Merge branch 'dev' into feature/cog-2728-bug-cors-issue-if-frontend-runs-on-any-port-other-than-3000

.github/actions/cognee_setup/action.yml

@@ -24,7 +24,7 @@ runs:
       uses: astral-sh/setup-uv@v4
       with:
         enable-cache: true
-    
+
     - name: Rebuild uv lockfile
       shell: bash
       run: |

.github/workflows/basic_tests.yml

@@ -188,6 +188,7 @@ jobs:
         uses: ./.github/actions/cognee_setup
         with:
           python-version: ${{ inputs.python-version }}
+          extra-dependencies: "baml"
 
       - name: Run Simple Examples
         run: uv run python ./examples/python/simple_example.py

.github/workflows/e2e_tests.yml

@@ -166,9 +166,6 @@ jobs:
             python-version: '3.11.x'
             extra-dependencies: "aws"
 
-        - name: Dependencies already installed
-          run: echo "Dependencies already installed in setup"
-
         - name: Run S3 Bucket Test
           env:
             ENV: 'dev'

.github/workflows/test_s3_file_storage.yml

@@ -18,6 +18,7 @@ jobs:
         uses: ./.github/actions/cognee_setup
         with:
           python-version: '3.11.x'
+          extra-dependencies: "aws"
 
       - name: Run S3 File Storage Test
         env:

cognee/api/client.py

@@ -3,7 +3,6 @@
 import os
 
 import uvicorn
-import sentry_sdk
 from traceback import format_exc
 from contextlib import asynccontextmanager
 from fastapi import Request
@@ -42,11 +41,18 @@ from cognee.modules.users.methods.get_authenticated_user import REQUIRE_AUTHENTI
 logger = get_logger()
 
 if os.getenv("ENV", "prod") == "prod":
-    sentry_sdk.init(
-        dsn=os.getenv("SENTRY_REPORTING_URL"),
-        traces_sample_rate=1.0,
-        profiles_sample_rate=1.0,
-    )
+    try:
+        import sentry_sdk
+
+        sentry_sdk.init(
+            dsn=os.getenv("SENTRY_REPORTING_URL"),
+            traces_sample_rate=1.0,
+            profiles_sample_rate=1.0,
+        )
+    except ImportError:
+        logger.info(
+            "Sentry SDK not available. Install with 'pip install cognee\"[monitoring]\"' to enable error monitoring."
+        )
 
 
 app_environment = os.getenv("ENV", "prod")

cognee/eval_framework/modal_eval_dashboard.py

@@ -1,6 +1,6 @@
 import os
 import json
-import pandas as pd
+
 import subprocess
 import modal
 import streamlit as st
@@ -78,6 +78,14 @@ def main():
             }
         )
 
+    try:
+        import pandas as pd
+    except ImportError:
+        st.error(
+            "Pandas is required for the evaluation dashboard. Install with 'pip install cognee\"[evals]\"' to use this feature."
+        )
+        return
+
     df = pd.DataFrame(records)
     if df.empty:
         st.warning("No JSON files found in the volume.")

cognee/infrastructure/data/utils/extract_keywords.py

@@ -1,48 +0,0 @@
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-from cognee.infrastructure.data.exceptions.exceptions import KeywordExtractionError
-from cognee.shared.utils import extract_pos_tags
-
-
-def extract_keywords(text: str) -> list[str]:
-    """
-    Extract keywords from the provided text string.
-
-    This function raises an KeyWordExtractionError if the input text is empty. It processes the
-    text to extract parts of speech, focusing on nouns, and uses TF-IDF to identify the most
-    relevant keywords based on their frequency. The function returns a list of up to 15
-    keywords, each having more than 3 characters.
-
-    Parameters:
-    -----------
-
-        - text (str): The input text from which to extract keywords.
-
-    Returns:
-    --------
-
-        - list[str]: A list of keywords extracted from the text, containing up to 15 nouns
-          with more than 3 characters.
-    """
-    if len(text) == 0:
-        raise KeywordExtractionError()
-
-    tags = extract_pos_tags(text)
-    nouns = [word for (word, tag) in tags if tag == "NN"]
-
-    vectorizer = TfidfVectorizer()
-    tfidf = vectorizer.fit_transform(nouns)
-
-    top_nouns = sorted(
-        vectorizer.vocabulary_, key=lambda x: tfidf[0, vectorizer.vocabulary_[x]], reverse=True
-    )
-
-    keywords = []
-
-    for word in top_nouns:
-        if len(word) > 3:
-            keywords.append(word)
-        if len(keywords) >= 15:
-            break
-
-    return keywords

cognee/infrastructure/databases/relational/create_relational_engine.py

@@ -39,8 +39,16 @@ def create_relational_engine(
         connection_string = f"sqlite+aiosqlite:///{db_path}/{db_name}"
 
     if db_provider == "postgres":
-        connection_string = (
-            f"postgresql+asyncpg://{db_username}:{db_password}@{db_host}:{db_port}/{db_name}"
-        )
+        try:
+            # Test if asyncpg is available
+            import asyncpg
+
+            connection_string = (
+                f"postgresql+asyncpg://{db_username}:{db_password}@{db_host}:{db_port}/{db_name}"
+            )
+        except ImportError:
+            raise ImportError(
+                "PostgreSQL dependencies are not installed. Please install with 'pip install cognee\"[postgres]\"' or 'pip install cognee\"[postgres-binary]\"' to use PostgreSQL functionality."
+            )
 
     return SQLAlchemyAdapter(connection_string)

cognee/infrastructure/databases/vector/create_vector_engine.py

@@ -66,7 +66,12 @@ def create_vector_engine(
             f"postgresql+asyncpg://{db_username}:{db_password}@{db_host}:{db_port}/{db_name}"
         )
 
