fix: set config before using it

cognee-frontend/src/ui/App/Logo/TextLogo.v2.tsx

@@ -1,4 +1,4 @@
-export default function TextLogo({ width = 285, height = 81, color = 'currentColor' }) {
+export default function TextLogo({ width = 285, height = 81, color = 'white' }) {
   return (
     <svg width={width} height={height} viewBox="0 0 285 81" fill="none" xmlns="http://www.w3.org/2000/svg">
       <path d="M42.0964 46.4597C39.6678 49.6212 36.2632 51.8922 32.4114 52.92C28.5596 53.9479 24.4762 53.6749 20.7954 52.1436C17.1147 50.6123 14.0426 47.9083 12.0565 44.4517C10.0704 40.9951 9.2813 36.9793 9.81189 33.0282C10.3425 29.0771 12.163 25.4118 14.9907 22.6016C17.8184 19.7914 21.4949 17.9937 25.4493 17.4877C29.4036 16.9816 33.4144 17.7956 36.8586 19.8032" stroke={color} strokeWidth="6.03175" strokeLinecap="round"/>

cognee-frontend/src/ui/Partials/Wizard/WizardContent/WizardContent.module.css

@@ -1,6 +1,7 @@
 .wizardContent {
   width: 100%;
   max-width: 400px;
+  height: max-content;
   background: linear-gradient(90deg, #D82EB5 0.52%, #9245FD 103.83%);
   padding: 24px;
   margin: 0 auto;

cognee/__init__.py

@@ -1,6 +1,6 @@
-from .api.v1.config.config import config
+# from .api.v1.config.config import config
-from .api.v1.add.add import add
+# from .api.v1.add.add import add
-from .api.v1.cognify.cognify import cognify
+# from .api.v1.cognify.cognify import cognify
-from .api.v1.datasets.datasets import datasets
+# from .api.v1.datasets.datasets import datasets
-from .api.v1.search.search import search, SearchType
+# from .api.v1.search.search import search, SearchType
-from .api.v1.prune import prune
+# from .api.v1.prune import prune

cognee/api/client.py

@@ -19,12 +19,7 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)
 
-from cognee.config import Config
+app = FastAPI(debug = True)
-
-config = Config()
-config.load()
-
-app = FastAPI(debug=True)
 
 origins = [
     "http://frontend:3000",
@@ -59,12 +54,12 @@ class Payload(BaseModel):
 
 @app.get("/datasets", response_model=list)
 async def get_datasets():
-    from cognee import datasets
+    from cognee.api.v1.datasets.datasets import datasets
     return datasets.list_datasets()
 
 @app.delete("/datasets/{dataset_id}", response_model=dict)
 async def delete_dataset(dataset_id: str):
-    from cognee import datasets
+    from cognee.api.v1.datasets.datasets import datasets
     datasets.delete_dataset(dataset_id)
     return JSONResponse(
         status_code=200,
@@ -73,22 +68,23 @@ async def delete_dataset(dataset_id: str):
 
 @app.get("/datasets/{dataset_id}/graph", response_model=list)
 async def get_dataset_graph(dataset_id: str):
-    from cognee import utils
+    from cognee.utils import render_graph
-    from cognee.infrastructure import infrastructure_config
+    from cognee.infrastructure.databases.graph import get_graph_config
     from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client
 
-    graph_engine = infrastructure_config.get_config()["graph_engine"]
+    graph_config = get_graph_config()
+    graph_engine = graph_config.graph_engine
     graph_client = await get_graph_client(graph_engine)
-    graph_url = await utils.render_graph(graph_client.graph)
+    graph_url = await render_graph(graph_client.graph)
 
     return JSONResponse(
-        status_code=200,
+        status_code = 200,
-        content=str(graph_url),
+        content = str(graph_url),
     )
 
 @app.get("/datasets/{dataset_id}/data", response_model=list)
 async def get_dataset_data(dataset_id: str):
-    from cognee import datasets
+    from cognee.api.v1.datasets.datasets import datasets
     dataset_data = datasets.list_data(dataset_id)
     if dataset_data is None:
         raise HTTPException(status_code=404, detail=f"Dataset ({dataset_id}) not found.")
@@ -105,7 +101,7 @@ async def get_dataset_data(dataset_id: str):
 
 @app.get("/datasets/status", response_model=dict)
 async def get_dataset_status(datasets: Annotated[List[str], Query(alias="dataset")] = None):
-    from cognee import datasets as cognee_datasets
+    from cognee.api.v1.datasets.datasets import datasets as cognee_datasets
     datasets_statuses = cognee_datasets.get_status(datasets)
 
     return JSONResponse(
@@ -115,7 +111,7 @@ async def get_dataset_status(datasets: Annotated[List[str], Query(alias="dataset
 
 @app.get("/datasets/{dataset_id}/data/{data_id}/raw", response_class=FileResponse)
 async def get_raw_data(dataset_id: str, data_id: str):
-    from cognee import datasets
+    from cognee.api.v1.datasets.datasets import datasets
     dataset_data = datasets.list_data(dataset_id)
     if dataset_data is None:
         raise HTTPException(status_code=404, detail=f"Dataset ({dataset_id}) not found.")
@@ -134,7 +130,7 @@ async def add(
     data: List[UploadFile] = File(...),
 ):
     """ This endpoint is responsible for adding data to the graph."""
-    from cognee import add as cognee_add
+    from cognee.api.v1.add import add as cognee_add
     try:
         if isinstance(data, str) and data.startswith("http"):
             if "github" in data:
@@ -178,7 +174,7 @@ class CognifyPayload(BaseModel):
 @app.post("/cognify", response_model=dict)
 async def cognify(payload: CognifyPayload):
     """ This endpoint is responsible for the cognitive processing of the content."""
-    from cognee import cognify as cognee_cognify
+    from cognee.api.v1.cognify.cognify import cognify as cognee_cognify
     try:
         await cognee_cognify(payload.datasets)
         return JSONResponse(
@@ -197,7 +193,7 @@ class SearchPayload(BaseModel):
 @app.post("/search", response_model=dict)
 async def search(payload: SearchPayload):
     """ This endpoint is responsible for searching for nodes in the graph."""
-    from cognee import search as cognee_search
+    from cognee.api.v1.search import search as cognee_search
     try:
         search_type = payload.query_params["searchType"]
         params = {
@@ -254,17 +250,21 @@ def start_api_server(host: str = "0.0.0.0", port: int = 8000):
     port (int): The port for the server.
     """
     try:
-        logger.info(f"Starting server at {host}:{port}")
+        logger.info("Starting server at %s:%s", host, port)
-        from cognee import config, prune
+
+        from cognee.base_config import get_base_config
+
+        base_config = get_base_config()
         data_directory_path = os.path.abspath(".data_storage")
-        config.data_root_directory(data_directory_path)
+        base_config.data_root_directory = data_directory_path
 
         cognee_directory_path = os.path.abspath(".cognee_system")
-        config.system_root_directory(cognee_directory_path)
+        base_config.system_root_directory = cognee_directory_path
 
-        asyncio.run(prune.prune_system())
+        from cognee.modules.data.deletion import prune_system
+        asyncio.run(prune_system())
 
-        uvicorn.run(app, host=host, port=port)
+        uvicorn.run(app, host = host, port = port)
     except Exception as e:
         logger.exception(f"Failed to start server: {e}")
         # Here you could add any cleanup code or error recovery code.

cognee/api/v1/add/add.py

@@ -4,7 +4,6 @@ import asyncio
 import dlt
 import duckdb
 import cognee.modules.ingestion as ingestion
-from cognee.infrastructure import infrastructure_config
 from cognee.infrastructure.files.storage import LocalStorage
 from cognee.modules.discovery import discover_directory_datasets
 from cognee.utils import send_telemetry
@@ -12,9 +11,6 @@ from cognee.base_config import get_base_config
 base_config = get_base_config()
 from cognee.infrastructure.databases.relational.config import get_relationaldb_config
 
