community: Cassandra Vector Store: extend metadata-related methods (#…

…27078)

**Description:** this PR adds a set of methods to deal with metadata
associated to the vector store entries. These, while essential to the
Graph-related extension of the `Cassandra` vector store, are also useful
in themselves. These are (all come in their sync+async versions):

- `[a]delete_by_metadata_filter`
- `[a]replace_metadata`
- `[a]get_by_document_id`
- `[a]metadata_search`

Additionally, a `[a]similarity_search_with_embedding_id_by_vector`
method is introduced to better serve the store's internal working (esp.
related to reranking logic).

**Issue:** no issue number, but now all Document's returned bear their
`.id` consistently (as a consequence of a slight refactoring in how the
raw entries read from DB are made back into `Document` instances).

**Dependencies:** (no new deps: packaging comes through langchain-core
already; `cassio` is now required to be version 0.1.10+)


**Add tests and docs**
Added integration tests for the relevant newly-introduced methods.
(Docs will be updated in a separate PR).

**Lint and test** Lint and (updated) test all pass.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/langchain_community/vectorstores/cassandra.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import asyncio
+import importlib.metadata
 import typing
 import uuid
 from typing import (
@@ -18,6 +19,7 @@
 )
 
 import numpy as np
+from packaging.version import Version  # this is a lancghain-core dependency
 
 if typing.TYPE_CHECKING:
     from cassandra.cluster import Session
@@ -30,6 +32,7 @@
 from langchain_community.vectorstores.utils import maximal_marginal_relevance
 
 CVST = TypeVar("CVST", bound="Cassandra")
+MIN_CASSIO_VERSION = Version("0.1.10")
 
 
 class Cassandra(VectorStore):
@@ -110,6 +113,15 @@ def __init__(
                 "Could not import cassio python package. "
                 "Please install it with `pip install cassio`."
             )
+        cassio_version = Version(importlib.metadata.version("cassio"))
+
+        if cassio_version is not None and cassio_version < MIN_CASSIO_VERSION:
+            msg = (
+                "Cassio version not supported. Please upgrade cassio "
+                f"to version {MIN_CASSIO_VERSION} or higher."
+            )
+            raise ImportError(msg)
+
         if not table_name:
             raise ValueError("Missing required parameter 'table_name'.")
         self.embedding = embedding
@@ -143,6 +155,9 @@ def __init__(
             **kwargs,
         )
 
+        if self.session is None:
+            self.session = self.table.session
+
     @property
     def embeddings(self) -> Embeddings:
         return self.embedding
@@ -231,6 +246,70 @@ async def adelete(
             await self.adelete_by_document_id(document_id)
         return True
 
+    def delete_by_metadata_filter(
+        self,
+        filter: dict[str, Any],
+        *,
+        batch_size: int = 50,
+    ) -> int:
+        """Delete all documents matching a certain metadata filtering condition.
+
+        This operation does not use the vector embeddings in any way, it simply
+        removes all documents whose metadata match the provided condition.
+
+        Args:
+            filter: Filter on the metadata to apply. The filter cannot be empty.
+            batch_size: amount of deletions per each batch (until exhaustion of
+                the matching documents).
+
+        Returns:
+            A number expressing the amount of deleted documents.
+        """
+        if not filter:
+            msg = (
+                "Method `delete_by_metadata_filter` does not accept an empty "
+                "filter. Use the `clear()` method if you really want to empty "
+                "the vector store."
+            )
+            raise ValueError(msg)
+
+        return self.table.find_and_delete_entries(
+            metadata=filter,
+            batch_size=batch_size,
+        )
+
+    async def adelete_by_metadata_filter(
+        self,
+        filter: dict[str, Any],
+        *,
+        batch_size: int = 50,
+    ) -> int:
+        """Delete all documents matching a certain metadata filtering condition.
+
+        This operation does not use the vector embeddings in any way, it simply
+        removes all documents whose metadata match the provided condition.
+
+        Args:
+            filter: Filter on the metadata to apply. The filter cannot be empty.
+            batch_size: amount of deletions per each batch (until exhaustion of
+                the matching documents).
+
+        Returns:
+            A number expressing the amount of deleted documents.
+        """
+        if not filter:
+            msg = (
+                "Method `delete_by_metadata_filter` does not accept an empty "
+                "filter. Use the `clear()` method if you really want to empty "
+                "the vector store."
+            )
+            raise ValueError(msg)
+
+        return await self.table.afind_and_delete_entries(
+            metadata=filter,
+            batch_size=batch_size,
+        )
+
     def add_texts(
         self,
         texts: Iterable[str],
@@ -333,6 +412,180 @@ async def send_concurrently(
             await asyncio.gather(*tasks)
         return ids
 
