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chore: Update vectorstore search to support inline embedding #201

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Apr 22, 2025
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chore: Update vectorstore search to support inline embedding #201

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6 changes: 4 additions & 2 deletions langchain_postgres/v2/async_vectorstore.py
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Original file line number Diff line number Diff line change
Expand Up @@ -561,10 +561,12 @@ async def __query_collection(
inline_embed_func = getattr(self.embedding_service, "embed_query_inline", None)
if not embedding and callable(inline_embed_func) and "query" in kwargs:
query_embedding = self.embedding_service.embed_query_inline(kwargs["query"]) # type: ignore
embedding_data_string = f"{query_embedding}"
else:
query_embedding = f"{[float(dimension) for dimension in embedding]}"
stmt = f"""SELECT {column_names}, {search_function}("{self.embedding_column}", :query_embedding) as distance
FROM "{self.schema_name}"."{self.table_name}" {param_filter} ORDER BY "{self.embedding_column}" {operator} :query_embedding LIMIT :k;
embedding_data_string = ":query_embedding"
stmt = f"""SELECT {column_names}, {search_function}("{self.embedding_column}", {embedding_data_string}) as distance
FROM "{self.schema_name}"."{self.table_name}" {param_filter} ORDER BY "{self.embedding_column}" {operator} {embedding_data_string} LIMIT :k;
"""
param_dict = {"query_embedding": query_embedding, "k": k}
if filter_dict:
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