-relational_config = get_relationaldb_config()
-
-
 async def add(data: Union[BinaryIO, List[BinaryIO], str, List[str]], dataset_name: str = None):
     if isinstance(data, str):
         # data is a data directory path
@@ -54,8 +50,6 @@ async def add_files(file_paths: List[str], dataset_name: str):
     # infra_config = infrastructure_config.get_config()
     data_directory_path = base_config.data_root_directory
 
-    LocalStorage.ensure_directory_exists(relational_config.database_directory_path)
-
     processed_file_paths = []
 
     for file_path in file_paths:
@@ -73,6 +67,7 @@ async def add_files(file_paths: List[str], dataset_name: str):
         else:
             processed_file_paths.append(file_path)
 
+    relational_config = get_relationaldb_config()
     db = duckdb.connect(relational_config.db_file_path)
 
     destination = dlt.destinations.duckdb(

cognee/api/v1/add/add_standalone.py

@@ -1,55 +0,0 @@
-import asyncio
-from uuid import UUID, uuid4
-from typing import Union, BinaryIO, List
-import cognee.modules.ingestion as ingestion
-from cognee.infrastructure import infrastructure_config
-from cognee.infrastructure.databases.relational.config import get_relationaldb_config
-
-relational_config = get_relationaldb_config()
-class DatasetException(Exception):
-    message: str
-
-    def __init__(self, message: str):
-        self.message = message
-
-
-async def add_standalone(
-    data: Union[str, BinaryIO, List[Union[str, BinaryIO]]],
-    dataset_id: UUID = uuid4(),
-    dataset_name: str = None
-):
-    db_engine = relational_config.database_engine
-    if db_engine.is_db_done is not True:
-        await db_engine.ensure_tables()
-
-    if not data:
-        raise DatasetException("Data must be provided to cognee.add(data: str)")
-
-    if isinstance(data, list):
-        promises = []
-
-        for data_item in data:
-            promises.append(add_standalone(data_item, dataset_id, dataset_name))
-
-        results = await asyncio.gather(*promises)
-
-        return results
-
-
-    if is_data_path(data):
-        with open(data.replace("file://", ""), "rb") as file:
-            return await add_standalone(file, dataset_id, dataset_name)
-
-    classified_data = ingestion.classify(data)
-
-    data_id = ingestion.identify(classified_data)
-
-    await ingestion.save(dataset_id, dataset_name, data_id, classified_data)
-
-    return dataset_id
-
-    # await ingestion.vectorize(dataset_id, dataset_name, data_id, classified_data)
-
-
-def is_data_path(data: str) -> bool:
-    return False if not isinstance(data, str) else data.startswith("file://")

cognee/api/v1/cognify/cognify.py

@@ -5,9 +5,7 @@ import logging
 import nltk
 from nltk.corpus import stopwords
 from cognee.config import Config
-from cognee.infrastructure.data.chunking.LangchainChunkingEngine import LangchainChunkEngine
 from cognee.infrastructure.databases.graph.config import get_graph_config
-from cognee.infrastructure.databases.vector.embeddings.DefaultEmbeddingEngine import LiteLLMEmbeddingEngine
 from cognee.modules.cognify.graph.add_node_connections import group_nodes_by_layer, \
     graph_ready_output, connect_nodes_in_graph
 from cognee.modules.cognify.graph.add_data_chunks import add_data_chunks, add_data_chunks_basic_rag
@@ -21,38 +19,23 @@ from cognee.modules.cognify.graph.add_cognitive_layers import add_cognitive_laye
 # from cognee.modules.cognify.graph.initialize_graph import initialize_graph
 from cognee.infrastructure.files.utils.guess_file_type import guess_file_type, FileTypeException
 from cognee.infrastructure.files.utils.extract_text_from_file import extract_text_from_file
-from cognee.infrastructure import infrastructure_config
 from cognee.modules.data.get_content_categories import get_content_categories
 from cognee.modules.data.get_content_summary import get_content_summary
 from cognee.modules.data.get_cognitive_layers import get_cognitive_layers
 from cognee.modules.data.get_layer_graphs import get_layer_graphs
-from cognee.shared.data_models import ChunkStrategy, KnowledgeGraph
+from cognee.shared.data_models import KnowledgeGraph
 from cognee.utils import send_telemetry
 from cognee.modules.tasks import create_task_status_table, update_task_status
 from cognee.shared.SourceCodeGraph import SourceCodeGraph
 from asyncio import Lock
 from cognee.modules.tasks import get_task_status
-from cognee.base_config import get_base_config
 from cognee.infrastructure.data.chunking.config import get_chunk_config
 from cognee.modules.cognify.config import get_cognify_config
-from cognee.infrastructure.databases.vector.embeddings.config import get_embedding_config
 from cognee.infrastructure.databases.relational.config import get_relationaldb_config
 
-graph_config = get_graph_config()
 config = Config()
 config.load()
 
-
-relational_config = get_relationaldb_config()
-
-
-cognify_config = get_cognify_config()
-chunk_config = get_chunk_config()
-base_config = get_base_config()
-embedding_config = get_embedding_config()
-
-# aclient = instructor.patch(OpenAI())
-
 USER_ID = "default_user"
 
 logger = logging.getLogger("cognify")
@@ -66,10 +49,11 @@ async def cognify(datasets: Union[str, List[str]] = None):
     stopwords.ensure_loaded()
     create_task_status_table()
 
+    graph_config = get_graph_config()
     graph_db_type = graph_config.graph_engine
-
     graph_client = await get_graph_client(graph_db_type)
 
+    relational_config = get_relationaldb_config()
     db_engine = relational_config.database_engine
 
     if datasets is None or len(datasets) == 0:
@@ -108,7 +92,7 @@ async def cognify(datasets: Union[str, List[str]] = None):
         if dataset_name in added_dataset:
             dataset_files.append((added_dataset, db_engine.get_files_metadata(added_dataset)))
 
-
+    chunk_config = get_chunk_config()
     chunk_engine = chunk_config.chunk_engine
     chunk_strategy = chunk_config.chunk_strategy
 
@@ -190,10 +174,11 @@ async def cognify(datasets: Union[str, List[str]] = None):
 async def process_text(chunk_collection: str, chunk_id: str, input_text: str, file_metadata: dict, document_id: str):
     print(f"Processing chunk ({chunk_id}) from document ({file_metadata['id']}).")
 
+    graph_config = get_graph_config()
     graph_client = await get_graph_client(graph_config.graph_engine)
-    print("graph_client", graph_client)
+    cognify_config = get_cognify_config()
-
     graph_topology = cognify_config.graph_model
+
     if graph_topology == SourceCodeGraph:
         classified_categories = [{"data_type": "text", "category_name": "Code and functions"}]
     elif graph_topology == KnowledgeGraph:
@@ -227,7 +212,7 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi
 
 
     if cognify_config.connect_documents is True:
-        db_engine = relational_config.database_engine
+        db_engine = get_relationaldb_config().database_engine
         relevant_documents_to_connect = db_engine.fetch_cognify_data(excluded_document_id = document_id)
 
         list_of_nodes = []
@@ -258,52 +243,52 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi
 
 
 
-if __name__ == "__main__":
+# if __name__ == "__main__":
 
-    async def test():
+#     async def test():
-        # await prune.prune_system()
+#         # await prune.prune_system()
-        # #
+#         # #
-        # from cognee.api.v1.add import add
+#         # from cognee.api.v1.add import add
-        # data_directory_path = os.path.abspath("../../../.data")
+#         # data_directory_path = os.path.abspath("../../../.data")
-        # # print(data_directory_path)
+#         # # print(data_directory_path)
-        # # config.data_root_directory(data_directory_path)
+#         # # config.data_root_directory(data_directory_path)
-        # # cognee_directory_path = os.path.abspath("../.cognee_system")
+#         # # cognee_directory_path = os.path.abspath("../.cognee_system")
-        # # config.system_root_directory(cognee_directory_path)
+#         # # config.system_root_directory(cognee_directory_path)
-        #
+#         #
-        # await add("data://" +data_directory_path, "example")
+#         # await add("data://" +data_directory_path, "example")
 
