198 commits

Author	SHA1	Message	Date
Ken Dreyer	dc4665af17	feat!: change bedrock bearer token env variable to match AWS docs & boto3 convention (#4152) ... Rename `AWS_BEDROCK_API_KEY` to `AWS_BEARER_TOKEN_BEDROCK` to align with the naming convention used in AWS Bedrock documentation and the AWS web console UI. This reduces confusion when developers compare LLS docs with AWS docs. Closes #4147	2025-11-21 09:48:05 -05:00
Ashwin Bharambe	d649c3663e	fix: enforce allowed_models during inference requests (#4197) ... The `allowed_models` configuration was only being applied when listing models via the `/v1/models` endpoint, but the actual inference requests weren't checking this restriction. This meant users could directly request any model the provider supports by specifying it in their inference call, completely bypassing the intended cost controls. The fix adds validation to all three inference methods (chat completions, completions, and embeddings) that checks the requested model against the allowed_models list before making the provider API call. ### Test plan Added unit tests	2025-11-19 14:49:44 -08:00
Ian Miller	0757d5a917	feat(responses)!: implement support for OpenAI compatible prompts in Responses API (#3965) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR is responsible for providing actual implementation of OpenAI compatible prompts in Responses API. This is the follow up PR with actual implementation after introducing #3942 The need of this functionality was initiated in #3514. > Note, https://github.com/llamastack/llama-stack/pull/3514 is divided on three separate PRs. Current PR is the third of three. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes #3321 ## 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.* --> Manual testing, CI workflow with added unit tests Comprehensive manual testing with new implementation: **Test Prompts with Images with text on them in Responses API:** I used this image for testing purposes: [iphone 17 image](https://github.com/user-attachments/assets/9e2ee821-e394-4bbd-b1c8-d48a3fa315de) 1. Upload an image: ``` curl -X POST http://localhost:8321/v1/files \ -H "Content-Type: multipart/form-data" \ -F "file=@/Users/ianmiller/iphone.jpeg" \ -F "purpose=assistants" ``` `{"object":"file","id":"file-d6d375f238e14f21952cc40246bc8504","bytes":556241,"created_at":1761750049,"expires_at":1793286049,"filename":"iphone.jpeg","purpose":"assistants"}%` 2. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.", "variables": ["product_name", "description", "product_photo"] }' ``` `{"prompt":"You are a product analysis expert. Analyze the following product:\n\nProduct Name: {{product_name}}\nDescription: {{description}}\n\nImage: {{product_photo}}\n\nProvide a detailed analysis including quality assessment, target audience, and pricing recommendations.","version":1,"prompt_id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":["product_name","description","product_photo"],"is_default":false}%` 3. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Accept: application/json, text/event-stream" \ -H "Content-Type: application/json" \ -d '{ "input": "Please analyze this product", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62", "version": "1", "variables": { "product_name": { "type": "input_text", "text": "iPhone 17 Pro Max" }, "product_photo": { "type": "input_image", "file_id": "file-d6d375f238e14f21952cc40246bc8504", "detail": "high" } } } }' ``` `{"created_at":1761750427,"error":null,"id":"resp_f897f914-e3b8-4783-8223-3ed0d32fcbc6","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"### Product Analysis: iPhone 17 Pro Max\n\n**Quality Assessment:**\n\n- **Display & Design:**\n - The 6.9-inch display is large, ideal for streaming and productivity.\n - Anti-reflective technology and 120Hz refresh rate enhance viewing experience, providing smoother visuals and reducing glare.\n - Titanium frame suggests a premium build, offering durability and a sleek appearance.\n\n- **Performance:**\n - The Apple A19 Pro chip promises significant performance improvements, likely leading to faster processing and efficient multitasking.\n - 12GB RAM is substantial for a smartphone, ensuring smooth operation for demanding apps and games.\n\n- **Camera System:**\n - The triple 48MP camera setup (wide, ultra-wide, telephoto) is designed for versatile photography needs, capturing high-resolution photos and videos.\n - The 24MP front camera will appeal to selfie enthusiasts and content creators needing quality front-facing shots.\n\n- **Connectivity:**\n - Wi-Fi 7 support indicates future-proof wireless capabilities, providing faster and more reliable internet connectivity.\n\n**Target Audience:**\n\n- **Tech Enthusiasts:** Individuals interested in cutting-edge technology and performance.\n- **Content Creators:** Users who need a robust camera system for photo and video production.\n- **Luxury Consumers:** Those who prefer premium materials and top-of-the-line specs.\n- **Professionals:** Users who require efficient multitasking and productivity features.\n\n**Pricing Recommendations:**\n\n- Given the premium specifications, a higher price point is expected. Consider pricing competitively within the high-end smartphone market while justifying cost through unique features like the titanium frame and advanced connectivity options.\n- Positioning around the $1,200 to $1,500 range would align with expectations for top-tier devices, catering to its target audience while ensuring profitability.\n\nOverall, the iPhone 17 Pro Max showcases a blend of innovative features and premium design, aimed at users seeking high performance and superior aesthetics.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_66f4d844-4d9e-4102-80fc-eb75b34b6dbd","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_7be2208cb82cdbc35356354dae1f335d1e9b7baeca21ea62","variables":{"product_name":{"text":"iPhone 17 Pro Max","type":"input_text"},"product_photo":{"detail":"high","type":"input_image","file_id":"file-d6d375f238e14f21952cc40246bc8504","image_url":null}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":830,"output_tokens":394,"total_tokens":1224,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%` **Test Prompts with PDF files in Responses API:** I used this PDF file for testing purposes: [invoicesample.pdf](https://github.com/user-attachments/files/22958943/invoicesample.pdf) 1. Upload PDF: ``` curl -X POST http://localhost:8321/v1/files \ -H "Content-Type: multipart/form-data" \ -F "file=@/Users/ianmiller/invoicesample.pdf" \ -F "purpose=assistants" ``` `{"object":"file","id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","bytes":149568,"created_at":1761750730,"expires_at":1793286730,"filename":"invoicesample.pdf","purpose":"assistants"}%` 2. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis", "variables": ["invoice_doc"] }' ``` `{"prompt":"You are an accounting and financial analysis expert. Analyze the following invoice document:\n\nInvoice Document: {{invoice_doc}}\n\nProvide a comprehensive analysis","version":1,"prompt_id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":["invoice_doc"],"is_default":false}%` 3. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Content-Type: application/json" \ -d '{ "input": "Please provide a detailed analysis of this invoice", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc", "version": "1", "variables": { "invoice_doc": { "type": "input_file", "file_id": "file-7fbb1043a4bb468cab60ffe4b8631d8e", "filename": "invoicesample.pdf" } } } }' ``` `{"created_at":1761750881,"error":null,"id":"resp_da866913-db06-4702-8000-174daed9dbbb","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"Here's a detailed analysis of the invoice provided:\n\n### Seller Information\n- **Business Name:** The invoice features a logo with \"Sunny Farm\" indicating the business identity.\n- **Address:** 123 Somewhere St, Melbourne VIC 3000\n- **Contact Information:** Phone number (03) 1234 5678\n\n### Buyer Information\n- **Name:** Denny Gunawan\n- **Address:** 221 Queen St, Melbourne VIC 3000\n\n### Transaction Details\n- **Invoice Number:** #20130304\n- **Date of Transaction:** Not explicitly mentioned, likely inferred from the invoice number or needs clarification.