-        from .pgvector.PGVectorAdapter import PGVectorAdapter
+        try:
+            from .pgvector.PGVectorAdapter import PGVectorAdapter
+        except ImportError:
+            raise ImportError(
+                "PostgreSQL dependencies are not installed. Please install with 'pip install cognee\"[postgres]\"' or 'pip install cognee\"[postgres-binary]\"' to use PGVector functionality."
+            )
 
         return PGVectorAdapter(
             connection_string,

cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py

@@ -205,9 +205,12 @@ class LanceDBAdapter(VectorDBInterface):
         collection = await self.get_collection(collection_name)
 
         if len(data_point_ids) == 1:
-            results = await collection.query().where(f"id = '{data_point_ids[0]}'").to_pandas()
+            results = await collection.query().where(f"id = '{data_point_ids[0]}'")
         else:
-            results = await collection.query().where(f"id IN {tuple(data_point_ids)}").to_pandas()
+            results = await collection.query().where(f"id IN {tuple(data_point_ids)}")
+
+        # Convert query results to list format
+        results_list = results.to_list() if hasattr(results, "to_list") else list(results)
 
         return [
             ScoredResult(
@@ -215,7 +218,7 @@ class LanceDBAdapter(VectorDBInterface):
                 payload=result["payload"],
                 score=0,
             )
-            for result in results.to_dict("index").values()
+            for result in results_list
         ]
 
     async def search(
@@ -242,9 +245,7 @@ class LanceDBAdapter(VectorDBInterface):
         if limit == 0:
             return []
 
-        results = await collection.vector_search(query_vector).limit(limit).to_pandas()
-
-        result_values = list(results.to_dict("index").values())
+        result_values = await collection.vector_search(query_vector).limit(limit).to_list()
 
         if not result_values:
             return []

cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py

@@ -9,7 +9,6 @@ from sqlalchemy.exc import ProgrammingError
 from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential
 from asyncpg import DeadlockDetectedError, DuplicateTableError, UniqueViolationError
 
-
 from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.engine import DataPoint
 from cognee.infrastructure.engine.utils import parse_id

cognee/infrastructure/files/storage/S3FileStorage.py

@@ -1,6 +1,5 @@
 import os
-import s3fs
-from typing import BinaryIO, Union
+from typing import BinaryIO, Union, TYPE_CHECKING
 from contextlib import asynccontextmanager
 
 from cognee.infrastructure.files.storage.s3_config import get_s3_config
@@ -8,17 +7,27 @@ from cognee.infrastructure.utils.run_async import run_async
 from cognee.infrastructure.files.storage.FileBufferedReader import FileBufferedReader
 from .storage import Storage
 
+if TYPE_CHECKING:
+    import s3fs
+
 
 class S3FileStorage(Storage):
     """
-    Manage local file storage operations such as storing, retrieving, and managing files on
-    the filesystem.
+    Manage S3 file storage operations such as storing, retrieving, and managing files on
+    S3-compatible storage.
     """
 
     storage_path: str
-    s3: s3fs.S3FileSystem
+    s3: "s3fs.S3FileSystem"
 
     def __init__(self, storage_path: str):
+        try:
+            import s3fs
+        except ImportError:
+            raise ImportError(
+                's3fs is required for S3FileStorage. Install it with: pip install cognee"[aws]"'
+            )
+
         self.storage_path = storage_path
         s3_config = get_s3_config()
         if s3_config.aws_access_key_id is not None and s3_config.aws_secret_access_key is not None:

cognee/infrastructure/files/utils/open_data_file.py

@@ -4,7 +4,6 @@ from urllib.parse import urlparse
 from contextlib import asynccontextmanager
 
 from cognee.infrastructure.files.utils.get_data_file_path import get_data_file_path
-from cognee.infrastructure.files.storage.S3FileStorage import S3FileStorage
 from cognee.infrastructure.files.storage.LocalFileStorage import LocalFileStorage
 
 
@@ -23,23 +22,17 @@ async def open_data_file(file_path: str, mode: str = "rb", encoding: str = None,
             yield file
 
     elif file_path.startswith("s3://"):
+        try:
+            from cognee.infrastructure.files.storage.S3FileStorage import S3FileStorage
+        except ImportError:
+            raise ImportError(
+                "S3 dependencies are not installed. Please install with 'pip install cognee\"[aws]\"' to use S3 functionality."
+            )
+
         normalized_url = get_data_file_path(file_path)
         s3_dir_path = os.path.dirname(normalized_url)
         s3_filename = os.path.basename(normalized_url)
 
