast_to) + for arg in args: + arg_name = arg.__name__.split(".")[-1] + value_name = value.__class__.__name__.split(".")[-1] + if arg_name == value_name: + return arg(**value_dict) + + # assume we have the correct association between the server-side type and the client-side type + return cast_to(**value_dict) + + return value + + +def convert_to_pydantic(annotation: Any, value: Any) -> Any: + if isinstance(annotation, type) and annotation in {str, int, float, bool}: + return value + + origin = get_origin(annotation) + if origin is list: + item_type = get_args(annotation)[0] + try: + return [convert_to_pydantic(item_type, item) for item in value] + except Exception: + print(f"Error converting list {value}") + return value + + elif origin is dict: + key_type, val_type = get_args(annotation) + try: + return {k: convert_to_pydantic(val_type, v) for k, v in value.items()} + except Exception: + print(f"Error converting dict {value}") + return value + + try: + # Handle Pydantic models and discriminated unions + return TypeAdapter(annotation).validate_python(value) + except Exception as e: + cprint( + f"Warning: direct client failed to convert parameter {value} into {annotation}: {e}", + "yellow", + ) + return value + + +class LlamaStackAsLibraryClient(LlamaStackClient): + def __init__( + self, + config_path_or_template_name: str, + custom_provider_registry: Optional[ProviderRegistry] = None, + ): + super().__init__() + self.async_client = AsyncLlamaStackAsLibraryClient( + config_path_or_template_name, custom_provider_registry + ) + self.pool_executor = ThreadPoolExecutor(max_workers=4) + + def initialize(self): + if in_notebook(): + import nest_asyncio + + nest_asyncio.apply() + + return asyncio.run(self.async_client.initialize()) + + def request(self, *args, **kwargs): + if kwargs.get("stream"): + return stream_across_asyncio_run_boundary( + lambda: self.async_client.request(*args, **kwargs), + self.pool_executor, + ) + else: + return asyncio.run(self.async_client.request(*args, **kwargs)) + + +class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient): + def __init__( + self, + config_path_or_template_name: str, + custom_provider_registry: Optional[ProviderRegistry] = None, + ): + super().__init__() + + # when using the library client, we should not log to console since many + # of our logs are intended for server-side usage + os.environ["TELEMETRY_SINKS"] = "sqlite" + + if config_path_or_template_name.endswith(".yaml"): + config_path = Path(config_path_or_template_name) + if not config_path.exists(): + raise ValueError(f"Config file {config_path} does not exist") + config_dict = replace_env_vars(yaml.safe_load(config_path.read_text())) + config = parse_and_maybe_upgrade_config(config_dict) + else: + # template + config = get_stack_run_config_from_template(config_path_or_template_name) + + self.config_path_or_template_name = config_path_or_template_name + self.config = config + self.custom_provider_registry = custom_provider_registry + + async def initialize(self): + try: + self.impls = await construct_stack( + self.config, self.custom_provider_registry + ) + except ModuleNotFoundError as _e: + cprint( + "Using llama-stack as a library requires installing dependencies depending on the template (providers) you choose.\n", + "yellow", + ) + if self.config_path_or_template_name.endswith(".yaml"): + print_pip_install_help(self.config.providers) + else: + prefix = "!" if in_notebook() else "" + cprint( + f"Please run:\n\n{prefix}llama stack build --template {self.config_path_or_template_name} --image-type venv\n\n", + "yellow", + ) + return False + + console = Console() + console.print(f"Using config [blue]{self.config_path_or_template_name}[/blue]:") + console.print(yaml.dump(self.config.model_dump(), indent=2)) + + endpoints = get_all_api_endpoints() + endpoint_impls = {} + for api, api_endpoints in endpoints.items(): + for endpoint in api_endpoints: + impl = self.impls[api] + func = getattr(impl, endpoint.name) + endpoint_impls[endpoint.route] = func + + self.endpoint_impls = endpoint_impls + return True + + async def request( + self, + cast_to: Any, + options: Any, + *, + stream=False, + stream_cls=None, + ): + if not self.endpoint_impls: + raise ValueError("Client not initialized") + + params = options.params or {} + params |= options.json_data or {} + if stream: + return self._call_streaming(options.url, params, cast_to) + else: + return await self._call_non_streaming(options.url, params, cast_to) + + async def _call_non_streaming( + self, path: str, body: dict = None, cast_to: Any = None + ): + func = self.endpoint_impls.get(path) + if not func: + raise ValueError(f"No endpoint found for {path}") + + body = self._convert_body(path, body) + return convert_pydantic_to_json_value(await func(**body), cast_to) + + async def _call_streaming(self, path: str, body: dict = None, cast_to: Any = None): + func = self.endpoint_impls.get(path) + if not func: + raise ValueError(f"No endpoint found for {path}") + + body = self._convert_body(path, body) + async for chunk in await func(**body): + yield convert_pydantic_to_json_value(chunk, cast_to) + + def _convert_body(self, path: str, body: Optional[dict] = None) -> dict: + if not body: + return {} + + func = self.endpoint_impls[path] + sig = inspect.signature(func) + + # Strip NOT_GIVENs to use the defaults in signature + body = {k: v for k, v in body.items() if v is not NOT_GIVEN} + + # Convert parameters to Pydantic models where needed + converted_body = {} + for param_name, param in sig.parameters.items(): + if param_name in body: + value = body.get(param_name) + converted_body[param_name] = convert_to_pydantic( + param.annotation, value + ) + return converted_body diff --git a/llama_stack/distribution/request_headers.py b/llama_stack/distribution/request_headers.py index 27ef3046a..41952edfd 100644 --- a/llama_stack/distribution/request_headers.py +++ b/llama_stack/distribution/request_headers.py @@ -35,7 +35,7 @@ class NeedsRequestProviderData: provider_data = validator(**val) return provider_data except Exception as e: - log.error("Error parsing provider data", e) + log.error(f"Error parsing provider data: {e}") def set_request_provider_data(headers: Dict[str, str]): diff --git a/llama_stack/distribution/routers/routers.py b/llama_stack/distribution/routers/routers.py index 5a62b6d64..5b75a525b 100644 --- a/llama_stack/distribution/routers/routers.py +++ b/llama_stack/distribution/routers/routers.py @@ -222,6 +222,12 @@ class DatasetIORouter(DatasetIO): filter_condition=filter_condition, ) + async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: + return await self.routing_table.get_provider_impl(dataset_id).append_rows( + dataset_id=dataset_id, + rows=rows, + ) + class ScoringRouter(Scoring): def __init__( diff --git a/llama_stack/distribution/routers/routing_tables.py b/llama_stack/distribution/routers/routing_tables.py index 4df693b26..2fb5a5e1c 100644 --- a/llama_stack/distribution/routers/routing_tables.py +++ b/llama_stack/distribution/routers/routing_tables.py @@ -57,6 +57,8 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None: return await p.unregister_memory_bank(obj.identifier) elif api == Api.inference: return await p.unregister_model(obj.identifier) + elif api == Api.datasetio: + return await p.unregister_dataset(obj.identifier) else: raise ValueError(f"Unregister not supported for {api}") @@ -354,6 +356,12 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets): ) await self.register_object(dataset) + async def unregister_dataset(self, dataset_id: str) -> None: + dataset = await self.get_dataset(dataset_id) + if dataset is None: + raise ValueError(f"Dataset {dataset_id} not found") + await self.unregister_object(dataset) + class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions): async def list_scoring_functions(self) -> List[ScoringFn]: diff --git a/llama_stack/distribution/server/server.py b/llama_stack/distribution/server/server.py index 8116e2b39..8f24f3eaf 100644 --- a/llama_stack/distribution/server/server.py +++ b/llama_stack/distribution/server/server.py @@ -43,9 +43,9 @@ from llama_stack.distribution.stack import ( replace_env_vars, validate_env_pair, ) -from llama_stack.providers.inline.meta_reference.telemetry.console import ( - ConsoleConfig, - ConsoleTelemetryImpl, +from llama_stack.providers.inline.telemetry.meta_reference.config import TelemetryConfig +from llama_stack.providers.inline.telemetry.meta_reference.telemetry import ( + TelemetryAdapter, ) from .endpoints import get_all_api_endpoints @@ -217,7 +217,7 @@ class TracingMiddleware: async def __call__(self, scope, receive, send): path = scope["path"] - await start_trace(path, {"location": "server"}) + await start_trace(path, {"__location__": "server"}) try: return await self.app(scope, receive, send) finally: @@ -290,7 +290,7 @@ def main(): if Api.telemetry in impls: setup_logger(impls[Api.telemetry]) else: - setup_logger(ConsoleTelemetryImpl(ConsoleConfig())) + setup_logger(TelemetryAdapter(TelemetryConfig())) all_endpoints = get_all_api_endpoints() diff --git a/llama_stack/distribution/tests/library_client_test.py b/llama_stack/distribution/tests/library_client_test.py new file mode 100644 index 000000000..955640c2b --- /dev/null +++ b/llama_stack/distribution/tests/library_client_test.py @@ -0,0 +1,128 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import argparse +import os + +from llama_stack.distribution.library_client import LlamaStackAsLibraryClient +from llama_stack_client.lib.agents.agent import Agent +from llama_stack_client.lib.agents.event_logger import EventLogger as AgentEventLogger +from llama_stack_client.lib.inference.event_logger import EventLogger +from llama_stack_client.types import Attachment, UserMessage +from llama_stack_client.types.agent_create_params import AgentConfig + + +def main(config_path: str): + client = LlamaStackAsLibraryClient(config_path) + if not client.initialize(): + return + + models = client.models.list() + print("\nModels:") + for model in models: + print(model) + + if not models: + print("No models found, skipping chat completion test") + return + + model_id = models[0].identifier + response = client.inference.chat_completion( + messages=[UserMessage(content="What is the capital of France?", role="user")], + model_id=model_id, + stream=False, + ) + print("\nChat completion response (non-stream):") + print(response) + + response = client.inference.chat_completion( + messages=[UserMessage(content="What is the capital of France?", role="user")], + model_id=model_id, + stream=True, + ) + + print("\nChat completion response (stream):") + for log in EventLogger().log(response): + log.print() + + print("\nAgent test:") + agent_config = AgentConfig( + model=model_id, + instructions="You are a helpful assistant", + sampling_params={ + "strategy": "greedy", + "temperature": 1.0, + "top_p": 0.9, + }, + tools=( + [ + { + "type": "brave_search", + "engine": "brave", + "api_key": os.getenv("BRAVE_SEARCH_API_KEY"), + } + ] + if os.getenv("BRAVE_SEARCH_API_KEY") + else [] + ) + + ( + [ + { + "type": "code_interpreter", + } + ] + ), + tool_choice="required", + input_shields=[], + output_shields=[], + enable_session_persistence=False, + ) + agent = Agent(client, agent_config) + user_prompts = [ + "Hello", + "Which players played in the winning team of the NBA western conference semifinals of 2024, please use tools", + ] + user_prompts = [ + ( + "Here is a csv, can you describe it ?", + [ + Attachment( + content="https://raw.githubusercontent.com/meta-llama/llama-stack-apps/main/examples/resources/inflation.csv", + mime_type="test/csv", + ) + ], + ), + ("Which year ended with the highest inflation ?", None), + ( + "What macro economic situations that led to such high inflation in that period?", + None, + ), + ("Plot average yearly inflation as a time series", None), + ] + + session_id = agent.create_session("test-session") + + for prompt, attachments in user_prompts: + response = agent.create_turn( + messages=[ + { + "role": "user", + "content": prompt, + } + ], + attachments=attachments, + session_id=session_id, + ) + + for log in AgentEventLogger().log(response): + log.print() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("config_path", help="Path to the config YAML file") + args = parser.parse_args() + main(args.config_path) diff --git a/llama_stack/distribution/ui/README.md b/llama_stack/distribution/ui/README.md new file mode 100644 index 000000000..c0a2597af --- /dev/null +++ b/llama_stack/distribution/ui/README.md @@ -0,0 +1,42 @@ +# (Experimental) LLama Stack UI + +## Docker Setup + +:warning: This is a work in progress. + +## Developer Setup + +1. Start up Llama Stack API server. More details [here](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html). + +``` +llama stack build --template together --image-type conda + +llama stack run together +``` + +2. (Optional) Register datasets and eval tasks as resources. If you want to run pre-configured evaluation flows (e.g. Evaluations (Generation + Scoring) Page). + +```bash +$ llama-stack-client datasets register \ +--dataset-id "mmlu" \ +--provider-id "huggingface" \ +--url "https://huggingface.co/datasets/llamastack/evals" \ +--metadata '{"path": "llamastack/evals", "name": "evals__mmlu__details", "split": "train"}' \ +--schema '{"input_query": {"type": "string"}, "expected_answer": {"type": "string", "chat_completion_input": {"type": "string"}}}' +``` + +```bash +$ llama-stack-client eval_tasks register \ +--eval-task-id meta-reference-mmlu \ +--provider-id meta-reference \ +--dataset-id mmlu \ +--scoring-functions basic::regex_parser_multiple_choice_answer +``` + +3. Start Streamlit UI + +```bash +cd llama_stack/distribution/ui +pip install -r requirements.txt +streamlit run app.py +``` diff --git a/llama_stack/providers/remote/telemetry/__init__.py b/llama_stack/distribution/ui/__init__.py similarity index 100% rename from llama_stack/providers/remote/telemetry/__init__.py rename to llama_stack/distribution/ui/__init__.py diff --git a/llama_stack/distribution/ui/app.py b/llama_stack/distribution/ui/app.py new file mode 100644 index 000000000..87a80e235 --- /dev/null +++ b/llama_stack/distribution/ui/app.py @@ -0,0 +1,57 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. +import streamlit as st + + +def main(): + # Evaluation pages + application_evaluation_page = st.Page( + "page/evaluations/app_eval.py", + title="Evaluations (Scoring)", + icon="📊", + default=False, + ) + native_evaluation_page = st.Page( + "page/evaluations/native_eval.py", + title="Evaluations (Generation + Scoring)", + icon="📊", + default=False, + ) + + # Playground pages + chat_page = st.Page( + "page/playground/chat.py", title="Chat", icon="💬", default=True + ) + rag_page = st.Page("page/playground/rag.py", title="RAG", icon="💬", default=False) + + # Distribution pages + resources_page = st.Page( + "page/distribution/resources.py", title="Resources", icon="🔍", default=False + ) + provider_page = st.Page( + "page/distribution/providers.py", + title="API Providers", + icon="🔍", + default=False, + ) + + pg = st.navigation( + { + "Playground": [ + chat_page, + rag_page, + application_evaluation_page, + native_evaluation_page, + ], + "Inspect": [provider_page, resources_page], + }, + expanded=False, + ) + pg.run() + + +if __name__ == "__main__": + main() diff --git a/llama_stack/distribution/ui/modules/__init__.py b/llama_stack/distribution/ui/modules/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/distribution/ui/modules/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/distribution/ui/modules/api.py b/llama_stack/distribution/ui/modules/api.py new file mode 100644 index 000000000..d3852caee --- /dev/null +++ b/llama_stack/distribution/ui/modules/api.py @@ -0,0 +1,36 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os + +from typing import Optional + +from llama_stack_client import LlamaStackClient + + +class LlamaStackApi: + def __init__(self): + self.client = LlamaStackClient( + base_url=os.environ.get("LLAMA_STACK_ENDPOINT", "http://localhost:5000"), + provider_data={ + "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY", ""), + "together_api_key": os.environ.get("TOGETHER_API_KEY", ""), + "openai_api_key": os.environ.get("OPENAI_API_KEY", ""), + }, + ) + + def run_scoring( + self, row, scoring_function_ids: list[str], scoring_params: Optional[dict] + ): + """Run scoring on a single row""" + if not scoring_params: + scoring_params = {fn_id: None for fn_id in scoring_function_ids} + return self.client.scoring.score( + input_rows=[row], scoring_functions=scoring_params + ) + + +llama_stack_api = LlamaStackApi() diff --git a/llama_stack/distribution/ui/modules/utils.py b/llama_stack/distribution/ui/modules/utils.py new file mode 100644 index 000000000..67cce98fa --- /dev/null +++ b/llama_stack/distribution/ui/modules/utils.py @@ -0,0 +1,42 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import base64 +import os + +import pandas as pd +import streamlit as st + + +def process_dataset(file): + if file is None: + return "No file uploaded", None + + try: + # Determine file type and read accordingly + file_ext = os.path.splitext(file.name)[1].lower() + if file_ext == ".csv": + df = pd.read_csv(file) + elif file_ext in [".xlsx", ".xls"]: + df = pd.read_excel(file) + else: + return "Unsupported file format. Please upload a CSV or Excel file.", None + + return df + + except Exception as e: + st.error(f"Error processing file: {str(e)}") + return None + + +def data_url_from_file(file) -> str: + file_content = file.getvalue() + base64_content = base64.b64encode(file_content).decode("utf-8") + mime_type = file.type + + data_url = f"data:{mime_type};base64,{base64_content}" + + return data_url diff --git a/llama_stack/distribution/ui/page/__init__.py b/llama_stack/distribution/ui/page/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/distribution/ui/page/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/distribution/ui/page/distribution/datasets.py b/llama_stack/distribution/ui/page/distribution/datasets.py new file mode 100644 index 000000000..44e314cde --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/datasets.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def datasets(): + st.header("Datasets") + + datasets_info = { + d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list() + } + + selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys())) + st.json(datasets_info[selected_dataset], expanded=True) diff --git a/llama_stack/distribution/ui/page/distribution/eval_tasks.py b/llama_stack/distribution/ui/page/distribution/eval_tasks.py new file mode 100644 index 000000000..4957fb178 --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/eval_tasks.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def eval_tasks(): + # Eval Tasks Section + st.header("Eval Tasks") + + eval_tasks_info = { + d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list() + } + + selected_eval_task = st.selectbox( + "Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect" + ) + st.json(eval_tasks_info[selected_eval_task], expanded=True) diff --git a/llama_stack/distribution/ui/page/distribution/memory_banks.py b/llama_stack/distribution/ui/page/distribution/memory_banks.py new file mode 100644 index 000000000..f28010bf2 --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/memory_banks.py @@ -0,0 +1,23 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def memory_banks(): + st.header("Memory Banks") + memory_banks_info = { + m.identifier: m.to_dict() for m in llama_stack_api.client.memory_banks.list() + } + + if len(memory_banks_info) > 0: + selected_memory_bank = st.selectbox( + "Select a memory bank", list(memory_banks_info.keys()) + ) + st.json(memory_banks_info[selected_memory_bank]) + else: + st.info("No memory banks found") diff --git a/llama_stack/distribution/ui/page/distribution/models.py b/llama_stack/distribution/ui/page/distribution/models.py new file mode 100644 index 000000000..70b166f2e --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/models.