16 commits

Author	SHA1	Message	Date
Charlie Doern	d5cd0eea14	feat!: standardize base_url for inference (#4177) ... # What does this PR do? Completes #3732 by removing runtime URL transformations and requiring users to provide full URLs in configuration. All providers now use 'base_url' consistently and respect the exact URL provided without appending paths like /v1 or /openai/v1 at runtime. BREAKING CHANGE: Users must update configs to include full URL paths (e.g., http://localhost:11434/v1 instead of http://localhost:11434). Closes #3732 ## Test Plan Existing tests should pass even with the URL changes, due to default URLs being altered. Add unit test to enforce URL standardization across remote inference providers (verifies all use 'base_url' field with HttpUrl | None type) Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-19 08:44:28 -08:00
Charlie Doern	a078f089d9	fix: rename llama_stack_api dir (#4155) ... # What does this PR do? the directory structure was src/llama-stack-api/llama_stack_api instead it should just be src/llama_stack_api to match the other packages. update the structure and pyproject/linting config --------- Signed-off-by: Charlie Doern <cdoern@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-13 15:04:36 -08:00
Charlie Doern	840ad75fe9	feat: split API and provider specs into separate llama-stack-api pkg (#3895) ... # What does this PR do? Extract API definitions and provider specifications into a standalone llama-stack-api package that can be published to PyPI independently of the main llama-stack server. see: https://github.com/llamastack/llama-stack/pull/2978 and https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942 Motivation External providers currently import from llama-stack, which overrides the installed version and causes dependency conflicts. This separation allows external providers to: - Install only the type definitions they need without server dependencies - Avoid version conflicts with the installed llama-stack package - Be versioned and released independently This enables us to re-enable external provider module tests that were previously blocked by these import conflicts. Changes - Created llama-stack-api package with minimal dependencies (pydantic, jsonschema) - Moved APIs, providers datatypes, strong_typing, and schema_utils - Updated all imports from llama_stack.* to llama_stack_api.* - Configured local editable install for development workflow - Updated linting and type-checking configuration for both packages Next Steps - Publish llama-stack-api to PyPI - Update external provider dependencies - Re-enable external provider module tests Pre-cursor PRs to this one: - #4093 - #3954 - #4064 These PRs moved key pieces _out_ of the Api pkg, limiting the scope of change here. relates to #3237 ## Test Plan Package builds successfully and can be imported independently. All pre-commit hooks pass with expected exclusions maintained. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-13 11:51:17 -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
Ashwin Bharambe	f49cb0b717	chore: Stack server no longer depends on llama-stack-client (#4094) ... This dependency has been bothering folks for a long time (cc @leseb). We really needed it due to "library client" which is primarily used for our tests and is not a part of the Stack server. Anyone who needs to use the library client can certainly install `llama-stack-client` in their environment to make that work. Updated the notebook references to install `llama-stack-client` additionally when setting things up.	2025-11-07 09:54:09 -08:00
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
Ashwin Bharambe	bef1b044bd	refactor(passthrough): use AsyncOpenAI instead of AsyncLlamaStackClient (#4085) ... We'd like to remove the dependence of `llama-stack` on `llama-stack-client`. This is a necessary step. A few small cleanups - Enables `embeddings` now also - Remove ModelRegistryHelper dependency (unused) - Consolidate to auth_credential field via RemoteInferenceProviderConfig - Implement list_models() to fetch from downstream /v1/models ## Test Plan Tested using this script https://gist.github.com/ashwinb/6356463d10f989c0682ab3bff8589581 Output: ``` Listing models from downstream server... Available models: ['passthrough/ollama/nomic-embed-text:latest', 'passthrough/ollama/all-minilm:l6-v2', 'passthrough/ollama/llama3.2-vision:11b', 'passthrough/ollama/llama3.2-vision:latest', 'passthrough/ollama/llama-guard3:1b', 'passthrough/o