+    def replace_metadata(
+        self,
+        id_to_metadata: dict[str, dict],
+        *,
+        batch_size: int = 50,
+    ) -> None:
+        """Replace the metadata of documents.
+
+        For each document to update, identified by its ID, the new metadata
+        dictionary completely replaces what is on the store. This includes
+        passing empty metadata `{}` to erase the currently-stored information.
+
+        Args:
+            id_to_metadata: map from the Document IDs to modify to the
+                new metadata for updating.
+                Keys in this dictionary that do not correspond to an existing
+                document will not cause an error, rather will result in new
+                rows being written into the Cassandra table but without an
+                associated vector: hence unreachable through vector search.
+            batch_size: Number of concurrent requests to send to the server.
+
+        Returns:
+            None if the writes succeed (otherwise an error is raised).
+        """
+        ids_and_metadatas = list(id_to_metadata.items())
+        for i in range(0, len(ids_and_metadatas), batch_size):
+            batch_i_m = ids_and_metadatas[i : i + batch_size]
+            futures = [
+                self.table.put_async(
+                    row_id=doc_id,
+                    metadata=doc_md,
+                )
+                for doc_id, doc_md in batch_i_m
+            ]
+            for future in futures:
+                future.result()
+        return
+
+    async def areplace_metadata(
+        self,
+        id_to_metadata: dict[str, dict],
+        *,
+        concurrency: int = 50,
+    ) -> None:
+        """Replace the metadata of documents.
+
+        For each document to update, identified by its ID, the new metadata
+        dictionary completely replaces what is on the store. This includes
+        passing empty metadata `{}` to erase the currently-stored information.
+
+        Args:
+            id_to_metadata: map from the Document IDs to modify to the
+                new metadata for updating.
+                Keys in this dictionary that do not correspond to an existing
+                document will not cause an error, rather will result in new
+                rows being written into the Cassandra table but without an
+                associated vector: hence unreachable through vector search.
+            concurrency: Number of concurrent queries to the database.
+                Defaults to 50.
+
+        Returns:
+            None if the writes succeed (otherwise an error is raised).
+        """
+        ids_and_metadatas = list(id_to_metadata.items())
+
+        sem = asyncio.Semaphore(concurrency)
+
+        async def send_concurrently(doc_id: str, doc_md: dict) -> None:
+            async with sem:
+                await self.table.aput(
+                    row_id=doc_id,
+                    metadata=doc_md,
+                )
+
+        for doc_id, doc_md in ids_and_metadatas:
+            tasks = [asyncio.create_task(send_concurrently(doc_id, doc_md))]
+            await asyncio.gather(*tasks)
+
+        return
+
+    @staticmethod
+    def _row_to_document(row: Dict[str, Any]) -> Document:
+        return Document(
+            id=row["row_id"],
+            page_content=row["body_blob"],
+            metadata=row["metadata"],
+        )
+
+    def get_by_document_id(self, document_id: str) -> Document | None:
+        """Get by document ID.
+
+        Args:
+            document_id: the document ID to get.
+        """
+        row = self.table.get(row_id=document_id)
+        if row is None:
+            return None
+        return self._row_to_document(row=row)
+
+    async def aget_by_document_id(self, document_id: str) -> Document | None:
+        """Get by document ID.
+
+        Args:
+            document_id: the document ID to get.
+        """
+        row = await self.table.aget(row_id=document_id)
+        if row is None:
+            return None
+        return self._row_to_document(row=row)
+
+    def metadata_search(
+        self,
+        metadata: dict[str, Any] = {},  # noqa: B006
+        n: int = 5,
+    ) -> Iterable[Document]:
+        """Get documents via a metadata search.
+
+        Args:
+            metadata: the metadata to query for.
+        """
+        rows = self.table.find_entries(metadata=metadata, n=n)
+        return [self._row_to_document(row=row) for row in rows if row]
+
+    async def ametadata_search(
+        self,
+        metadata: dict[str, Any] = {},  # noqa: B006
+        n: int = 5,
+    ) -> Iterable[Document]:
+        """Get documents via a metadata search.
+
+        Args:
+            metadata: the metadata to query for.
+        """
+        rows = await self.table.afind_entries(metadata=metadata, n=n)
+        return [self._row_to_document(row=row) for row in rows]
+
+    async def asimilarity_search_with_embedding_id_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        filter: Optional[Dict[str, str]] = None,
+        body_search: Optional[Union[str, List[str]]] = None,
+    ) -> List[Tuple[Document, List[float], str]]:
+        """Return docs most similar to embedding vector.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            filter: Filter on the metadata to apply.
+            body_search: Document textual search terms to apply.
+                Only supported by Astra DB at the moment.
+        Returns:
+            List of (Document, embedding, id), the most similar to the query vector.
+        """
+        kwargs: Dict[str, Any] = {}
+        if filter is not None:
+            kwargs["metadata"] = filter
+        if body_search is not None:
+            kwargs["body_search"] = body_search
+
+        hits = await self.table.aann_search(
+            vector=embedding,
+            n=k,
+            **kwargs,
+        )
+        return [
+            (
+                self._row_to_document(row=hit),
+                hit["vector"],
+                hit["row_id"],
+            )
+            for hit in hits
+        ]
+
     @staticmethod
     def _search_to_documents(
         hits: Iterable[Dict[str, Any]],
@@ -341,10 +594,7 @@ def _search_to_documents(
         # (1=most relevant), as required by this class' contract.
         return [
             (
-                Document(
-                    page_content=hit["body_blob"],
-                    metadata=hit["metadata"],
-                ),
+                Cassandra._row_to_document(row=hit),
                 0.5 + 0.5 * hit["distance"],
                 hit["row_id"],
             )
@@ -375,7 +625,6 @@ def similarity_search_with_score_id_by_vector(
             kwargs["metadata"] = filter
         if body_search is not None:
             kwargs["body_search"] = body_search
-
         hits = self.table.metric_ann_search(
             vector=embedding,
             n=k,
@@ -712,13 +961,7 @@ def _mmr_search_to_documents(
             for pf_index, pf_hit in enumerate(prefetch_hits)
             if pf_index in mmr_chosen_indices
         ]
-        return [
-            Document(
-                page_content=hit["body_blob"],
-                metadata=hit["metadata"],
-            )
-            for hit in mmr_hits
-        ]
+        return [Cassandra._row_to_document(row=hit) for hit in mmr_hits]
 
     def max_marginal_relevance_search_by_vector(
         self,