Merge 011f4b0d1e into 4237eb4aaa

docs/docs/providers/agents/index.mdx

@@ -13,6 +13,6 @@ title: Agents
 
 Agents
 
-    APIs for creating and interacting with agentic systems.
+APIs for creating and interacting with agentic systems.
 
 This section contains documentation for all available providers for the **agents** API.

docs/docs/providers/batches/index.mdx

@@ -19,14 +19,14 @@ title: Batches
 ## Overview
 
 The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+particularly useful for processing large datasets, batch evaluation workflows, and
+cost-effective inference at scale.
 
-    The API is designed to allow use of openai client libraries for seamless integration.
+The API is designed to allow use of openai client libraries for seamless integration.
 
-    This API provides the following extensions:
+This API provides the following extensions:
  - idempotent batch creation
 
-    Note: This API is currently under active development and may undergo changes.
+Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.

docs/docs/providers/eval/index.mdx

@@ -13,6 +13,6 @@ title: Eval
 
 Evaluations
 
-    Llama Stack Evaluation API for running evaluations on model and agent candidates.
+Llama Stack Evaluation API for running evaluations on model and agent candidates.
 
 This section contains documentation for all available providers for the **eval** API.

docs/docs/providers/files/index.mdx

@@ -13,6 +13,6 @@ title: Files
 
 Files
 
-    This API is used to upload documents that can be used with other Llama Stack APIs.
+This API is used to upload documents that can be used with other Llama Stack APIs.
 
 This section contains documentation for all available providers for the **files** API.

docs/docs/providers/inference/index.mdx

@@ -18,11 +18,11 @@ title: Inference
 
 Inference
 
-    Llama Stack Inference API for generating completions, chat completions, and embeddings.
+Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Three kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
-    - Rerank models: these models reorder the documents based on their relevance to a query.
+This API provides the raw interface to the underlying models. Three kinds of models are supported:
+- LLM models: these models generate "raw" and "chat" (conversational) completions.
+- Embedding models: these models generate embeddings to be used for semantic search.
+- Rerank models: these models reorder the documents based on their relevance to a query.
 
 This section contains documentation for all available providers for the **inference** API.

docs/docs/providers/safety/index.mdx

@@ -13,6 +13,6 @@ title: Safety
 
 Safety
 
-    OpenAI-compatible Moderations API.
+OpenAI-compatible Moderations API.
 
 This section contains documentation for all available providers for the **safety** API.

src/llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 import asyncio
+import heapq
 import json
 from typing import Any
 from urllib.parse import urlparse
@@ -16,6 +17,7 @@ from llama_stack.log import get_logger
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
 from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import ChunkForDeletion, EmbeddingIndex, VectorStoreWithIndex
+from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator
 from llama_stack_api import (
     Chunk,
     Files,
@@ -99,8 +101,55 @@ class ChromaIndex(EmbeddingIndex):
     async def delete(self):
         await maybe_await(self.client.delete_collection(self.collection.name))
 
-    async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
-        raise NotImplementedError("Keyword search is not supported in Chroma")
+    async def query_keyword(
+        self,
+        query_string: str,
+        k: int,
+        score_threshold: float,
+    ) -> QueryChunksResponse:
+        """
+        Perform keyword search using Chroma's built-in where_document feature.
+
+        Args:
+            query_string: The text query for keyword search
+            k: Number of results to return
+            score_threshold: Minimum similarity score threshold
+
+        Returns:
+            QueryChunksResponse with combined results
+        """
+        try:
+            results = await maybe_await(
+                self.collection.query(
+                    query_texts=[query_string],
+                    where_document={"$contains": query_string},
+                    n_results=k,
+                    include=["documents", "distances"],
+                )
+            )
+        except Exception as e:
+            log.error(f"Chroma client keyword search failed: {e}")
+            raise
+
+        distances = results["distances"][0] if results["distances"] else []
+        documents = results["documents"][0] if results["documents"] else []
+
+        chunks = []
+        scores = []
+
+        for dist, doc in zip(distances, documents, strict=False):
+            doc_data = json.loads(doc)
+            chunk = Chunk(**doc_data)
+
+            score = 1.0 / (1.0 + float(dist)) if dist is not None else 1.0
+
+            if score < score_threshold:
+                continue
+
+            chunks.append(chunk)
+            scores.append(score)
+
+        return QueryChunksResponse(chunks=chunks, scores=scores)
 
