Merge branch 'dev' into fix/litellm-dimensions-handling

2026-01-12 15:03:07 +08:00 · 2026-01-12 15:03:07 +08:00 · 03b7923a3e
commit 03b7923a3e
parent 2e679b69e5 ab990f7c5c
114 changed files with 1373 additions and 612 deletions
--- a/.coderabbit.yaml
+++ b/.coderabbit.yaml
@ -3,7 +3,7 @@
 language: en
 early_access: false
 enable_free_tier: true
-reviews:  
+reviews:
  profile: chill
  instructions: >-
    # Code Review Instructions
@ -118,10 +118,10 @@ reviews:
          - E117
          - D208
        line_length: 100
-        dummy_variable_rgx: '^(_.*|junk|extra)$'  # Variables starting with '_' or named 'junk' or 'extras', are considered dummy variables        
+        dummy_variable_rgx: '^(_.*|junk|extra)$'  # Variables starting with '_' or named 'junk' or 'extras', are considered dummy variables
    markdownlint:
      enabled: true
    yamllint:
      enabled: true
 chat:
-  auto_reply: true
+  auto_reply: true
--- a/cognee/api/.env.example
+++ b/cognee/api/.env.example
@ -2,4 +2,8 @@
 # Example:
 # CORS_ALLOWED_ORIGINS="https://yourdomain.com,https://another.com"
 # For local development, you might use:
-# CORS_ALLOWED_ORIGINS="http://localhost:3000"
+# CORS_ALLOWED_ORIGINS="http://localhost:3000"
+
+LLM_API_KEY="your-openai-api-key"
+LLM_MODEL="openai/gpt-4o-mini"
+LLM_PROVIDER="openai"
--- a/.gitguardian.yml
+++ b/.gitguardian.yml
@ -28,4 +28,4 @@ secret-scan:
    - path: 'docker-compose.yml'
      comment: 'Development docker compose with test credentials (neo4j/pleaseletmein, postgres cognee/cognee)'
    - path: 'deployment/helm/docker-compose-helm.yml'
-      comment: 'Helm deployment docker compose with test postgres credentials (cognee/cognee)'
+      comment: 'Helm deployment docker compose with test postgres credentials (cognee/cognee)'
--- a/.github/ISSUE_TEMPLATE/bug_report.yml
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@ -8,7 +8,7 @@ body:
    attributes:
      value: |
        Thanks for taking the time to fill out this bug report! Please provide a clear and detailed description.
-        
+
  - type: textarea
    id: description
    attributes:
@ -17,7 +17,7 @@ body:
      placeholder: Describe the bug in detail...
    validations:
      required: true
-      
+
  - type: textarea
    id: reproduction
    attributes:
@ -29,7 +29,7 @@ body:
        3. See error...
    validations:
      required: true
-      
+
  - type: textarea
    id: expected
    attributes:
@ -38,7 +38,7 @@ body:
      placeholder: Describe what you expected...
    validations:
      required: true
-      
+
  - type: textarea
    id: actual
    attributes:
@ -47,7 +47,7 @@ body:
      placeholder: Describe what actually happened...
    validations:
      required: true
-      
+
  - type: textarea
    id: environment
    attributes:
@ -61,7 +61,7 @@ body:
        - Database: [e.g. Neo4j]
    validations:
      required: true
-      
+
  - type: textarea
    id: logs
    attributes:
@ -71,7 +71,7 @@ body:
      render: shell
    validations:
      required: false
-      
+
  - type: textarea
    id: additional
    attributes:
@ -80,7 +80,7 @@ body:
      placeholder: Any additional information...
    validations:
      required: false
-      
+
  - type: checkboxes
    id: checklist
    attributes:
--- a/.github/ISSUE_TEMPLATE/documentation.yml
+++ b/.github/ISSUE_TEMPLATE/documentation.yml
@ -8,7 +8,7 @@ body:
    attributes:
      value: |
        Thanks for helping improve our documentation! Please provide details about the documentation issue or improvement.
-        
+
  - type: dropdown
    id: doc-type
    attributes:
@ -22,7 +22,7 @@ body:
        - New documentation request
    validations:
      required: true
-      
+
  - type: textarea
    id: location
    attributes:
@ -31,7 +31,7 @@ body:
      placeholder: https://cognee.ai/docs/... or specific file/section
    validations:
      required: true
-      
+
  - type: textarea
    id: issue
    attributes:
@ -40,7 +40,7 @@ body:
      placeholder: The documentation is unclear about...
    validations:
      required: true
-      
+
  - type: textarea
    id: suggestion
    attributes:
@ -49,7 +49,7 @@ body:
      placeholder: I suggest changing this to...
    validations:
      required: false
-      
+
  - type: textarea
    id: additional
    attributes:
@ -58,7 +58,7 @@ body:
      placeholder: Additional context...
    validations:
      required: false
-      
+
  - type: checkboxes
    id: checklist
    attributes:
@ -71,4 +71,3 @@ body:
          required: true
        - label: I have specified the location of the documentation issue
          required: true
-
--- a/.github/ISSUE_TEMPLATE/feature_request.yml
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@ -8,7 +8,7 @@ body:
    attributes:
      value: |
        Thanks for suggesting a new feature! Please provide a clear and detailed description of your idea.
-        
+
  - type: textarea
    id: problem
    attributes:
@ -17,7 +17,7 @@ body:
      placeholder: I'm always frustrated when...
    validations:
      required: true
-      
+
  - type: textarea
    id: solution
    attributes:
@ -26,7 +26,7 @@ body:
      placeholder: I would like to see...
    validations:
      required: true
-      
+
  - type: textarea
    id: alternatives
    attributes:
@ -35,7 +35,7 @@ body:
      placeholder: I have also considered...
    validations:
      required: false
-      
+
  - type: textarea
    id: use-case
    attributes:
@ -44,7 +44,7 @@ body:
      placeholder: This feature would help me...
    validations:
      required: true
-      
+
  - type: textarea
    id: implementation
    attributes:
@ -53,7 +53,7 @@ body:
      placeholder: This could be implemented by...
    validations:
      required: false
-      
+
  - type: textarea
    id: additional
    attributes:
@ -62,7 +62,7 @@ body:
      placeholder: Additional context...
    validations:
      required: false
-      
+
  - type: checkboxes
    id: checklist
    attributes:
@ -75,4 +75,3 @@ body:
          required: true
        - label: I have described my specific use case
          required: true
-
--- a/.github/actions/setup_neo4j/action.yml
+++ b/.github/actions/setup_neo4j/action.yml
@ -34,14 +34,14 @@ runs:
          -e NEO4J_apoc_export_file_enabled=true \
          -e NEO4J_apoc_import_file_enabled=true \
          neo4j:${{ inputs.neo4j-version }}
-    
+
    - name: Wait for Neo4j to be ready
      shell: bash
      run: |
        echo "Waiting for Neo4j to start..."
        timeout=60
        counter=0
-        
+
        while [ $counter -lt $timeout ]; do
          if docker exec neo4j-test cypher-shell -u neo4j -p "${{ inputs.neo4j-password }}" "RETURN 1" > /dev/null 2>&1; then
            echo "Neo4j is ready!"
@ -51,13 +51,13 @@ runs:
          sleep 2
          counter=$((counter + 2))
        done
-        
+
        if [ $counter -ge $timeout ]; then
          echo "Neo4j failed to start within $timeout seconds"
          docker logs neo4j-test
          exit 1
        fi
-    
+
    - name: Verify GDS is available
      shell: bash
      run: |
--- a/.github/core-team.txt
+++ b/.github/core-team.txt
@ -8,5 +8,3 @@ lxobr
 pazone
 siillee
 vasilije1990
-
-
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@ -10,26 +10,21 @@ DO NOT use AI-generated descriptions. We want to understand your thought process
 <!--
 * Key requirements to the new feature or modification;
 * Proof that the changes work and meet the requirements;
-* Include instructions on how to verify the changes. Describe how to test it locally;
-* Proof that it's sufficiently tested.
 -->

 ## Type of Change
 <!-- Please check the relevant option -->
 - [ ] Bug fix (non-breaking change that fixes an issue)
 - [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to change)
- [ ] Documentation update
 - [ ] Code refactoring
- [ ] Performance improvement
 - [ ] Other (please specify):

-## Screenshots/Videos (if applicable)
-<!-- Add screenshots or videos to help explain your changes -->
+## Screenshots
+<!-- ADD SCREENSHOT OF LOCAL TESTS PASSING-->

