48 commits

Author	SHA1	Message	Date
Francisco Arceo	31b088978a	fix: Fix /vector-stores/create API when vector store with duplicate name (#2617) ... # What does this PR do? Resolves https://github.com/meta-llama/llama-stack/issues/2735 Currently, if you test against OpenAI's Vector Stores API the `client.vector_stores.search` call fails with an invalid vector_db during routing (see the script referenced in the clickable item under the Test Plan section). This PR ensures that `client.vector_stores.search()` is compatible with OpenAI's Vector Stores API. Two biggest changes: 1. The `name`, which was previously used as the `vector_db_id`, has been changed to be consistent with OpenAI's `vs_{uuid}` format. 2. The vector store ID has to be referenced by the ID, the name is not reliable as every `client.vector_stores.create` results in a new vector store. NOTE: I believe this is a breaking change for end users as they'll need to update their VectorDB identifiers. ## Test Plan Unit tests: ```bash ./scripts/unit-tests.sh tests/unit/providers/vector_io/ -v ``` Integration tests: ```bash ENABLE_MILVUS=milvus llama stack run /Users/farceo/dev/llama-stack/llama_stack/templates/starter/run.yaml --image-type venv LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/vector_io/test_openai_vector_stores.py --embedding-model=all-MiniLM-L6-v2 -vv ``` Unit tests and test script below 👇 <details> <summary>Click here for script used to test OpenAI and Llama Stack Vector Store implementation</summary> ```python import json import argparse from openai import OpenAI, pagination import logging from colorama import Fore, Style, init import traceback import os # Initialize colorama for color support in terminal init(autoreset=True) # Setup basic logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') DEMO_VECTOR_STORE_NAME = "Support FAQ FJA" global DEMO_VECTOR_STORE_ID global DEMO_VECTOR_STORE_ID2 def colored_print(color, text): """Prints text to the console with the specified color.""" print(f"{color}{text}{Style.RESET_ALL}") def log_and_print(color, message, level=logging.INFO): """Logs a message and prints it to the console with the specified color.""" logging.log(level, message) colored_print(color, message) def run_tests(client, prefix="openai"): """ Runs all tests using the provided OpenAI client and saves the output to JSON files with the given prefix. """ # Create the directory if it doesn't exist os.makedirs('openai_testing', exist_ok=True) # Default values in case tests fail global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 DEMO_VECTOR_STORE_ID = None DEMO_VECTOR_STORE_ID2 = None def test_idempotent_vector_store_creation(): """ Test that creating a vector store with the same name is idempotent. """ log_and_print(Fore.BLUE, "Starting vector store creation test...") try: vector_store = client.vector_stores.create( name=DEMO_VECTOR_STORE_NAME, ) # Attempt to create the same vector store again vector_store2 = client.vector_stores.create( name=DEMO_VECTOR_STORE_NAME, ) # Check instead of assert if vector_store2.id != vector_store.id: log_and_print(Fore.YELLOW, f"FAILED IDEMPOTENCY: the same VectorStore name for {prefix.upper()} does not return the same ID", level=logging.WARNING) else: log_and_print(Fore.GREEN, f"PASSED IDEMPOTENCY: f{vector_store2.id} == {vector_store.id} the same VectorStore name for {prefix.upper()} returns the same ID") vector_store_data = vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.create = {json.dumps(vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_create.json', 'w') as f: json.dump(vector_store_data, f, indent=2) global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 DEMO_VECTOR_STORE_ID = vector_store.id DEMO_VECTOR_STORE_ID2 = vector_store2.id return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 except Exception as e: log_and_print(Fore.RED, f"Idempotent vector store creation test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) # Create a fallback vector store ID if needed if 'vector_store' in locals() and vector_store: DEMO_VECTOR_STORE_ID = vector_store.id return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 def test_vector_store_list(): """ Test listing vector stores. """ log_and_print(Fore.BLUE, "Starting vector store list test...") try: vector_stores = client.vector_stores.list() # Check instead of assert if not isinstance(vector_stores, pagination.SyncCursorPage): log_and_print(Fore.YELLOW, f"FAILED: Expected a list of vector stores, got {type(vector_stores)}", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Vector store list test passed!") vector_stores_data = vector_stores.to_dict() log_and_print(Fore.WHITE, f"vector_stores.list = {json.dumps(vector_stores_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_list.json', 'w') as f: json.dump(vector_stores_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Vector store list test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_retrieve_vector_store(): """ Test retrieving a specific vector store. """ log_and_print(Fore.BLUE, "Starting retrieve vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping retrieve vector store test - no vector store ID available", level=logging.WARNING) return try: vector_store = client.vector_stores.retrieve( vector_store_id=DEMO_VECTOR_STORE_ID, ) # Check instead of assert if vector_store.id != DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "FAILED: Retrieved vector store ID does not match", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Retrieve vector store test passed!") vector_store_data = vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.retrieve = {json.dumps(vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_retrieve.json', 'w') as f: json.dump(vector_store_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Retrieve vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_modify_vector_store(): """ Test modifying a vector store. """ log_and_print(Fore.BLUE, "Starting modify vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping modify vector store test - no vector store ID available", level=logging.WARNING) return try: updated_vector_store = client.vector_stores.update( vector_store_id=DEMO_VECTOR_STORE_ID, name="Updated Support FAQ FJA", ) # Check instead of assert if updated_vector_store.name != "Updated Support FAQ FJA": log_and_print(Fore.YELLOW, "FAILED: Vector store name was not updated correctly", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Modify vector store test passed!") updated_vector_store_data = updated_vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.modify = {json.dumps(updated_vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_modify.json', 'w') as f: json.dump(updated_vector_store_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Modify vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_delete_vector_store(): """ Test deleting a vector store. """ log_and_print(Fore.BLUE, "Starting delete vector store test...") if not DEMO_VECTOR_STORE_ID2: log_and_print(Fore.YELLOW, "Skipping delete vector store test - no second vector store ID available", level=logging.WARNING) return try: response = client.vector_stores.delete( vector_store_id=DEMO_VECTOR_STORE_ID2, ) log_and_print(Fore.GREEN, "Delete vector store test passed!") response_data = response.to_dict() log_and_print(Fore.WHITE, f"Vector store delete response = {json.dumps(response_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_delete.json', 'w') as f: json.dump(response_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Delete vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_create_vector_store_file(): log_and_print(Fore.BLUE, "Starting create vector store file test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping create vector store file test - no vector store ID available", level=logging.WARNING) return try: # create jsonl of files as an example with open("mydata.jsonl", "w") as f: f.write('{"text": "What is the return policy?", "metadata": {"category": "support"}}\n') f.write('{"text": "How do I reset my password?", "metadata": {"category": "support"}}\n') f.write('{"text": "Where can I find my order history?", "metadata": {"category": "support"}}\n') f.write('{"text": "What are the shipping options?", "metadata": {"category": "support"}}\n') f.write('{"text": "What is your favorite banana?", "metadata": {"category": "support"}}\n') # Create a simple text file if my_data_small.txt doesn't exist if not os.path.exists("my_data_small.txt"): with open("my_data_small.txt", "w") as f: f.write("This is a test file for vector store testing.