PGVector - Retrieving all documents with metadata filter

[sunnstix]

Hi, I am building a service where I follow along with traditional use cases of PGVector including population of the database using vector_store.add_documents and common retrieval methods including vector_store.similarity_search. However, my application also requires me to be able to retrieve all of the documents in the vectorstore annotated with particular metadata and I cannot find documentation on how to perform retrieval without using some form of vector search.

Example:

embeddings = OpenAIEmbeddings()
vector_store = PGVector(
    embeddings=embeddings,
    collection_name=PG_COLLECTION,
    connection=PG_CONN_STR,
)
vector_store.add_documents(
    documents=[
        Document(page_content="Content of File A - Part 1", metadata={"id": "file_a"}),
        Document(page_content="Content of File A - Part 2", metadata={"id": "file_a"}),
        Document(page_content="Content of File B - Part 1", metadata={"id": "file_b"}),
        Document(page_content="Content of File B - Part 2", metadata={"id": "file_b"}),
    ]
)

# Now I want to retrieve all of the documents that have id="file_b" in their metadata
# However, all of the documented retrieval functions require a query
vector_store.as_retriever(search_kwargs={"filter": {"id": "file_b"}}).invoke("")

Is there a way to perform retrieval with PGVecttor using just a metadata query?

[dosubot[bot]]

Hey @sunnstix! I'm here to help you with any bugs, questions, or contributions you have. Let's tackle this together!

Yes, you can perform retrieval with PGVector using just a metadata query without using vector search. The vectorstore supports a variety of filters that can be applied against the metadata fields of the documents. These filters include operators for equality, inequality, comparison, and logical operations, such as $eq, $ne, $lt, $lte, $gt, $gte, $in, $nin, $between, $like, $ilike, $and, and $or.

For example, you can use a metadata filter to retrieve documents based on specific criteria without relying on vector similarity:

results = vector_store.similarity_search(
    "", k=10, filter={"id": {"$eq": "file_b"}}
)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

In this example, the filter parameter is used to specify a metadata query, allowing retrieval based on metadata. This approach enables you to perform retrieval using metadata queries alone, without the need for vector-based similarity searches ^[1][2].

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[sunnstix]

@dosu Are you confident in the behavior that providing an empty query string "" will result in all results being found?

[dosubot[bot]]

The behavior of providing an empty query string "" in the similarity_search method is not explicitly documented in the LangChain codebase. However, based on the test cases and examples available, it appears that using an empty query string with a metadata filter should allow you to retrieve documents based solely on the metadata criteria specified in the filter. This means that the query string itself does not contribute to the search results, and the filtering is done entirely based on the metadata ^[1][2].

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[sunnstix]

@dosu Instead of getting the documents, how can I get the postgres ids?

[dosubot[bot]]

To retrieve the PostgreSQL IDs instead of the documents when using PGVector, you can modify the similarity_search_with_score_by_vector method to return the custom_id field from the EmbeddingStore instead of the document content. Here's how you can adjust the query to achieve this:

def similarity_search_with_score_by_vector(
    self,
    embedding: List[float],
    k: int = 4,
    filter: Optional[dict] = None,
) -> List[Tuple[str, float]]:  # Change return type to Tuple[str, float]
    with Session(self._conn) as session:
        collection = self.get_collection(session)
        set_enable_seqscan_stmt = sqlalchemy.text("SET enable_seqscan = off")
        session.execute(set_enable_seqscan_stmt)
        if not collection:
            raise ValueError("Collection not found")

        filter_by = EmbeddingStore.collection_id == collection.uuid

        if filter is not None:
            filter_clauses = []
            for key, value in filter.items():
                IN = "in"
                if isinstance(value, dict) and IN in map(str.lower, value):
                    value_case_insensitive = {
                        k.lower(): v for k, v in value.items()
                    }
                    filter_by_metadata = EmbeddingStore.cmetadata[key].astext.in_(
                        value_case_insensitive[IN]
                    )
                    filter_clauses.append(filter_by_metadata)
                elif isinstance(value, dict) and "substring" in map(
                    str.lower, value
                ):
                    filter_by_metadata = EmbeddingStore.cmetadata[key].astext.ilike(
                        f"%{value['substring']}%"
                    )
                    filter_clauses.append(filter_by_metadata)
                else:
                    filter_by_metadata = EmbeddingStore.cmetadata[
                        key
                    ].astext == str(value)
                    filter_clauses.append(filter_by_metadata)

            filter_by = sqlalchemy.and_(filter_by, *filter_clauses)

        results: List[QueryResult] = (
            session.query(
                EmbeddingStore.custom_id,  # Select custom_id instead of document
                func.abs(EmbeddingStore.embedding.op("<->")(embedding)).label(
                    "distance"
                ),
            )
            .filter(filter_by)
            .order_by(
                func.abs(EmbeddingStore.embedding.op("<->")(embedding)).asc()
            )
            .limit(k)
            .all()
        )

    ids_and_scores = [
        (result.custom_id, result.distance)  # Return custom_id and distance
        for result in results
    ]
    return ids_and_scores

This modification will return a list of tuples containing the custom_id and the distance score, allowing you to retrieve the PostgreSQL IDs instead of the documents ^[1].

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[sunnstix]

@dosu Your answer about using an empty string was incorrect. Are there any other ways to retrieves all documents matching a metadata filter using PGVector?

[dosubot[bot]]

To retrieve all documents matching a metadata filter using PGVector without relying on an empty query string, you can use the filter parameter in the similarity_search_with_score method. This allows you to specify metadata conditions directly. Here's an example of how you can achieve this:

results = vector_store.similarity_search_with_score(
    query="",  # The query string can be empty or omitted
    k=100,  # Set k to a large number to retrieve all matching documents
    filter={"id": {"$eq": "file_b"}}  # Use the filter to specify metadata criteria
)

for doc, score in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

This approach uses the filter parameter to retrieve documents based on metadata conditions, effectively bypassing the need for a query string. The key is to set k to a sufficiently large number to ensure all matching documents are retrieved ^[1].

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[sunnstix]

@dosu Doing so raises ValueError: All texts must be non-empty and cannot be just whitespace. Providing an empty string for the query parameter is not valid. Evaluate other methods to do this

[dosubot[bot]]

To retrieve all documents matching a metadata filter using PGVector without providing an empty string for the query parameter, you can use the similarity_search_with_score method with a filter. This method allows you to specify a filter directly without needing to provide a query string. Here's an example:

def retrieve_documents_with_metadata_filter(pgvector: PGVector, filter: Dict[str, Any]) -> List[Document]:
    """Retrieve documents matching a metadata filter."""
    # Use similarity_search_with_score with a filter and an arbitrary query
    output = pgvector.similarity_search_with_score("arbitrary_query", k=5, filter=filter)
    return [doc for doc, score in output]

# Example usage
pgvector_instance = PGVector.from_texts(
    texts=["foo", "bar", "baz"],
    collection_name="test_collection_filter",
    embedding=FakeEmbeddingsWithAdaDimension(),
    connection_string=CONNECTION_STRING,
    pre_delete_collection=True,
)

# Define your metadata filter
metadata_filter = {"page": "0"}

# Retrieve documents
documents = retrieve_documents_with_metadata_filter(pgvector_instance, metadata_filter)

In this example, the "arbitrary_query" is used as a placeholder since the actual query string is not needed when using a filter. The filter parameter is used to specify the metadata criteria for retrieving documents ^[1].

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[sunnstix]

This makes sense. The query doesn't matter because it retrieves k documents regardless