 ## Pre-submission Checklist
 <!-- Please check all boxes that apply before submitting your PR -->
- [ ] **I have tested my changes thoroughly before submitting this PR**
+- [ ] **I have tested my changes thoroughly before submitting this PR** (See `CONTRIBUTING.md`)
 - [ ] **This PR contains minimal changes necessary to address the issue/feature**
 - [ ] My code follows the project's coding standards and style guidelines
 - [ ] I have added tests that prove my fix is effective or that my feature works
--- a/.github/release-drafter.yml
+++ b/.github/release-drafter.yml
@ -3,7 +3,7 @@ tag-template: 'v$NEXT_PATCH_VERSION'

 categories:
  - title: 'Features'
-    labels: ['feature', 'enhancement']    
+    labels: ['feature', 'enhancement']
  - title: 'Bug Fixes'
    labels: ['bug', 'fix']
  - title: 'Maintenance'
--- a/.github/workflows/basic_tests.yml
+++ b/.github/workflows/basic_tests.yml
@ -34,43 +34,6 @@ env:
  ENV: 'dev'

 jobs:
-
-  lint:
-    name: Run Linting
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: ${{ inputs.python-version }}
-
-      - name: Run Linting
-        uses: astral-sh/ruff-action@v2
-
-  format-check:
-    name: Run Formatting Check
-    runs-on: ubuntu-22.04
-    steps:
-      - name: Check out repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Cognee Setup
-        uses: ./.github/actions/cognee_setup
-        with:
-          python-version: ${{ inputs.python-version }}
-
-      - name: Run Formatting Check
-        uses: astral-sh/ruff-action@v2
-        with:
-          args: "format --check"
-
  unit-tests:
    name: Run Unit Tests
    runs-on: ubuntu-22.04
--- a/.github/workflows/disable_independent_workflows.sh
+++ b/.github/workflows/disable_independent_workflows.sh
@ -31,54 +31,54 @@ WORKFLOWS=(
 for workflow in "${WORKFLOWS[@]}"; do
  if [ -f "$workflow" ]; then
    echo "Processing $workflow..."
-    
+
    # Create a backup
    cp "$workflow" "${workflow}.bak"
-    
+
    # Check if the file begins with a workflow_call trigger
    if grep -q "workflow_call:" "$workflow"; then
      echo "$workflow already has workflow_call trigger, skipping..."
      continue
    fi
-    
+
    # Get the content after the 'on:' section
    on_line=$(grep -n "^on:" "$workflow" | cut -d ':' -f1)
-    
+
    if [ -z "$on_line" ]; then
      echo "Warning: No 'on:' section found in $workflow, skipping..."
      continue
    fi
-    
+
    # Create a new file with the modified content
    {
      # Copy the part before 'on:'
      head -n $((on_line-1)) "$workflow"
-      
+
      # Add the new on: section that only includes workflow_call
      echo "on:"
      echo "  workflow_call:"
      echo "    secrets:"
      echo "      inherit: true"
-      
+
      # Find where to continue after the original 'on:' section
      next_section=$(awk "NR > $on_line && /^[a-z]/ {print NR; exit}" "$workflow")
-      
+
      if [ -z "$next_section" ]; then
        next_section=$(wc -l < "$workflow")
        next_section=$((next_section+1))
      fi
-      
+
      # Copy the rest of the file starting from the next section
      tail -n +$next_section "$workflow"
    } > "${workflow}.new"
-    
+
    # Replace the original with the new version
    mv "${workflow}.new" "$workflow"
-    
+
    echo "Modified $workflow to only run when called from test-suites.yml"
  else
    echo "Warning: $workflow not found, skipping..."
  fi
 done

-echo "Finished modifying workflows!" 
+echo "Finished modifying workflows!"
--- a/.github/workflows/dockerhub.yml
+++ b/.github/workflows/dockerhub.yml
@ -45,4 +45,4 @@ jobs:
          cache-to: type=registry,ref=cognee/cognee:buildcache,mode=max

      - name: Image digest
-        run: echo ${{ steps.build.outputs.digest }}
+        run: echo ${{ steps.build.outputs.digest }}
--- a/.github/workflows/label-core-team.yml
+++ b/.github/workflows/label-core-team.yml
@ -72,5 +72,3 @@ jobs:
            } catch (error) {
              core.warning(`Failed to add label: ${error.message}`);
            }
-
-
--- a/.github/workflows/load_tests.yml
+++ b/.github/workflows/load_tests.yml
@ -66,5 +66,3 @@ jobs:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
        run: uv run python ./cognee/tests/test_load.py
-  
-    
--- a/.github/workflows/pre_test.yml
+++ b/.github/workflows/pre_test.yml
@ -5,7 +5,7 @@ permissions:
  contents: read
 jobs:
  check-uv-lock:
-    name: Validate uv lockfile and project metadata
+    name: Lockfile and Pre-commit Hooks
    runs-on: ubuntu-22.04
    steps:
      - name: Check out repository
@ -17,6 +17,9 @@ jobs:
        uses: astral-sh/setup-uv@v4
        with:
          enable-cache: true
-      
+
      - name: Validate uv lockfile and project metadata
        run: uv lock --check || { echo "'uv lock --check' failed."; echo "Run 'uv lock' and push your changes."; exit 1; }
+
+      - name: Run pre-commit hooks
+        uses: pre-commit/action@v3.0.1
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@ -42,10 +42,10 @@ jobs:

          echo "tag=${TAG}" >> "$GITHUB_OUTPUT"
          echo "version=${VERSION}" >> "$GITHUB_OUTPUT"
-          
+
          git tag "${TAG}"
          git push origin "${TAG}"
-          
+

      - name: Create GitHub Release
        uses: softprops/action-gh-release@v2
@ -54,8 +54,8 @@ jobs:
          generate_release_notes: true
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-  
-  release-pypi-package:    
+
+  release-pypi-package:
    needs: release-github
    name: Release PyPI Package from ${{ inputs.flavour }}
    permissions:
@ -67,25 +67,25 @@ jobs:
        uses: actions/checkout@v4
        with:
          ref: ${{ inputs.flavour }}
-      
+
      - name: Install uv
        uses: astral-sh/setup-uv@v7
-      
+
      - name: Install Python
        run: uv python install
-      
+
      - name: Install dependencies
        run: uv sync --locked --all-extras
-      
+
      - name: Build distributions
        run: uv build
-      
+
      - name: Publish ${{ inputs.flavour }} release to PyPI
        env:
          UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }}
        run: uv publish
-  
-  release-docker-image:    
+
+  release-docker-image:
    needs: release-github
    name: Release Docker Image from ${{ inputs.flavour }}
    permissions:
@ -128,7 +128,7 @@ jobs:
          context: .
          platforms: linux/amd64,linux/arm64
          push: true
-          tags: | 
+          tags: |
            cognee/cognee:${{ needs.release-github.outputs.version }}
            cognee/cognee:latest
          labels: |
@ -163,4 +163,4 @@ jobs:
            -H "Authorization: Bearer ${{ secrets.REPO_DISPATCH_PAT_TOKEN }}" \
            -H "X-GitHub-Api-Version: 2022-11-28" \
            https://api.github.com/repos/topoteretes/cognee-community/dispatches \
-            -d '{"event_type":"new-main-release","client_payload":{"caller_repo":"'"${GITHUB_REPOSITORY}"'"}}'
+            -d '{"event_type":"new-main-release","client_payload":{"caller_repo":"'"${GITHUB_REPOSITORY}"'"}}'
--- a/.github/workflows/release_test.yml
+++ b/.github/workflows/release_test.yml
@ -15,4 +15,3 @@ jobs:
    name: Load Tests
    uses: ./.github/workflows/load_tests.yml
    secrets: inherit
-
--- a/.github/workflows/test_different_operating_systems.yml
+++ b/.github/workflows/test_different_operating_systems.yml
@ -10,7 +10,7 @@ on:
        required: false
        type: string
        default: '["3.10.x", "3.12.x", "3.13.x"]'
-      os: 
+      os:
        required: false
        type: string
        default: '["ubuntu-22.04", "macos-15", "windows-latest"]'
--- a/.github/workflows/test_llms.yml
+++ b/.github/workflows/test_llms.yml
@ -173,4 +173,4 @@ jobs:
          EMBEDDING_MODEL: "amazon.titan-embed-text-v2:0"
          EMBEDDING_DIMENSIONS: "1024"
          EMBEDDING_MAX_TOKENS: "8191"
-        run: uv run python ./examples/python/simple_example.py
+        run: uv run python ./examples/python/simple_example.py
--- a/.github/workflows/test_suites.yml
+++ b/.github/workflows/test_suites.yml
@ -18,11 +18,11 @@ env:
  RUNTIME__LOG_LEVEL: ERROR
  ENV: 'dev'

-jobs: 
+jobs:
  pre-test:
    name: basic checks
    uses: ./.github/workflows/pre_test.yml
-    
+
  basic-tests:
    name: Basic Tests
    uses: ./.github/workflows/basic_tests.yml
--- a/.gitignore
+++ b/.gitignore
@ -147,6 +147,8 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+mise.toml
+deployment/helm/values-local.yml

 # Spyder project settings
 .spyderproject
--- a/.mergify.yml
+++ b/.mergify.yml
@ -6,4 +6,4 @@ pull_request_rules:
    actions:
      backport:
        branches:
-          - main
+          - main
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -7,6 +7,7 @@ repos:
    -   id: trailing-whitespace
    -   id: end-of-file-fixer
    -   id: check-yaml
+        exclude: ^deployment/helm/templates/
    -   id: check-added-large-files
 - repo: https://github.com/astral-sh/ruff-pre-commit
  # Ruff version.
--- a/AGENTS.md
+++ b/AGENTS.md
@ -128,5 +128,3 @@ MCP server and Frontend:
 ## CI Mirrors Local Commands

 Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises.
-
-
--- a/CLAUDE.md
+++ b/CLAUDE.md
@ -0,0 +1,588 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Cognee is an open-source AI memory platform that transforms raw data into persistent knowledge graphs for AI agents. It replaces traditional RAG (Retrieval-Augmented Generation) with an ECL (Extract, Cognify, Load) pipeline combining vector search, graph databases, and LLM-powered entity extraction.
+
+**Requirements**: Python 3.9 - 3.12
+
+## Development Commands
+
+### Setup
+```bash
+# Create virtual environment (recommended: uv)
+uv venv && source .venv/bin/activate
+
+# Install with pip, poetry, or uv
+uv pip install -e .
+
+# Install with dev dependencies
+uv pip install -e ".[dev]"
+
+# Install with specific extras
+uv pip install -e ".[postgres,neo4j,docs,chromadb]"
+
+# Set up pre-commit hooks
+pre-commit install
+```
+
+### Available Installation Extras
+- **postgres** / **postgres-binary** - PostgreSQL + PGVector support
+- **neo4j** - Neo4j graph database support
+- **neptune** - AWS Neptune support
+- **chromadb** - ChromaDB vector database
+- **docs** - Document processing (unstructured library)
+- **scraping** - Web scraping (Tavily, BeautifulSoup, Playwright)
+- **langchain** - LangChain integration
+- **llama-index** - LlamaIndex integration
+- **anthropic** - Anthropic Claude models
+- **gemini** - Google Gemini models
+- **ollama** - Ollama local models
+- **mistral** - Mistral AI models
+- **groq** - Groq API support
+- **llama-cpp** - Llama.cpp local inference
+- **huggingface** - HuggingFace transformers
+- **aws** - S3 storage backend
+- **redis** - Redis caching
+- **graphiti** - Graphiti-core integration
+- **baml** - BAML structured output
+- **dlt** - Data load tool (dlt) integration
+- **docling** - Docling document processing
+- **codegraph** - Code graph extraction
+- **evals** - Evaluation tools
+- **deepeval** - DeepEval testing framework
+- **posthog** - PostHog analytics
+- **monitoring** - Sentry + Langfuse observability
+- **distributed** - Modal distributed execution
+- **dev** - All development tools (pytest, mypy, ruff, etc.)
+- **debug** - Debugpy for debugging
+
+### Testing
+```bash
+# Run all tests
+pytest
+
+# Run with coverage
+pytest --cov=cognee --cov-report=html
+
+# Run specific test file
+pytest cognee/tests/test_custom_model.py
+
+# Run specific test function
+pytest cognee/tests/test_custom_model.py::test_function_name
+
+# Run async tests
+pytest -v cognee/tests/integration/
+
+# Run unit tests only
+pytest cognee/tests/unit/
+
+# Run integration tests only
+pytest cognee/tests/integration/
+```
+
+### Code Quality
+```bash
+# Run ruff linter
+ruff check .
+
+# Run ruff formatter
+ruff format .
+
+# Run both linting and formatting (pre-commit)
+pre-commit run --all-files
+
+# Type checking with mypy
+mypy cognee/
+
+# Run pylint
+pylint cognee/
+```
+
+### Running Cognee
+```bash
+# Using Python SDK
+python examples/python/simple_example.py
+
+# Using CLI
+cognee-cli add "Your text here"
+cognee-cli cognify
+cognee-cli search "Your query"
+cognee-cli delete --all
+
+# Launch full stack with UI
+cognee-cli -ui
+```
+
+## Architecture Overview
+
+### Core Workflow: add → cognify → search/memify
+
+1. **add()** - Ingest data (files, URLs, text) into datasets
+2. **cognify()** - Extract entities/relationships and build knowledge graph
+3. **search()** - Query knowledge using various retrieval strategies
+4. **memify()** - Enrich graph with additional context and rules
+
+### Key Architectural Patterns
+
+#### 1. Pipeline-Based Processing
+All data flows through task-based pipelines (`cognee/modules/pipelines/`). Tasks are composable units that can run sequentially or in parallel. Example pipeline tasks: `classify_documents`, `extract_graph_from_data`, `add_data_points`.
+
+#### 2. Interface-Based Database Adapters
+Multiple backends are supported through adapter interfaces:
+- **Graph**: Kuzu (default), Neo4j, Neptune via `GraphDBInterface`
+- **Vector**: LanceDB (default), ChromaDB, PGVector via `VectorDBInterface`
+- **Relational**: SQLite (default), PostgreSQL
+
+Key files:
+- `cognee/infrastructure/databases/graph/graph_db_interface.py`
+- `cognee/infrastructure/databases/vector/vector_db_interface.py`
+
+#### 3. Multi-Tenant Access Control
+User → Dataset → Data hierarchy with permission-based filtering. Enable with `ENABLE_BACKEND_ACCESS_CONTROL=True`. Each user+dataset combination can have isolated graph/vector databases (when using supported backends: Kuzu, LanceDB, SQLite, Postgres).
+
+### Layer Structure
+
+```
+API Layer (cognee/api/v1/)
+    ↓
+Main Functions (add, cognify, search, memify)
+    ↓
+Pipeline Orchestrator (cognee/modules/pipelines/)
+    ↓
+Task Execution Layer (cognee/tasks/)
+    ↓
+Domain Modules (graph, retrieval, ingestion, etc.)
+    ↓
+Infrastructure Adapters (LLM, databases)
+    ↓
+External Services (OpenAI, Kuzu, LanceDB, etc.)
+```
+
+### Critical Data Flow Paths
+
+#### ADD: Data Ingestion
+`add()` → `resolve_data_directories` → `ingest_data` → `save_data_item_to_storage` → Create Dataset + Data records in relational DB
+
+Key files: `cognee/api/v1/add/add.py`, `cognee/tasks/ingestion/ingest_data.py`
+
+#### COGNIFY: Knowledge Graph Construction
+`cognify()` → `classify_documents` → `extract_chunks_from_documents` → `extract_graph_from_data` (LLM extracts entities/relationships using Instructor) → `summarize_text` → `add_data_points` (store in graph + vector DBs)
+
+Key files:
+- `cognee/api/v1/cognify/cognify.py`
+- `cognee/tasks/graph/extract_graph_from_data.py`
+- `cognee/tasks/storage/add_data_points.py`
+
+#### SEARCH: Retrieval
+`search(query_text, query_type)` → route to retriever type → filter by permissions → return results
+
+Available search types (from `cognee/modules/search/types/SearchType.py`):
+- **GRAPH_COMPLETION** (default) - Graph traversal + LLM completion
+- **GRAPH_SUMMARY_COMPLETION** - Uses pre-computed summaries with graph context
+- **GRAPH_COMPLETION_COT** - Chain-of-thought reasoning over graph
+- **GRAPH_COMPLETION_CONTEXT_EXTENSION** - Extended context graph retrieval
+- **TRIPLET_COMPLETION** - Triplet-based (subject-predicate-object) search
+- **RAG_COMPLETION** - Traditional RAG with chunks
+- **CHUNKS** - Vector similarity search over chunks
+- **CHUNKS_LEXICAL** - Lexical (keyword) search over chunks
+- **SUMMARIES** - Search pre-computed document summaries
+- **CYPHER** - Direct Cypher query execution (requires `ALLOW_CYPHER_QUERY=True`)
+- **NATURAL_LANGUAGE** - Natural language to structured query
+- **TEMPORAL** - Time-aware graph search
+- **FEELING_LUCKY** - Automatic search type selection
+- **FEEDBACK** - User feedback-based refinement
+- **CODING_RULES** - Code-specific search rules
+
+Key files:
+- `cognee/api/v1/search/search.py`
+- `cognee/modules/retrieval/context_providers/TripletSearchContextProvider.py`
+- `cognee/modules/search/types/SearchType.py`
+
+### Core Data Models
+
+#### Engine Models (`cognee/infrastructure/engine/models/`)
+- **DataPoint** - Base class for all graph nodes (versioned, with metadata)
+- **Edge** - Graph relationships (source, target, relationship type)
+- **Triplet** - (Subject, Predicate, Object) representation
+
+#### Graph Models (`cognee/shared/data_models.py`)
+- **KnowledgeGraph** - Container for nodes and edges
+- **Node** - Entity (id, name, type, description)
+- **Edge** - Relationship (source_node_id, target_node_id, relationship_name)
+
+### Key Infrastructure Components
+
+#### LLM Gateway (`cognee/infrastructure/llm/LLMGateway.py`)
+Unified interface for multiple LLM providers: OpenAI, Anthropic, Gemini, Ollama, Mistral, Bedrock. Uses Instructor for structured output extraction.
+
+#### Embedding Engines
+Factory pattern for embeddings: `cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py`
+
+#### Document Loaders
+Support for PDF, DOCX, CSV, images, audio, code files in `cognee/infrastructure/files/`
+
+## Important Configuration
+
+### Environment Setup
+Copy `.env.template` to `.env` and configure:
+
+```bash
+# Minimal setup (defaults to OpenAI + local file-based databases)
+LLM_API_KEY="your_openai_api_key"
+LLM_MODEL="openai/gpt-4o-mini"  # Default model
+```
+
+**Important**: If you configure only LLM or only embeddings, the other defaults to OpenAI. Ensure you have a working OpenAI API key, or configure both to avoid unexpected defaults.
+
+Default databases (no extra setup needed):
+- **Relational**: SQLite (metadata and state storage)
+- **Vector**: LanceDB (embeddings for semantic search)
+- **Graph**: Kuzu (knowledge graph and relationships)
+
+All stored in `.venv` by default. Override with `DATA_ROOT_DIRECTORY` and `SYSTEM_ROOT_DIRECTORY`.
+
+### Switching Databases
+
+#### Relational Databases
+```bash
+# PostgreSQL (requires postgres extra: pip install cognee[postgres])
+DB_PROVIDER=postgres
+DB_HOST=localhost
+DB_PORT=5432
+DB_USERNAME=cognee
+DB_PASSWORD=cognee
+DB_NAME=cognee_db
+```
+
+#### Vector Databases
+Supported: lancedb (default), pgvector, chromadb, qdrant, weaviate, milvus
+```bash
+# ChromaDB (requires chromadb extra)
+VECTOR_DB_PROVIDER=chromadb
+
+# PGVector (requires postgres extra)
+VECTOR_DB_PROVIDER=pgvector
+VECTOR_DB_URL=postgresql://cognee:cognee@localhost:5432/cognee_db
+```
+
+#### Graph Databases
+Supported: kuzu (default), neo4j, neptune, kuzu-remote
+```bash
+# Neo4j (requires neo4j extra: pip install cognee[neo4j])
+GRAPH_DATABASE_PROVIDER=neo4j
+GRAPH_DATABASE_URL=bolt://localhost:7687
+GRAPH_DATABASE_NAME=neo4j
+GRAPH_DATABASE_USERNAME=neo4j
+GRAPH_DATABASE_PASSWORD=yourpassword
+
+# Remote Kuzu
+GRAPH_DATABASE_PROVIDER=kuzu-remote
+GRAPH_DATABASE_URL=http://localhost:8000
+GRAPH_DATABASE_USERNAME=your_username
+GRAPH_DATABASE_PASSWORD=your_password
+```
+
+### LLM Provider Configuration
+
+Supported providers: OpenAI (default), Azure OpenAI, Google Gemini, Anthropic, AWS Bedrock, Ollama, LM Studio, Custom (OpenAI-compatible APIs)
+
+#### OpenAI (Recommended - Minimal Setup)
+```bash