\n") created_file = client.files.create( file=open("my_data_small.txt", "rb"), purpose="assistants", ) created_file_data = created_file.to_dict() log_and_print(Fore.WHITE, f"Created file {json.dumps(created_file_data, indent=2)}") with open(f'openai_testing/{prefix}_file_create.json', 'w') as f: json.dump(created_file_data, f, indent=2) retrieved_files = client.files.retrieve(created_file.id) retrieved_files_data = retrieved_files.to_dict() log_and_print(Fore.WHITE, f"Retrieved file {json.dumps(retrieved_files_data, indent=2)}") with open(f'openai_testing/{prefix}_file_retrieve.json', 'w') as f: json.dump(retrieved_files_data, f, indent=2) vector_store_file = client.vector_stores.files.create( vector_store_id=DEMO_VECTOR_STORE_ID, file_id=created_file.id, ) log_and_print(Fore.GREEN, "Create vector store file test passed!") except Exception as e: log_and_print(Fore.RED, f"Create vector store file test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_search_vector_store(): """ Test searching a vector store. """ log_and_print(Fore.BLUE, "Starting search vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping search vector store test - no vector store ID available", level=logging.WARNING) return try: query = "What is the banana policy?" search_results = client.vector_stores.search( vector_store_id=DEMO_VECTOR_STORE_ID, query=query, max_num_results=10, ranking_options={ 'ranker': 'default-2024-11-15', 'score_threshold': 0.0, }, rewrite_query=False, ) # Check instead of assert if not isinstance(search_results, pagination.SyncPage): log_and_print(Fore.YELLOW, f"FAILED: Expected a list of search results, got {type(search_results)}", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Search vector store test passed!") search_results_dict = search_results.to_dict() log_and_print(Fore.WHITE, f"Search results = {search_results_dict}") with open(f'openai_testing/{prefix}_vector_store_search.json', 'w') as f: json.dump(search_results_dict, f, indent=2) log_and_print(Fore.WHITE, f"vector_stores.search = {search_results.to_json()}") except Exception as e: log_and_print(Fore.RED, f"Search vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) # Run all tests in sequence, even if some fail test_results = [] try: result = test_idempotent_vector_store_creation() if result and len(result) == 2: DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 = result test_results.append(True) except Exception as e: log_and_print(Fore.RED, f"Vector store creation test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) test_results.append(False) for test_func in [ test_vector_store_list, test_retrieve_vector_store, test_modify_vector_store, test_delete_vector_store, test_create_vector_store_file, test_search_vector_store ]: try: test_func() test_results.append(True) except Exception as e: log_and_print(Fore.RED, f"{test_func.__name__} failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) test_results.append(False) if all(test_results): log_and_print(Fore.GREEN, f"All {prefix} tests completed successfully!") else: failed_count = test_results.count(False) log_and_print(Fore.YELLOW, f"{failed_count} {prefix} test(s) failed, but script completed.") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run OpenAI and/or LlamaStack tests.") parser.add_argument( "--provider", type=str, default="llama", choices=["openai", "llama", "both"], help="Specify which environment to test: openai, llama, or both. Default is both.", ) args = parser.parse_args() try: if args.provider in ("openai", "both"): openai_client = OpenAI() run_tests(openai_client, prefix="openai") if args.provider in ("llama", "both"): llama_client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none") run_tests(llama_client, prefix="llama") log_and_print(Fore.GREEN, "All tests completed!") except Exception as e: log_and_print(Fore.RED, f"Tests failed to complete: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) ``` </details> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-15 11:24:41 -04:00
Varsha	4ae5656c2f	feat: Implement keyword search in milvus (#2231) ... # What does this PR do? This PR adds the keyword search implementation for Milvus. Along with the implementation for remote Milvus, the tests require us to start a Milvus containers locally. In order to verify the implementation, run: ``` pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s --tb=short --disable-warnings --asyncio-mode=auto ``` You can also test the changes using the below script: ``` #!/usr/bin/env python3 import asyncio import os import uuid from typing import List from llama_stack_client import ( Agent, AgentEventLogger, LlamaStackClient, RAGDocument ) class MilvusRAGDemo: def __init__(self, base_url: str = "http://localhost:8321/"): self.client = LlamaStackClient(base_url=base_url) self.vector_db_id = f"milvus_rag_demo_{uuid.uuid4().hex[:8]}" self.model_id = None self.embedding_model_id = None self.embedding_dimension = None def setup_models(self): """Get available models and select appropriate ones for LLM and embeddings.""" models = self.client.models.list() # Select embedding model embedding_models = [m for m in models if m.model_type == "embedding"] if not embedding_models: raise ValueError("No embedding models found") self.embedding_model_id = embedding_models[0].identifier self.embedding_dimension = embedding_models[0].metadata["embedding_dimension"] def register_vector_db(self): print(f"Registering Milvus vector database: {self.vector_db_id}") response = self.client.vector_dbs.register( vector_db_id=self.vector_db_id, embedding_model=self.embedding_model_id, embedding_dimension=self.embedding_dimension, provider_id="milvus-remote", # Use remote Milvus ) print(f"Vector database registered successfully") return response def insert_documents(self): """Insert sample documents into the vector database.""" print("\nInserting sample documents...") # Sample documents about different topics documents = [ RAGDocument( document_id="ai_ml_basics", content=""" Artificial Intelligence (AI) and Machine Learning (ML) are transforming the world. AI refers to the simulation of human intelligence in machines, while ML is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. Deep learning, a subset of ML, uses neural networks with multiple layers to process complex patterns in data. Key concepts in AI/ML include: - Supervised Learning: Training with labeled data - Unsupervised Learning: Finding patterns in unlabeled data - Reinforcement Learning: Learning through trial and error - Neural Networks: Computing systems inspired by biological brains """, mime_type="text/plain", metadata={"topic": "technology", "category": "ai_ml"}, ), ] # Insert documents with chunking self.client.tool_runtime.rag_tool.insert( documents=documents, vector_db_id=self.vector_db_id, chunk_size_in_tokens=200, # Smaller chunks for better granularity ) print(f"Inserted {len(documents)} documents with chunking") def test_keyword_search(self): """Test keyword-based search using BM25.""" queries = [ "neural networks", "Python frameworks", "data cleaning", ] for query in queries: response = self.client.vector_io.query( vector_db_id=self.vector_db_id, query=query, params={ "mode": "keyword", # Keyword search "max_chunks": 3, "score_threshold": 0.0, } ) for i, (chunk, score) in enumerate(zip(response.chunks, response.scores)): print(f" {i+1}. Score: {score:.4f}") print(f" Content: {chunk.content[:100]}...") print(f" Metadata: {chunk.metadata}") def run_demo(self): try: self.setup_models() self.register_vector_db() self.insert_documents() self.test_keyword_search() except Exception as e: print(f"Error during demo: {e}") raise def main(): """Main function to run the demo.""" # Check if Llama Stack server is running demo = MilvusRAGDemo() try: demo.run_demo() except Exception as e: print(f"Demo failed: {e}") if __name__ == "__main__": main() ``` [//]: # (## Documentation) --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-07-14 19:39:55 -04:00