-        text = """import subprocess
+#         text = """import subprocess
-                def show_all_processes():
+#                 def show_all_processes():
-                    process = subprocess.Popen(['ps', 'aux'], stdout=subprocess.PIPE)
+#                     process = subprocess.Popen(['ps', 'aux'], stdout=subprocess.PIPE)
-                    output, error = process.communicate()
+#                     output, error = process.communicate()
                 
-                    if error:
+#                     if error:
-                        print(f"Error: {error}")
+#                         print(f"Error: {error}")
-                    else:
+#                     else:
-                        print(output.decode())
+#                         print(output.decode())
                 
-                show_all_processes()"""
+#                 show_all_processes()"""
 
-        from cognee.api.v1.add import add
+#         from cognee.api.v1.add import add
 
-        await add([text], "example_dataset")
+#         await add([text], "example_dataset")
 
-        infrastructure_config.set_config( {"chunk_engine": LangchainChunkEngine() , "chunk_strategy": ChunkStrategy.CODE,'embedding_engine': LiteLLMEmbeddingEngine() })
+#         infrastructure_config.set_config( {"chunk_engine": LangchainChunkEngine() , "chunk_strategy": ChunkStrategy.CODE,'embedding_engine': LiteLLMEmbeddingEngine() })
-        from cognee.shared.SourceCodeGraph import SourceCodeGraph
+#         from cognee.shared.SourceCodeGraph import SourceCodeGraph
-        from cognee.api.v1.config import config
+#         from cognee.api.v1.config import config
 
-        # config.set_graph_model(SourceCodeGraph)
+#         # config.set_graph_model(SourceCodeGraph)
-        # config.set_classification_model(CodeContentPrediction)
+#         # config.set_classification_model(CodeContentPrediction)
-        # graph = await cognify()
+#         # graph = await cognify()
-        vector_client = infrastructure_config.get_config("vector_engine")
+#         vector_client = infrastructure_config.get_config("vector_engine")
 
-        out = await vector_client.search(collection_name ="basic_rag", query_text="show_all_processes", limit=10)
+#         out = await vector_client.search(collection_name ="basic_rag", query_text="show_all_processes", limit=10)
 
-        print("results", out)
+#         print("results", out)
-        #
+#         #
-        # from cognee.utils import render_graph
+#         # from cognee.utils import render_graph
-        #
+#         #
-        # await render_graph(graph, include_color=True, include_nodes=False, include_size=False)
+#         # await render_graph(graph, include_color=True, include_nodes=False, include_size=False)
 
-    import asyncio
+#     import asyncio
-    asyncio.run(test())
+#     asyncio.run(test())

cognee/api/v1/config/config.py

@@ -4,80 +4,93 @@ from cognee.infrastructure.databases.graph.config import get_graph_config
 from cognee.infrastructure.data.chunking.config import get_chunk_config
 from cognee.modules.cognify.config import get_cognify_config
 
-
-cognify_config = get_cognify_config()
-chunk_config = get_chunk_config()
-graph_config = get_graph_config()
-base_config = get_base_config()
-
 class config():
     @staticmethod
     def system_root_directory(system_root_directory: str):
+        base_config = get_base_config()
         base_config.system_root_directory = system_root_directory
 
+
     @staticmethod
     def data_root_directory(data_root_directory: str):
+        base_config = get_base_config()
         base_config.data_root_directory = data_root_directory
 
+
     @staticmethod
     def monitoring_tool(monitoring_tool: object):
+        base_config = get_base_config()
         base_config.monitoring_tool = monitoring_tool
 
+
     @staticmethod
     def set_classification_model(classification_model: object):
+        cognify_config = get_cognify_config()
         cognify_config.classification_model =  classification_model
 
+
     @staticmethod
     def set_summarization_model(summarization_model: object):
+        cognify_config = get_cognify_config()
         cognify_config.summarization_model=summarization_model
 
 
     @staticmethod
     def set_labeling_model(labeling_model: object):
+        cognify_config = get_cognify_config()
         cognify_config.labeling_model =labeling_model
 
 
     @staticmethod
     def set_graph_model(graph_model: object):
-        graph_config.graph_model =graph_model
+        graph_config = get_graph_config()
+        graph_config.graph_model = graph_model
 
 
     @staticmethod
     def set_cognitive_layer_model(cognitive_layer_model: object):
-        cognify_config.cognitive_layer_model =cognitive_layer_model
+        cognify_config = get_cognify_config()
+        cognify_config.cognitive_layer_model = cognitive_layer_model
 
 
     @staticmethod
     def set_graph_engine(graph_engine: object):
-        graph_config.graph_engine =graph_engine
+        graph_config = get_graph_config()
+        graph_config.graph_engine = graph_engine
 
     @staticmethod
     def llm_provider(llm_provider: str):
+        graph_config = get_graph_config()
         graph_config.llm_provider = llm_provider
 
     @staticmethod
     def llm_endpoint(llm_endpoint: str):
+        graph_config = get_graph_config()
         graph_config.llm_endpoint = llm_endpoint
 
     @staticmethod
     def llm_model(llm_model: str):
+        graph_config = get_graph_config()
         graph_config.llm_model = llm_model
 
     @staticmethod
     def intra_layer_score_treshold(intra_layer_score_treshold: str):
-        cognify_config.intra_layer_score_treshold =intra_layer_score_treshold
+        cognify_config = get_cognify_config()
+        cognify_config.intra_layer_score_treshold = intra_layer_score_treshold
 
 
     @staticmethod
     def connect_documents(connect_documents: bool):
+        cognify_config = get_cognify_config()
         cognify_config.connect_documents = connect_documents
 
     @staticmethod
     def set_chunk_strategy(chunk_strategy: object):
+        chunk_config = get_chunk_config()
         chunk_config.chunk_strategy = chunk_strategy
 
 
     @staticmethod
     def set_graph_topology(graph_topology: object):
-        get_cognify_config.graph_topology =graph_topology
+        cognify_config = get_cognify_config()
-
+        cognify_config.graph_topology = graph_topology

cognee/api/v1/datasets/datasets.py

@@ -3,12 +3,11 @@ from cognee.modules.discovery import discover_directory_datasets
 from cognee.modules.tasks import get_task_status
 from cognee.infrastructure.databases.relational.config import get_relationaldb_config
 
-relational_config = get_relationaldb_config()
-
 class datasets():
     @staticmethod
     def list_datasets():
-        db = relational_config.db_engine
+        relational_config = get_relationaldb_config()
+        db = relational_config.database_engine
         return db.get_datasets()
 
     @staticmethod
@@ -17,7 +16,8 @@ class datasets():
 
     @staticmethod
     def list_data(dataset_name: str):
-        db = relational_config.db_engine
+        relational_config = get_relationaldb_config()
+        db = relational_config.database_engine
         try:
             return db.get_files_metadata(dataset_name)
         except CatalogException:
@@ -32,7 +32,8 @@ class datasets():
 