+LLM_API_KEY="your_openai_api_key"
+LLM_MODEL="openai/gpt-4o-mini"  # or gpt-4o, gpt-4-turbo, etc.
+LLM_PROVIDER="openai"
+```
+
+#### Azure OpenAI
+```bash
+LLM_PROVIDER="azure"
+LLM_MODEL="azure/gpt-4o-mini"
+LLM_ENDPOINT="https://YOUR-RESOURCE.openai.azure.com/openai/deployments/gpt-4o-mini"
+LLM_API_KEY="your_azure_api_key"
+LLM_API_VERSION="2024-12-01-preview"
+```
+
+#### Google Gemini (requires gemini extra)
+```bash
+LLM_PROVIDER="gemini"
+LLM_MODEL="gemini/gemini-2.0-flash-exp"
+LLM_API_KEY="your_gemini_api_key"
+```
+
+#### Anthropic Claude (requires anthropic extra)
+```bash
+LLM_PROVIDER="anthropic"
+LLM_MODEL="claude-3-5-sonnet-20241022"
+LLM_API_KEY="your_anthropic_api_key"
+```
+
+#### Ollama (Local - requires ollama extra)
+```bash
+LLM_PROVIDER="ollama"
+LLM_MODEL="llama3.1:8b"
+LLM_ENDPOINT="http://localhost:11434/v1"
+LLM_API_KEY="ollama"
+EMBEDDING_PROVIDER="ollama"
+EMBEDDING_MODEL="nomic-embed-text:latest"
+EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
+HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
+```
+
+#### Custom / OpenRouter / vLLM
+```bash
+LLM_PROVIDER="custom"
+LLM_MODEL="openrouter/google/gemini-2.0-flash-lite-preview-02-05:free"
+LLM_ENDPOINT="https://openrouter.ai/api/v1"
+LLM_API_KEY="your_api_key"
+```
+
+#### AWS Bedrock (requires aws extra)
+```bash
+LLM_PROVIDER="bedrock"
+LLM_MODEL="anthropic.claude-3-sonnet-20240229-v1:0"
+AWS_REGION="us-east-1"
+AWS_ACCESS_KEY_ID="your_access_key"
+AWS_SECRET_ACCESS_KEY="your_secret_key"
+# Optional for temporary credentials:
+# AWS_SESSION_TOKEN="your_session_token"
+```
+
+#### LLM Rate Limiting
+```bash
+LLM_RATE_LIMIT_ENABLED=true
+LLM_RATE_LIMIT_REQUESTS=60  # Requests per interval
+LLM_RATE_LIMIT_INTERVAL=60  # Interval in seconds
+```
+
+#### Instructor Mode (Structured Output)
+```bash
+# LLM_INSTRUCTOR_MODE controls how structured data is extracted
+# Each LLM has its own default (e.g., gpt-4o models use "json_schema_mode")
+# Override if needed:
+LLM_INSTRUCTOR_MODE="json_schema_mode"  # or "tool_call", "md_json", etc.
+```
+
+### Structured Output Framework
+```bash
+# Use Instructor (default, via litellm)
+STRUCTURED_OUTPUT_FRAMEWORK="instructor"
+
+# Or use BAML (requires baml extra: pip install cognee[baml])
+STRUCTURED_OUTPUT_FRAMEWORK="baml"
+BAML_LLM_PROVIDER=openai
+BAML_LLM_MODEL="gpt-4o-mini"
+BAML_LLM_API_KEY="your_api_key"
+```
+
+### Storage Backend
+```bash
+# Local filesystem (default)
+STORAGE_BACKEND="local"
+
+# S3 (requires aws extra: pip install cognee[aws])
+STORAGE_BACKEND="s3"
+STORAGE_BUCKET_NAME="your-bucket-name"
+AWS_REGION="us-east-1"
+AWS_ACCESS_KEY_ID="your_access_key"
+AWS_SECRET_ACCESS_KEY="your_secret_key"
+DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data"
+SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system"
+```
+
+## Extension Points
+
+### Adding New Functionality
+
+1. **New Task Type**: Create task function in `cognee/tasks/`, return Task object, register in pipeline
+2. **New Database Backend**: Implement `GraphDBInterface` or `VectorDBInterface` in `cognee/infrastructure/databases/`
+3. **New LLM Provider**: Add configuration in LLM config (uses litellm)
+4. **New Document Processor**: Extend loaders in `cognee/modules/data/processing/`
+5. **New Search Type**: Add to `SearchType` enum and implement retriever in `cognee/modules/retrieval/`
+6. **Custom Graph Models**: Define Pydantic models extending `DataPoint` in your code
+
+### Working with Ontologies
+Cognee supports ontology-based entity extraction to ground knowledge graphs in standardized semantic frameworks (e.g., OWL ontologies).
+
+Configuration:
+```bash
+ONTOLOGY_RESOLVER=rdflib  # Default: uses rdflib and OWL files
+MATCHING_STRATEGY=fuzzy   # Default: fuzzy matching with 80% similarity
+ONTOLOGY_FILE_PATH=/path/to/your/ontology.owl  # Full path to ontology file
+```
+
+Implementation: `cognee/modules/ontology/`
+
+## Branching Strategy
+
+**IMPORTANT**: Always branch from `dev`, not `main`. The `dev` branch is the active development branch.
+
+```bash
+git checkout dev
+git pull origin dev
+git checkout -b feature/your-feature-name
+```
+
+## Code Style
+
+- Ruff for linting and formatting (configured in `pyproject.toml`)
+- Line length: 100 characters
+- Pre-commit hooks run ruff automatically
+- Type hints encouraged (mypy checks enabled)
+
+## Testing Strategy
+
+Tests are organized in `cognee/tests/`:
+- `unit/` - Unit tests for individual modules
+- `integration/` - Full pipeline integration tests
+- `cli_tests/` - CLI command tests
+- `tasks/` - Task-specific tests
+
+When adding features, add corresponding tests. Integration tests should cover the full add → cognify → search flow.
+
+## API Structure
+
+FastAPI application with versioned routes under `cognee/api/v1/`:
+- `/add` - Data ingestion
+- `/cognify` - Knowledge graph processing
+- `/search` - Query interface
+- `/memify` - Graph enrichment
+- `/datasets` - Dataset management
+- `/users` - Authentication (if `REQUIRE_AUTHENTICATION=True`)
+- `/visualize` - Graph visualization server
+
+## Python SDK Entry Points
+
+Main functions exported from `cognee/__init__.py`:
+- `add(data, dataset_name)` - Ingest data
+- `cognify(datasets)` - Build knowledge graph
+- `search(query_text, query_type)` - Query knowledge
+- `memify(extraction_tasks, enrichment_tasks)` - Enrich graph
+- `delete(data_id)` - Remove data
+- `config()` - Configuration management
+- `datasets()` - Dataset operations
+
+All functions are async - use `await` or `asyncio.run()`.
+
+## Security Considerations
+
+Several security environment variables in `.env`:
+- `ACCEPT_LOCAL_FILE_PATH` - Allow local file paths (default: True)
+- `ALLOW_HTTP_REQUESTS` - Allow HTTP requests from Cognee (default: True)
+- `ALLOW_CYPHER_QUERY` - Allow raw Cypher queries (default: True)
+- `REQUIRE_AUTHENTICATION` - Enable API authentication (default: False)
+- `ENABLE_BACKEND_ACCESS_CONTROL` - Multi-tenant isolation (default: True)
+
+For production deployments, review and tighten these settings.
+
+## Common Patterns
+
+### Creating a Custom Pipeline Task
+```python
+from cognee.modules.pipelines.tasks.Task import Task
+
+async def my_custom_task(data):
+    # Your logic here
+    processed_data = process(data)
+    return processed_data
+
+# Use in pipeline
+task = Task(my_custom_task)
+```
+
+### Accessing Databases Directly
+```python
+from cognee.infrastructure.databases.graph import get_graph_engine
+from cognee.infrastructure.databases.vector import get_vector_engine
+
+graph_engine = await get_graph_engine()
+vector_engine = await get_vector_engine()
+```
+
+### Using LLM Gateway
+```python
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+
+llm_client = get_llm_client()
+response = await llm_client.acreate_structured_output(
+    text_input="Your prompt",
+    system_prompt="System instructions",
+    response_model=YourPydanticModel
+)
+```
+
+## Key Concepts
+
+### Datasets
+Datasets are project-level containers that support organization, permissions, and isolated processing workflows. Each user can have multiple datasets with different access permissions.
+
+```python
+# Create/use a dataset
+await cognee.add(data, dataset_name="my_project")
+await cognee.cognify(datasets=["my_project"])
+```
+
+### DataPoints
+Atomic knowledge units that form the foundation of graph structures. All graph nodes extend the `DataPoint` base class with versioning and metadata support.
+
+### Permissions System
+Multi-tenant architecture with users, roles, and Access Control Lists (ACLs):
+- Read, write, delete, and share permissions per dataset
+- Enable with `ENABLE_BACKEND_ACCESS_CONTROL=True`
+- Supports isolated databases per user+dataset (Kuzu, LanceDB, SQLite, Postgres)
+
+### Graph Visualization
+Launch visualization server:
+```bash
+# Via CLI
+cognee-cli -ui  # Launches full stack with UI at http://localhost:3000
+
+# Via Python
+from cognee.api.v1.visualize import start_visualization_server
+await start_visualization_server(port=8080)
+```
+
+## Debugging & Troubleshooting
+
+### Debug Configuration
+- Set `LITELLM_LOG="DEBUG"` for verbose LLM logs (default: "ERROR")
+- Enable debug mode: `ENV="development"` or `ENV="debug"`
+- Disable telemetry: `TELEMETRY_DISABLED=1`
+- Check logs in structured format (uses structlog)
+- Use `debugpy` optional dependency for debugging: `pip install cognee[debug]`
+
+### Common Issues
+
+**Ollama + OpenAI Embeddings NoDataError**
+- Issue: Mixing Ollama with OpenAI embeddings can cause errors
+- Solution: Configure both LLM and embeddings to use the same provider, or ensure `HUGGINGFACE_TOKENIZER` is set when using Ollama
+
+**LM Studio Structured Output**
+- Issue: LM Studio requires explicit instructor mode
+- Solution: Set `LLM_INSTRUCTOR_MODE="json_schema_mode"` (or appropriate mode)
+
+**Default Provider Fallback**
+- Issue: Configuring only LLM or only embeddings defaults the other to OpenAI
+- Solution: Always configure both LLM and embedding providers, or ensure valid OpenAI API key
+
+**Permission Denied on Search**
+- Behavior: Returns empty list rather than error (prevents information leakage)
+- Solution: Check dataset permissions and user access rights
+
+**Database Connection Issues**
+- Check: Verify database URLs, credentials, and that services are running
+- Docker users: Use `DB_HOST=host.docker.internal` for local databases
+
+**Rate Limiting Errors**
+- Enable client-side rate limiting: `LLM_RATE_LIMIT_ENABLED=true`
+- Adjust limits: `LLM_RATE_LIMIT_REQUESTS` and `LLM_RATE_LIMIT_INTERVAL`
+
+## Resources
+
+- [Documentation](https://docs.cognee.ai/)
+- [Discord Community](https://discord.gg/NQPKmU5CCg)
+- [GitHub Issues](https://github.com/topoteretes/cognee/issues)
+- [Example Notebooks](examples/python/)
+- [Research Paper](https://arxiv.org/abs/2505.24478) - Optimizing knowledge graphs for LLM reasoning
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@ -1,16 +1,16 @@
 > [!IMPORTANT]
 > **Note for contributors:** When branching out, create a new branch from the `dev` branch.