-        # if "/" in s3_path:
-        #     s3_dir = "/".join(s3_path.split("/")[:-1])
-        #     s3_filename = s3_path.split("/")[-1]
-        # else:
-        #     s3_dir = ""
-        #     s3_filename = s3_path
-
-        # Extract filesystem path from S3 URL structure
-        # file_dir_path = (
-        #     f"s3://{parsed_url.netloc}/{s3_dir}" if s3_dir else f"s3://{parsed_url.netloc}"
-        # )
-        # file_name = s3_filename
-
         file_storage = S3FileStorage(s3_dir_path)
 
         async with file_storage.open(s3_filename, mode=mode, **kwargs) as file:

cognee/infrastructure/llm/config.py

@@ -1,9 +1,13 @@
 import os
-from typing import Optional, ClassVar
+from typing import Optional, ClassVar, Any
 from functools import lru_cache
 from pydantic_settings import BaseSettings, SettingsConfigDict
 from pydantic import model_validator
-from baml_py import ClientRegistry
+
+try:
+    from baml_py import ClientRegistry
+except ImportError:
+    ClientRegistry = None
 
 
 class LLMConfig(BaseSettings):
@@ -65,27 +69,36 @@ class LLMConfig(BaseSettings):
     fallback_endpoint: str = ""
     fallback_model: str = ""
 
-    baml_registry: ClassVar[ClientRegistry] = ClientRegistry()
+    baml_registry: Optional[Any] = None
 
     model_config = SettingsConfigDict(env_file=".env", extra="allow")
 
     def model_post_init(self, __context) -> None:
         """Initialize the BAML registry after the model is created."""
-        raw_options = {
-            "model": self.baml_llm_model,
-            "temperature": self.baml_llm_temperature,
-            "api_key": self.baml_llm_api_key,
-            "base_url": self.baml_llm_endpoint,
-            "api_version": self.baml_llm_api_version,
-        }
+        # Check if BAML is selected as structured output framework but not available
+        if self.structured_output_framework.lower() == "baml" and ClientRegistry is None:
+            raise ImportError(
+                "BAML is selected as structured output framework but not available. "
+                "Please install with 'pip install cognee\"[baml]\"' to use BAML extraction features."
+            )
+        elif self.structured_output_framework.lower() == "baml" and ClientRegistry is not None:
+            self.baml_registry = ClientRegistry()
 
-        # Note: keep the item only when the value is not None or an empty string (they would override baml default values)
-        options = {k: v for k, v in raw_options.items() if v not in (None, "")}
-        self.baml_registry.add_llm_client(
-            name=self.baml_llm_provider, provider=self.baml_llm_provider, options=options
-        )
-        # Sets the primary client
-        self.baml_registry.set_primary(self.baml_llm_provider)
+            raw_options = {
+                "model": self.baml_llm_model,
+                "temperature": self.baml_llm_temperature,
+                "api_key": self.baml_llm_api_key,
+                "base_url": self.baml_llm_endpoint,
+                "api_version": self.baml_llm_api_version,
+            }
+
+            # Note: keep the item only when the value is not None or an empty string (they would override baml default values)
+            options = {k: v for k, v in raw_options.items() if v not in (None, "")}
+            self.baml_registry.add_llm_client(
+                name=self.baml_llm_provider, provider=self.baml_llm_provider, options=options
+            )
+            # Sets the primary client
+            self.baml_registry.set_primary(self.baml_llm_provider)
 
     @model_validator(mode="after")
     def ensure_env_vars_for_ollama(self) -> "LLMConfig":

cognee/infrastructure/llm/structured_output_framework/baml/baml_src/extraction/acreate_structured_output.py

@@ -53,7 +53,8 @@ async def acreate_structured_output(
 
     # Transform BAML response to proper pydantic reponse model
     if response_model is str:
-        return str(result)
+        # Note: when a response model is set to string in python, result is stored in text property in the BAML response model
+        return str(result.text)
     return response_model.model_validate(result.dict())
 
 

cognee/modules/ingestion/data_types/TextData.py

@@ -1,7 +1,6 @@
 from typing import BinaryIO
 from contextlib import asynccontextmanager
 import hashlib
-from cognee.infrastructure.data.utils.extract_keywords import extract_keywords
 from .IngestionData import IngestionData
 
 

cognee/modules/visualization/cognee_network_visualization.py

@@ -1,6 +1,5 @@
 import os
 import json
-import networkx
 
 from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.files.storage.LocalFileStorage import LocalFileStorage
@@ -9,6 +8,8 @@ logger = get_logger()
 
 
 async def cognee_network_visualization(graph_data, destination_file_path: str = None):
+    import networkx
+
     nodes_data, edges_data = graph_data
 
     G = networkx.DiGraph()
@@ -104,7 +105,7 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
             .nodes circle { stroke: white; stroke-width: 0.5px; filter: drop-shadow(0 0 5px rgba(255,255,255,0.3)); }
             .node-label { font-size: 5px; font-weight: bold; fill: white; text-anchor: middle; dominant-baseline: middle; font-family: 'Inter', sans-serif; pointer-events: none; }
             .edge-label { font-size: 3px; fill: rgba(255, 255, 255, 0.7); text-anchor: middle; dominant-baseline: middle; font-family: 'Inter', sans-serif; pointer-events: none; }
-            
+
             .tooltip {
                 position: absolute;
                 text-align: left;
@@ -166,7 +167,7 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
                     // Create tooltip content for edge
                     var content = "<strong>Edge Information</strong><br/>";
                     content += "Relationship: " + d.relation + "<br/>";
-                    
+
                     // Show all weights
                     if (d.all_weights && Object.keys(d.all_weights).length > 0) {
                         content += "<strong>Weights:</strong><br/>";
@@ -176,23 +177,23 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
                     } else if (d.weight !== null && d.weight !== undefined) {
                         content += "Weight: " + d.weight + "<br/>";
                     }
-                    
+
                     if (d.relationship_type) {
                         content += "Type: " + d.relationship_type + "<br/>";
                     }
-                    
+
                     // Add other edge properties
                     if (d.edge_info) {
                         Object.keys(d.edge_info).forEach(function(key) {
-                            if (key !== 'weight' && key !== 'weights' && key !== 'relationship_type' && 
-                                key !== 'source_node_id' && key !== 'target_node_id' && 
-                                key !== 'relationship_name' && key !== 'updated_at' && 
+                            if (key !== 'weight' && key !== 'weights' && key !== 'relationship_type' &&
+                                key !== 'source_node_id' && key !== 'target_node_id' &&
+                                key !== 'relationship_name' && key !== 'updated_at' &&
                                 !key.startsWith('weight_')) {
                                 content += key + ": " + d.edge_info[key] + "<br/>";
                             }
                         });
                     }
-                    
+
                     tooltip.html(content)
                         .style("left", (d3.event.pageX + 10) + "px")
                         .style("top", (d3.event.pageY - 10) + "px")