Francisco Arceo	33f0d83ad3	chore: Move vector store kvstore implementation into openai_vector_store_mixin.py (#2748)	2025-07-14 18:10:35 -04:00
Francisco Arceo	6a6b66ae4f	chore: Adding unit tests for OpenAI vector stores and migrating SQLite-vec registry to kvstore (#2665) ... # What does this PR do? This PR refactors and the VectorIO backend logic for `sqlite-vec` and adds unit tests and fixtures to make it easy to test both `sqlite-vec` and `milvus`. Key changes: - `sqlite-vec` migrated to `kvstore` registry - added in-memory cache for sqlite-vec to be consistent with `milvus` - default fixtures moved to `conftest.py` - removed redundant tests from sqlite`-vec` - made `test_vector_io_openai_vector_stores.py` more easily extensible ## Test Plan Unit tests added testing inline providers. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-10 14:22:13 -04:00
pgustafs	d39660afed	fix(remote:milvus): add missing files_api parameter and kvstore configuration (#2630) ... - Fix constructor call missing files_api parameter - Add kvstore field to MilvusVectorIOConfig - Resolves #2626 # What does this PR do? [https://github.com/meta-llama/llama-stack/issues/2626] ## Problem The `MilvusVectorIOAdapter` fails to initialize due to two missing configuration issues: 1. Missing `files_api` parameter in the constructor call 2. Missing `kvstore` field in the `MilvusVectorIOConfig` class ## Root Cause 1. The adapter constructor expects 3 parameters `(config, inference_api, files_api)` but the `get_adapter_impl` function only passes 2 parameters 2. The `MilvusVectorIOConfig` class lacks the `kvstore` field that the adapter's `initialize()` method expects for metadata persistence ## Solution - Added `files_api = deps.get(Api.files, None)` to safely retrieve files API from dependencies - Pass the files_api parameter to MilvusVectorIOAdapter constructor - Added `kvstore: KVStoreConfig | None = None` field to MilvusVectorIOConfig - Maintains backward compatibility since both files_api and kvstore can be None Closes #2626 ## Test Plan - [x] Tested with Milvus configuration - server starts successfully ```yaml vector_io: - provider_id: milvus provider_type: remote::milvus config: uri: http://localhost:19530 token: root:Milvus kvstore: type: sqlite namespace: null db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/milvus_store.db ``` - [x] Vector operations work as expected ```python from llama_stack_client import LlamaStackClient from llama_stack_client.types.shared_params.document import Document as RAGDocument from llama_stack_client.lib.agents.agent import Agent from llama_stack_client.lib.agents.event_logger import EventLogger as AgentEventLogger import os endpoint = os.getenv("LLAMA_STACK_ENDPOINT") model = os.getenv("INFERENCE_MODEL") # Initialize the client client = LlamaStackClient(base_url=endpoint) vector_db_id = "my_documents" response = client.vector_dbs.register( vector_db_id=vector_db_id, embedding_model="all-MiniLM-L6-v2", embedding_dimension=384, provider_id="milvus", ) urls = ["getting_started/Red_Hat_AI_Inference_Server-3.0-Getting_started-en-US.pdf", "vllm_server_arguments/Red_Hat_AI_Inference_Server-3.0-vLLM_server_arguments-en-US.pdf"] documents = [ RAGDocument( document_id=f"num-{i}", content=f"https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/3.0/pdf/{url}", mime_type="application/pdf", metadata={}, ) for i, url in enumerate(urls) ] client.tool_runtime.rag_tool.insert( documents=documents, vector_db_id=vector_db_id, chunk_size_in_tokens=512, ) rag_agent = Agent( client, model=model, # Define instructions for the agent (system prompt) instructions="You are a helpful assistant", enable_session_persistence=False, # Define tools available to the agent tools=[ { "name": "builtin::rag/knowledge_search", "args": { "vector_db_ids": [vector_db_id], }, } ], ) session_id = rag_agent.create_session("test-session") user_prompts = [ "How to start the AI Inference Server container image? use the knowledge_search tool to get information.", ] for prompt in user_prompts: print(f"User> {prompt}") response = rag_agent.create_turn( messages=[{"role": "user", "content": prompt}], session_id=session_id, ) for log in AgentEventLogger().log(response): log.print() ``` server logs: ``` INFO 2025-07-04 22:18:30,385 __main__:577 server: Listening on ['::', '0.0.0.0']:5000 INFO: Started server process [769725] INFO: Waiting for application startup. INFO 2025-07-04 22:18:30,390 __main__:158 server: Starting up INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit) INFO 2025-07-04 22:18:52,193 llama_stack.distribution.routing_tables.common:200 core: Setting owner for vector_db 'my_documents' to 20:18:52.194 [START] /v1/vector-dbs INFO: 192.168.1.249:64170 - "POST /v1/vector-dbs HTTP/1.1" 200 OK 20:18:52.216 [END] /v1/vector-dbs [StatusCode.OK] (21.89ms) 20:18:52.222 [START] /v1/tool-runtime/rag-tool/insert INFO 2025-07-04 22:18:56,265 llama_stack.providers.utils.inference.embedding_mixin:102 uncategorized: Loading sentence transformer for all-MiniLM-L6-v2... WARNING 2025-07-04 22:18:59,214 opentelemetry.trace:537 uncategorized: Overriding of current TracerProvider is not allowed INFO 2025-07-04 22:18:59,339 sentence_transformers.SentenceTransformer:219 uncategorized: Use pytorch device_name: cuda:0 INFO 2025-07-04 22:18:59,340 sentence_transformers.SentenceTransformer:227 uncategorized: Load pretrained SentenceTransformer: all-MiniLM-L6-v2 INFO: 192.168.1.249:64170 - "POST /v1/tool-runtime/rag-tool/insert HTTP/1.1" 200 OK INFO: 192.168.1.249:64170 - "POST /v1/agents HTTP/1.1" 200 OK INFO: 192.168.1.249:64170 - "GET /v1/tools?toolgroup_id=builtin%3A%3Arag%2Fknowledge_search HTTP/1.1" 200 OK INFO: 192.168.1.249:64170 - "POST /v1/agents/b1f6f063-1691-4780-8d9e-facd81708b91/session HTTP/1.1" 200 OK 20:19:01.834 [END] /v1/tool-runtime/rag-tool/insert [StatusCode.OK] (9612.06ms) 20:19:01.839 [START] /v1/agents INFO: 192.168.1.249:64170 - "POST /v1/agents/b1f6f063-1691-4780-8d9e-facd81708b91/session/d2706302-bb54-421d-a890-5e25df9cb47f/turn HTTP/1.1" 200 OK 20:19:01.839 [END] /v1/agents [StatusCode.OK] (0.18ms) 20:19:01.844 [START] /v1/tools INFO 2025-07-04 22:19:01,853 llama_stack.providers.remote.inference.vllm.vllm:330 uncategorized: Initializing vLLM client with base_url=http://192.168.1.183:8080/v1 20:19:01.858 [END] /v1/tools [StatusCode.OK] (14.92ms) 20:19:01.868 [START] /v1/agents/{agent_id}/session 20:19:01.868 [END] /v1/agents/{agent_id}/session [StatusCode.OK] (0.37ms) 20:19:01.873 [START] /v1/agents/{agent_id}/session/{session_id}/turn 20:19:01.885 [START] inference 20:19:05.506 [END] inference [StatusCode.OK] (3621.19ms) INFO 2025-07-04 22:19:05,537 llama_stack.providers.inline.agents.meta_reference.agent_instance:890 agents: executing tool call: knowledge_search with args: {'query': 'How to start the AI Inference Server container image'} 20:19:05.538 [START] tool_execution 20:19:05.928 [END] tool_execution [StatusCode.OK] (390.08ms) 20:19:05.538 [INFO] executing tool call: knowledge_search with args: {'query': 'How to start the AI Inference Server container image'} 20:19:05.935 [START] inference 20:19:17.539 [END] inference [StatusCode.OK] (11603.76ms) 20:19:17.560 [END] /v1/agents/{agent_id}/session/{session_id}/turn [StatusCode.OK] (15686.62ms) ``` - [x] No regressions in functionality - [x] Configuration properly accepts kvstore settings --------- Co-authored-by: Peter Gustafsson <peter.gustafsson6@gmail.com> Co-authored-by: raghotham <rsm@meta.com> Co-authored-by: Francisco Arceo <farceo@redhat.com>	2025-07-09 10:08:14 +02:00
Francisco Arceo	83c89265e0	chore: Adding unit tests for Milvus and OpenAI compatibility (#2640) ... # What does this PR do? - Enabling Unit tests for Milvus to start to test OpenAI compatibility and fixing a few bugs. - Also fixed an inconsistency in the Milvus config between remote and inline. - Added pymilvus to extras for testing in CI I'm going to refactor this later to include the other inline providers so that we can catch issues sooner. I have another PR where I've been testing to find other bugs in the implementation (and required changes drafted here: https://github.com/meta-llama/llama-stack/pull/2617). ## 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>	2025-07-08 00:50:16 -07:00