-# 🎉 Welcome to **cognee**! 
+# 🎉 Welcome to **cognee**!

-We're excited that you're interested in contributing to our project! 
-We want to ensure that every user and contributor feels welcome, included and supported to participate in cognee community. 
+We're excited that you're interested in contributing to our project!
+We want to ensure that every user and contributor feels welcome, included and supported to participate in cognee community.
 This guide will help you get started and ensure your contributions can be efficiently integrated into the project.

 ## 🌟 Quick Links

 - [Code of Conduct](CODE_OF_CONDUCT.md)
- [Discord Community](https://discord.gg/bcy8xFAtfd)  
+- [Discord Community](https://discord.gg/bcy8xFAtfd)
 - [Issue Tracker](https://github.com/topoteretes/cognee/issues)
 - [Cognee Docs](https://docs.cognee.ai)

@ -62,6 +62,11 @@ Looking for a place to start? Try filtering for [good first issues](https://gith

 ## 2. 🛠️ Development Setup

+### Required tools
+* [Python](https://www.python.org/downloads/)
+* [uv](https://docs.astral.sh/uv/getting-started/installation/)
+* pre-commit: `uv run pip install pre-commit && pre-commit install`
+
 ### Fork and Clone

 1. Fork the [**cognee**](https://github.com/topoteretes/cognee) repository
@ -93,29 +98,31 @@ git checkout -b feature/your-feature-name
 4. **Commits**: Write clear commit messages

 ### Running Tests
+
+Rename `.env.example` into `.env` and provide your OPENAI_API_KEY as LLM_API_KEY
+
 ```shell
-python cognee/cognee/tests/test_library.py
+uv run python cognee/tests/test_library.py
 ```

 ### Running Simple Example

-Change .env.example into .env and provide your OPENAI_API_KEY as LLM_API_KEY
+Rename `.env.example` into `.env` and provide your OPENAI_API_KEY as LLM_API_KEY

 Make sure to run ```shell uv sync ``` in the root cloned folder or set up a virtual environment to run cognee

 ```shell
-python cognee/cognee/examples/python/simple_example.py
+python examples/python/simple_example.py
 ```
-or 
+or

 ```shell
-uv run python cognee/cognee/examples/python/simple_example.py
+uv run python examples/python/simple_example.py
 ```

 ## 4. 📤 Submitting Changes

-1. Install ruff on your system
-2. Run ```ruff format .``` and ``` ruff check ``` and fix the issues
+1. Make sure that `pre-commit` and hooks are installed. See `Required tools` section for more information. Try executing `pre-commit run` if you are not sure.
 3. Push your changes:
 ```shell
 git add .
--- a/3
+++ b/3
@ -16,9 +16,6 @@ ARG DEBUG
 # Set environment variable based on the build argument
 ENV DEBUG=${DEBUG}

-# if you located in China, you can use aliyun mirror to speed up
-#RUN sed -i 's@deb.debian.org@mirrors.ustc.edu.cn@g' /etc/apt/sources.list.d/debian.sources
-
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
    gcc \
--- a/README.md
+++ b/README.md
@ -65,12 +65,12 @@ Use your data to build personalized and dynamic memory for AI Agents. Cognee let

 ## About Cognee

-Cognee is an open-source tool and platform that transforms your raw data into persistent and dynamic AI memory for Agents. It combines vector search with graph databases to make your documents both searchable by meaning and connected by relationships. 
+Cognee is an open-source tool and platform that transforms your raw data into persistent and dynamic AI memory for Agents. It combines vector search with graph databases to make your documents both searchable by meaning and connected by relationships.

 You can use Cognee in two ways:

 1. [Self-host Cognee Open Source](https://docs.cognee.ai/getting-started/installation), which stores all data locally by default.
-2. [Connect to Cognee Cloud](https://platform.cognee.ai/), and get the same OSS stack on managed infrastructure for easier development and productionization. 
+2. [Connect to Cognee Cloud](https://platform.cognee.ai/), and get the same OSS stack on managed infrastructure for easier development and productionization.

 ### Cognee Open Source (self-hosted):

@ -81,8 +81,8 @@ You can use Cognee in two ways:
 - Offers high customizability through user-defined tasks, modular pipelines, and built-in search endpoints

 ### Cognee Cloud (managed):
- Hosted web UI dashboard 
- Automatic version updates 
+- Hosted web UI dashboard
+- Automatic version updates
 - Resource usage analytics
 - GDPR compliant, enterprise-grade security

@ -119,7 +119,7 @@ To integrate other LLM providers, see our [LLM Provider Documentation](https://d

 ### Step 3: Run the Pipeline

-Cognee will take your documents, generate a knowledge graph from them and then query the graph based on combined relationships. 
+Cognee will take your documents, generate a knowledge graph from them and then query the graph based on combined relationships.

 Now, run a minimal pipeline:

@ -157,7 +157,7 @@ As you can see, the output is generated from the document we previously stored i
  Cognee turns documents into AI memory.
 ```

-### Use the Cognee CLI 
+### Use the Cognee CLI

 As an alternative, you can get started with these essential commands:

--- a/alembic/README
+++ b/alembic/README
@ -1 +1 @@
-Generic single-database configuration with an async dbapi.
+Generic single-database configuration with an async dbapi.
--- a/alembic/versions/1a58b986e6e1_enable_delete_for_old_tutorial_notebooks.py
+++ b/alembic/versions/1a58b986e6e1_enable_delete_for_old_tutorial_notebooks.py
@ -14,7 +14,7 @@ import sqlalchemy as sa

 # revision identifiers, used by Alembic.
 revision: str = "1a58b986e6e1"
-down_revision: Union[str, None] = "46a6ce2bd2b2"
+down_revision: Union[str, None] = "e1ec1dcb50b6"
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None

--- a/assets/community/README.pt.md
+++ b/assets/community/README.pt.md
@ -43,10 +43,10 @@ Saiba mais sobre os [casos de uso](https://docs.cognee.ai/use-cases) e [avaliaç

 ## Funcionalidades

- Conecte e recupere suas conversas passadas, documentos, imagens e transcrições de áudio  
- Reduza alucinações, esforço de desenvolvimento e custos  
- Carregue dados em bancos de dados de grafos e vetores usando apenas Pydantic  
- Transforme e organize seus dados enquanto os coleta de mais de 30 fontes diferentes  
+- Conecte e recupere suas conversas passadas, documentos, imagens e transcrições de áudio
+- Reduza alucinações, esforço de desenvolvimento e custos
+- Carregue dados em bancos de dados de grafos e vetores usando apenas Pydantic
+- Transforme e organize seus dados enquanto os coleta de mais de 30 fontes diferentes

 ## Primeiros Passos

@ -108,7 +108,7 @@ if __name__ == '__main__':
 Exemplo do output:
 ```
  O Processamento de Linguagem Natural (NLP) é um campo interdisciplinar e transdisciplinar que envolve ciência da computação e recuperação de informações. Ele se concentra na interação entre computadores e a linguagem humana, permitindo que as máquinas compreendam e processem a linguagem natural.
-  
+
 ```

 Visualização do grafo:
--- a/assets/community/README.ru.md
+++ b/assets/community/README.ru.md
@ -141,7 +141,7 @@ if __name__ == '__main__':
 2. Простая демонстрация GraphRAG
 [Видео](https://github.com/user-attachments/assets/d80b0776-4eb9-4b8e-aa22-3691e2d44b8f)

-3. Cognee с Ollama 
+3. Cognee с Ollama
 [Видео](https://github.com/user-attachments/assets/8621d3e8-ecb8-4860-afb2-5594f2ee17db)

