fix(chromadb): queries without embeddings result in program crash (#1…

…1328)

llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/llama_index/vector_stores/chroma/base.py

@@ -291,13 +291,26 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
         else:
             where = kwargs.pop("where", {})
 
-        results = self._collection.query(
+        if not query.query_embedding:
+            return self._get(limit=query.similarity_top_k, where=where, **kwargs)
+
+        return self._query(
             query_embeddings=query.query_embedding,
             n_results=query.similarity_top_k,
             where=where,
             **kwargs,
         )
 
+    def _query(
+        self, query_embeddings: List["float"], n_results: int, where: dict, **kwargs
+    ) -> VectorStoreQueryResult:
+        results = self._collection.query(
+            query_embeddings=query_embeddings,
+            n_results=n_results,
+            where=where,
+            **kwargs,
+        )
+
         logger.debug(f"> Top {len(results['documents'])} nodes:")
         nodes = []
         similarities = []
@@ -338,3 +351,48 @@ def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResul
             ids.append(node_id)
 
         return VectorStoreQueryResult(nodes=nodes, similarities=similarities, ids=ids)
+
+    def _get(self, limit: int, where: dict, **kwargs) -> VectorStoreQueryResult:
+        results = self._collection.get(
+            limit=limit,
+            where=where,
+            **kwargs,
+        )
+
+        logger.debug(f"> Top {len(results['documents'])} nodes:")
+        nodes = []
+        ids = []
+
+        if not results["ids"]:
+            results["ids"] = [[]]
+
+        for node_id, text, metadata in zip(
+            results["ids"][0], results["documents"], results["metadatas"]
+        ):
+            try:
+                node = metadata_dict_to_node(metadata)
+                node.set_content(text)
+            except Exception:
+                # NOTE: deprecated legacy logic for backward compatibility
+                metadata, node_info, relationships = legacy_metadata_dict_to_node(
+                    metadata
+                )
+
+                node = TextNode(
+                    text=text,
+                    id_=node_id,
+                    metadata=metadata,
+                    start_char_idx=node_info.get("start", None),
+                    end_char_idx=node_info.get("end", None),
+                    relationships=relationships,
+                )
+
+            nodes.append(node)
+
+            logger.debug(
+                f"> [Node {node_id}] [Similarity score: N/A - using get()] "
+                f"{truncate_text(str(text), 100)}"
+            )
+            ids.append(node_id)
+
+        return VectorStoreQueryResult(nodes=nodes, ids=ids)

llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/test_chromadb.py

@@ -4,7 +4,13 @@
 import pytest
 from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
 from llama_index.vector_stores.chroma import ChromaVectorStore
-from llama_index.core.vector_stores.types import VectorStoreQuery
+from llama_index.core.vector_stores.types import (
+    VectorStoreQuery,
+    MetadataFilter,
+    MetadataFilters,
+    FilterOperator,
+    FilterCondition,
+)
 
 ##
 # Run tests
@@ -159,3 +165,34 @@ async def test_add_to_chromadb_and_query(
         )
     assert res.nodes
     assert res.nodes[0].get_content() == "lorem ipsum"
+
+
+@pytest.mark.asyncio()
+@pytest.mark.parametrize("use_async", [True, False])
+async def test_add_to_chromadb_and_query_by_metafilters_only(
+    vector_store: ChromaVectorStore,
+    node_embeddings: List[TextNode],
+    use_async: bool,
+) -> None:
+    filters = MetadataFilters(
+        filters=[
+            MetadataFilter(
+                key="author", value="Marie Curie", operator=FilterOperator.EQ
+            )
+        ],
+        condition=FilterCondition.AND,
+    )
+
+    if use_async:
+        await vector_store.async_add(node_embeddings)
+        res = await vector_store.aquery(
+            VectorStoreQuery(filters=filters, similarity_top_k=1)
+        )
+    else:
+        vector_store.add(node_embeddings)
+        res = vector_store.query(VectorStoreQuery(filters=filters, similarity_top_k=1))
+
+    assert (
+        res.nodes[0].get_content()
+        == "I was taught that the way of progress was neither swift nor easy."
+    )