Merge branch 'dev' into multi-tenant-neo4j

.env.template

@@ -21,6 +21,10 @@ LLM_PROVIDER="openai"
 LLM_ENDPOINT=""
 LLM_API_VERSION=""
 LLM_MAX_TOKENS="16384"
+# Instructor's modes determine how structured data is requested from and extracted from LLM responses
+# You can change this type (i.e. mode) via this env variable
+# Each LLM has its own default value, e.g. gpt-5 models have "json_schema_mode"
+LLM_INSTRUCTOR_MODE=""
 
 EMBEDDING_PROVIDER="openai"
 EMBEDDING_MODEL="openai/text-embedding-3-large"

cognee-mcp/src/server.py

@@ -194,7 +194,6 @@ async def cognify(
 
     Prerequisites:
         - **LLM_API_KEY**: Must be configured (required for entity extraction and graph generation)
-        - **Data Added**: Must have data previously added via `cognee.add()`
         - **Vector Database**: Must be accessible for embeddings storage
         - **Graph Database**: Must be accessible for relationship storage
 

cognee/api/client.py

@@ -23,6 +23,7 @@ from cognee.api.v1.settings.routers import get_settings_router
 from cognee.api.v1.datasets.routers import get_datasets_router
 from cognee.api.v1.cognify.routers import get_code_pipeline_router, get_cognify_router
 from cognee.api.v1.search.routers import get_search_router
+from cognee.api.v1.ontologies.routers.get_ontology_router import get_ontology_router
 from cognee.api.v1.memify.routers import get_memify_router
 from cognee.api.v1.add.routers import get_add_router
 from cognee.api.v1.delete.routers import get_delete_router
@@ -263,6 +264,8 @@ app.include_router(
 
 app.include_router(get_datasets_router(), prefix="/api/v1/datasets", tags=["datasets"])
 
+app.include_router(get_ontology_router(), prefix="/api/v1/ontologies", tags=["ontologies"])
+
 app.include_router(get_settings_router(), prefix="/api/v1/settings", tags=["settings"])
 
 app.include_router(get_visualize_router(), prefix="/api/v1/visualize", tags=["visualize"])

cognee/api/v1/cognify/routers/get_cognify_router.py

@@ -41,6 +41,9 @@ class CognifyPayloadDTO(InDTO):
     custom_prompt: Optional[str] = Field(
         default="", description="Custom prompt for entity extraction and graph generation"
     )
+    ontology_key: Optional[List[str]] = Field(
+        default=None, description="Reference to one or more previously uploaded ontologies"
+    )
 
 
 def get_cognify_router() -> APIRouter:
@@ -68,6 +71,7 @@ def get_cognify_router() -> APIRouter:
         - **dataset_ids** (Optional[List[UUID]]): List of existing dataset UUIDs to process. UUIDs allow processing of datasets not owned by the user (if permitted).
         - **run_in_background** (Optional[bool]): Whether to execute processing asynchronously. Defaults to False (blocking).
         - **custom_prompt** (Optional[str]): Custom prompt for entity extraction and graph generation. If provided, this prompt will be used instead of the default prompts for knowledge graph extraction.
+        - **ontology_key** (Optional[List[str]]): Reference to one or more previously uploaded ontology files to use for knowledge graph construction.
 
         ## Response
         - **Blocking execution**: Complete pipeline run information with entity counts, processing duration, and success/failure status
@@ -82,7 +86,8 @@ def get_cognify_router() -> APIRouter:
         {
             "datasets": ["research_papers", "documentation"],
             "run_in_background": false,
-            "custom_prompt": "Extract entities focusing on technical concepts and their relationships. Identify key technologies, methodologies, and their interconnections."
+            "custom_prompt": "Extract entities focusing on technical concepts and their relationships. Identify key technologies, methodologies, and their interconnections.",
+            "ontology_key": ["medical_ontology_v1"]
         }
         ```
 
@@ -108,13 +113,35 @@ def get_cognify_router() -> APIRouter:
             )
 
         from cognee.api.v1.cognify import cognify as cognee_cognify
+        from cognee.api.v1.ontologies.ontologies import OntologyService
 
         try:
             datasets = payload.dataset_ids if payload.dataset_ids else payload.datasets
+            config_to_use = None
+
+            if payload.ontology_key:
+                ontology_service = OntologyService()
+                ontology_contents = ontology_service.get_ontology_contents(
+                    payload.ontology_key, user
+                )
+
+                from cognee.modules.ontology.ontology_config import Config
+                from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import (
+                    RDFLibOntologyResolver,
+                )
+                from io import StringIO
+
+                ontology_streams = [StringIO(content) for content in ontology_contents]
+                config_to_use: Config = {
+                    "ontology_config": {
+                        "ontology_resolver": RDFLibOntologyResolver(ontology_file=ontology_streams)
+                    }
+                }
 
             cognify_run = await cognee_cognify(
                 datasets,
                 user,
+                config=config_to_use,
                 run_in_background=payload.run_in_background,
                 custom_prompt=payload.custom_prompt,
             )

cognee/api/v1/ontologies/__init__.py

@@ -0,0 +1,4 @@
+from .ontologies import OntologyService
+from .routers.get_ontology_router import get_ontology_router
+
+__all__ = ["OntologyService", "get_ontology_router"]

cognee/api/v1/ontologies/ontologies.py

@@ -0,0 +1,183 @@
+import os
+import json
+import tempfile
+from pathlib import Path
+from datetime import datetime, timezone
+from typing import Optional, List
+from dataclasses import dataclass
+
+
+@dataclass
+class OntologyMetadata:
+    ontology_key: str
+    filename: str
+    size_bytes: int
+    uploaded_at: str
+    description: Optional[str] = None
+
+
+class OntologyService:
+    def __init__(self):
+        pass
+
+    @property
+    def base_dir(self) -> Path:
+        return Path(tempfile.gettempdir()) / "ontologies"
+
+    def _get_user_dir(self, user_id: str) -> Path:
+        user_dir = self.base_dir / str(user_id)
+        user_dir.mkdir(parents=True, exist_ok=True)
+        return user_dir
+
+    def _get_metadata_path(self, user_dir: Path) -> Path:
+        return user_dir / "metadata.json"
+
+    def _load_metadata(self, user_dir: Path) -> dict:
+        metadata_path = self._get_metadata_path(user_dir)
+        if metadata_path.exists():
+            with open(metadata_path, "r") as f:
+                return json.load(f)
+        return {}
+
+    def _save_metadata(self, user_dir: Path, metadata: dict):
+        metadata_path = self._get_metadata_path(user_dir)
+        with open(metadata_path, "w") as f:
+            json.dump(metadata, f, indent=2)
+
+    async def upload_ontology(
+        self, ontology_key: str, file, user, description: Optional[str] = None
+    ) -> OntologyMetadata:
+        if not file.filename.lower().endswith(".owl"):
+            raise ValueError("File must be in .owl format")
+
+        user_dir = self._get_user_dir(str(user.id))
+        metadata = self._load_metadata(user_dir)
+
+        if ontology_key in metadata:
+            raise ValueError(f"Ontology key '{ontology_key}' already exists")
+
+        content = await file.read()
+        if len(content) > 10 * 1024 * 1024:
+            raise ValueError("File size exceeds 10MB limit")
+
+        file_path = user_dir / f"{ontology_key}.owl"
+        with open(file_path, "wb") as f:
+            f.write(content)
+
+        ontology_metadata = {
+            "filename": file.filename,
+            "size_bytes": len(content),
+            "uploaded_at": datetime.now(timezone.utc).isoformat(),
+            "description": description,
+        }
+        metadata[ontology_key] = ontology_metadata
+        self._save_metadata(user_dir, metadata)
+
+        return OntologyMetadata(
+            ontology_key=ontology_key,
+            filename=file.filename,
+            size_bytes=len(content),
+            uploaded_at=ontology_metadata["uploaded_at"],
+            description=description,
+        )
+
+    async def upload_ontologies(
+        self, ontology_key: List[str], files: List, user, descriptions: Optional[List[str]] = None
+    ) -> List[OntologyMetadata]:
+        """
+        Upload ontology files with their respective keys.
+
+        Args:
+            ontology_key: List of unique keys for each ontology
+            files: List of UploadFile objects (same length as keys)
+            user: Authenticated user
+            descriptions: Optional list of descriptions for each file
+
+        Returns:
+            List of OntologyMetadata objects for uploaded files
+
+        Raises:
+            ValueError: If keys duplicate, file format invalid, or array lengths don't match
+        """
+        if len(ontology_key) != len(files):
+            raise ValueError("Number of keys must match number of files")
+
+        if len(set(ontology_key)) != len(ontology_key):
+            raise ValueError("Duplicate ontology keys not allowed")
+
+        if descriptions and len(descriptions) != len(files):
+            raise ValueError("Number of descriptions must match number of files")
+
+        results = []
+        user_dir = self._get_user_dir(str(user.id))
+        metadata = self._load_metadata(user_dir)
+
+        for i, (key, file) in enumerate(zip(ontology_key, files)):
+            if key in metadata:
+                raise ValueError(f"Ontology key '{key}' already exists")
+
+            if not file.filename.lower().endswith(".owl"):
+                raise ValueError(f"File '{file.filename}' must be in .owl format")
+
+            content = await file.read()
+            if len(content) > 10 * 1024 * 1024:
+                raise ValueError(f"File '{file.filename}' exceeds 10MB limit")
+
+            file_path = user_dir / f"{key}.owl"
+            with open(file_path, "wb") as f:
+                f.write(content)
+
+            ontology_metadata = {
+                "filename": file.filename,
+                "size_bytes": len(content),
+                "uploaded_at": datetime.now(timezone.utc).isoformat(),
+                "description": descriptions[i] if descriptions else None,
+            }
+            metadata[key] = ontology_metadata
+
+            results.append(
+                OntologyMetadata(
+                    ontology_key=key,
+                    filename=file.filename,
+                    size_bytes=len(content),
+                    uploaded_at=ontology_metadata["uploaded_at"],
+                    description=descriptions[i] if descriptions else None,
+                )
+            )
+
+        self._save_metadata(user_dir, metadata)
+        return results
+
+    def get_ontology_contents(self, ontology_key: List[str], user) -> List[str]:
+        """
+        Retrieve ontology content for one or more keys.
+
+        Args:
+            ontology_key: List of ontology keys to retrieve (can contain single item)
+            user: Authenticated user
+
+        Returns:
+            List of ontology content strings
+
+        Raises:
+            ValueError: If any ontology key not found
+        """
+        user_dir = self._get_user_dir(str(user.id))
+        metadata = self._load_metadata(user_dir)
+
+        contents = []
+        for key in ontology_key:
+            if key not in metadata:
+                raise ValueError(f"Ontology key '{key}' not found")
+
+            file_path = user_dir / f"{key}.owl"
+            if not file_path.exists():
+                raise ValueError(f"Ontology file for key '{key}' not found")
+
+            with open(file_path, "r", encoding="utf-8") as f:
+                contents.append(f.read())
+        return contents
+
+    def list_ontologies(self, user) -> dict:
+        user_dir = self._get_user_dir(str(user.id))
+        return self._load_metadata(user_dir)

cognee/api/v1/ontologies/routers/get_ontology_router.py

@@ -0,0 +1,107 @@
+from fastapi import APIRouter, File, Form, UploadFile, Depends, HTTPException
+from fastapi.responses import JSONResponse
+from typing import Optional, List
+
+from cognee.modules.users.models import User
+from cognee.modules.users.methods import get_authenticated_user
+from cognee.shared.utils import send_telemetry
+from cognee import __version__ as cognee_version
+from ..ontologies import OntologyService
+
+
+def get_ontology_router() -> APIRouter:
+    router = APIRouter()
+    ontology_service = OntologyService()
+
+    @router.post("", response_model=dict)
+    async def upload_ontology(
+        ontology_key: str = Form(...),
+        ontology_file: List[UploadFile] = File(...),
+        descriptions: Optional[str] = Form(None),
+        user: User = Depends(get_authenticated_user),
+    ):
+        """
+        Upload ontology files with their respective keys for later use in cognify operations.
+
+        Supports both single and multiple file uploads:
+        - Single file: ontology_key=["key"], ontology_file=[file]
+        - Multiple files: ontology_key=["key1", "key2"], ontology_file=[file1, file2]
+
+        ## Request Parameters
+        - **ontology_key** (str): JSON array string of user-defined identifiers for the ontologies
+        - **ontology_file** (List[UploadFile]): OWL format ontology files
+        - **descriptions** (Optional[str]): JSON array string of optional descriptions
+
+        ## Response
+        Returns metadata about uploaded ontologies including keys, filenames, sizes, and upload timestamps.
+
+        ## Error Codes
+        - **400 Bad Request**: Invalid file format, duplicate keys, array length mismatches, file size exceeded
+        - **500 Internal Server Error**: File system or processing errors
+        """
+        send_telemetry(
+            "Ontology Upload API Endpoint Invoked",
+            user.id,
+            additional_properties={
+                "endpoint": "POST /api/v1/ontologies",
+                "cognee_version": cognee_version,
+            },
+        )
+
+        try:
+            import json
+
+            ontology_keys = json.loads(ontology_key)
+            description_list = json.loads(descriptions) if descriptions else None
+
+            if not isinstance(ontology_keys, list):
+                raise ValueError("ontology_key must be a JSON array")
+
+            results = await ontology_service.upload_ontologies(
+                ontology_keys, ontology_file, user, description_list
+            )
+
+            return {
+                "uploaded_ontologies": [
+                    {
+                        "ontology_key": result.ontology_key,
+                        "filename": result.filename,
+                        "size_bytes": result.size_bytes,
+                        "uploaded_at": result.uploaded_at,
+                        "description": result.description,
+                    }
+                    for result in results
+                ]
+            }
+        except (json.JSONDecodeError, ValueError) as e:
+            return JSONResponse(status_code=400, content={"error": str(e)})
+        except Exception as e:
+            return JSONResponse(status_code=500, content={"error": str(e)})
+
+    @router.get("", response_model=dict)
+    async def list_ontologies(user: User = Depends(get_authenticated_user)):
+        """
+        List all uploaded ontologies for the authenticated user.
+
+        ## Response
+        Returns a dictionary mapping ontology keys to their metadata including filename, size, and upload timestamp.
+
+        ## Error Codes
+        - **500 Internal Server Error**: File system or processing errors
+        """
+        send_telemetry(
+            "Ontology List API Endpoint Invoked",
+            user.id,
+            additional_properties={
+                "endpoint": "GET /api/v1/ontologies",
+                "cognee_version": cognee_version,
+            },
+        )
+
+        try:
+            metadata = ontology_service.list_ontologies(user)
+            return metadata
+        except Exception as e:
+            return JSONResponse(status_code=500, content={"error": str(e)})
+
+    return router

cognee/api/v1/search/search.py

@@ -31,6 +31,8 @@ async def search(
     only_context: bool = False,
     use_combined_context: bool = False,
     session_id: Optional[str] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> Union[List[SearchResult], CombinedSearchResult]:
     """
     Search and query the knowledge graph for insights, information, and connections.
@@ -200,6 +202,8 @@ async def search(
         only_context=only_context,
         use_combined_context=use_combined_context,
         session_id=session_id,
+        wide_search_top_k=wide_search_top_k,
+        triplet_distance_penalty=triplet_distance_penalty,
     )
 
     return filtered_search_results

cognee/cli/commands/cognify_command.py

@@ -22,7 +22,7 @@ relationships, and creates semantic connections for enhanced search and reasonin
 
 Processing Pipeline:
 1. **Document Classification**: Identifies document types and structures
-2. **Permission Validation**: Ensures user has processing rights  
+2. **Permission Validation**: Ensures user has processing rights
 3. **Text Chunking**: Breaks content into semantically meaningful segments
 4. **Entity Extraction**: Identifies key concepts, people, places, organizations
 5. **Relationship Detection**: Discovers connections between entities
@@ -97,6 +97,13 @@ After successful cognify processing, use `cognee search` to query the knowledge
                             chunker_class = LangchainChunker
                         except ImportError:
                             fmt.warning("LangchainChunker not available, using TextChunker")
+                    elif args.chunker == "CsvChunker":
+                        try:
+                            from cognee.modules.chunking.CsvChunker import CsvChunker
+
+                            chunker_class = CsvChunker
+                        except ImportError:
+                            fmt.warning("CsvChunker not available, using TextChunker")
 
                     result = await cognee.cognify(
                         datasets=datasets,

cognee/cli/config.py

@@ -26,7 +26,7 @@ SEARCH_TYPE_CHOICES = [
 ]
 
