Merge c71bcd5479 into 2f58d87c22

docs/source/providers/agents/index.md

@@ -4,12 +4,12 @@
 
 Agents API for creating and interacting with agentic systems.
 
-    Main functionalities provided by this API:
+Main functionalities provided by this API:
-    - Create agents with specific instructions and ability to use tools.
+- Create agents with specific instructions and ability to use tools.
-    - Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
+- Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
-    - Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
+- Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
-    - Agents can be provided with various shields (see the Safety API for more details).
+- Agents can be provided with various shields (see the Safety API for more details).
-    - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
+- Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
 
 This section contains documentation for all available providers for the **agents** API.
 

docs/source/providers/batches/index.md

@@ -3,15 +3,15 @@
 ## Overview
 
 The Batches API enables efficient processing of multiple requests in a single operation,
-    particularly useful for processing large datasets, batch evaluation workflows, and
+particularly useful for processing large datasets, batch evaluation workflows, and
-    cost-effective inference at scale.
+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
+ - 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/source/providers/inference/index.md

@@ -4,9 +4,9 @@
 
 Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
+This API provides the raw interface to the underlying models. Two kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
+- LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
+- Embedding models: these models generate embeddings to be used for semantic search.
 
 This section contains documentation for all available providers for the **inference** API.
 

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
@@ -30,6 +31,7 @@ from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
 )
+from llama_stack.providers.utils.vector_io.vector_utils import WeightedInMemoryAggregator
 
 from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
 
@@ -114,7 +116,38 @@ class ChromaIndex(EmbeddingIndex):
         k: int,
         score_threshold: float,
     ) -> QueryChunksResponse:
-        raise NotImplementedError("Keyword search is not supported in Chroma")
+        results = await maybe_await(
+            self.collection.query(
+                query_texts=[query_string],
+                where_document={"$contains": query_string},
+                n_results=k,
+                include=["documents", "distances"],
+            )
+        )
+
+        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):
+            try:
+                doc_data = json.loads(doc)
+                chunk = Chunk(**doc_data)
+            except Exception:
+                log.exception(f"Failed to load chunk: {doc}")
+                continue
+
+            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."""
@@ -130,7 +163,57 @@ 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, VectorDBsProtocolPrivate):

pyproject.toml

@@ -25,8 +25,8 @@ classifiers = [
 ]
 dependencies = [
     "aiohttp",
-    "fastapi>=0.115.0,<1.0",                  # server
+    "fastapi>=0.115.0,<1.0", # server
-    "fire",                                   # for MCP in LLS client
+    "fire", # for MCP in LLS client
     "httpx",
     "huggingface-hub>=0.34.0,<1.0",
     "jinja2>=3.1.6",
@@ -43,12 +43,12 @@ dependencies = [
     "tiktoken",
     "pillow",
     "h11>=0.16.0",
-    "python-multipart>=0.0.20",               # For fastapi Form
+    "python-multipart>=0.0.20", # For fastapi Form
-    "uvicorn>=0.34.0",                        # server
+    "uvicorn>=0.34.0", # server
-    "opentelemetry-sdk>=1.30.0",              # server
+    "opentelemetry-sdk>=1.30.0", # server
     "opentelemetry-exporter-otlp-proto-http>=1.30.0", # server
-    "aiosqlite>=0.21.0",                      # server - for metadata store
+    "aiosqlite>=0.21.0", # server - for metadata store
-    "asyncpg",                                # for metadata store
+    "asyncpg", # for metadata store
 ]
 
 [project.optional-dependencies]

uv.lock

@@ -1767,6 +1767,7 @@ dependencies = [
     { name = "opentelemetry-exporter-otlp-proto-http" },
     { name = "opentelemetry-sdk" },
     { name = "pillow" },
+    { name = "pre-commit" },
     { name = "prompt-toolkit" },
     { name = "pydantic" },
     { name = "python-dotenv" },
@@ -1892,6 +1893,7 @@ requires-dist = [
     { name = "opentelemetry-sdk", specifier = ">=1.30.0" },
     { name = "pandas", marker = "extra == 'ui'" },
     { name = "pillow" },
+    { name = "pre-commit", specifier = ">=4.2.0" },
     { name = "prompt-toolkit" },
     { name = "pydantic", specifier = ">=2.11.9" },
     { name = "python-dotenv" },