Francisco Arceo	4afd619c56	chore: Add support for vector-stores files api for Milvus (#2582) ... # What does this PR do? ### Summary This pull request implements support for the OpenAI Vector Store Files API for the Milvus vector store provider in `llama_stack`. It enables storing, loading, updating, and deleting file metadata and file contents in Milvus collections, allowing OpenAI vector store files to be managed directly within Milvus. ### Main Changes - **Milvus Vector Store Files API Implementation** - Implements all required methods for storing, loading, updating, and deleting vector store file metadata and contents (`_save_openai_vector_store_file`, `_load_openai_vector_store_file`, `_load_openai_vector_store_file_contents`, `_update_openai_vector_store_file`, `_delete_openai_vector_store_file_from_storage`). - Uses two Milvus collections: `openai_vector_store_files` (for metadata) and `openai_vector_store_files_contents` (for chunked file contents). - Collections are created dynamically if they do not exist, with appropriate schema definitions. - **Collection Name Sanitization** - Adds a `sanitize_collection_name` utility to ensure Milvus collection names only contain valid characters (letters, numbers, underscores). - **Testing** - Updates test skip logic to include `"inline::milvus"` for cases where the OpenAI Vector Store Files API is not supported, improving integration test accuracy. - **Other Improvements** - Passes `kvstore` to `MilvusIndex` for consistency. - Removes obsolete NotImplementedErrors and legacy code for file storage. ## Test Plan CI and tested via a test script ## Notes - `VectorDB` currently uses the `name` as the `identifier` in `openai_create_vector_store`. We need to add `name` as a field to `VectorDB` and generate the `identifier` upon creation. OpenAI is not idempotent with respect to the `name` field that they pass (i.e., you can pass the same name multiple times and OpenAI will generate a new identifier). I'll add a follow up PR for this. - The `Files` api needs to use `files-` as a prefix in the identifier. I have updated the Vector Store to use the OpenAI prefix `vs_*`. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-03 12:15:33 -07:00
Sébastien Han	c9a49a80e8	docs: auto generated documentation for providers (#2543) ... # What does this PR do? Simple approach to get some provider pages in the docs. Add or update description fields in the provider configuration class using Pydantic’s Field, ensuring these descriptions are clear and complete, as they will be used to auto-generate provider documentation via ./scripts/distro_codegen.py instead of editing the docs manually. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-30 15:13:20 +02:00
Francisco Arceo	cc19b56c87	chore: OpenAI compatibility for Milvus (#2470) ... # What does this PR do? Closes https://github.com/meta-llama/llama-stack/issues/2461 ## Test Plan Tested with the `ollama` distriubtion template and updated the vector_io provider to: ```yaml vector_io: - provider_id: milvus provider_type: inline::milvus config: db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/milvus_store.db kvstore: type: sqlite db_name: milvus_registry.db ``` Ran the stack ```bash llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv --env OLLAMA_URL="http://0.0.0.0:11434" ``` Ran the tests: ``` pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ``` Output passed. Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-06-27 16:00:36 -07:00
Ben Browning	0883944bc3	fix: Some missed env variable changes from PR 2490 (#2538) ... # What does this PR do? Some templates were still using the old environment variable substition syntax instead of the new one and were not getting substituted properly. Also, some places didn't handle the new None vs old empty string ("") values that come from the conditional environment variable substitution. This gets the starter and remote-vllm distributions starting again, and I tested various permutations of the starter as chroma and pgvector needed some adjustments to their config classes to handle the new possible `None` values. And, I had to tweak our `Provider` class to also handle `None` values, for cases where we disable providers in the starter config via environment variables. This may not have caught everything that was missed, but I did grep around quite a bit to try and find anything lingering. ## Test Plan The following permutations now all run (or attempt to run to the point of complaining that they can't connect to chroma, vllm, etc) when before they failed immediately on startup because of bad environment variable substitions: ``` uv run llama stack run llama_stack/templates/starter/run.yaml ENABLE_SQLITE_VEC=true uv run llama stack run llama_stack/templates/starter/run.yaml ENABLE_PGVECTOR=true uv run llama stack run llama_stack/templates/starter/run.yaml ENABLE_CHROMADB=true uv run llama stack run llama_stack/templates/starter/run.yaml uv run llama stack run llama_stack/templates/remote-vllm/run.yaml ``` <!-- 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: Ben Browning <bbrownin@redhat.com> Co-authored-by: raghotham <rsm@meta.com>	2025-06-26 17:59:15 -07:00
Hardik Shah	eb01a3f1c5	ci: vector_io provider integration tests (#2537) ... Runs integration tests for `vector_io` across the provider matrix. This new workflow adds CI testing across - `inline::faiss`, `remote::chroma`.	2025-06-26 17:04:32 -07:00
Sébastien Han	43c1f39bd6	refactor(env)!: enhanced environment variable substitution (#2490) ... # What does this PR do? This commit significantly improves the environment variable substitution functionality in Llama Stack configuration files: * The version field in configuration files has been changed from string to integer type for better type consistency across build and run configurations. * The environment variable substitution system for ${env.FOO:} was fixed and properly returns an error * The environment variable substitution system for ${env.FOO+} returns None instead of an empty strings, it better matches type annotations in config fields * The system includes automatic type conversion for boolean, integer, and float values. * The error messages have been enhanced to provide clearer guidance when environment variables are missing, including suggestions for using default values or conditional syntax. * Comprehensive documentation has been added to the configuration guide explaining all supported syntax patterns, best practices, and runtime override capabilities. * Multiple provider configurations have been updated to use the new conditional syntax for optional API keys, making the system more flexible for different deployment scenarios. The telemetry configuration has been improved to properly handle optional endpoints with appropriate validation, ensuring that required endpoints are specified when their corresponding sinks are enabled. * There were many instances of ${env.NVIDIA_API_KEY:} that should have caused the code to fail. However, due to a bug, the distro server was still being started, and early validation wasn’t triggered. As a result, failures were likely being handled downstream by the providers. I’ve maintained similar behavior by using ${env.NVIDIA_API_KEY:+}, though I believe this is incorrect for many configurations. I’ll leave it to each provider to correct it as needed. * Environment variable substitution now uses the same syntax as Bash parameter expansion. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-26 08:20:08 +05:30