 # Chunker choices
-CHUNKER_CHOICES = ["TextChunker", "LangchainChunker"]
+CHUNKER_CHOICES = ["TextChunker", "LangchainChunker", "CsvChunker"]
 
 # Output format choices
 OUTPUT_FORMAT_CHOICES = ["json", "pretty", "simple"]

cognee/eval_framework/Dockerfile

@@ -0,0 +1,29 @@
+FROM python:3.11-slim
+
+# Set environment variables
+ENV PIP_NO_CACHE_DIR=true
+ENV PATH="${PATH}:/root/.poetry/bin"
+ENV PYTHONPATH=/app
+ENV SKIP_MIGRATIONS=true
+
+# System dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    libpq-dev \
+    git \
+    curl \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY pyproject.toml poetry.lock README.md /app/
+
+RUN pip install poetry
+
+RUN poetry config virtualenvs.create false
+
+RUN poetry install --extras distributed --extras evals --extras deepeval --no-root
+
+COPY cognee/ /app/cognee
+COPY distributed/ /app/distributed

cognee/eval_framework/answer_generation/answer_generation_executor.py

@@ -35,6 +35,16 @@ class AnswerGeneratorExecutor:
             retrieval_context = await retriever.get_context(query_text)
             search_results = await retriever.get_completion(query_text, retrieval_context)
 
+            ############
+            #:TODO This is a quick fix until we don't structure retriever results properly but lets not leave it like this...this is needed now due to the changed combined retriever structure..
+            if isinstance(retrieval_context, list):
+                retrieval_context = await retriever.convert_retrieved_objects_to_context(
+                    triplets=retrieval_context
+                )
+
+            if isinstance(search_results, str):
+                search_results = [search_results]
+            #############
             answer = {
                 "question": query_text,
                 "answer": search_results[0],

cognee/eval_framework/answer_generation/run_question_answering_module.py

@@ -35,7 +35,7 @@ async def create_and_insert_answers_table(questions_payload):
 
 
 async def run_question_answering(
-    params: dict, system_prompt="answer_simple_question.txt", top_k: Optional[int] = None
+    params: dict, system_prompt="answer_simple_question_benchmark.txt", top_k: Optional[int] = None
 ) -> List[dict]:
     if params.get("answering_questions"):
         logger.info("Question answering started...")

cognee/eval_framework/eval_config.py

@@ -14,7 +14,7 @@ class EvalConfig(BaseSettings):
 
     # Question answering params
     answering_questions: bool = True
-    qa_engine: str = "cognee_completion"  # Options: 'cognee_completion' or 'cognee_graph_completion' or 'cognee_graph_completion_cot' or 'cognee_graph_completion_context_extension'
+    qa_engine: str = "cognee_graph_completion"  # Options: 'cognee_completion' or 'cognee_graph_completion' or 'cognee_graph_completion_cot' or 'cognee_graph_completion_context_extension'
 
     # Evaluation params
     evaluating_answers: bool = True
@@ -25,7 +25,7 @@ class EvalConfig(BaseSettings):
         "EM",
         "f1",
     ]  # Use only 'correctness' for DirectLLM
-    deepeval_model: str = "gpt-5-mini"
+    deepeval_model: str = "gpt-4o-mini"
 
     # Metrics params
     calculate_metrics: bool = True

cognee/eval_framework/modal_run_eval.py

@@ -2,7 +2,6 @@ import modal
 import os
 import asyncio
 import datetime
-import hashlib
 import json
 from cognee.shared.logging_utils import get_logger
 from cognee.eval_framework.eval_config import EvalConfig
@@ -10,6 +9,9 @@ from cognee.eval_framework.corpus_builder.run_corpus_builder import run_corpus_b
 from cognee.eval_framework.answer_generation.run_question_answering_module import (
     run_question_answering,
 )
+import pathlib
+from os import path
+from modal import Image
 from cognee.eval_framework.evaluation.run_evaluation_module import run_evaluation
 from cognee.eval_framework.metrics_dashboard import create_dashboard
 
@@ -38,22 +40,19 @@ def read_and_combine_metrics(eval_params: dict) -> dict:
 
 app = modal.App("modal-run-eval")
 
-image = (
-    modal.Image.from_dockerfile(path="Dockerfile_modal", force_build=False)
-    .copy_local_file("pyproject.toml", "pyproject.toml")
-    .copy_local_file("poetry.lock", "poetry.lock")
-    .env(
-        {
-            "ENV": os.getenv("ENV"),
-            "LLM_API_KEY": os.getenv("LLM_API_KEY"),
-            "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"),
-        }
-    )
-    .pip_install("protobuf", "h2", "deepeval", "gdown", "plotly")
+image = Image.from_dockerfile(
+    path=pathlib.Path(path.join(path.dirname(__file__), "Dockerfile")).resolve(),
+    force_build=False,
+).add_local_python_source("cognee")
+
+
+@app.function(
+    image=image,
+    max_containers=10,
+    timeout=86400,
+    volumes={"/data": vol},
+    secrets=[modal.Secret.from_name("eval_secrets")],
 )
-
-
-@app.function(image=image, concurrency_limit=10, timeout=86400, volumes={"/data": vol})
 async def modal_run_eval(eval_params=None):
     """Runs evaluation pipeline and returns combined metrics results."""
     if eval_params is None:
@@ -105,18 +104,7 @@ async def main():
     configs = [
         EvalConfig(
             task_getter_type="Default",
-            number_of_samples_in_corpus=10,
-            benchmark="HotPotQA",
-            qa_engine="cognee_graph_completion",
-            building_corpus_from_scratch=True,
-            answering_questions=True,
-            evaluating_answers=True,
-            calculate_metrics=True,
-            dashboard=True,
-        ),
-        EvalConfig(
-            task_getter_type="Default",
-            number_of_samples_in_corpus=10,
+            number_of_samples_in_corpus=25,
             benchmark="TwoWikiMultiHop",
             qa_engine="cognee_graph_completion",
             building_corpus_from_scratch=True,
@@ -127,7 +115,7 @@ async def main():
         ),
         EvalConfig(
             task_getter_type="Default",
-            number_of_samples_in_corpus=10,
+            number_of_samples_in_corpus=25,
             benchmark="Musique",
             qa_engine="cognee_graph_completion",
             building_corpus_from_scratch=True,

cognee/infrastructure/databases/graph/graph_db_interface.py

@@ -398,3 +398,18 @@ class GraphDBInterface(ABC):
             - node_id (Union[str, UUID]): Unique identifier of the node for which to retrieve connections.
         """
         raise NotImplementedError
+
+    @abstractmethod
+    async def get_filtered_graph_data(
+        self, attribute_filters: List[Dict[str, List[Union[str, int]]]]
+    ) -> Tuple[List[Node], List[EdgeData]]:
+        """
+        Retrieve nodes and edges filtered by the provided attribute criteria.
+
+        Parameters:
+        -----------
+
+            - attribute_filters: A list of dictionaries where keys are attribute names and values
+              are lists of attribute values to filter by.
+        """
+        raise NotImplementedError

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

@@ -12,6 +12,7 @@ from contextlib import asynccontextmanager
 from concurrent.futures import ThreadPoolExecutor
 from typing import Dict, Any, List, Union, Optional, Tuple, Type
 
+from cognee.exceptions import CogneeValidationError
 from cognee.shared.logging_utils import get_logger
 from cognee.infrastructure.utils.run_sync import run_sync
 from cognee.infrastructure.files.storage import get_file_storage
@@ -1186,6 +1187,11 @@ class KuzuAdapter(GraphDBInterface):
               A tuple with two elements: a list of tuples of (node_id, properties) and a list of
               tuples of (source_id, target_id, relationship_name, properties).
         """
+
+        import time
+
+        start_time = time.time()
+
         try:
             nodes_query = """
             MATCH (n:Node)
@@ -1249,6 +1255,11 @@ class KuzuAdapter(GraphDBInterface):
                             },
                         )
                     )
+
+            retrieval_time = time.time() - start_time
+            logger.info(
+                f"Retrieved {len(nodes)} nodes and {len(edges)} edges in {retrieval_time:.2f} seconds"
+            )
             return formatted_nodes, formatted_edges
         except Exception as e:
             logger.error(f"Failed to get graph data: {e}")
@@ -1417,6 +1428,92 @@ class KuzuAdapter(GraphDBInterface):
                 formatted_edges.append((source_id, target_id, rel_type, props))
         return formatted_nodes, formatted_edges
 
+    async def get_id_filtered_graph_data(self, target_ids: list[str]):
+        """
+        Retrieve graph data filtered by specific node IDs, including their direct neighbors
+        and only edges where one endpoint matches those IDs.
+
+        Returns:
+            nodes: List[dict]   -> Each dict includes "id" and all node properties
+            edges: List[dict]   -> Each dict includes "source", "target", "type", "properties"
+        """
+        import time
+
+        start_time = time.time()
+
+        try:
+            if not target_ids:
+                logger.warning("No target IDs provided for ID-filtered graph retrieval.")
+                return [], []
+
+            if not all(isinstance(x, str) for x in target_ids):
+                raise CogneeValidationError("target_ids must be a list of strings")
+
+            query = """
+            MATCH (n:Node)-[r]->(m:Node)
+            WHERE n.id IN $target_ids OR m.id IN $target_ids
+            RETURN n.id, {
+                name: n.name,
+                type: n.type,
+                properties: n.properties
+            }, m.id, {
+                name: m.name,
+                type: m.type,
+                properties: m.properties
+            }, r.relationship_name, r.properties
+            """
+
+            result = await self.query(query, {"target_ids": target_ids})
+
+            if not result:
+                logger.info("No data returned for the supplied IDs")
+                return [], []
+
+            nodes_dict = {}
+            edges = []
+
+            for n_id, n_props, m_id, m_props, r_type, r_props_raw in result:
+                if n_props.get("properties"):
+                    try:
+                        additional_props = json.loads(n_props["properties"])
+                        n_props.update(additional_props)
+                        del n_props["properties"]
+                    except json.JSONDecodeError:
+                        logger.warning(f"Failed to parse properties JSON for node {n_id}")
+
+                if m_props.get("properties"):
+                    try:
+                        additional_props = json.loads(m_props["properties"])
+                        m_props.update(additional_props)
+                        del m_props["properties"]
+                    except json.JSONDecodeError:
+                        logger.warning(f"Failed to parse properties JSON for node {m_id}")
+
+                nodes_dict[n_id] = (n_id, n_props)
+                nodes_dict[m_id] = (m_id, m_props)
+
+                edge_props = {}
+                if r_props_raw:
+                    try:
+                        edge_props = json.loads(r_props_raw)
+                    except (json.JSONDecodeError, TypeError):
+                        logger.warning(f"Failed to parse edge properties for {n_id}->{m_id}")
+
+                source_id = edge_props.get("source_node_id", n_id)
+                target_id = edge_props.get("target_node_id", m_id)
+                edges.append((source_id, target_id, r_type, edge_props))
+
+            retrieval_time = time.time() - start_time
+            logger.info(
+                f"ID-filtered retrieval: {len(nodes_dict)} nodes and {len(edges)} edges in {retrieval_time:.2f}s"
+            )
+
+            return list(nodes_dict.values()), edges
+
+        except Exception as e:
+            logger.error(f"Error during ID-filtered graph data retrieval: {str(e)}")
+            raise
+
     async def get_graph_metrics(self, include_optional=False) -> Dict[str, Any]:
         """
         Get metrics on graph structure and connectivity.

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

@@ -964,6 +964,63 @@ class Neo4jAdapter(GraphDBInterface):
             logger.error(f"Error during graph data retrieval: {str(e)}")
             raise
 
+    async def get_id_filtered_graph_data(self, target_ids: list[str]):
+        """
+        Retrieve graph data filtered by specific node IDs, including their direct neighbors
+        and only edges where one endpoint matches those IDs.
+
+        This version uses a single Cypher query for efficiency.
+        """
+        import time
+
+        start_time = time.time()
+
+        try:
+            if not target_ids:
+                logger.warning("No target IDs provided for ID-filtered graph retrieval.")
+                return [], []
+
+            query = """
+            MATCH ()-[r]-()
+            WHERE startNode(r).id IN $target_ids
+               OR endNode(r).id IN $target_ids
+            WITH DISTINCT r, startNode(r) AS a, endNode(r) AS b
+            RETURN
+                properties(a) AS n_properties,
+                properties(b) AS m_properties,
+                type(r) AS type,
+                properties(r) AS properties
+            """
+
+            result = await self.query(query, {"target_ids": target_ids})
+
+            nodes_dict = {}
+            edges = []
+
+            for record in result:
+                n_props = record["n_properties"]
+                m_props = record["m_properties"]
+                r_props = record["properties"]
+                r_type = record["type"]
+
+                nodes_dict[n_props["id"]] = (n_props["id"], n_props)
+                nodes_dict[m_props["id"]] = (m_props["id"], m_props)
+
+                source_id = r_props.get("source_node_id", n_props["id"])
+                target_id = r_props.get("target_node_id", m_props["id"])
+                edges.append((source_id, target_id, r_type, r_props))
+
+            retrieval_time = time.time() - start_time
+            logger.info(
+                f"ID-filtered retrieval: {len(nodes_dict)} nodes and {len(edges)} edges in {retrieval_time:.2f}s"
+            )
+
+            return list(nodes_dict.values()), edges
+
+        except Exception as e:
+            logger.error(f"Error during ID-filtered graph data retrieval: {str(e)}")
+            raise
+
     async def get_nodeset_subgraph(
         self, node_type: Type[Any], node_name: List[str]
     ) -> Tuple[List[Tuple[int, dict]], List[Tuple[int, int, str, dict]]]:

cognee/infrastructure/files/utils/guess_file_type.py

@@ -55,6 +55,10 @@ def guess_file_type(file: BinaryIO, name: Optional[str] = None) -> filetype.Type
         file_type = Type("text/plain", "txt")
         return file_type
 
+    if ext in [".csv"]:
+        file_type = Type("text/csv", "csv")
+        return file_type
+
     file_type = filetype.guess(file)
 
     # If file type could not be determined consider it a plain text file as they don't have magic number encoding

cognee/infrastructure/llm/config.py

@@ -38,6 +38,7 @@ class LLMConfig(BaseSettings):
     """
 
     structured_output_framework: str = "instructor"
+    llm_instructor_mode: str = ""
     llm_provider: str = "openai"
     llm_model: str = "openai/gpt-5-mini"
     llm_endpoint: str = ""
@@ -181,6 +182,7 @@ class LLMConfig(BaseSettings):
               instance.
         """
         return {
+            "llm_instructor_mode": self.llm_instructor_mode.lower(),
             "provider": self.llm_provider,
             "model": self.llm_model,
             "endpoint": self.llm_endpoint,

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/anthropic/adapter.py

@@ -28,13 +28,16 @@ class AnthropicAdapter(LLMInterface):
 
     name = "Anthropic"
     model: str
+    default_instructor_mode = "anthropic_tools"
 
-    def __init__(self, max_completion_tokens: int, model: str = None):
+    def __init__(self, max_completion_tokens: int, model: str = None, instructor_mode: str = None):
         import anthropic
 
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
         self.aclient = instructor.patch(
             create=anthropic.AsyncAnthropic(api_key=get_llm_config().llm_api_key).messages.create,
-            mode=instructor.Mode.ANTHROPIC_TOOLS,
+            mode=instructor.Mode(self.instructor_mode),
         )
 
         self.model = model

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py

@@ -41,6 +41,7 @@ class GeminiAdapter(LLMInterface):
     name: str
     model: str
     api_key: str
+    default_instructor_mode = "json_mode"
 
     def __init__(
         self,
@@ -49,6 +50,7 @@ class GeminiAdapter(LLMInterface):
         model: str,
         api_version: str,
         max_completion_tokens: int,
+        instructor_mode: str = None,
         fallback_model: str = None,
         fallback_api_key: str = None,
         fallback_endpoint: str = None,
@@ -63,7 +65,11 @@ class GeminiAdapter(LLMInterface):
         self.fallback_api_key = fallback_api_key
         self.fallback_endpoint = fallback_endpoint
 
