feat: add falkordb adapter

cognee/infrastructure/databases/graph/neo4j_driver/adapter.py

@@ -7,7 +7,6 @@ from contextlib import asynccontextmanager
 from neo4j import AsyncSession
 from neo4j import AsyncGraphDatabase
 from neo4j.exceptions import Neo4jError
-from networkx import predecessor
 from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface
 
 logger = logging.getLogger("Neo4jAdapter")

cognee/infrastructure/databases/vector/falkordb/FalkorDBAdapter.py

@@ -1,57 +1,113 @@
-
+import asyncio
-from typing import List, Dict, Optional, Any
-
 from falkordb import FalkorDB
-from qdrant_client import AsyncQdrantClient, models
-from ..vector_db_interface import VectorDBInterface
 from ..models.DataPoint import DataPoint
+from ..vector_db_interface import VectorDBInterface
 from ..embeddings.EmbeddingEngine import EmbeddingEngine
 
 
-
-
 class FalcorDBAdapter(VectorDBInterface):
     def __init__(
         self,
         graph_database_url: str,
-        graph_database_username: str,
-        graph_database_password: str,
         graph_database_port: int,
-        driver: Optional[Any] = None,
         embedding_engine =  EmbeddingEngine,
-        graph_name: str = "DefaultGraph",
     ):
         self.driver = FalkorDB(
             host = graph_database_url,
             port = graph_database_port)
-        self.graph_name = graph_name
         self.embedding_engine = embedding_engine
 
 
-
     async def embed_data(self, data: list[str]) -> list[list[float]]:
         return await self.embedding_engine.embed_text(data)
 
+    async def has_collection(self, collection_name: str) -> bool:
+        collections = self.driver.list_graphs()
+
+        return collection_name in collections
 
     async def create_collection(self, collection_name: str, payload_schema = None):
-        pass
+        self.driver.select_graph(collection_name)
 
+    async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
+        graph = self.driver.select_graph(collection_name)
 
-    async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
+        def stringify_properties(properties: dict) -> str:
-        pass
+            return ",".join(f"{key}:'{value}'" for key, value in properties.items())
+            
+        def create_data_point_query(data_point: DataPoint):
+            node_label = type(data_point.payload).__name__
+            node_properties = stringify_properties(data_point.payload.dict())
+          
+            return f"""CREATE (:{node_label} {{{node_properties}}})"""
+
+        query = " ".join([create_data_point_query(data_point) for data_point in data_points])
+
+        graph.query(query)
 
     async def retrieve(self, collection_name: str, data_point_ids: list[str]):
-        pass
+        graph = self.driver.select_graph(collection_name)
+
+        return graph.query(
+            f"MATCH (node) WHERE node.id IN $node_ids RETURN node",
+            {
+                "node_ids": data_point_ids,
+            },
+        )
 
     async def search(
         self,
         collection_name: str,
         query_text: str = None,
-        query_vector: List[float] = None,
+        query_vector: list[float] = None,
         limit: int = 10,
         with_vector: bool = False,
     ):
-        pass
+        if query_text is None and query_vector is None:
+            raise ValueError("One of query_text or query_vector must be provided!")
+
+        if query_text and not query_vector:
+            query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
+
+        graph = self.driver.select_graph(collection_name)
+
+        query = f"""
+            CALL db.idx.vector.queryNodes(
+                null,
+                'text',
+                {limit},
+                {query_vector}
+            ) YIELD node, score
+        """
+
+        result = graph.query(query)
+
+        return result
+
+    async def batch_search(
+        self,
+        collection_name: str,
+        query_texts: list[str],
+        limit: int = None,
+        with_vectors: bool = False,
+    ):
+        query_vectors = await self.embedding_engine.embed_text(query_texts)
+
+        return await asyncio.gather(
+            *[self.search(
+                collection_name = collection_name,
+                query_vector = query_vector,
+                limit = limit,
+                with_vector = with_vectors,
+            ) for query_vector in query_vectors]
+        )
 
     async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
-        pass
+        graph = self.driver.select_graph(collection_name)
+
+        return graph.query(
+            f"MATCH (node) WHERE node.id IN $node_ids DETACH DELETE node",
+            {
+                "node_ids": data_point_ids,
+            },
+        )

cognee/modules/data/processing/document_types/__init__.py

@@ -1,3 +1,4 @@
+from .Document import Document
 from .PdfDocument import PdfDocument
 from .TextDocument import TextDocument
 from .ImageDocument import ImageDocument

