7 commits

Author	SHA1	Message	Date
Ashwin Bharambe	492f79ca9b	fix: harden storage semantics (#4118) ... Fixes issues in the storage system by guaranteeing immediate durability for responses and ensuring background writers stay alive. Three related fixes: * Responses to the OpenAI-compatible API now write directly to Postgres/SQLite inside the request instead of detouring through an async queue that might never drain; this restores the expected read-after-write behavior and removes the "response not found" races reported by users. * The access-control shim was stamping owner_principal/access_attributes as SQL NULL, which Postgres interprets as non-public rows; fixing it to use the empty-string/JSON-null pattern means conversations and responses stored without an authenticated user stay queryable (matching SQLite). * The inference-store queue remains for batching, but its worker tasks now start lazily on the live event loop so server startup doesn't cancel them—writes keep flowing even when the stack is launched via llama stack run. Closes #4115 ### Test Plan Added a matrix entry to test our "base" suite against Postgres as the store.	2025-11-12 10:35:39 -08:00
Dennis Kennetz	209a78b618	feat: add oci genai service as chat inference provider (#3876) ... # What does this PR do? Adds OCI GenAI PaaS models for openai chat completion endpoints. ## Test Plan In an OCI tenancy with access to GenAI PaaS, perform the following steps: 1. Ensure you have IAM policies in place to use service (check docs included in this PR) 2. For local development, [setup OCI cli](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliinstall.htm) and configure the CLI with your region, tenancy, and auth [here](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliconfigure.htm) 3. Once configured, go through llama-stack setup and run llama-stack (uses config based auth) like: ```bash OCI_AUTH_TYPE=config_file \ OCI_CLI_PROFILE=CHICAGO \ OCI_REGION=us-chicago-1 \ OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..aaaaaaaa5...5a \ llama stack run oci ``` 4. Hit the `models` endpoint to list models after server is running: ```bash curl http://localhost:8321/v1/models | jq ... { "identifier": "meta.llama-4-scout-17b-16e-instruct", "provider_resource_id": "ocid1.generativeaimodel.oc1.us-chicago-1.am...q", "provider_id": "oci", "type": "model", "metadata": { "display_name": "meta.llama-4-scout-17b-16e-instruct", "capabilities": [ "CHAT" ], "oci_model_id": "ocid1.generativeaimodel.oc1.us-chicago-1.a...q" }, "model_type": "llm" }, ... ``` 5. Use the "display_name" field to use the model in a `/chat/completions` request: ```bash # Streaming result curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "meta.llama-4-scout-17b-16e-instruct", "stream": true, "temperature": 0.9, "messages": [ { "role": "system", "content": "You are a funny comedian. You can be crass." }, { "role": "user", "content": "Tell me a funny joke about programming." } ] }' # Non-streaming result curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "meta.llama-4-scout-17b-16e-instruct", "stream": false, "temperature": 0.9, "messages": [ { "role": "system", "content": "You are a funny comedian. You can be crass." }, { "role": "user", "content": "Tell me a funny joke about programming." } ] }' ``` 6. Try out other models from the `/models` endpoint.	2025-11-10 16:16:24 -05:00
Sumanth Kamenani	e894e36eea	feat: add OpenAI-compatible Bedrock provider (#3748) ... Implements AWS Bedrock inference provider using OpenAI-compatible endpoint for Llama models available through Bedrock. Closes: #3410 ## What does this PR do? Adds AWS Bedrock as an inference provider using the OpenAI-compatible endpoint. This lets us use Bedrock models (GPT-OSS, Llama) through the standard llama-stack inference API. The implementation uses LiteLLM's OpenAI client under the hood, so it gets all the OpenAI compatibility features. The provider handles per-request API key overrides via headers. ## Test Plan **Tested the following scenarios:** - Non-streaming completion - basic request/response flow - Streaming completion - SSE streaming with chunked responses - Multi-turn conversations - context retention across turns - Tool calling - function calling with proper tool_calls format # Bedrock OpenAI-Compatible Provider - Test Results **Model:** `bedrock-inference/openai.gpt-oss-20b-1:0` --- ## Test 1: Model Listing **Request:** ```http GET /v1/models HTTP/1.1 ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: application/json { "data": [ {"identifier": "bedrock-inference/openai.gpt-oss-20b-1:0", ...}, {"identifier": "bedrock-inference/openai.gpt-oss-40b-1:0", ...