\n\n### Items Purchased\n1. **Apple**\n - Price: $5.00/kg\n - Quantity: 1 kg\n - Subtotal: $5.00\n\n2. **Orange**\n - Price: $1.99/kg\n - Quantity: 2 kg\n - Subtotal: $3.98\n\n3. **Watermelon**\n - Price: $1.69/kg\n - Quantity: 3 kg\n - Subtotal: $5.07\n\n4. **Mango**\n - Price: $9.56/kg\n - Quantity: 2 kg\n - Subtotal: $19.12\n\n5. **Peach**\n - Price: $2.99/kg\n - Quantity: 1 kg\n - Subtotal: $2.99\n\n### Financial Summary\n- **Subtotal for Items:** $36.00\n- **GST (Goods and Services Tax):** 10% of $36.00, which amounts to $3.60\n- **Total Amount Due:** $39.60\n\n### Notes\n- The invoice includes a placeholder text: \"Lorem ipsum dolor sit amet...\" which is typically used as filler text. This might indicate a section intended for terms, conditions, or additional notes that haven’t been completed.\n\n### Visual and Design Elements\n- The invoice uses a simple and clear layout, featuring the business logo prominently and stating essential information such as contact and transaction details in a structured manner.\n- There is a \"Thank You\" note at the bottom, which adds a professional and courteous touch.\n\n### Considerations\n- Ensure the date of the transaction is clear if there are any future references needed.\n- Replace filler text with relevant terms and conditions or any special instructions pertaining to the transaction.\n\nThis invoice appears standard, representing a small business transaction with clearly itemized products and applicable taxes.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_39f3b39e-4684-4444-8e4d-e7395f88c9dc","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_72e2a184a86f32a568b6afb5455dca5c16bf3cc3f80092dc","variables":{"invoice_doc":{"type":"input_file","file_data":null,"file_id":"file-7fbb1043a4bb468cab60ffe4b8631d8e","file_url":null,"filename":"invoicesample.pdf"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":529,"output_tokens":513,"total_tokens":1042,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%` **Test simple text Prompt in Responses API:** 1. Create prompt: ``` curl -X POST http://localhost:8321/v1/prompts \ -H "Content-Type: application/json" \ -d '{ "prompt": "Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.", "variables": ["name", "company", "role", "tone"] }' ``` `{"prompt":"Hello {{name}}! You are working at {{company}}. Your role is {{role}} at {{company}}. Remember, {{name}}, to be {{tone}}.","version":1,"prompt_id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":["name","company","role","tone"],"is_default":false}%` 2. Create response: ``` curl -X POST http://localhost:8321/v1/responses \ -H "Accept: application/json, text/event-stream" \ -H "Content-Type: application/json" \ -d '{ "input": "What is the capital of Ireland?", "model": "openai/gpt-4o", "store": true, "prompt": { "id": "pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef", "version": "1", "variables": { "name": { "type": "input_text", "text": "Alice" }, "company": { "type": "input_text", "text": "Dummy Company" }, "role": { "type": "input_text", "text": "Geography expert" }, "tone": { "type": "input_text", "text": "professional and helpful" } } } }' ``` `{"created_at":1761751097,"error":null,"id":"resp_1b037b95-d9ae-4ad0-8e76-d953897ecaef","model":"openai/gpt-4o","object":"response","output":[{"content":[{"text":"The capital of Ireland is Dublin.","type":"output_text","annotations":[]}],"role":"assistant","type":"message","id":"msg_8e7c72b6-2aa2-4da6-8e57-da4e12fa3ce2","status":"completed"}],"parallel_tool_calls":false,"previous_response_id":null,"prompt":{"id":"pmpt_f340a3164a4f65d975c774ffe38ea42d15e7ce4a835919ef","variables":{"name":{"text":"Alice","type":"input_text"},"company":{"text":"Dummy Company","type":"input_text"},"role":{"text":"Geography expert","type":"input_text"},"tone":{"text":"professional and helpful","type":"input_text"}},"version":"1"},"status":"completed","temperature":null,"text":{"format":{"type":"text"}},"top_p":null,"tools":[],"truncation":null,"usage":{"input_tokens":47,"output_tokens":7,"total_tokens":54,"input_tokens_details":{"cached_tokens":0},"output_tokens_details":{"reasoning_tokens":0}},"instructions":null}%`	2025-11-19 11:48:11 -08:00
Roy Belio	f18870a221	fix: Pydantic validation error with list-type metadata in vector search (#3797) (#4173) ... # Fix for Issue #3797 ## Problem Vector store search failed with Pydantic ValidationError when chunk metadata contained list-type values. **Error:** ``` ValidationError: 3 validation errors for VectorStoreSearchResponse attributes.tags.str: Input should be a valid string attributes.tags.float: Input should be a valid number attributes.tags.bool: Input should be a valid boolean ``` **Root Cause:** - `Chunk.metadata` accepts `dict[str, Any]` (any type allowed) - `VectorStoreSearchResponse.attributes` requires `dict[str, str | float | bool]` (primitives only) - Direct assignment at line 641 caused validation failure for non-primitive types ## Solution Added utility function to filter metadata to primitive types before creating search response. ## Impact **Fixed:** - Vector search works with list metadata (e.g., `tags: ["transformers", "gpu"]`) - Lists become searchable as comma-separated strings - No ValidationError on search responses **Preserved:** - Full metadata still available in `VectorStoreContent.metadata` - No API schema changes - Backward compatible with existing primitive metadata **Affected:** All vector store providers using `OpenAIVectorStoreMixin`: FAISS, Chroma, Qdrant, Milvus, Weaviate, PGVector, SQLite-vec ## Testing tests/unit/providers/vector_io/test_vector_utils.py::test_sanitize_metadata_for_attributes --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>	2025-11-19 10:16:34 -08:00
Anik	4e9633f7c3	feat: Make Safety API an optional dependency for meta-reference agents provider (#4169) ... # What does this PR do? Change Safety API from required to optional dependency, following the established pattern used for other optional dependencies in Llama Stack. The provider now starts successfully without Safety API configured. Requests that explicitly include guardrails will receive a clear error message when Safety API is unavailable. This enables local development and testing without Safety API while maintaining clear error messages when guardrail features are requested. Closes #4165 Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com> ## 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.* --> 1. New unit tests added in `tests/unit/providers/agents/meta_reference/test_safety_optional.py` 2. Integration tests performed with the files in https://gist.github.com/anik120/c33cef497ec7085e1fe2164e0705b8d6 (i) test with `test_integration_no_safety_fail.yaml`: Config WITHOUT Safety API, should fail with helpful error since `required_safety_api` is `true` by default ``` $ uv run llama stack run test_integration_no_safety_fail.yaml 2>&1 | grep -B 5 -A 15 "ValueError.*Safety\|Safety API is required" File "/Users/anbhatta/go/src/github.com/llamastack/llama-stack/src/llama_stack/providers/inline/agents/meta_reference /__init__.py", line 27, in get_provider_impl raise ValueError( ...<9 lines>... ) ValueError: Safety API is required but not configured. To run without safety checks, explicitly set in your configuration: providers: agents: - provider_id: meta-reference provider_type: inline::meta-reference config: require_safety_api: false Warning: This disables all safety guardrails for this agents provider. ``` (ii) test with `test_integration_no_safety_works.yaml` Config WITHOUT Safety API, **but** `require_safety_api=false` is explicitly set, should succeed ``` $ uv run llama stack run test_integration_no_safety_works.yaml INFO 2025-11-16 09:49:10,044 llama_stack.cli.stack.run:169 cli: Using run configuration: /Users/anbhatta/go/src/github.com/llamastack/llama-stack/test_integration_no_safety_works.yaml INFO 2025-11-16 09:49:10,052 llama_stack.cli.stack.run:228 cli: HTTPS enabled with certificates: Key: None Cert: None . . . INFO 2025-11-16 09:49:38,528 llama_stack.core.stack:495 core: starting registry refresh task INFO 2025-11-16 09:49:38,534 uvicorn.error:62 uncategorized: Application startup complete. INFO 2025-11-16 09:49:38,535 uvicorn.error:216 uncategorized: Uvicorn running on http://0.0.0.0:8321 (Press CTRL+C ``` Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com> Signed-off-by: Anik Bhattacharjee <anbhatta@redhat.com>	2025-11-19 10:04:24 -08:00
