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251 | class FirestoreVectorStore(BasePydanticVectorStore):
"""Firestore Vector Store."""
stores_text: bool = True
flat_metadata: bool = True
collection_name: str
batch_size: Optional[int] = DEFAULT_BATCH_SIZE
embedding_key: str = "embedding"
text_key: str = "text"
metadata_key: str = "metadata"
distance_strategy: DistanceMeasure = DistanceMeasure.COSINE
_client: Client
def __init__(
self,
client: Optional[Client] = None,
**kwargs: Any,
) -> None:
"""Initialize params."""
super().__init__(**kwargs)
object.__setattr__(self, "_client", client_with_user_agent(client))
@classmethod
def class_name(cls) -> str:
return "FirestoreVectorStore"
@property
def client(self) -> Any:
return self._client
def add(
self,
nodes: List[BaseNode],
) -> List[str]:
"""Add nodes to vector store."""
ids = []
entries = []
for node in nodes:
node_id = node.node_id
metadata = node_to_metadata_dict(
node,
remove_text=not self.stores_text,
flat_metadata=self.flat_metadata,
)
entry = {
self.embedding_key: node.get_embedding(),
self.metadata_key: metadata,
}
ids.append(node_id)
entries.append(entry)
self._upsert_batch(entries, ids)
return ids
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""Delete nodes using with ref_doc_id."""
docs = (
self._client.collection(self.collection_name)
.where("metadata.ref_doc_id", "==", ref_doc_id)
.stream()
)
self._delete_batch([doc.id for doc in docs])
def query(self, query: VectorStoreQuery, **kwargs: Any) -> VectorStoreQueryResult:
"""Query vector store."""
if query.query_embedding is None:
raise ValueError("Query embedding is required.")
filters = _to_firestore_filter(query.filters) if query.filters else None
results = self._similarity_search(
query.query_embedding, query.similarity_top_k, filters=filters, **kwargs
)
top_k_ids = []
top_k_nodes = []
top_k_similarities = []
LOGGER.debug(f"Found {len(results)} results.")
for result in results:
# Convert the Firestore document to dict
result_dict = result.to_dict() or {}
metadata = result_dict.get(self.metadata_key) or {}
fir_vec: Optional[Vector] = result_dict.get(self.embedding_key)
if fir_vec is None:
raise ValueError(
"Embedding is missing in Firestore document.", result.id
)
embedding = list(fir_vec.to_map_value()["value"])
# Convert metadata to node, and add text if available
node = metadata_dict_to_node(metadata, text=result_dict.get(self.text_key))
# Keep track of the top k ids and nodes
top_k_ids.append(result.id)
top_k_nodes.append(node)
top_k_similarities.append(
similarity(
query.query_embedding,
embedding,
self._distance_to_similarity_mode(self.distance_strategy),
)
)
return VectorStoreQueryResult(
nodes=top_k_nodes, ids=top_k_ids, similarities=top_k_similarities
)
def _distance_to_similarity_mode(self, distance: DistanceMeasure) -> SimilarityMode:
"""Convert Firestore's distance measure to similarity mode."""
return {
DistanceMeasure.COSINE: SimilarityMode.DEFAULT,
DistanceMeasure.EUCLIDEAN: SimilarityMode.EUCLIDEAN,
DistanceMeasure.DOT_PRODUCT: SimilarityMode.DOT_PRODUCT,
}.get(distance, SimilarityMode.DEFAULT)
def _delete_batch(self, ids: List[str]) -> None:
"""Delete batch of vectors from Firestore."""
db_batch = self._client.batch()
for batch in more_itertools.chunked(ids, DEFAULT_BATCH_SIZE):
for doc_id in batch:
doc = self._client.collection(self.collection_name).document(doc_id)
db_batch.delete(doc)
db_batch.commit()
def _upsert_batch(self, entries: List[dict], ids: Optional[List[str]]) -> None:
"""Upsert batch of vectors to Firestore."""
if ids and len(ids) != len(entries):
raise ValueError("Length of ids and entries should be the same.")
db_batch = self._client.batch()
for batch in more_itertools.chunked(entries, DEFAULT_BATCH_SIZE):
for i, entry in enumerate(batch):
# Convert the embedding array to a Firestore Vector
entry[self.embedding_key] = Vector(entry[self.embedding_key])
doc = self._client.collection(self.collection_name).document(
ids[i] if ids else None
)
db_batch.set(doc, entry, merge=True)
db_batch.commit()
def _similarity_search(
self,
query: List[float],
k: int,
filters: Union[BaseFilter, BaseCompositeFilter, None] = None,
) -> List[DocumentSnapshot]:
wfilters = None
collection = self._client.collection(self.collection_name)
if filters:
wfilters = collection.where(filter=filters)
results = (wfilters or collection).find_nearest(
vector_field=self.embedding_key,
query_vector=Vector(query),
distance_measure=self.distance_strategy,
limit=k,
)
return results.get()
|
