add query_keyword function

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

@@ -10,7 +10,6 @@ from typing import Any
 from urllib.parse import urlparse
 
 import chromadb
-from chromadb.api.models.AsyncCollection import AsyncCollection
 from numpy.typing import NDArray
 
 from llama_stack.apis.files import Files
@@ -114,13 +113,41 @@ 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 parse document: {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_chunk(self, chunk_id: str) -> None:
-        if isinstance(self.collection, AsyncCollection):
-            await self.collection.delete([chunk_id])
-        else:
-            self.collection.delete([chunk_id])
+        await maybe_await(self.collection.delete([chunk_id]))
 
     async def query_hybrid(
         self,