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
Ashwin Bharambe	bd5ad2963e	refactor(storage): make { kvstore, sqlstore } as llama stack "internal" APIs (#4181) ... These primitives (used both by the Stack as well as provider implementations) can be thought of fruitfully as internal-only APIs which can themselves have multiple implementations. We use the new `llama_stack_api.internal` namespace for this. In addition: the change moves kv/sql store impls, configs, and dependency helpers under `core/storage` ## Testing `pytest tests/unit/utils/test_authorized_sqlstore.py`, other existing CI	2025-11-18 13:15:16 -08:00
Mike Sager	cc88789071	test: Restore responses unit tests (#4153) ... # What does this PR do? Restores the responses unit tests that were inadvertently deleted in PR [#4055 ](https://github.com/llamastack/llama-stack/pull/4055) ## Test Plan I ran the unit tests that I restored. They all passed with one exception: tests/unit/providers/agents/meta_reference/test_openai_responses.py::test_reuse_mcp_tool_list AttributeError: module 'llama_stack.providers.utils.tools' has no attribute 'mcp' It's coming from this line: @patch("llama_stack.providers.utils.tools.mcp.list_mcp_tools") The mcp.py module (and \_\_init\_\_.py) exists under tools. There are some 'from mcp ....' imports (mcp package in this case) within it that python may be interpreting as circular imports (or maybe I'm overlooking something).	2025-11-14 13:16:03 -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
Charlie Doern	43adc23ef6	refactor: remove dead inference API code and clean up imports (#4093) ... # What does this PR do? Delete ~2,000 lines of dead code from the old bespoke inference API that was replaced by OpenAI-only API. This includes removing unused type conversion functions, dead provider methods, and event_logger.py. Clean up imports across the codebase to remove references to deleted types. This eliminates unnecessary code and dependencies, helping isolate the API package as a self-contained module. This is the last interdependency between the .api package and "exterior" packages, meaning that now every other package in llama stack imports the API, not the other way around. ## Test Plan this is a structural change, no tests needed. --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-11-10 15:29:24 -08:00
Juan Pérez de Algaba	6147321083	fix: Vector store persistence across server restarts (#3977) ... # What does this PR do? This PR fixes a bug in LlamaStack 0.3.0 where vector stores created via the OpenAI-compatible API (`POST /v1/vector_stores`) would fail with `VectorStoreNotFoundError` after server restart when attempting operations like `vector_io.insert()` or `vector_io.query()`. The bug affected **6 vector IO providers**: `pgvector`, `sqlite_vec`, `chroma`, `milvus`, `qdrant`, and `weaviate`. Created with the assistance of: claude-4.5-sonnet ## Root Cause All affected providers had a broken `_get_and_cache_vector_store_index()` method that: 1. Did not load existing vector stores from persistent storage during initialization 2. Attempted to use `vector_store_table` (which was either `None` or a `KVStore` without the required `get_vector_store()` method) 3. Could not reload vector stores after server restart or cache miss ## Solution This PR implements a consistent pattern across all 6 providers: 1. **Load vector stores during initialization** - Pre-populate the cache from KV store on startup 2. **Fix lazy loading** - Modified `_get_and_cache_vector_store_index()` to load directly from KV store instead of relying on `vector_store_table` 3. **Remove broken dependency** - Eliminated reliance on the `vector_store_table` pattern ## Testing steps ### 1.1 Configure the stack Create or use an existing configuration with a vector IO provider. **Example `run.yaml`:** ```yaml vector_io_store: - provider_id: pgvector provider_type: remote::pgvector config: host: localhost port: 5432 db: llamastack user: llamastack password: llamastack inference: - provider_id: sentence-transformers provider_type: inline::sentence-transformers config: model: sentence-transformers/all-MiniLM-L6-v2 ``` ### 1.2 Start the server ```bash llama stack run run.yaml --port 5000 ``` Wait for the server to fully start. You should see: ``` INFO: Started server process INFO: Application startup complete ``` --- ## Step 2: Create a Vector Store ### 2.1 Create via API ```bash curl -X POST http://localhost:5000/v1/vector_stores \ -H "Content-Type: application/json" \ -d '{ "name": "test-persistence-store", "extra_body": { "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", "embedding_dimension": 384, "provider_id": "pgvector" } }' | jq ``` ### 2.2 Expected Response ```json { "id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d", "object": "vector_store", "name": "test-persistence-store", "status": "completed", "created_at": 1730304000, "file_counts": { "total": 0, "completed": 0, "in_progress": 0, "failed": 0, "cancelled": 0 }, "usage_bytes": 0 } ``` **Save the `id` field** (e.g., `vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d`) — you’ll need it for the next steps. --- ## Step 3: Insert Data (Before Restart) ### 3.1 Insert chunks into the vector store ```bash export VS_ID="vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d" curl -X POST http://localhost:5000/vector-io/insert \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"chunks\": [ { \"content\": \"Python is a high-level programming language known for its readability.\", \"metadata\": {\"source\": \"doc1\", \"page\": 1} }, { \"content\": \"Machine learning enables computers to learn from data without explicit programming.\", \"metadata\": {\"source\": \"doc2\", \"page\": 1} }, { \"content\": \"Neural networks are inspired by biological neurons in the brain.\", \"metadata\": {\"source\": \"doc3\", \"page\": 1} } ] }" ``` ### 3.2 Expected Response Status: **200 OK** Response: *Empty or success confirmation* --- ## Step 4: Query Data (Before Restart – Baseline) ### 4.1 Query the vector store ```bash curl -X POST http://localhost:5000/vector-io/query \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"query\": \"What is machine learning?\" }" | jq ``` ### 4.2 Expected Response ```json { "chunks": [ { "content": "Machine learning enables computers to learn from data without explicit programming.", "metadata": {"source": "doc2", "page": 1} }, { "content": "Neural networks are inspired by biological neurons in the brain.", "metadata": {"source": "doc3", "page": 1} } ], "scores": [0.85, 0.72] } ``` **Checkpoint:** Works correctly before restart. --- ## Step 5: Restart the Server (Critical Test) ### 5.1 Stop the server In the terminal where it’s running: ``` Ctrl + C ``` Wait for: ``` Shutting down... ``` ### 5.2 Restart the server ```bash llama stack run run.yaml --port 5000 ``` Wait for: ``` INFO: Started server process INFO: Application startup complete ``` The vector store cache is now empty, but data should persist. --- ## Step 6: Verify Vector Store Exists (After Restart) ### 6.1 List vector stores ```bash curl http://localhost:5000/v1/vector_stores | jq ``` ### 6.2 Expected Response ```json { "object": "list", "data": [ { "id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d", "name": "test-persistence-store", "status": "completed" } ] } ``` **Checkpoint:** Vector store should be listed. --- ## Step 7: Insert Data (After Restart – THE BUG TEST) ### 7.1 Insert new chunks ```bash curl -X POST http://localhost:5000/vector-io/insert \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"chunks\": [ { \"content\": \"This chunk was inserted AFTER the server restart.\", \"metadata\": {\"source\": \"post-restart\", \"test\": true} } ] }" ``` ### 7.2 Expected Results **With Fix (Correct):** ``` Status: 200 OK Response: Success ``` **Without Fix (Bug):** ```json { "detail": "VectorStoreNotFoundError: Vector Store 'vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d' not found." } ``` **Critical Test:** If insertion succeeds, the fix works. --- ## Step 8: Query Data (After Restart – Verification) ### 8.1 Query all data ```bash curl -X POST http://localhost:5000/vector-io/query \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"query\": \"restart\" }" | jq ``` ### 8.2 Expected Response ```json { "chunks": [ { "content": "This chunk was inserted AFTER the server restart.", "metadata": {"source": "post-restart", "test": true} } ], "scores": [0.95] } ``` **Checkpoint:** Both old and new data are queryable. --- ## Step 9: Multiple Restart Test (Extra Verification) ### 9.1 Restart again ```bash Ctrl + C llama stack run run.yaml --port 5000 ``` ### 9.2 Query after restart ```bash curl -X POST http://localhost:5000/vector-io/query \ -H "Content-Type: application/json" \ -d "{ \"vector_store_id\": \"$VS_ID\", \"query\": \"programming\" }" | jq ``` **Expected:** Works correctly across multiple restarts. --------- Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>	2025-11-09 00:05:00 -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