     @staticmethod
     def delete_dataset(dataset_id: str):
-        db = relational_config.db_engine
+        relational_config = get_relationaldb_config()
+        db = relational_config.database_engine
         try:
             return db.delete_table(dataset_id)
         except CatalogException:

cognee/api/v1/prune/prune.py

@@ -1,11 +1,11 @@
 from cognee.modules.data.deletion import prune_system
 from cognee.base_config import get_base_config
 from cognee.infrastructure.files.storage import LocalStorage
-base_config = get_base_config()
 
 class prune():
     @staticmethod
     async def prune_data():
+        base_config = get_base_config()
         data_root_directory = base_config.data_root_directory
         LocalStorage.remove_all(data_root_directory)
 

cognee/api/v1/search/search.py

@@ -11,21 +11,19 @@ from cognee.modules.search.graph.search_categories import search_categories
 from cognee.modules.search.graph.search_neighbour import search_neighbour
 from cognee.modules.search.graph.search_summary import search_summary
 from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client
-from cognee.infrastructure import infrastructure_config
 from cognee.utils import send_telemetry
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
 
 class SearchType(Enum):
-    ADJACENT = 'ADJACENT'
+    ADJACENT = "ADJACENT"
-    SIMILARITY = 'SIMILARITY'
+    SIMILARITY = "SIMILARITY"
-    CATEGORIES = 'CATEGORIES'
+    CATEGORIES = "CATEGORIES"
-    NEIGHBOR = 'NEIGHBOR'
+    NEIGHBOR = "NEIGHBOR"
-    SUMMARY = 'SUMMARY'
+    SUMMARY = "SUMMARY"
-    SUMMARY_CLASSIFICATION = 'SUMMARY_CLASSIFICATION'
+    SUMMARY_CLASSIFICATION = "SUMMARY_CLASSIFICATION"
-    NODE_CLASSIFICATION = 'NODE_CLASSIFICATION'
+    NODE_CLASSIFICATION = "NODE_CLASSIFICATION"
-    DOCUMENT_CLASSIFICATION = 'DOCUMENT_CLASSIFICATION',
+    DOCUMENT_CLASSIFICATION = "DOCUMENT_CLASSIFICATION",
-    CYPHER = 'CYPHER'
+    CYPHER = "CYPHER"
 
     @staticmethod
     def from_str(name: str):
@@ -38,7 +36,7 @@ class SearchParameters(BaseModel):
     search_type: SearchType
     params: Dict[str, Any]
 
-    @field_validator('search_type', mode='before')
+    @field_validator("search_type", mode="before")
     def convert_string_to_enum(cls, value):
         if isinstance(value, str):
             return SearchType.from_str(value)
@@ -51,6 +49,7 @@ async def search(search_type: str, params: Dict[str, Any]) -> List:
 
 
 async def specific_search(query_params: List[SearchParameters]) -> List:
+    graph_config = get_graph_config()
     graph_client = await get_graph_client(graph_config.graph_engine)
     graph = graph_client.graph
 

cognee/base_config.py

@@ -1,6 +1,5 @@
 from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
-
 from cognee.root_dir import get_absolute_path
 from cognee.shared.data_models import MonitoringTool
 

cognee/config.py

@@ -7,9 +7,9 @@ from typing import Optional, Dict, Any
 from dataclasses import dataclass, field
 from pathlib import Path
 from dotenv import load_dotenv
-from cognee.root_dir import get_absolute_path
+
-from cognee.shared.data_models import ChunkStrategy, DefaultGraphModel
 logging.basicConfig(level=logging.DEBUG)
+
 def load_dontenv():
     base_dir = Path(__file__).resolve().parent.parent
     # Load the .env file from the base directory
@@ -33,27 +33,6 @@ class Config:
         )
     )
 
-
-    system_root_directory = get_absolute_path(".cognee_system")
-    logging.info("system_root_directory: %s", system_root_directory)
-    data_root_directory = os.getenv("DATA_PATH", get_absolute_path(".data"))
-
-    vectordb: str = os.getenv("VECTORDB", "weaviate")
-
-    qdrant_path: str = os.getenv("QDRANT_PATH", None)
-    qdrant_url: str = os.getenv("QDRANT_URL", None)
-    qdrant_api_key: str = os.getenv("QDRANT_API_KEY", None)
-
-
-
-    graph_filename = os.getenv("GRAPH_NAME", "cognee_graph.pkl")
-
-    # Model parameters
-    llm_provider: str = os.getenv("LLM_PROVIDER", "openai") #openai, or custom or ollama
-    llm_model: str = os.getenv("LLM_MODEL", "gpt-4")
-    llm_api_key: str = os.getenv("LLM_API_KEY", os.getenv("OPENAI_API_KEY"))
-    llm_endpoint: str = os.getenv("LLM_ENDPOINT", None)
-
     # custom_model: str = os.getenv("CUSTOM_LLM_MODEL", "llama3-70b-8192") #"mistralai/Mixtral-8x7B-Instruct-v0.1"
     # custom_endpoint: str = os.getenv("CUSTOM_ENDPOINT", "https://api.endpoints.anyscale.com/v1") #"https://api.endpoints.anyscale.com/v1" # pass claude endpoint
     # custom_key: Optional[str] = os.getenv("CUSTOM_LLM_API_KEY")
@@ -63,7 +42,6 @@ class Config:
     # openai_model: str = os.getenv("OPENAI_MODEL", "gpt-4o" ) #"gpt-4o"
     # model_endpoint: str = "openai"
     # llm_api_key: Optional[str] = os.getenv("OPENAI_API_KEY")
-    openai_temperature: float = float(os.getenv("OPENAI_TEMPERATURE", 0.0))
     # openai_embedding_model = "text-embedding-3-large"
     # openai_embedding_dimensions = 3072
     # litellm_embedding_model = "text-embedding-3-large"
@@ -77,23 +55,9 @@ class Config:
     embedding_dimensions: int = 1024
     connect_documents: bool = False
 
-    # Database parameters
-    graph_database_provider: str = os.getenv("GRAPH_DB_PROVIDER", "NETWORKX")
-    graph_topology:str = DefaultGraphModel
-    cognitive_layers_limit: int = 2
-
-    from cognee.shared.data_models import MonitoringTool
-
-    # Monitoring tool
-    monitoring_tool: str = os.getenv("MONITORING_TOOL", MonitoringTool.LANGFUSE)
-
-    weaviate_url: str = os.getenv("WEAVIATE_URL")
-    weaviate_api_key: str = os.getenv("WEAVIATE_API_KEY")
-
     # Model parameters and configuration for interlayer scoring
     intra_layer_score_treshold: float = 0.98
 
-
     # Client ID
     anon_clientid: Optional[str] = field(default_factory=lambda: uuid.uuid4().hex)
 

cognee/infrastructure/InfrastructureConfig.py

@@ -1,29 +1,18 @@
 import logging
-import os
-
 from cognee.config import Config
 from .data.chunking.config import get_chunk_config
-from .databases.relational import DatabaseEngine
 from .llm.llm_interface import LLMInterface
 from .llm.get_llm_client import get_llm_client
-from .files.storage import LocalStorage
 from ..shared.data_models import GraphDBType, DefaultContentPrediction, KnowledgeGraph, SummarizedContent, \
     LabeledContent, DefaultCognitiveLayer
 
 logging.basicConfig(level=logging.DEBUG)
+
 config = Config()
 config.load()
-from cognee.infrastructure.databases.relational.config import get_relationaldb_config
+
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-vector_db_config = get_vectordb_config()
-relational = get_relationaldb_config()
 chunk_config = get_chunk_config()
 class InfrastructureConfig():
-
-    system_root_directory: str = config.system_root_directory
-    data_root_directory: str = config.data_root_directory
-    llm_provider: str = config.llm_provider
-    database_engine: DatabaseEngine = None
     graph_engine: GraphDBType = None
     llm_engine: LLMInterface = None
     classification_model = None
@@ -34,28 +23,14 @@ class InfrastructureConfig():
     intra_layer_score_treshold = None
     embedding_engine = None
     connect_documents = config.connect_documents
-    database_directory_path: str = None
-    database_file_path: str = None
     chunk_strategy = chunk_config.chunk_strategy
     chunk_engine = None
-    graph_topology = config.graph_topology
-    monitoring_tool = config.monitoring_tool
     llm_provider: str = None
     llm_model: str = None
     llm_endpoint: str = None
     llm_api_key: str = None
 
     def get_config(self, config_entity: str = None) -> dict:
-
-        if (config_entity is None or config_entity == "database_engine") and self.database_engine is None:
-
-
-            db_path = os.path.join(self.system_root_directory,relational.db_path)
-
-            LocalStorage.ensure_directory_exists(db_path)
-
-            self.database_engine = relational.db_engine
-
         if self.graph_engine is None:
             self.graph_engine = GraphDBType.NETWORKX
 