cognee/shared/utils.py

@@ -4,7 +4,6 @@ import os
 import ssl
 import requests
 from datetime import datetime, timezone
-import matplotlib.pyplot as plt
 import http.server
 import socketserver
 from threading import Thread
@@ -30,37 +29,6 @@ def create_secure_ssl_context() -> ssl.SSLContext:
     return ssl.create_default_context()
 
 
-def get_entities(tagged_tokens):
-    import nltk
-
-    nltk.download("maxent_ne_chunker", quiet=True)
-
-    from nltk.chunk import ne_chunk
-
-    return ne_chunk(tagged_tokens)
-
-
-def extract_pos_tags(sentence):
-    """Extract Part-of-Speech (POS) tags for words in a sentence."""
-    import nltk
-
-    # Ensure that the necessary NLTK resources are downloaded
-    nltk.download("words", quiet=True)
-    nltk.download("punkt", quiet=True)
-    nltk.download("averaged_perceptron_tagger", quiet=True)
-
-    from nltk.tag import pos_tag
-    from nltk.tokenize import word_tokenize
-
-    # Tokenize the sentence into words
-    tokens = word_tokenize(sentence)
-
-    # Tag each word with its corresponding POS tag
-    pos_tags = pos_tag(tokens)
-
-    return pos_tags
-
-
 def get_anonymous_id():
     """Creates or reads a anonymous user id"""
     tracking_id = os.getenv("TRACKING_ID", None)