Francisco Arceo	82f13fe83e	feat: Add ChunkMetadata to Chunk (#2497) ... # What does this PR do? Adding `ChunkMetadata` so we can properly delete embeddings later. More specifically, this PR refactors and extends the chunk metadata handling in the vector database and introduces a distinction between metadata used for model context and backend-only metadata required for chunk management, storage, and retrieval. It also improves chunk ID generation and propagation throughout the stack, enhances test coverage, and adds new utility modules. ```python class ChunkMetadata(BaseModel): """ `ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional information about the chunk that will NOT be inserted into the context during inference, but is required for backend functionality. Use `metadata` in `Chunk` for metadata that will be used during inference. """ document_id: str | None = None chunk_id: str | None = None source: str | None = None created_timestamp: int | None = None updated_timestamp: int | None = None chunk_window: str | None = None chunk_tokenizer: str | None = None chunk_embedding_model: str | None = None chunk_embedding_dimension: int | None = None content_token_count: int | None = None metadata_token_count: int | None = None ``` Eventually we can migrate the document_id out of the `metadata` field. I've introduced the changes so that `ChunkMetadata` is backwards compatible with `metadata`. <!-- If resolving an issue, uncomment and update the line below --> Closes https://github.com/meta-llama/llama-stack/issues/2501 ## Test Plan Added unit tests --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-06-25 15:55:23 -04:00
Varsha	cfee63bd0d	feat: Add search_mode support to OpenAI vector store API (#2500) ... # What does this PR do? Add search_mode parameter (vector/keyword/hybrid) to openai_search_vector_store method. Fixes OpenAPI code generation by using str instead of Literal type. Closes: #2459 ## 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: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-24 20:38:47 -04:00
Ashwin Bharambe	73c18feac4	fix: update the signature of openai_list_files_in_vector_store in all VectorIO impls (#2503)	2025-06-24 18:55:56 +05:30
Ben Browning	f394c7f2d9	feat: Add missing Vector Store Files API surface (#2468) ... # What does this PR do? This adds the ability to list, retrieve, update, and delete Vector Store Files. It implements these new APIs for the faiss and sqlite-vec providers, since those are the two that also have the rest of the vector store files implementation. Closes #2445 ## Test Plan ### test_openai_vector_stores Integration Tests There are a number of new integration tests added, which I ran for each provider as outlined below. faiss (from ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` sqlite-vec (from starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` ### file_search verification tests I also ensured the file_search verification tests continue to work, both for faiss and sqlite-vec. faiss (ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` sqlite-vec (starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=together/meta-llama/Llama-3.2-3B-Instruct-Turbo ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-19 11:08:24 -04:00
ehhuang	db2cd9e8f3	feat: support filters in file search (#2472) ... # What does this PR do? Move to use vector_stores.search for file search tool in Responses, which supports filters. closes #2435 ## Test Plan Added e2e test with fitlers. myenv ❯ llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search and filters' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct	2025-06-18 21:50:55 -07:00
Varsha	2e8054bede	feat: Implement hybrid search in SQLite-vec (#2312) ... # What does this PR do? Add support for hybrid search mode in SQLite-vec provider, which combines keyword and vector search for better results. The implementation: - Adds hybrid search mode as a new option alongside vector and keyword search - Implements query_hybrid method in SQLiteVecIndex that: - First performs keyword search to get candidate matches - Then applies vector similarity search on those candidates - Updates documentation to reflect the new search mode This change improves search quality by leveraging both semantic similarity and keyword matching, while maintaining backward compatibility with existing vector and keyword search modes. ## Test Plan ``` pytest tests/unit/providers/vector_io/test_sqlite_vec.py -v -s --tb=short /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:217: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =============================================================================================== test session starts =============================================================================================== platform darwin -- Python 3.10.16, pytest-8.3.5, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.6-arm64-arm-64bit', 'Packages': {'pytest': '8.3.5', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'asyncio': '0.26.0', 'nbval': '0.11.0', 'cov': '6.1.1'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, anyio-4.8.0, asyncio-0.26.0, nbval-0.11.0, cov-6.1.1 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 10 items tests/unit/providers/vector_io/test_sqlite_vec.py::test_add_chunks PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search_k_greater_than_results PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_no_keyword_matches PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_score_threshold PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_different_embedding PASSED ``` --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-13 15:54:06 -04:00
Ben Browning	941f505eb0	feat: File search tool for Responses API (#2426) ... # What does this PR do? This is an initial working prototype of wiring up the `file_search` builtin tool for the Responses API to our existing rag knowledge search tool. This is me seeing what I could pull together on top of the bits we already have merged. This may not be the ideal way to implement this, and things like how I shuffle the vector store ids from the original response API tool request to the actual tool execution feel a bit hacky (grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see what I mean). ## Test Plan I stubbed in some new tests to exercise this using text and pdf documents. Note that this is currently under tests/verification only because it sometimes flakes with tool calling of the small Llama-3.2-3B model we run in CI (and that I use as an example below). We'd want to make the test a bit more robust in some way if we moved this over to tests/integration and ran it in CI. ### OpenAI SaaS (to verify test correctness) ``` pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=https://api.openai.com/v1 \ --model=gpt-4o ``` ### Fireworks with faiss vector store ``` llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct ``` ### Ollama with faiss vector store This sometimes flakes on Ollama because the quantized small model doesn't always choose to call the tool to answer the user's question. But, it often works. ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` ### OpenAI provider with sqlite-vec vector store ``` llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=openai/gpt-4o-mini ``` ### Ensure existing vector store integration tests still pass ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io \ --text-model "meta-llama/Llama-3.2-3B-Instruct" \ --embedding-model=all-MiniLM-L6-v2 ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-13 14:32:48 -04:00
Hardik Shah	0bc1747ed8	feat: update search for vector_stores (#2441) ... Updated the `search` functionality return response to match openai. ## Test Plan ``` pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-12 15:34:22 -07:00
Ibrahim Haroon	35c2817d0a	fix(weaviate): handle case where distance is 0 by setting score to infinity (#2415) ... # What does this PR do? Fixes provider weaviate `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then sets `score` to infinity, which represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="weaviate", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ```	2025-06-12 11:23:59 -04:00
Hardik Shah	de37a04c3e	fix: set appropriate defaults for params (#2434) ... Setting defaults to be `| None` else they get marked as required params in open-api spec.	2025-06-11 17:30:34 -07:00