@@ -86,29 +61,14 @@ class InfrastructureConfig():
         if self.chunk_engine is None:
             self.chunk_engine = chunk_config.chunk_engine
 
-        if self.graph_topology is None:
-            self.graph_topology = config.graph_topology
-
         if (config_entity is None or config_entity == "llm_engine") and self.llm_engine is None:
             self.llm_engine = get_llm_client()
 
-        if (config_entity is None or config_entity == "database_directory_path") and self.database_directory_path is None:
-            self.database_directory_path = self.system_root_directory + "/" + relational.db_path
-        if self.database_directory_path is None:
-            self.database_directory_path = self.system_root_directory + "/" + relational.db_path
-
-        if (config_entity is None or config_entity == "database_file_path") and self.database_file_path is None:
-            self.database_file_path = self.system_root_directory + "/" + relational.db_path + "/" + relational.db_name
-
         if config_entity is not None:
             return getattr(self, config_entity)
 
         return {
             "llm_engine": self.llm_engine,
-            "database_engine": self.database_engine,
-            "system_root_directory": self.system_root_directory,
-            "data_root_directory": self.data_root_directory,
-            "graph_engine": self.graph_engine,
             "classification_model": self.classification_model,
             "summarization_model": self.summarization_model,
             "labeling_model": self.labeling_model,
@@ -118,29 +78,11 @@ class InfrastructureConfig():
             "intra_layer_score_treshold": self.intra_layer_score_treshold,
             "embedding_engine": self.embedding_engine,
             "connect_documents": self.connect_documents,
-            "database_directory_path": self.database_directory_path,
-            "database_path": self.database_file_path,
             "chunk_strategy": self.chunk_strategy,
             "chunk_engine": self.chunk_engine,
-            "graph_topology": self.graph_topology
         }
 
     def set_config(self, new_config: dict):
-        if "system_root_directory" in new_config:
-            self.system_root_directory = new_config["system_root_directory"]
-
-        if "data_root_directory" in new_config:
-            self.data_root_directory = new_config["data_root_directory"]
-
-        if "database_engine" in new_config:
-            self.database_engine = new_config["database_engine"]
-
-        if "llm_engine" in new_config:
-            self.llm_engine = new_config["llm_engine"]
-
-        if "graph_engine" in new_config:
-            self.graph_engine = new_config["graph_engine"]
-
         if "classification_model" in new_config:
             self.classification_model = new_config["classification_model"]
 
@@ -150,12 +92,6 @@ class InfrastructureConfig():
         if "labeling_model" in new_config:
             self.labeling_model = new_config["labeling_model"]
 
-        if "graph_model" in new_config:
-            self.graph_model = new_config["graph_model"]
-
-        if "llm_provider" in new_config:
-            self.llm_provider = new_config["llm_provider"]
-
         if "cognitive_layer_model" in new_config:
             self.cognitive_layer_model = new_config["cognitive_layer_model"]
 
@@ -174,7 +110,4 @@ class InfrastructureConfig():
         if "chunk_engine" in new_config:
             self.chunk_engine = new_config["chunk_engine"]
 
-        if "graph_topology" in new_config:
-            self.graph_topology = new_config["graph_topology"]
-
 infrastructure_config = InfrastructureConfig()

cognee/infrastructure/databases/graph/__init__.py

@@ -0,0 +1 @@
+from .config import get_graph_config

cognee/infrastructure/databases/graph/config.py

@@ -2,11 +2,8 @@
 import os
 from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
-from cognee.base_config import get_base_config
 from cognee.infrastructure.databases.relational.config import get_relationaldb_config
 from cognee.shared.data_models import DefaultGraphModel, GraphDBType
-relational_config = get_relationaldb_config()
-base_config = get_base_config()
 
 class GraphConfig(BaseSettings):
     graph_filename: str = "cognee_graph.pkl"
@@ -15,7 +12,7 @@ class GraphConfig(BaseSettings):
     graph_database_username: str = ""
     graph_database_password: str = ""
     graph_database_port: int = 123
-    graph_file_path: str = os.path.join(relational_config.database_directory_path,graph_filename)
+    graph_file_path: str = os.path.join(get_relationaldb_config().db_path, graph_filename)
     graph_engine: object =  GraphDBType.NETWORKX
     graph_model: object = DefaultGraphModel
 

cognee/infrastructure/databases/graph/get_graph_client.py

@@ -4,11 +4,11 @@ from cognee.shared.data_models import GraphDBType
 from .config import get_graph_config
 from .graph_db_interface import GraphDBInterface
 from .networkx.adapter import NetworkXAdapter
-config = get_graph_config()
 
 
 async def get_graph_client(graph_type: GraphDBType, graph_file_name: str = None) -> GraphDBInterface :
     """Factory function to get the appropriate graph client based on the graph type."""
+    config = get_graph_config()
 
     if graph_type == GraphDBType.NEO4J:
         try:

cognee/infrastructure/databases/relational/config.py

@@ -1,28 +1,24 @@
 import os
 from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
-from cognee.infrastructure.databases.relational import DuckDBAdapter
 from cognee.base_config import get_base_config
-config = get_base_config()
+from .create_relational_engine import create_relational_engine
 
 class RelationalConfig(BaseSettings):
-    db_path: str =  os.path.join(config.system_root_directory, "databases")
+    db_path: str =  os.path.join(get_base_config().system_root_directory, "databases")
     db_name: str =  "cognee.db"
     db_host: str =  "localhost"
     db_port: str =  "5432"
     db_user: str = "cognee"
     db_password: str =  "cognee"
-    db_engine: object = DuckDBAdapter(
+    database_engine: object = create_relational_engine(db_path, db_name)
-        db_name=db_name,
+    db_file_path: str = os.path.join(db_path, db_name)
-        db_path=db_path
-    )
-    database_engine: object = db_engine
-    db_file_path:str = os.path.join(db_path, db_name)
-    database_path: str = os.path.join(config.system_root_directory, "databases")
-    database_directory_path: str = db_path
 
     model_config = SettingsConfigDict(env_file = ".env", extra = "allow")
 
+    def create_engine(self):
+        return create_relational_engine(self.db_path, self.db_name)
+
     def to_dict(self) -> dict:
         return {
             "db_path": self.db_path,
@@ -31,8 +27,7 @@ class RelationalConfig(BaseSettings):
             "db_port": self.db_port,
             "db_user": self.db_user,
             "db_password": self.db_password,
-            "db_engine": self.db_engine,
+            "db_engine": self.database_engine,
-            "database_path": self.database_path,
         }
 
 @lru_cache

cognee/infrastructure/databases/relational/create_relational_engine.py

@@ -0,0 +1,10 @@
+from cognee.infrastructure.files.storage import LocalStorage
+from cognee.infrastructure.databases.relational import DuckDBAdapter
+
+def create_relational_engine(db_path: str, db_name: str):
+    LocalStorage.ensure_directory_exists(db_path)
+
+    return DuckDBAdapter(
+        db_name = db_name,
+        db_path = db_path,
+    )

cognee/infrastructure/databases/vector/config.py

@@ -5,33 +5,28 @@ from cognee.infrastructure.databases.relational.config import get_relationaldb_c
 from cognee.infrastructure.databases.vector.embeddings.config import get_embedding_config
 from .create_vector_engine import create_vector_engine
 