-        self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
+        self.aclient = instructor.from_litellm(
+            litellm.acompletion, mode=instructor.Mode(self.instructor_mode)
+        )
 
     @retry(
         stop=stop_after_delay(128),

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py

@@ -41,6 +41,7 @@ class GenericAPIAdapter(LLMInterface):
     name: str
     model: str
     api_key: str
+    default_instructor_mode = "json_mode"
 
     def __init__(
         self,
@@ -49,6 +50,7 @@ class GenericAPIAdapter(LLMInterface):
         model: str,
         name: str,
         max_completion_tokens: int,
+        instructor_mode: str = None,
         fallback_model: str = None,
         fallback_api_key: str = None,
         fallback_endpoint: str = None,
@@ -63,7 +65,11 @@ class GenericAPIAdapter(LLMInterface):
         self.fallback_api_key = fallback_api_key
         self.fallback_endpoint = fallback_endpoint
 
-        self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
+        self.aclient = instructor.from_litellm(
+            litellm.acompletion, mode=instructor.Mode(self.instructor_mode)
+        )
 
     @retry(
         stop=stop_after_delay(128),

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py

@@ -81,6 +81,7 @@ def get_llm_client(raise_api_key_error: bool = True):
             model=llm_config.llm_model,
             transcription_model=llm_config.transcription_model,
             max_completion_tokens=max_completion_tokens,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
             streaming=llm_config.llm_streaming,
             fallback_api_key=llm_config.fallback_api_key,
             fallback_endpoint=llm_config.fallback_endpoint,
@@ -101,6 +102,7 @@ def get_llm_client(raise_api_key_error: bool = True):
             llm_config.llm_model,
             "Ollama",
             max_completion_tokens=max_completion_tokens,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
         )
 
     elif provider == LLMProvider.ANTHROPIC:
@@ -109,7 +111,9 @@ def get_llm_client(raise_api_key_error: bool = True):
         )
 
         return AnthropicAdapter(
-            max_completion_tokens=max_completion_tokens, model=llm_config.llm_model
+            max_completion_tokens=max_completion_tokens,
+            model=llm_config.llm_model,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
         )
 
     elif provider == LLMProvider.CUSTOM:
@@ -126,6 +130,7 @@ def get_llm_client(raise_api_key_error: bool = True):
             llm_config.llm_model,
             "Custom",
             max_completion_tokens=max_completion_tokens,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
             fallback_api_key=llm_config.fallback_api_key,
             fallback_endpoint=llm_config.fallback_endpoint,
             fallback_model=llm_config.fallback_model,
@@ -145,6 +150,7 @@ def get_llm_client(raise_api_key_error: bool = True):
             max_completion_tokens=max_completion_tokens,
             endpoint=llm_config.llm_endpoint,
             api_version=llm_config.llm_api_version,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
         )
 
     elif provider == LLMProvider.MISTRAL:
@@ -160,21 +166,7 @@ def get_llm_client(raise_api_key_error: bool = True):
             model=llm_config.llm_model,
             max_completion_tokens=max_completion_tokens,
             endpoint=llm_config.llm_endpoint,
-        )
-
-    elif provider == LLMProvider.MISTRAL:
-        if llm_config.llm_api_key is None:
-            raise LLMAPIKeyNotSetError()
-
-        from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import (
-            MistralAdapter,
-        )
-
-        return MistralAdapter(
-            api_key=llm_config.llm_api_key,
-            model=llm_config.llm_model,
-            max_completion_tokens=max_completion_tokens,
-            endpoint=llm_config.llm_endpoint,
+            instructor_mode=llm_config.llm_instructor_mode.lower(),
         )
 
     else:

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py

@@ -37,16 +37,26 @@ class MistralAdapter(LLMInterface):
     model: str
     api_key: str
     max_completion_tokens: int
+    default_instructor_mode = "mistral_tools"
 
-    def __init__(self, api_key: str, model: str, max_completion_tokens: int, endpoint: str = None):
+    def __init__(
+        self,
+        api_key: str,
+        model: str,
+        max_completion_tokens: int,
+        endpoint: str = None,
+        instructor_mode: str = None,
+    ):
         from mistralai import Mistral
 
         self.model = model
         self.max_completion_tokens = max_completion_tokens
 
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
         self.aclient = instructor.from_litellm(
             litellm.acompletion,
-            mode=instructor.Mode.MISTRAL_TOOLS,
+            mode=instructor.Mode(self.instructor_mode),
             api_key=get_llm_config().llm_api_key,
         )
 

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/ollama/adapter.py

@@ -42,8 +42,16 @@ class OllamaAPIAdapter(LLMInterface):
     - aclient
     """
 
+    default_instructor_mode = "json_mode"
+
     def __init__(
-        self, endpoint: str, api_key: str, model: str, name: str, max_completion_tokens: int
+        self,
+        endpoint: str,
+        api_key: str,
+        model: str,
+        name: str,
+        max_completion_tokens: int,
+        instructor_mode: str = None,
     ):
         self.name = name
         self.model = model
@@ -51,8 +59,11 @@ class OllamaAPIAdapter(LLMInterface):
         self.endpoint = endpoint
         self.max_completion_tokens = max_completion_tokens
 
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
+
         self.aclient = instructor.from_openai(
-            OpenAI(base_url=self.endpoint, api_key=self.api_key), mode=instructor.Mode.JSON
+            OpenAI(base_url=self.endpoint, api_key=self.api_key),
+            mode=instructor.Mode(self.instructor_mode),
         )
 
     @retry(

cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py

@@ -56,6 +56,7 @@ class OpenAIAdapter(LLMInterface):
     model: str
     api_key: str
     api_version: str
+    default_instructor_mode = "json_schema_mode"
 
     MAX_RETRIES = 5
 
@@ -69,19 +70,21 @@ class OpenAIAdapter(LLMInterface):
         model: str,
         transcription_model: str,
         max_completion_tokens: int,
+        instructor_mode: str = None,
         streaming: bool = False,
         fallback_model: str = None,
         fallback_api_key: str = None,
         fallback_endpoint: str = None,
     ):
+        self.instructor_mode = instructor_mode if instructor_mode else self.default_instructor_mode
         # TODO: With gpt5 series models OpenAI expects JSON_SCHEMA as a mode for structured outputs.
         #       Make sure all new gpt models will work with this mode as well.
         if "gpt-5" in model:
             self.aclient = instructor.from_litellm(
-                litellm.acompletion, mode=instructor.Mode.JSON_SCHEMA
+                litellm.acompletion, mode=instructor.Mode(self.instructor_mode)
             )
             self.client = instructor.from_litellm(
-                litellm.completion, mode=instructor.Mode.JSON_SCHEMA
+                litellm.completion, mode=instructor.Mode(self.instructor_mode)
             )
         else:
             self.aclient = instructor.from_litellm(litellm.acompletion)

cognee/infrastructure/loaders/LoaderEngine.py

@@ -31,6 +31,7 @@ class LoaderEngine:
             "pypdf_loader",
             "image_loader",
             "audio_loader",
+            "csv_loader",
             "unstructured_loader",
             "advanced_pdf_loader",
         ]

cognee/infrastructure/loaders/core/__init__.py

@@ -3,5 +3,6 @@
 from .text_loader import TextLoader
 from .audio_loader import AudioLoader
 from .image_loader import ImageLoader
+from .csv_loader import CsvLoader
 
-__all__ = ["TextLoader", "AudioLoader", "ImageLoader"]
+__all__ = ["TextLoader", "AudioLoader", "ImageLoader", "CsvLoader"]

cognee/infrastructure/loaders/core/csv_loader.py

@@ -0,0 +1,93 @@
+import os
+from typing import List
+import csv
+from cognee.infrastructure.loaders.LoaderInterface import LoaderInterface
+from cognee.infrastructure.files.storage import get_file_storage, get_storage_config
+from cognee.infrastructure.files.utils.get_file_metadata import get_file_metadata
+
+
+class CsvLoader(LoaderInterface):
+    """
+    Core CSV file loader that handles basic CSV file formats.
+    """
+
+    @property
+    def supported_extensions(self) -> List[str]:
+        """Supported text file extensions."""
+        return [
+            "csv",
+        ]
+
+    @property
+    def supported_mime_types(self) -> List[str]:
+        """Supported MIME types for text content."""
+        return [
+            "text/csv",
+        ]
+
+    @property
+    def loader_name(self) -> str:
+        """Unique identifier for this loader."""
+        return "csv_loader"
+
+    def can_handle(self, extension: str, mime_type: str) -> bool:
+        """
+        Check if this loader can handle the given file.
+
+        Args:
+            extension: File extension
+            mime_type: Optional MIME type
+
+        Returns:
+            True if file can be handled, False otherwise
+        """
+        if extension in self.supported_extensions and mime_type in self.supported_mime_types:
+            return True
+
+        return False
+
+    async def load(self, file_path: str, encoding: str = "utf-8", **kwargs):
+        """
+        Load and process the csv file.
+
+        Args:
+            file_path: Path to the file to load
+            encoding: Text encoding to use (default: utf-8)
+            **kwargs: Additional configuration (unused)
+
+        Returns:
+            LoaderResult containing the file content and metadata
+
+        Raises:
+            FileNotFoundError: If file doesn't exist
+            UnicodeDecodeError: If file cannot be decoded with specified encoding
+            OSError: If file cannot be read
+        """
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        with open(file_path, "rb") as f:
+            file_metadata = await get_file_metadata(f)
+        # Name ingested file of current loader based on original file content hash
+        storage_file_name = "text_" + file_metadata["content_hash"] + ".txt"
+
+        row_texts = []
+        row_index = 1
+
+        with open(file_path, "r", encoding=encoding, newline="") as file:
+            reader = csv.DictReader(file)
+            for row in reader:
+                pairs = [f"{str(k)}: {str(v)}" for k, v in row.items()]
+                row_text = ", ".join(pairs)
+                row_texts.append(f"Row {row_index}:\n{row_text}\n")
+                row_index += 1
+
+        content = "\n".join(row_texts)
+
+        storage_config = get_storage_config()
+        data_root_directory = storage_config["data_root_directory"]
+        storage = get_file_storage(data_root_directory)
+
+        full_file_path = await storage.store(storage_file_name, content)
+
+        return full_file_path

cognee/infrastructure/loaders/core/text_loader.py

@@ -16,7 +16,7 @@ class TextLoader(LoaderInterface):
     @property
     def supported_extensions(self) -> List[str]:
         """Supported text file extensions."""
-        return ["txt", "md", "csv", "json", "xml", "yaml", "yml", "log"]
+        return ["txt", "md", "json", "xml", "yaml", "yml", "log"]
 
     @property
     def supported_mime_types(self) -> List[str]:
@@ -24,7 +24,6 @@ class TextLoader(LoaderInterface):
         return [
             "text/plain",
             "text/markdown",
-            "text/csv",
             "application/json",
             "text/xml",
             "application/xml",

cognee/infrastructure/loaders/external/advanced_pdf_loader.py

@@ -227,12 +227,3 @@ class AdvancedPdfLoader(LoaderInterface):
         if value is None:
             return ""
         return str(value).replace("\xa0", " ").strip()
-
-
-if __name__ == "__main__":
-    loader = AdvancedPdfLoader()
-    asyncio.run(
-        loader.load(
-            "/Users/xiaotao/work/cognee/cognee/infrastructure/loaders/external/attention_is_all_you_need.pdf"
-        )
-    )

cognee/infrastructure/loaders/supported_loaders.py

@@ -1,5 +1,5 @@
 from cognee.infrastructure.loaders.external import PyPdfLoader
-from cognee.infrastructure.loaders.core import TextLoader, AudioLoader, ImageLoader
+from cognee.infrastructure.loaders.core import TextLoader, AudioLoader, ImageLoader, CsvLoader
 
 # Registry for loader implementations
 supported_loaders = {
@@ -7,6 +7,7 @@ supported_loaders = {
     TextLoader.loader_name: TextLoader,
     ImageLoader.loader_name: ImageLoader,
     AudioLoader.loader_name: AudioLoader,
+    CsvLoader.loader_name: CsvLoader,
 }
 
 # Try adding optional loaders

cognee/modules/chunking/CsvChunker.py

@@ -0,0 +1,35 @@
+from cognee.shared.logging_utils import get_logger
+
+
+from cognee.tasks.chunks import chunk_by_row
+from cognee.modules.chunking.Chunker import Chunker
+from .models.DocumentChunk import DocumentChunk
+
+logger = get_logger()
+
+
+class CsvChunker(Chunker):
+    async def read(self):
+        async for content_text in self.get_text():
+            if content_text is None:
+                continue
+
+            for chunk_data in chunk_by_row(content_text, self.max_chunk_size):
+                if chunk_data["chunk_size"] <= self.max_chunk_size:
+                    yield DocumentChunk(
+                        id=chunk_data["chunk_id"],
+                        text=chunk_data["text"],
+                        chunk_size=chunk_data["chunk_size"],
+                        is_part_of=self.document,
+                        chunk_index=self.chunk_index,
+                        cut_type=chunk_data["cut_type"],
+                        contains=[],
+                        metadata={
+                            "index_fields": ["text"],
+                        },
+                    )
+                    self.chunk_index += 1
+                else:
+                    raise ValueError(
+                        f"Chunk size is larger than the maximum chunk size {self.max_chunk_size}"
+                    )

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

@@ -0,0 +1,33 @@
+import io
+import csv
+from typing import Type
+
+from cognee.modules.chunking.Chunker import Chunker
+from cognee.infrastructure.files.utils.open_data_file import open_data_file
+from .Document import Document
+
+
+class CsvDocument(Document):
+    type: str = "csv"
+    mime_type: str = "text/csv"
+
+    async def read(self, chunker_cls: Type[Chunker], max_chunk_size: int):
+        async def get_text():
+            async with open_data_file(
+                self.raw_data_location, mode="r", encoding="utf-8", newline=""
+            ) as file:
+                content = file.read()
+                file_like_obj = io.StringIO(content)
+                reader = csv.DictReader(file_like_obj)
+
+                for row in reader:
+                    pairs = [f"{str(k)}: {str(v)}" for k, v in row.items()]
+                    row_text = ", ".join(pairs)
+                    if not row_text.strip():
+                        break
+                    yield row_text
+
+        chunker = chunker_cls(self, max_chunk_size=max_chunk_size, get_text=get_text)
+
+        async for chunk in chunker.read():
+            yield chunk

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

@@ -4,3 +4,4 @@ from .TextDocument import TextDocument
 from .ImageDocument import ImageDocument
 from .AudioDocument import AudioDocument
 from .UnstructuredDocument import UnstructuredDocument
+from .CsvDocument import CsvDocument

cognee/modules/graph/cognee_graph/CogneeGraph.py

@@ -56,6 +56,68 @@ class CogneeGraph(CogneeAbstractGraph):
     def get_edges(self) -> List[Edge]:
         return self.edges
 