cognee/tasks/graph/infer_data_ontology.py

@@ -1,309 +0,0 @@
-# PROPOSED TO BE DEPRECATED
-
-"""This module contains the OntologyEngine class which is responsible for adding graph ontology from a JSON or CSV file."""
-
-import csv
-import json
-from cognee.shared.logging_utils import get_logger
-from datetime import datetime, timezone
-from fastapi import status
-from typing import Any, Dict, List, Optional, Union, Type
-
-import aiofiles
-import pandas as pd
-from pydantic import BaseModel
-
-from cognee.modules.graph.exceptions import EntityNotFoundError
-from cognee.modules.ingestion.exceptions import IngestionError
-
-from cognee.infrastructure.data.chunking.config import get_chunk_config
-from cognee.infrastructure.data.chunking.get_chunking_engine import get_chunk_engine
-from cognee.infrastructure.databases.graph.get_graph_engine import get_graph_engine
-from cognee.infrastructure.files.utils.extract_text_from_file import extract_text_from_file
-from cognee.infrastructure.files.utils.guess_file_type import guess_file_type, FileTypeException
-from cognee.modules.data.methods.add_model_class_to_graph import (
-    add_model_class_to_graph,
-)
-from cognee.tasks.graph.models import NodeModel, GraphOntology
-from cognee.shared.data_models import KnowledgeGraph
-from cognee.modules.engine.utils import generate_node_id, generate_node_name
-from cognee.infrastructure.llm.LLMGateway import LLMGateway
-
-logger = get_logger("task:infer_data_ontology")
-
-
-async def extract_ontology(content: str, response_model: Type[BaseModel]):
-    """
-    Extracts structured ontology from the provided content using a pre-defined LLM client.
-
-    This asynchronous function retrieves a system prompt from a file and utilizes an LLM
-    client to create a structured output based on the input content and specified response
-    model.
-
-    Parameters:
-    -----------
-
-        - content (str): The content from which to extract the ontology.
-        - response_model (Type[BaseModel]): The model that defines the structure of the
-          output ontology.
-
-    Returns:
-    --------
-
-        The structured ontology extracted from the content.
-    """
-
-    system_prompt = LLMGateway.read_query_prompt("extract_ontology.txt")
-
-    ontology = await LLMGateway.acreate_structured_output(content, system_prompt, response_model)
-
-    return ontology
-
-
-class OntologyEngine:
-    """
-    Manage ontology data and operations for graph structures, providing methods for data
-    loading, flattening models, and adding ontological relationships to a graph database.
-
-    Public methods:
-
-    - flatten_model
-    - recursive_flatten
-    - load_data
-    - add_graph_ontology
-    """
-
-    async def flatten_model(
-        self, model: NodeModel, parent_id: Optional[str] = None
-    ) -> Dict[str, Any]:
-        """
-        Flatten the model to a dictionary including optional parent ID and relationship details
-        if available.
-
-        Parameters:
-        -----------
-
-            - model (NodeModel): The NodeModel instance to flatten.
-            - parent_id (Optional[str]): An optional ID of the parent node for hierarchical
-              purposes. (default None)
-
-        Returns:
-        --------
-
-            - Dict[str, Any]: A dictionary representation of the model with flattened
-              attributes.
-        """
-        result = model.dict()
-        result["parent_id"] = parent_id
-        if model.default_relationship:
-            result.update(
-                {
-                    "relationship_type": model.default_relationship.type,
-                    "relationship_source": model.default_relationship.source,
-                    "relationship_target": model.default_relationship.target,
-                }
-            )
-        return result
-
-    async def recursive_flatten(
-        self, items: Union[List[Dict[str, Any]], Dict[str, Any]], parent_id: Optional[str] = None
-    ) -> List[Dict[str, Any]]:
-        """
-        Recursively flatten a hierarchical structure of models into a flat list of dictionaries.
-
-        Parameters:
-        -----------
-
-            - items (Union[List[Dict[str, Any]], Dict[str, Any]]): A list or dictionary
-              containing models to flatten.
-            - parent_id (Optional[str]): An optional ID of the parent node to maintain hierarchy
-              during flattening. (default None)
-
-        Returns:
-        --------
-
-            - List[Dict[str, Any]]: A flat list of dictionaries representing the hierarchical
-              model structure.
-        """
-        flat_list = []
-
-        if isinstance(items, list):
-            for item in items:
-                flat_list.extend(await self.recursive_flatten(item, parent_id))
-        elif isinstance(items, dict):
-            model = NodeModel.model_validate(items)
-            flat_list.append(await self.flatten_model(model, parent_id))
-            for child in model.children:
-                flat_list.extend(await self.recursive_flatten(child, model.node_id))
-        return flat_list
-
-    async def load_data(self, file_path: str) -> Union[List[Dict[str, Any]], Dict[str, Any]]:
-        """
-        Load data from a specified JSON or CSV file and return it in a structured format.
-
-        Parameters:
-        -----------
-
-            - file_path (str): The path to the file to load data from.
-
-        Returns:
-        --------
-
-            - Union[List[Dict[str, Any]], Dict[str, Any]]: Parsed data from the file as either a
-              list of dictionaries or a single dictionary depending on content type.
-        """
-        try:
-            if file_path.endswith(".json"):
-                async with aiofiles.open(file_path, mode="r") as f:
-                    data = await f.read()
-                    return json.loads(data)
-            elif file_path.endswith(".csv"):
-                async with aiofiles.open(file_path, mode="r") as f:
-                    content = await f.read()
-                    reader = csv.DictReader(content.splitlines())
-                    return list(reader)
-            else:
-                raise IngestionError(message="Unsupported file format")
-        except Exception as e:
-            raise IngestionError(
-                message=f"Failed to load data from {file_path}: {e}",
-                status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
-            )
-
-    async def add_graph_ontology(self, file_path: str = None, documents: list = None):
-        """
-        Add graph ontology from a JSON or CSV file, or infer relationships from provided
-        document content. Raise exceptions for invalid file types or missing entities.
-
-        Parameters:
-        -----------
-
-            - file_path (str): Optional path to a file containing data to be loaded. (default
-              None)
-            - documents (list): Optional list of document objects for content extraction if no
-              file path is provided. (default None)
-        """
-        if file_path is None:
-            initial_chunks_and_ids = []
-
-            chunk_config = get_chunk_config()
-            chunk_engine = get_chunk_engine()
-            chunk_strategy = chunk_config.chunk_strategy
-
-            for base_file in documents:
-                with open(base_file.raw_data_location, "rb") as file:
-                    try:
-                        file_type = guess_file_type(file)
-                        text = extract_text_from_file(file, file_type)
-
-                        subchunks, chunks_with_ids = chunk_engine.chunk_data(
-                            chunk_strategy,
-                            text,
-                            chunk_config.chunk_size,
-                            chunk_config.chunk_overlap,
-                        )
-
-                        if chunks_with_ids[0][0] == 1:
-                            initial_chunks_and_ids.append({base_file.id: chunks_with_ids})
-
-                    except FileTypeException:
-                        logger.warning(
-                            "File (%s) has an unknown file type. We are skipping it.", file["id"]
-                        )
-
-            ontology = await extract_ontology(str(initial_chunks_and_ids), GraphOntology)
-            graph_client = await get_graph_engine()
-
-            await graph_client.add_nodes(
-                [
-                    (
-                        node.id,
-                        dict(
-                            uuid=generate_node_id(node.id),
-                            name=generate_node_name(node.name),
-                            type=generate_node_id(node.id),
-                            description=node.description,
-                            updated_at=datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"),
-                        ),
-                    )
-                    for node in ontology.nodes
-                ]
-            )
-
-            await graph_client.add_edges(
-                (
-                    generate_node_id(edge.source_id),
-                    generate_node_id(edge.target_id),
-                    edge.relationship_type,
-                    dict(
-                        source_node_id=generate_node_id(edge.source_id),
-                        target_node_id=generate_node_id(edge.target_id),
-                        relationship_name=edge.relationship_type,
-                        updated_at=datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S"),
-                    ),
-                )
-                for edge in ontology.edges
-            )
-
-        else:
-            dataset_level_information = documents[0][1]
-
-            # Extract the list of valid IDs from the explanations
-            valid_ids = {item["id"] for item in dataset_level_information}
-            try:
-                data = await self.load_data(file_path)
-                flt_ontology = await self.recursive_flatten(data)
-                df = pd.DataFrame(flt_ontology)
-                graph_client = await get_graph_engine()
-
-                for _, row in df.iterrows():
-                    node_data = row.to_dict()
-                    node_id = node_data.pop("node_id", None)
-                    if node_id in valid_ids:
-                        await graph_client.add_node(node_id, node_data)
-                    if node_id not in valid_ids:
-                        raise EntityNotFoundError(
-                            message=f"Node ID {node_id} not found in the dataset"
-                        )
-                    if pd.notna(row.get("relationship_source")) and pd.notna(
-                        row.get("relationship_target")
-                    ):
-                        await graph_client.add_edge(
-                            row["relationship_source"],
-                            row["relationship_target"],
-                            relationship_name=row["relationship_type"],
-                            edge_properties={
-                                "source_node_id": row["relationship_source"],
-                                "target_node_id": row["relationship_target"],
-                                "relationship_name": row["relationship_type"],
-                                "updated_at": datetime.now(timezone.utc).strftime(
-                                    "%Y-%m-%d %H:%M:%S"
-                                ),
-                            },
-                        )
-
-                return
-            except Exception as e:
-                raise RuntimeError(f"Failed to add graph ontology from {file_path}: {e}") from e
-
-
-async def infer_data_ontology(documents, ontology_model=KnowledgeGraph, root_node_id=None):
-    """
-    Infer data ontology from provided documents and optionally add it to a graph.
-
-    Parameters:
-    -----------
-
-        - documents: The documents from which to infer the ontology.
-        - ontology_model: The ontology model to use for the inference, defaults to
-          KnowledgeGraph. (default KnowledgeGraph)
-        - root_node_id: An optional root node identifier for the ontology. (default None)
-    """
-    if ontology_model == KnowledgeGraph:
-        ontology_engine = OntologyEngine()
-        root_node_id = await ontology_engine.add_graph_ontology(documents=documents)
-    else:
-        graph_engine = await get_graph_engine()
-        await add_model_class_to_graph(ontology_model, graph_engine)
-
-    yield (documents, root_node_id)