-embeddings_config = get_embedding_config()
-relational_config = get_relationaldb_config()
-
-lancedb_path = os.path.join(relational_config.database_directory_path, "cognee.lancedb")
-
 class VectorConfig(BaseSettings):
-    vector_db_url: str = lancedb_path
+    vector_db_url: str = os.path.join(get_relationaldb_config().db_path, "cognee.lancedb")
     vector_db_key: str = ""
     vector_engine_provider: str = "lancedb"
     vector_engine: object = create_vector_engine(
         {
             "vector_db_key": None,
-            "vector_db_url": lancedb_path,
+            "vector_db_url": vector_db_url,
             "vector_db_provider": "lancedb",
         },
-        embeddings_config.embedding_engine,
+        get_embedding_config().embedding_engine,
     )
 
     model_config = SettingsConfigDict(env_file = ".env", extra = "allow")
 
     def create_engine(self):
         if self.vector_engine_provider == "lancedb":
-            self.vector_db_url = lancedb_path
+            self.vector_db_url = os.path.join(get_relationaldb_config().db_path, "cognee.lancedb")
 
         self.vector_engine = create_vector_engine(
             get_vectordb_config().to_dict(),
-            embeddings_config.embedding_engine,
+            get_embedding_config().embedding_engine,
         )
 
     def to_dict(self) -> dict:

cognee/infrastructure/files/storage/LocalStorage.py

@@ -35,7 +35,7 @@ class LocalStorage(Storage):
     @staticmethod
     def ensure_directory_exists(file_path: str):
         if not os.path.exists(file_path):
-            os.makedirs(file_path)
+            os.makedirs(file_path, exist_ok = True)
 
     def remove(self, file_path: str):
         os.remove(self.storage_path + "/" + file_path)

cognee/infrastructure/llm/config.py

@@ -6,6 +6,7 @@ class LLMConfig(BaseSettings):
     llm_model: str = "gpt-4o"
     llm_endpoint: str = ""
     llm_api_key: str = ""
+    llm_temperature: float = 0.0
 
     model_config = SettingsConfigDict(env_file = ".env", extra = "allow")
 

cognee/modules/cognify/config.py

@@ -1,9 +1,6 @@
 from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
-
+from cognee.shared.data_models import DefaultContentPrediction, LabeledContent, SummarizedContent, \
-from cognee.root_dir import get_absolute_path
-
-from cognee.shared.data_models import MonitoringTool, DefaultContentPrediction, LabeledContent, SummarizedContent, \
     DefaultCognitiveLayer, DefaultGraphModel, KnowledgeGraph
 
 
@@ -23,7 +20,6 @@ class CognifyConfig(BaseSettings):
     graph_model:object = KnowledgeGraph
 
 
-
     model_config = SettingsConfigDict(env_file = ".env", extra = "allow")
 
     def to_dict(self) -> dict:

cognee/modules/cognify/graph/add_cognitive_layer_graphs.py

@@ -6,8 +6,7 @@ from cognee.infrastructure.databases.vector import DataPoint
 # from cognee.utils import extract_pos_tags, extract_named_entities, extract_sentiment_vader
 from cognee.infrastructure.databases.graph.config import get_graph_config
 from cognee.infrastructure.databases.vector.config import get_vectordb_config
-graph_config = get_graph_config()
+
-vectordb_config = get_vectordb_config()
 class GraphLike(TypedDict):
     nodes: List
     edges: List
@@ -19,7 +18,10 @@ async def add_cognitive_layer_graphs(
     chunk_id: str,
     layer_graphs: List[Tuple[str, GraphLike]],
 ):
+    vectordb_config = get_vectordb_config()
     vector_client = vectordb_config.vector_engine
+
+    graph_config = get_graph_config()
     graph_model = graph_config.graph_model
 
     for (layer_id, layer_graph) in layer_graphs:

cognee/modules/cognify/graph/add_data_chunks.py

@@ -2,17 +2,15 @@
 from typing import TypedDict
 from pydantic import BaseModel, Field
 from cognee.infrastructure.databases.vector.config import get_vectordb_config
-
 from cognee.infrastructure.databases.vector import DataPoint
 
-config = get_vectordb_config()
-
 class TextChunk(TypedDict):
     text: str
     chunk_id: str
     file_metadata: dict
 
 async def add_data_chunks(dataset_data_chunks: dict[str, list[TextChunk]]):
+    config = get_vectordb_config()
     vector_client = config.vector_engine
 
     identified_chunks = []
@@ -55,6 +53,7 @@ async def add_data_chunks(dataset_data_chunks: dict[str, list[TextChunk]]):
 
 
 async def add_data_chunks_basic_rag(dataset_data_chunks: dict[str, list[TextChunk]]):
+    config = get_vectordb_config()
     vector_client = config.vector_engine
 
     identified_chunks = []

cognee/modules/cognify/graph/add_label_nodes.py

@@ -4,11 +4,10 @@ from datetime import datetime
 from pydantic import BaseModel
 
 from cognee.infrastructure.databases.vector import DataPoint
-from cognee.infrastructure.databases.graph.config import get_graph_config
 from cognee.infrastructure.databases.vector.config import get_vectordb_config
-graph_config = get_graph_config()
+
-vectordb_config = get_vectordb_config()
 async def add_label_nodes(graph_client, parent_node_id: str, keywords: List[str]) -> None:
+    vectordb_config = get_vectordb_config()
     vector_client = vectordb_config.vector_engine
 
     keyword_nodes = []

cognee/modules/cognify/graph/add_node_connections.py

@@ -4,9 +4,6 @@ import uuid
 from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client
 from cognee.shared.data_models import GraphDBType
 from cognee.infrastructure.databases.graph.config import get_graph_config
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-graph_config = get_graph_config()
-vectordb_config = get_vectordb_config()
 
 
 async def group_nodes_by_layer(node_descriptions):
@@ -42,6 +39,7 @@ async def connect_nodes_in_graph(graph, relationship_dict, score_threshold=0.9):
         for relationship in relationships:
 
             if relationship['score'] > score_threshold:
+                graph_config = get_graph_config()
 
                 # For NetworkX
                 if graph_config.graph_engine == GraphDBType.NETWORKX:

cognee/modules/cognify/graph/create.py

@@ -1,14 +1,9 @@
 """ This module is responsible for creating a semantic graph """
 from typing import  Optional, Any
 from pydantic import BaseModel
-
-# from cognee.infrastructure import infrastructure_config
 from cognee.shared.data_models import GraphDBType
-
 from cognee.infrastructure.databases.graph.config import get_graph_config
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
+
-graph_config = get_graph_config()
-vectordb_config = get_vectordb_config()
 async def generate_node_id(instance: BaseModel) -> str:
     for field in ["id", "doc_id", "location_id", "type_id", "node_id"]:
         if hasattr(instance, field):
@@ -46,7 +41,8 @@ async def add_node(client, parent_id: Optional[str], node_id: str, node_data: di
             # print('NODE ID', node_id)
             # print('NODE DATA', node_data)
             result = await client.add_node(node_id, node_properties = node_data)
-            print("added node", result)
+
+            graph_config = get_graph_config()
 
             # Add an edge if a parent ID is provided and the graph engine is NETWORKX
             if parent_id and "default_relationship" in node_data and graph_config.graph_engine == GraphDBType.NETWORKX:

cognee/modules/cognify/llm/resolve_cross_graph_references.py

@@ -1,8 +1,5 @@
 from typing import Dict, List
-from cognee.infrastructure.databases.graph.config import get_graph_config
 from cognee.infrastructure.databases.vector.config import get_vectordb_config
-graph_config = get_graph_config()
-vectordb_config = get_vectordb_config()
 
 async def resolve_cross_graph_references(nodes_by_layer: Dict):
     results = []
@@ -19,6 +16,7 @@ async def resolve_cross_graph_references(nodes_by_layer: Dict):
     return results
 
 async def get_nodes_by_layer(layer_id: str, layer_nodes: List):
+    vectordb_config = get_vectordb_config()
     vector_engine = vectordb_config.vector_engine
 
     score_points = await vector_engine.batch_search(

cognee/modules/data/extraction/knowledge_graph/extract_knowledge_graph_module.py

@@ -3,12 +3,9 @@ import dspy
 import nltk
 from nltk.corpus import stopwords
 from nltk.tokenize import word_tokenize
-from cognee.config import Config
+from cognee.infrastructure.llm import get_llm_config
 from cognee.shared.data_models import KnowledgeGraph, Node, Edge
-from cognee.utils import num_tokens_from_string, trim_text_to_max_tokens
+from cognee.utils import trim_text_to_max_tokens
-
-config = Config()
-config.load()
 