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def models(): + # Models Section + st.header("Models") + models_info = { + m.identifier: m.to_dict() for m in llama_stack_api.client.models.list() + } + + selected_model = st.selectbox("Select a model", list(models_info.keys())) + st.json(models_info[selected_model]) diff --git a/llama_stack/distribution/ui/page/distribution/providers.py b/llama_stack/distribution/ui/page/distribution/providers.py new file mode 100644 index 000000000..69f6bd771 --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/providers.py @@ -0,0 +1,20 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def providers(): + st.header("🔍 API Providers") + apis_providers_info = llama_stack_api.client.providers.list() + # selected_api = st.selectbox("Select an API", list(apis_providers_info.keys())) + for api in apis_providers_info.keys(): + st.markdown(f"###### {api}") + st.dataframe([p.to_dict() for p in apis_providers_info[api]], width=500) + + +providers() diff --git a/llama_stack/distribution/ui/page/distribution/resources.py b/llama_stack/distribution/ui/page/distribution/resources.py new file mode 100644 index 000000000..6b3ea0e3a --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/resources.py @@ -0,0 +1,52 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from page.distribution.datasets import datasets +from page.distribution.eval_tasks import eval_tasks +from page.distribution.memory_banks import memory_banks +from page.distribution.models import models +from page.distribution.scoring_functions import scoring_functions +from page.distribution.shields import shields + +from streamlit_option_menu import option_menu + + +def resources_page(): + options = [ + "Models", + "Memory Banks", + "Shields", + "Scoring Functions", + "Datasets", + "Eval Tasks", + ] + icons = ["magic", "memory", "shield", "file-bar-graph", "database", "list-task"] + selected_resource = option_menu( + None, + options, + icons=icons, + orientation="horizontal", + styles={ + "nav-link": { + "font-size": "12px", + }, + }, + ) + if selected_resource == "Eval Tasks": + eval_tasks() + elif selected_resource == "Memory Banks": + memory_banks() + elif selected_resource == "Datasets": + datasets() + elif selected_resource == "Models": + models() + elif selected_resource == "Scoring Functions": + scoring_functions() + elif selected_resource == "Shields": + shields() + + +resources_page() diff --git a/llama_stack/distribution/ui/page/distribution/scoring_functions.py b/llama_stack/distribution/ui/page/distribution/scoring_functions.py new file mode 100644 index 000000000..581ae0db7 --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/scoring_functions.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def scoring_functions(): + st.header("Scoring Functions") + + scoring_functions_info = { + s.identifier: s.to_dict() + for s in llama_stack_api.client.scoring_functions.list() + } + + selected_scoring_function = st.selectbox( + "Select a scoring function", list(scoring_functions_info.keys()) + ) + st.json(scoring_functions_info[selected_scoring_function], expanded=True) diff --git a/llama_stack/distribution/ui/page/distribution/shields.py b/llama_stack/distribution/ui/page/distribution/shields.py new file mode 100644 index 000000000..18bbfc008 --- /dev/null +++ b/llama_stack/distribution/ui/page/distribution/shields.py @@ -0,0 +1,20 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + + +def shields(): + # Shields Section + st.header("Shields") + + shields_info = { + s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list() + } + + selected_shield = st.selectbox("Select a shield", list(shields_info.keys())) + st.json(shields_info[selected_shield]) diff --git a/llama_stack/distribution/ui/page/evaluations/__init__.py b/llama_stack/distribution/ui/page/evaluations/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/distribution/ui/page/evaluations/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/distribution/ui/page/evaluations/app_eval.py b/llama_stack/distribution/ui/page/evaluations/app_eval.py new file mode 100644 index 000000000..5ec47ed45 --- /dev/null +++ b/llama_stack/distribution/ui/page/evaluations/app_eval.py @@ -0,0 +1,148 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json + +import pandas as pd +import streamlit as st + +from modules.api import llama_stack_api +from modules.utils import process_dataset + + +def application_evaluation_page(): + + st.set_page_config(page_title="Evaluations (Scoring)", page_icon="🦙") + st.title("📊 Evaluations (Scoring)") + + # File uploader + uploaded_file = st.file_uploader("Upload Dataset", type=["csv", "xlsx", "xls"]) + + if uploaded_file is None: + st.error("No file uploaded") + return + + # Process uploaded file + df = process_dataset(uploaded_file) + if df is None: + st.error("Error processing file") + return + + # Display dataset information + st.success("Dataset loaded successfully!") + + # Display dataframe preview + st.subheader("Dataset Preview") + st.dataframe(df) + + # Select Scoring Functions to Run Evaluation On + st.subheader("Select Scoring Functions") + scoring_functions = llama_stack_api.client.scoring_functions.list() + scoring_functions = {sf.identifier: sf for sf in scoring_functions} + scoring_functions_names = list(scoring_functions.keys()) + selected_scoring_functions = st.multiselect( + "Choose one or more scoring functions", + options=scoring_functions_names, + help="Choose one or more scoring functions.", + ) + + available_models = llama_stack_api.client.models.list() + available_models = [m.identifier for m in available_models] + + scoring_params = {} + if selected_scoring_functions: + st.write("Selected:") + for scoring_fn_id in selected_scoring_functions: + scoring_fn = scoring_functions[scoring_fn_id] + st.write(f"- **{scoring_fn_id}**: {scoring_fn.description}") + new_params = None + if scoring_fn.params: + new_params = {} + for param_name, param_value in scoring_fn.params.to_dict().items(): + if param_name == "type": + new_params[param_name] = param_value + continue + + if param_name == "judge_model": + value = st.selectbox( + f"Select **{param_name}** for {scoring_fn_id}", + options=available_models, + index=0, + key=f"{scoring_fn_id}_{param_name}", + ) + new_params[param_name] = value + else: + value = st.text_area( + f"Enter value for **{param_name}** in {scoring_fn_id} in valid JSON format", + value=json.dumps(param_value, indent=2), + height=80, + ) + try: + new_params[param_name] = json.loads(value) + except json.JSONDecodeError: + st.error( + f"Invalid JSON for **{param_name}** in {scoring_fn_id}" + ) + + st.json(new_params) + scoring_params[scoring_fn_id] = new_params + + # Add run evaluation button & slider + total_rows = len(df) + num_rows = st.slider("Number of rows to evaluate", 1, total_rows, total_rows) + + if st.button("Run Evaluation"): + progress_text = "Running evaluation..." + progress_bar = st.progress(0, text=progress_text) + rows = df.to_dict(orient="records") + if num_rows < total_rows: + rows = rows[:num_rows] + + # Create separate containers for progress text and results + progress_text_container = st.empty() + results_container = st.empty() + output_res = {} + for i, r in enumerate(rows): + # Update progress + progress = i / len(rows) + progress_bar.progress(progress, text=progress_text) + + # Run evaluation for current row + score_res = llama_stack_api.run_scoring( + r, + scoring_function_ids=selected_scoring_functions, + scoring_params=scoring_params, + ) + + for k in r.keys(): + if k not in output_res: + output_res[k] = [] + output_res[k].append(r[k]) + + for fn_id in selected_scoring_functions: + if fn_id not in output_res: + output_res[fn_id] = [] + output_res[fn_id].append(score_res.results[fn_id].score_rows[0]) + + # Display current row results using separate containers + progress_text_container.write( + f"Expand to see current processed result ({i+1}/{len(rows)})" + ) + results_container.json( + score_res.to_json(), + expanded=2, + ) + + progress_bar.progress(1.0, text="Evaluation complete!") + + # Display results in dataframe + if output_res: + output_df = pd.DataFrame(output_res) + st.subheader("Evaluation Results") + st.dataframe(output_df) + + +application_evaluation_page() diff --git a/llama_stack/distribution/ui/page/evaluations/native_eval.py b/llama_stack/distribution/ui/page/evaluations/native_eval.py new file mode 100644 index 000000000..b8cc8bfa6 --- /dev/null +++ b/llama_stack/distribution/ui/page/evaluations/native_eval.py @@ -0,0 +1,257 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json + +import pandas as pd + +import streamlit as st + +from modules.api import llama_stack_api + + +def select_eval_task_1(): + # Select Eval Tasks + st.subheader("1. Choose An Eval Task") + eval_tasks = llama_stack_api.client.eval_tasks.list() + eval_tasks = {et.identifier: et for et in eval_tasks} + eval_tasks_names = list(eval_tasks.keys()) + selected_eval_task = st.selectbox( + "Choose an eval task.", + options=eval_tasks_names, + help="Choose an eval task. Each eval task is parameterized by a dataset, and list of scoring functions.", + ) + with st.expander("View Eval Task"): + st.json(eval_tasks[selected_eval_task], expanded=True) + + st.session_state["selected_eval_task"] = selected_eval_task + st.session_state["eval_tasks"] = eval_tasks + if st.button("Confirm", key="confirm_1"): + st.session_state["selected_eval_task_1_next"] = True + + +def define_eval_candidate_2(): + if not st.session_state.get("selected_eval_task_1_next", None): + return + + st.subheader("2. Define Eval Candidate") + st.info( + """ + Define the configurations for the evaluation candidate model or agent used for generation. + Select "model" if you want to run generation with inference API, or "agent" if you want to run generation with agent API through specifying AgentConfig. + """ + ) + with st.expander("Define Eval Candidate", expanded=True): + # Define Eval Candidate + candidate_type = st.radio("Candidate Type", ["model", "agent"]) + + available_models = llama_stack_api.client.models.list() + available_models = [model.identifier for model in available_models] + selected_model = st.selectbox( + "Choose a model", + available_models, + index=0, + ) + + # Sampling Parameters + st.markdown("##### Sampling Parameters") + strategy = st.selectbox( + "Strategy", + ["greedy", "top_p", "top_k"], + index=0, + ) + temperature = st.slider( + "Temperature", + min_value=0.0, + max_value=1.0, + value=0.0, + step=0.1, + help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable", + ) + top_p = st.slider( + "Top P", + min_value=0.0, + max_value=1.0, + value=0.95, + step=0.1, + ) + max_tokens = st.slider( + "Max Tokens", + min_value=0, + max_value=4096, + value=512, + step=1, + help="The maximum number of tokens to generate", + ) + repetition_penalty = st.slider( + "Repetition Penalty", + min_value=1.0, + max_value=2.0, + value=1.0, + step=0.1, + help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.", + ) + if candidate_type == "model": + eval_candidate = { + "type": "model", + "model": selected_model, + "sampling_params": { + "strategy": strategy, + "temperature": temperature, + "top_p": top_p, + "max_tokens": max_tokens, + "repetition_penalty": repetition_penalty, + }, + } + elif candidate_type == "agent": + system_prompt = st.text_area( + "System Prompt", + value="You are a helpful AI assistant.", + help="Initial instructions given to the AI to set its behavior and context", + ) + tools_json = st.text_area( + "Tools Configuration (JSON)", + value=json.dumps( + [ + { + "type": "brave_search", + "engine": "brave", + "api_key": "ENTER_BRAVE_API_KEY_HERE", + } + ] + ), + help="Enter tool configurations in JSON format. Each tool should have a name, description, and parameters.", + height=200, + ) + try: + tools = json.loads(tools_json) + except json.JSONDecodeError: + st.error("Invalid JSON format for tools configuration") + tools = [] + eval_candidate = { + "type": "agent", + "config": { + "model": selected_model, + "instructions": system_prompt, + "tools": tools, + "tool_choice": "auto", + "tool_prompt_format": "json", + "input_shields": [], + "output_shields": [], + "enable_session_persistence": False, + }, + } + st.session_state["eval_candidate"] = eval_candidate + + if st.button("Confirm", key="confirm_2"): + st.session_state["selected_eval_candidate_2_next"] = True + + +def run_evaluation_3(): + if not st.session_state.get("selected_eval_candidate_2_next", None): + return + + st.subheader("3. Run Evaluation") + # Add info box to explain configurations being used + st.info( + """ + Review the configurations that will be used for this evaluation run, make any necessary changes, and then click the "Run Evaluation" button. + """ + ) + selected_eval_task = st.session_state["selected_eval_task"] + eval_tasks = st.session_state["eval_tasks"] + eval_candidate = st.session_state["eval_candidate"] + + dataset_id = eval_tasks[selected_eval_task].dataset_id + rows = llama_stack_api.client.datasetio.get_rows_paginated( + dataset_id=dataset_id, + rows_in_page=-1, + ) + total_rows = len(rows.rows) + # Add number of examples control + num_rows = st.number_input( + "Number of Examples to Evaluate", + min_value=1, + max_value=total_rows, + value=5, + help="Number of examples from the dataset to evaluate. ", + ) + + eval_task_config = { + "type": "benchmark", + "eval_candidate": eval_candidate, + "scoring_params": {}, + } + + with st.expander("View Evaluation Task", expanded=True): + st.json(eval_tasks[selected_eval_task], expanded=True) + with st.expander("View Evaluation Task Configuration", expanded=True): + st.json(eval_task_config, expanded=True) + + # Add run button and handle evaluation + if st.button("Run Evaluation"): + + progress_text = "Running evaluation..." + progress_bar = st.progress(0, text=progress_text) + rows = rows.rows + if num_rows < total_rows: + rows = rows[:num_rows] + + # Create separate containers for progress text and results + progress_text_container = st.empty() + results_container = st.empty() + output_res = {} + for i, r in enumerate(rows): + # Update progress + progress = i / len(rows) + progress_bar.progress(progress, text=progress_text) + # Run evaluation for current row + eval_res = llama_stack_api.client.eval.evaluate_rows( + task_id=selected_eval_task, + input_rows=[r], + scoring_functions=eval_tasks[selected_eval_task].scoring_functions, + task_config=eval_task_config, + ) + + for k in r.keys(): + if k not in output_res: + output_res[k] = [] + output_res[k].append(r[k]) + + for k in eval_res.generations[0].keys(): + if k not in output_res: + output_res[k] = [] + output_res[k].append(eval_res.generations[0][k]) + + for scoring_fn in eval_tasks[selected_eval_task].scoring_functions: + if scoring_fn not in output_res: + output_res[scoring_fn] = [] + output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0]) + + progress_text_container.write( + f"Expand to see current processed result ({i+1}/{len(rows)})" + ) + results_container.json(eval_res, expanded=2) + + progress_bar.progress(1.0, text="Evaluation complete!") + # Display results in dataframe + if output_res: + output_df = pd.DataFrame(output_res) + st.subheader("Evaluation Results") + st.dataframe(output_df) + + +def native_evaluation_page(): + + st.set_page_config(page_title="Evaluations (Generation + Scoring)", page_icon="🦙") + st.title("📊 Evaluations (Generation + Scoring)") + + select_eval_task_1() + define_eval_candidate_2() + run_evaluation_3() + + +native_evaluation_page() diff --git a/llama_stack/distribution/ui/page/playground/__init__.py b/llama_stack/distribution/ui/page/playground/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/distribution/ui/page/playground/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/distribution/ui/page/playground/chat.py b/llama_stack/distribution/ui/page/playground/chat.py new file mode 100644 index 000000000..157922d3b --- /dev/null +++ b/llama_stack/distribution/ui/page/playground/chat.py @@ -0,0 +1,123 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from modules.api import llama_stack_api + +# Sidebar configurations +with st.sidebar: + st.header("Configuration") + available_models = llama_stack_api.client.models.list() + available_models = [model.identifier for model in available_models] + selected_model = st.selectbox( + "Choose a model", + available_models, + index=0, + ) + + temperature = st.slider( + "Temperature", + min_value=0.0, + max_value=1.0, + value=0.0, + step=0.1, + help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable", + ) + + top_p = st.slider( + "Top P", + min_value=0.0, + max_value=1.0, + value=0.95, + step=0.1, + ) + + max_tokens = st.slider( + "Max Tokens", + min_value=0, + max_value=4096, + value=512, + step=1, + help="The maximum number of tokens to generate", + ) + + repetition_penalty = st.slider( + "Repetition Penalty", + min_value=1.0, + max_value=2.0, + value=1.0, + step=0.1, + help="Controls the likelihood for generating the same word or phrase multiple times in the same sentence or paragraph. 1 implies no penalty, 2 will strongly discourage model to repeat words or phrases.", + ) + + stream = st.checkbox("Stream", value=True) + system_prompt = st.text_area( + "System Prompt", + value="You are a helpful AI assistant.", + help="Initial instructions given to the AI to set its behavior and context", + ) + + # Add clear chat button to sidebar + if st.button("Clear Chat", use_container_width=True): + st.session_state.messages = [] + st.rerun() + + +# Main chat interface +st.title("🦙 Chat") + + +# Initialize chat history +if "messages" not in st.session_state: + st.session_state.messages = [] + +# Display chat messages +for message in st.session_state.messages: + with st.chat_message(message["role"]): + st.markdown(message["content"]) + +# Chat input +if prompt := st.chat_input("Example: What is Llama Stack?"): + # Add user message to chat history + st.session_state.messages.append({"role": "user", "content": prompt}) + + # Display user message + with st.chat_message("user"): + st.markdown(prompt) + + # Display assistant response + with st.chat_message("assistant"): + message_placeholder = st.empty() + full_response = "" + + response = llama_stack_api.client.inference.chat_completion( + messages=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt}, + ], + model_id=selected_model, + stream=stream, + sampling_params={ + "temperature": temperature, + "top_p": top_p, + "max_tokens": max_tokens, + "repetition_penalty": repetition_penalty, + }, + ) + + if stream: + for chunk in response: + if chunk.event.event_type == "progress": + full_response += chunk.event.delta + message_placeholder.markdown(full_response + "▌") + message_placeholder.markdown(full_response) + else: + full_response = response + message_placeholder.markdown(full_response.completion_message.content) + + st.session_state.messages.append( + {"role": "assistant", "content": full_response} + ) diff --git a/llama_stack/distribution/ui/page/playground/rag.py b/llama_stack/distribution/ui/page/playground/rag.py new file mode 100644 index 000000000..ffcaf1afd --- /dev/null +++ b/llama_stack/distribution/ui/page/playground/rag.py @@ -0,0 +1,188 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import streamlit as st +from llama_stack_client.lib.agents.agent import Agent +from llama_stack_client.lib.agents.event_logger import EventLogger +from llama_stack_client.types.agent_create_params import AgentConfig +from llama_stack_client.types.memory_insert_params import Document + +from modules.api import llama_stack_api +from modules.utils import data_url_from_file + + +def rag_chat_page(): + st.title("🦙 RAG") + + with st.sidebar: + # File/Directory Upload Section + st.subheader("Upload Documents") + uploaded_files = st.file_uploader( + "Upload file(s) or directory", + accept_multiple_files=True, + type=["txt", "pdf", "doc", "docx"], # Add more file types as needed + ) + # Process uploaded files + if uploaded_files: + st.success(f"Successfully uploaded {len(uploaded_files)} files") + # Add memory bank name input field + memory_bank_name = st.text_input( + "Memory Bank Name", + value="rag_bank", + help="Enter a unique identifier for this memory bank", + ) + if st.button("Create Memory Bank"): + documents = [ + Document( + document_id=uploaded_file.name, + content=data_url_from_file(uploaded_file), + ) + for i, uploaded_file in enumerate(uploaded_files) + ] + + providers = llama_stack_api.client.providers.list() + llama_stack_api.client.memory_banks.register( + memory_bank_id=memory_bank_name, # Use the user-provided name + params={ + "embedding_model": "all-MiniLM-L6-v2", + "chunk_size_in_tokens": 512, + "overlap_size_in_tokens": 64, + }, + provider_id=providers["memory"][0].provider_id, + ) + + # insert documents using the custom bank name + llama_stack_api.client.memory.insert( + bank_id=memory_bank_name, # Use the user-provided name + documents=documents, + ) + st.success("Memory bank created successfully!") + + st.subheader("Configure Agent") + # select memory banks + memory_banks = llama_stack_api.client.memory_banks.list() + memory_banks = [bank.identifier for bank in memory_banks] + selected_memory_banks = st.multiselect( + "Select Memory Banks", + memory_banks, + ) + memory_bank_configs = [ + {"bank_id": bank_id, "type": "vector"} for bank_id in selected_memory_banks + ] + + available_models = llama_stack_api.client.models.list() + available_models = [model.identifier for model in available_models] + selected_model = st.selectbox( + "Choose a model", + available_models, + index=0, + ) + system_prompt = st.text_area( + "System Prompt", + value="You are a helpful assistant. ", + help="Initial instructions given to the AI to set its behavior and context", + ) + temperature = st.slider( + "Temperature", + min_value=0.0, + max_value=1.0, + value=0.0, + step=0.1, + help="Controls the randomness of the response. Higher values make the output more creative and unexpected, lower values make it more conservative and predictable", + ) + + top_p = st.slider( + "Top P", + min_value=0.0, + max_value=1.0, + value=0.95, + step=0.1, + ) + + # Add clear chat button to sidebar + if st.button("Clear Chat", use_container_width=True): + st.session_state.messages = [] + st.rerun() + + # Chat Interface + if "messages" not in st.session_state: + st.session_state.messages = [] + + # Display chat history + for message in st.session_state.messages: + with st.chat_message(message["role"]): + st.markdown(message["content"]) + + selected_model = llama_stack_api.client.models.list()[0].identifier + + agent_config = AgentConfig( + model=selected_model, + instructions=system_prompt, + sampling_params={ + "strategy": "greedy", + "temperature": temperature, + "top_p": top_p, + }, + tools=[ + { + "type": "memory", + "memory_bank_configs": memory_bank_configs, + "query_generator_config": {"type": "default", "sep": " "}, + "max_tokens_in_context": 4096, + "max_chunks": 10, + } + ], + tool_choice="auto", + tool_prompt_format="json", + input_shields=[], + output_shields=[], + enable_session_persistence=False, + ) + + agent = Agent(llama_stack_api.client, agent_config) + session_id = agent.create_session("rag-session") + + # Chat input + if prompt := st.chat_input("Ask a question about your documents"): + # Add user message to chat history + st.session_state.messages.append({"role": "user", "content": prompt}) + + # Display user message + with st.chat_message("user"): + st.markdown(prompt) + + response = agent.create_turn( + messages=[ + { + "role": "user", + "content": prompt, + } + ], + session_id=session_id, + ) + + # Display assistant response + with st.chat_message("assistant"): + retrieval_message_placeholder = st.empty() + message_placeholder = st.empty() + full_response = "" + retrieval_response = "" + for log in EventLogger().log(response): + log.print() + if log.role == "memory_retrieval": + retrieval_response += log.content.replace("====", "").strip() + retrieval_message_placeholder.info(retrieval_response) + else: + full_response += log.content + message_placeholder.markdown(full_response + "▌") + message_placeholder.markdown(full_response) + + st.session_state.messages.append( + {"role": "assistant", "content": full_response} + ) + + +rag_chat_page() diff --git a/llama_stack/distribution/ui/requirements.txt b/llama_stack/distribution/ui/requirements.txt new file mode 100644 index 000000000..39f2b3d27 --- /dev/null +++ b/llama_stack/distribution/ui/requirements.txt @@ -0,0 +1,4 @@ +streamlit +pandas +llama-stack-client>=0.0.55 +streamlit-option-menu diff --git a/llama_stack/providers/datatypes.py b/llama_stack/providers/datatypes.py index 080204e45..8e89bcc72 100644 --- a/llama_stack/providers/datatypes.py +++ b/llama_stack/providers/datatypes.py @@ -63,6 +63,8 @@ class MemoryBanksProtocolPrivate(Protocol): class DatasetsProtocolPrivate(Protocol): async def register_dataset(self, dataset: Dataset) -> None: ... + async def unregister_dataset(self, dataset_id: str) -> None: ... + class ScoringFunctionsProtocolPrivate(Protocol): async def list_scoring_functions(self) -> List[ScoringFn]: ... diff --git a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py index 8f800ad6f..f08bdb032 100644 --- a/llama_stack/providers/inline/agents/meta_reference/agent_instance.py +++ b/llama_stack/providers/inline/agents/meta_reference/agent_instance.py @@ -10,9 +10,7 @@ import logging import os import re import secrets -import shutil import string -import tempfile import uuid from datetime import datetime from typing import AsyncGenerator, List, Tuple @@ -57,6 +55,7 @@ class ChatAgent(ShieldRunnerMixin): self, agent_id: str, agent_config: AgentConfig, + tempdir: str, inference_api: Inference, memory_api: Memory, memory_banks_api: MemoryBanks, @@ -65,14 +64,13 @@ class ChatAgent(ShieldRunnerMixin): ): self.agent_id = agent_id self.agent_config = agent_config + self.tempdir = tempdir self.inference_api = inference_api self.memory_api = memory_api self.memory_banks_api = memory_banks_api self.safety_api = safety_api self.storage = AgentPersistence(agent_id, persistence_store) - self.tempdir = tempfile.mkdtemp() - builtin_tools = [] for tool_defn in agent_config.tools: if isinstance(tool_defn, WolframAlphaToolDefinition): @@ -103,9 +101,6 @@ class ChatAgent(ShieldRunnerMixin): output_shields=agent_config.output_shields, ) - def __del__(self): - shutil.rmtree(self.tempdir) - def turn_to_messages(self, turn: Turn) -> List[Message]: messages = [] @@ -144,87 +139,91 @@ class ChatAgent(ShieldRunnerMixin): async def create_session(self, name: str) -> str: return await self.storage.create_session(name) - @tracing.span("create_and_execute_turn") async def create_and_execute_turn( self, request: AgentTurnCreateRequest ) -> AsyncGenerator: - assert request.stream is True, "Non-streaming not supported" + with tracing.span("create_and_execute_turn") as span: + span.set_attribute("session_id", request.session_id) + span.set_attribute("agent_id", self.agent_id) + span.set_attribute("request", request.model_dump_json()) + assert request.stream is True, "Non-streaming not supported" - session_info = await self.storage.get_session_info(request.session_id) - if session_info is None: - raise ValueError(f"Session {request.session_id} not found") + session_info = await self.storage.get_session_info(request.session_id) + if session_info is None: + raise ValueError(f"Session {request.session_id} not found") - turns = await self.storage.get_session_turns(request.session_id) + turns = await self.storage.get_session_turns(request.session_id) - messages = [] - if self.agent_config.instructions != "": - messages.append(SystemMessage(content=self.agent_config.instructions)) + messages = [] + if self.agent_config.instructions != "": + messages.append(SystemMessage(content=self.agent_config.instructions)) - for i, turn in enumerate(turns): - messages.extend(self.turn_to_messages(turn)) + for i, turn in enumerate(turns): + messages.extend(self.turn_to_messages(turn)) - messages.extend(request.messages) + messages.extend(request.messages) - turn_id = str(uuid.uuid4()) - start_time = datetime.now() - yield AgentTurnResponseStreamChunk( - event=AgentTurnResponseEvent( - payload=AgentTurnResponseTurnStartPayload( - turn_id=turn_id, + turn_id = str(uuid.uuid4()) + span.set_attribute("turn_id", turn_id) + start_time = datetime.now() + yield AgentTurnResponseStreamChunk( + event=AgentTurnResponseEvent( + payload=AgentTurnResponseTurnStartPayload( + turn_id=turn_id, + ) ) ) - ) - steps = [] - output_message = None - async for chunk in self.run( - session_id=request.session_id, - turn_id=turn_id, - input_messages=messages, - attachments=request.attachments or [], - sampling_params=self.agent_config.sampling_params, - stream=request.stream, - ): - if isinstance(chunk, CompletionMessage): - log.info( - f"{chunk.role.capitalize()}: {chunk.content}", - ) - output_message = chunk - continue - - assert isinstance( - chunk, AgentTurnResponseStreamChunk - ), f"Unexpected type {type(chunk)}" - event = chunk.event - if ( - event.payload.event_type - == AgentTurnResponseEventType.step_complete.value + steps = [] + output_message = None + async for chunk in self.run( + session_id=request.session_id, + turn_id=turn_id, + input_messages=messages, + attachments=request.attachments or [], + sampling_params=self.agent_config.sampling_params, + stream=request.stream, ): - steps.append(event.payload.step_details) + if isinstance(chunk, CompletionMessage): + # log.info( + # f"{chunk.role.capitalize()}: {chunk.content}", + # ) + output_message = chunk + continue - yield chunk + assert isinstance( + chunk, AgentTurnResponseStreamChunk + ), f"Unexpected type {type(chunk)}" + event = chunk.event + if ( + event.payload.event_type + == AgentTurnResponseEventType.step_complete.value + ): + steps.append(event.payload.step_details) - assert output_message is not None + yield chunk - turn = Turn( - turn_id=turn_id, - session_id=request.session_id, - input_messages=request.messages, - output_message=output_message, - started_at=start_time, - completed_at=datetime.now(), - steps=steps, - ) - await self.storage.add_turn_to_session(request.session_id, turn) + assert output_message is not None - chunk = AgentTurnResponseStreamChunk( - event=AgentTurnResponseEvent( - payload=AgentTurnResponseTurnCompletePayload( - turn=turn, + turn = Turn( + turn_id=turn_id, + session_id=request.session_id, + input_messages=request.messages, + output_message=output_message, + started_at=start_time, + completed_at=datetime.now(), + steps=steps, + ) + await self.storage.add_turn_to_session(request.session_id, turn) + + chunk = AgentTurnResponseStreamChunk( + event=AgentTurnResponseEvent( + payload=AgentTurnResponseTurnCompletePayload( + turn=turn, + ) ) ) - ) - yield chunk + yield chunk async def run( self, @@ -273,7 +272,6 @@ class ChatAgent(ShieldRunnerMixin): yield final_response - @tracing.span("run_shields") async def run_multiple_shields_wrapper( self, turn_id: str, @@ -281,23 +279,47 @@ class ChatAgent(ShieldRunnerMixin): shields: List[str], touchpoint: str, ) -> AsyncGenerator: - if len(shields) == 0: - return + with tracing.span("run_shields") as span: + span.set_attribute("turn_id", turn_id) + span.set_attribute("input", [m.model_dump_json() for m in messages]) + if len(shields) == 0: + span.set_attribute("output", "no shields") + return - step_id = str(uuid.uuid4()) - try: - yield AgentTurnResponseStreamChunk( - event=AgentTurnResponseEvent( - payload=AgentTurnResponseStepStartPayload( - step_type=StepType.shield_call.value, - step_id=step_id, - metadata=dict(touchpoint=touchpoint), + step_id = str(uuid.uuid4()) + try: + yield AgentTurnResponseStreamChunk( + event=AgentTurnResponseEvent( + payload=AgentTurnResponseStepStartPayload( + step_type=StepType.shield_call.value, + step_id=step_id, + metadata=dict(touchpoint=touchpoint), + ) ) ) - ) - await self.run_multiple_shields(messages, shields) + await self.run_multiple_shields(messages, shields) + + except SafetyException as e: + yield AgentTurnResponseStreamChunk( + event=AgentTurnResponseEvent( + payload=AgentTurnResponseStepCompletePayload( + step_type=StepType.shield_call.value, + step_details=ShieldCallStep( + step_id=step_id, + turn_id=turn_id, + violation=e.violation, + ), + ) + ) + ) + span.set_attribute("output", e.violation.model_dump_json()) + + yield CompletionMessage( + content=str(e), + stop_reason=StopReason.end_of_turn, + ) + yield False - except SafetyException as e: yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepCompletePayload( @@ -305,30 +327,12 @@ class ChatAgent(ShieldRunnerMixin): step_details=ShieldCallStep( step_id=step_id, turn_id=turn_id, - violation=e.violation, + violation=None, ), ) ) ) - - yield CompletionMessage( - content=str(e), - stop_reason=StopReason.end_of_turn, - ) - yield False - - yield AgentTurnResponseStreamChunk( - event=AgentTurnResponseEvent( - payload=AgentTurnResponseStepCompletePayload( - step_type=StepType.shield_call.value, - step_details=ShieldCallStep( - step_id=step_id, - turn_id=turn_id, - violation=None, - ), - ) - ) - ) + span.set_attribute("output", "no violations") async def _run( self, @@ -356,10 +360,15 @@ class ChatAgent(ShieldRunnerMixin): # TODO: find older context from the session and either replace it # or append with a sliding window. this is really a very simplistic implementation - with tracing.span("retrieve_rag_context"): + with tracing.span("retrieve_rag_context") as span: rag_context, bank_ids = await self._retrieve_context( session_id, input_messages, attachments ) + span.set_attribute( + "input", [m.model_dump_json() for m in input_messages] + ) + span.set_attribute("output", rag_context) + span.set_attribute("bank_ids", bank_ids) step_id = str(uuid.uuid4()) yield AgentTurnResponseStreamChunk( @@ -396,11 +405,11 @@ class ChatAgent(ShieldRunnerMixin): n_iter = 0 while True: msg = input_messages[-1] - if len(str(msg)) > 1000: - msg_str = f"{str(msg)[:500]}......{str(msg)[-500:]}" - else: - msg_str = str(msg) - log.info(f"{msg_str}") + # if len(str(msg)) > 1000: + # msg_str = f"{str(msg)[:500]}......{str(msg)[-500:]}" + # else: + # msg_str = str(msg) + # log.info(f"{msg_str}") step_id = str(uuid.uuid4()) yield AgentTurnResponseStreamChunk( @@ -416,7 +425,7 @@ class ChatAgent(ShieldRunnerMixin): content = "" stop_reason = None - with tracing.span("inference"): + with tracing.span("inference") as span: async for chunk in await self.inference_api.chat_completion( self.agent_config.model, input_messages, @@ -436,14 +445,13 @@ class ChatAgent(ShieldRunnerMixin): if isinstance(delta, ToolCallDelta): if delta.parse_status == ToolCallParseStatus.success: tool_calls.append(delta.content) - if stream: yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( payload=AgentTurnResponseStepProgressPayload( step_type=StepType.inference.value, step_id=step_id, - model_response_text_delta="", + text_delta="", tool_call_delta=delta, ) ) @@ -457,7 +465,7 @@ class ChatAgent(ShieldRunnerMixin): payload=AgentTurnResponseStepProgressPayload( step_type=StepType.inference.value, step_id=step_id, - model_response_text_delta=event.delta, + text_delta=event.delta, ) ) ) @@ -466,6 +474,13 @@ class ChatAgent(ShieldRunnerMixin): if event.stop_reason is not None: stop_reason = event.stop_reason + span.set_attribute("stop_reason", stop_reason) + span.set_attribute( + "input", [m.model_dump_json() for m in input_messages] + ) + span.set_attribute( + "output", f"content: {content} tool_calls: {tool_calls}" + ) stop_reason = stop_reason or StopReason.out_of_tokens @@ -499,12 +514,12 @@ class ChatAgent(ShieldRunnerMixin): ) if n_iter >= self.agent_config.max_infer_iters: - log.info("Done with MAX iterations, exiting.") + # log.info("Done with MAX iterations, exiting.") yield message break if stop_reason == StopReason.out_of_tokens: - log.info("Out of token budget, exiting.") + # log.info("Out of token budget, exiting.") yield message break @@ -518,10 +533,10 @@ class ChatAgent(ShieldRunnerMixin): message.content = [message.content] + attachments yield message else: - log.info(f"Partial message: {str(message)}") + # log.info(f"Partial message: {str(message)}") input_messages = input_messages + [message] else: - log.info(f"{str(message)}") + # log.info(f"{str(message)}") try: tool_call = message.tool_calls[0] @@ -549,7 +564,13 @@ class ChatAgent(ShieldRunnerMixin): ) ) - with tracing.span("tool_execution"): + with tracing.span( + "tool_execution", + { + "tool_name": tool_call.tool_name, + "input": message.model_dump_json(), + }, + ) as span: result_messages = await execute_tool_call_maybe( self.tools_dict, [message], @@ -558,6 +579,7 @@ class ChatAgent(ShieldRunnerMixin): len(result_messages) == 1 ), "Currently not supporting multiple messages" result_message = result_messages[0] + span.set_attribute("output", result_message.model_dump_json()) yield AgentTurnResponseStreamChunk( event=AgentTurnResponseEvent( @@ -778,7 +800,7 @@ async def attachment_message(tempdir: str, urls: List[URL]) -> ToolResponseMessa path = urlparse(uri).path basename = os.path.basename(path) filepath = f"{tempdir}/{make_random_string() + basename}" - log.info(f"Downloading {url} -> {filepath}") + # log.info(f"Downloading {url} -> {filepath}") async with httpx.AsyncClient() as client: r = await client.get(uri) diff --git a/llama_stack/providers/inline/agents/meta_reference/agents.py b/llama_stack/providers/inline/agents/meta_reference/agents.py index f33aadde3..dec5ec960 100644 --- a/llama_stack/providers/inline/agents/meta_reference/agents.py +++ b/llama_stack/providers/inline/agents/meta_reference/agents.py @@ -6,9 +6,13 @@ import json import logging +import shutil +import tempfile import uuid from typing import AsyncGenerator +from termcolor import colored + from llama_stack.apis.inference import Inference from llama_stack.apis.memory import Memory from llama_stack.apis.memory_banks import MemoryBanks @@ -40,10 +44,20 @@ class MetaReferenceAgentsImpl(Agents): self.memory_banks_api = memory_banks_api self.in_memory_store = InmemoryKVStoreImpl() + self.tempdir = tempfile.mkdtemp() async def initialize(self) -> None: self.persistence_store = await kvstore_impl(self.config.persistence_store) + # check if "bwrap" is available + if not shutil.which("bwrap"): + print( + colored( + "Warning: `bwrap` is not available. Code interpreter tool will not work correctly.", + "yellow", + ) + ) + async def create_agent( self, agent_config: AgentConfig, @@ -82,6 +96,7 @@ class MetaReferenceAgentsImpl(Agents): return ChatAgent( agent_id=agent_id, agent_config=agent_config, + tempdir=self.tempdir, inference_api=self.inference_api, safety_api=self.safety_api, memory_api=self.memory_api, diff --git a/llama_stack/providers/inline/datasetio/localfs/datasetio.py b/llama_stack/providers/inline/datasetio/localfs/datasetio.py index 4de1850ae..736e5d8b9 100644 --- a/llama_stack/providers/inline/datasetio/localfs/datasetio.py +++ b/llama_stack/providers/inline/datasetio/localfs/datasetio.py @@ -3,14 +3,17 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional +from typing import Any, Dict, List, Optional import pandas from llama_models.llama3.api.datatypes import * # noqa: F403 from llama_stack.apis.datasetio import * # noqa: F403 +import base64 +import os from abc import ABC, abstractmethod from dataclasses import dataclass +from urllib.parse import urlparse from llama_stack.providers.datatypes import DatasetsProtocolPrivate from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url @@ -97,6 +100,9 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): dataset_impl=dataset_impl, ) + async def unregister_dataset(self, dataset_id: str) -> None: + del self.dataset_infos[dataset_id] + async def get_rows_paginated( self, dataset_id: str, @@ -128,3 +134,41 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): total_count=len(rows), next_page_token=str(end), ) + + async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: + dataset_info = self.dataset_infos.get(dataset_id) + if dataset_info is None: + raise ValueError(f"Dataset with id {dataset_id} not found") + + dataset_impl = dataset_info.dataset_impl + dataset_impl.load() + + new_rows_df = pandas.DataFrame(rows) + new_rows_df = dataset_impl._validate_dataset_schema(new_rows_df) + dataset_impl.df = pandas.concat( + [dataset_impl.df, new_rows_df], ignore_index=True + ) + + url = str(dataset_info.dataset_def.url) + parsed_url = urlparse(url) + + if parsed_url.scheme == "file" or not parsed_url.scheme: + file_path = parsed_url.path + os.makedirs(os.path.dirname(file_path), exist_ok=True) + dataset_impl.df.to_csv(file_path, index=False) + elif parsed_url.scheme == "data": + # For data URLs, we need to update the base64-encoded content + if not parsed_url.path.startswith("text/csv;base64,"): + raise ValueError("Data URL must be a base64-encoded CSV") + + csv_buffer = dataset_impl.df.to_csv(index=False) + base64_content = base64.b64encode(csv_buffer.encode("utf-8")).decode( + "utf-8" + ) + dataset_info.dataset_def.url = URL( + uri=f"data:text/csv;base64,{base64_content}" + ) + else: + raise ValueError( + f"Unsupported URL scheme: {parsed_url.scheme}. Only file:// and data: URLs are supported for writing." + ) diff --git a/llama_stack/providers/inline/eval/__init__.py b/llama_stack/providers/inline/eval/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/providers/inline/eval/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/providers/inline/eval/meta_reference/config.py b/llama_stack/providers/inline/eval/meta_reference/config.py index 8538d32ad..95b780cca 100644 --- a/llama_stack/providers/inline/eval/meta_reference/config.py +++ b/llama_stack/providers/inline/eval/meta_reference/config.py @@ -3,12 +3,13 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. +from pydantic import BaseModel + from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR from llama_stack.providers.utils.kvstore.config import ( KVStoreConfig, SqliteKVStoreConfig, ) -from pydantic import BaseModel class MetaReferenceEvalConfig(BaseModel): diff --git a/llama_stack/providers/inline/eval/meta_reference/eval.py b/llama_stack/providers/inline/eval/meta_reference/eval.py index c6cacfcc3..453215e41 100644 --- a/llama_stack/providers/inline/eval/meta_reference/eval.py +++ b/llama_stack/providers/inline/eval/meta_reference/eval.py @@ -4,7 +4,9 @@ # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. from enum import Enum +from typing import Any, Dict, List, Optional from llama_models.llama3.api.datatypes import * # noqa: F403 +from tqdm import tqdm from .....apis.common.job_types import Job from .....apis.eval.eval import Eval, EvalTaskConfig, EvaluateResponse, JobStatus @@ -17,7 +19,6 @@ from llama_stack.apis.inference import Inference from llama_stack.apis.scoring import Scoring from llama_stack.providers.datatypes import EvalTasksProtocolPrivate from llama_stack.providers.utils.kvstore import kvstore_impl -from tqdm import tqdm from .config import MetaReferenceEvalConfig diff --git a/llama_stack/providers/inline/memory/faiss/faiss.py b/llama_stack/providers/inline/memory/faiss/faiss.py index dfefefeb8..78de13120 100644 --- a/llama_stack/providers/inline/memory/faiss/faiss.py +++ b/llama_stack/providers/inline/memory/faiss/faiss.py @@ -27,7 +27,6 @@ from llama_stack.providers.utils.memory.vector_store import ( BankWithIndex, EmbeddingIndex, ) -from llama_stack.providers.utils.telemetry import tracing from .config import FaissImplConfig @@ -95,7 +94,6 @@ class FaissIndex(EmbeddingIndex): await self.kvstore.delete(f"faiss_index:v1::{self.bank_id}") - @tracing.span(name="add_chunks") async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray): indexlen = len(self.id_by_index) for i, chunk in enumerate(chunks): diff --git a/llama_stack/providers/inline/meta_reference/telemetry/__init__.py b/llama_stack/providers/inline/meta_reference/telemetry/__init__.py deleted file mode 100644 index 4a0c2f6ee..000000000 --- a/llama_stack/providers/inline/meta_reference/telemetry/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from .config import ConsoleConfig - - -async def get_provider_impl(config: ConsoleConfig, _deps): - from .console import ConsoleTelemetryImpl - - impl = ConsoleTelemetryImpl(config) - await impl.initialize() - return impl diff --git a/llama_stack/providers/inline/meta_reference/telemetry/config.py b/llama_stack/providers/inline/meta_reference/telemetry/config.py deleted file mode 100644 index a1db1d4d8..000000000 --- a/llama_stack/providers/inline/meta_reference/telemetry/config.py +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from enum import Enum - -from llama_models.schema_utils import json_schema_type - -from pydantic import BaseModel - - -class LogFormat(Enum): - TEXT = "text" - JSON = "json" - - -@json_schema_type -class ConsoleConfig(BaseModel): - log_format: LogFormat = LogFormat.TEXT diff --git a/llama_stack/providers/inline/meta_reference/telemetry/console.py b/llama_stack/providers/inline/meta_reference/telemetry/console.py index d8ef49481..838aaa4e1 100644 --- a/llama_stack/providers/inline/meta_reference/telemetry/console.py +++ b/llama_stack/providers/inline/meta_reference/telemetry/console.py @@ -5,7 +5,7 @@ # the root directory of this source tree. import json -from typing import Optional +from typing import List, Optional from .config import LogFormat @@ -49,8 +49,27 @@ class ConsoleTelemetryImpl(Telemetry): if formatted: print(formatted) - async def get_trace(self, trace_id: str) -> Trace: - raise NotImplementedError() + async def query_traces( + self, + attribute_conditions: Optional[List[QueryCondition]] = None, + attribute_keys_to_return: Optional[List[str]] = None, + limit: Optional[int] = 100, + offset: Optional[int] = 0, + order_by: Optional[List[str]] = None, + ) -> List[Trace]: + raise NotImplementedError("Console telemetry does not support trace querying") + + async def get_spans( + self, + span_id: str, + attribute_conditions: Optional[List[QueryCondition]] = None, + attribute_keys_to_return: Optional[List[str]] = None, + max_depth: Optional[int] = None, + limit: Optional[int] = 100, + offset: Optional[int] = 0, + order_by: Optional[List[str]] = None, + ) -> SpanWithChildren: + raise NotImplementedError("Console telemetry does not support span querying") COLORS = { diff --git a/llama_stack/providers/inline/scoring/__init__.py b/llama_stack/providers/inline/scoring/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/providers/inline/scoring/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/providers/inline/scoring/braintrust/__init__.py b/llama_stack/providers/inline/scoring/braintrust/__init__.py index f442a6c3b..2ddc58bd2 100644 --- a/llama_stack/providers/inline/scoring/braintrust/__init__.py +++ b/llama_stack/providers/inline/scoring/braintrust/__init__.py @@ -5,11 +5,17 @@ # the root directory of this source tree. from typing import Dict +from pydantic import BaseModel + from llama_stack.distribution.datatypes import Api, ProviderSpec from .config import BraintrustScoringConfig +class BraintrustProviderDataValidator(BaseModel): + openai_api_key: str + + async def get_provider_impl( config: BraintrustScoringConfig, deps: Dict[Api, ProviderSpec], diff --git a/llama_stack/providers/inline/scoring/braintrust/braintrust.py b/llama_stack/providers/inline/scoring/braintrust/braintrust.py index 00817bb33..8b22a8930 100644 --- a/llama_stack/providers/inline/scoring/braintrust/braintrust.py +++ b/llama_stack/providers/inline/scoring/braintrust/braintrust.py @@ -12,9 +12,12 @@ from llama_stack.apis.common.type_system import * # noqa: F403 from llama_stack.apis.datasetio import * # noqa: F403 from llama_stack.apis.datasets import * # noqa: F403 -# from .scoring_fn.braintrust_scoring_fn import BraintrustScoringFn +import os + from autoevals.llm import Factuality from autoevals.ragas import AnswerCorrectness + +from llama_stack.distribution.request_headers import NeedsRequestProviderData from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate from llama_stack.providers.utils.scoring.aggregation_utils import aggregate_average @@ -24,7 +27,9 @@ from .scoring_fn.fn_defs.answer_correctness import answer_correctness_fn_def from .scoring_fn.fn_defs.factuality import factuality_fn_def -class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate): +class BraintrustScoringImpl( + Scoring, ScoringFunctionsProtocolPrivate, NeedsRequestProviderData +): def __init__( self, config: BraintrustScoringConfig, @@ -79,12 +84,25 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate): f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'." ) + async def set_api_key(self) -> None: + # api key is in the request headers + if not self.config.openai_api_key: + provider_data = self.get_request_provider_data() + if provider_data is None or not provider_data.openai_api_key: + raise ValueError( + 'Pass OpenAI API Key in the header X-LlamaStack-ProviderData as { "openai_api_key": }' + ) + self.config.openai_api_key = provider_data.openai_api_key + + os.environ["OPENAI_API_KEY"] = self.config.openai_api_key + async def score_batch( self, dataset_id: str, scoring_functions: List[str], save_results_dataset: bool = False, ) -> ScoreBatchResponse: + await self.set_api_key() await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id) all_rows = await self.datasetio_api.get_rows_paginated( dataset_id=dataset_id, @@ -105,6 +123,7 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate): async def score_row( self, input_row: Dict[str, Any], scoring_fn_identifier: Optional[str] = None ) -> ScoringResultRow: + await self.set_api_key() assert scoring_fn_identifier is not None, "scoring_fn_identifier cannot be None" expected_answer = input_row["expected_answer"] generated_answer = input_row["generated_answer"] @@ -118,6 +137,7 @@ class BraintrustScoringImpl(Scoring, ScoringFunctionsProtocolPrivate): async def score( self, input_rows: List[Dict[str, Any]], scoring_functions: List[str] ) -> ScoreResponse: + await self.set_api_key() res = {} for scoring_fn_id in scoring_functions: if scoring_fn_id not in self.supported_fn_defs_registry: diff --git a/llama_stack/providers/inline/scoring/braintrust/config.py b/llama_stack/providers/inline/scoring/braintrust/config.py index fef6df5c8..e12249432 100644 --- a/llama_stack/providers/inline/scoring/braintrust/config.py +++ b/llama_stack/providers/inline/scoring/braintrust/config.py @@ -6,4 +6,14 @@ from llama_stack.apis.scoring import * # noqa: F401, F403 -class BraintrustScoringConfig(BaseModel): ... +class BraintrustScoringConfig(BaseModel): + openai_api_key: Optional[str] = Field( + default=None, + description="The OpenAI API Key", + ) + + @classmethod + def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + return { + "openai_api_key": "${env.OPENAI_API_KEY:}", + } diff --git a/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/llm_as_judge_base.py b/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/llm_as_judge_base.py index b00b9a7db..0b18bac01 100644 --- a/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/llm_as_judge_base.py +++ b/llama_stack/providers/inline/scoring/llm_as_judge/scoring_fn/fn_defs/llm_as_judge_base.py @@ -5,7 +5,7 @@ # the root directory of this source tree. from llama_stack.apis.common.type_system import NumberType -from llama_stack.apis.scoring_functions import ScoringFn +from llama_stack.apis.scoring_functions import LLMAsJudgeScoringFnParams, ScoringFn llm_as_judge_base = ScoringFn( @@ -14,4 +14,8 @@ llm_as_judge_base = ScoringFn( return_type=NumberType(), provider_id="llm-as-judge", provider_resource_id="llm-as-judge-base", + params=LLMAsJudgeScoringFnParams( + judge_model="meta-llama/Llama-3.1-405B-Instruct", + prompt_template="Enter custom LLM as Judge Prompt Template", + ), ) diff --git a/llama_stack/providers/inline/telemetry/__init__.py b/llama_stack/providers/inline/telemetry/__init__.py new file mode 100644 index 000000000..756f351d8 --- /dev/null +++ b/llama_stack/providers/inline/telemetry/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. diff --git a/llama_stack/providers/inline/telemetry/meta_reference/__init__.py b/llama_stack/providers/inline/telemetry/meta_reference/__init__.py new file mode 100644 index 000000000..2905e2f6a --- /dev/null +++ b/llama_stack/providers/inline/telemetry/meta_reference/__init__.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import Any, Dict + +from .config import TelemetryConfig, TelemetrySink + +__all__ = ["TelemetryConfig", "TelemetrySink"] + + +async def get_provider_impl(config: TelemetryConfig, deps: Dict[str, Any]): + from .telemetry import TelemetryAdapter + + impl = TelemetryAdapter(config, deps) + await impl.initialize() + return impl diff --git a/llama_stack/providers/inline/telemetry/meta_reference/config.py b/llama_stack/providers/inline/telemetry/meta_reference/config.py new file mode 100644 index 000000000..41d62c268 --- /dev/null +++ b/llama_stack/providers/inline/telemetry/meta_reference/config.py @@ -0,0 +1,58 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from enum import Enum +from typing import Any, Dict, List + +from pydantic import BaseModel, Field, field_validator + +from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR + + +class TelemetrySink(str, Enum): + OTEL = "otel" + SQLITE = "sqlite" + CONSOLE = "console" + + +class TelemetryConfig(BaseModel): + otel_endpoint: str = Field( + default="http://localhost:4318/v1/traces", + description="The OpenTelemetry collector endpoint URL", + ) + service_name: str = Field( + default="llama-stack", + description="The service name to use for telemetry", + ) + sinks: List[TelemetrySink] = Field( + default=[TelemetrySink.CONSOLE, TelemetrySink.SQLITE], + description="List of telemetry sinks to enable (possible values: otel, sqlite, console)", + ) + sqlite_db_path: str = Field( + default=(RUNTIME_BASE_DIR / "trace_store.db").as_posix(), + description="The path to the SQLite database to use for storing traces", + ) + + @field_validator("sinks", mode="before") + @classmethod + def validate_sinks(cls, v): + if isinstance(v, str): + return [TelemetrySink(sink.strip()) for sink in v.split(",")] + return v + + @classmethod + def sample_run_config( + cls, __distro_dir__: str = "runtime", db_name: str = "trace_store.db" + ) -> Dict[str, Any]: + return { + "service_name": "${env.OTEL_SERVICE_NAME:llama-stack}", + "sinks": "${env.TELEMETRY_SINKS:console,sqlite}", + "sqlite_db_path": "${env.SQLITE_DB_PATH:~/.llama/" + + __distro_dir__ + + "/" + + db_name + + "}", + } diff --git a/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py b/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py new file mode 100644 index 000000000..2f00b21b8 --- /dev/null +++ b/llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py @@ -0,0 +1,117 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +from datetime import datetime + +from opentelemetry.sdk.trace import ReadableSpan +from opentelemetry.sdk.trace.export import SpanProcessor +from opentelemetry.trace.status import StatusCode + +# Colors for console output +COLORS = { + "reset": "\033[0m", + "bold": "\033[1m", + "dim": "\033[2m", + "red": "\033[31m", + "green": "\033[32m", + "yellow": "\033[33m", + "blue": "\033[34m", + "magenta": "\033[35m", + "cyan": "\033[36m", + "white": "\033[37m", +} + + +class ConsoleSpanProcessor(SpanProcessor): + + def __init__(self, print_attributes: bool = False): + self.print_attributes = print_attributes + + def on_start(self, span: ReadableSpan, parent_context=None) -> None: + if span.attributes and span.attributes.get("__autotraced__"): + return + + timestamp = datetime.utcfromtimestamp(span.start_time / 1e9).strftime( + "%H:%M:%S.%f" + )[:-3] + + print( + f"{COLORS['dim']}{timestamp}{COLORS['reset']} " + f"{COLORS['magenta']}[START]{COLORS['reset']} " + f"{COLORS['dim']}{span.name}{COLORS['reset']}" + ) + + def on_end(self, span: ReadableSpan) -> None: + if span.attributes and span.attributes.get("__autotraced__"): + return + + timestamp = datetime.utcfromtimestamp(span.end_time / 1e9).strftime( + "%H:%M:%S.%f" + )[:-3] + + span_context = ( + f"{COLORS['dim']}{timestamp}{COLORS['reset']} " + f"{COLORS['magenta']}[END]{COLORS['reset']} " + f"{COLORS['dim']}{span.name}{COLORS['reset']}" + ) + + if span.status.status_code == StatusCode.ERROR: + span_context += f"{COLORS['reset']} {COLORS['red']}[ERROR]{COLORS['reset']}" + elif span.status.status_code != StatusCode.UNSET: + span_context += f"{COLORS['reset']} [{span.status.status_code}]" + + duration_ms = (span.end_time - span.start_time) / 1e6 + span_context += f"{COLORS['reset']} ({duration_ms:.2f}ms)" + + print(span_context) + + if self.print_attributes and span.attributes: + for key, value in span.attributes.items(): + if key.startswith("__"): + continue + str_value = str(value) + if len(str_value) > 1000: + str_value = str_value[:997] + "..." + print(f" {COLORS['dim']}{key}: {str_value}{COLORS['reset']}") + + for event in span.events: + event_time = datetime.utcfromtimestamp(event.timestamp / 1e9).strftime( + "%H:%M:%S.%f" + )[:-3] + + severity = event.attributes.get("severity", "info") + message = event.attributes.get("message", event.name) + if isinstance(message, (dict, list)): + message = json.dumps(message, indent=2) + + severity_colors = { + "error": f"{COLORS['bold']}{COLORS['red']}", + "warn": f"{COLORS['bold']}{COLORS['yellow']}", + "info": COLORS["white"], + "debug": COLORS["dim"], + } + msg_color = severity_colors.get(severity, COLORS["white"]) + + print( + f" {event_time} " + f"{msg_color}[{severity.upper()}] " + f"{message}{COLORS['reset']}" + ) + + if event.attributes: + for key, value in event.attributes.items(): + if key.startswith("__") or key in ["message", "severity"]: + continue + print(f" {COLORS['dim']}{key}: {value}{COLORS['reset']}") + + def shutdown(self) -> None: + """Shutdown the processor.""" + pass + + def force_flush(self, timeout_millis: float = None) -> bool: + """Force flush any pending spans.""" + return True diff --git a/llama_stack/providers/inline/telemetry/meta_reference/sqlite_span_processor.py b/llama_stack/providers/inline/telemetry/meta_reference/sqlite_span_processor.py new file mode 100644 index 000000000..553dd5000 --- /dev/null +++ b/llama_stack/providers/inline/telemetry/meta_reference/sqlite_span_processor.py @@ -0,0 +1,242 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +import os +import sqlite3 +import threading +from datetime import datetime, timedelta +from typing import Dict + +from opentelemetry.sdk.trace import SpanProcessor +from opentelemetry.trace import Span + + +class SQLiteSpanProcessor(SpanProcessor): + def __init__(self, conn_string, ttl_days=30): + """Initialize the SQLite span processor with a connection string.""" + self.conn_string = conn_string + self.ttl_days = ttl_days + self.cleanup_task = None + self._thread_local = threading.local() + self._connections: Dict[int, sqlite3.Connection] = {} + self._lock = threading.Lock() + self.setup_database() + + def _get_connection(self) -> sqlite3.Connection: + """Get a thread-specific database connection.""" + thread_id = threading.get_ident() + with self._lock: + if thread_id not in self._connections: + conn = sqlite3.connect(self.conn_string) + self._connections[thread_id] = conn + return self._connections[thread_id] + + def setup_database(self): + """Create the necessary tables if they don't exist.""" + # Create directory if it doesn't exist + os.makedirs(os.path.dirname(self.conn_string), exist_ok=True) + + conn = self._get_connection() + cursor = conn.cursor() + + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS traces ( + trace_id TEXT PRIMARY KEY, + service_name TEXT, + root_span_id TEXT, + start_time TIMESTAMP, + end_time TIMESTAMP, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """ + ) + + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS spans ( + span_id TEXT PRIMARY KEY, + trace_id TEXT REFERENCES traces(trace_id), + parent_span_id TEXT, + name TEXT, + start_time TIMESTAMP, + end_time TIMESTAMP, + attributes TEXT, + status TEXT, + kind TEXT + ) + """ + ) + + cursor.execute( + """ + CREATE TABLE IF NOT EXISTS span_events ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + span_id TEXT REFERENCES spans(span_id), + name TEXT, + timestamp TIMESTAMP, + attributes TEXT + ) + """ + ) + + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_traces_created_at + ON traces(created_at) + """ + ) + + conn.commit() + cursor.close() + + # Start periodic cleanup in a separate thread + self.cleanup_task = threading.Thread(target=self._periodic_cleanup, daemon=True) + self.cleanup_task.start() + + def _cleanup_old_data(self): + """Delete records older than TTL.""" + try: + conn = self._get_connection() + cutoff_date = (datetime.now() - timedelta(days=self.ttl_days)).isoformat() + cursor = conn.cursor() + + # Delete old span events + cursor.execute( + """ + DELETE FROM span_events + WHERE span_id IN ( + SELECT span_id FROM spans + WHERE trace_id IN ( + SELECT trace_id FROM traces + WHERE created_at < ? + ) + ) + """, + (cutoff_date,), + ) + + # Delete old spans + cursor.execute( + """ + DELETE FROM spans + WHERE trace_id IN ( + SELECT trace_id FROM traces + WHERE created_at < ? + ) + """, + (cutoff_date,), + ) + + # Delete old traces + cursor.execute("DELETE FROM traces WHERE created_at < ?", (cutoff_date,)) + + conn.commit() + cursor.close() + except Exception as e: + print(f"Error during cleanup: {e}") + + def _periodic_cleanup(self): + """Run cleanup periodically.""" + import time + + while True: + time.sleep(3600) # Sleep for 1 hour + self._cleanup_old_data() + + def on_start(self, span: Span, parent_context=None): + """Called when a span starts.""" + pass + + def on_end(self, span: Span): + """Called when a span ends. Export the span data to SQLite.""" + try: + conn = self._get_connection() + cursor = conn.cursor() + + trace_id = format(span.get_span_context().trace_id, "032x") + span_id = format(span.get_span_context().span_id, "016x") + service_name = span.resource.attributes.get("service.name", "unknown") + + parent_span_id = None + parent_context = span.parent + if parent_context: + parent_span_id = format(parent_context.span_id, "016x") + + # Insert into traces + cursor.execute( + """ + INSERT INTO traces ( + trace_id, service_name, root_span_id, start_time, end_time + ) VALUES (?, ?, ?, ?, ?) + ON CONFLICT(trace_id) DO UPDATE SET + root_span_id = COALESCE(root_span_id, excluded.root_span_id), + start_time = MIN(excluded.start_time, start_time), + end_time = MAX(excluded.end_time, end_time) + """, + ( + trace_id, + service_name, + (span_id if not