pyproject.toml

@@ -20,56 +20,47 @@ classifiers = [
     "Operating System :: Microsoft :: Windows",
 ]
 dependencies = [
-    "openai>=1.80.1,<2.0.0",
+    "openai>=1.80.1",
     "python-dotenv>=1.0.1,<2.0.0",
     "pydantic>=2.10.5,<3.0.0",
     "pydantic-settings>=2.2.1,<3",
     "typing_extensions>=4.12.2,<5.0.0",
-    "nltk>=3.9.1,<4.0.0",
     "numpy>=1.26.4, <=4.0.0",
-    "pandas>=2.2.2,<3.0.0",
-    # Note: New s3fs and boto3 versions don't work well together
-    # Always use comaptible fixed versions of these two dependencies
-    "s3fs[boto3]==2025.3.2",
     "sqlalchemy>=2.0.39,<3.0.0",
     "aiosqlite>=0.20.0,<1.0.0",
     "tiktoken>=0.8.0,<1.0.0",
-    "litellm>=1.71.0, <2.0.0",
+    "litellm>=1.76.0",
     "instructor>=1.9.1,<2.0.0",
-    "langfuse>=2.32.0,<3",
     "filetype>=1.2.0,<2.0.0",
     "aiohttp>=3.11.14,<4.0.0",
     "aiofiles>=23.2.1,<24.0.0",
     "rdflib>=7.1.4,<7.2.0",
     "pypdf>=4.1.0,<7.0.0",
     "jinja2>=3.1.3,<4",
-    "matplotlib>=3.8.3,<4",
-    "networkx>=3.4.2,<4",
     "lancedb>=0.24.0,<1.0.0",
     "nbformat>=5.7.0,<6.0.0",
     "alembic>=1.13.3,<2",
     "pre-commit>=4.0.1,<5",
-    "scikit-learn>=1.6.1,<2",
     "limits>=4.4.1,<5",
     "fastapi>=0.115.7,<1.0.0",
     "python-multipart>=0.0.20,<1.0.0",
     "fastapi-users[sqlalchemy]>=14.0.1,<15.0.0",
-    "dlt[sqlalchemy]>=1.9.0,<2",
-    "sentry-sdk[fastapi]>=2.9.0,<3",
     "structlog>=25.2.0,<26",
-    "baml-py (==0.206.0)",
     "pympler>=1.1,<2.0.0",
     "onnxruntime>=1.0.0,<2.0.0",
-    "pylance>=0.22.0,<1.0.0",
     "kuzu (==0.11.0)",
     "python-magic-bin<0.5 ; platform_system == 'Windows'", # Only needed for Windows
+    "fastembed<=0.6.0 ",
+    "networkx>=3.4.2,<4",
     "uvicorn>=0.34.0,<1.0.0",
     "gunicorn>=20.1.0,<24",
     "websockets>=15.0.1,<16.0.0"
+
 ]
 