Ashwin Bharambe	a8a8aa56c0	chore!: remove the agents (sessions and turns) API (#4055) ... - Removes the deprecated agents (sessions and turns) API that was marked alpha in 0.3.0 - Cleans up unused imports and orphaned types after the API removal - Removes `SessionNotFoundError` and `AgentTurnInputType` which are no longer needed The agents API is completely superseded by the Responses + Conversations APIs, and the client SDK Agent class already uses those implementations. Corresponding client-side PR: https://github.com/llamastack/llama-stack-client-python/pull/295	2025-11-04 09:38:39 -08:00
Mustafa Elbehery	a6ddbae0ed	chore(test): migrate unit tests from unittest to pytest nvidia test eval (#3249) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR migrates `unittest` to `pytest` in `tests/unit/providers/nvidia/test_eval.py`. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Part of https://github.com/llamastack/llama-stack/issues/2680 Supersedes https://github.com/llamastack/llama-stack/pull/2791 Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>	2025-11-04 10:29:07 +01:00
Matthew Farrellee	1263448de2	fix: allowed_models config did not filter models (#4030) ... # What does this PR do? closes #4022 ## Test Plan ci w/ new tests Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-11-03 11:43:39 -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
Charlie Doern	e8ecc99524	fix!: remove chunk_id property from Chunk class (#3954) ... # What does this PR do? chunk_id in the Chunk class executes actual logic to compute a chunk ID. This sort of logic should not live in the API spec. Instead, the providers should be in charge of calling generate_chunk_id, and pass it to `Chunk`. this removes the incorrect dependency between Provider impl and API impl Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-10-29 18:59:59 -07:00
Ashwin Bharambe	c9d4b6c54f	chore(mypy): part-04 resolve mypy errors in meta_reference agents (#3969) ... ## Summary Fixes all mypy type errors in `providers/inline/agents/meta_reference/` and removes exclusions from pyproject.toml. ## Changes - Fix type annotations for Safety API message parameters (OpenAIMessageParam) - Add Action enum usage in access control checks - Correct method signatures to match API supertype (parameter ordering) - Handle optional return types with proper None checks - Remove 3 meta_reference exclusions from mypy config **Files fixed:** 25 errors across 3 files (safety.py, persistence.py, agents.py)	2025-10-29 13:37:28 -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
Luis Tomas Bolivar	63422e5b36	fix!: Enhance response API support to not fail with tool calling (#3385) ... # What does this PR do? Introduces two main fixes to enhance the stability of Responses API when dealing with tool calling responses and structured outputs. ### Changes Made 1. It added OpenAIResponseOutputMessageMCPCall and ListTools to OpenAIResponseInput but https://github.com/llamastack/llama-stack/pull/3810 got merge that did the same in a different way. Still this PR does it in a way that keep the sync between OpenAIResponsesOutput and the allowed objects in OpenAIResponseInput. 2. Add protection in case self.ctx.response_format does not have type attribute BREAKING CHANGE: OpenAIResponseInput now uses OpenAIResponseOutput union type. This is semantically equivalent - all previously accepted types are still supported via the OpenAIResponseOutput union. This improves type consistency and maintainability.	2025-10-27 09:33:02 -07:00
dependabot[bot]	8885cea8d7	fix(conversations)!: update Conversations API definitions (was: bump openai from 1.107.0 to 2.5.0) (#3847) ... Bumps [openai](https://github.com/openai/openai-python) from 1.107.0 to 2.5.0. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/openai/openai-python/releases">openai's releases</a>.</em></p> <blockquote> <h2>v2.5.0</h2> <h2>2.5.0 (2025-10-17)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.4.0...v2.5.0">v2.4.0...v2.5.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> api update (<a href="8b280d57d6">8b280d5</a>)</li> </ul> <h3>Chores</h3> <ul> <li>bump <code>httpx-aiohttp</code> version to 0.1.9 (<a href="67f2f0afe5">67f2f0a</a>)</li> </ul> <h2>v2.4.0</h2> <h2>2.4.0 (2025-10-16)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.3.0...v2.4.0">v2.3.0...v2.4.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> Add support for gpt-4o-transcribe-diarize on audio/transcriptions endpoint (<a href="bdbe9b8f44">bdbe9b8</a>)</li> </ul> <h3>Chores</h3> <ul> <li>fix dangling comment (<a href="da14e99606">da14e99</a>)</li> <li><strong>internal:</strong> detect missing future annotations with ruff (<a href="2672b8f072">2672b8f</a>)</li> </ul> <h2>v2.3.0</h2> <h2>2.3.0 (2025-10-10)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.2.0...v2.3.0">v2.2.0...v2.3.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> comparison filter in/not in (<a href="aa49f626a6">aa49f62</a>)</li> </ul> <h3>Chores</h3> <ul> <li><strong>package:</strong> bump jiter to &gt;=0.10.0 to support Python 3.14 (<a href="https://redirect.github.com/openai/openai-python/issues/2618">#2618</a>) (<a href="aa445cab5c">aa445ca</a>)</li> </ul> <h2>v2.2.0</h2> <h2>2.2.0 (2025-10-06)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.1.0...v2.2.0">v2.1.0...v2.2.0</a></p> <h3>Features</h3> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/openai/openai-python/blob/main/CHANGELOG.md">openai's changelog</a>.</em></p> <blockquote> <h2>2.5.0 (2025-10-17)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.4.0...v2.5.0">v2.4.0...v2.5.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> api update (<a href="8b280d57d6">8b280d5</a>)</li> </ul> <h3>Chores</h3> <ul> <li>bump <code>httpx-aiohttp</code> version to 0.1.9 (<a href="67f2f0afe5">67f2f0a</a>)</li> </ul> <h2>2.4.0 (2025-10-16)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.3.0...v2.4.0">v2.3.0...v2.4.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> Add support for gpt-4o-transcribe-diarize on audio/transcriptions endpoint (<a href="bdbe9b8f44">bdbe9b8</a>)</li> </ul> <h3>Chores</h3> <ul> <li>fix dangling comment (<a href="da14e99606">da14e99</a>)</li> <li><strong>internal:</strong> detect missing future annotations with ruff (<a href="2672b8f072">2672b8f</a>)</li> </ul> <h2>2.3.0 (2025-10-10)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.2.0...v2.3.0">v2.2.0...v2.3.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> comparison filter in/not in (<a href="aa49f626a6">aa49f62</a>)</li> </ul> <h3>Chores</h3> <ul> <li><strong>package:</strong> bump jiter to &gt;=0.10.0 to support Python 3.14 (<a href="https://redirect.github.com/openai/openai-python/issues/2618">#2618</a>) (<a href="aa445cab5c">aa445ca</a>)</li> </ul> <h2>2.2.0 (2025-10-06)</h2> <p>Full Changelog: <a href="https://github.com/openai/openai-python/compare/v2.1.0...v2.2.0">v2.1.0...v2.2.0</a></p> <h3>Features</h3> <ul> <li><strong>api:</strong> dev day 2025 launches (<a href="38ac0093eb">38ac009</a>)</li> </ul> <h3>Bug Fixes</h3> <!-- raw HTML omitted --> </blockquote> <p>... (truncated)</p> </details> <details> <summary>Commits</summary> <ul> <li><a href="513ae76253"><code>513ae76</code></a> release: 2.5.0 (<a href="https://redirect.github.com/openai/openai-python/issues/2694">#2694</a>)</li> <li><a href="ebf32212f7"><code>ebf3221</code></a> release: 2.4.0</li> <li><a href="e043d7b164"><code>e043d7b</code></a> chore: fix dangling comment</li> <li><a href="25cbb74f83"><code>25cbb74</code></a> feat(api): Add support for gpt-4o-transcribe-diarize on audio/transcriptions ...</li> <li><a href="8cdfd0650e"><code>8cdfd06</code></a> codegen metadata</li> <li><a href="d5c64434b7"><code>d5c6443</code></a> codegen metadata</li> <li><a href="b20a9e7b81"><code>b20a9e7</code></a> chore(internal): detect missing future annotations with ruff</li> <li><a href="e5f93f5dae"><code>e5f93f5</code></a> release: 2.3.0</li> <li><a href="044878859c"><code>0448788</code></a> feat(api): comparison filter in/not in</li> <li><a href="85a91ade61"><code>85a91ad</code></a> chore(package): bump jiter to &gt;=0.10.0 to support Python 3.14 (<a href="https://redirect.github.com/openai/openai-python/issues/2618">#2618</a>)</li> <li>Additional commits viewable in <a href="https://github.com/openai/openai-python/compare/v1.107.0...v2.5.0">compare view</a></li> </ul> </details> <br /> [](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores) Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. 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You can achieve the same result by closing it manually - `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself) </details> --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-22 12:32:48 -07:00
