Add Cassandra vector store implementation

dictionary.txt

@@ -63,6 +63,7 @@ numpy
 pypi
 nbformat
 semversioner
+cassio
 
 # Library Methods
 iterrows

graphrag/index/verbs/text/embed/text_embed.py

@@ -75,7 +75,7 @@ async def text_embed(
             max_tokens: !ENV ${GRAPHRAG_MAX_TOKENS:6000} # The max tokens to use for openai
             organization: !ENV ${GRAPHRAG_OPENAI_ORGANIZATION} # The organization to use for openai
         vector_store: # The optional configuration for the vector store
-            type: lancedb # The type of vector store to use, available options are: azure_ai_search, lancedb
+            type: lancedb # The type of vector store to use, available options are: azure_ai_search, lancedb, cassandra
             <...>
     ```
     """

graphrag/vector_stores/__init__.py

@@ -5,12 +5,14 @@
 
 from .azure_ai_search import AzureAISearch
 from .base import BaseVectorStore, VectorStoreDocument, VectorStoreSearchResult
+from .cassandra import CassandraVectorStore
 from .lancedb import LanceDBVectorStore
 from .typing import VectorStoreFactory, VectorStoreType
 
 __all__ = [
     "AzureAISearch",
     "BaseVectorStore",
+    "CassandraVectorStore",
     "LanceDBVectorStore",
     "VectorStoreDocument",
     "VectorStoreFactory",

graphrag/vector_stores/cassandra.py

@@ -0,0 +1,122 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""The Apache Cassandra vector store implementation package."""
+
+from typing import Any
+
+import cassio
+from cassio.table import MetadataVectorCassandraTable
+from typing_extensions import override
+
+from graphrag.model.types import TextEmbedder
+
+from .base import (
+    DEFAULT_VECTOR_SIZE,
+    BaseVectorStore,
+    VectorStoreDocument,
+    VectorStoreSearchResult,
+)
+
+
+class CassandraVectorStore(BaseVectorStore):
+    """The Apache Cassandra vector storage implementation."""
+
+    def __init__(
+        self,
+        collection_name: str,
+        token: str | None = None,
+        database_id: str | None = None,
+        keyspace: str | None = None,
+        **kwargs: Any,
+    ):
+        super().__init__(collection_name)
+        cassio.init(
+            token=token,
+            database_id=database_id,
+            keyspace=keyspace,
+            **kwargs,
+        )
+
+    @override
+    def connect(self, keyspace: str | None = None, **kwargs: Any) -> None:
+        self.db_connection = cassio.config.resolve_session()
+        self.keyspace = cassio.config.resolve_keyspace(keyspace)
+
+    @override
+    def load_documents(
+        self, documents: list[VectorStoreDocument], overwrite: bool = True
+    ) -> None:
+        if overwrite:
+            self.db_connection.execute(
+                f"DROP TABLE IF EXISTS {self.keyspace}.{self.collection_name};"
+            )
+
+        if not documents:
+            return
+
+        if not self.document_collection or overwrite:
+            dimension = DEFAULT_VECTOR_SIZE
+            for doc in documents:
+                if doc.vector:
+                    dimension = len(doc.vector)
+                    break
+            self.document_collection = MetadataVectorCassandraTable(
+                table=self.collection_name,
+                vector_dimension=dimension,
+                primary_key_type="TEXT",
+            )
+
+        futures = [
+            self.document_collection.put_async(
+                row_id=doc.id,
+                body_blob=doc.text,
+                vector=doc.vector,
+                metadata=doc.attributes,
+            )
+            for doc in documents
+            if doc.vector
+        ]
+
+        for future in futures:
+            future.result()
+
+    @override
+    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
+        msg = "Cassandra vector store doesn't support filtering by IDs."
+        raise NotImplementedError(msg)
+
+    @override
+    def similarity_search_by_vector(
+        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+    ) -> list[VectorStoreSearchResult]:
+        response = self.document_collection.metric_ann_search(
+            vector=query_embedding,
+            n=k,
+            metric="cos",
+            **kwargs,
+        )
+
+        return [
+            VectorStoreSearchResult(
+                document=VectorStoreDocument(
+                    id=doc["row_id"],
+                    text=doc["body_blob"],
+                    vector=doc["vector"],
+                    attributes=doc["metadata"],
+                ),
+                score=doc["distance"],
+            )
+            for doc in response
+        ]
+
+    @override
+    def similarity_search_by_text(
+        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+    ) -> list[VectorStoreSearchResult]:
+        query_embedding = text_embedder(text)
+        if query_embedding:
+            return self.similarity_search_by_vector(
+                query_embedding=query_embedding, k=k, **kwargs
+            )
+        return []

graphrag/vector_stores/typing.py

@@ -6,6 +6,7 @@
 from enum import Enum
 from typing import ClassVar
 
+from . import BaseVectorStore, CassandraVectorStore
 from .azure_ai_search import AzureAISearch
 from .lancedb import LanceDBVectorStore
 
@@ -15,6 +16,7 @@ class VectorStoreType(str, Enum):
 
     LanceDB = "lancedb"
     AzureAISearch = "azure_ai_search"
+    Cassandra = "cassandra"
 
 
 class VectorStoreFactory:
@@ -30,13 +32,15 @@ def register(cls, vector_store_type: str, vector_store: type):
     @classmethod
     def get_vector_store(
         cls, vector_store_type: VectorStoreType | str, kwargs: dict
-    ) -> LanceDBVectorStore | AzureAISearch:
+    ) -> BaseVectorStore:
         """Get the vector store type from a string."""
         match vector_store_type:
             case VectorStoreType.LanceDB:
                 return LanceDBVectorStore(**kwargs)
             case VectorStoreType.AzureAISearch:
                 return AzureAISearch(**kwargs)
+            case VectorStoreType.Cassandra:
+                return CassandraVectorStore(**kwargs)
             case _:
                 if vector_store_type in cls.vector_store_types:
                     return cls.vector_store_types[vector_store_type](**kwargs)

pyproject.toml

@@ -50,6 +50,7 @@ datashaper = "^0.0.49"
 azure-search-documents = "^11.4.0"
 lancedb = "^0.13.0"
 
+
 # Async IO
 aiolimiter = "^1.1.0"
 aiofiles = "^24.1.0"
@@ -87,6 +88,7 @@ azure-identity = "^1.17.1"
 json-repair = "^0.28.4"
 
 future = "^1.0.0" # Needed until graspologic fixes their dependency
+cassio = "^0.1.9"
 
 [tool.poetry.group.dev.dependencies]
 coverage = "^7.6.0"