 [project.optional-dependencies]
 api=[]
+
 distributed = [
     "modal>=1.0.5,<2.0.0",
 ]
@@ -115,15 +106,16 @@ codegraph = [
 evals = [
     "plotly>=6.0.0,<7",
     "gdown>=5.2.0,<6",
+    "pandas>=2.2.2,<3.0.0",
+    "matplotlib>=3.8.3,<4",
 ]
-gui = [
-    "pyside6>=6.8.3,<7",
-    "qasync>=0.27.1,<0.28",
-]
+
 graphiti = ["graphiti-core>=0.7.0,<0.8"]
 # Note: New s3fs and boto3 versions don't work well together
 # Always use comaptible fixed versions of these two dependencies
 aws = ["s3fs[boto3]==2025.3.2"]
+dlt = ["dlt[sqlalchemy]>=1.9.0,<2"]
+baml = ["baml-py (==0.206.0)"]
 dev = [
     "pytest>=7.4.0,<8",
     "pytest-cov>=6.1.1,<7.0.0",
@@ -142,6 +134,8 @@ dev = [
 ]
 debug = ["debugpy>=1.8.9,<2.0.0"]
 
+monitoring = ["sentry-sdk[fastapi]>=2.9.0,<3", "langfuse>=2.32.0,<3"]
+
 [project.urls]
 Homepage = "https://www.cognee.ai"
 Repository = "https://github.com/topoteretes/cognee"

uv.lock

@@ -3,16 +3,16 @@ revision = 2
 requires-python = ">=3.10, <=3.13"
 resolution-markers = [
     "python_full_version >= '3.13' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
-    "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
-    "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
-    "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
     "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
-    "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
-    "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
-    "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
     "python_full_version >= '3.13' and sys_platform == 'emscripten'",
+    "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
+    "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
     "python_full_version == '3.12.*' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
+    "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
     "python_full_version == '3.11.*' and sys_platform == 'emscripten'",
+    "python_full_version < '3.11' and platform_python_implementation != 'PyPy' and sys_platform != 'emscripten'",
+    "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform != 'emscripten'",
     "python_full_version < '3.11' and sys_platform == 'emscripten'",
 ]
 
@@ -222,7 +222,7 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
@@ -230,9 +230,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213, upload-time = "2025-08-04T08:54:24.882Z" },
+    { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
 ]
 
 [[package]]
@@ -297,14 +297,14 @@ wheels = [
 
 [[package]]
 name = "asgiref"
-version = "3.9.1"
+version = "3.9.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/90/61/0aa957eec22ff70b830b22ff91f825e70e1ef732c06666a805730f28b36b/asgiref-3.9.1.tar.gz", hash = "sha256:a5ab6582236218e5ef1648f242fd9f10626cfd4de8dc377db215d5d5098e3142", size = 36870, upload-time = "2025-07-08T09:07:43.344Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/7f/bf/0f3ecda32f1cb3bf1dca480aca08a7a8a3bdc4bed2343a103f30731565c9/asgiref-3.9.2.tar.gz", hash = "sha256:a0249afacb66688ef258ffe503528360443e2b9a8d8c4581b6ebefa58c841ef1", size = 36894, upload-time = "2025-09-23T15:00:55.136Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7c/3c/0464dcada90d5da0e71018c04a140ad6349558afb30b3051b4264cc5b965/asgiref-3.9.1-py3-none-any.whl", hash = "sha256:f3bba7092a48005b5f5bacd747d36ee4a5a61f4a269a6df590b43144355ebd2c", size = 23790, upload-time = "2025-07-08T09:07:41.548Z" },
+    { url = "https://files.pythonhosted.org/packages/c7/d1/69d02ce34caddb0a7ae088b84c356a625a93cd4ff57b2f97644c03fad905/asgiref-3.9.2-py3-none-any.whl", hash = "sha256:0b61526596219d70396548fc003635056856dba5d0d086f86476f10b33c75960", size = 23788, upload-time = "2025-09-23T15:00:53.627Z" },
 ]
 