Jiayi Ni	bb1ebb3c6b	feat: Add rerank models and rerank API change (#3831) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> - Extend the model type to include rerank models. - Implement `rerank()` method in inference router. - Add `rerank_model_list` to `OpenAIMixin` to enable providers to register and identify rerank models - Update documentation. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## 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.* --> ``` pytest tests/unit/providers/utils/inference/test_openai_mixin.py ```	2025-10-22 12:02:28 -07:00
slekkala1	eb2b240594	fix: remove consistency checks (#3881) ... # What does this PR do? metadata is conflicting with the default embedding model set on server side via extra body, removing the check and just letting metadata take precedence over extra body `ValueError: Embedding model inconsistent between metadata ('text-embedding-3-small') and extra_body ('sentence-transformers/nomic-ai/nomic-embed-text-v1.5')` ## Test Plan CI	2025-10-21 14:40:14 -07:00
Ashwin Bharambe	bd3c473208	revert: "chore(cleanup)!: remove tool_runtime.rag_tool" (#3877) ... Reverts llamastack/llama-stack#3871 This PR broke RAG (even from Responses -- there _is_ a dependency)	2025-10-21 11:22:06 -07:00
Ashwin Bharambe	0e96279bee	chore(cleanup)!: remove tool_runtime.rag_tool (#3871) ... Kill the `builtin::rag` tool group completely since it is no longer targeted. We use the Responses implementation for knowledge_search which uses the `openai_vector_stores` pathway. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-10-20 22:26:21 -07:00
Ashwin Bharambe	122de785c4	chore(cleanup)!: kill vector_db references as far as possible (#3864) ... There should not be "vector db" anywhere.	2025-10-20 20:06:16 -07:00
Francisco Arceo	48581bf651	chore: Updating how default embedding model is set in stack (#3818) ... # What does this PR do? Refactor setting default vector store provider and embedding model to use an optional `vector_stores` config in the `StackRunConfig` and clean up code to do so (had to add back in some pieces of VectorDB). Also added remote Qdrant and Weaviate to starter distro (based on other PR where inference providers were added for UX). New config is simply (default for Starter distro): ```yaml vector_stores: default_provider_id: faiss default_embedding_model: provider_id: sentence-transformers model_id: nomic-ai/nomic-embed-text-v1.5 ``` ## Test Plan CI and Unit tests. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-20 14:22:45 -07:00
Ashwin Bharambe	2c43285e22	feat(stores)!: use backend storage references instead of configs (#3697) ... **This PR changes configurations in a backward incompatible way.** Run configs today repeat full SQLite/Postgres snippets everywhere a store is needed, which means duplicated credentials, extra connection pools, and lots of drift between files. This PR introduces named storage backends so the stack and providers can share a single catalog and reference those backends by name. ## Key Changes - Add `storage.backends` to `StackRunConfig`, register each KV/SQL backend once at startup, and validate that references point to the right family. - Move server stores under `storage.stores` with lightweight references (backend + namespace/table) instead of full configs. - Update every provider/config/doc to use the new reference style; docs/codegen now surface the simplified YAML. ## Migration Before: ```yaml metadata_store: type: sqlite db_path: ~/.llama/distributions/foo/registry.db inference_store: type: postgres host: ${env.POSTGRES_HOST} port: ${env.POSTGRES_PORT} db: ${env.POSTGRES_DB} user: ${env.POSTGRES_USER} password: ${env.POSTGRES_PASSWORD} conversations_store: type: postgres host: ${env.POSTGRES_HOST} port: ${env.POSTGRES_PORT} db: ${env.POSTGRES_DB} user: ${env.POSTGRES_USER} password: ${env.POSTGRES_PASSWORD} ``` After: ```yaml storage: backends: kv_default: type: kv_sqlite db_path: ~/.llama/distributions/foo/kvstore.db sql_default: type: sql_postgres host: ${env.POSTGRES_HOST} port: ${env.POSTGRES_PORT} db: ${env.POSTGRES_DB} user: ${env.POSTGRES_USER} password: ${env.POSTGRES_PASSWORD} stores: metadata: backend: kv_default namespace: registry inference: backend: sql_default table_name: inference_store max_write_queue_size: 10000 num_writers: 4 conversations: backend: sql_default table_name: openai_conversations ``` Provider configs follow the same pattern—for example, a Chroma vector adapter switches from: ```yaml providers: vector_io: - provider_id: chromadb provider_type: remote::chromadb config: url: ${env.CHROMADB_URL} kvstore: type: sqlite db_path: ~/.llama/distributions/foo/chroma.db ``` to: ```yaml providers: vector_io: - provider_id: chromadb provider_type: remote::chromadb config: url: ${env.CHROMADB_URL} persistence: backend: kv_default namespace: vector_io::chroma_remote ``` Once the backends are declared, everything else just points at them, so rotating credentials or swapping to Postgres happens in one place and the stack reuses a single connection pool.	2025-10-20 13:20:09 -07:00
Shabana Baig	add64e8e2a	feat: Add instructions parameter in response object (#3741) ... # Problem The current inline provider appends the user provided instructions to messages as a system prompt, but the returned response object does not contain the instructions field (as specified in the OpenAI responses spec). # What does this PR do? This pull request adds the instruction field to the response object definition and updates the inline provider. It also ensures that instructions from previous response is not carried over to the next response (as specified in the openAI spec). Closes #[3566](https://github.com/llamastack/llama-stack/issues/3566) ## Test Plan - Tested manually for change in model response w.r.t supplied instructions field. - Added unit test to check that the instructions from previous response is not carried over to the next response. - Added integration tests to check instructions parameter in the returned response object. - Added new recordings for the integration tests. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-10-20 13:10:37 -07:00
Ashwin Bharambe	185de61d8e	fix(openai_mixin): no yelling for model listing if API keys are not provided (#3826) ... As indicated in the title. Our `starter` distribution enables all remote providers _very intentionally_ because we believe it creates an easier, more welcoming experience to new folks using the software. If we do that, and then slam the logs with errors making them question their life choices, it is not so good :) Note that this fix is limited in scope. If you ever try to actually instantiate the OpenAI client from a code path without an API key being present, you deserve to fail hard. ## Test Plan Run `llama stack run starter` with `OPENAI_API_KEY` set. No more wall of text, just one message saying "listed 96 models".	2025-10-16 10:12:13 -07:00
Ashwin Bharambe	07fc8013eb	fix(tests): reduce some test noise (#3825) ... a bunch of logger.info()s are good for server code to help debug in production, but we don't want them killing our unit test output :) --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-10-16 09:52:16 -07:00