parent_span_id else None), + datetime.fromtimestamp(span.start_time / 1e9).isoformat(), + datetime.fromtimestamp(span.end_time / 1e9).isoformat(), + ), + ) + + # Insert into spans + cursor.execute( + """ + INSERT INTO spans ( + span_id, trace_id, parent_span_id, name, + start_time, end_time, attributes, status, + kind + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + span_id, + trace_id, + parent_span_id, + span.name, + datetime.fromtimestamp(span.start_time / 1e9).isoformat(), + datetime.fromtimestamp(span.end_time / 1e9).isoformat(), + json.dumps(dict(span.attributes)), + span.status.status_code.name, + span.kind.name, + ), + ) + + for event in span.events: + cursor.execute( + """ + INSERT INTO span_events ( + span_id, name, timestamp, attributes + ) VALUES (?, ?, ?, ?) + """, + ( + span_id, + event.name, + datetime.fromtimestamp(event.timestamp / 1e9).isoformat(), + json.dumps(dict(event.attributes)), + ), + ) + + conn.commit() + cursor.close() + except Exception as e: + print(f"Error exporting span to SQLite: {e}") + + def shutdown(self): + """Cleanup any resources.""" + with self._lock: + for conn in self._connections.values(): + if conn: + conn.close() + self._connections.clear() + + def force_flush(self, timeout_millis=30000): + """Force export of spans.""" + pass diff --git a/llama_stack/providers/remote/telemetry/opentelemetry/opentelemetry.py b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py similarity index 66% rename from llama_stack/providers/remote/telemetry/opentelemetry/opentelemetry.py rename to llama_stack/providers/inline/telemetry/meta_reference/telemetry.py index c9830fd9d..2e4a778e4 100644 --- a/llama_stack/providers/remote/telemetry/opentelemetry/opentelemetry.py +++ b/llama_stack/providers/inline/telemetry/meta_reference/telemetry.py @@ -5,6 +5,7 @@ # the root directory of this source tree. import threading +from typing import Any, Dict, List, Optional from opentelemetry import metrics, trace from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter @@ -16,10 +17,21 @@ from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.semconv.resource import ResourceAttributes +from llama_stack.providers.inline.telemetry.meta_reference.console_span_processor import ( + ConsoleSpanProcessor, +) + +from llama_stack.providers.inline.telemetry.meta_reference.sqlite_span_processor import ( + SQLiteSpanProcessor, +) +from llama_stack.providers.utils.telemetry.dataset_mixin import TelemetryDatasetMixin +from llama_stack.providers.utils.telemetry.sqlite_trace_store import SQLiteTraceStore from llama_stack.apis.telemetry import * # noqa: F403 -from .config import OpenTelemetryConfig +from llama_stack.distribution.datatypes import Api + +from .config import TelemetryConfig, TelemetrySink _GLOBAL_STORAGE = { "active_spans": {}, @@ -45,9 +57,10 @@ def is_tracing_enabled(tracer): return span.is_recording() -class OpenTelemetryAdapter(Telemetry): - def __init__(self, config: OpenTelemetryConfig): +class TelemetryAdapter(TelemetryDatasetMixin, Telemetry): + def __init__(self, config: TelemetryConfig, deps: Dict[str, Any]) -> None: self.config = config + self.datasetio_api = deps[Api.datasetio] resource = Resource.create( { @@ -57,22 +70,29 @@ class OpenTelemetryAdapter(Telemetry): provider = TracerProvider(resource=resource) trace.set_tracer_provider(provider) - otlp_exporter = OTLPSpanExporter( - endpoint=self.config.otel_endpoint, - ) - span_processor = BatchSpanProcessor(otlp_exporter) - trace.get_tracer_provider().add_span_processor(span_processor) - # Set up metrics - metric_reader = PeriodicExportingMetricReader( - OTLPMetricExporter( + if TelemetrySink.OTEL in self.config.sinks: + otlp_exporter = OTLPSpanExporter( endpoint=self.config.otel_endpoint, ) - ) - metric_provider = MeterProvider( - resource=resource, metric_readers=[metric_reader] - ) - metrics.set_meter_provider(metric_provider) - self.meter = metrics.get_meter(__name__) + span_processor = BatchSpanProcessor(otlp_exporter) + trace.get_tracer_provider().add_span_processor(span_processor) + metric_reader = PeriodicExportingMetricReader( + OTLPMetricExporter( + endpoint=self.config.otel_endpoint, + ) + ) + metric_provider = MeterProvider( + resource=resource, metric_readers=[metric_reader] + ) + metrics.set_meter_provider(metric_provider) + self.meter = metrics.get_meter(__name__) + if TelemetrySink.SQLITE in self.config.sinks: + trace.get_tracer_provider().add_span_processor( + SQLiteSpanProcessor(self.config.sqlite_db_path) + ) + self.trace_store = SQLiteTraceStore(self.config.sqlite_db_path) + if TelemetrySink.CONSOLE in self.config.sinks: + trace.get_tracer_provider().add_span_processor(ConsoleSpanProcessor()) self._lock = _global_lock async def initialize(self) -> None: @@ -83,15 +103,17 @@ class OpenTelemetryAdapter(Telemetry): trace.get_tracer_provider().shutdown() metrics.get_meter_provider().shutdown() - async def log_event(self, event: Event) -> None: + async def log_event(self, event: Event, ttl_seconds: int = 604800) -> None: if isinstance(event, UnstructuredLogEvent): - self._log_unstructured(event) + self._log_unstructured(event, ttl_seconds) elif isinstance(event, MetricEvent): self._log_metric(event) elif isinstance(event, StructuredLogEvent): - self._log_structured(event) + self._log_structured(event, ttl_seconds) + else: + raise ValueError(f"Unknown event type: {event}") - def _log_unstructured(self, event: UnstructuredLogEvent) -> None: + def _log_unstructured(self, event: UnstructuredLogEvent, ttl_seconds: int) -> None: with self._lock: # Use global storage instead of instance storage span_id = string_to_span_id(event.span_id) @@ -104,6 +126,7 @@ class OpenTelemetryAdapter(Telemetry): attributes={ "message": event.message, "severity": event.severity.value, + "__ttl__": ttl_seconds, **event.attributes, }, timestamp=timestamp_ns, @@ -154,11 +177,14 @@ class OpenTelemetryAdapter(Telemetry): ) return _GLOBAL_STORAGE["up_down_counters"][name] - def _log_structured(self, event: StructuredLogEvent) -> None: + def _log_structured(self, event: StructuredLogEvent, ttl_seconds: int) -> None: with self._lock: span_id = string_to_span_id(event.span_id) trace_id = string_to_trace_id(event.trace_id) tracer = trace.get_tracer(__name__) + if event.attributes is None: + event.attributes = {} + event.attributes["__ttl__"] = ttl_seconds if isinstance(event.payload, SpanStartPayload): # Check if span already exists to prevent duplicates @@ -170,7 +196,6 @@ class OpenTelemetryAdapter(Telemetry): parent_span_id = string_to_span_id(event.payload.parent_span_id) parent_span = _GLOBAL_STORAGE["active_spans"].get(parent_span_id) - # Create a new trace context with the trace_id context = trace.Context(trace_id=trace_id) if parent_span: context = trace.set_span_in_context(parent_span, context) @@ -179,14 +204,9 @@ class OpenTelemetryAdapter(Telemetry): name=event.payload.name, context=context, attributes=event.attributes or {}, - start_time=int(event.timestamp.timestamp() * 1e9), ) _GLOBAL_STORAGE["active_spans"][span_id] = span - # Set as current span using context manager - with trace.use_span(span, end_on_exit=False): - pass # Let the span continue beyond this block - elif isinstance(event.payload, SpanEndPayload): span = _GLOBAL_STORAGE["active_spans"].get(span_id) if span: @@ -199,10 +219,33 @@ class OpenTelemetryAdapter(Telemetry): else trace.Status(status_code=trace.StatusCode.ERROR) ) span.set_status(status) - span.end(end_time=int(event.timestamp.timestamp() * 1e9)) - - # Remove from active spans + span.end() _GLOBAL_STORAGE["active_spans"].pop(span_id, None) + else: + raise ValueError(f"Unknown structured log event: {event}") - async def get_trace(self, trace_id: str) -> Trace: - raise NotImplementedError("Trace retrieval not implemented yet") + async def query_traces( + self, + attribute_filters: Optional[List[QueryCondition]] = None, + limit: Optional[int] = 100, + offset: Optional[int] = 0, + order_by: Optional[List[str]] = None, + ) -> List[Trace]: + return await self.trace_store.query_traces( + attribute_filters=attribute_filters, + limit=limit, + offset=offset, + order_by=order_by, + ) + + async def get_span_tree( + self, + span_id: str, + attributes_to_return: Optional[List[str]] = None, + max_depth: Optional[int] = None, + ) -> SpanWithChildren: + return await self.trace_store.get_span_tree( + span_id=span_id, + attributes_to_return=attributes_to_return, + max_depth=max_depth, + ) diff --git a/llama_stack/providers/remote/telemetry/sample/__init__.py b/llama_stack/providers/inline/telemetry/sample/__init__.py similarity index 100% rename from llama_stack/providers/remote/telemetry/sample/__init__.py rename to llama_stack/providers/inline/telemetry/sample/__init__.py diff --git a/llama_stack/providers/remote/telemetry/sample/config.py b/llama_stack/providers/inline/telemetry/sample/config.py similarity index 100% rename from llama_stack/providers/remote/telemetry/sample/config.py rename to llama_stack/providers/inline/telemetry/sample/config.py diff --git a/llama_stack/providers/remote/telemetry/sample/sample.py b/llama_stack/providers/inline/telemetry/sample/sample.py similarity index 100% rename from llama_stack/providers/remote/telemetry/sample/sample.py rename to llama_stack/providers/inline/telemetry/sample/sample.py diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py index c8d061f6c..13d463ad8 100644 --- a/llama_stack/providers/registry/inference.py +++ b/llama_stack/providers/registry/inference.py @@ -61,6 +61,17 @@ def available_providers() -> List[ProviderSpec]: config_class="llama_stack.providers.remote.inference.sample.SampleConfig", ), ), + remote_provider_spec( + api=Api.inference, + adapter=AdapterSpec( + adapter_type="cerebras", + pip_packages=[ + "cerebras_cloud_sdk", + ], + module="llama_stack.providers.remote.inference.cerebras", + config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig", + ), + ), remote_provider_spec( api=Api.inference, adapter=AdapterSpec( diff --git a/llama_stack/providers/registry/scoring.py b/llama_stack/providers/registry/scoring.py index 2da9797bc..f31ff44d7 100644 --- a/llama_stack/providers/registry/scoring.py +++ b/llama_stack/providers/registry/scoring.py @@ -44,5 +44,6 @@ def available_providers() -> List[ProviderSpec]: Api.datasetio, Api.datasets, ], + provider_data_validator="llama_stack.providers.inline.scoring.braintrust.BraintrustProviderDataValidator", ), ] diff --git a/llama_stack/providers/registry/telemetry.py b/llama_stack/providers/registry/telemetry.py index ac537e076..d367bf894 100644 --- a/llama_stack/providers/registry/telemetry.py +++ b/llama_stack/providers/registry/telemetry.py @@ -14,9 +14,13 @@ def available_providers() -> List[ProviderSpec]: InlineProviderSpec( api=Api.telemetry, provider_type="inline::meta-reference", - pip_packages=[], - module="llama_stack.providers.inline.meta_reference.telemetry", - config_class="llama_stack.providers.inline.meta_reference.telemetry.ConsoleConfig", + pip_packages=[ + "opentelemetry-sdk", + "opentelemetry-exporter-otlp-proto-http", + ], + api_dependencies=[Api.datasetio], + module="llama_stack.providers.inline.telemetry.meta_reference", + config_class="llama_stack.providers.inline.telemetry.meta_reference.config.TelemetryConfig", ), remote_provider_spec( api=Api.telemetry, @@ -27,18 +31,4 @@ def available_providers() -> List[ProviderSpec]: config_class="llama_stack.providers.remote.telemetry.sample.SampleConfig", ), ), - remote_provider_spec( - api=Api.telemetry, - adapter=AdapterSpec( - adapter_type="opentelemetry-jaeger", - pip_packages=[ - "opentelemetry-api", - "opentelemetry-sdk", - "opentelemetry-exporter-jaeger", - "opentelemetry-semantic-conventions", - ], - module="llama_stack.providers.remote.telemetry.opentelemetry", - config_class="llama_stack.providers.remote.telemetry.opentelemetry.OpenTelemetryConfig", - ), - ), ] diff --git a/llama_stack/providers/remote/datasetio/huggingface/huggingface.py b/llama_stack/providers/remote/datasetio/huggingface/huggingface.py index c2e4506bf..db52270a7 100644 --- a/llama_stack/providers/remote/datasetio/huggingface/huggingface.py +++ b/llama_stack/providers/remote/datasetio/huggingface/huggingface.py @@ -3,7 +3,7 @@ # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. -from typing import Optional +from typing import Any, Dict, List, Optional from llama_stack.apis.datasetio import * # noqa: F403 @@ -64,6 +64,11 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): ) self.dataset_infos[dataset_def.identifier] = dataset_def + async def unregister_dataset(self, dataset_id: str) -> None: + key = f"{DATASETS_PREFIX}{dataset_id}" + await self.kvstore.delete(key=key) + del self.dataset_infos[dataset_id] + async def get_rows_paginated( self, dataset_id: str, @@ -95,3 +100,22 @@ class HuggingfaceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate): total_count=len(rows), next_page_token=str(end), ) + + async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: + dataset_def = self.dataset_infos[dataset_id] + loaded_dataset = load_hf_dataset(dataset_def) + + # Convert rows to HF Dataset format + new_dataset = hf_datasets.Dataset.from_list(rows) + + # Concatenate the new rows with existing dataset + updated_dataset = hf_datasets.concatenate_datasets( + [loaded_dataset, new_dataset] + ) + + if dataset_def.metadata.get("path", None): + updated_dataset.push_to_hub(dataset_def.metadata["path"]) + else: + raise NotImplementedError( + "Uploading to URL-based datasets is not supported yet" + ) diff --git a/llama_stack/providers/remote/inference/cerebras/__init__.py b/llama_stack/providers/remote/inference/cerebras/__init__.py new file mode 100644 index 000000000..a24bb2c70 --- /dev/null +++ b/llama_stack/providers/remote/inference/cerebras/__init__.py @@ -0,0 +1,21 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .config import CerebrasImplConfig + + +async def get_adapter_impl(config: CerebrasImplConfig, _deps): + from .cerebras import CerebrasInferenceAdapter + + assert isinstance( + config, CerebrasImplConfig + ), f"Unexpected config type: {type(config)}" + + impl = CerebrasInferenceAdapter(config) + + await impl.initialize() + + return impl diff --git a/llama_stack/providers/remote/inference/cerebras/cerebras.py b/llama_stack/providers/remote/inference/cerebras/cerebras.py new file mode 100644 index 000000000..65022f85e --- /dev/null +++ b/llama_stack/providers/remote/inference/cerebras/cerebras.py @@ -0,0 +1,191 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import AsyncGenerator + +from cerebras.cloud.sdk import AsyncCerebras + +from llama_models.llama3.api.chat_format import ChatFormat + +from llama_models.llama3.api.datatypes import Message +from llama_models.llama3.api.tokenizer import Tokenizer + +from llama_stack.apis.inference import * # noqa: F403 + +from llama_models.datatypes import CoreModelId + +from llama_stack.providers.utils.inference.model_registry import ( + build_model_alias, + ModelRegistryHelper, +) +from llama_stack.providers.utils.inference.openai_compat import ( + get_sampling_options, + process_chat_completion_response, + process_chat_completion_stream_response, + process_completion_response, + process_completion_stream_response, +) +from llama_stack.providers.utils.inference.prompt_adapter import ( + chat_completion_request_to_prompt, + completion_request_to_prompt, +) + +from .config import CerebrasImplConfig + + +model_aliases = [ + build_model_alias( + "llama3.1-8b", + CoreModelId.llama3_1_8b_instruct.value, + ), + build_model_alias( + "llama3.1-70b", + CoreModelId.llama3_1_70b_instruct.value, + ), +] + + +class CerebrasInferenceAdapter(ModelRegistryHelper, Inference): + def __init__(self, config: CerebrasImplConfig) -> None: + ModelRegistryHelper.__init__( + self, + model_aliases=model_aliases, + ) + self.config = config + self.formatter = ChatFormat(Tokenizer.get_instance()) + + self.client = AsyncCerebras( + base_url=self.config.base_url, api_key=self.config.api_key + ) + + async def initialize(self) -> None: + return + + async def shutdown(self) -> None: + pass + + async def completion( + self, + model_id: str, + content: InterleavedTextMedia, + sampling_params: Optional[SamplingParams] = SamplingParams(), + response_format: Optional[ResponseFormat] = None, + stream: Optional[bool] = False, + logprobs: Optional[LogProbConfig] = None, + ) -> AsyncGenerator: + model = await self.model_store.get_model(model_id) + request = CompletionRequest( + model=model.provider_resource_id, + content=content, + sampling_params=sampling_params, + response_format=response_format, + stream=stream, + logprobs=logprobs, + ) + if stream: + return self._stream_completion( + request, + ) + else: + return await self._nonstream_completion(request) + + async def _nonstream_completion( + self, request: CompletionRequest + ) -> CompletionResponse: + params = self._get_params(request) + + r = await self.client.completions.create(**params) + + return process_completion_response(r, self.formatter) + + async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator: + params = self._get_params(request) + + stream = await self.client.completions.create(**params) + + async for chunk in process_completion_stream_response(stream, self.formatter): + yield chunk + + async def chat_completion( + self, + model_id: str, + messages: List[Message], + sampling_params: Optional[SamplingParams] = SamplingParams(), + tools: Optional[List[ToolDefinition]] = None, + tool_choice: Optional[ToolChoice] = ToolChoice.auto, + tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json, + response_format: Optional[ResponseFormat] = None, + stream: Optional[bool] = False, + logprobs: Optional[LogProbConfig] = None, + ) -> AsyncGenerator: + model = await self.model_store.get_model(model_id) + request = ChatCompletionRequest( + model=model.provider_resource_id, + messages=messages, + sampling_params=sampling_params, + tools=tools or [], + tool_choice=tool_choice, + tool_prompt_format=tool_prompt_format, + response_format=response_format, + stream=stream, + logprobs=logprobs, + ) + + if stream: + return self._stream_chat_completion(request) + else: + return await self._nonstream_chat_completion(request) + + async def _nonstream_chat_completion( + self, request: CompletionRequest + ) -> CompletionResponse: + params = self._get_params(request) + + r = await self.client.completions.create(**params) + + return process_chat_completion_response(r, self.formatter) + + async def _stream_chat_completion( + self, request: CompletionRequest + ) -> AsyncGenerator: + params = self._get_params(request) + + stream = await self.client.completions.create(**params) + + async for chunk in process_chat_completion_stream_response( + stream, self.formatter + ): + yield chunk + + def _get_params( + self, request: Union[ChatCompletionRequest, CompletionRequest] + ) -> dict: + if request.sampling_params and request.sampling_params.top_k: + raise ValueError("`top_k` not supported by Cerebras") + + prompt = "" + if type(request) == ChatCompletionRequest: + prompt = chat_completion_request_to_prompt( + request, self.get_llama_model(request.model), self.formatter + ) + elif type(request) == CompletionRequest: + prompt = completion_request_to_prompt(request, self.formatter) + else: + raise ValueError(f"Unknown request type {type(request)}") + + return { + "model": request.model, + "prompt": prompt, + "stream": request.stream, + **get_sampling_options(request.sampling_params), + } + + async def embeddings( + self, + model_id: str, + contents: List[InterleavedTextMedia], + ) -> EmbeddingsResponse: + raise NotImplementedError() diff --git a/llama_stack/providers/remote/inference/cerebras/config.py b/llama_stack/providers/remote/inference/cerebras/config.py new file mode 100644 index 000000000..9bae6ca4d --- /dev/null +++ b/llama_stack/providers/remote/inference/cerebras/config.py @@ -0,0 +1,32 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import os +from typing import Any, Dict, Optional + +from llama_models.schema_utils import json_schema_type +from pydantic import BaseModel, Field + +DEFAULT_BASE_URL = "https://api.cerebras.ai" + + +@json_schema_type +class CerebrasImplConfig(BaseModel): + base_url: str = Field( + default=os.environ.get("CEREBRAS_BASE_URL", DEFAULT_BASE_URL), + description="Base URL for the Cerebras API", + ) + api_key: Optional[str] = Field( + default=os.environ.get("CEREBRAS_API_KEY"), + description="Cerebras API Key", + ) + + @classmethod + def sample_run_config(cls, **kwargs) -> Dict[str, Any]: + return { + "base_url": DEFAULT_BASE_URL, + "api_key": "${env.CEREBRAS_API_KEY}", + } diff --git a/llama_stack/providers/remote/inference/nvidia/config.py b/llama_stack/providers/remote/inference/nvidia/config.py