llama/llama3.2:1b', 'passthrough/ollama/all-minilm:latest', 'passthrough/ollama/llama3.2:3b', 'passthrough/ollama/llama3.2:3b-instruct-fp16', 'passthrough/bedrock/meta.llama3-1-8b-instruct-v1:0', 'passthrough/bedrock/meta.llama3-1-70b-instruct -v1:0', 'passthrough/bedrock/meta.llama3-1-405b-instruct-v1:0', 'passthrough/sentence-transformers/nomic-ai/nomic-embed-text-v1.5'] Using LLM model: passthrough/ollama/llama3.2-vision:11b Making inference request... Response: 4. --- Testing streaming --- Streamed response: ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='1', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='', index=0, logprobs=None)], created=1762381674, m odel='passthrough/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None) ... 5ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='stop', index=0, logprobs=None)], created=1762381674, model='passthrou gh/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None) ```	2025-11-05 18:15:11 -08:00
Wojciech-Rebisz	07c28cd519	fix: Avoid model_limits KeyError (#4060) ... # What does this PR do? It avoids model_limit KeyError while trying to get embedding models for Watsonx <!-- If resolving an issue, uncomment and update the line below --> Closes https://github.com/llamastack/llama-stack/issues/4059 ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* --> Start server with watsonx distro: ```bash llama stack list-deps watsonx | xargs -L1 uv pip install uv run llama stack run watsonx ``` Run ```python client = LlamaStackClient(base_url=base_url) client.models.list() ``` Check if there is any embedding model available (currently there is not a single one)	2025-11-05 10:34:40 -08:00
Nathan Weinberg	62b3ad349a	fix: return to hardcoded model IDs for Vertex AI (#4041) ... # What does this PR do? partial revert of b67aef2 Vertex AI doesn't offer an endpoint for listing models from Google's Model Garden Return to hardcoded values until such an endpoint is available Closes #3988 ## Test Plan Server side, set up your Vertex AI env vars (`VERTEX_AI_PROJECT`, `VERTEX_AI_LOCATION`, and `GOOGLE_APPLICATION_CREDENTIALS`) and run the starter distribution ```bash $ llama stack list-deps starter | xargs -L1 uv pip install $ llama stack run starter ``` Client side, formerly broken cURL requests now working ```bash $ curl http://127.0.0.1:8321/v1/models | jq '.data | map(select(.provider_id == "vertexai"))' [ { "identifier": "vertexai/vertex_ai/gemini-2.0-flash", "provider_resource_id": "vertex_ai/gemini-2.0-flash", "provider_id": "vertexai", "type": "model", "metadata": {}, "model_type": "llm" }, { "identifier": "vertexai/vertex_ai/gemini-2.5-flash", "provider_resource_id": "vertex_ai/gemini-2.5-flash", "provider_id": "vertexai", "type": "model", "metadata": {}, "model_type": "llm" }, { "identifier": "vertexai/vertex_ai/gemini-2.5-pro", "provider_resource_id": "vertex_ai/gemini-2.5-pro", "provider_id": "vertexai", "type": "model", "metadata": {}, "model_type": "llm" } ] $ curl -fsS http://127.0.0.1:8321/v1/openai/v1/chat/completions -H "Content-Type: application/json" -d "{\"model\": \"vertexai/vertex_a i/gemini-2.5-flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"max_tokens\": 128, \"temperature\": 0.0}" | jq { "id": "p8oIaYiQF8_PptQPo-GH8QQ", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "Hello there! How can I help you today?", "refusal": null, "role": "assistant", "annotations": null, "audio": null, "function_call": null, "tool_calls": null } } ], ... ``` Signed-off-by: Nathan Weinberg <nweinber@redhat.com>	2025-11-03 17:38:16 -08:00
Jiayi Ni	fa7699d2c3	feat: Add rerank API for NVIDIA Inference Provider (#3329) ... # What does this PR do? Add rerank API for NVIDIA Inference Provider. <!-- If resolving an issue, uncomment and update the line below --> Closes #3278 ## Test Plan Unit test: ``` pytest tests/unit/providers/nvidia/test_rerank_inference.py ``` Integration test: ``` pytest -s -v tests/integration/inference/test_rerank.py --stack-config="inference=nvidia" --rerank-model=nvidia/nvidia/nv-rerankqa-mistral-4b-v3 --env NVIDIA_API_KEY="" --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com" ```	2025-10-30 21:42:09 -07:00