 # """Instructions:
 # You are a top-tier algorithm designed for extracting information from text in structured formats to build a knowledge graph.
@@ -41,7 +38,9 @@ def are_all_nodes_connected(graph: KnowledgeGraph) -> bool:
 
 
 class ExtractKnowledgeGraph(dspy.Module):
-    def __init__(self, lm = dspy.OpenAI(model = config.llm_model, api_key = config.llm_api_key, model_type = "chat", max_tokens = 4096)):
+    llm_config = get_llm_config()
+
+    def __init__(self, lm = dspy.OpenAI(model = llm_config.llm_model, api_key = llm_config.llm_api_key, model_type = "chat", max_tokens = 4096)):
         super().__init__()
         self.lm = lm
         dspy.settings.configure(lm=self.lm)
@@ -50,7 +49,7 @@ class ExtractKnowledgeGraph(dspy.Module):
 
     def forward(self, context: str, question: str):
         context = remove_stop_words(context)
-        context = trim_text_to_max_tokens(context, 1500, config.llm_model)
+        context = trim_text_to_max_tokens(context, 1500, self.llm_config.llm_model)
       
         with dspy.context(lm = self.lm):
             graph = self.generate_graph(text = context).graph

cognee/modules/ingestion/add_data_to_dataset.py

@@ -1,47 +0,0 @@
-import logging
-from cognee.infrastructure import infrastructure_config
-from cognee.infrastructure.data import Dataset, Data
-from cognee.infrastructure.files import remove_file_from_storage
-from cognee.infrastructure.databases.relational import DatabaseEngine
-from cognee.infrastructure.databases.relational.config import get_relationaldb_config
-config = get_relationaldb_config()
-
-logger = logging.getLogger(__name__)
-
-async def add_data_to_dataset(dataset: Dataset, data: Data):
-    db_engine: DatabaseEngine = config.database_engine
-
-    existing_dataset = (await db_engine.query_entity(dataset)).scalar()
-    existing_data = (await db_engine.query_entity(data)).scalar()
-
-    if existing_dataset:
-        if existing_data:
-            await remove_old_raw_data(existing_data.raw_data_location)
-
-            def update_raw_data():
-                existing_data.raw_data_location = data.raw_data_location
-
-            await db_engine.update(update_raw_data)
-
-            if existing_dataset.id == dataset.id and dataset.name is not None:
-                def update_name(): existing_dataset.name = dataset.name
-                await db_engine.update(update_name)
-        else:
-            await db_engine.update(lambda: existing_dataset.data.append(data))
-    else:
-        if existing_data:
-            await remove_old_raw_data(existing_data.raw_data_location)
-
-            existing_data.raw_data_location = data.raw_data_location
-
-            await db_engine.update(lambda: dataset.data.append(existing_data))
-        else:
-            await db_engine.update(lambda: dataset.data.append(data))
-
-        await db_engine.create(dataset)
-
-async def remove_old_raw_data(data_location: str):
-    try:
-        await remove_file_from_storage(data_location)
-    except Exception:
-        logger.error("Failed to remove old raw data file: %s", data_location)

cognee/modules/ingestion/save.py

@@ -1,27 +0,0 @@
-import asyncio
-from uuid import UUID, uuid4
-from cognee.infrastructure.data import Data, Dataset
-from .add_data_to_dataset import add_data_to_dataset
-from .data_types import IngestionData
-
-async def save(dataset_id: UUID, dataset_name: str, data_id: UUID, data: IngestionData):
-    file_path = uuid4().hex + "." + data.get_extension()
-
-    promises = []
-
-    promises.append(
-        add_data_to_dataset(
-            Dataset(
-                id = dataset_id,
-                name = dataset_name if dataset_name else dataset_id.hex
-            ),
-            Data(
-                id = data_id,
-                raw_data_location = file_path,
-                name = data.metadata["name"],
-                meta_data = data.metadata
-            )
-        )
-    )
-
-    await asyncio.gather(*promises)

cognee/modules/search/graph/search_adjacent.py

@@ -5,7 +5,7 @@ from typing import Union, Dict
 import networkx as nx
 from cognee.shared.data_models import GraphDBType
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
+
 async def search_adjacent(graph: Union[nx.Graph, any], query: str, other_param: dict = None) -> Dict[str, str]:
     """
     Find the neighbours of a given node in the graph and return their descriptions.
@@ -23,7 +23,9 @@ async def search_adjacent(graph: Union[nx.Graph, any], query: str, other_param:
 
     if node_id is None:
         return {}
-    from cognee.infrastructure import infrastructure_config
+
+    graph_config = get_graph_config()
+
     if graph_config.graph_engine == GraphDBType.NETWORKX:
         if node_id not in graph:
             return {}

cognee/modules/search/graph/search_categories.py

@@ -1,19 +1,11 @@
-from typing import Union, Dict
-import re
-
-from pydantic import BaseModel
-
-from cognee.modules.search.llm.extraction.categorize_relevant_category import categorize_relevant_category
-
 """ Search categories in the graph and return their summary attributes. """
-
+from typing import Union
-from cognee.shared.data_models import GraphDBType, DefaultContentPrediction
+import re
 import networkx as nx
-
+from pydantic import BaseModel
+from cognee.shared.data_models import GraphDBType
+from cognee.modules.search.llm.extraction.categorize_relevant_category import categorize_relevant_category
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-vector_config = get_vectordb_config()
 
 def strip_exact_regex(s, substring):
     # Escaping substring to be used in a regex pattern
@@ -25,7 +17,7 @@ def strip_exact_regex(s, substring):
 class DefaultResponseModel(BaseModel):
     document_id: str
 
-async def search_categories(query:str, graph: Union[nx.Graph, any], query_label: str=None, infrastructure_config: Dict=None):
+async def search_categories(query:str, graph: Union[nx.Graph, any], query_label: str=None):
     """
     Filter nodes in the graph that contain the specified label and return their summary attributes.
     This function supports both NetworkX graphs and Neo4j graph databases.
@@ -33,7 +25,6 @@ async def search_categories(query:str, graph: Union[nx.Graph, any], query_label:
     Parameters:
     - graph (Union[nx.Graph, AsyncSession]): The graph object or Neo4j session.
     - query_label (str): The label to filter nodes by.
-    - infrastructure_config (Dict): Configuration that includes the graph engine type.
 