     async def delete_chunks(self, chunks_for_deletion: list[ChunkForDeletion]) -> None:
         """Delete a single chunk from the Chroma collection by its ID."""
@@ -116,7 +165,57 @@ class ChromaIndex(EmbeddingIndex):
         reranker_type: str,
         reranker_params: dict[str, Any] | None = None,
     ) -> QueryChunksResponse:
-        raise NotImplementedError("Hybrid search is not supported in Chroma")
+        """
+        Hybrid search combining vector similarity and keyword search using configurable reranking.
+        Args:
+            embedding: The query embedding vector
+            query_string: The text query for keyword search
+            k: Number of results to return
+            score_threshold: Minimum similarity score threshold
+            reranker_type: Type of reranker to use ("rrf" or "weighted")
+            reranker_params: Parameters for the reranker
+        Returns:
+            QueryChunksResponse with combined results
+        """
+        if reranker_params is None:
+            reranker_params = {}
+
+        # Get results from both search methods
+        vector_response = await self.query_vector(embedding, k, score_threshold)
+        keyword_response = await self.query_keyword(query_string, k, score_threshold)
+
+        # Convert responses to score dictionaries using chunk_id
+        vector_scores = {
+            chunk.chunk_id: score for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False)
+        }
+        keyword_scores = {
+            chunk.chunk_id: score
+            for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False)
+        }
+
+        # Combine scores using the reranking utility
+        combined_scores = WeightedInMemoryAggregator.combine_search_results(
+            vector_scores, keyword_scores, reranker_type, reranker_params
+        )
+
+        # Efficient top-k selection because it only tracks the k best candidates it's seen so far
+        top_k_items = heapq.nlargest(k, combined_scores.items(), key=lambda x: x[1])
+
+        # Filter by score threshold
+        filtered_items = [(doc_id, score) for doc_id, score in top_k_items if score >= score_threshold]
+
+        # Create a map of chunk_id to chunk for both responses
+        chunk_map = {c.chunk_id: c for c in vector_response.chunks + keyword_response.chunks}
+
+        # Use the map to look up chunks by their IDs
+        chunks = []
+        scores = []
+        for doc_id, score in filtered_items:
+            if doc_id in chunk_map:
+                chunks.append(chunk_map[doc_id])
+                scores.append(score)
+
+        return QueryChunksResponse(chunks=chunks, scores=scores)
 
 
 class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtocolPrivate):

src/llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -92,6 +92,13 @@ class OpenAIVectorStoreMixin(ABC):
         self.kvstore = kvstore
         self._last_file_batch_cleanup_time = 0
         self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
+        self._vector_store_locks: dict[str, asyncio.Lock] = {}
+
+    def _get_vector_store_lock(self, vector_store_id: str) -> asyncio.Lock:
+        """Get or create a lock for a specific vector store."""
+        if vector_store_id not in self._vector_store_locks:
+            self._vector_store_locks[vector_store_id] = asyncio.Lock()
+        return self._vector_store_locks[vector_store_id]
 
     async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
         """Save vector store metadata to persistent storage."""
@@ -831,7 +838,12 @@ class OpenAIVectorStoreMixin(ABC):
         await self._save_openai_vector_store_file(vector_store_id, file_id, file_info, dict_chunks)
 
         # Update file_ids and file_counts in vector store metadata
+        # Use lock to prevent race condition when multiple files are attached concurrently
+        async with self._get_vector_store_lock(vector_store_id):
             store_info = self.openai_vector_stores[vector_store_id].copy()
+            # Deep copy file_counts to avoid mutating shared dict
+            store_info["file_counts"] = store_info["file_counts"].copy()
+            store_info["file_ids"] = store_info["file_ids"].copy()
             store_info["file_ids"].append(file_id)
             store_info["file_counts"]["total"] += 1
             store_info["file_counts"][vector_store_file_object.status] += 1
@@ -839,9 +851,6 @@ class OpenAIVectorStoreMixin(ABC):
             # Save updated vector store to persistent storage
             await self._save_openai_vector_store(vector_store_id, store_info)
 
-        # Update vector store in-memory cache
-        self.openai_vector_stores[vector_store_id] = store_info
-
         return vector_store_file_object
 
     async def openai_list_files_in_vector_store(

tests/integration/vector_io/test_openai_vector_stores.py

@@ -61,6 +61,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
             "remote::milvus",
             "remote::pgvector",
             "remote::weaviate",
+            "remote::chromadb",
         ],
         "hybrid": [
             "inline::milvus",
@@ -68,6 +69,7 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
             "remote::milvus",
             "remote::pgvector",
             "remote::weaviate",
+            "remote::chromadb",
         ],
     }
     supported_providers = search_mode_support.get(search_mode, [])