index c50143043..28be43f4c 100644 --- a/llama_stack/providers/remote/inference/nvidia/config.py +++ b/llama_stack/providers/remote/inference/nvidia/config.py @@ -35,7 +35,9 @@ class NVIDIAConfig(BaseModel): """ url: str = Field( - default="https://integrate.api.nvidia.com", + default_factory=lambda: os.getenv( + "NVIDIA_BASE_URL", "https://integrate.api.nvidia.com" + ), description="A base url for accessing the NVIDIA NIM", ) api_key: Optional[str] = Field( diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py index 74c0b8601..d6fa20835 100644 --- a/llama_stack/providers/remote/inference/ollama/ollama.py +++ b/llama_stack/providers/remote/inference/ollama/ollama.py @@ -180,7 +180,6 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate): async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator: params = await self._get_params(request) r = await self.client.generate(**params) - assert isinstance(r, dict) choice = OpenAICompatCompletionChoice( finish_reason=r["done_reason"] if r["done"] else None, @@ -270,7 +269,6 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate): r = await self.client.chat(**params) else: r = await self.client.generate(**params) - assert isinstance(r, dict) if "message" in r: choice = OpenAICompatCompletionChoice( diff --git a/llama_stack/providers/remote/inference/vllm/vllm.py b/llama_stack/providers/remote/inference/vllm/vllm.py index 0f4034478..57f3db802 100644 --- a/llama_stack/providers/remote/inference/vllm/vllm.py +++ b/llama_stack/providers/remote/inference/vllm/vllm.py @@ -100,6 +100,7 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): tool_prompt_format=tool_prompt_format, stream=stream, logprobs=logprobs, + response_format=response_format, ) if stream: return self._stream_chat_completion(request, self.client) @@ -180,6 +181,16 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate): self.formatter, ) + if fmt := request.response_format: + if fmt.type == ResponseFormatType.json_schema.value: + input_dict["extra_body"] = { + "guided_json": request.response_format.json_schema + } + elif fmt.type == ResponseFormatType.grammar.value: + raise NotImplementedError("Grammar response format not supported yet") + else: + raise ValueError(f"Unknown response format {fmt.type}") + return { "model": request.model, **input_dict, diff --git a/llama_stack/providers/remote/telemetry/opentelemetry/__init__.py b/llama_stack/providers/remote/telemetry/opentelemetry/__init__.py deleted file mode 100644 index 0842afe2d..000000000 --- a/llama_stack/providers/remote/telemetry/opentelemetry/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from .config import OpenTelemetryConfig - - -async def get_adapter_impl(config: OpenTelemetryConfig, _deps): - from .opentelemetry import OpenTelemetryAdapter - - impl = OpenTelemetryAdapter(config) - await impl.initialize() - return impl diff --git a/llama_stack/providers/remote/telemetry/opentelemetry/config.py b/llama_stack/providers/remote/telemetry/opentelemetry/config.py deleted file mode 100644 index 5e9dff1a1..000000000 --- a/llama_stack/providers/remote/telemetry/opentelemetry/config.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from typing import Any, Dict - -from pydantic import BaseModel, Field - - -class OpenTelemetryConfig(BaseModel): - otel_endpoint: str = Field( - default="http://localhost:4318/v1/traces", - description="The OpenTelemetry collector endpoint URL", - ) - service_name: str = Field( - default="llama-stack", - description="The service name to use for telemetry", - ) - - @classmethod - def sample_run_config(cls, **kwargs) -> Dict[str, Any]: - return { - "otel_endpoint": "${env.OTEL_ENDPOINT:http://localhost:4318/v1/traces}", - "service_name": "${env.OTEL_SERVICE_NAME:llama-stack}", - } diff --git a/llama_stack/providers/tests/datasetio/test_datasetio.py b/llama_stack/providers/tests/datasetio/test_datasetio.py index dd2cbd019..7d88b6115 100644 --- a/llama_stack/providers/tests/datasetio/test_datasetio.py +++ b/llama_stack/providers/tests/datasetio/test_datasetio.py @@ -81,6 +81,18 @@ class TestDatasetIO: assert len(response) == 1 assert response[0].identifier == "test_dataset" + with pytest.raises(Exception) as exc_info: + # unregister a dataset that does not exist + await datasets_impl.unregister_dataset("test_dataset2") + + await datasets_impl.unregister_dataset("test_dataset") + response = await datasets_impl.list_datasets() + assert isinstance(response, list) + assert len(response) == 0 + + with pytest.raises(Exception) as exc_info: + await datasets_impl.unregister_dataset("test_dataset") + @pytest.mark.asyncio async def test_get_rows_paginated(self, datasetio_stack): datasetio_impl, datasets_impl = datasetio_stack diff --git a/llama_stack/providers/tests/eval/conftest.py b/llama_stack/providers/tests/eval/conftest.py index 171fae51a..b310439ce 100644 --- a/llama_stack/providers/tests/eval/conftest.py +++ b/llama_stack/providers/tests/eval/conftest.py @@ -6,10 +6,14 @@ import pytest +from ..agents.fixtures import AGENTS_FIXTURES + from ..conftest import get_provider_fixture_overrides from ..datasetio.fixtures import DATASETIO_FIXTURES from ..inference.fixtures import INFERENCE_FIXTURES +from ..memory.fixtures import MEMORY_FIXTURES +from ..safety.fixtures import SAFETY_FIXTURES from ..scoring.fixtures import SCORING_FIXTURES from .fixtures import EVAL_FIXTURES @@ -20,6 +24,9 @@ DEFAULT_PROVIDER_COMBINATIONS = [ "scoring": "basic", "datasetio": "localfs", "inference": "fireworks", + "agents": "meta_reference", + "safety": "llama_guard", + "memory": "faiss", }, id="meta_reference_eval_fireworks_inference", marks=pytest.mark.meta_reference_eval_fireworks_inference, @@ -30,6 +37,9 @@ DEFAULT_PROVIDER_COMBINATIONS = [ "scoring": "basic", "datasetio": "localfs", "inference": "together", + "agents": "meta_reference", + "safety": "llama_guard", + "memory": "faiss", }, id="meta_reference_eval_together_inference", marks=pytest.mark.meta_reference_eval_together_inference, @@ -40,6 +50,9 @@ DEFAULT_PROVIDER_COMBINATIONS = [ "scoring": "basic", "datasetio": "huggingface", "inference": "together", + "agents": "meta_reference", + "safety": "llama_guard", + "memory": "faiss", }, id="meta_reference_eval_together_inference_huggingface_datasetio", marks=pytest.mark.meta_reference_eval_together_inference_huggingface_datasetio, @@ -75,6 +88,9 @@ def pytest_generate_tests(metafunc): "scoring": SCORING_FIXTURES, "datasetio": DATASETIO_FIXTURES, "inference": INFERENCE_FIXTURES, + "agents": AGENTS_FIXTURES, + "safety": SAFETY_FIXTURES, + "memory": MEMORY_FIXTURES, } combinations = ( get_provider_fixture_overrides(metafunc.config, available_fixtures) diff --git a/llama_stack/providers/tests/eval/fixtures.py b/llama_stack/providers/tests/eval/fixtures.py index a6b404d0c..50dc9c16e 100644 --- a/llama_stack/providers/tests/eval/fixtures.py +++ b/llama_stack/providers/tests/eval/fixtures.py @@ -40,14 +40,30 @@ async def eval_stack(request): providers = {} provider_data = {} - for key in ["datasetio", "eval", "scoring", "inference"]: + for key in [ + "datasetio", + "eval", + "scoring", + "inference", + "agents", + "safety", + "memory", + ]: fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}") providers[key] = fixture.providers if fixture.provider_data: provider_data.update(fixture.provider_data) test_stack = await construct_stack_for_test( - [Api.eval, Api.datasetio, Api.inference, Api.scoring], + [ + Api.eval, + Api.datasetio, + Api.inference, + Api.scoring, + Api.agents, + Api.safety, + Api.memory, + ], providers, provider_data, ) diff --git a/llama_stack/providers/tests/inference/fixtures.py b/llama_stack/providers/tests/inference/fixtures.py index a427eef12..21e122149 100644 --- a/llama_stack/providers/tests/inference/fixtures.py +++ b/llama_stack/providers/tests/inference/fixtures.py @@ -17,6 +17,7 @@ from llama_stack.providers.inline.inference.meta_reference import ( ) from llama_stack.providers.remote.inference.bedrock import BedrockConfig +from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig from llama_stack.providers.remote.inference.nvidia import NVIDIAConfig from llama_stack.providers.remote.inference.ollama import OllamaImplConfig @@ -64,6 +65,21 @@ def inference_meta_reference(inference_model) -> ProviderFixture: ) +@pytest.fixture(scope="session") +def inference_cerebras() -> ProviderFixture: + return ProviderFixture( + providers=[ + Provider( + provider_id="cerebras", + provider_type="remote::cerebras", + config=CerebrasImplConfig( + api_key=get_env_or_fail("CEREBRAS_API_KEY"), + ).model_dump(), + ) + ], + ) + + @pytest.fixture(scope="session") def inference_ollama(inference_model) -> ProviderFixture: inference_model = ( @@ -206,6 +222,7 @@ INFERENCE_FIXTURES = [ "vllm_remote", "remote", "bedrock", + "cerebras", "nvidia", "tgi", ] diff --git a/llama_stack/providers/tests/inference/test_text_inference.py b/llama_stack/providers/tests/inference/test_text_inference.py index f0f1d0eb2..b84761219 100644 --- a/llama_stack/providers/tests/inference/test_text_inference.py +++ b/llama_stack/providers/tests/inference/test_text_inference.py @@ -94,6 +94,7 @@ class TestInference: "remote::tgi", "remote::together", "remote::fireworks", + "remote::cerebras", ): pytest.skip("Other inference providers don't support completion() yet") @@ -139,6 +140,8 @@ class TestInference: "remote::tgi", "remote::together", "remote::fireworks", + "remote::vllm", + "remote::cerebras", ): pytest.skip( "Other inference providers don't support structured output in completions yet" @@ -198,6 +201,7 @@ class TestInference: "remote::fireworks", "remote::tgi", "remote::together", + "remote::vllm", "remote::nvidia", ): pytest.skip("Other inference providers don't support structured output yet") @@ -211,7 +215,15 @@ class TestInference: response = await inference_impl.chat_completion( model_id=inference_model, messages=[ - SystemMessage(content="You are a helpful assistant."), + # we include context about Michael Jordan in the prompt so that the test is + # focused on the funtionality of the model and not on the information embedded + # in the model. Llama 3.2 3B Instruct tends to think MJ played for 14 seasons. + SystemMessage( + content=( + "You are a helpful assistant.\n\n" + "Michael Jordan was born in 1963. He played basketball for the Chicago Bulls for 15 seasons." + ) + ), UserMessage(content="Please give me information about Michael Jordan."), ], stream=False, diff --git a/llama_stack/providers/tests/scoring/fixtures.py b/llama_stack/providers/tests/scoring/fixtures.py index d89b211ef..a9f088e07 100644 --- a/llama_stack/providers/tests/scoring/fixtures.py +++ b/llama_stack/providers/tests/scoring/fixtures.py @@ -10,9 +10,10 @@ import pytest_asyncio from llama_stack.apis.models import ModelInput from llama_stack.distribution.datatypes import Api, Provider - +from llama_stack.providers.inline.scoring.braintrust import BraintrustScoringConfig from llama_stack.providers.tests.resolver import construct_stack_for_test from ..conftest import ProviderFixture, remote_stack_fixture +from ..env import get_env_or_fail @pytest.fixture(scope="session") @@ -40,7 +41,9 @@ def scoring_braintrust() -> ProviderFixture: Provider( provider_id="braintrust", provider_type="inline::braintrust", - config={}, + config=BraintrustScoringConfig( + openai_api_key=get_env_or_fail("OPENAI_API_KEY"), + ).model_dump(), ) ], ) diff --git a/llama_stack/providers/utils/telemetry/dataset_mixin.py b/llama_stack/providers/utils/telemetry/dataset_mixin.py new file mode 100644 index 000000000..7a59801f4 --- /dev/null +++ b/llama_stack/providers/utils/telemetry/dataset_mixin.py @@ -0,0 +1,87 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from typing import List, Optional + +from llama_stack.apis.datasetio import DatasetIO +from llama_stack.apis.telemetry import QueryCondition, Span, SpanWithChildren + + +class TelemetryDatasetMixin: + """Mixin class that provides dataset-related functionality for telemetry providers.""" + + datasetio_api: DatasetIO + + async def save_spans_to_dataset( + self, + attribute_filters: List[QueryCondition], + attributes_to_save: List[str], + dataset_id: str, + max_depth: Optional[int] = None, + ) -> None: + spans = await self.query_spans( + attribute_filters=attribute_filters, + attributes_to_return=attributes_to_save, + max_depth=max_depth, + ) + + rows = [ + { + "trace_id": span.trace_id, + "span_id": span.span_id, + "parent_span_id": span.parent_span_id, + "name": span.name, + "start_time": span.start_time, + "end_time": span.end_time, + **{attr: span.attributes.get(attr) for attr in attributes_to_save}, + } + for span in spans + ] + + await self.datasetio_api.append_rows(dataset_id=dataset_id, rows=rows) + + async def query_spans( + self, + attribute_filters: List[QueryCondition], + attributes_to_return: List[str], + max_depth: Optional[int] = None, + ) -> List[Span]: + traces = await self.query_traces(attribute_filters=attribute_filters) + spans = [] + + for trace in traces: + span_tree = await self.get_span_tree( + span_id=trace.root_span_id, + attributes_to_return=attributes_to_return, + max_depth=max_depth, + ) + + def extract_spans(span: SpanWithChildren) -> List[Span]: + result = [] + if span.attributes and all( + attr in span.attributes and span.attributes[attr] is not None + for attr in attributes_to_return + ): + result.append( + Span( + trace_id=trace.root_span_id, + span_id=span.span_id, + parent_span_id=span.parent_span_id, + name=span.name, + start_time=span.start_time, + end_time=span.end_time, + attributes=span.attributes, + ) + ) + + for child in span.children: + result.extend(extract_spans(child)) + + return result + + spans.extend(extract_spans(span_tree)) + + return spans diff --git a/llama_stack/providers/utils/telemetry/sqlite_trace_store.py b/llama_stack/providers/utils/telemetry/sqlite_trace_store.py new file mode 100644 index 000000000..8d9035216 --- /dev/null +++ b/llama_stack/providers/utils/telemetry/sqlite_trace_store.py @@ -0,0 +1,178 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import json +from datetime import datetime +from typing import List, Optional, Protocol + +import aiosqlite + +from llama_stack.apis.telemetry import QueryCondition, SpanWithChildren, Trace + + +class TraceStore(Protocol): + async def query_traces( + self, + attribute_filters: Optional[List[QueryCondition]] = None, + limit: Optional[int] = 100, + offset: Optional[int] = 0, + order_by: Optional[List[str]] = None, + ) -> List[Trace]: ... + + async def get_span_tree( + self, + span_id: str, + attributes_to_return: Optional[List[str]] = None, + max_depth: Optional[int] = None, + ) -> SpanWithChildren: ... + + +class SQLiteTraceStore(TraceStore): + def __init__(self, conn_string: str): + self.conn_string = conn_string + + async def query_traces( + self, + attribute_filters: Optional[List[QueryCondition]] = None, + limit: Optional[int] = 100, + offset: Optional[int] = 0, + order_by: Optional[List[str]] = None, + ) -> List[Trace]: + def build_where_clause() -> tuple[str, list]: + if not attribute_filters: + return "", [] + + ops_map = {"eq": "=", "ne": "!=", "gt": ">", "lt": "<"} + + conditions = [ + f"json_extract(s.attributes, '$.{condition.key}') {ops_map[condition.op.value]} ?" + for condition in attribute_filters + ] + params = [condition.value for condition in attribute_filters] + where_clause = " WHERE " + " AND ".join(conditions) + return where_clause, params + + def build_order_clause() -> str: + if not order_by: + return "" + + order_clauses = [] + for field in order_by: + desc = field.startswith("-") + clean_field = field[1:] if desc else field + order_clauses.append(f"t.{clean_field} {'DESC' if desc else 'ASC'}") + return " ORDER BY " + ", ".join(order_clauses) + + # Build the main query + base_query = """ + WITH matching_traces AS ( + SELECT DISTINCT t.trace_id + FROM traces t + JOIN spans s ON t.trace_id = s.trace_id + {where_clause} + ), + filtered_traces AS ( + SELECT t.trace_id, t.root_span_id, t.start_time, t.end_time + FROM matching_traces mt + JOIN traces t ON mt.trace_id = t.trace_id + LEFT JOIN spans s ON t.trace_id = s.trace_id + {order_clause} + ) + SELECT DISTINCT trace_id, root_span_id, start_time, end_time + FROM filtered_traces + LIMIT {limit} OFFSET {offset} + """ + + where_clause, params = build_where_clause() + query = base_query.format( + where_clause=where_clause, + order_clause=build_order_clause(), + limit=limit, + offset=offset, + ) + + # Execute query and return results + async with aiosqlite.connect(self.conn_string) as conn: + conn.row_factory = aiosqlite.Row + async with conn.execute(query, params) as cursor: + rows = await cursor.fetchall() + return [ + Trace( + trace_id=row["trace_id"], + root_span_id=row["root_span_id"], + start_time=datetime.fromisoformat(row["start_time"]), + end_time=datetime.fromisoformat(row["end_time"]), + ) + for row in rows + ] + + async def get_span_tree( + self, + span_id: str, + attributes_to_return: Optional[List[str]] = None, + max_depth: Optional[int] = None, + ) -> SpanWithChildren: + # Build the attributes selection + attributes_select = "s.attributes" + if attributes_to_return: + json_object = ", ".join( + f"'{key}', json_extract(s.attributes, '$.{key}')" + for key in attributes_to_return + ) + attributes_select = f"json_object({json_object})" + + # SQLite CTE query with filtered attributes + query = f""" + WITH RECURSIVE span_tree AS ( + SELECT s.*, 1 as depth, {attributes_select} as filtered_attributes + FROM spans s + WHERE s.span_id = ? + + UNION ALL + + SELECT s.*, st.depth + 1, {attributes_select} as filtered_attributes + FROM spans s + JOIN span_tree st ON s.parent_span_id = st.span_id + WHERE (? IS NULL OR st.depth < ?) + ) + SELECT * + FROM span_tree + ORDER BY depth, start_time + """ + + async with aiosqlite.connect(self.conn_string) as conn: + conn.row_factory = aiosqlite.Row + async with conn.execute(query, (span_id, max_depth, max_depth)) as cursor: + rows = await cursor.fetchall() + + if not rows: + raise ValueError(f"Span {span_id} not found") + + # Build span tree + spans_by_id = {} + root_span = None + + for row in rows: + span = SpanWithChildren( + span_id=row["span_id"], + trace_id=row["trace_id"], + parent_span_id=row["parent_span_id"], + name=row["name"], + start_time=datetime.fromisoformat(row["start_time"]), + end_time=datetime.fromisoformat(row["end_time"]), + attributes=json.loads(row["filtered_attributes"]), + status=row["status"].lower(), + children=[], + ) + + spans_by_id[span.span_id] = span + + if span.span_id == span_id: + root_span = span + elif span.parent_span_id in spans_by_id: + spans_by_id[span.parent_span_id].children.append(span) + + return root_span diff --git a/llama_stack/providers/utils/telemetry/trace_protocol.py b/llama_stack/providers/utils/telemetry/trace_protocol.py new file mode 100644 index 000000000..3fcce08e9 --- /dev/null +++ b/llama_stack/providers/utils/telemetry/trace_protocol.py @@ -0,0 +1,129 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +import asyncio +import inspect +import json +from functools import wraps +from typing import Any, AsyncGenerator, Callable, Type, TypeVar + +from pydantic import BaseModel + +T = TypeVar("T") + + +def serialize_value(value: Any) -> str: + """Helper function to serialize values to string representation.""" + try: + if isinstance(value, BaseModel): + return value.model_dump_json() + elif isinstance(value, list) and value and isinstance(value[0], BaseModel): + return json.dumps([item.model_dump_json() for item in value]) + elif hasattr(value, "to_dict"): + return json.dumps(value.to_dict()) + elif isinstance(value, (dict, list, int, float, str, bool)): + return json.dumps(value) + else: + return str(value) + except Exception: + return str(value) + + +def trace_protocol(cls: Type[T]) -> Type[T]: + """ + A class decorator that automatically traces all methods in a protocol/base class + and its inheriting classes. + """ + + def trace_method(method: Callable) -> Callable: + from llama_stack.providers.utils.telemetry import tracing + + is_async = asyncio.iscoroutinefunction(method) + is_async_gen = inspect.isasyncgenfunction(method) + + def create_span_context(self: Any, *args: Any, **kwargs: Any) -> tuple: + class_name = self.__class__.__name__ + method_name = method.__name__ + + span_type = ( + "async_generator" if is_async_gen else "async" if is_async else "sync" + ) + span_attributes = { + "__autotraced__": True, + "__class__": class_name, + "__method__": method_name, + "__type__": span_type, + "__args__": serialize_value(args), + } + + return class_name, method_name, span_attributes + + @wraps(method) + async def async_gen_wrapper( + self: Any, *args: Any, **kwargs: Any + ) -> AsyncGenerator: + class_name, method_name, span_attributes = create_span_context( + self, *args, **kwargs + ) + + with tracing.span(f"{class_name}.