+    async def _get_nodeset_subgraph(
+        self,
+        adapter,
+        node_type,
+        node_name,
+    ):
+        """Retrieve subgraph based on node type and name."""
+        logger.info("Retrieving graph filtered by node type and node name (NodeSet).")
+        nodes_data, edges_data = await adapter.get_nodeset_subgraph(
+            node_type=node_type, node_name=node_name
+        )
+        if not nodes_data or not edges_data:
+            raise EntityNotFoundError(
+                message="Nodeset does not exist, or empty nodeset projected from the database."
+            )
+        return nodes_data, edges_data
+
+    async def _get_full_or_id_filtered_graph(
+        self,
+        adapter,
+        relevant_ids_to_filter,
+    ):
+        """Retrieve full or ID-filtered graph with fallback."""
+        if relevant_ids_to_filter is None:
+            logger.info("Retrieving full graph.")
+            nodes_data, edges_data = await adapter.get_graph_data()
+            if not nodes_data or not edges_data:
+                raise EntityNotFoundError(message="Empty graph projected from the database.")
+            return nodes_data, edges_data
+
+        get_graph_data_fn = getattr(adapter, "get_id_filtered_graph_data", adapter.get_graph_data)
+        if getattr(adapter.__class__, "get_id_filtered_graph_data", None):
+            logger.info("Retrieving ID-filtered graph from database.")
+            nodes_data, edges_data = await get_graph_data_fn(target_ids=relevant_ids_to_filter)
+        else:
+            logger.info("Retrieving full graph from database.")
+            nodes_data, edges_data = await get_graph_data_fn()
+        if hasattr(adapter, "get_id_filtered_graph_data") and (not nodes_data or not edges_data):
+            logger.warning(
+                "Id filtered graph returned empty, falling back to full graph retrieval."
+            )
+            logger.info("Retrieving full graph")
+            nodes_data, edges_data = await adapter.get_graph_data()
+
+        if not nodes_data or not edges_data:
+            raise EntityNotFoundError("Empty graph projected from the database.")
+        return nodes_data, edges_data
+
+    async def _get_filtered_graph(
+        self,
+        adapter,
+        memory_fragment_filter,
+    ):
+        """Retrieve graph filtered by attributes."""
+        logger.info("Retrieving graph filtered by memory fragment")
+        nodes_data, edges_data = await adapter.get_filtered_graph_data(
+            attribute_filters=memory_fragment_filter
+        )
+        if not nodes_data or not edges_data:
+            raise EntityNotFoundError(message="Empty filtered graph projected from the database.")
+        return nodes_data, edges_data
+
     async def project_graph_from_db(
         self,
         adapter: Union[GraphDBInterface],
@@ -67,40 +129,39 @@ class CogneeGraph(CogneeAbstractGraph):
         memory_fragment_filter=[],
         node_type: Optional[Type] = None,
         node_name: Optional[List[str]] = None,
+        relevant_ids_to_filter: Optional[List[str]] = None,
+        triplet_distance_penalty: float = 3.5,
     ) -> None:
         if node_dimension < 1 or edge_dimension < 1:
             raise InvalidDimensionsError()
         try:
+            if node_type is not None and node_name not in [None, [], ""]:
+                nodes_data, edges_data = await self._get_nodeset_subgraph(
+                    adapter, node_type, node_name
+                )
+            elif len(memory_fragment_filter) == 0:
+                nodes_data, edges_data = await self._get_full_or_id_filtered_graph(
+                    adapter, relevant_ids_to_filter
+                )
+            else:
+                nodes_data, edges_data = await self._get_filtered_graph(
+                    adapter, memory_fragment_filter
+                )
+
             import time
 
             start_time = time.time()
-
-            # Determine projection strategy
-            if node_type is not None and node_name not in [None, [], ""]:
-                nodes_data, edges_data = await adapter.get_nodeset_subgraph(
-                    node_type=node_type, node_name=node_name
-                )
-                if not nodes_data or not edges_data:
-                    raise EntityNotFoundError(
-                        message="Nodeset does not exist, or empty nodetes projected from the database."
-                    )
-            elif len(memory_fragment_filter) == 0:
-                nodes_data, edges_data = await adapter.get_graph_data()
-                if not nodes_data or not edges_data:
-                    raise EntityNotFoundError(message="Empty graph projected from the database.")
-            else:
-                nodes_data, edges_data = await adapter.get_filtered_graph_data(
-                    attribute_filters=memory_fragment_filter
-                )
-                if not nodes_data or not edges_data:
-                    raise EntityNotFoundError(
-                        message="Empty filtered graph projected from the database."
-                    )
-
             # Process nodes
             for node_id, properties in nodes_data:
                 node_attributes = {key: properties.get(key) for key in node_properties_to_project}
-                self.add_node(Node(str(node_id), node_attributes, dimension=node_dimension))
+                self.add_node(
+                    Node(
+                        str(node_id),
+                        node_attributes,
+                        dimension=node_dimension,
+                        node_penalty=triplet_distance_penalty,
+                    )
+                )
 
             # Process edges
             for source_id, target_id, relationship_type, properties in edges_data:
@@ -118,6 +179,7 @@ class CogneeGraph(CogneeAbstractGraph):
                         attributes=edge_attributes,
                         directed=directed,
                         dimension=edge_dimension,
+                        edge_penalty=triplet_distance_penalty,
                     )
                     self.add_edge(edge)
 

cognee/modules/graph/cognee_graph/CogneeGraphElements.py

@@ -20,13 +20,17 @@ class Node:
     status: np.ndarray
 
     def __init__(
-        self, node_id: str, attributes: Optional[Dict[str, Any]] = None, dimension: int = 1
+        self,
+        node_id: str,
+        attributes: Optional[Dict[str, Any]] = None,
+        dimension: int = 1,
+        node_penalty: float = 3.5,
     ):
         if dimension <= 0:
             raise InvalidDimensionsError()
         self.id = node_id
         self.attributes = attributes if attributes is not None else {}
-        self.attributes["vector_distance"] = float("inf")
+        self.attributes["vector_distance"] = node_penalty
         self.skeleton_neighbours = []
         self.skeleton_edges = []
         self.status = np.ones(dimension, dtype=int)
@@ -105,13 +109,14 @@ class Edge:
         attributes: Optional[Dict[str, Any]] = None,
         directed: bool = True,
         dimension: int = 1,
+        edge_penalty: float = 3.5,
     ):
         if dimension <= 0:
             raise InvalidDimensionsError()
         self.node1 = node1
         self.node2 = node2
         self.attributes = attributes if attributes is not None else {}
-        self.attributes["vector_distance"] = float("inf")
+        self.attributes["vector_distance"] = edge_penalty
         self.directed = directed
         self.status = np.ones(dimension, dtype=int)
 

cognee/modules/notebooks/operations/run_in_local_sandbox.py

@@ -2,6 +2,8 @@ import io
 import sys
 import traceback
 
+import cognee
+
 
 def wrap_in_async_handler(user_code: str) -> str:
     return (
@@ -34,6 +36,7 @@ def run_in_local_sandbox(code, environment=None, loop=None):
 
     environment["print"] = customPrintFunction
     environment["running_loop"] = loop
+    environment["cognee"] = cognee
 
     try:
         exec(code, environment)

cognee/modules/ontology/rdf_xml/RDFLibOntologyResolver.py

@@ -2,7 +2,7 @@ import os
 import difflib
 from cognee.shared.logging_utils import get_logger
 from collections import deque
-from typing import List, Tuple, Dict, Optional, Any, Union
+from typing import List, Tuple, Dict, Optional, Any, Union, IO
 from rdflib import Graph, URIRef, RDF, RDFS, OWL
 
 from cognee.modules.ontology.exceptions import (
@@ -26,44 +26,76 @@ class RDFLibOntologyResolver(BaseOntologyResolver):
 
     def __init__(
         self,
-        ontology_file: Optional[Union[str, List[str]]] = None,
+        ontology_file: Optional[Union[str, List[str], IO, List[IO]]] = None,
         matching_strategy: Optional[MatchingStrategy] = None,
     ) -> None:
         super().__init__(matching_strategy)
         self.ontology_file = ontology_file
         try:
-            files_to_load = []
+            self.graph = None
             if ontology_file is not None:
-                if isinstance(ontology_file, str):
+                files_to_load = []
+                file_objects = []
+
+                if hasattr(ontology_file, "read"):
+                    file_objects = [ontology_file]
+                elif isinstance(ontology_file, str):
                     files_to_load = [ontology_file]
                 elif isinstance(ontology_file, list):
-                    files_to_load = ontology_file
+                    if all(hasattr(item, "read") for item in ontology_file):
+                        file_objects = ontology_file
+                    else:
+                        files_to_load = ontology_file
                 else:
                     raise ValueError(
-                        f"ontology_file must be a string, list of strings, or None. Got: {type(ontology_file)}"
+                        f"ontology_file must be a string, list of strings, file-like object, list of file-like objects, or None. Got: {type(ontology_file)}"
                     )
 
-            if files_to_load:
-                self.graph = Graph()
-                loaded_files = []
-                for file_path in files_to_load:
-                    if os.path.exists(file_path):
-                        self.graph.parse(file_path)
-                        loaded_files.append(file_path)
-                        logger.info("Ontology loaded successfully from file: %s", file_path)
-                    else:
-                        logger.warning(
-                            "Ontology file '%s' not found. Skipping this file.",
-                            file_path,
+                if file_objects:
+                    self.graph = Graph()
+                    loaded_objects = []
+                    for file_obj in file_objects:
+                        try:
+                            content = file_obj.read()
+                            self.graph.parse(data=content, format="xml")
+                            loaded_objects.append(file_obj)
+                            logger.info("Ontology loaded successfully from file object")
+                        except Exception as e:
+                            logger.warning("Failed to parse ontology file object: %s", str(e))
+
+                    if not loaded_objects:
+                        logger.info(
+                            "No valid ontology file objects found. No owl ontology will be attached to the graph."
                         )
+                        self.graph = None
+                    else:
+                        logger.info("Total ontology file objects loaded: %d", len(loaded_objects))
 
-                if not loaded_files:
-                    logger.info(
-                        "No valid ontology files found. No owl ontology will be attached to the graph."
-                    )
-                    self.graph = None
+                elif files_to_load:
+                    self.graph = Graph()
+                    loaded_files = []
+                    for file_path in files_to_load:
+                        if os.path.exists(file_path):
+                            self.graph.parse(file_path)
+                            loaded_files.append(file_path)
+                            logger.info("Ontology loaded successfully from file: %s", file_path)
+                        else:
+                            logger.warning(
+                                "Ontology file '%s' not found. Skipping this file.",
+                                file_path,
+                            )
+
+                    if not loaded_files:
+                        logger.info(
+                            "No valid ontology files found. No owl ontology will be attached to the graph."
+                        )
+                        self.graph = None
+                    else:
+                        logger.info("Total ontology files loaded: %d", len(loaded_files))
                 else:
-                    logger.info("Total ontology files loaded: %d", len(loaded_files))
+                    logger.info(
+                        "No ontology file provided. No owl ontology will be attached to the graph."
+                    )
             else:
                 logger.info(
                     "No ontology file provided. No owl ontology will be attached to the graph."

cognee/modules/retrieval/graph_completion_context_extension_retriever.py

@@ -39,6 +39,8 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
         node_type: Optional[Type] = None,
         node_name: Optional[List[str]] = None,
         save_interaction: bool = False,
+        wide_search_top_k: Optional[int] = 100,
+        triplet_distance_penalty: Optional[float] = 3.5,
     ):
         super().__init__(
             user_prompt_path=user_prompt_path,
@@ -48,6 +50,8 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
             node_name=node_name,
             save_interaction=save_interaction,
             system_prompt=system_prompt,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
 
     async def get_completion(

cognee/modules/retrieval/graph_completion_cot_retriever.py

@@ -65,6 +65,8 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
         node_type: Optional[Type] = None,
         node_name: Optional[List[str]] = None,
         save_interaction: bool = False,
+        wide_search_top_k: Optional[int] = 100,
+        triplet_distance_penalty: Optional[float] = 3.5,
     ):
         super().__init__(
             user_prompt_path=user_prompt_path,
@@ -74,6 +76,8 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
             node_type=node_type,
             node_name=node_name,
             save_interaction=save_interaction,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
         self.validation_system_prompt_path = validation_system_prompt_path
         self.validation_user_prompt_path = validation_user_prompt_path

cognee/modules/retrieval/graph_completion_retriever.py

@@ -47,6 +47,8 @@ class GraphCompletionRetriever(BaseGraphRetriever):
         node_type: Optional[Type] = None,
         node_name: Optional[List[str]] = None,
         save_interaction: bool = False,
+        wide_search_top_k: Optional[int] = 100,
+        triplet_distance_penalty: Optional[float] = 3.5,
     ):
         """Initialize retriever with prompt paths and search parameters."""
         self.save_interaction = save_interaction
@@ -54,8 +56,10 @@ class GraphCompletionRetriever(BaseGraphRetriever):
         self.system_prompt_path = system_prompt_path
         self.system_prompt = system_prompt
         self.top_k = top_k if top_k is not None else 5
+        self.wide_search_top_k = wide_search_top_k
         self.node_type = node_type
         self.node_name = node_name
+        self.triplet_distance_penalty = triplet_distance_penalty
 
     async def resolve_edges_to_text(self, retrieved_edges: list) -> str:
         """
@@ -105,6 +109,8 @@ class GraphCompletionRetriever(BaseGraphRetriever):
             collections=vector_index_collections or None,
             node_type=self.node_type,
             node_name=self.node_name,
+            wide_search_top_k=self.wide_search_top_k,
+            triplet_distance_penalty=self.triplet_distance_penalty,
         )
 
         return found_triplets
@@ -141,6 +147,10 @@ class GraphCompletionRetriever(BaseGraphRetriever):
 
         return triplets
 
+    async def convert_retrieved_objects_to_context(self, triplets: List[Edge]):
+        context = await self.resolve_edges_to_text(triplets)
+        return context
+
     async def get_completion(
         self,
         query: str,

cognee/modules/retrieval/graph_summary_completion_retriever.py

@@ -26,6 +26,8 @@ class GraphSummaryCompletionRetriever(GraphCompletionRetriever):
         node_type: Optional[Type] = None,
         node_name: Optional[List[str]] = None,
         save_interaction: bool = False,
+        wide_search_top_k: Optional[int] = 100,
+        triplet_distance_penalty: Optional[float] = 3.5,
     ):
         """Initialize retriever with default prompt paths and search parameters."""
         super().__init__(
@@ -36,6 +38,8 @@ class GraphSummaryCompletionRetriever(GraphCompletionRetriever):
             node_name=node_name,
             save_interaction=save_interaction,
             system_prompt=system_prompt,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
         self.summarize_prompt_path = summarize_prompt_path
 

cognee/modules/retrieval/temporal_retriever.py

@@ -47,6 +47,8 @@ class TemporalRetriever(GraphCompletionRetriever):
         top_k: Optional[int] = 5,
         node_type: Optional[Type] = None,
         node_name: Optional[List[str]] = None,
+        wide_search_top_k: Optional[int] = 100,
+        triplet_distance_penalty: Optional[float] = 3.5,
     ):
         super().__init__(
             user_prompt_path=user_prompt_path,
@@ -54,6 +56,8 @@ class TemporalRetriever(GraphCompletionRetriever):
             top_k=top_k,
             node_type=node_type,
             node_name=node_name,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
         self.user_prompt_path = user_prompt_path
         self.system_prompt_path = system_prompt_path

cognee/modules/retrieval/utils/brute_force_triplet_search.py

@@ -58,6 +58,8 @@ async def get_memory_fragment(
     properties_to_project: Optional[List[str]] = None,
     node_type: Optional[Type] = None,
     node_name: Optional[List[str]] = None,
+    relevant_ids_to_filter: Optional[List[str]] = None,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> CogneeGraph:
     """Creates and initializes a CogneeGraph memory fragment with optional property projections."""
     if properties_to_project is None:
@@ -74,6 +76,8 @@ async def get_memory_fragment(
             edge_properties_to_project=["relationship_name", "edge_text"],
             node_type=node_type,
             node_name=node_name,
+            relevant_ids_to_filter=relevant_ids_to_filter,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
 
     except EntityNotFoundError:
@@ -95,6 +99,8 @@ async def brute_force_triplet_search(
     memory_fragment: Optional[CogneeGraph] = None,
     node_type: Optional[Type] = None,
     node_name: Optional[List[str]] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> List[Edge]:
     """
     Performs a brute force search to retrieve the top triplets from the graph.
@@ -107,6 +113,8 @@ async def brute_force_triplet_search(
         memory_fragment (Optional[CogneeGraph]): Existing memory fragment to reuse.
         node_type: node type to filter
         node_name: node name to filter
+        wide_search_top_k (Optional[int]): Number of initial elements to retrieve from collections
+        triplet_distance_penalty (Optional[float]): Default distance penalty in graph projection
 
     Returns:
         list: The top triplet results.
@@ -116,10 +124,10 @@ async def brute_force_triplet_search(
     if top_k <= 0:
         raise ValueError("top_k must be a positive integer.")
 