Ashwin Bharambe	f70aa99c97	fix(models)!: always prefix models with provider_id when registering (#3822) ... **!!BREAKING CHANGE!!** The lookup is also straightforward -- we always look for this identifier and don't try to find a match for something without the provider_id prefix. Note that, this ideally means we need to update the `register_model()` API also (we should kill "identifier" from there) but I am not doing that as part of this PR. ## Test Plan Existing unit tests	2025-10-16 06:47:39 -07:00
slekkala1	99141c29b1	feat: Add responses and safety impl extra_body (#3781) ... # What does this PR do? Have closed the previous PR due to merge conflicts with multiple PRs Addressed all comments from https://github.com/llamastack/llama-stack/pull/3768 (sorry for carrying over to this one) ## Test Plan Added UTs and integration tests	2025-10-15 15:01:37 -07:00
Sumanth Kamenani	bc8b377a7c	fix(vector-io): handle missing document_id in insert_chunks (#3521) ... Fixed KeyError when chunks don't have document_id in metadata or chunk_metadata. Updated logging to safely extract document_id using getattr and RAG memory to handle different document_id locations. Added test for missing document_id scenarios. Fixes issue #3494 where /v1/vector-io/insert would crash with KeyError. Fixed KeyError when chunks don't have document_id in metadata or chunk_metadata. Updated logging to safely extract document_id using getattr and RAG memory to handle different document_id locations. Added test for missing document_id scenarios. # What does this PR do? Fixes a KeyError crash in `/v1/vector-io/insert` when chunks are missing `document_id` fields. The API was failing even though `document_id` is optional according to the schema. Closes #3494 ## Test Plan **Before fix:** - POST to `/v1/vector-io/insert` with chunks → 500 KeyError - Happened regardless of where `document_id` was placed **After fix:** - Same request works fine → 200 OK - Tested with Postman using FAISS backend - Added unit test covering missing `document_id` scenarios	2025-10-15 11:02:48 -07:00
Ashwin Bharambe	e9b4278a51	feat(responses)!: improve responses + conversations implementations (#3810) ... This PR updates the Conversation item related types and improves a couple critical parts of the implemenation: - it creates a streaming output item for the final assistant message output by the model. until now we only added content parts and included that message in the final response. - rewrites the conversation update code completely to account for items other than messages (tool calls, outputs, etc.) ## Test Plan Used the test script from https://github.com/llamastack/llama-stack-client-python/pull/281 for this ``` TEST_API_BASE_URL=http://localhost:8321/v1 \ pytest tests/integration/test_agent_turn_step_events.py::test_client_side_function_tool -xvs ```	2025-10-15 09:36:11 -07:00
slekkala1	ce8ea2f505	chore: Support embedding params from metadata for Vector Store (#3811) ... # What does this PR do? Support reading embedding model and dimensions from metadata for vector store ## Test Plan Unit Tests	2025-10-15 15:53:36 +02:00
Francisco Arceo	ef4bc70bbe	feat: Enable setting a default embedding model in the stack (#3803) ... # What does this PR do? Enables automatic embedding model detection for vector stores and by using a `default_configured` boolean that can be defined in the `run.yaml`. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan - Unit tests - Integration tests - Simple example below: Spin up the stack: ```bash uv run llama stack build --distro starter --image-type venv --run ``` Then test with OpenAI's client: ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none") vs = client.vector_stores.create() ``` Previously you needed: ```python vs = client.vector_stores.create( extra_body={ "embedding_model": "sentence-transformers/all-MiniLM-L6-v2", "embedding_dimension": 384, } ) ``` The `extra_body` is now unnecessary. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-10-14 18:25:13 -07:00
IAN MILLER	007efa6eb5	refactor: replace default all-MiniLM-L6-v2 embedding model by nomic-embed-text-v1.5 in Llama Stack (#3183) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> The purpose of this PR is to replace the Llama Stack's default embedding model by nomic-embed-text-v1.5. These are the key reasons why Llama Stack community decided to switch from all-MiniLM-L6-v2 to nomic-embed-text-v1.5: 1. The training data for [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2#training-data) includes a lot of data sets with various licensing terms, so it is tricky to know when/whether it is appropriate to use this model for commercial applications. 2. The model is not particularly competitive on major benchmarks. For example, if you look at the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) and click on Miscellaneous/BEIR to see English information retrieval accuracy, you see that the top of the leaderboard is dominated by enormous models but also that there are many, many models of relatively modest size whith much higher Retrieval scores. If you want to look closely at the data, I recommend clicking "Download Table" because it is easier to browse that way. More discussion info can be founded [here](https://github.com/llamastack/llama-stack/issues/2418) <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes #2418 ## 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.* --> 1. Run `./scripts/unit-tests.sh` 2. Integration tests via CI wokrflow --------- Signed-off-by: Sébastien Han <seb@redhat.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com> Co-authored-by: Sébastien Han <seb@redhat.com>	2025-10-14 10:44:20 -04:00
Ashwin Bharambe	ecc8a554d2	feat(api)!: support extra_body to embeddings and vector_stores APIs (#3794) ... Applies the same pattern from https://github.com/llamastack/llama-stack/pull/3777 to embeddings and vector_stores.create() endpoints. This should _not_ be a breaking change since (a) our tests were already using the `extra_body` parameter when passing in to the backend (b) but the backend probably wasn't extracting the parameters correctly. This PR will fix that. Updated APIs: `openai_embeddings(), openai_create_vector_store(), openai_create_vector_store_file_batch()`	2025-10-12 19:01:52 -07:00
ehhuang	06e4cd8e02	feat(api)!: BREAKING CHANGE: support passing extra_body through to providers (#3777) ... # What does this PR do? Allows passing through extra_body parameters to inference providers. With this, we removed the 2 vllm-specific parameters from completions API into `extra_body`. Before/After <img width="1883" height="324" alt="image" src="https://github.com/user-attachments/assets/acb27c08-c748-46c9-b1da-0de64e9908a1" /> closes #2720 ## Test Plan CI and added new test ``` ❯ uv run pytest -s -v tests/integration/ --stack-config=server:starter --inference-mode=record -k 'not( builtin_tool or safety_with_image or code_interpreter or test_rag ) and test_openai_completion_guided_choice' --setup=vllm --suite=base --color=yes Uninstalled 3 packages in 125ms Installed 3 packages in 19ms INFO 2025-10-10 14:29:54,317 tests.integration.conftest:118 tests: Applying setup 'vllm' for suite base INFO 2025-10-10 14:29:54,331 tests.integration.conftest:47 tests: Test stack config type: server (stack_config=server:starter) ============================================================================================================== test session starts ============================================================================================================== platform darwin -- Python 3.12.11, pytest-8.4.2, pluggy-1.6.0 -- /Users/erichuang/projects/llama-stack-1/.venv/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.11', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.2', 'pluggy': '1.6.0'}, 'Plugins': {'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0'}} rootdir: /Users/erichuang/projects/llama-stack-1 configfile: pyproject.toml plugins: anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 285 items / 284 deselected / 1 selected tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=vllm/Qwen/Qwen3-0.6B] instantiating llama_stack_client Starting llama stack server with config 'starter' on port 8321... Waiting for server at http://localhost:8321... (0.0s elapsed) Waiting for server at http://localhost:8321... (0.5s elapsed) Waiting for server at http://localhost:8321... (5.1s elapsed) Waiting for server at http://localhost:8321... (5.6s elapsed) Waiting for server at http://localhost:8321... (10.1s elapsed) Waiting for server at http://localhost:8321... (10.6s elapsed) Server is ready at http://localhost:8321 llama_stack_client instantiated in 11.773s PASSEDTerminating llama stack server process... Terminating process 98444 and its group... Server process and children terminated gracefully ============================================================================================================= slowest 10 durations ============================================================================================================== 11.88s setup tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=vllm/Qwen/Qwen3-0.6B] 3.02s call tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=vllm/Qwen/Qwen3-0.6B] 0.01s teardown tests/integration/inference/test_openai_completion.py::test_openai_completion_guided_choice[txt=vllm/Qwen/Qwen3-0.6B] ================================================================================================ 1 passed, 284 deselected, 3 warnings in 16.21s ================================================================================================= ```	2025-10-10 16:21:44 -07:00