 [[package]]
@@ -822,42 +822,33 @@ dependencies = [
     { name = "aiohttp" },
     { name = "aiosqlite" },
     { name = "alembic" },
-    { name = "baml-py" },
-    { name = "dlt", extra = ["sqlalchemy"] },
     { name = "fastapi" },
     { name = "fastapi-users", extra = ["sqlalchemy"] },
+    { name = "fastembed" },
     { name = "filetype" },
     { name = "gunicorn" },
     { name = "instructor" },
     { name = "jinja2" },
     { name = "kuzu" },
     { name = "lancedb" },
-    { name = "langfuse" },
     { name = "limits" },
     { name = "litellm" },
-    { name = "matplotlib" },
     { name = "nbformat" },
     { name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "networkx", version = "3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "nltk" },
     { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
     { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
     { name = "onnxruntime" },
     { name = "openai" },
-    { name = "pandas" },
     { name = "pre-commit" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
-    { name = "pylance" },
     { name = "pympler" },
     { name = "pypdf" },
     { name = "python-dotenv" },
     { name = "python-magic-bin", marker = "sys_platform == 'win32'" },
     { name = "python-multipart" },
     { name = "rdflib" },
-    { name = "s3fs", extra = ["boto3"] },
-    { name = "scikit-learn" },
-    { name = "sentry-sdk", extra = ["fastapi"] },
     { name = "sqlalchemy" },
     { name = "structlog" },
     { name = "tiktoken" },
@@ -873,6 +864,9 @@ anthropic = [
 aws = [
     { name = "s3fs", extra = ["boto3"] },
 ]
+baml = [
+    { name = "baml-py" },
+]
 chromadb = [
     { name = "chromadb" },
     { name = "pypika" },
@@ -908,11 +902,16 @@ dev = [
 distributed = [
     { name = "modal" },
 ]
+dlt = [
+    { name = "dlt", extra = ["sqlalchemy"] },
+]
 docs = [
     { name = "unstructured", extra = ["csv", "doc", "docx", "epub", "md", "odt", "org", "ppt", "pptx", "rst", "rtf", "tsv", "xlsx"] },
 ]
 evals = [
     { name = "gdown" },
+    { name = "matplotlib" },
+    { name = "pandas" },
     { name = "plotly" },
 ]
 falkordb = [
@@ -927,10 +926,6 @@ graphiti = [
 groq = [
     { name = "groq" },
 ]
-gui = [
-    { name = "pyside6" },
-    { name = "qasync" },
-]
 huggingface = [
     { name = "transformers" },
 ]
@@ -944,6 +939,10 @@ llama-index = [
 mistral = [
     { name = "mistral-common" },
 ]
+monitoring = [
+    { name = "langfuse" },
+    { name = "sentry-sdk", extra = ["fastapi"] },
+]
 neo4j = [
     { name = "neo4j" },
 ]
@@ -979,16 +978,17 @@ requires-dist = [
     { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.27" },
     { name = "asyncpg", marker = "extra == 'postgres'", specifier = ">=0.30.0,<1.0.0" },
     { name = "asyncpg", marker = "extra == 'postgres-binary'", specifier = ">=0.30.0,<1.0.0" },
-    { name = "baml-py", specifier = "==0.206.0" },
+    { name = "baml-py", marker = "extra == 'baml'", specifier = "==0.206.0" },
     { name = "chromadb", marker = "extra == 'chromadb'", specifier = ">=0.6,<0.7" },
     { name = "coverage", marker = "extra == 'dev'", specifier = ">=7.3.2,<8" },
     { name = "debugpy", marker = "extra == 'debug'", specifier = ">=1.8.9,<2.0.0" },
     { name = "deepeval", marker = "extra == 'deepeval'", specifier = ">=3.0.1,<4" },
     { name = "deptry", marker = "extra == 'dev'", specifier = ">=0.20.0,<0.21" },
-    { name = "dlt", extras = ["sqlalchemy"], specifier = ">=1.9.0,<2" },
+    { name = "dlt", extras = ["sqlalchemy"], marker = "extra == 'dlt'", specifier = ">=1.9.0,<2" },
     { name = "falkordb", marker = "extra == 'falkordb'", specifier = ">=1.0.9,<2.0.0" },
     { name = "fastapi", specifier = ">=0.115.7,<1.0.0" },
     { name = "fastapi-users", extras = ["sqlalchemy"], specifier = ">=14.0.1,<15.0.0" },
+    { name = "fastembed", specifier = "<=0.6.0" },
     { name = "fastembed", marker = "python_full_version < '3.13' and extra == 'codegraph'", specifier = "<=0.6.0" },
     { name = "filetype", specifier = ">=1.2.0,<2.0.0" },
     { name = "gdown", marker = "extra == 'evals'", specifier = ">=5.2.0,<6" },
@@ -1003,12 +1003,12 @@ requires-dist = [
     { name = "lancedb", specifier = ">=0.24.0,<1.0.0" },
     { name = "langchain-aws", marker = "extra == 'neptune'", specifier = ">=0.2.22" },
     { name = "langchain-text-splitters", marker = "extra == 'langchain'", specifier = ">=0.3.2,<1.0.0" },
-    { name = "langfuse", specifier = ">=2.32.0,<3" },
+    { name = "langfuse", marker = "extra == 'monitoring'", specifier = ">=2.32.0,<3" },
     { name = "langsmith", marker = "extra == 'langchain'", specifier = ">=0.2.3,<1.0.0" },
     { name = "limits", specifier = ">=4.4.1,<5" },
-    { name = "litellm", specifier = ">=1.71.0,<2.0.0" },
+    { name = "litellm", specifier = ">=1.76.0" },
     { name = "llama-index-core", marker = "extra == 'llama-index'", specifier = ">=0.12.11,<0.13" },
-    { name = "matplotlib", specifier = ">=3.8.3,<4" },
+    { name = "matplotlib", marker = "extra == 'evals'", specifier = ">=3.8.3,<4" },
     { name = "mistral-common", marker = "extra == 'mistral'", specifier = ">=1.5.2,<2" },
     { name = "mkdocs-material", marker = "extra == 'dev'", specifier = ">=9.5.42,<10" },
     { name = "mkdocs-minify-plugin", marker = "extra == 'dev'", specifier = ">=0.8.0,<0.9" },
@@ -1018,13 +1018,12 @@ requires-dist = [
     { name = "nbformat", specifier = ">=5.7.0,<6.0.0" },
     { name = "neo4j", marker = "extra == 'neo4j'", specifier = ">=5.28.0,<6" },
     { name = "networkx", specifier = ">=3.4.2,<4" },
-    { name = "nltk", specifier = ">=3.9.1,<4.0.0" },
     { name = "notebook", marker = "extra == 'dev'", specifier = ">=7.1.0,<8" },
     { name = "notebook", marker = "extra == 'notebook'", specifier = ">=7.1.0,<8" },
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