-    if memory_fragment is None:
-        memory_fragment = await get_memory_fragment(
-            properties_to_project, node_type=node_type, node_name=node_name
-        )
+    # Setting wide search limit based on the parameters
+    non_global_search = node_name is None
+
+    wide_search_limit = wide_search_top_k if non_global_search else None
 
     if collections is None:
         collections = [
@@ -140,7 +148,7 @@ async def brute_force_triplet_search(
     async def search_in_collection(collection_name: str):
         try:
             return await vector_engine.search(
-                collection_name=collection_name, query_vector=query_vector, limit=None
+                collection_name=collection_name, query_vector=query_vector, limit=wide_search_limit
             )
         except CollectionNotFoundError:
             return []
@@ -156,15 +164,38 @@ async def brute_force_triplet_search(
             return []
 
         # Final statistics
-        projection_time = time.time() - start_time
+        vector_collection_search_time = time.time() - start_time
         logger.info(
-            f"Vector collection retrieval completed: Retrieved distances from {sum(1 for res in results if res)} collections in {projection_time:.2f}s"
+            f"Vector collection retrieval completed: Retrieved distances from {sum(1 for res in results if res)} collections in {vector_collection_search_time:.2f}s"
         )
 
         node_distances = {collection: result for collection, result in zip(collections, results)}
 
         edge_distances = node_distances.get("EdgeType_relationship_name", None)
 
+        if wide_search_limit is not None:
+            relevant_ids_to_filter = list(
+                {
+                    str(getattr(scored_node, "id"))
+                    for collection_name, score_collection in node_distances.items()
+                    if collection_name != "EdgeType_relationship_name"
+                    and isinstance(score_collection, (list, tuple))
+                    for scored_node in score_collection
+                    if getattr(scored_node, "id", None)
+                }
+            )
+        else:
+            relevant_ids_to_filter = None
+
+        if memory_fragment is None:
+            memory_fragment = await get_memory_fragment(
+                properties_to_project=properties_to_project,
+                node_type=node_type,
+                node_name=node_name,
+                relevant_ids_to_filter=relevant_ids_to_filter,
+                triplet_distance_penalty=triplet_distance_penalty,
+            )
+
         await memory_fragment.map_vector_distances_to_graph_nodes(node_distances=node_distances)
         await memory_fragment.map_vector_distances_to_graph_edges(
             vector_engine=vector_engine, query_vector=query_vector, edge_distances=edge_distances

cognee/modules/search/methods/get_search_type_tools.py

@@ -37,6 +37,8 @@ async def get_search_type_tools(
     node_name: Optional[List[str]] = None,
     save_interaction: bool = False,
     last_k: Optional[int] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> list:
     search_tasks: dict[SearchType, List[Callable]] = {
         SearchType.SUMMARIES: [
@@ -67,6 +69,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_completion,
             GraphCompletionRetriever(
                 system_prompt_path=system_prompt_path,
@@ -75,6 +79,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_context,
         ],
         SearchType.GRAPH_COMPLETION_COT: [
@@ -85,6 +91,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_completion,
             GraphCompletionCotRetriever(
                 system_prompt_path=system_prompt_path,
@@ -93,6 +101,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_context,
         ],
         SearchType.GRAPH_COMPLETION_CONTEXT_EXTENSION: [
@@ -103,6 +113,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_completion,
             GraphCompletionContextExtensionRetriever(
                 system_prompt_path=system_prompt_path,
@@ -111,6 +123,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_context,
         ],
         SearchType.GRAPH_SUMMARY_COMPLETION: [
@@ -121,6 +135,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_completion,
             GraphSummaryCompletionRetriever(
                 system_prompt_path=system_prompt_path,
@@ -129,6 +145,8 @@ async def get_search_type_tools(
                 node_name=node_name,
                 save_interaction=save_interaction,
                 system_prompt=system_prompt,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             ).get_context,
         ],
         SearchType.CODE: [
@@ -145,8 +163,16 @@ async def get_search_type_tools(
         ],
         SearchType.FEEDBACK: [UserQAFeedback(last_k=last_k).add_feedback],
         SearchType.TEMPORAL: [
-            TemporalRetriever(top_k=top_k).get_completion,
-            TemporalRetriever(top_k=top_k).get_context,
+            TemporalRetriever(
+                top_k=top_k,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
+            ).get_completion,
+            TemporalRetriever(
+                top_k=top_k,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
+            ).get_context,
         ],
         SearchType.CHUNKS_LEXICAL: (
             lambda _r=JaccardChunksRetriever(top_k=top_k): [

cognee/modules/search/methods/no_access_control_search.py

@@ -24,6 +24,8 @@ async def no_access_control_search(
     last_k: Optional[int] = None,
     only_context: bool = False,
     session_id: Optional[str] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> Tuple[Any, Union[str, List[Edge]], List[Dataset]]:
     search_tools = await get_search_type_tools(
         query_type=query_type,
@@ -35,6 +37,8 @@ async def no_access_control_search(
         node_name=node_name,
         save_interaction=save_interaction,
         last_k=last_k,
+        wide_search_top_k=wide_search_top_k,
+        triplet_distance_penalty=triplet_distance_penalty,
     )
     graph_engine = await get_graph_engine()
     is_empty = await graph_engine.is_empty()

cognee/modules/search/methods/search.py

@@ -47,6 +47,8 @@ async def search(
     only_context: bool = False,
     use_combined_context: bool = False,
     session_id: Optional[str] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> Union[CombinedSearchResult, List[SearchResult]]:
     """
 
@@ -90,6 +92,8 @@ async def search(
             only_context=only_context,
             use_combined_context=use_combined_context,
             session_id=session_id,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
     else:
         search_results = [
@@ -105,6 +109,8 @@ async def search(
                 last_k=last_k,
                 only_context=only_context,
                 session_id=session_id,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             )
         ]
 
@@ -219,6 +225,8 @@ async def authorized_search(
     only_context: bool = False,
     use_combined_context: bool = False,
     session_id: Optional[str] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> Union[
     Tuple[Any, Union[List[Edge], str], List[Dataset]],
     List[Tuple[Any, Union[List[Edge], str], List[Dataset]]],
@@ -246,6 +254,8 @@ async def authorized_search(
             last_k=last_k,
             only_context=True,
             session_id=session_id,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
 
         context = {}
@@ -267,6 +277,8 @@ async def authorized_search(
             node_name=node_name,
             save_interaction=save_interaction,
             last_k=last_k,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
         search_tools = specific_search_tools
         if len(search_tools) == 2:
@@ -306,6 +318,7 @@ async def authorized_search(
         last_k=last_k,
         only_context=only_context,
         session_id=session_id,
+        wide_search_top_k=wide_search_top_k,
     )
 
     return search_results
@@ -325,6 +338,8 @@ async def search_in_datasets_context(
     only_context: bool = False,
     context: Optional[Any] = None,
     session_id: Optional[str] = None,
+    wide_search_top_k: Optional[int] = 100,
+    triplet_distance_penalty: Optional[float] = 3.5,
 ) -> List[Tuple[Any, Union[str, List[Edge]], List[Dataset]]]:
     """
     Searches all provided datasets and handles setting up of appropriate database context based on permissions.
@@ -345,6 +360,8 @@ async def search_in_datasets_context(
         only_context: bool = False,
         context: Optional[Any] = None,
         session_id: Optional[str] = None,
+        wide_search_top_k: Optional[int] = 100,
+        triplet_distance_penalty: Optional[float] = 3.5,
     ) -> Tuple[Any, Union[str, List[Edge]], List[Dataset]]:
         # Set database configuration in async context for each dataset user has access for
         await set_database_global_context_variables(dataset.id, dataset.owner_id)
@@ -378,6 +395,8 @@ async def search_in_datasets_context(
             node_name=node_name,
             save_interaction=save_interaction,
             last_k=last_k,
+            wide_search_top_k=wide_search_top_k,
+            triplet_distance_penalty=triplet_distance_penalty,
         )
         search_tools = specific_search_tools
         if len(search_tools) == 2:
@@ -413,6 +432,8 @@ async def search_in_datasets_context(
                 only_context=only_context,
                 context=context,
                 session_id=session_id,
+                wide_search_top_k=wide_search_top_k,
+                triplet_distance_penalty=triplet_distance_penalty,
             )
         )
 

cognee/tasks/chunks/__init__.py

@@ -1,4 +1,5 @@
 from .chunk_by_word import chunk_by_word
 from .chunk_by_sentence import chunk_by_sentence
 from .chunk_by_paragraph import chunk_by_paragraph
+from .chunk_by_row import chunk_by_row
 from .remove_disconnected_chunks import remove_disconnected_chunks

cognee/tasks/chunks/chunk_by_row.py

@@ -0,0 +1,94 @@
+from typing import Any, Dict, Iterator
+from uuid import NAMESPACE_OID, uuid5
+
+from cognee.infrastructure.databases.vector.embeddings import get_embedding_engine
+
+
+def _get_pair_size(pair_text: str) -> int:
+    """
+    Calculate the size of a given text in terms of tokens.
+
+    If an embedding engine's tokenizer is available, count the tokens for the provided word.
+    If the tokenizer is not available, assume the word counts as one token.
+
+    Parameters:
+    -----------
+
+        - pair_text (str): The key:value pair text for which the token size is to be calculated.
+
+    Returns:
+    --------
+
+        - int: The number of tokens representing the text, typically an integer, depending
+          on the tokenizer's output.
+    """
+    embedding_engine = get_embedding_engine()
+    if embedding_engine.tokenizer:
+        return embedding_engine.tokenizer.count_tokens(pair_text)
+    else:
+        return 3
+
+
+def chunk_by_row(
+    data: str,
+    max_chunk_size,
+) -> Iterator[Dict[str, Any]]:
+    """
+    Chunk the input text by row while enabling exact text reconstruction.
+
+    This function divides the given text data into smaller chunks on a line-by-line basis,
+    ensuring that the size of each chunk is less than or equal to the specified maximum
+    chunk size. It guarantees that when the generated chunks are concatenated, they
+    reproduce the original text accurately. The tokenization process is handled by
+    adapters compatible with the vector engine's embedding model.
+
+    Parameters:
+    -----------
+
+        - data (str): The input text to be chunked.
+        - max_chunk_size: The maximum allowed size for each chunk, in terms of tokens or
+          words.
+    """
+    current_chunk_list = []
+    chunk_index = 0
+    current_chunk_size = 0
+
+    lines = data.split("\n\n")
+    for line in lines:
+        pairs_text = line.split(", ")
+
+        for pair_text in pairs_text:
+            pair_size = _get_pair_size(pair_text)
+            if current_chunk_size > 0 and (current_chunk_size + pair_size > max_chunk_size):
+                # Yield current cut chunk
+                current_chunk = ", ".join(current_chunk_list)
+                chunk_dict = {
+                    "text": current_chunk,
+                    "chunk_size": current_chunk_size,
+                    "chunk_id": uuid5(NAMESPACE_OID, current_chunk),
+                    "chunk_index": chunk_index,
+                    "cut_type": "row_cut",
+                }
+
+                yield chunk_dict
+
+                # Start new chunk with current pair text
+                current_chunk_list = []
+                current_chunk_size = 0
+                chunk_index += 1
+
+            current_chunk_list.append(pair_text)
+            current_chunk_size += pair_size
+
+        # Yield row chunk
+        current_chunk = ", ".join(current_chunk_list)
+        if current_chunk:
+            chunk_dict = {
+                "text": current_chunk,
+                "chunk_size": current_chunk_size,
+                "chunk_id": uuid5(NAMESPACE_OID, current_chunk),
+                "chunk_index": chunk_index,
+                "cut_type": "row_end",
+            }
+
+            yield chunk_dict

cognee/tasks/documents/classify_documents.py

@@ -7,6 +7,7 @@ from cognee.modules.data.processing.document_types import (
     ImageDocument,
     TextDocument,
     UnstructuredDocument,
+    CsvDocument,
 )
 from cognee.modules.engine.models.node_set import NodeSet
 from cognee.modules.engine.utils.generate_node_id import generate_node_id
@@ -15,6 +16,7 @@ from cognee.tasks.documents.exceptions import WrongDataDocumentInputError
 EXTENSION_TO_DOCUMENT_CLASS = {
     "pdf": PdfDocument,  # Text documents
     "txt": TextDocument,
+    "csv": CsvDocument,
     "docx": UnstructuredDocument,
     "doc": UnstructuredDocument,
     "odt": UnstructuredDocument,

cognee/tests/integration/documents/CsvDocument_test.py

@@ -0,0 +1,70 @@
+import os
+import sys
+import uuid
+import pytest
+import pathlib
+from unittest.mock import patch
+
+from cognee.modules.chunking.CsvChunker import CsvChunker
+from cognee.modules.data.processing.document_types.CsvDocument import CsvDocument
+from cognee.tests.integration.documents.AudioDocument_test import mock_get_embedding_engine
+from cognee.tests.integration.documents.async_gen_zip import async_gen_zip
+
+chunk_by_row_module = sys.modules.get("cognee.tasks.chunks.chunk_by_row")
+
+
+GROUND_TRUTH = {
+    "chunk_size_10": [
+        {"token_count": 9, "len_text": 26, "cut_type": "row_cut", "chunk_index": 0},
+        {"token_count": 6, "len_text": 29, "cut_type": "row_end", "chunk_index": 1},
+        {"token_count": 9, "len_text": 25, "cut_type": "row_cut", "chunk_index": 2},
+        {"token_count": 6, "len_text": 30, "cut_type": "row_end", "chunk_index": 3},
+    ],
+    "chunk_size_128": [
+        {"token_count": 15, "len_text": 57, "cut_type": "row_end", "chunk_index": 0},
+        {"token_count": 15, "len_text": 57, "cut_type": "row_end", "chunk_index": 1},
+    ],
+}
+
+
+@pytest.mark.parametrize(
+    "input_file,chunk_size",
+    [("example_with_header.csv", 10), ("example_with_header.csv", 128)],
+)
+@patch.object(chunk_by_row_module, "get_embedding_engine", side_effect=mock_get_embedding_engine)
+@pytest.mark.asyncio
+async def test_CsvDocument(mock_engine, input_file, chunk_size):
+    # Define file paths of test data
+    csv_file_path = os.path.join(
+        pathlib.Path(__file__).parent.parent.parent,
+        "test_data",
+        input_file,
+    )
+
+    # Define test documents
+    csv_document = CsvDocument(
+        id=uuid.uuid4(),
+        name="example_with_header.csv",
+        raw_data_location=csv_file_path,
+        external_metadata="",
+        mime_type="text/csv",
+    )
+
+    # TEST CSV
+    ground_truth_key = f"chunk_size_{chunk_size}"
+    async for ground_truth, row_data in async_gen_zip(
+        GROUND_TRUTH[ground_truth_key],
+        csv_document.read(chunker_cls=CsvChunker, max_chunk_size=chunk_size),
+    ):
+        assert ground_truth["token_count"] == row_data.chunk_size, (
+            f'{ground_truth["token_count"] = } != {row_data.chunk_size = }'
+        )
+        assert ground_truth["len_text"] == len(row_data.text), (
+            f'{ground_truth["len_text"] = } != {len(row_data.text) = }'
+        )
+        assert ground_truth["cut_type"] == row_data.cut_type, (
+            f'{ground_truth["cut_type"] = } != {row_data.cut_type = }'
+        )
+        assert ground_truth["chunk_index"] == row_data.chunk_index, (
+            f'{ground_truth["chunk_index"] = } != {row_data.chunk_index = }'
+        )

cognee/tests/integration/web_url_crawler/test_default_url_crawler.py

@@ -5,7 +5,7 @@ from cognee.tasks.web_scraper import DefaultUrlCrawler
 @pytest.mark.asyncio
 async def test_fetch():
     crawler = DefaultUrlCrawler()
-    url = "https://en.wikipedia.org/wiki/Large_language_model"
+    url = "http://example.com/"
     results = await crawler.fetch_urls(url)
     assert len(results) == 1
     assert isinstance(results, dict)

cognee/tests/integration/web_url_crawler/test_tavily_crawler.py

@@ -11,7 +11,7 @@ skip_in_ci = pytest.mark.skipif(
 @skip_in_ci
 @pytest.mark.asyncio
 async def test_fetch():
-    url = "https://en.wikipedia.org/wiki/Large_language_model"
+    url = "http://example.com/"
     results = await fetch_with_tavily(url)
     assert isinstance(results, dict)
     assert len(results) == 1

cognee/tests/integration/web_url_crawler/test_url_adding_e2e.py

@@ -14,9 +14,7 @@ async def test_url_saves_as_html_file():
     await cognee.prune.prune_system(metadata=True)
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         assert file_path.endswith(".html")
         file = Path(file_path)
@@ -44,9 +42,7 @@ async def test_saved_html_is_valid():
     await cognee.prune.prune_system(metadata=True)
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         content = Path(file_path).read_text()
 