 ## Правила поведения
--- a/assets/community/README.zh.md
+++ b/assets/community/README.zh.md
@ -114,7 +114,7 @@ if __name__ == '__main__':
 示例输出：
 ```
  自然语言处理（NLP）是计算机科学和信息检索的跨学科领域。它关注计算机和人类语言之间的交互，使机器能够理解和处理自然语言。
-  
+
 ```
 图形可视化：
 <a href="https://rawcdn.githack.com/topoteretes/cognee/refs/heads/main/assets/graph_visualization.html"><img src="https://rawcdn.githack.com/topoteretes/cognee/refs/heads/main/assets/graph_visualization.png" width="100%" alt="图形可视化"></a>
--- a/cognee-frontend/package.json
+++ b/cognee-frontend/package.json
@ -13,7 +13,7 @@
    "classnames": "^2.5.1",
    "culori": "^4.0.1",
    "d3-force-3d": "^3.0.6",
-    "next": "^16.1.0",
+    "next": "^16.1.7",
    "react": "^19.2.3",
    "react-dom": "^19.2.3",
    "react-force-graph-2d": "^1.27.1",
@ -34,4 +34,4 @@
    "tailwindcss": "^4.1.7",
    "typescript": "^5"
  }
-}
+}
--- a/cognee-frontend/src/app/(graph)/CogneeAddWidget.tsx
+++ b/cognee-frontend/src/app/(graph)/CogneeAddWidget.tsx
@ -55,7 +55,7 @@ export default function CogneeAddWidget({ onData, useCloud = false }: CogneeAddW
    setTrue: setProcessingFilesInProgress,
    setFalse: setProcessingFilesDone,
  } = useBoolean(false);
-  
+
  const handleAddFiles = (dataset: Dataset, event: ChangeEvent<HTMLInputElement>) => {
    event.stopPropagation();

--- a/cognee-frontend/src/app/(graph)/GraphControls.tsx
+++ b/cognee-frontend/src/app/(graph)/GraphControls.tsx
@ -111,7 +111,7 @@ export default function GraphControls({ data, isAddNodeFormOpen, onGraphShapeCha

  const [isAuthShapeChangeEnabled, setIsAuthShapeChangeEnabled] = useState(true);
  const shapeChangeTimeout = useRef<number | null>(null);
-  
+
  useEffect(() => {
    onGraphShapeChange(DEFAULT_GRAPH_SHAPE);

--- a/cognee-frontend/src/app/(graph)/GraphVisualization.tsx
+++ b/cognee-frontend/src/app/(graph)/GraphVisualization.tsx
@ -57,7 +57,7 @@ export default function GraphVisualization({ ref, data, graphControls, className
    // Initial size calculation
    handleResize();

-    // ResizeObserver 
+    // ResizeObserver
    const resizeObserver = new ResizeObserver(() => {
      handleResize();
    });
@ -216,7 +216,7 @@ export default function GraphVisualization({ ref, data, graphControls, className
  }, [data, graphRef]);

  const [graphShape, setGraphShape] = useState<string>();
-  
+
  const zoomToFit: ForceGraphMethods["zoomToFit"] = (
    durationMs?: number,
    padding?: number,
@ -227,15 +227,15 @@ export default function GraphVisualization({ ref, data, graphControls, className
      // eslint-disable-next-line @typescript-eslint/no-explicit-any
      return undefined as any;
    }
-  
+
    return graphRef.current.zoomToFit?.(durationMs, padding, nodeFilter);
  };
-  
+
  useImperativeHandle(ref, () => ({
    zoomToFit,
    setGraphShape,
  }));
-  
+

  return (
    <div ref={containerRef} className={classNames("w-full h-full", className)} id="graph-container">
--- a/cognee-frontend/src/app/(graph)/example_data.json
+++ b/cognee-frontend/src/app/(graph)/example_data.json
@ -1373,4 +1373,4 @@
      "padding": 20
    }
  }
-} 
+}
--- a/cognee-frontend/src/app/dashboard/DatasetsAccordion.tsx
+++ b/cognee-frontend/src/app/dashboard/DatasetsAccordion.tsx
@ -134,7 +134,7 @@ export default function DatasetsAccordion({
  } = useBoolean(false);

  const [datasetToRemove, setDatasetToRemove] = useState<Dataset | null>(null);
-  
+
  const handleDatasetRemove = (dataset: Dataset) => {
    setDatasetToRemove(dataset);
    openRemoveDatasetModal();
--- a/cognee-frontend/src/app/plan/Plan.tsx
+++ b/cognee-frontend/src/app/plan/Plan.tsx
@ -45,7 +45,7 @@ export default function Plan() {
          <div className="bg-white rounded-xl px-5 py-5 mb-2">
            Affordable and transparent pricing
          </div>
-        
+
          <div className="grid grid-cols-3 gap-x-2.5">
            <div className="pt-13 py-4 px-5 mb-2.5 rounded-tl-xl rounded-tr-xl bg-white h-full">
              <div>Basic</div>
--- a/cognee-frontend/src/modules/chat/hooks/useChat.ts
+++ b/cognee-frontend/src/modules/chat/hooks/useChat.ts
@ -40,7 +40,7 @@ export default function useChat(dataset: Dataset) {
    setTrue: disableSearchRun,
    setFalse: enableSearchRun,
  } = useBoolean(false);
-  
+
  const refreshChat = useCallback(async () => {
    const data = await fetchMessages();
    return setMessages(data);
--- a/cognee-frontend/src/modules/ingestion/useDatasets.ts
+++ b/cognee-frontend/src/modules/ingestion/useDatasets.ts
@ -46,7 +46,7 @@ function useDatasets(useCloud = false) {
  //     checkDatasetStatuses(datasets);
  //   }, 50000);
  // }, [fetchDatasetStatuses]);
-  
+
  // useEffect(() => {
  //   return () => {
  //     if (statusTimeout.current !== null) {
--- a/cognee-frontend/src/modules/notebooks/createNotebook.ts
+++ b/cognee-frontend/src/modules/notebooks/createNotebook.ts
@ -7,7 +7,7 @@ export default function createNotebook(notebookName: string, instance: CogneeIns
    headers: {
      "Content-Type": "application/json",
    },
-  }).then((response: Response) => 
+  }).then((response: Response) =>
    response.ok ? response.json() : Promise.reject(response)
  );
 }
--- a/cognee-frontend/src/modules/notebooks/getNotebooks.ts
+++ b/cognee-frontend/src/modules/notebooks/getNotebooks.ts
@ -6,7 +6,7 @@ export default function getNotebooks(instance: CogneeInstance) {
    headers: {
      "Content-Type": "application/json",
    },
-  }).then((response: Response) => 
+  }).then((response: Response) =>
    response.ok ? response.json() : Promise.reject(response)
  );
 }
--- a/cognee-frontend/src/modules/notebooks/saveNotebook.ts
+++ b/cognee-frontend/src/modules/notebooks/saveNotebook.ts
@ -7,7 +7,7 @@ export default function saveNotebook(notebookId: string, notebookData: object, i
    headers: {
      "Content-Type": "application/json",
    },
-  }).then((response: Response) => 
+  }).then((response: Response) =>
    response.ok ? response.json() : Promise.reject(response)
  );
 }
--- a/cognee-frontend/src/ui/Icons/GitHubIcon.tsx
+++ b/cognee-frontend/src/ui/Icons/GitHubIcon.tsx
@ -7,4 +7,4 @@ export default function GitHubIcon({ width = 24, height = 24, color = 'currentCo
      </g>
    </svg>
  );
-}
+}
--- a/cognee-frontend/src/ui/Layout/Header.tsx
+++ b/cognee-frontend/src/ui/Layout/Header.tsx
@ -46,7 +46,7 @@ export default function Header({ user }: HeaderProps) {

    checkMCPConnection();
    const interval = setInterval(checkMCPConnection, 30000);
-    
+
    return () => clearInterval(interval);
  }, [setMCPConnected, setMCPDisconnected]);

--- a/cognee-frontend/src/ui/Partials/SearchView/SearchView.tsx
+++ b/cognee-frontend/src/ui/Partials/SearchView/SearchView.tsx
@ -90,7 +90,7 @@ export default function SearchView() {
    scrollToBottom();

    setSearchInputValue("");
-    
+
    // Pass topK to sendMessage
    sendMessage(chatInput, searchType, topK)
      .then(scrollToBottom)
@ -171,4 +171,4 @@ export default function SearchView() {
      </form>
    </div>
  );
-}
+}
--- a/cognee-frontend/src/ui/elements/Modal/index.ts
+++ b/cognee-frontend/src/ui/elements/Modal/index.ts
@ -1,3 +1,2 @@
 export { default as Modal } from "./Modal";
 export { default as useModal } from "./useModal";
-
--- a/cognee-frontend/src/ui/elements/Notebook/MarkdownPreview.tsx
+++ b/cognee-frontend/src/ui/elements/Notebook/MarkdownPreview.tsx
@ -74,4 +74,3 @@ function MarkdownPreview({ content, className = "" }: MarkdownPreviewProps) {
 }

 export default memo(MarkdownPreview);
-
--- a/cognee-frontend/src/ui/elements/Notebook/Notebook.tsx
+++ b/cognee-frontend/src/ui/elements/Notebook/Notebook.tsx
@ -534,7 +534,7 @@ function transformInsightsGraphData(triplets: Triplet[]) {
      target: string,
      label: string,
    }
-  } = {};          
+  } = {};

  for (const triplet of triplets) {
    nodes[triplet[0].id] = {
--- a/cognee-frontend/src/ui/elements/TextArea.tsx
+++ b/cognee-frontend/src/ui/elements/TextArea.tsx
@ -34,8 +34,8 @@ export default function TextArea({
    // Cache maxHeight on first calculation
    if (maxHeightRef.current === null) {
      const computedStyle = getComputedStyle(textarea);
-      maxHeightRef.current = computedStyle.maxHeight === "none" 
-        ? Infinity 
+      maxHeightRef.current = computedStyle.maxHeight === "none"
+        ? Infinity
        : parseInt(computedStyle.maxHeight) || Infinity;
    }