Hardik Shah	d55100d9b7	feat: OpenAIVectorIOMixin for vector_stores common logic (#2427) ... Extracts common OpenAI vector-store code into its own mixin so that all providers can share the same core logic. This also makes it easy for Llama Stack to support both vector-stores and Llama Stack APIs in the interim so that both share the same underlying vector-dbs. Each provider contains storage specific logic to `create / edit / delete / list` vector dbs while the plumbing logic is standardized in the common code. Ensured that this works well with both faiss and sqllite-vec. ### Test Plan ``` llama stack run starter pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-11 15:40:57 -07:00
Hardik Shah	5ac43268e8	feat: Add OpenAI compat /v1/vector_store APIs (#2423) ... Adding OpenAI compat `/v1/vector-store` apis. This PR implements the `faiss` provider with followup PRs coming up for other providers. Added routes to create, update, delete, list vector stores. Also added route to search a vector store Inserting into vector stores is missing and will be a follow up diff. ### Test Plan - Added new integration test for testing the faiss provider ``` pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-10 13:07:39 -07:00
Ibrahim Haroon	28ca00d0d9	fix(pgvector): handle case where distance is 0 by setting score to infinity (#2416) ... # What does this PR do? Fixes provider pgvector `query_vector` function for when the distance between the query embedding and an embedding within the vector db is 0 (identical vectors). Catches `ZeroDivisionError` and then sets `score` to infinity, which represent maximum similarity. <!-- If resolving an issue, uncomment and update the line below --> Closes [#2381] ## Test Plan Checkout this PR Execute this code and there will no longer be a `ZeroDivisionError` exception ``` from llama_stack_client import LlamaStackClient base_url = "http://localhost:8321" client = LlamaStackClient(base_url=base_url) models = client.models.list() embedding_model = ( em := next(m for m in models if m.model_type == "embedding") ).identifier embedding_dimension = 384 _ = client.vector_dbs.register( vector_db_id="foo_db", embedding_model=embedding_model, embedding_dimension=embedding_dimension, provider_id="pgvector", ) chunk = { "content": "foo", "mime_type": "text/plain", "metadata": { "document_id": "foo-id" } } client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk]) client.vector_io.query(vector_db_id="foo_db", query="foo") ```	2025-06-07 16:31:30 -04:00
Hardik Shah	1f48577a02	fix: ChromaDB provider (#2413) ... fixes the remote::chromaDB provider for vector_io by updating the method definition appropriately. Fixed impl to use score_threshold properly. ### Test Plan ``` # Start Chroma Docker docker run --rm \ --name chromadb \ -p 8800:8000 \ -v ~/chroma:/chroma/chroma \ -e IS_PERSISTENT=TRUE \ -e ANONYMIZED_TELEMETRY=FALSE \ chromadb/chroma:latest # run pytest CHROMADB_URL="http://localhost:8800" pytest -sv tests/integration/vector_io/test_vector_io.py --stack-config vector_io=remote::chromadb,inference=fireworks --embedding-model nomic-ai/nomic-embed-text-v1.5 ```	2025-06-06 11:25:58 -07:00
Varsha	e92301f2d7	feat(sqlite-vec): enable keyword search for sqlite-vec (#1439) ... # What does this PR do? This PR introduces support for keyword based FTS5 search with BM25 relevance scoring. It makes changes to the existing EmbeddingIndex base class in order to support a search_mode and query_str parameter, that can be used for keyword based search implementations. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan run ``` pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto ``` Output: ``` pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) ====================================================== test session starts ======================================================= platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.4-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0 asyncio: mode=auto, asyncio_default_fixture_loop_scope=None collected 7 items llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_add_chunks PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_fts PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_register_vector_db PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_unregister_vector_db PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED ``` For reference, with the implementation, the fts table looks like below: ``` Chunk ID: 9fbc39ce-c729-64a2-260f-c5ec9bb2a33e, Content: Sentence 0 from document 0 Chunk ID: 94062914-3e23-44cf-1e50-9e25821ba882, Content: Sentence 1 from document 0 Chunk ID: e6cfd559-4641-33ba-6ce1-7038226495eb, Content: Sentence 2 from document 0 Chunk ID: 1383af9b-f1f0-f417-4de5-65fe9456cc20, Content: Sentence 3 from document 0 Chunk ID: 2db19b1a-de14-353b-f4e1-085e8463361c, Content: Sentence 4 from document 0 Chunk ID: 9faf986a-f028-7714-068a-1c795e8f2598, Content: Sentence 5 from document 0 Chunk ID: ef593ead-5a4a-392f-7ad8-471a50f033e8, Content: Sentence 6 from document 0 Chunk ID: e161950f-021f-7300-4d05-3166738b94cf, Content: Sentence 7 from document 0 Chunk ID: 90610fc4-67c1-e740-f043-709c5978867a, Content: Sentence 8 from document 0 Chunk ID: 97712879-6fff-98ad-0558-e9f42e6b81d3, Content: Sentence 9 from document 0 Chunk ID: aea70411-51df-61ba-d2f0-cb2b5972c210, Content: Sentence 0 from document 1 Chunk ID: b678a463-7b84-92b8-abb2-27e9a1977e3c, Content: Sentence 1 from document 1 Chunk ID: 27bd63da-909c-1606-a109-75bdb9479882, Content: Sentence 2 from document 1 Chunk ID: a2ad49ad-f9be-5372-e0c7-7b0221d0b53e, Content: Sentence 3 from document 1 Chunk ID: cac53bcd-1965-082a-c0f4-ceee7323fc70, Content: Sentence 4 from document 1 ``` Query results: Result 1: Sentence 5 from document 0 Result 2: Sentence 5 from document 1 Result 3: Sentence 5 from document 2 [//]: # (## Documentation) --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-05-21 15:24:24 -04:00
Matthew Farrellee	9a6e91cd93	fix: chromadb type hint (#2136) ... ``` $ INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct \ CHROMADB_URL=http://localhost:8000 \ llama stack build --image-type conda --image-name llama \ --providers vector_io=remote::chromadb,inference=remote::ollama \ --run ... File ".../llama_stack/providers/remote/vector_io/chroma/chroma.py", line 31, in <module> ChromaClientType = chromadb.AsyncHttpClient | chromadb.PersistentClient TypeError: unsupported operand type(s) for |: 'function' and 'function' ``` issue: AsyncHttpClient and PersistentClient are functions that return AsyncClientAPI and ClientAPI types, respectively. | cannot be used to construct a type from functions. previously the code was Union[AsyncHttpClient, PersistentClient], which did not trigger an error # What does this PR do? Closes #2135	2025-05-12 06:27:01 -07:00
Divya	3022f7b642	feat: Adding TLS support for Remote::Milvus vector_io (#2011) ... # What does this PR do? For the Issue :- #[2010](https://github.com/meta-llama/llama-stack/issues/2010) Currently, if we try to connect the Llama stack server to a remote Milvus instance that has TLS enabled, the connection fails because TLS support is not implemented in the Llama stack codebase. As a result, users are unable to use secured Milvus deployments out of the box. After adding this , the user will be able to connect to remote::Milvus which is TLS enabled . if TLS enabled :- ``` vector_io: - provider_id: milvus provider_type: remote::milvus config: uri: "http://<host>:<port>" token: "<user>:<password>" secure: True server_pem_path: "path/to/server.pem" ``` [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan I have already tested it by connecting to a Milvus instance which is TLS enabled and i was able to start llama stack server .	2025-05-06 14:15:34 +02:00
Ihar Hrachyshka	9e6561a1ec	chore: enable pyupgrade fixes (#1806) ... # What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>	2025-05-01 14:23:50 -07:00