@@ -72,7 +68,7 @@ async def test_add_url():
     await cognee.prune.prune_data()
     await cognee.prune.prune_system(metadata=True)
 
-    await cognee.add("https://en.wikipedia.org/wiki/Large_language_model")
+    await cognee.add("http://example.com/")
 
 
 skip_in_ci = pytest.mark.skipif(
@@ -88,7 +84,7 @@ async def test_add_url_with_tavily():
     await cognee.prune.prune_data()
     await cognee.prune.prune_system(metadata=True)
 
-    await cognee.add("https://en.wikipedia.org/wiki/Large_language_model")
+    await cognee.add("http://example.com/")
 
 
 @pytest.mark.asyncio
@@ -98,7 +94,7 @@ async def test_add_url_without_incremental_loading():
 
     try:
         await cognee.add(
-            "https://en.wikipedia.org/wiki/Large_language_model",
+            "http://example.com/",
             incremental_loading=False,
         )
     except Exception as e:
@@ -112,7 +108,7 @@ async def test_add_url_with_incremental_loading():
 
     try:
         await cognee.add(
-            "https://en.wikipedia.org/wiki/Large_language_model",
+            "http://example.com/",
             incremental_loading=True,
         )
     except Exception as e:
@@ -125,7 +121,7 @@ async def test_add_url_can_define_preferred_loader_as_list_of_str():
     await cognee.prune.prune_system(metadata=True)
 
     await cognee.add(
-        "https://en.wikipedia.org/wiki/Large_language_model",
+        "http://example.com/",
         preferred_loaders=["beautiful_soup_loader"],
     )
 
@@ -144,7 +140,7 @@ async def test_add_url_with_extraction_rules():
 
     try:
         await cognee.add(
-            "https://en.wikipedia.org/wiki/Large_language_model",
+            "http://example.com/",
             preferred_loaders={"beautiful_soup_loader": {"extraction_rules": extraction_rules}},
         )
     except Exception as e:
@@ -163,9 +159,7 @@ async def test_loader_is_none_by_default():
     }
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         assert file_path.endswith(".html")
         file = Path(file_path)
@@ -196,9 +190,7 @@ async def test_beautiful_soup_loader_is_selected_loader_if_preferred_loader_prov
     }
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         assert file_path.endswith(".html")
         file = Path(file_path)
@@ -225,9 +217,7 @@ async def test_beautiful_soup_loader_works_with_and_without_arguments():
     await cognee.prune.prune_system(metadata=True)
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         assert file_path.endswith(".html")
         file = Path(file_path)
@@ -263,9 +253,7 @@ async def test_beautiful_soup_loader_successfully_loads_file_if_required_args_pr
     await cognee.prune.prune_system(metadata=True)
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         assert file_path.endswith(".html")
         file = Path(file_path)
@@ -302,9 +290,7 @@ async def test_beautiful_soup_loads_file_successfully():
     }
 
     try:
-        original_file_path = await save_data_item_to_storage(
-            "https://en.wikipedia.org/wiki/Large_language_model"
-        )
+        original_file_path = await save_data_item_to_storage("http://example.com/")
         file_path = get_data_file_path(original_file_path)
         assert file_path.endswith(".html")
         original_file = Path(file_path)

cognee/tests/test_cognee_server_start.py

@@ -7,6 +7,7 @@ import requests
 from pathlib import Path
 import sys
 import uuid
+import json
 
 
 class TestCogneeServerStart(unittest.TestCase):
@@ -90,12 +91,71 @@ class TestCogneeServerStart(unittest.TestCase):
             )
         }
 
-        payload = {"datasets": [dataset_name]}
+        ontology_key = f"test_ontology_{uuid.uuid4().hex[:8]}"
+        payload = {"datasets": [dataset_name], "ontology_key": [ontology_key]}
 
         add_response = requests.post(url, headers=headers, data=form_data, files=file, timeout=50)
         if add_response.status_code not in [200, 201]:
             add_response.raise_for_status()
 
+        ontology_content = b"""<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+         xmlns:owl="http://www.w3.org/2002/07/owl#"
+         xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
+         xmlns="http://example.org/ontology#"
+         xml:base="http://example.org/ontology">
+
+                <owl:Ontology rdf:about="http://example.org/ontology"/>
+
+                <!-- Classes -->
+                <owl:Class rdf:ID="Problem"/>
+                <owl:Class rdf:ID="HardwareProblem"/>
+                <owl:Class rdf:ID="SoftwareProblem"/>
+                <owl:Class rdf:ID="Concept"/>
+                <owl:Class rdf:ID="Object"/>
+                <owl:Class rdf:ID="Joke"/>
+                <owl:Class rdf:ID="Image"/>
+                <owl:Class rdf:ID="Person"/>
+
+                <rdf:Description rdf:about="#HardwareProblem">
+                    <rdfs:subClassOf rdf:resource="#Problem"/>
+                    <rdfs:comment>A failure caused by physical components.</rdfs:comment>
+                </rdf:Description>
+
+                <rdf:Description rdf:about="#SoftwareProblem">
+                    <rdfs:subClassOf rdf:resource="#Problem"/>
+                    <rdfs:comment>An error caused by software logic or configuration.</rdfs:comment>
+                </rdf:Description>
+
+                <rdf:Description rdf:about="#Person">
+                    <rdfs:comment>A human being or individual.</rdfs:comment>
+                </rdf:Description>
+
+                <!-- Individuals -->
+                <Person rdf:ID="programmers">
+                    <rdfs:label>Programmers</rdfs:label>
+                </Person>
+
+                <Object rdf:ID="light_bulb">
+                    <rdfs:label>Light Bulb</rdfs:label>
+                </Object>
+
+                <HardwareProblem rdf:ID="hardware_problem">
+                    <rdfs:label>Hardware Problem</rdfs:label>
+                </HardwareProblem>
+
+            </rdf:RDF>"""
+
+        ontology_response = requests.post(
+            "http://127.0.0.1:8000/api/v1/ontologies",
+            headers=headers,
+            files=[("ontology_file", ("test.owl", ontology_content, "application/xml"))],
+            data={
+                "ontology_key": json.dumps([ontology_key]),
+                "description": json.dumps(["Test ontology"]),
+            },
+        )
+        self.assertEqual(ontology_response.status_code, 200)
+
         # Cognify request
         url = "http://127.0.0.1:8000/api/v1/cognify"
         headers = {
@@ -107,6 +167,29 @@ class TestCogneeServerStart(unittest.TestCase):
         if cognify_response.status_code not in [200, 201]:
             cognify_response.raise_for_status()
 
+        datasets_response = requests.get("http://127.0.0.1:8000/api/v1/datasets", headers=headers)
+
+        datasets = datasets_response.json()
+        dataset_id = None
+        for dataset in datasets:
+            if dataset["name"] == dataset_name:
+                dataset_id = dataset["id"]
+                break
+
+        graph_response = requests.get(
+            f"http://127.0.0.1:8000/api/v1/datasets/{dataset_id}/graph", headers=headers
+        )
+        self.assertEqual(graph_response.status_code, 200)
+
+        graph_data = graph_response.json()
+        ontology_nodes = [
+            node for node in graph_data.get("nodes") if node.get("properties").get("ontology_valid")
+        ]
+
+        self.assertGreater(
+            len(ontology_nodes), 0, "No ontology nodes found - ontology was not integrated"
+        )
+
         # TODO: Add test to verify cognify pipeline is complete before testing search
 
         # Search request

cognee/tests/test_data/example_with_header.csv

@@ -0,0 +1,3 @@
+id,name,age,city,country
+1,Eric,30,Beijing,China
+2,Joe,35,Berlin,Germany

cognee/tests/unit/api/test_ontology_endpoint.py

@@ -0,0 +1,272 @@
+import pytest
+import uuid
+from fastapi.testclient import TestClient
+from unittest.mock import patch, Mock, AsyncMock
+from types import SimpleNamespace
+import importlib
+from cognee.api.client import app
+
+gau_mod = importlib.import_module("cognee.modules.users.methods.get_authenticated_user")
+
+
+@pytest.fixture
+def client():
+    return TestClient(app)
+
+
+@pytest.fixture
+def mock_user():
+    user = Mock()
+    user.id = "test-user-123"
+    return user
+
+
+@pytest.fixture
+def mock_default_user():
+    """Mock default user for testing."""
+    return SimpleNamespace(
+        id=str(uuid.uuid4()),
+        email="default@example.com",
+        is_active=True,
+        tenant_id=str(uuid.uuid4()),
+    )
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_upload_ontology_success(mock_get_default_user, client, mock_default_user):
+    """Test successful ontology upload"""
+    import json
+
+    mock_get_default_user.return_value = mock_default_user
+    ontology_content = (
+        b"<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'></rdf:RDF>"
+    )
+    unique_key = f"test_ontology_{uuid.uuid4().hex[:8]}"
+
+    response = client.post(
+        "/api/v1/ontologies",
+        files=[("ontology_file", ("test.owl", ontology_content, "application/xml"))],
+        data={"ontology_key": json.dumps([unique_key]), "description": json.dumps(["Test"])},
+    )
+
+    assert response.status_code == 200
+    data = response.json()
+    assert data["uploaded_ontologies"][0]["ontology_key"] == unique_key
+    assert "uploaded_at" in data["uploaded_ontologies"][0]
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_upload_ontology_invalid_file(mock_get_default_user, client, mock_default_user):
+    """Test 400 response for non-.owl files"""
+    mock_get_default_user.return_value = mock_default_user
+    unique_key = f"test_ontology_{uuid.uuid4().hex[:8]}"
+    response = client.post(
+        "/api/v1/ontologies",
+        files={"ontology_file": ("test.txt", b"not xml")},
+        data={"ontology_key": unique_key},
+    )
+    assert response.status_code == 400
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_upload_ontology_missing_data(mock_get_default_user, client, mock_default_user):
+    """Test 400 response for missing file or key"""
+    import json
+
+    mock_get_default_user.return_value = mock_default_user
+    # Missing file
+    response = client.post("/api/v1/ontologies", data={"ontology_key": json.dumps(["test"])})
+    assert response.status_code == 400
+
+    # Missing key
+    response = client.post(
+        "/api/v1/ontologies", files=[("ontology_file", ("test.owl", b"xml", "application/xml"))]
+    )
+    assert response.status_code == 400
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_upload_ontology_unauthorized(mock_get_default_user, client, mock_default_user):
+    """Test behavior when default user is provided (no explicit authentication)"""
+    import json
+
+    unique_key = f"test_ontology_{uuid.uuid4().hex[:8]}"
+    mock_get_default_user.return_value = mock_default_user
+    response = client.post(
+        "/api/v1/ontologies",
+        files=[("ontology_file", ("test.owl", b"<rdf></rdf>", "application/xml"))],
+        data={"ontology_key": json.dumps([unique_key])},
+    )
+
+    # The current system provides a default user when no explicit authentication is given
+    # This test verifies the system works with conditional authentication
+    assert response.status_code == 200
+    data = response.json()
+    assert data["uploaded_ontologies"][0]["ontology_key"] == unique_key
+    assert "uploaded_at" in data["uploaded_ontologies"][0]
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_upload_multiple_ontologies(mock_get_default_user, client, mock_default_user):
+    """Test uploading multiple ontology files in single request"""
+    import io
+
+    mock_get_default_user.return_value = mock_default_user
+    # Create mock files
+    file1_content = b"<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'></rdf:RDF>"
+    file2_content = b"<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'></rdf:RDF>"
+
+    files = [
+        ("ontology_file", ("vehicles.owl", io.BytesIO(file1_content), "application/xml")),
+        ("ontology_file", ("manufacturers.owl", io.BytesIO(file2_content), "application/xml")),
+    ]
+    data = {
+        "ontology_key": '["vehicles", "manufacturers"]',
+        "descriptions": '["Base vehicles", "Car manufacturers"]',
+    }
+
+    response = client.post("/api/v1/ontologies", files=files, data=data)
+
+    assert response.status_code == 200
+    result = response.json()
+    assert "uploaded_ontologies" in result
+    assert len(result["uploaded_ontologies"]) == 2
+    assert result["uploaded_ontologies"][0]["ontology_key"] == "vehicles"
+    assert result["uploaded_ontologies"][1]["ontology_key"] == "manufacturers"
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_upload_endpoint_accepts_arrays(mock_get_default_user, client, mock_default_user):
+    """Test that upload endpoint accepts array parameters"""
+    import io
+    import json
+
+    mock_get_default_user.return_value = mock_default_user
+    file_content = b"<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'></rdf:RDF>"
+
+    files = [("ontology_file", ("single.owl", io.BytesIO(file_content), "application/xml"))]
+    data = {
+        "ontology_key": json.dumps(["single_key"]),
+        "descriptions": json.dumps(["Single ontology"]),
+    }
+
+    response = client.post("/api/v1/ontologies", files=files, data=data)
+
+    assert response.status_code == 200
+    result = response.json()
+    assert result["uploaded_ontologies"][0]["ontology_key"] == "single_key"
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_cognify_with_multiple_ontologies(mock_get_default_user, client, mock_default_user):
+    """Test cognify endpoint accepts multiple ontology keys"""
+    payload = {
+        "datasets": ["test_dataset"],
+        "ontology_key": ["ontology1", "ontology2"],  # Array instead of string
+        "run_in_background": False,
+    }
+
+    response = client.post("/api/v1/cognify", json=payload)
+
+    # Should not fail due to ontology_key type
+    assert response.status_code in [200, 400, 409]  # May fail for other reasons, not type
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_complete_multifile_workflow(mock_get_default_user, client, mock_default_user):
+    """Test complete workflow: upload multiple ontologies → cognify with multiple keys"""
+    import io
+    import json
+
+    mock_get_default_user.return_value = mock_default_user
+    # Step 1: Upload multiple ontologies
+    file1_content = b"""<?xml version="1.0"?>
+    <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+             xmlns:owl="http://www.w3.org/2002/07/owl#">
+        <owl:Class rdf:ID="Vehicle"/>
+    </rdf:RDF>"""
+
+    file2_content = b"""<?xml version="1.0"?>
+    <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+             xmlns:owl="http://www.w3.org/2002/07/owl#">
+        <owl:Class rdf:ID="Manufacturer"/>
+    </rdf:RDF>"""
+
+    files = [
+        ("ontology_file", ("vehicles.owl", io.BytesIO(file1_content), "application/xml")),
+        ("ontology_file", ("manufacturers.owl", io.BytesIO(file2_content), "application/xml")),
+    ]
+    data = {
+        "ontology_key": json.dumps(["vehicles", "manufacturers"]),
+        "descriptions": json.dumps(["Vehicle ontology", "Manufacturer ontology"]),
+    }
+
+    upload_response = client.post("/api/v1/ontologies", files=files, data=data)
+    assert upload_response.status_code == 200
+
+    # Step 2: Verify ontologies are listed
+    list_response = client.get("/api/v1/ontologies")
+    assert list_response.status_code == 200
+    ontologies = list_response.json()
+    assert "vehicles" in ontologies
+    assert "manufacturers" in ontologies
+
+    # Step 3: Test cognify with multiple ontologies
+    cognify_payload = {
+        "datasets": ["test_dataset"],
+        "ontology_key": ["vehicles", "manufacturers"],
+        "run_in_background": False,
+    }
+
+    cognify_response = client.post("/api/v1/cognify", json=cognify_payload)
+    # Should not fail due to ontology handling (may fail for dataset reasons)
+    assert cognify_response.status_code != 400  # Not a validation error
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_multifile_error_handling(mock_get_default_user, client, mock_default_user):
+    """Test error handling for invalid multifile uploads"""
+    import io
+    import json
+
+    # Test mismatched array lengths
+    file_content = b"<rdf:RDF></rdf:RDF>"
+    files = [("ontology_file", ("test.owl", io.BytesIO(file_content), "application/xml"))]
+    data = {
+        "ontology_key": json.dumps(["key1", "key2"]),  # 2 keys, 1 file
+        "descriptions": json.dumps(["desc1"]),
+    }
+
+    response = client.post("/api/v1/ontologies", files=files, data=data)
+    assert response.status_code == 400
+    assert "Number of keys must match number of files" in response.json()["error"]
+
+    # Test duplicate keys
+    files = [
+        ("ontology_file", ("test1.owl", io.BytesIO(file_content), "application/xml")),
+        ("ontology_file", ("test2.owl", io.BytesIO(file_content), "application/xml")),
+    ]
+    data = {
+        "ontology_key": json.dumps(["duplicate", "duplicate"]),
+        "descriptions": json.dumps(["desc1", "desc2"]),
+    }
+
+    response = client.post("/api/v1/ontologies", files=files, data=data)
+    assert response.status_code == 400
+    assert "Duplicate ontology keys not allowed" in response.json()["error"]
+
+
+@patch.object(gau_mod, "get_default_user", new_callable=AsyncMock)
+def test_cognify_missing_ontology_key(mock_get_default_user, client, mock_default_user):
+    """Test cognify with non-existent ontology key"""
+    mock_get_default_user.return_value = mock_default_user
+
+    payload = {
+        "datasets": ["test_dataset"],
+        "ontology_key": ["nonexistent_key"],
+        "run_in_background": False,
+    }
+
+    response = client.post("/api/v1/cognify", json=payload)
+    assert response.status_code == 409
+    assert "Ontology key 'nonexistent_key' not found" in response.json()["error"]