{method_name}", span_attributes) as span: + try: + count = 0 + async for item in method(self, *args, **kwargs): + yield item + count += 1 + finally: + span.set_attribute("chunk_count", count) + + @wraps(method) + async def async_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any: + class_name, method_name, span_attributes = create_span_context( + self, *args, **kwargs + ) + + with tracing.span(f"{class_name}.{method_name}", span_attributes) as span: + try: + result = await method(self, *args, **kwargs) + span.set_attribute("output", serialize_value(result)) + return result + except Exception as e: + span.set_attribute("error", str(e)) + raise + + @wraps(method) + def sync_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any: + class_name, method_name, span_attributes = create_span_context( + self, *args, **kwargs + ) + + with tracing.span(f"{class_name}.{method_name}", span_attributes) as span: + try: + result = method(self, *args, **kwargs) + span.set_attribute("output", serialize_value(result)) + return result + except Exception as _e: + raise + + if is_async_gen: + return async_gen_wrapper + elif is_async: + return async_wrapper + else: + return sync_wrapper + + original_init_subclass = getattr(cls, "__init_subclass__", None) + + def __init_subclass__(cls_child, **kwargs): # noqa: N807 + if original_init_subclass: + original_init_subclass(**kwargs) + + for name, method in vars(cls_child).items(): + if inspect.isfunction(method) and not name.startswith("_"): + setattr(cls_child, name, trace_method(method)) # noqa: B010 + + cls.__init_subclass__ = classmethod(__init_subclass__) + + return cls diff --git a/llama_stack/providers/utils/telemetry/tracing.py b/llama_stack/providers/utils/telemetry/tracing.py index b53dc0df9..54558afdc 100644 --- a/llama_stack/providers/utils/telemetry/tracing.py +++ b/llama_stack/providers/utils/telemetry/tracing.py @@ -69,7 +69,7 @@ class TraceContext: self.logger = logger self.trace_id = trace_id - def push_span(self, name: str, attributes: Dict[str, Any] = None): + def push_span(self, name: str, attributes: Dict[str, Any] = None) -> Span: current_span = self.get_current_span() span = Span( span_id=generate_short_uuid(), @@ -94,6 +94,7 @@ class TraceContext: ) self.spans.append(span) + return span def pop_span(self, status: SpanStatus = SpanStatus.OK): span = self.spans.pop() @@ -203,12 +204,13 @@ class SpanContextManager: def __init__(self, name: str, attributes: Dict[str, Any] = None): self.name = name self.attributes = attributes + self.span = None def __enter__(self): global CURRENT_TRACE_CONTEXT context = CURRENT_TRACE_CONTEXT if context: - context.push_span(self.name, self.attributes) + self.span = context.push_span(self.name, self.attributes) return self def __exit__(self, exc_type, exc_value, traceback): @@ -217,11 +219,24 @@ class SpanContextManager: if context: context.pop_span() + def set_attribute(self, key: str, value: Any): + if self.span: + if self.span.attributes is None: + self.span.attributes = {} + self.span.attributes[key] = value + async def __aenter__(self): - return self.__enter__() + global CURRENT_TRACE_CONTEXT + context = CURRENT_TRACE_CONTEXT + if context: + self.span = context.push_span(self.name, self.attributes) + return self async def __aexit__(self, exc_type, exc_value, traceback): - self.__exit__(exc_type, exc_value, traceback) + global CURRENT_TRACE_CONTEXT + context = CURRENT_TRACE_CONTEXT + if context: + context.pop_span() def __call__(self, func: Callable): @wraps(func) @@ -246,3 +261,11 @@ class SpanContextManager: def span(name: str, attributes: Dict[str, Any] = None): return SpanContextManager(name, attributes) + + +def get_current_span() -> Optional[Span]: + global CURRENT_TRACE_CONTEXT + context = CURRENT_TRACE_CONTEXT + if context: + return context.get_current_span() + return None diff --git a/llama_stack/templates/bedrock/bedrock.py b/llama_stack/templates/bedrock/bedrock.py index cf3c342fe..c52b56612 100644 --- a/llama_stack/templates/bedrock/bedrock.py +++ b/llama_stack/templates/bedrock/bedrock.py @@ -6,6 +6,9 @@ from pathlib import Path +from llama_stack.distribution.datatypes import Provider + +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -16,10 +19,19 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["remote::bedrock"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } + name = "bedrock" + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) return DistributionTemplate( - name="bedrock", + name=name, distro_type="self_hosted", description="Use AWS Bedrock for running LLM inference and safety", docker_image=None, @@ -27,7 +39,11 @@ def get_distribution_template() -> DistributionTemplate: providers=providers, default_models=[], run_configs={ - "run.yaml": RunConfigSettings(), + "run.yaml": RunConfigSettings( + provider_overrides={ + "memory": [memory_provider], + }, + ), }, run_config_env_vars={ "LLAMASTACK_PORT": ( diff --git a/llama_stack/templates/bedrock/build.yaml b/llama_stack/templates/bedrock/build.yaml index c73db3eae..cd36c320e 100644 --- a/llama_stack/templates/bedrock/build.yaml +++ b/llama_stack/templates/bedrock/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/bedrock/run.yaml b/llama_stack/templates/bedrock/run.yaml index 1f632a1f2..47885b536 100644 --- a/llama_stack/templates/bedrock/run.yaml +++ b/llama_stack/templates/bedrock/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: bedrock apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -34,9 +37,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/bedrock}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/bedrock/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/cerebras/__init__.py b/llama_stack/templates/cerebras/__init__.py new file mode 100644 index 000000000..9f9929b52 --- /dev/null +++ b/llama_stack/templates/cerebras/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from .cerebras import get_distribution_template # noqa: F401 diff --git a/llama_stack/templates/cerebras/build.yaml b/llama_stack/templates/cerebras/build.yaml new file mode 100644 index 000000000..a1fe93099 --- /dev/null +++ b/llama_stack/templates/cerebras/build.yaml @@ -0,0 +1,17 @@ +version: '2' +name: cerebras +distribution_spec: + description: Use Cerebras for running LLM inference + docker_image: null + providers: + inference: + - remote::cerebras + safety: + - inline::llama-guard + memory: + - inline::meta-reference + agents: + - inline::meta-reference + telemetry: + - inline::meta-reference +image_type: conda diff --git a/llama_stack/templates/cerebras/cerebras.py b/llama_stack/templates/cerebras/cerebras.py new file mode 100644 index 000000000..58e05adf8 --- /dev/null +++ b/llama_stack/templates/cerebras/cerebras.py @@ -0,0 +1,71 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the terms described in the LICENSE file in +# the root directory of this source tree. + +from pathlib import Path + +from llama_models.sku_list import all_registered_models + +from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.remote.inference.cerebras import CerebrasImplConfig +from llama_stack.providers.remote.inference.cerebras.cerebras import model_aliases + +from llama_stack.templates.template import DistributionTemplate, RunConfigSettings + + +def get_distribution_template() -> DistributionTemplate: + providers = { + "inference": ["remote::cerebras"], + "safety": ["inline::llama-guard"], + "memory": ["inline::meta-reference"], + "agents": ["inline::meta-reference"], + "telemetry": ["inline::meta-reference"], + } + + inference_provider = Provider( + provider_id="cerebras", + provider_type="remote::cerebras", + config=CerebrasImplConfig.sample_run_config(), + ) + + core_model_to_hf_repo = { + m.descriptor(): m.huggingface_repo for m in all_registered_models() + } + default_models = [ + ModelInput( + model_id=core_model_to_hf_repo[m.llama_model], + provider_model_id=m.provider_model_id, + ) + for m in model_aliases + ] + + return DistributionTemplate( + name="cerebras", + distro_type="self_hosted", + description="Use Cerebras for running LLM inference", + docker_image=None, + template_path=Path(__file__).parent / "doc_template.md", + providers=providers, + default_models=default_models, + run_configs={ + "run.yaml": RunConfigSettings( + provider_overrides={ + "inference": [inference_provider], + }, + default_models=default_models, + default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], + ), + }, + run_config_env_vars={ + "LLAMASTACK_PORT": ( + "5001", + "Port for the Llama Stack distribution server", + ), + "CEREBRAS_API_KEY": ( + "", + "Cerebras API Key", + ), + }, + ) diff --git a/llama_stack/templates/cerebras/doc_template.md b/llama_stack/templates/cerebras/doc_template.md new file mode 100644 index 000000000..77fc6f478 --- /dev/null +++ b/llama_stack/templates/cerebras/doc_template.md @@ -0,0 +1,60 @@ +# Cerebras Distribution + +The `llamastack/distribution-{{ name }}` distribution consists of the following provider configurations. + +{{ providers_table }} + +{% if run_config_env_vars %} +### Environment Variables + +The following environment variables can be configured: + +{% for var, (default_value, description) in run_config_env_vars.items() %} +- `{{ var }}`: {{ description }} (default: `{{ default_value }}`) +{% endfor %} +{% endif %} + +{% if default_models %} +### Models + +The following models are available by default: + +{% for model in default_models %} +- `{{ model.model_id }} ({{ model.provider_model_id }})` +{% endfor %} +{% endif %} + + +### Prerequisite: API Keys + +Make sure you have access to a Cerebras API Key. You can get one by visiting [cloud.cerebras.ai](https://cloud.cerebras.ai/). + + +## Running Llama Stack with Cerebras + +You can do this via Conda (build code) or Docker which has a pre-built image. + +### Via Docker + +This method allows you to get started quickly without having to build the distribution code. + +```bash +LLAMA_STACK_PORT=5001 +docker run \ + -it \ + -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ./run.yaml:/root/my-run.yaml \ + llamastack/distribution-{{ name }} \ + --yaml-config /root/my-run.yaml \ + --port $LLAMA_STACK_PORT \ + --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY +``` + +### Via Conda + +```bash +llama stack build --template cerebras --image-type conda +llama stack run ./run.yaml \ + --port 5001 \ + --env CEREBRAS_API_KEY=$CEREBRAS_API_KEY +``` diff --git a/llama_stack/templates/cerebras/run.yaml b/llama_stack/templates/cerebras/run.yaml new file mode 100644 index 000000000..451e2b076 --- /dev/null +++ b/llama_stack/templates/cerebras/run.yaml @@ -0,0 +1,66 @@ +version: '2' +image_name: cerebras +docker_image: null +conda_env: cerebras +apis: +- agents +- inference +- memory +- safety +- telemetry +providers: + inference: + - provider_id: cerebras + provider_type: remote::cerebras + config: + base_url: https://api.cerebras.ai + api_key: ${env.CEREBRAS_API_KEY} + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: {} + memory: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + kvstore: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/faiss_store.db + agents: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + persistence_store: + type: sqlite + namespace: null + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/agents_store.db + telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/cerebras/trace_store.db} +metadata_store: + namespace: null + type: sqlite + db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/cerebras}/registry.db +models: +- metadata: {} + model_id: meta-llama/Llama-3.1-8B-Instruct + provider_id: null + provider_model_id: llama3.1-8b +- metadata: {} + model_id: meta-llama/Llama-3.1-70B-Instruct + provider_id: null + provider_model_id: llama3.1-70b +shields: +- params: null + shield_id: meta-llama/Llama-Guard-3-8B + provider_id: null + provider_shield_id: null +memory_banks: [] +datasets: [] +scoring_fns: [] +eval_tasks: [] diff --git a/llama_stack/templates/fireworks/build.yaml b/llama_stack/templates/fireworks/build.yaml index c16e3f5d6..30ea347ae 100644 --- a/llama_stack/templates/fireworks/build.yaml +++ b/llama_stack/templates/fireworks/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/fireworks/fireworks.py b/llama_stack/templates/fireworks/fireworks.py index 5f744cae0..64387e4b7 100644 --- a/llama_stack/templates/fireworks/fireworks.py +++ b/llama_stack/templates/fireworks/fireworks.py @@ -9,6 +9,7 @@ from pathlib import Path from llama_models.sku_list import all_registered_models from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.fireworks import FireworksImplConfig from llama_stack.providers.remote.inference.fireworks.fireworks import MODEL_ALIASES @@ -22,13 +23,23 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } + name = "fireworks" + inference_provider = Provider( provider_id="fireworks", provider_type="remote::fireworks", config=FireworksImplConfig.sample_run_config(), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) core_model_to_hf_repo = { m.descriptor(): m.huggingface_repo for m in all_registered_models() @@ -42,7 +53,7 @@ def get_distribution_template() -> DistributionTemplate: ] return DistributionTemplate( - name="fireworks", + name=name, distro_type="self_hosted", description="Use Fireworks.AI for running LLM inference", docker_image=None, @@ -53,6 +64,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=default_models, default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], diff --git a/llama_stack/templates/fireworks/run.yaml b/llama_stack/templates/fireworks/run.yaml index 6add39c3a..70e2c1e5c 100644 --- a/llama_stack/templates/fireworks/run.yaml +++ b/llama_stack/templates/fireworks/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: fireworks apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -36,9 +39,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/fireworks}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/fireworks/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/hf-endpoint/build.yaml b/llama_stack/templates/hf-endpoint/build.yaml index 798cb3961..523cf5d83 100644 --- a/llama_stack/templates/hf-endpoint/build.yaml +++ b/llama_stack/templates/hf-endpoint/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/hf-endpoint/hf_endpoint.py b/llama_stack/templates/hf-endpoint/hf_endpoint.py index af00114ba..297fdae51 100644 --- a/llama_stack/templates/hf-endpoint/hf_endpoint.py +++ b/llama_stack/templates/hf-endpoint/hf_endpoint.py @@ -5,6 +5,7 @@ # the root directory of this source tree. from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.tgi import InferenceEndpointImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -16,13 +17,21 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "hf-endpoint" inference_provider = Provider( provider_id="hf-endpoint", provider_type="remote::hf::endpoint", config=InferenceEndpointImplConfig.sample_run_config(), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -34,7 +43,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="hf-endpoint", + name=name, distro_type="self_hosted", description="Use (an external) Hugging Face Inference Endpoint for running LLM inference", docker_image=None, @@ -45,6 +54,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), @@ -59,7 +69,8 @@ def get_distribution_template() -> DistributionTemplate: endpoint_name="${env.SAFETY_INFERENCE_ENDPOINT_NAME}", ), ), - ] + ], + "memory": [memory_provider], }, default_models=[ inference_model, diff --git a/llama_stack/templates/hf-endpoint/run-with-safety.yaml b/llama_stack/templates/hf-endpoint/run-with-safety.yaml index d518f29b8..845abf0dc 100644 --- a/llama_stack/templates/hf-endpoint/run-with-safety.yaml +++ b/llama_stack/templates/hf-endpoint/run-with-safety.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: hf-endpoint apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -41,9 +44,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-endpoint/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/hf-endpoint/run.yaml b/llama_stack/templates/hf-endpoint/run.yaml index ff4e90606..815ee7f03 100644 --- a/llama_stack/templates/hf-endpoint/run.yaml +++ b/llama_stack/templates/hf-endpoint/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: hf-endpoint apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -36,9 +39,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-endpoint}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-endpoint/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/hf-serverless/build.yaml b/llama_stack/templates/hf-serverless/build.yaml index 3c03a98c1..af7eb60fe 100644 --- a/llama_stack/templates/hf-serverless/build.yaml +++ b/llama_stack/templates/hf-serverless/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/hf-serverless/hf_serverless.py b/llama_stack/templates/hf-serverless/hf_serverless.py index 5434de986..835495bb9 100644 --- a/llama_stack/templates/hf-serverless/hf_serverless.py +++ b/llama_stack/templates/hf-serverless/hf_serverless.py @@ -5,6 +5,7 @@ # the root directory of this source tree. from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.tgi import InferenceAPIImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -16,13 +17,22 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } + name = "hf-serverless" inference_provider = Provider( provider_id="hf-serverless", provider_type="remote::hf::serverless", config=InferenceAPIImplConfig.sample_run_config(), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -34,7 +44,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="hf-serverless", + name=name, distro_type="self_hosted", description="Use (an external) Hugging Face Inference Endpoint for running LLM inference", docker_image=None, @@ -45,6 +55,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), @@ -59,7 +70,8 @@ def get_distribution_template() -> DistributionTemplate: repo="${env.SAFETY_MODEL}", ), ), - ] + ], + "memory": [memory_provider], }, default_models=[ inference_model, diff --git a/llama_stack/templates/hf-serverless/run-with-safety.yaml b/llama_stack/templates/hf-serverless/run-with-safety.yaml index e7591bbf0..82276ca8f 100644 --- a/llama_stack/templates/hf-serverless/run-with-safety.yaml +++ b/llama_stack/templates/hf-serverless/run-with-safety.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: hf-serverless apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -41,9 +44,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-serverless/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/hf-serverless/run.yaml b/llama_stack/templates/hf-serverless/run.yaml index d7ec02f6a..6f87c04e2 100644 --- a/llama_stack/templates/hf-serverless/run.yaml +++ b/llama_stack/templates/hf-serverless/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: hf-serverless apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -36,9 +39,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/hf-serverless}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/hf-serverless/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/meta-reference-gpu/build.yaml b/llama_stack/templates/meta-reference-gpu/build.yaml index ef075d098..300b75b14 100644 --- a/llama_stack/templates/meta-reference-gpu/build.yaml +++ b/llama_stack/templates/meta-reference-gpu/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/meta-reference-gpu/doc_template.md b/llama_stack/templates/meta-reference-gpu/doc_template.md index 865944476..421812dbc 100644 --- a/llama_stack/templates/meta-reference-gpu/doc_template.md +++ b/llama_stack/templates/meta-reference-gpu/doc_template.md @@ -29,7 +29,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ ls ~/.llama/checkpoints @@ -50,6 +50,7 @@ LLAMA_STACK_PORT=5001 docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-{{ name }} \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct @@ -61,6 +62,7 @@ If you are using Llama Stack Safety / Shield APIs, use: docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-{{ name }} \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ diff --git a/llama_stack/templates/meta-reference-gpu/meta_reference.py b/llama_stack/templates/meta-reference-gpu/meta_reference.py index f254bc920..0aff9f39c 100644 --- a/llama_stack/templates/meta-reference-gpu/meta_reference.py +++ b/llama_stack/templates/meta-reference-gpu/meta_reference.py @@ -10,6 +10,7 @@ from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput from llama_stack.providers.inline.inference.meta_reference import ( MetaReferenceInferenceConfig, ) +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -20,8 +21,11 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "meta-reference-gpu" inference_provider = Provider( provider_id="meta-reference-inference", provider_type="inline::meta-reference", @@ -30,6 +34,11 @@ def get_distribution_template() -> DistributionTemplate: checkpoint_dir="${env.INFERENCE_CHECKPOINT_DIR:null}", ), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -41,7 +50,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="meta-reference-gpu", + name=name, distro_type="self_hosted", description="Use Meta Reference for running LLM inference", template_path=Path(__file__).parent / "doc_template.md", @@ -51,6 +60,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), @@ -67,6 +77,7 @@ def get_distribution_template() -> DistributionTemplate: ), ), ], + "memory": [memory_provider], }, default_models=[ inference_model, diff --git a/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml b/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml index f82e0c938..044c1e7fd 100644 --- a/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml +++ b/llama_stack/templates/meta-reference-gpu/run-with-safety.