ehhuang	80d58ab519	chore: refactor (chat)completions endpoints to use shared params struct (#3761) ... # What does this PR do? Converts openai(_chat)_completions params to pydantic BaseModel to reduce code duplication across all providers. ## Test Plan CI --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/llamastack/llama-stack/pull/3761). * #3777 * __->__ #3761	2025-10-10 15:46:34 -07:00
Varsha	32fde8d9a8	feat: Add /v1/embeddings endpoint to batches API (#3384) ... # What does this PR do? This PR extends the Llama Stack Batches API to support the /v1/embeddings endpoint, enabling efficient batch processing of embedding requests alongside the existing /v1/chat/completions and /v1/completions support. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> Closes: https://github.com/llamastack/llama-stack/issues/3145 ## 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.* --> ``` (stack-client) ➜ llama-stack git:(support/embeddings-api) conda activate stack-client && python -m pytest tests/unit/providers/batches/test_reference.py -v ============================================================================================================================================ test session starts ============================================================================================================================================= platform darwin -- Python 3.12.11, pytest-7.4.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.12.11', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '7.4.4', 'pluggy': '1.5.0'}, 'Plugins': {'asyncio': '0.23.8', 'cov': '6.0.0', 'timeout': '2.2.0', 'socket': '0.7.0', 'xdist': '3.8.0', 'html': '3.1.1', 'langsmith': '0.3.39', 'anyio': '4.8.0', 'metadata': '3.0.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: asyncio-0.23.8, cov-6.0.0, timeout-2.2.0, socket-0.7.0, xdist-3.8.0, html-3.1.1, langsmith-0.3.39, anyio-4.8.0, metadata-3.0.0 asyncio: mode=Mode.AUTO collected 46 items tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_and_retrieve_batch_success PASSED [ 2%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_batch_without_metadata PASSED [ 4%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_batch_completion_window PASSED [ 6%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_batch_invalid_endpoints[/v1/invalid/endpoint] PASSED [ 8%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_batch_invalid_endpoints[] PASSED [ 10%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_batch_invalid_metadata PASSED [ 13%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_retrieve_batch_not_found PASSED [ 15%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_cancel_batch_success PASSED [ 17%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_cancel_batch_invalid_statuses[failed] PASSED [ 19%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_cancel_batch_invalid_statuses[expired] PASSED [ 21%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_cancel_batch_invalid_statuses[completed] PASSED [ 23%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_cancel_batch_not_found PASSED [ 26%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_list_batches_empty PASSED [ 28%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_list_batches_single_batch PASSED [ 30%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_list_batches_multiple_batches PASSED [ 32%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_list_batches_with_limit PASSED [ 34%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_list_batches_with_pagination PASSED [ 36%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_list_batches_invalid_after PASSED [ 39%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_kvstore_persistence PASSED [ 41%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_file_not_found PASSED [ 43%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_file_exists_empty_content PASSED [ 45%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_file_mixed_valid_invalid_json PASSED [ 47%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_model PASSED [ 50%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_chat_completions[custom_id-custom_id-missing_required_parameter-Missing required parameter: custom_id] PASSED [ 52%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_chat_completions[method-method-missing_required_parameter-Missing required parameter: method] PASSED [ 54%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_chat_completions[url-url-missing_required_parameter-Missing required parameter: url] PASSED [ 56%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_chat_completions[body-body-missing_required_parameter-Missing required parameter: body] PASSED [ 58%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_chat_completions[model-body.model-invalid_request-Model parameter is required] PASSED [ 60%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_chat_completions[messages-body.messages-invalid_request-Messages parameter is required] PASSED [ 63%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_completions[custom_id-custom_id-missing_required_parameter-Missing required parameter: custom_id] PASSED [ 65%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_completions[method-method-missing_required_parameter-Missing required parameter: method] PASSED [ 67%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_completions[url-url-missing_required_parameter-Missing required parameter: url] PASSED [ 69%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_completions[body-body-missing_required_parameter-Missing required parameter: body] PASSED [ 71%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_completions[model-body.model-invalid_request-Model parameter is required] PASSED [ 73%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_missing_parameters_completions[prompt-body.prompt-invalid_request-Prompt parameter is required] PASSED [ 76%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_url_mismatch PASSED [ 78%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_multiple_errors_per_request PASSED [ 80%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_request_format PASSED [ 82%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_parameter_types[custom_id-custom_id-12345-Custom_id must be a string] PASSED [ 84%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_parameter_types[url-url-123-URL must be a string] PASSED [ 86%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_parameter_types[method-method-invalid_value2-Method must be a string] PASSED [ 89%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_parameter_types[body-body-invalid_value3-Body must be a JSON dictionary object] PASSED [ 91%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_parameter_types[model-body.model-123-Model must be a string] PASSED [ 93%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_validate_input_invalid_parameter_types[messages-body.messages-invalid messages format-Messages must be an array] PASSED [ 95%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_max_concurrent_batches PASSED [ 97%] tests/unit/providers/batches/test_reference.py::TestReferenceBatchesImpl::test_create_batch_embeddings_endpoint PASSED [100%] ``` --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-10 13:25:58 -07:00