cognee/tests/unit/modules/graph/cognee_graph_elements_test.py

@@ -9,7 +9,7 @@ def test_node_initialization():
     """Test that a Node is initialized correctly."""
     node = Node("node1", {"attr1": "value1"}, dimension=2)
     assert node.id == "node1"
-    assert node.attributes == {"attr1": "value1", "vector_distance": np.inf}
+    assert node.attributes == {"attr1": "value1", "vector_distance": 3.5}
     assert len(node.status) == 2
     assert np.all(node.status == 1)
 
@@ -96,7 +96,7 @@ def test_edge_initialization():
     edge = Edge(node1, node2, {"weight": 10}, directed=False, dimension=2)
     assert edge.node1 == node1
     assert edge.node2 == node2
-    assert edge.attributes == {"vector_distance": np.inf, "weight": 10}
+    assert edge.attributes == {"vector_distance": 3.5, "weight": 10}
     assert edge.directed is False
     assert len(edge.status) == 2
     assert np.all(edge.status == 1)

cognee/tests/unit/modules/graph/cognee_graph_test.py

@@ -1,4 +1,5 @@
 import pytest
+from unittest.mock import AsyncMock
 
 from cognee.modules.graph.exceptions import EntityNotFoundError, EntityAlreadyExistsError
 from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
@@ -11,6 +12,30 @@ def setup_graph():
     return CogneeGraph()
 
 
+@pytest.fixture
+def mock_adapter():
+    """Fixture to create a mock adapter for database operations."""
+    adapter = AsyncMock()
+    return adapter
+
+
+@pytest.fixture
+def mock_vector_engine():
+    """Fixture to create a mock vector engine."""
+    engine = AsyncMock()
+    engine.search = AsyncMock()
+    return engine
+
+
+class MockScoredResult:
+    """Mock class for vector search results."""
+
+    def __init__(self, id, score, payload=None):
+        self.id = id
+        self.score = score
+        self.payload = payload or {}
+
+
 def test_add_node_success(setup_graph):
     """Test successful addition of a node."""
     graph = setup_graph
@@ -73,3 +98,433 @@ def test_get_edges_nonexistent_node(setup_graph):
     graph = setup_graph
     with pytest.raises(EntityNotFoundError, match="Node with id nonexistent does not exist."):
         graph.get_edges_from_node("nonexistent")
+
+
+@pytest.mark.asyncio
+async def test_project_graph_from_db_full_graph(setup_graph, mock_adapter):
+    """Test projecting a full graph from database."""
+    graph = setup_graph
+
+    nodes_data = [
+        ("1", {"name": "Node1", "description": "First node"}),
+        ("2", {"name": "Node2", "description": "Second node"}),
+    ]
+    edges_data = [
+        ("1", "2", "CONNECTS_TO", {"relationship_name": "connects"}),
+    ]
+
+    mock_adapter.get_graph_data = AsyncMock(return_value=(nodes_data, edges_data))
+
+    await graph.project_graph_from_db(
+        adapter=mock_adapter,
+        node_properties_to_project=["name", "description"],
+        edge_properties_to_project=["relationship_name"],
+    )
+
+    assert len(graph.nodes) == 2
+    assert len(graph.edges) == 1
+    assert graph.get_node("1") is not None
+    assert graph.get_node("2") is not None
+    assert graph.edges[0].node1.id == "1"
+    assert graph.edges[0].node2.id == "2"
+
+
+@pytest.mark.asyncio
+async def test_project_graph_from_db_id_filtered(setup_graph, mock_adapter):
+    """Test projecting an ID-filtered graph from database."""
+    graph = setup_graph
+
+    nodes_data = [
+        ("1", {"name": "Node1"}),
+        ("2", {"name": "Node2"}),
+    ]
+    edges_data = [
+        ("1", "2", "CONNECTS_TO", {"relationship_name": "connects"}),
+    ]
+
+    mock_adapter.get_id_filtered_graph_data = AsyncMock(return_value=(nodes_data, edges_data))
+
+    await graph.project_graph_from_db(
+        adapter=mock_adapter,
+        node_properties_to_project=["name"],
+        edge_properties_to_project=["relationship_name"],
+        relevant_ids_to_filter=["1", "2"],
+    )
+
+    assert len(graph.nodes) == 2
+    assert len(graph.edges) == 1
+    mock_adapter.get_id_filtered_graph_data.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_project_graph_from_db_nodeset_subgraph(setup_graph, mock_adapter):
+    """Test projecting a nodeset subgraph filtered by node type and name."""
+    graph = setup_graph
+
+    nodes_data = [
+        ("1", {"name": "Alice", "type": "Person"}),
+        ("2", {"name": "Bob", "type": "Person"}),
+    ]
+    edges_data = [
+        ("1", "2", "KNOWS", {"relationship_name": "knows"}),
+    ]
+
+    mock_adapter.get_nodeset_subgraph = AsyncMock(return_value=(nodes_data, edges_data))
+
+    await graph.project_graph_from_db(
+        adapter=mock_adapter,
+        node_properties_to_project=["name", "type"],
+        edge_properties_to_project=["relationship_name"],
+        node_type="Person",
+        node_name=["Alice"],
+    )
+
+    assert len(graph.nodes) == 2
+    assert graph.get_node("1") is not None
+    assert len(graph.edges) == 1
+    mock_adapter.get_nodeset_subgraph.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_project_graph_from_db_empty_graph(setup_graph, mock_adapter):
+    """Test projecting empty graph raises EntityNotFoundError."""
+    graph = setup_graph
+
+    mock_adapter.get_graph_data = AsyncMock(return_value=([], []))
+
+    with pytest.raises(EntityNotFoundError, match="Empty graph projected from the database."):
+        await graph.project_graph_from_db(
+            adapter=mock_adapter,
+            node_properties_to_project=["name"],
+            edge_properties_to_project=[],
+        )
+
+
+@pytest.mark.asyncio
+async def test_project_graph_from_db_missing_nodes(setup_graph, mock_adapter):
+    """Test that edges referencing missing nodes raise error."""
+    graph = setup_graph
+
+    nodes_data = [
+        ("1", {"name": "Node1"}),
+    ]
+    edges_data = [
+        ("1", "999", "CONNECTS_TO", {"relationship_name": "connects"}),
+    ]
+
+    mock_adapter.get_graph_data = AsyncMock(return_value=(nodes_data, edges_data))
+
+    with pytest.raises(EntityNotFoundError, match="Edge references nonexistent nodes"):
+        await graph.project_graph_from_db(
+            adapter=mock_adapter,
+            node_properties_to_project=["name"],
+            edge_properties_to_project=["relationship_name"],
+        )
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_to_graph_nodes(setup_graph):
+    """Test mapping vector distances to graph nodes."""
+    graph = setup_graph
+
+    node1 = Node("1", {"name": "Node1"})
+    node2 = Node("2", {"name": "Node2"})
+    graph.add_node(node1)
+    graph.add_node(node2)
+
+    node_distances = {
+        "Entity_name": [
+            MockScoredResult("1", 0.95),
+            MockScoredResult("2", 0.87),
+        ]
+    }
+
+    await graph.map_vector_distances_to_graph_nodes(node_distances)
+
+    assert graph.get_node("1").attributes.get("vector_distance") == 0.95
+    assert graph.get_node("2").attributes.get("vector_distance") == 0.87
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_partial_node_coverage(setup_graph):
+    """Test mapping vector distances when only some nodes have results."""
+    graph = setup_graph
+
+    node1 = Node("1", {"name": "Node1"})
+    node2 = Node("2", {"name": "Node2"})
+    node3 = Node("3", {"name": "Node3"})
+    graph.add_node(node1)
+    graph.add_node(node2)
+    graph.add_node(node3)
+
+    node_distances = {
+        "Entity_name": [
+            MockScoredResult("1", 0.95),
+            MockScoredResult("2", 0.87),
+        ]
+    }
+
+    await graph.map_vector_distances_to_graph_nodes(node_distances)
+
+    assert graph.get_node("1").attributes.get("vector_distance") == 0.95
+    assert graph.get_node("2").attributes.get("vector_distance") == 0.87
+    assert graph.get_node("3").attributes.get("vector_distance") == 3.5
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_multiple_categories(setup_graph):
+    """Test mapping vector distances from multiple collection categories."""
+    graph = setup_graph
+
+    # Create nodes
+    node1 = Node("1")
+    node2 = Node("2")
+    node3 = Node("3")
+    node4 = Node("4")
+    graph.add_node(node1)
+    graph.add_node(node2)
+    graph.add_node(node3)
+    graph.add_node(node4)
+
+    node_distances = {
+        "Entity_name": [
+            MockScoredResult("1", 0.95),
+            MockScoredResult("2", 0.87),
+        ],
+        "TextSummary_text": [
+            MockScoredResult("3", 0.92),
+        ],
+    }
+
+    await graph.map_vector_distances_to_graph_nodes(node_distances)
+
+    assert graph.get_node("1").attributes.get("vector_distance") == 0.95
+    assert graph.get_node("2").attributes.get("vector_distance") == 0.87
+    assert graph.get_node("3").attributes.get("vector_distance") == 0.92
+    assert graph.get_node("4").attributes.get("vector_distance") == 3.5
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_to_graph_edges_with_payload(setup_graph, mock_vector_engine):
+    """Test mapping vector distances to edges when edge_distances provided."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+
+    edge = Edge(
+        node1,
+        node2,
+        attributes={"edge_text": "CONNECTS_TO", "relationship_type": "connects"},
+    )
+    graph.add_edge(edge)
+
+    edge_distances = [
+        MockScoredResult("e1", 0.92, payload={"text": "CONNECTS_TO"}),
+    ]
+
+    await graph.map_vector_distances_to_graph_edges(
+        vector_engine=mock_vector_engine,
+        query_vector=[0.1, 0.2, 0.3],
+        edge_distances=edge_distances,
+    )
+
+    assert graph.edges[0].attributes.get("vector_distance") == 0.92
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_to_graph_edges_search(setup_graph, mock_vector_engine):
+    """Test mapping edge distances when searching for them."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+
+    edge = Edge(
+        node1,
+        node2,
+        attributes={"edge_text": "CONNECTS_TO", "relationship_type": "connects"},
+    )
+    graph.add_edge(edge)
+
+    mock_vector_engine.search.return_value = [
+        MockScoredResult("e1", 0.88, payload={"text": "CONNECTS_TO"}),
+    ]
+
+    await graph.map_vector_distances_to_graph_edges(
+        vector_engine=mock_vector_engine,
+        query_vector=[0.1, 0.2, 0.3],
+        edge_distances=None,
+    )
+
+    mock_vector_engine.search.assert_called_once()
+    assert graph.edges[0].attributes.get("vector_distance") == 0.88
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_partial_edge_coverage(setup_graph, mock_vector_engine):
+    """Test mapping edge distances when only some edges have results."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    node3 = Node("3")
+    graph.add_node(node1)
+    graph.add_node(node2)
+    graph.add_node(node3)
+
+    edge1 = Edge(node1, node2, attributes={"edge_text": "CONNECTS_TO"})
+    edge2 = Edge(node2, node3, attributes={"edge_text": "DEPENDS_ON"})
+    graph.add_edge(edge1)
+    graph.add_edge(edge2)
+
+    edge_distances = [
+        MockScoredResult("e1", 0.92, payload={"text": "CONNECTS_TO"}),
+    ]
+
+    await graph.map_vector_distances_to_graph_edges(
+        vector_engine=mock_vector_engine,
+        query_vector=[0.1, 0.2, 0.3],
+        edge_distances=edge_distances,
+    )
+
+    assert graph.edges[0].attributes.get("vector_distance") == 0.92
+    assert graph.edges[1].attributes.get("vector_distance") == 3.5
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_edges_fallback_to_relationship_type(
+    setup_graph, mock_vector_engine
+):
+    """Test that edge mapping falls back to relationship_type when edge_text is missing."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+
+    edge = Edge(
+        node1,
+        node2,
+        attributes={"relationship_type": "KNOWS"},
+    )
+    graph.add_edge(edge)
+
+    edge_distances = [
+        MockScoredResult("e1", 0.85, payload={"text": "KNOWS"}),
+    ]
+
+    await graph.map_vector_distances_to_graph_edges(
+        vector_engine=mock_vector_engine,
+        query_vector=[0.1, 0.2, 0.3],
+        edge_distances=edge_distances,
+    )
+
+    assert graph.edges[0].attributes.get("vector_distance") == 0.85
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_no_edge_matches(setup_graph, mock_vector_engine):
+    """Test edge mapping when no edges match the distance results."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+
+    edge = Edge(
+        node1,
+        node2,
+        attributes={"edge_text": "CONNECTS_TO", "relationship_type": "connects"},
+    )
+    graph.add_edge(edge)
+
+    edge_distances = [
+        MockScoredResult("e1", 0.92, payload={"text": "SOME_OTHER_EDGE"}),
+    ]
+
+    await graph.map_vector_distances_to_graph_edges(
+        vector_engine=mock_vector_engine,
+        query_vector=[0.1, 0.2, 0.3],
+        edge_distances=edge_distances,
+    )
+
+    assert graph.edges[0].attributes.get("vector_distance") == 3.5
+
+
+@pytest.mark.asyncio
+async def test_map_vector_distances_invalid_query_vector(setup_graph, mock_vector_engine):
+    """Test that invalid query vector raises error."""
+    graph = setup_graph
+
+    with pytest.raises(ValueError, match="Failed to generate query embedding"):
+        await graph.map_vector_distances_to_graph_edges(
+            vector_engine=mock_vector_engine,
+            query_vector=[],
+            edge_distances=None,
+        )
+
+
+@pytest.mark.asyncio
+async def test_calculate_top_triplet_importances(setup_graph):
+    """Test calculating top triplet importances by score."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    node3 = Node("3")
+    node4 = Node("4")
+
+    node1.add_attribute("vector_distance", 0.9)
+    node2.add_attribute("vector_distance", 0.8)
+    node3.add_attribute("vector_distance", 0.7)
+    node4.add_attribute("vector_distance", 0.6)
+
+    graph.add_node(node1)
+    graph.add_node(node2)
+    graph.add_node(node3)
+    graph.add_node(node4)
+
+    edge1 = Edge(node1, node2)
+    edge2 = Edge(node2, node3)
+    edge3 = Edge(node3, node4)
+
+    edge1.add_attribute("vector_distance", 0.85)
+    edge2.add_attribute("vector_distance", 0.75)
+    edge3.add_attribute("vector_distance", 0.65)
+
+    graph.add_edge(edge1)
+    graph.add_edge(edge2)
+    graph.add_edge(edge3)
+
+    top_triplets = await graph.calculate_top_triplet_importances(k=2)
+
+    assert len(top_triplets) == 2
+
+    assert top_triplets[0] == edge3
+    assert top_triplets[1] == edge2
+
+
+@pytest.mark.asyncio
+async def test_calculate_top_triplet_importances_default_distances(setup_graph):
+    """Test calculating importances when nodes/edges have no vector distances."""
+    graph = setup_graph
+
+    node1 = Node("1")
+    node2 = Node("2")
+    graph.add_node(node1)
+    graph.add_node(node2)
+
+    edge = Edge(node1, node2)
+    graph.add_edge(edge)
+
+    top_triplets = await graph.calculate_top_triplet_importances(k=1)
+
+    assert len(top_triplets) == 1
+    assert top_triplets[0] == edge