Francisco Arceo	74a2584cdb	chore: Updating Milvus Client calls to be non-blocking (#1830) ... # What does this PR do? This PR converts blocking Milvus Client calls to non-blocking. Another one for https://github.com/meta-llama/llama-stack/issues/1489 ## Test Plan I ran the integration tests from https://github.com/meta-llama/llama-stack/pull/1467 with: ```python pytest -s -v tests/integration/vector_io/test_vector_io.py \ --stack-config inference=sentence-transformers,vector_io=inline::milvus \ --embedding-model all-miniLM-L6-V2 --env MILVUS_DB_PATH=/tmp/moo.db INFO 2025-03-28 21:35:22,726 tests.integration.conftest:41 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS /Users/farceo/dev/llama-stack/.venv/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =============================================================================================================================================================================================================================================================== test session starts =============================================================================================================================================================================================================================================================== platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/farceo/dev/llama-stack/.venv/bin/python3 cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-15.3.1-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'cov': '6.0.0', 'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0', 'nbval': '0.11.0'}} rootdir: /Users/farceo/dev/llama-stack configfile: pyproject.toml plugins: cov-6.0.0, html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0, nbval-0.11.0 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None collected 7 items tests/integration/vector_io/test_vector_io.py::test_vector_db_retrieve[emb=all-miniLM-L6-V2] PASSED tests/integration/vector_io/test_vector_io.py::test_vector_db_register[emb=all-miniLM-L6-V2] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case0] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case1] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case2] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case3] PASSED tests/integration/vector_io/test_vector_io.py::test_insert_chunks[emb=all-miniLM-L6-V2-test_case4] PASSED ========================================================================================================================================================================================================================================================= 7 passed, 2 warnings in 40.33s ========================================================================================================================================================================================================================================================== ``` [//]: # (## Documentation) Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-03-28 22:14:07 -04:00
Daniele Martinoli	cca9bd6cc3	feat: Qdrant inline provider (#1273) ... # What does this PR do? Removed local execution option from the remote Qdrant provider and introduced an explicit inline provider for the embedded execution. Updated the ollama template to include this option: this part can be reverted in case we don't want to have two default `vector_io` providers. (Closes #1082) ## Test Plan Build and run an ollama distro: ```bash llama stack build --template ollama --image-type conda llama stack run --image-type conda ollama ``` Run one of the sample ingestionapplicatinos like [rag_with_vector_db.py](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py), but replace this line: ```py selected_vector_provider = vector_providers[0] ``` with the following, to use the `qdrant` provider: ```py selected_vector_provider = vector_providers[1] ``` After running the test code, verify the timestamp of the Qdrant store: ```bash % ls -ltr ~/.llama/distributions/ollama/qdrant.db/collection/test_vector_db_* total 784 -rw-r--r--@ 1 dmartino staff 401408 Feb 26 10:07 storage.sqlite ``` [//]: # (## Documentation) --------- Signed-off-by: Daniele Martinoli <dmartino@redhat.com> Co-authored-by: Francisco Arceo <farceo@redhat.com>	2025-03-18 14:04:21 -07:00
Ashwin Bharambe	d072b5fa0c	test: add unit test to ensure all config types are instantiable (#1601)	2025-03-12 22:29:58 -07:00
Ashwin Bharambe	330cc9d09d	feat: add Milvus vectorDB (#1467) ... # What does this PR do? See https://github.com/meta-llama/llama-stack/pull/1171 which is the original PR. Author: @zc277584121 feat: add [Milvus](https://milvus.io/) vectorDB note: I use the MilvusClient to implement it instead of AsyncMilvusClient, because when I tested AsyncMilvusClient, it would raise issues about evenloop, which I think AsyncMilvusClient SDK is not robust enough to be compatible with llama_stack framework. ## Test Plan have passed the unit test and ene2end test Here is my end2end test logs, including the client code, client log, server logs from inline and remote settings [test_end2end_logs.zip](https://github.com/user-attachments/files/18964391/test_end2end_logs.zip) --------- Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Cheney Zhang <chen.zhang@zilliz.com>	2025-03-06 20:59:31 -08:00
Alexey Rybak	d57cffb495	fix(pgvector): replace hyphens with underscores in table names (#1385) ... # What does this PR do? Fix SQL syntax errors caused by hyphens in Vector DB IDs by sanitizing table # (Closes #1332 ) ## Test Plan Test confirms table names with hyphens are properly converted to underscores	2025-03-04 07:06:35 -08:00
Ashwin Bharambe	6609d4ada4	feat: allow conditionally enabling providers in run.yaml (#1321) ... # What does this PR do? We want to bundle a bunch of (typically remote) providers in a distro template and be able to configure them "on the fly" via environment variables. So far, we have been able to do this with simple env var replacements. However, sometimes you want to only conditionally enable providers (because the relevant remote services may not be alive, or relevant.) This was not possible until now. To aid this, we add a simple (bash-like) env var replacement enhancement: `${env.FOO+bar}` evaluates to `bar` if the variable is SET and evaluates to empty string if it is not. On top of that, we update our main resolver to ignore any provider whose ID is null. This allows using the distro like this: ```bash llama stack run dev --env CHROMADB_URL=http://localhost:6001 --env ENABLE_CHROMADB=1 ``` when only Chroma is UP. This disables the other `pgvector` provider in the run configuration. ## Test Plan Hard code `chromadb` as the vector io provider inside `test_vector_io.py` and run: ```bash LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -s -v tests/client-sdk/vector_io/ --embedding-model all-MiniLM-L6-v2 ```	2025-03-01 11:19:14 -08:00
Ashwin Bharambe	314ee09ae3	chore: move all Llama Stack types from llama-models to llama-stack (#1098) ... llama-models should have extremely minimal cruft. Its sole purpose should be didactic -- show the simplest implementation of the llama models and document the prompt formats, etc. This PR is the complement to https://github.com/meta-llama/llama-models/pull/279 ## Test Plan Ensure all `llama` CLI `model` sub-commands work: ```bash llama model list llama model download --model-id ... llama model prompt-format -m ... ``` Ran tests: ```bash cd tests/client-sdk LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/ LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/ LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/ ``` Create a fresh venv `uv venv && source .venv/bin/activate` and run `llama stack build --template fireworks --image-type venv` followed by `llama stack run together --image-type venv` <-- the server runs Also checked that the OpenAPI generator can run and there is no change in the generated files as a result. ```bash cd docs/openapi_generator sh run_openapi_generator.sh ```	2025-02-14 09:10:59 -08:00
Sébastien Han	c0ee512980	build: configure ruff from pyproject.toml (#1100) ... # What does this PR do? - Remove hardcoded configurations from pre-commit. - Allow configuration to be set via pyproject.toml. - Merge .ruff.toml settings into pyproject.toml. - Ensure the linter and formatter use the defined configuration instead of being overridden by pre-commit. Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (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.*] [//]: # (## Documentation) Signed-off-by: Sébastien Han <seb@redhat.com>	2025-02-14 09:01:57 -08:00
