feat: Implement hybrid search in SQLite-vec

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>

docs/_static/llama-stack-spec.html

@@ -13994,7 +13994,7 @@
                     },
                     "mode": {
                         "type": "string",
-                        "description": "Search mode for retrieval—either \"vector\" or \"keyword\". Default \"vector\"."
+                        "description": "Search mode for retrieval—either \"vector\", \"keyword\", or \"hybrid\". Default \"vector\"."
                     }
                 },
                 "additionalProperties": false,

docs/_static/llama-stack-spec.yaml

@@ -9756,7 +9756,8 @@ components:
         mode:
           type: string
           description: >-
-            Search mode for retrieval—either "vector" or "keyword". Default "vector".
+            Search mode for retrieval—either "vector", "keyword", or "hybrid". Default
+            "vector".
       additionalProperties: false
       required:
         - query_generator_config

docs/source/providers/vector_io/sqlite-vec.md

@@ -66,6 +66,39 @@ To use sqlite-vec in your Llama Stack project, follow these steps:
 2. Configure your Llama Stack project to use SQLite-Vec.
 3. Start storing and querying vectors.
 
+The SQLite-vec provider supports three search modes:
+
+1. **Vector Search** (`mode="vector"`): Performs pure vector similarity search using the embeddings.
+2. **Keyword Search** (`mode="keyword"`): Performs full-text search using SQLite's FTS5.
+3. **Hybrid Search** (`mode="hybrid"`): Combines both vector and keyword search for better results. First performs keyword search to get candidate matches, then applies vector similarity search on those candidates.
+
+Example with hybrid search:
+```python
+response = await vector_io.query_chunks(
+    vector_db_id="my_db",
+    query="your query here",
+    params={"mode": "hybrid", "max_chunks": 3, "score_threshold": 0.7},
+)
+```
+
+Example with explicit vector search:
+```python
+response = await vector_io.query_chunks(
+    vector_db_id="my_db",
+    query="your query here",
+    params={"mode": "vector", "max_chunks": 3, "score_threshold": 0.7},
+)
+```
+
+Example with keyword search:
+```python
+response = await vector_io.query_chunks(
+    vector_db_id="my_db",
+    query="your query here",
+    params={"mode": "keyword", "max_chunks": 3, "score_threshold": 0.7},
+)
+```
+
 ## Supported Search Modes
 
 The sqlite-vec provider supports both vector-based and keyword-based (full-text) search modes.