examples/python/GraphModel.py

@@ -0,0 +1,62 @@
+
+from typing import Optional
+from uuid import UUID
+from datetime import datetime
+from pydantic import BaseModel
+
+
+async def add_data_points(collection_name: str, data_points: list):
+    pass
+
+
+
+class Summary(BaseModel):
+    id: UUID
+    text: str
+    chunk: "Chunk"
+    created_at: datetime
+    updated_at: Optional[datetime]
+
+    vector_index = ["text"]
+
+class Chunk(BaseModel):
+    id: UUID
+    text: str
+    summary: Summary
+    document: "Document"
+    created_at: datetime
+    updated_at: Optional[datetime]
+    word_count: int
+    chunk_index: int
+    cut_type: str
+
+    vector_index = ["text"]
+
+class Document(BaseModel):
+    id: UUID
+    chunks: list[Chunk]
+    created_at: datetime
+    updated_at: Optional[datetime]
+
+class EntityType(BaseModel):
+    id: UUID
+    name: str
+    description: str
+    created_at: datetime
+    updated_at: Optional[datetime]
+
+    vector_index = ["name"]
+
+class Entity(BaseModel):
+    id: UUID
+    name: str
+    type: EntityType
+    description: str
+    chunks: list[Chunk]
+    created_at: datetime
+    updated_at: Optional[datetime]
+
+    vector_index = ["name"]
+
+class OntologyModel(BaseModel):
+    chunks: list[Chunk]

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
 [[package]]
 name = "aiofiles"
@@ -1490,6 +1490,19 @@ files = [
 [package.extras]
 tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
+[[package]]
+name = "falkordb"
+version = "1.0.9"
+description = "Python client for interacting with FalkorDB database"
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "falkordb-1.0.9.tar.gz", hash = "sha256:177008e63c7e4d9ebbdfeb8cad24b0e49175bb0f6e96cac9b4ffb641c0eff0f1"},
+]
+
+[package.dependencies]
+redis = ">=5.0.1,<6.0.0"
+
 [[package]]
 name = "fastapi"
 version = "0.109.2"
@@ -3685,7 +3698,6 @@ optional = false
 python-versions = ">=3.6"
 files = [
     {file = "mkdocs-redirects-1.2.1.tar.gz", hash = "sha256:9420066d70e2a6bb357adf86e67023dcdca1857f97f07c7fe450f8f1fb42f861"},
-    {file = "mkdocs_redirects-1.2.1-py3-none-any.whl", hash = "sha256:497089f9e0219e7389304cffefccdfa1cac5ff9509f2cb706f4c9b221726dffb"},
 ]
 
 [package.dependencies]
@@ -5771,6 +5783,24 @@ files = [
 [package.extras]
 test = ["pytest (>=3.0)", "pytest-asyncio"]
 
+[[package]]
+name = "redis"
+version = "5.1.1"
+description = "Python client for Redis database and key-value store"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "redis-5.1.1-py3-none-any.whl", hash = "sha256:f8ea06b7482a668c6475ae202ed8d9bcaa409f6e87fb77ed1043d912afd62e24"},
+    {file = "redis-5.1.1.tar.gz", hash = "sha256:f6c997521fedbae53387307c5d0bf784d9acc28d9f1d058abeac566ec4dbed72"},
+]
+
+[package.dependencies]
+async-timeout = {version = ">=4.0.3", markers = "python_full_version < \"3.11.3\""}
+
+[package.extras]
+hiredis = ["hiredis (>=3.0.0)"]
+ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"]
+
 [[package]]
 name = "referencing"
 version = "0.35.1"
@@ -6292,11 +6322,6 @@ files = [
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
     {file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
-    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
-    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
-    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
-    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
-    {file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
     {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
@@ -7766,4 +7791,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<3.12"
-content-hash = "70a0072dce8de95d64b862f9a9df48aaec84c8d8515ae018fce4426a0dcacf88"
+content-hash = "fef56656ead761cab7d5c3d0bf1fa5a54608db73b14616d08e5fb152dba91236"

pyproject.toml

@@ -72,6 +72,7 @@ asyncpg = "^0.29.0"
 alembic = "^1.13.3"
 pgvector = "^0.3.5"
 psycopg2 = {version = "^2.9.10", optional = true}
+falkordb = "^1.0.9"
 
 [tool.poetry.extras]
 filesystem = ["s3fs", "botocore"]