Yuan Tang	5858777ff0	fix: Update VectorIO config classes in registry (#1079) ... This was missed in https://github.com/meta-llama/llama-stack/pull/1023. ``` Traceback (most recent call last): File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/yutang/repos/llama-stack/llama_stack/distribution/server/server.py", line 488, in <module> main() File "/home/yutang/repos/llama-stack/llama_stack/distribution/server/server.py", line 389, in main impls = asyncio.run(construct_stack(config)) File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/asyncio/runners.py", line 44, in run return loop.run_until_complete(main) File "/home/yutang/.conda/envs/distribution-myenv/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete return future.result() File "/home/yutang/repos/llama-stack/llama_stack/distribution/stack.py", line 202, in construct_stack impls = await resolve_impls(run_config, provider_registry or get_provider_registry(), dist_registry) File "/home/yutang/repos/llama-stack/llama_stack/distribution/resolver.py", line 230, in resolve_impls impl = await instantiate_provider( File "/home/yutang/repos/llama-stack/llama_stack/distribution/resolver.py", line 312, in instantiate_provider config_type = instantiate_class_type(provider_spec.config_class) File "/home/yutang/repos/llama-stack/llama_stack/distribution/utils/dynamic.py", line 13, in instantiate_class_type return getattr(module, class_name) AttributeError: module 'llama_stack.providers.inline.vector_io.faiss' has no attribute 'FaissImplConfig' ``` --------- Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>	2025-02-13 15:39:13 -08:00
Yuan Tang	8ff27b58fa	chore: Consistent naming for VectorIO providers (#1023) ... # What does this PR do? This changes all VectorIO providers classes to follow the pattern `<ProviderName>VectorIOConfig` and `<ProviderName>VectorIOAdapter`. All API endpoints for VectorIOs are currently consistent with `/vector-io`. Note that API endpoint for VectorDB stay unchanged as `/vector-dbs`. ## Test Plan I don't have a way to test all providers. This is a simple renaming so things should work as expected. --------- Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>	2025-02-13 13:15:49 -05:00
Sébastien Han	e4a1579e63	build: format codebase imports using ruff linter (#1028) ... # What does this PR do? - Configured ruff linter to automatically fix import sorting issues. - Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are applied. - Enabled the 'I' selection to focus on import-related linting rules. - Ran the linter, and formatted all codebase imports accordingly. - Removed the black dep from the "dev" group since we use ruff Signed-off-by: Sébastien Han <seb@redhat.com> [//]: # (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.*] [//]: # (## Documentation) [//]: # (- [ ] Added a Changelog entry if the change is significant) Signed-off-by: Sébastien Han <seb@redhat.com>	2025-02-13 10:06:21 -08:00
Bill Murdock	3856927ee8	fix: Update Qdrant support post-refactor (#1022) ... # What does this PR do? I tried running the Qdrant provider and found some bugs. See #1021 for details. @terrytangyuan wrote there: > Please feel free to submit your changes in a PR. I fixed similar issues for pgvector provider. This might be an issue introduced from a refactoring. So I am submitting this PR. Closes #1021 ## Test Plan Here are the highlights for what I did to test this: References: - https://llama-stack.readthedocs.io/en/latest/getting_started/index.html - https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py - https://github.com/meta-llama/llama-stack/blob/main/docs/zero_to_hero_guide/README.md#build-configure-and-run-llama-stack Install and run Qdrant server: ``` podman pull qdrant/qdrant mkdir qdrant-data podman run -p 6333:6333 -v $(pwd)/qdrant-data:/qdrant/storage qdrant/qdrant ``` Install and run Llama Stack from the venv-support PR (mainly because I didn't want to install conda): ``` brew install cmake # Should just need this once git clone https://github.com/meta-llama/llama-models.git gh repo clone cdoern/llama-stack cd llama-stack gh pr checkout 1018 # This is the checkout that introduces venv support for build/run. Otherwise you have to use conda. Eventually this wil be part of main, hopefully. uv sync --extra dev uv pip install -e . source .venv/bin/activate uv pip install qdrant_client LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack build --template ollama --image-type venv ``` ``` edit llama_stack/templates/ollama/run.yaml ``` in that editor under: ``` vector_io: ``` add: ``` - provider_id: qdrant provider_type: remote::qdrant config: {} ``` see https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/vector_io/qdrant/config.py#L14 for config options (but I didn't need any) ``` LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack run ollama --image-type venv \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env SAFETY_MODEL=$SAFETY_MODEL \ --env OLLAMA_URL=$OLLAMA_URL ``` Then I tested it out in a notebook. Key highlights included: ``` qdrant_provider = None for provider in client.providers.list(): if provider.api == "vector_io" and provider.provider_id == "qdrant": qdrant_provider = provider qdrant_provider assert qdrant_provider is not None, "QDrant is not a provider. You need to edit the run yaml file you use in your `llama stack run` call" vector_db_id = f"test-vector-db-{uuid.uuid4().hex}" client.vector_dbs.register( vector_db_id=vector_db_id, embedding_model="all-MiniLM-L6-v2", embedding_dimension=384, provider_id=qdrant_provider.provider_id, ) ``` Other than that, I just followed what was in https://llama-stack.readthedocs.io/en/latest/getting_started/index.html It would be good to have automated tests for this in the future, but that would be a big undertaking. Signed-off-by: Bill Murdock <bmurdock@redhat.com>	2025-02-10 18:08:33 -05:00
Yuan Tang	a79a083e39	Fix broken pgvector provider and memory leaks (#947) ... This PR fixes the broken pgvector provider as well as wraps all cursor object creations with context manager to ensure that they get properly closed to avoid potential memory leaks. ``` > pytest llama_stack/providers/tests/vector_io/test_vector_io.py -m "pgvector" --env EMBEDDING_DIMENSION=384 --env PGVECTOR_PORT=7432 --env PGVECTOR_DB=db --env PGVECTOR_USER=user --env PGVECTOR_PASSWORD=pass -v -s --tb=short --disable-warnings llama_stack/providers/tests/vector_io/test_vector_io.py::TestVectorIO::test_banks_list[-pgvector] PASSED llama_stack/providers/tests/vector_io/test_vector_io.py::TestVectorIO::test_banks_register[-pgvector] PASSED llama_stack/providers/tests/vector_io/test_vector_io.py::TestVectorIO::test_query_documents[-pgvector] The scores are: [0.8168284974053789, 0.8080469278964486, 0.8050996198466661] PASSED ``` --------- Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>	2025-02-05 09:32:05 -08:00
Yuan Tang	83a51c7bfb	Properly close PGVector DB connection during shutdown() (#931) ... The connection to the DB was not closed during shutdown. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>	2025-02-02 21:23:13 -08:00
Yuan Tang	34ab7a3b6c	Fix precommit check after moving to ruff (#927) ... Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>	2025-02-02 06:46:45 -08:00
Ashwin Bharambe	f2feb7d15c	Fix Chroma adapter (#893) ... Chroma method had the wrong signature. ## Test Plan Start Chroma: `chroma run --path /tmp/foo/chroma2 --host localhost --port 6001` Modify run.yaml to include Chroma server pointing to localhost:6001 and run `llama stack run` Then: ```bash LLAMA_STACK_BASE_URL=http://localhost:8321 pytest -s -v agents/test_agents.py -k rag ``` passes	2025-01-28 13:19:47 -08:00
Ashwin Bharambe	c9e5578151	[memory refactor][5/n] Migrate all vector_io providers (#835) ... See https://github.com/meta-llama/llama-stack/issues/827 for the broader design. This PR finishes off all the stragglers and migrates everything to the new naming.	2025-01-22 10:17:59 -08:00
Ashwin Bharambe	3ae8585b65	[memory refactor][1/n] Rename Memory -> VectorIO, MemoryBanks -> VectorDBs (#828) ... See https://github.com/meta-llama/llama-stack/issues/827 for the broader design. This is the first part: - delete other kinds of memory banks (keyvalue, keyword, graph) for now; we will introduce a keyvalue store API as part of this design but not use it in the RAG tool yet. - renaming of the APIs	2025-01-22 09:59:30 -08:00