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: meta-reference-gpu apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -43,9 +46,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/meta-reference-gpu/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/meta-reference-gpu/run.yaml b/llama_stack/templates/meta-reference-gpu/run.yaml index b125169a3..e8fdb10c2 100644 --- a/llama_stack/templates/meta-reference-gpu/run.yaml +++ b/llama_stack/templates/meta-reference-gpu/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: meta-reference-gpu apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -37,9 +40,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-gpu}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/meta-reference-gpu/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/meta-reference-quantized-gpu/build.yaml b/llama_stack/templates/meta-reference-quantized-gpu/build.yaml index 961864dac..9d866de18 100644 --- a/llama_stack/templates/meta-reference-quantized-gpu/build.yaml +++ b/llama_stack/templates/meta-reference-quantized-gpu/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/meta-reference-quantized-gpu/doc_template.md b/llama_stack/templates/meta-reference-quantized-gpu/doc_template.md index 567d83941..daa380d20 100644 --- a/llama_stack/templates/meta-reference-quantized-gpu/doc_template.md +++ b/llama_stack/templates/meta-reference-quantized-gpu/doc_template.md @@ -31,7 +31,7 @@ The following environment variables can be configured: ## Prerequisite: Downloading Models -Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. +Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints. ``` $ ls ~/.llama/checkpoints @@ -52,6 +52,7 @@ LLAMA_STACK_PORT=5001 docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-{{ name }} \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct @@ -63,6 +64,7 @@ If you are using Llama Stack Safety / Shield APIs, use: docker run \ -it \ -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \ + -v ~/.llama:/root/.llama \ llamastack/distribution-{{ name }} \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ diff --git a/llama_stack/templates/meta-reference-quantized-gpu/meta_reference.py b/llama_stack/templates/meta-reference-quantized-gpu/meta_reference.py index 1ff5d31d6..1d611ae5f 100644 --- a/llama_stack/templates/meta-reference-quantized-gpu/meta_reference.py +++ b/llama_stack/templates/meta-reference-quantized-gpu/meta_reference.py @@ -10,6 +10,7 @@ from llama_stack.distribution.datatypes import ModelInput, Provider from llama_stack.providers.inline.inference.meta_reference import ( MetaReferenceQuantizedInferenceConfig, ) +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -20,8 +21,11 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "meta-reference-quantized-gpu" inference_provider = Provider( provider_id="meta-reference-inference", provider_type="inline::meta-reference-quantized", @@ -30,13 +34,18 @@ def get_distribution_template() -> DistributionTemplate: checkpoint_dir="${env.INFERENCE_CHECKPOINT_DIR:null}", ), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", provider_id="meta-reference-inference", ) return DistributionTemplate( - name="meta-reference-quantized-gpu", + name=name, distro_type="self_hosted", description="Use Meta Reference with fp8, int4 quantization for running LLM inference", template_path=Path(__file__).parent / "doc_template.md", @@ -46,6 +55,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), diff --git a/llama_stack/templates/meta-reference-quantized-gpu/run.yaml b/llama_stack/templates/meta-reference-quantized-gpu/run.yaml index e1104b623..0232ec51c 100644 --- a/llama_stack/templates/meta-reference-quantized-gpu/run.yaml +++ b/llama_stack/templates/meta-reference-quantized-gpu/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: meta-reference-quantized-gpu apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -39,9 +42,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/meta-reference-quantized-gpu}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/meta-reference-quantized-gpu/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/ollama/build.yaml b/llama_stack/templates/ollama/build.yaml index 106449309..a021e4993 100644 --- a/llama_stack/templates/ollama/build.yaml +++ b/llama_stack/templates/ollama/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/ollama/doc_template.md b/llama_stack/templates/ollama/doc_template.md index cfefce33d..a75583592 100644 --- a/llama_stack/templates/ollama/doc_template.md +++ b/llama_stack/templates/ollama/doc_template.md @@ -114,9 +114,9 @@ llama stack run ./run-with-safety.yaml \ ### (Optional) Update Model Serving Configuration -> [!NOTE] -> Please check the [OLLAMA_SUPPORTED_MODELS](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers.remote/inference/ollama/ollama.py) for the supported Ollama models. - +```{note} +Please check the [model_aliases](https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/inference/ollama/ollama.py#L45) for the supported Ollama models. +``` To serve a new model with `ollama` ```bash diff --git a/llama_stack/templates/ollama/ollama.py b/llama_stack/templates/ollama/ollama.py index b30c75bb5..c24dfa6e9 100644 --- a/llama_stack/templates/ollama/ollama.py +++ b/llama_stack/templates/ollama/ollama.py @@ -7,6 +7,7 @@ from pathlib import Path from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.ollama import OllamaImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -18,13 +19,21 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "ollama" inference_provider = Provider( provider_id="ollama", provider_type="remote::ollama", config=OllamaImplConfig.sample_run_config(), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -36,7 +45,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="ollama", + name=name, distro_type="self_hosted", description="Use (an external) Ollama server for running LLM inference", docker_image=None, @@ -47,6 +56,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), @@ -54,7 +64,8 @@ def get_distribution_template() -> DistributionTemplate: provider_overrides={ "inference": [ inference_provider, - ] + ], + "memory": [memory_provider], }, default_models=[ inference_model, diff --git a/llama_stack/templates/ollama/run-with-safety.yaml b/llama_stack/templates/ollama/run-with-safety.yaml index 6c86677b3..fcb1b2dba 100644 --- a/llama_stack/templates/ollama/run-with-safety.yaml +++ b/llama_stack/templates/ollama/run-with-safety.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: ollama apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -35,9 +38,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/ollama/run.yaml b/llama_stack/templates/ollama/run.yaml index b2d6f2c18..2e739aac2 100644 --- a/llama_stack/templates/ollama/run.yaml +++ b/llama_stack/templates/ollama/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: ollama apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -35,9 +38,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/ollama}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/ollama/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/remote-vllm/run-with-safety.yaml b/llama_stack/templates/remote-vllm/run-with-safety.yaml index c0849e2d0..ac8cf6f4a 100644 --- a/llama_stack/templates/remote-vllm/run-with-safety.yaml +++ b/llama_stack/templates/remote-vllm/run-with-safety.yaml @@ -45,7 +45,10 @@ providers: telemetry: - provider_id: meta-reference provider_type: inline::meta-reference - config: {} + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/remote-vllm/run.yaml b/llama_stack/templates/remote-vllm/run.yaml index 3457afdd6..27c5df53c 100644 --- a/llama_stack/templates/remote-vllm/run.yaml +++ b/llama_stack/templates/remote-vllm/run.yaml @@ -39,7 +39,10 @@ providers: telemetry: - provider_id: meta-reference provider_type: inline::meta-reference - config: {} + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/remote-vllm/trace_store.db} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/remote-vllm/vllm.py b/llama_stack/templates/remote-vllm/vllm.py index c3858f7e5..f5ccfcf16 100644 --- a/llama_stack/templates/remote-vllm/vllm.py +++ b/llama_stack/templates/remote-vllm/vllm.py @@ -7,6 +7,7 @@ from pathlib import Path from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -19,7 +20,7 @@ def get_distribution_template() -> DistributionTemplate: "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], } - + name = "remote-vllm" inference_provider = Provider( provider_id="vllm-inference", provider_type="remote::vllm", @@ -27,6 +28,11 @@ def get_distribution_template() -> DistributionTemplate: url="${env.VLLM_URL}", ), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -38,7 +44,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="remote-vllm", + name=name, distro_type="self_hosted", description="Use (an external) vLLM server for running LLM inference", template_path=Path(__file__).parent / "doc_template.md", @@ -48,6 +54,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), @@ -63,6 +70,7 @@ def get_distribution_template() -> DistributionTemplate: ), ), ], + "memory": [memory_provider], }, default_models=[ inference_model, diff --git a/llama_stack/templates/template.py b/llama_stack/templates/template.py index bf74b95d1..e82be6394 100644 --- a/llama_stack/templates/template.py +++ b/llama_stack/templates/template.py @@ -44,36 +44,37 @@ class RunConfigSettings(BaseModel): provider_configs[api_str] = api_providers continue - provider_type = provider_types[0] - provider_id = provider_type.split("::")[-1] + provider_configs[api_str] = [] + for provider_type in provider_types: + provider_id = provider_type.split("::")[-1] - api = Api(api_str) - if provider_type not in provider_registry[api]: - raise ValueError( - f"Unknown provider type: {provider_type} for API: {api_str}" + api = Api(api_str) + if provider_type not in provider_registry[api]: + raise ValueError( + f"Unknown provider type: {provider_type} for API: {api_str}" + ) + + config_class = provider_registry[api][provider_type].config_class + assert ( + config_class is not None + ), f"No config class for provider type: {provider_type} for API: {api_str}" + + config_class = instantiate_class_type(config_class) + if hasattr(config_class, "sample_run_config"): + config = config_class.sample_run_config( + __distro_dir__=f"distributions/{name}" + ) + else: + config = {} + + provider_configs[api_str].append( + Provider( + provider_id=provider_id, + provider_type=provider_type, + config=config, + ) ) - config_class = provider_registry[api][provider_type].config_class - assert ( - config_class is not None - ), f"No config class for provider type: {provider_type} for API: {api_str}" - - config_class = instantiate_class_type(config_class) - if hasattr(config_class, "sample_run_config"): - config = config_class.sample_run_config( - __distro_dir__=f"distributions/{name}" - ) - else: - config = {} - - provider_configs[api_str] = [ - Provider( - provider_id=provider_id, - provider_type=provider_type, - config=config, - ) - ] - # Get unique set of APIs from providers apis = list(sorted(providers.keys())) diff --git a/llama_stack/templates/tgi/build.yaml b/llama_stack/templates/tgi/build.yaml index 0f7602e2f..d90b505df 100644 --- a/llama_stack/templates/tgi/build.yaml +++ b/llama_stack/templates/tgi/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/tgi/run-with-safety.yaml b/llama_stack/templates/tgi/run-with-safety.yaml index ebf082cd6..a7375a90f 100644 --- a/llama_stack/templates/tgi/run-with-safety.yaml +++ b/llama_stack/templates/tgi/run-with-safety.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: tgi apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -39,9 +42,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/tgi/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/tgi/run.yaml b/llama_stack/templates/tgi/run.yaml index 352afabb5..a3e21075f 100644 --- a/llama_stack/templates/tgi/run.yaml +++ b/llama_stack/templates/tgi/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: tgi apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -35,9 +38,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/tgi}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/tgi/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/tgi/tgi.py b/llama_stack/templates/tgi/tgi.py index caa341df3..83818a598 100644 --- a/llama_stack/templates/tgi/tgi.py +++ b/llama_stack/templates/tgi/tgi.py @@ -7,6 +7,7 @@ from pathlib import Path from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.tgi import TGIImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -18,8 +19,11 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "tgi" inference_provider = Provider( provider_id="tgi-inference", provider_type="remote::tgi", @@ -27,6 +31,11 @@ def get_distribution_template() -> DistributionTemplate: url="${env.TGI_URL}", ), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -38,7 +47,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="tgi", + name=name, distro_type="self_hosted", description="Use (an external) TGI server for running LLM inference", docker_image=None, @@ -49,6 +58,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), @@ -64,6 +74,7 @@ def get_distribution_template() -> DistributionTemplate: ), ), ], + "memory": [memory_provider], }, default_models=[ inference_model, diff --git a/llama_stack/templates/together/build.yaml b/llama_stack/templates/together/build.yaml index a4402ba93..6930b7692 100644 --- a/llama_stack/templates/together/build.yaml +++ b/llama_stack/templates/together/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/together/run.yaml b/llama_stack/templates/together/run.yaml index 855ba0626..529bf7873 100644 --- a/llama_stack/templates/together/run.yaml +++ b/llama_stack/templates/together/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: together apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -36,9 +39,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/together}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/together/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/together/together.py b/llama_stack/templates/together/together.py index 16265b04f..6656cfe44 100644 --- a/llama_stack/templates/together/together.py +++ b/llama_stack/templates/together/together.py @@ -9,6 +9,7 @@ from pathlib import Path from llama_models.sku_list import all_registered_models from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.providers.remote.inference.together import TogetherImplConfig from llama_stack.providers.remote.inference.together.together import MODEL_ALIASES @@ -22,13 +23,21 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "together" inference_provider = Provider( provider_id="together", provider_type="remote::together", config=TogetherImplConfig.sample_run_config(), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) core_model_to_hf_repo = { m.descriptor(): m.huggingface_repo for m in all_registered_models() @@ -42,7 +51,7 @@ def get_distribution_template() -> DistributionTemplate: ] return DistributionTemplate( - name="together", + name=name, distro_type="self_hosted", description="Use Together.AI for running LLM inference", docker_image=None, @@ -53,6 +62,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=default_models, default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")], diff --git a/llama_stack/templates/vllm-gpu/build.yaml b/llama_stack/templates/vllm-gpu/build.yaml index 6792a855f..4289296ec 100644 --- a/llama_stack/templates/vllm-gpu/build.yaml +++ b/llama_stack/templates/vllm-gpu/build.yaml @@ -16,4 +16,13 @@ distribution_spec: - inline::meta-reference telemetry: - inline::meta-reference + eval: + - inline::meta-reference + datasetio: + - remote::huggingface + - inline::localfs + scoring: + - inline::basic + - inline::llm-as-judge + - inline::braintrust image_type: conda diff --git a/llama_stack/templates/vllm-gpu/run.yaml b/llama_stack/templates/vllm-gpu/run.yaml index a140ad403..8353dbd51 100644 --- a/llama_stack/templates/vllm-gpu/run.yaml +++ b/llama_stack/templates/vllm-gpu/run.yaml @@ -4,9 +4,12 @@ docker_image: null conda_env: vllm-gpu apis: - agents +- datasetio +- eval - inference - memory - safety +- scoring - telemetry providers: inference: @@ -39,9 +42,34 @@ providers: namespace: null db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/vllm-gpu}/agents_store.db telemetry: + - provider_id: meta-reference + provider_type: inline::meta-reference + config: + service_name: ${env.OTEL_SERVICE_NAME:llama-stack} + sinks: ${env.TELEMETRY_SINKS:console,sqlite} + sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/vllm-gpu/trace_store.db} + eval: - provider_id: meta-reference provider_type: inline::meta-reference config: {} + datasetio: + - provider_id: huggingface + provider_type: remote::huggingface + config: {} + - provider_id: localfs + provider_type: inline::localfs + config: {} + scoring: + - provider_id: basic + provider_type: inline::basic + config: {} + - provider_id: llm-as-judge + provider_type: inline::llm-as-judge + config: {} + - provider_id: braintrust + provider_type: inline::braintrust + config: + openai_api_key: ${env.OPENAI_API_KEY:} metadata_store: namespace: null type: sqlite diff --git a/llama_stack/templates/vllm-gpu/vllm.py b/llama_stack/templates/vllm-gpu/vllm.py index 78fcf4f57..10b448b5c 100644 --- a/llama_stack/templates/vllm-gpu/vllm.py +++ b/llama_stack/templates/vllm-gpu/vllm.py @@ -6,6 +6,7 @@ from llama_stack.distribution.datatypes import ModelInput, Provider from llama_stack.providers.inline.inference.vllm import VLLMConfig +from llama_stack.providers.inline.memory.faiss.config import FaissImplConfig from llama_stack.templates.template import DistributionTemplate, RunConfigSettings @@ -16,13 +17,21 @@ def get_distribution_template() -> DistributionTemplate: "safety": ["inline::llama-guard"], "agents": ["inline::meta-reference"], "telemetry": ["inline::meta-reference"], + "eval": ["inline::meta-reference"], + "datasetio": ["remote::huggingface", "inline::localfs"], + "scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"], } - + name = "vllm-gpu" inference_provider = Provider( provider_id="vllm", provider_type="inline::vllm", config=VLLMConfig.sample_run_config(), ) + memory_provider = Provider( + provider_id="faiss", + provider_type="inline::faiss", + config=FaissImplConfig.sample_run_config(f"distributions/{name}"), + ) inference_model = ModelInput( model_id="${env.INFERENCE_MODEL}", @@ -30,7 +39,7 @@ def get_distribution_template() -> DistributionTemplate: ) return DistributionTemplate( - name="vllm-gpu", + name=name, distro_type="self_hosted", description="Use a built-in vLLM engine for running LLM inference", docker_image=None, @@ -41,6 +50,7 @@ def get_distribution_template() -> DistributionTemplate: "run.yaml": RunConfigSettings( provider_overrides={ "inference": [inference_provider], + "memory": [memory_provider], }, default_models=[inference_model], ), diff --git a/requirements.txt b/requirements.txt index b5b7587d0..ce5918fa5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,8 +2,8 @@ blobfile fire httpx huggingface-hub -llama-models>=0.0.55 -llama-stack-client>=0.0.55 +llama-models>=0.0.61 +llama-stack-client>=0.0.61 prompt-toolkit python-dotenv pydantic>=2 diff --git a/setup.py b/setup.py index a4efd08c6..cab3f7d68 100644 --- a/setup.py +++ b/setup.py @@ -16,7 +16,7 @@ def read_requirements(): setup( name="llama_stack", - version="0.0.55", + version="0.0.61", author="Meta Llama", author_email="llama-oss@meta.com", description="Llama Stack",