Ashwin Bharambe	1394403360	feat(responses): implement usage tracking in streaming responses (#3771) ... Implementats usage accumulation to StreamingResponseOrchestrator. The most important part was to pass `stream_options = { "include_usage": true }` to the chat_completion call. This means I will have to record all responses tests again because request hash will change :) Test changes: - Add usage assertions to streaming and non-streaming tests - Update test recordings with actual usage data from OpenAI	2025-10-10 12:27:03 -07:00
Francisco Arceo	e7d21e1ee3	feat: Add support for Conversations in Responses API (#3743) ... # What does this PR do? This PR adds support for Conversations in Responses. <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan Unit tests Integration tests <Details> <Summary>Manual testing with this script: (click to expand)</Summary> ```python from openai import OpenAI client = OpenAI() client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none") def test_conversation_create(): print("Testing conversation create...") conversation = client.conversations.create( metadata={"topic": "demo"}, items=[ {"type": "message", "role": "user", "content": "Hello!"} ] ) print(f"Created: {conversation}") return conversation def test_conversation_retrieve(conv_id): print(f"Testing conversation retrieve for {conv_id}...") retrieved = client.conversations.retrieve(conv_id) print(f"Retrieved: {retrieved}") return retrieved def test_conversation_update(conv_id): print(f"Testing conversation update for {conv_id}...") updated = client.conversations.update( conv_id, metadata={"topic": "project-x"} ) print(f"Updated: {updated}") return updated def test_conversation_delete(conv_id): print(f"Testing conversation delete for {conv_id}...") deleted = client.conversations.delete(conv_id) print(f"Deleted: {deleted}") return deleted def test_conversation_items_create(conv_id): print(f"Testing conversation items create for {conv_id}...") items = client.conversations.items.create( conv_id, items=[ { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "Hello!"}] }, { "type": "message", "role": "user", "content": [{"type": "input_text", "text": "How are you?"}] } ] ) print(f"Items created: {items}") return items def test_conversation_items_list(conv_id): print(f"Testing conversation items list for {conv_id}...") items = client.conversations.items.list(conv_id, limit=10) print(f"Items list: {items}") return items def test_conversation_item_retrieve(conv_id, item_id): print(f"Testing conversation item retrieve for {conv_id}/{item_id}...") item = client.conversations.items.retrieve(conversation_id=conv_id, item_id=item_id) print(f"Item retrieved: {item}") return item def test_conversation_item_delete(conv_id, item_id): print(f"Testing conversation item delete for {conv_id}/{item_id}...") deleted = client.conversations.items.delete(conversation_id=conv_id, item_id=item_id) print(f"Item deleted: {deleted}") return deleted def test_conversation_responses_create(): print("\nTesting conversation create for a responses example...") conversation = client.conversations.create() print(f"Created: {conversation}") response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "What are the 5 Ds of dodgeball?"}], conversation=conversation.id, ) print(f"Created response: {response} for conversation {conversation.id}") return response, conversation def test_conversations_responses_create_followup( conversation, content="Repeat what you just said but add 'this is my second time saying this'", ): print(f"Using: {conversation.id}") response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": content}], conversation=conversation.id, ) print(f"Created response: {response} for conversation {conversation.id}") conv_items = client.conversations.items.list(conversation.id) print(f"\nRetrieving list of items for conversation {conversation.id}:") print(conv_items.model_dump_json(indent=2)) def test_response_with_fake_conv_id(): fake_conv_id = "conv_zzzzzzzzz5dc81908289d62779d2ac510a2b0b602ef00a44" print(f"Using {fake_conv_id}") try: response = client.responses.create( model="gpt-4.1", input=[{"role": "user", "content": "say hello"}], conversation=fake_conv_id, ) print(f"Created response: {response} for conversation {fake_conv_id}") except Exception as e: print(f"failed to create response for conversation {fake_conv_id} with error {e}") def main(): print("Testing OpenAI Conversations API...") # Create conversation conversation = test_conversation_create() conv_id = conversation.id # Retrieve conversation test_conversation_retrieve(conv_id) # Update conversation test_conversation_update(conv_id) # Create items items = test_conversation_items_create(conv_id) # List items items_list = test_conversation_items_list(conv_id) # Retrieve specific item if items_list.data: item_id = items_list.data[0].id test_conversation_item_retrieve(conv_id, item_id) # Delete item test_conversation_item_delete(conv_id, item_id) # Delete conversation test_conversation_delete(conv_id) response, conversation2 = test_conversation_responses_create() print('\ntesting reseponse retrieval') test_conversation_retrieve(conversation2.id) print('\ntesting responses follow up') test_conversations_responses_create_followup(conversation2) print('\ntesting responses follow up x2!') test_conversations_responses_create_followup( conversation2, content="Repeat what you just said but add 'this is my third time saying this'", ) test_response_with_fake_conv_id() print("All tests completed!") if __name__ == "__main__": main() ``` </Details> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-10 11:57:40 -07:00
grs	8bf07f91cb	feat: reuse previous mcp tool listings where possible (#3710) ... # What does this PR do? This PR checks whether, if a previous response is linked, there are mcp_list_tools objects that can be reused instead of listing the tools explicitly every time. Closes #3106 ## Test Plan Tested manually. Added unit tests to cover new behaviour. --------- Signed-off-by: Gordon Sim <gsim@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-10-10 09:28:25 -07:00
Matthew Farrellee	0066d986c5	feat: use SecretStr for inference provider auth credentials (#3724) ... # What does this PR do? use SecretStr for OpenAIMixin providers - RemoteInferenceProviderConfig now has auth_credential: SecretStr - the default alias is api_key (most common name) - some providers override to use api_token (RunPod, vLLM, Databricks) - some providers exclude it (Ollama, TGI, Vertex AI) addresses #3517 ## Test Plan ci w/ new tests	2025-10-10 07:32:50 -07:00
Ashwin Bharambe	e039b61d26	feat(responses)!: add in_progress, failed, content part events (#3765) ... ## Summary - add schema + runtime support for response.in_progress / response.failed / response.incomplete - stream content parts with proper indexes and reasoning slots - align tests + docs with the richer event payloads ## Testing - uv run pytest tests/unit/providers/agents/meta_reference/test_openai_responses.py::test_create_openai_response_with_string_input - uv run pytest tests/unit/providers/agents/meta_reference/test_response_conversion_utils.py	2025-10-10 07:27:34 -07:00
Ashwin Bharambe	a055a32ee4	fix(tests): remove chroma and qdrant from vector io unit tests (#3759) ... These vector databases are already thoroughly tested in integration tests. Unit tests now focus on sqlite_vec, faiss, and pgvector with mocked dependencies, removing the need for external service dependencies. ## Changes: - Deleted test_qdrant.py unit test file - Removed chroma/qdrant fixtures and parametrization from conftest.py - Fixed SqliteKVStoreConfig import to use correct location - Removed chromadb, qdrant-client, pymilvus, milvus-lite, and weaviate-client from unit test dependencies in pyproject.toml	2025-10-09 14:36:34 -07:00
Francisco Arceo	b96640eca3	chore: Removing Weaviate, PGVector, and Milvus from unit tests (#3742) ... # What does this PR do? Removing Weaviate, PostGres, and Milvus unit tests <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## 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.* --> Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-10-08 12:25:51 -07:00