Ashwin Bharambe	da8f014b96	feat(models): list models available via provider_data header (#3968) ... ## Summary When users provide API keys via `X-LlamaStack-Provider-Data` header, `models.list()` now returns models they can access from those providers, not just pre-registered models from the registry. This complements the routing fix from f88416ef8 which enabled inference calls with `provider_id/model_id` format for unregistered models. Users can now discover which models are available to them before making inference requests. The implementation reuses `NeedsRequestProviderData.get_request_provider_data()` to validate credentials, then dynamically fetches models from providers without caching them since they're user-specific. Registry models take precedence to respect any pre-configured aliases. ## Test Script ```python #!/usr/bin/env python3 import json import os from openai import OpenAI # Test 1: Without provider_data header client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="dummy") models = client.models.list() anthropic_without = [m.id for m in models.data if m.id and "anthropic" in m.id] print(f"Without header: {len(models.data)} models, {len(anthropic_without)} anthropic") # Test 2: With provider_data header containing Anthropic API key anthropic_api_key = os.environ["ANTHROPIC_API_KEY"] client_with_key = OpenAI( base_url="http://localhost:8321/v1/openai/v1", api_key="dummy", default_headers={ "X-LlamaStack-Provider-Data": json.dumps({"anthropic_api_key": anthropic_api_key}) } ) models_with_key = client_with_key.models.list() anthropic_with = [m.id for m in models_with_key.data if m.id and "anthropic" in m.id] print(f"With header: {len(models_with_key.data)} models, {len(anthropic_with)} anthropic") print(f"Anthropic models: {anthropic_with}") assert len(anthropic_with) > len(anthropic_without), "Should have more anthropic models with API key" print("\n✓ Test passed!") ``` Run with a stack that has Anthropic provider configured (but without API key in config): ```bash ANTHROPIC_API_KEY=sk-ant-... python test_provider_data_models.py ```	2025-10-29 14:03:03 -07:00
Ashwin Bharambe	94b0592240	fix(mypy): add type stubs and fix typing issues (#3938) ... Adds type stubs and fixes mypy errors for better type coverage. Changes: - Added type_checking dependency group with type stubs (torchtune, trl, etc.) - Added lm-format-enforcer to pre-commit hook - Created HFAutoModel Protocol for type-safe HuggingFace model handling - Added mypy.overrides for untyped libraries (torchtune, fairscale, etc.) - Fixed type issues in post-training providers, databricks, and api_recorder Note: ~1,200 errors remain in excluded files (see pyproject.toml exclude list). --------- Co-authored-by: Claude <noreply@anthropic.com>	2025-10-28 11:00:09 -07:00
Ashwin Bharambe	1d385b5b75	fix(mypy): resolve OpenAI SDK and provider type issues (#3936) ... ## Summary - Fix OpenAI SDK NotGiven/Omit type mismatches in embeddings calls - Fix incorrect OpenAIChatCompletionChunk import in vllm provider - Refactor to avoid type:ignore comments by using conditional kwargs ## Changes **openai_mixin.py (9 errors fixed):** - Build kwargs conditionally for embeddings.create() to avoid NotGiven/Omit mismatch - Only include parameters when they have actual values (not None) **gemini.py (9 errors fixed):** - Apply same conditional kwargs pattern - Add missing Any import **vllm.py (2 errors fixed):** - Use correct OpenAIChatCompletionChunk from llama_stack.apis.inference - Remove incorrect alias from openai package ## Technical Notes The OpenAI SDK has a type system quirk where `NOT_GIVEN` has type `NotGiven` but parameter signatures expect `Omit`. By only passing parameters with actual values, we avoid this mismatch entirely without needing `# type: ignore` comments. 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude <noreply@anthropic.com>	2025-10-28 10:54:29 -07:00
Ashwin Bharambe	d009dc29f7	fix(mypy): resolve provider utility and testing type issues (#3935) ... Fixes mypy type errors in provider utilities and testing infrastructure: - `mcp.py`: Cast incompatible client types, wrap image data properly - `batches.py`: Rename walrus variable to avoid shadowing - `api_recorder.py`: Use cast for Pydantic field annotation No functional changes. --------- Co-authored-by: Claude <noreply@anthropic.com>	2025-10-28 10:37:27 -07:00
Matthew Farrellee	a9b00db421	feat: add provider data keys for Cerebras, Databricks, NVIDIA, and RunPod (#3734) ... # What does this PR do? add provider-data key passing support to Cerebras, Databricks, NVIDIA and RunPod also, added missing tests for Fireworks, Anthropic, Gemini, SambaNova, and vLLM addresses #3517 ## Test Plan ci w/ new tests --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-27 13:09:35 -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