cognee/tests/unit/modules/retrieval/test_brute_force_triplet_search.py

@@ -0,0 +1,582 @@
+import pytest
+from unittest.mock import AsyncMock, patch
+
+from cognee.modules.retrieval.utils.brute_force_triplet_search import (
+    brute_force_triplet_search,
+    get_memory_fragment,
+)
+from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
+from cognee.modules.graph.exceptions.exceptions import EntityNotFoundError
+
+
+class MockScoredResult:
+    """Mock class for vector search results."""
+
+    def __init__(self, id, score, payload=None):
+        self.id = id
+        self.score = score
+        self.payload = payload or {}
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_empty_query():
+    """Test that empty query raises ValueError."""
+    with pytest.raises(ValueError, match="The query must be a non-empty string."):
+        await brute_force_triplet_search(query="")
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_none_query():
+    """Test that None query raises ValueError."""
+    with pytest.raises(ValueError, match="The query must be a non-empty string."):
+        await brute_force_triplet_search(query=None)
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_negative_top_k():
+    """Test that negative top_k raises ValueError."""
+    with pytest.raises(ValueError, match="top_k must be a positive integer."):
+        await brute_force_triplet_search(query="test query", top_k=-1)
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_zero_top_k():
+    """Test that zero top_k raises ValueError."""
+    with pytest.raises(ValueError, match="top_k must be a positive integer."):
+        await brute_force_triplet_search(query="test query", top_k=0)
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_wide_search_limit_global_search():
+    """Test that wide_search_limit is applied for global search (node_name=None)."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        await brute_force_triplet_search(
+            query="test",
+            node_name=None,  # Global search
+            wide_search_top_k=75,
+        )
+
+        for call in mock_vector_engine.search.call_args_list:
+            assert call[1]["limit"] == 75
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_wide_search_limit_filtered_search():
+    """Test that wide_search_limit is None for filtered search (node_name provided)."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        await brute_force_triplet_search(
+            query="test",
+            node_name=["Node1"],
+            wide_search_top_k=50,
+        )
+
+        for call in mock_vector_engine.search.call_args_list:
+            assert call[1]["limit"] is None
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_wide_search_default():
+    """Test that wide_search_top_k defaults to 100."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        await brute_force_triplet_search(query="test", node_name=None)
+
+        for call in mock_vector_engine.search.call_args_list:
+            assert call[1]["limit"] == 100
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_default_collections():
+    """Test that default collections are used when none provided."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        await brute_force_triplet_search(query="test")
+
+        expected_collections = [
+            "Entity_name",
+            "TextSummary_text",
+            "EntityType_name",
+            "DocumentChunk_text",
+        ]
+
+        call_collections = [
+            call[1]["collection_name"] for call in mock_vector_engine.search.call_args_list
+        ]
+        assert call_collections == expected_collections
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_custom_collections():
+    """Test that custom collections are used when provided."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    custom_collections = ["CustomCol1", "CustomCol2"]
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        await brute_force_triplet_search(query="test", collections=custom_collections)
+
+        call_collections = [
+            call[1]["collection_name"] for call in mock_vector_engine.search.call_args_list
+        ]
+        assert call_collections == custom_collections
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_all_collections_empty():
+    """Test that empty list is returned when all collections return no results."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        results = await brute_force_triplet_search(query="test")
+        assert results == []
+
+
+# Tests for query embedding
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_embeds_query():
+    """Test that query is embedded before searching."""
+    query_text = "test query"
+    expected_vector = [0.1, 0.2, 0.3]
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[expected_vector])
+    mock_vector_engine.search = AsyncMock(return_value=[])
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+        return_value=mock_vector_engine,
+    ):
+        await brute_force_triplet_search(query=query_text)
+
+        mock_vector_engine.embedding_engine.embed_text.assert_called_once_with([query_text])
+
+        for call in mock_vector_engine.search.call_args_list:
+            assert call[1]["query_vector"] == expected_vector
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_extracts_node_ids_global_search():
+    """Test that node IDs are extracted from search results for global search."""
+    scored_results = [
+        MockScoredResult("node1", 0.95),
+        MockScoredResult("node2", 0.87),
+        MockScoredResult("node3", 0.92),
+    ]
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=scored_results)
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment_fn,
+    ):
+        await brute_force_triplet_search(query="test", node_name=None)
+
+        call_kwargs = mock_get_fragment_fn.call_args[1]
+        assert set(call_kwargs["relevant_ids_to_filter"]) == {"node1", "node2", "node3"}
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_reuses_provided_fragment():
+    """Test that provided memory fragment is reused instead of creating new one."""
+    provided_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[MockScoredResult("n1", 0.95)])
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment"
+        ) as mock_get_fragment,
+    ):
+        await brute_force_triplet_search(
+            query="test",
+            memory_fragment=provided_fragment,
+            node_name=["node"],
+        )
+
+        mock_get_fragment.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_creates_fragment_when_not_provided():
+    """Test that memory fragment is created when not provided."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[MockScoredResult("n1", 0.95)])
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment,
+    ):
+        await brute_force_triplet_search(query="test", node_name=["node"])
+
+        mock_get_fragment.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_passes_top_k_to_importance_calculation():
+    """Test that custom top_k is passed to importance calculation."""
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(return_value=[MockScoredResult("n1", 0.95)])
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ),
+    ):
+        custom_top_k = 15
+        await brute_force_triplet_search(query="test", top_k=custom_top_k, node_name=["n"])
+
+        mock_fragment.calculate_top_triplet_importances.assert_called_once_with(k=custom_top_k)
+
+
+@pytest.mark.asyncio
+async def test_get_memory_fragment_returns_empty_graph_on_entity_not_found():
+    """Test that get_memory_fragment returns empty graph when entity not found."""
+    mock_graph_engine = AsyncMock()
+    mock_graph_engine.project_graph_from_db = AsyncMock(
+        side_effect=EntityNotFoundError("Entity not found")
+    )
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_graph_engine",
+        return_value=mock_graph_engine,
+    ):
+        fragment = await get_memory_fragment()
+
+        assert isinstance(fragment, CogneeGraph)
+        assert len(fragment.nodes) == 0
+
+
+@pytest.mark.asyncio
+async def test_get_memory_fragment_returns_empty_graph_on_error():
+    """Test that get_memory_fragment returns empty graph on generic error."""
+    mock_graph_engine = AsyncMock()
+    mock_graph_engine.project_graph_from_db = AsyncMock(side_effect=Exception("Generic error"))
+
+    with patch(
+        "cognee.modules.retrieval.utils.brute_force_triplet_search.get_graph_engine",
+        return_value=mock_graph_engine,
+    ):
+        fragment = await get_memory_fragment()
+
+        assert isinstance(fragment, CogneeGraph)
+        assert len(fragment.nodes) == 0
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_deduplicates_node_ids():
+    """Test that duplicate node IDs across collections are deduplicated."""
+
+    def search_side_effect(*args, **kwargs):
+        collection_name = kwargs.get("collection_name")
+        if collection_name == "Entity_name":
+            return [
+                MockScoredResult("node1", 0.95),
+                MockScoredResult("node2", 0.87),
+            ]
+        elif collection_name == "TextSummary_text":
+            return [
+                MockScoredResult("node1", 0.90),
+                MockScoredResult("node3", 0.92),
+            ]
+        else:
+            return []
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(side_effect=search_side_effect)
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment_fn,
+    ):
+        await brute_force_triplet_search(query="test", node_name=None)
+
+        call_kwargs = mock_get_fragment_fn.call_args[1]
+        assert set(call_kwargs["relevant_ids_to_filter"]) == {"node1", "node2", "node3"}
+        assert len(call_kwargs["relevant_ids_to_filter"]) == 3
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_excludes_edge_collection():
+    """Test that EdgeType_relationship_name collection is excluded from ID extraction."""
+
+    def search_side_effect(*args, **kwargs):
+        collection_name = kwargs.get("collection_name")
+        if collection_name == "Entity_name":
+            return [MockScoredResult("node1", 0.95)]
+        elif collection_name == "EdgeType_relationship_name":
+            return [MockScoredResult("edge1", 0.88)]
+        else:
+            return []
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(side_effect=search_side_effect)
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment_fn,
+    ):
+        await brute_force_triplet_search(
+            query="test",
+            node_name=None,
+            collections=["Entity_name", "EdgeType_relationship_name"],
+        )
+
+        call_kwargs = mock_get_fragment_fn.call_args[1]
+        assert call_kwargs["relevant_ids_to_filter"] == ["node1"]
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_skips_nodes_without_ids():
+    """Test that nodes without ID attribute are skipped."""
+
+    class ScoredResultNoId:
+        """Mock result without id attribute."""
+
+        def __init__(self, score):
+            self.score = score
+
+    def search_side_effect(*args, **kwargs):
+        collection_name = kwargs.get("collection_name")
+        if collection_name == "Entity_name":
+            return [
+                MockScoredResult("node1", 0.95),
+                ScoredResultNoId(0.90),
+                MockScoredResult("node2", 0.87),
+            ]
+        else:
+            return []
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(side_effect=search_side_effect)
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment_fn,
+    ):
+        await brute_force_triplet_search(query="test", node_name=None)
+
+        call_kwargs = mock_get_fragment_fn.call_args[1]
+        assert set(call_kwargs["relevant_ids_to_filter"]) == {"node1", "node2"}
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_handles_tuple_results():
+    """Test that both list and tuple results are handled correctly."""
+
+    def search_side_effect(*args, **kwargs):
+        collection_name = kwargs.get("collection_name")
+        if collection_name == "Entity_name":
+            return (
+                MockScoredResult("node1", 0.95),
+                MockScoredResult("node2", 0.87),
+            )
+        else:
+            return []
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(side_effect=search_side_effect)
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment_fn,
+    ):
+        await brute_force_triplet_search(query="test", node_name=None)
+
+        call_kwargs = mock_get_fragment_fn.call_args[1]
+        assert set(call_kwargs["relevant_ids_to_filter"]) == {"node1", "node2"}
+
+
+@pytest.mark.asyncio
+async def test_brute_force_triplet_search_mixed_empty_collections():
+    """Test ID extraction with mixed empty and non-empty collections."""
+
+    def search_side_effect(*args, **kwargs):
+        collection_name = kwargs.get("collection_name")
+        if collection_name == "Entity_name":
+            return [MockScoredResult("node1", 0.95)]
+        elif collection_name == "TextSummary_text":
+            return []
+        elif collection_name == "EntityType_name":
+            return [MockScoredResult("node2", 0.92)]
+        else:
+            return []
+
+    mock_vector_engine = AsyncMock()
+    mock_vector_engine.embedding_engine = AsyncMock()
+    mock_vector_engine.embedding_engine.embed_text = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
+    mock_vector_engine.search = AsyncMock(side_effect=search_side_effect)
+
+    mock_fragment = AsyncMock(
+        map_vector_distances_to_graph_nodes=AsyncMock(),
+        map_vector_distances_to_graph_edges=AsyncMock(),
+        calculate_top_triplet_importances=AsyncMock(return_value=[]),
+    )
+
+    with (
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine",
+            return_value=mock_vector_engine,
+        ),
+        patch(
+            "cognee.modules.retrieval.utils.brute_force_triplet_search.get_memory_fragment",
+            return_value=mock_fragment,
+        ) as mock_get_fragment_fn,
+    ):
+        await brute_force_triplet_search(query="test", node_name=None)
+
+        call_kwargs = mock_get_fragment_fn.call_args[1]
+        assert set(call_kwargs["relevant_ids_to_filter"]) == {"node1", "node2"}

cognee/tests/unit/processing/chunks/chunk_by_row_test.py

@@ -0,0 +1,52 @@
+from itertools import product
+
+import numpy as np
+import pytest
+
+from cognee.infrastructure.databases.vector.embeddings import get_embedding_engine
+from cognee.tasks.chunks import chunk_by_row
+
+INPUT_TEXTS = "name: John, age: 30, city: New York, country: USA"
+max_chunk_size_vals = [8, 32]
+
+
+@pytest.mark.parametrize(
+    "input_text,max_chunk_size",
+    list(product([INPUT_TEXTS], max_chunk_size_vals)),
+)
+def test_chunk_by_row_isomorphism(input_text, max_chunk_size):
+    chunks = chunk_by_row(input_text, max_chunk_size)
+    reconstructed_text = ", ".join([chunk["text"] for chunk in chunks])
+    assert reconstructed_text == input_text, (
+        f"texts are not identical: {len(input_text) = }, {len(reconstructed_text) = }"
+    )
+
+
+@pytest.mark.parametrize(
+    "input_text,max_chunk_size",
+    list(product([INPUT_TEXTS], max_chunk_size_vals)),
+)
+def test_row_chunk_length(input_text, max_chunk_size):
+    chunks = list(chunk_by_row(data=input_text, max_chunk_size=max_chunk_size))
+    embedding_engine = get_embedding_engine()
+
+    chunk_lengths = np.array(
+        [embedding_engine.tokenizer.count_tokens(chunk["text"]) for chunk in chunks]
+    )
+
+    larger_chunks = chunk_lengths[chunk_lengths > max_chunk_size]
+    assert np.all(chunk_lengths <= max_chunk_size), (
+        f"{max_chunk_size = }: {larger_chunks} are too large"
+    )
+
+
+@pytest.mark.parametrize(
+    "input_text,max_chunk_size",
+    list(product([INPUT_TEXTS], max_chunk_size_vals)),
+)
+def test_chunk_by_row_chunk_numbering(input_text, max_chunk_size):
+    chunks = chunk_by_row(data=input_text, max_chunk_size=max_chunk_size)
+    chunk_indices = np.array([chunk["chunk_index"] for chunk in chunks])
+    assert np.all(chunk_indices == np.arange(len(chunk_indices))), (
+        f"{chunk_indices = } are not monotonically increasing"
+    )

poetry.lock

@@ -11656,7 +11656,9 @@ groups = ["main"]
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@@ -11691,12 +11693,20 @@ files = [
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pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "cognee"
 
-version = "0.3.9"
+version = "0.5.0.dev0"
 description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
 authors = [
     { name = "Vasilije Markovic" },
@@ -156,7 +156,6 @@ Homepage = "https://www.cognee.ai"
 Repository = "https://github.com/topoteretes/cognee"
 
 [project.scripts]
-cognee = "cognee.cli._cognee:main"
 cognee-cli = "cognee.cli._cognee:main"
 
 [build-system]

uv.lock

@@ -929,7 +929,7 @@ wheels = [
 
 [[package]]
 name = "cognee"
-version = "0.3.9"
+version = "0.5.0.dev0"
 source = { editable = "." }
 dependencies = [
     { name = "aiofiles" },
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