     Returns:
     - Union[Dict, List[Dict]]: For NetworkX, returns a dictionary where keys are node identifiers,
@@ -41,21 +32,22 @@ async def search_categories(query:str, graph: Union[nx.Graph, any], query_label:
       each representing a node with 'nodeId' and 'summary'.
     """
     # Determine which client is in use based on the configuration
-    from cognee.infrastructure import infrastructure_config
+    graph_config = get_graph_config()
+
     if graph_config.graph_engine == GraphDBType.NETWORKX:
 
         categories_and_ids = [
-            {'document_id': strip_exact_regex(_, "DATA_SUMMARY__"), 'Summary': data['summary']}
+            {"document_id": strip_exact_regex(_, "DATA_SUMMARY__"), "Summary": data["summary"]}
             for _, data in graph.nodes(data=True)
-            if 'summary' in data
+            if "summary" in data
         ]
         connected_nodes = []
         for id in categories_and_ids:
             print("id", id)
-            connected_nodes.append(list(graph.neighbors(id['document_id'])))
+            connected_nodes.append(list(graph.neighbors(id["document_id"])))
         check_relevant_category = await categorize_relevant_category(query, categories_and_ids, response_model=DefaultResponseModel )
-        connected_nodes = list(graph.neighbors(check_relevant_category['document_id']))
+        connected_nodes = list(graph.neighbors(check_relevant_category["document_id"]))
-        descriptions = {node: graph.nodes[node].get('description', 'No desc available') for node in connected_nodes}
+        descriptions = {node: graph.nodes[node].get("description", "No desc available") for node in connected_nodes}
         return descriptions
 
     elif graph_config.graph_engine == GraphDBType.NEO4J:

cognee/modules/search/graph/search_cypher.py

@@ -1,24 +1,15 @@
 
-from typing import Union, Dict
-import re
-
 import networkx as nx
-from pydantic import BaseModel
+from typing import Union
-
-from cognee.modules.search.llm.extraction.categorize_relevant_category import categorize_relevant_category
 from cognee.shared.data_models import GraphDBType
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-vector_config = get_vectordb_config()
 
 async def search_cypher(query:str, graph: Union[nx.Graph, any]):
     """
     Use a Cypher query to search the graph and return the results.
     """
+    graph_config = get_graph_config()
 
-
-    from cognee.infrastructure import infrastructure_config
     if graph_config.graph_engine == GraphDBType.NEO4J:
         result = await graph.run(query)
         return result

cognee/modules/search/graph/search_neighbour.py

@@ -1,15 +1,10 @@
 """ Fetches the context of a given node in the graph"""
-from typing import Union, Dict
-
-from neo4j import AsyncSession
-
-from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client
 import networkx as nx
+from typing import Union
+from neo4j import AsyncSession
 from cognee.shared.data_models import GraphDBType
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
+
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-vector_config = get_vectordb_config()
 async def search_neighbour(graph: Union[nx.Graph, any], query: str,
                            other_param: dict = None):
     """
@@ -25,19 +20,20 @@ async def search_neighbour(graph: Union[nx.Graph, any], query: str,
     Returns:
     - List[str]: A list of 'description' attributes of nodes that share the same 'layer_uuid' with the specified node.
     """
-    from cognee.infrastructure import infrastructure_config
     node_id = other_param.get('node_id') if other_param else query
 
     if node_id is None:
         return []
 
+    graph_config = get_graph_config()
+
     if graph_config.graph_engine == GraphDBType.NETWORKX:
         relevant_context = []
-        target_layer_uuid = graph.nodes[node_id].get('layer_uuid')
+        target_layer_uuid = graph.nodes[node_id].get("layer_uuid")
 
         for n, attr in graph.nodes(data=True):
-            if attr.get('layer_uuid') == target_layer_uuid and 'description' in attr:
+            if attr.get("layer_uuid") == target_layer_uuid and "description" in attr:
-                relevant_context.append(attr['description'])
+                relevant_context.append(attr["description"])
 
         return relevant_context
 

cognee/modules/search/graph/search_summary.py

@@ -1,23 +1,16 @@
-
+import re
-
-
 from typing import Union, Dict
 import networkx as nx
-from cognee.infrastructure import infrastructure_config
-
 from cognee.modules.search.llm.extraction.categorize_relevant_summary import categorize_relevant_summary
 from cognee.shared.data_models import GraphDBType, ResponseSummaryModel
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
-from cognee.infrastructure.databases.vector.config import get_vectordb_config
-vector_config = get_vectordb_config()
-import re
 
 def strip_exact_regex(s, substring):
     # Escaping substring to be used in a regex pattern
     pattern = re.escape(substring)
     # Regex to match the exact substring at the start and end
     return re.sub(f"^{pattern}|{pattern}$", "", s)
+
 async def search_summary( query: str,  graph: Union[nx.Graph, any]) -> Dict[str, str]:
     """
     Filter nodes based on a condition (such as containing 'SUMMARY' in their identifiers) and return their summary attributes.
@@ -32,13 +25,13 @@ async def search_summary( query: str,  graph: Union[nx.Graph, any]) -> Dict[str,
     Returns:
     - Dict[str, str]: A dictionary where keys are node identifiers containing the query string, and values are their 'summary' attributes.
     """
+    graph_config = get_graph_config()
 
     if graph_config.graph_engine == GraphDBType.NETWORKX:
-        print("graph", graph)
         summaries_and_ids = [
-            {'document_id': strip_exact_regex(_, "DATA_SUMMARY__"), 'Summary': data['summary']}
+            {"document_id": strip_exact_regex(_, "DATA_SUMMARY__"), "Summary": data["summary"]}
             for _, data in graph.nodes(data=True)
-            if 'summary' in data
+            if "summary" in data
         ]
         print("summaries_and_ids", summaries_and_ids)
         check_relevant_summary = await categorize_relevant_summary(query, summaries_and_ids, response_model=ResponseSummaryModel)

cognee/modules/search/vector/search_similarity.py

@@ -1,10 +1,10 @@
 from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client
 from cognee.infrastructure.databases.graph.config import get_graph_config
-graph_config = get_graph_config()
 from cognee.infrastructure.databases.vector.config import get_vectordb_config
-vector_config = get_vectordb_config()
 
 async def search_similarity(query: str, graph):
+    graph_config = get_graph_config()
+
     graph_db_type = graph_config.graph_engine
 
     graph_client = await get_graph_client(graph_db_type)
@@ -17,6 +17,8 @@ async def search_similarity(query: str, graph):
 
     graph_nodes = []
 
+    vector_config = get_vectordb_config()
+
     for layer_id in unique_layer_uuids:
         vector_engine = vector_config.vector_engine
 

cognee/modules/tasks/create_task_status_table.py

@@ -1,10 +1,8 @@
-from cognee.infrastructure.InfrastructureConfig import infrastructure_config
 from cognee.infrastructure.databases.relational.config import get_relationaldb_config
 
-config = get_relationaldb_config()
-
 def create_task_status_table():
-    db_engine = config.db_engine
+    config = get_relationaldb_config()
+    db_engine = config.database_engine
 
     db_engine.create_table("cognee_task_status", [
         dict(name = "data_id", type = "STRING"),

cognee/modules/tasks/get_task_status.py

@@ -2,7 +2,7 @@ from cognee.infrastructure.databases.relational.config import get_relationaldb_c
 
 def get_task_status(data_ids: [str]):
     relational_config = get_relationaldb_config()
-    db_engine = relational_config.db_engine
+    db_engine = relational_config.database_engine
 
     formatted_data_ids = ", ".join([f"'{data_id}'" for data_id in data_ids])
 

cognee/modules/tasks/update_task_status.py

@@ -1,8 +1,6 @@
-from cognee.infrastructure.InfrastructureConfig import infrastructure_config
 from cognee.infrastructure.databases.relational.config import get_relationaldb_config
 
-config = get_relationaldb_config()
-
 def update_task_status(data_id: str, status: str):
-    db_engine = config.db_engine
+    config = get_relationaldb_config()
+    db_engine = config.database_engine
     db_engine.insert_data("cognee_task_status", [dict(data_id = data_id, status = status)])

evals/simple_rag_vs_cognee_eval.py

@@ -2,9 +2,8 @@ from deepeval.dataset import EvaluationDataset
 from pydantic import BaseModel
 
 
-from typing import List, Type
+from typing import List, Type, Dict
 from deepeval.test_case import LLMTestCase
-from deepeval.dataset import Golden
 import dotenv
 dotenv.load_dotenv()
 
@@ -42,7 +41,6 @@ print(dataset)
 
 
 import logging
-from typing import List, Dict
 from cognee.infrastructure import infrastructure_config
 
 logger = logging.getLogger(__name__)