} ] } ``` --- ## Test 2: Non-Streaming Completion **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "bedrock-inference/openai.gpt-oss-20b-1:0", "messages": [{"role": "user", "content": "Say 'Hello from Bedrock' and nothing else"}], "stream": false } ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: application/json { "choices": [{ "finish_reason": "stop", "message": {"content": "...Hello from Bedrock"} }], "usage": {"prompt_tokens": 79, "completion_tokens": 50, "total_tokens": 129} } ``` --- ## Test 3: Streaming Completion **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "bedrock-inference/openai.gpt-oss-20b-1:0", "messages": [{"role": "user", "content": "Count from 1 to 5"}], "stream": true } ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: text/event-stream [6 SSE chunks received] Final content: "1, 2, 3, 4, 5" ``` --- ## Test 4: Error Handling - Invalid Model **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "invalid-model-id", "messages": [{"role": "user", "content": "Hello"}], "stream": false } ``` **Response:** ```http HTTP/1.1 404 Not Found Content-Type: application/json { "detail": "Model 'invalid-model-id' not found. Use 'client.models.list()' to list available Models." } ``` --- ## Test 5: Multi-Turn Conversation **Request 1:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "My name is Alice"}] } ``` **Response 1:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Nice to meet you, Alice! How can I help you today?"} }] } ``` **Request 2 (with history):** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [ {"role": "user", "content": "My name is Alice"}, {"role": "assistant", "content": "...Nice to meet you, Alice!..."}, {"role": "user", "content": "What is my name?"} ] } ``` **Response 2:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Your name is Alice."} }], "usage": {"prompt_tokens": 183, "completion_tokens": 42} } ``` **Context retained across turns** --- ## Test 6: System Messages **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [ {"role": "system", "content": "You are Shakespeare. Respond only in Shakespearean English."}, {"role": "user", "content": "Tell me about the weather"} ] } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "Lo! I heed thy request..."} }], "usage": {"completion_tokens": 813} } ``` --- ## Test 7: Tool Calling **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "What's the weather in San Francisco?"}], "tools": [{ "type": "function", "function": { "name": "get_weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}} } }] } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "finish_reason": "tool_calls", "message": { "tool_calls": [{ "function": {"name": "get_weather", "arguments": "{\"location\":\"San Francisco\"}"} }] } }] } ``` --- ## Test 8: Sampling Parameters **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "Say hello"}], "temperature": 0.7, "top_p": 0.9 } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Hello! 👋 How can I help you today?"} }] } ``` --- ## Test 9: Authentication Error Handling ### Subtest A: Invalid API Key **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": "invalid-fake-key-12345"} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 400 Bad Request { "detail": "Invalid value: Authentication failed: Error code: 401 - {'error': {'message': 'Invalid API Key format: Must start with pre-defined prefix', ...}}" } ``` --- ### Subtest B: Empty API Key (Fallback to Config) **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": ""} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Hello! How can I assist you today?"} }] } ``` **Fell back to config key** --- ### Subtest C: Malformed Token **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": "not-a-valid-bedrock-token-format"} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 400 Bad Request { "detail": "Invalid value: Authentication failed: Error code: 401 - {'error': {'message': 'Invalid API Key format: Must start with pre-defined prefix', ...}}" } ```	2025-11-06 17:18:18 -08:00
Roy Belio	c672a5d792	feat: ability to use postgres as store for starter distro (#4076) ... ## What does this PR do? The starter distribution now comes with all the required packages to support persistent stores—like the agent store, metadata, and inference—using PostgreSQL. Users can enable PostgreSQL support by setting the `ENABLE_POSTGRES_STORE=1` environment variable. This PR consolidates the functionality from the removed `postgres-demo` distribution into the starter distribution, reducing maintenance overhead. **Closes: #2619** **Supersedes: #2851** (rebased and updated) ## Changes Made 1. **Added PostgreSQL support to starter distribution** - New `run-with-postgres-store.yaml` configuration - Automatic config switching via `ENABLE_POSTGRES_STORE` environment variable - Removed separate `postgres-demo` distribution 2. **Updated to new build system** - Integrated postgres switching logic into Containerfile entrypoint - Uses new `storage_backends` and `storage_stores` API - Properly configured both PostgreSQL KV store and SQL store 3. **Updated dependencies** - Added `psycopg2-binary` and `asyncpg` to starter distribution - All postgres-related dependencies automatically included ## How to Use ### With Docker (PostgreSQL): ```bash docker run \ -e ENABLE_POSTGRES_STORE=1 \ -e POSTGRES_HOST=your_postgres_host \ -e POSTGRES_PORT=5432 \ -e POSTGRES_DB=llamastack \ -e POSTGRES_USER=llamastack \ -e POSTGRES_PASSWORD=llamastack \ -e OPENAI_API_KEY=your_key \ llamastack/distribution-starter ``` ### PostgreSQL environment variables: - `POSTGRES_HOST`: Postgres host (default: `localhost`) - `POSTGRES_PORT`: Postgres port (default: `5432`) - `POSTGRES_DB`: Postgres database name (default: `llamastack`) - `POSTGRES_USER`: Postgres username (default: `llamastack`) - `POSTGRES_PASSWORD`: Postgres password (default: `llamastack`) ## Test Plan All pre-commit hooks pass (mypy, ruff, distro-codegen) `llama stack list-deps starter` confirms psycopg2-binary is included Storage configuration correctly uses PostgreSQL backends Container builds successfully with postgres support ## Credits Original work by @leseb in #2851. Rebased and updated by @r-bit-rry to work with latest main. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Sébastien Han @leseb --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: Sébastien Han <seb@redhat.com>	2025-11-05 15:37:06 -08:00
raghotham	feabcdd67b	docs: add documentation on how to use custom run yaml in docker (#3949) ... as title test plan: ```yaml # custom-ollama-run.yaml version: 2 image_name: starter external_providers_dir: /.llama/providers.d apis: - inference - vector_io - files - safety - tool_runtime - agents providers: inference: # Single Ollama provider for all models - provider_id: ollama provider_type: remote::ollama config: url: ${env.OLLAMA_URL:=http://localhost:11434} vector_io: - provider_id: faiss provider_type: inline::faiss config: persistence: namespace: vector_io::faiss backend: kv_default files: - provider_id: meta-reference-files provider_type: inline::localfs config: storage_dir: /.llama/files metadata_store: table_name: files_metadata backend: sql_default safety: - provider_id: llama-guard provider_type: inline::llama-guard config: excluded_categories: [] tool_runtime: - provider_id: rag-runtime provider_type: inline::rag-runtime agents: - provider_id: meta-reference provider_type: inline::meta-reference config: persistence: agent_state: namespace: agents backend: kv_default responses: table_name: responses backend: sql_default max_write_queue_size: 10000 num_writers: 4 storage: backends: kv_default: type: kv_sqlite db_path: /.llama/kvstore.db sql_default: type: sql_sqlite db_path: /.llama/sql_store.db stores: metadata: namespace: registry backend: kv_default inference: table_name: inference_store backend: sql_default max_write_queue_size: 10000 num_writers: 4 conversations: table_name: openai_conversations backend: sql_default registered_resources: models: # All models use the same 'ollama' provider - model_id: llama3.2-vision:latest provider_id: ollama provider_model_id: llama3.2-vision:latest model_type: llm - model_id: llama3.2:3b provider_id: ollama provider_model_id: llama3.2:3b model_type: llm # Embedding models - model_id: nomic-embed-text-v2-moe provider_id: ollama provider_model_id: toshk0/nomic-embed-text-v2-moe:Q6_K model_type: embedding metadata: embedding_dimension: 768 shields: [] vector_dbs: [] datasets: [] scoring_fns: [] benchmarks: [] tool_groups: [] server: port: 8321 telemetry: enabled: true vector_stores: default_provider_id: faiss default_embedding_model: provider_id: ollama model_id: toshk0/nomic-embed-text-v2-moe:Q6_K ``` ```bash docker run -it --pull always -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT -v ~/.llama:/root/.llama -v $CUSTOM_RUN_CONFIG:/app/custom-run.yaml -e RUN_CONFIG_PATH=/app/custom-run.yaml -e OLLAMA_URL=http://host.docker.internal:11434/ llamastack/distribution-starter:0.3.0 --port $LLAMA_STACK_PORT ```	2025-10-28 16:05:44 -07:00
Sébastien Han	d10bfb5121	chore: remove leftover llama_stack directory (#3940) ... # What does this PR do? Followup on https://github.com/llamastack/llama-stack/pull/3920 where the llama_stack directory was moved under src. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-10-28 05:09:08 -07:00
Ashwin Bharambe	471b1b248b	chore(package): migrate to src/ layout (#3920) ... Migrates package structure to src/ layout following Python packaging best practices. All code moved from `llama_stack/` to `src/llama_stack/`. Public API unchanged - imports remain `import llama_stack.*`. Updated build configs, pre-commit hooks, scripts, and GitHub workflows accordingly. All hooks pass, package builds cleanly. **Developer note**: Reinstall after pulling: `pip install -e .`	2025-10-27 12:02:21 -07:00