--- a/cognee-frontend/src/ui/elements/index.ts
+++ b/cognee-frontend/src/ui/elements/index.ts
@ -10,4 +10,4 @@ export { default as NeutralButton } from "./NeutralButton";
 export { default as StatusIndicator } from "./StatusIndicator";
 export { default as StatusDot } from "./StatusDot";
 export { default as Accordion } from "./Accordion";
-export { default as Notebook } from "./Notebook"; 
+export { default as Notebook } from "./Notebook";
--- a/cognee-frontend/src/utils/fetch.ts
+++ b/cognee-frontend/src/utils/fetch.ts
@ -57,7 +57,7 @@ export default async function fetch(url: string, options: RequestInit = {}, useC
          new Error("Backend server is not responding. Please check if the server is running.")
        );
      }
-      
+
      if (error.detail === undefined) {
        return Promise.reject(
          new Error("No connection to the server.")
@ -74,7 +74,7 @@ export default async function fetch(url: string, options: RequestInit = {}, useC
 fetch.checkHealth = async () => {
  const maxRetries = 5;
  const retryDelay = 1000; // 1 second
-  
+
  for (let i = 0; i < maxRetries; i++) {
    try {
      const response = await global.fetch(`${backendApiUrl.replace("/api", "")}/health`);
@ -90,7 +90,7 @@ fetch.checkHealth = async () => {
      await new Promise(resolve => setTimeout(resolve, retryDelay));
    }
  }
-  
+
  throw new Error("Backend server is not responding after multiple attempts");
 };

--- a/cognee-mcp/README.md
+++ b/cognee-mcp/README.md
@ -105,14 +105,14 @@ If you'd rather run cognee-mcp in a container, you have two options:
      ```bash
      # For HTTP transport (recommended for web deployments)
      docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
-      # For SSE transport  
+      # For SSE transport
      docker run -e TRANSPORT_MODE=sse --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
      # For stdio transport (default)
      docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
      ```
-      
+
      **Installing optional dependencies at runtime:**
-      
+
      You can install optional dependencies when running the container by setting the `EXTRAS` environment variable:
      ```bash
      # Install a single optional dependency group at runtime
@ -122,7 +122,7 @@ If you'd rather run cognee-mcp in a container, you have two options:
        --env-file ./.env \
        -p 8000:8000 \
        --rm -it cognee/cognee-mcp:main
-      
+
      # Install multiple optional dependency groups at runtime (comma-separated)
      docker run \
        -e TRANSPORT_MODE=sse \
@ -131,7 +131,7 @@ If you'd rather run cognee-mcp in a container, you have two options:
        -p 8000:8000 \
        --rm -it cognee/cognee-mcp:main
      ```
-      
+
      **Available optional dependency groups:**
      - `aws` - S3 storage support
      - `postgres` / `postgres-binary` - PostgreSQL database support
@ -160,7 +160,7 @@ If you'd rather run cognee-mcp in a container, you have two options:
   # With stdio transport (default)
   docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
   ```
-   
+
   **With runtime installation of optional dependencies:**
   ```bash
   # Install optional dependencies from Docker Hub image
@ -357,7 +357,7 @@ You can configure both transports simultaneously for testing:
      "url": "http://localhost:8000/sse"
    },
    "cognee-http": {
-      "type": "http", 
+      "type": "http",
      "url": "http://localhost:8000/mcp"
    }
  }
--- a/cognee-mcp/entrypoint.sh
+++ b/cognee-mcp/entrypoint.sh
@ -7,11 +7,11 @@ echo "Environment: $ENVIRONMENT"
 # Install optional dependencies if EXTRAS is set
 if [ -n "$EXTRAS" ]; then
    echo "Installing optional dependencies: $EXTRAS"
-    
+
    # Get the cognee version that's currently installed
    COGNEE_VERSION=$(uv pip show cognee | grep "Version:" | awk '{print $2}')
    echo "Current cognee version: $COGNEE_VERSION"
-    
+
    # Build the extras list for cognee
    IFS=',' read -ra EXTRA_ARRAY <<< "$EXTRAS"
    # Combine base extras from pyproject.toml with requested extras
@ -28,11 +28,11 @@ if [ -n "$EXTRAS" ]; then
            fi
        fi
    done
-    
+
    echo "Installing cognee with extras: $ALL_EXTRAS"
    echo "Running: uv pip install 'cognee[$ALL_EXTRAS]==$COGNEE_VERSION'"
    uv pip install "cognee[$ALL_EXTRAS]==$COGNEE_VERSION"
-    
+
    # Verify installation
    echo ""
    echo "✓ Optional dependencies installation completed"
@ -93,19 +93,19 @@ if [ -n "$API_URL" ]; then
    if echo "$API_URL" | grep -q "localhost" || echo "$API_URL" | grep -q "127.0.0.1"; then
        echo "⚠️  Warning: API_URL contains localhost/127.0.0.1"
        echo "   Original: $API_URL"
-        
+
        # Try to use host.docker.internal (works on Mac/Windows and recent Linux with Docker Desktop)
        FIXED_API_URL=$(echo "$API_URL" | sed 's/localhost/host.docker.internal/g' | sed 's/127\.0\.0\.1/host.docker.internal/g')
-        
+
        echo "   Converted to: $FIXED_API_URL"
        echo "   This will work on Mac/Windows/Docker Desktop."
        echo "   On Linux without Docker Desktop, you may need to:"
        echo "     - Use --network host, OR"
        echo "     - Set API_URL=http://172.17.0.1:8000 (Docker bridge IP)"
-        
+
        API_URL="$FIXED_API_URL"
    fi
-    
+
    API_ARGS="--api-url $API_URL"
    if [ -n "$API_TOKEN" ]; then
        API_ARGS="$API_ARGS --api-token $API_TOKEN"
--- a/cognee-starter-kit/.env.template
+++ b/cognee-starter-kit/.env.template
@ -16,4 +16,4 @@ EMBEDDING_API_VERSION=""


 GRAPHISTRY_USERNAME=""
-GRAPHISTRY_PASSWORD=""
+GRAPHISTRY_PASSWORD=""
--- a/cognee-starter-kit/README.md
+++ b/cognee-starter-kit/README.md
@ -14,7 +14,7 @@ This starter kit is deprecated. Its examples have been integrated into the `/new
 # Cognee Starter Kit
 Welcome to the <a href="https://github.com/topoteretes/cognee">cognee</a> Starter Repo! This repository is designed to help you get started quickly by providing a structured dataset and pre-built data pipelines using cognee to build powerful knowledge graphs.

-You can use this repo to ingest, process, and visualize data in minutes. 
+You can use this repo to ingest, process, and visualize data in minutes.

 By following this guide, you will:

@ -80,7 +80,7 @@ Custom model uses custom pydantic model for graph extraction. This script catego
 python src/pipelines/custom-model.py
 ```

-## Graph preview 
+## Graph preview

 cognee provides a visualize_graph function that will render the graph for you.

--- a/cognee/api/v1/search/routers/get_search_router.py
+++ b/cognee/api/v1/search/routers/get_search_router.py
@ -8,12 +8,14 @@ from fastapi.encoders import jsonable_encoder

 from cognee.modules.search.types import SearchType, SearchResult, CombinedSearchResult
 from cognee.api.DTO import InDTO, OutDTO
-from cognee.modules.users.exceptions.exceptions import PermissionDeniedError
+from cognee.modules.users.exceptions.exceptions import PermissionDeniedError, UserNotFoundError
 from cognee.modules.users.models import User
 from cognee.modules.search.operations import get_history
 from cognee.modules.users.methods import get_authenticated_user
 from cognee.shared.utils import send_telemetry
 from cognee import __version__ as cognee_version
+from cognee.infrastructure.databases.exceptions import DatabaseNotCreatedError
+from cognee.exceptions import CogneeValidationError


 # Note: Datasets sent by name will only map to datasets owned by the request sender
@ -138,6 +140,17 @@ def get_search_router() -> APIRouter:
            )

            return jsonable_encoder(results)
+        except (DatabaseNotCreatedError, UserNotFoundError, CogneeValidationError) as e:
+            # Return a clear 422 with actionable guidance instead of leaking a stacktrace
+            status_code = getattr(e, "status_code", 422)
+            return JSONResponse(
+                status_code=status_code,
+                content={
+                    "error": "Search prerequisites not met",
+                    "detail": str(e),
+                    "hint": "Run `await cognee.add(...)` then `await cognee.cognify()` before searching.",
+                },
+            )
        except PermissionDeniedError:
            return []
        except Exception as error:
--- a/cognee/api/v1/search/search.py
+++ b/cognee/api/v1/search/search.py
@ -11,6 +11,9 @@ from cognee.modules.data.methods import get_authorized_existing_datasets
 from cognee.modules.data.exceptions import DatasetNotFoundError
 from cognee.context_global_variables import set_session_user_context_variable
 from cognee.shared.logging_utils import get_logger
+from cognee.infrastructure.databases.exceptions import DatabaseNotCreatedError
+from cognee.exceptions import CogneeValidationError
+from cognee.modules.users.exceptions.exceptions import UserNotFoundError

 logger = get_logger()

@ -176,7 +179,18 @@ async def search(
        datasets = [datasets]

    if user is None:
-        user = await get_default_user()
+        try:
+            user = await get_default_user()
+        except (DatabaseNotCreatedError, UserNotFoundError) as error:
+            # Provide a clear, actionable message instead of surfacing low-level stacktraces
+            raise CogneeValidationError(
+                message=(
+                    "Search prerequisites not met: no database/default user found. "
+                    "Initialize Cognee before searching by:\n"
+                    "• running `await cognee.add(...)` followed by `await cognee.cognify()`."
+                ),
+                name="SearchPreconditionError",
+            ) from error

    await set_session_user_context_variable(user)

--- a/cognee/api/v1/sync/routers/get_sync_router.py
+++ b/cognee/api/v1/sync/routers/get_sync_router.py
@ -71,7 +71,7 @@ def get_sync_router() -> APIRouter:
          -H "Content-Type: application/json" \\
          -H "Cookie: auth_token=your-token" \\
          -d '{"dataset_ids": ["123e4567-e89b-12d3-a456-426614174000", "456e7890-e12b-34c5-d678-901234567000"]}'
-        
+
        # Sync all user datasets (empty request body or null dataset_ids)
        curl -X POST "http://localhost:8000/api/v1/sync" \\
          -H "Content-Type: application/json" \\
@ -88,7 +88,7 @@ def get_sync_router() -> APIRouter:
        - **413 Payload Too Large**: Dataset too large for current cloud plan
        - **429 Too Many Requests**: Rate limit exceeded

-        ## Notes  
+        ## Notes
        - Sync operations run in the background - you get an immediate response
        - Use the returned run_id to track progress (status API coming soon)
        - Large datasets are automatically chunked for efficient transfer
@ -179,7 +179,7 @@ def get_sync_router() -> APIRouter:
        ```

        ## Example Responses
-        
+
        **No running syncs:**
        ```json
        {
--- a/cognee/cli/commands/add_command.py
+++ b/cognee/cli/commands/add_command.py
@ -21,7 +21,7 @@ binary streams, then stores them in a specified dataset for further processing.

 Supported Input Types:
 - **Text strings**: Direct text content
- **File paths**: Local file paths (absolute paths starting with "/")  
+- **File paths**: Local file paths (absolute paths starting with "/")
 - **File URLs**: "file:///absolute/path" or "file://relative/path"
 - **S3 paths**: "s3://bucket-name/path/to/file"
 - **Lists**: Multiple files or text strings in a single call
--- a/cognee/cli/commands/config_command.py
+++ b/cognee/cli/commands/config_command.py
@ -17,7 +17,7 @@ The `cognee config` command allows you to view and modify configuration settings

 You can:
 - View all current configuration settings
- Get specific configuration values  
+- Get specific configuration values
 - Set configuration values
 - Unset (reset to default) specific configuration values
 - Reset all configuration to defaults
--- a/cognee/infrastructure/databases/hybrid/neptune_analytics/NeptuneAnalyticsAdapter.py
+++ b/cognee/infrastructure/databases/hybrid/neptune_analytics/NeptuneAnalyticsAdapter.py
@ -290,7 +290,7 @@ class NeptuneAnalyticsAdapter(NeptuneGraphDB, VectorDBInterface):
        query_string = f"""
        CALL neptune.algo.vectors.topKByEmbeddingWithFiltering({{
                topK: {limit},
-                embedding: {embedding}, 
+                embedding: {embedding},
                nodeFilter: {{ equals: {{property: '{self._COLLECTION_PREFIX}', value: '{collection_name}'}} }}
              }}
            )
@ -299,7 +299,7 @@ class NeptuneAnalyticsAdapter(NeptuneGraphDB, VectorDBInterface):

        if with_vector:
            query_string += """
-        WITH node, score, id(node) as node_id 
+        WITH node, score, id(node) as node_id
        MATCH (n)
        WHERE id(n) = id(node)
        CALL neptune.algo.vectors.get(n)
--- a/cognee/infrastructure/llm/prompts/extract_query_time.txt
+++ b/cognee/infrastructure/llm/prompts/extract_query_time.txt
@ -10,4 +10,4 @@ Extraction rules:
 5. Current-time references ("now", "current", "today"): If the query explicitly refers to the present, set both starts_at and ends_at to now (the ingestion timestamp).
 6. "Who is" and "Who was" questions: These imply a general identity or biographical inquiry without a specific temporal scope. Set both starts_at and ends_at to None.
 7. Ordering rule: Always ensure the earlier date is assigned to starts_at and the later date to ends_at.
-8. No temporal information: If no valid or inferable time reference is found, set both starts_at and ends_at to None.
+8. No temporal information: If no valid or inferable time reference is found, set both starts_at and ends_at to None.
--- a/cognee/infrastructure/llm/prompts/generate_event_entity_prompt.txt
+++ b/cognee/infrastructure/llm/prompts/generate_event_entity_prompt.txt
@ -22,4 +22,4 @@ The `attributes` should be a list of dictionaries, each containing:
 - Relationships should be technical with one or at most two words. If two words, use underscore camelcase style
 - Relationships could imply general meaning like: subject, object, participant, recipient, agent, instrument, tool, source, cause, effect, purpose, manner, resource, etc.
 - You can combine two words to form a relationship name: subject_role, previous_owner, etc.
- Focus on how the entity specifically relates to the event
+- Focus on how the entity specifically relates to the event
--- a/cognee/infrastructure/llm/prompts/generate_event_graph_prompt.txt
+++ b/cognee/infrastructure/llm/prompts/generate_event_graph_prompt.txt
@ -27,4 +27,4 @@ class Event(BaseModel):
    time_from: Optional[Timestamp] = None
    time_to: Optional[Timestamp] = None
    location: Optional[str] = None
-```
+```
--- a/cognee/infrastructure/llm/prompts/generate_graph_prompt.txt
+++ b/cognee/infrastructure/llm/prompts/generate_graph_prompt.txt
@ -19,8 +19,8 @@ The aim is to achieve simplicity and clarity in the knowledge graph.
  - **Naming Convention**: Use snake_case for relationship names, e.g., `acted_in`.
 # 3. Coreference Resolution
  - **Maintain Entity Consistency**: When extracting entities, it's vital to ensure consistency.
-  If an entity, such as "John Doe", is mentioned multiple times in the text but is referred to by different names or pronouns (e.g., "Joe", "he"),
-  always use the most complete identifier for that entity throughout the knowledge graph. In this example, use "John Doe" as the Persons ID.
+  If an entity, is mentioned multiple times in the text but is referred to by different names or pronouns,
+  always use the most complete identifier for that entity throughout the knowledge graph.
 Remember, the knowledge graph should be coherent and easily understandable, so maintaining consistency in entity references is crucial.
 # 4. Strict Compliance
 Adhere to the rules strictly. Non-compliance will result in termination
--- a/cognee/infrastructure/llm/prompts/generate_graph_prompt_guided.txt
+++ b/cognee/infrastructure/llm/prompts/generate_graph_prompt_guided.txt
@ -22,7 +22,7 @@ You are an advanced algorithm designed to extract structured information to buil
 3. **Coreference Resolution**:
   - Maintain one consistent node ID for each real-world entity.
   - Resolve aliases, acronyms, and pronouns to the most complete form.
-   - *Example*: Always use "John Doe" even if later referred to as "Doe" or "he".
+   - *Example*: Always use full identifier even if later referred to as in a similar but slightly different way

 **Property & Data Guidelines**:

--- a/cognee/infrastructure/llm/prompts/generate_graph_prompt_oneshot.txt
+++ b/cognee/infrastructure/llm/prompts/generate_graph_prompt_oneshot.txt
@ -42,10 +42,10 @@ You are an advanced algorithm designed to extract structured information from un
 - **Rule**: Resolve all aliases, acronyms, and pronouns to one canonical identifier.

 > **One-Shot Example**:
-> **Input**: "John Doe is an author. Later, Doe published a book. He is well-known."
+> **Input**: "X is an author. Later, Doe published a book. He is well-known."
 > **Output Node**:
 > ```
-> John Doe (Person)
+> X (Person)
 > ```

 ---
--- a/cognee/infrastructure/llm/prompts/generate_graph_prompt_simple.txt
+++ b/cognee/infrastructure/llm/prompts/generate_graph_prompt_simple.txt
@ -15,7 +15,7 @@ You are an advanced algorithm that extracts structured data into a knowledge gra
   - Properties are key-value pairs; do not use escaped quotes.

 3. **Coreference Resolution**
-   - Use a single, complete identifier for each entity (e.g., always "John Doe" not "Joe" or "he").
+   - Use a single, complete identifier for each entity

 4. **Relationship Labels**:
   - Use descriptive, lowercase, snake_case names for edges.
--- a/cognee/infrastructure/llm/prompts/generate_graph_prompt_strict.txt
+++ b/cognee/infrastructure/llm/prompts/generate_graph_prompt_strict.txt
@ -26,7 +26,7 @@ Use **basic atomic types** for node labels. Always prefer general types over spe
  - Good: "Alan Turing", "Google Inc.", "World War II"
  - Bad: "Entity_001", "1234", "he", "they"
 - Never use numeric or autogenerated IDs.
- Prioritize **most complete form** of entity names for consistency (e.g., always use "John Doe" instead of "John" or "he").
+- Prioritize **most complete form** of entity names for consistency

 2. Dates, Numbers, and Properties
 ---------------------------------
--- a/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt
+++ b/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt
@ -2,12 +2,12 @@ You are an expert query analyzer for a **GraphRAG system**. Your primary goal is

 Here are the available `SearchType` tools and their specific functions:

- **`SUMMARIES`**: The `SUMMARIES` search type retrieves summarized information from the knowledge graph. 
+- **`SUMMARIES`**: The `SUMMARIES` search type retrieves summarized information from the knowledge graph.

-  **Best for:** 
+  **Best for:**

-  - Getting concise overviews of topics 
-  - Summarizing large amounts of information 
+  - Getting concise overviews of topics
+  - Summarizing large amounts of information
  - Quick understanding of complex subjects

  **Best for:**
@ -16,7 +16,7 @@ Here are the available `SearchType` tools and their specific functions:
  - Understanding relationships between concepts
  - Exploring the structure of your knowledge graph

-* **`CHUNKS`**: The `CHUNKS` search type retrieves specific facts and information chunks from the knowledge graph. 
+* **`CHUNKS`**: The `CHUNKS` search type retrieves specific facts and information chunks from the knowledge graph.

  **Best for:**

@ -122,4 +122,4 @@ Response: `NATURAL_LANGUAGE`



-Your response MUST be a single word, consisting of only the chosen `SearchType` name. Do not provide any explanation.
+Your response MUST be a single word, consisting of only the chosen `SearchType` name. Do not provide any explanation.
--- a/cognee/infrastructure/llm/prompts/test.txt
+++ b/cognee/infrastructure/llm/prompts/test.txt
@ -1 +1 @@
-Respond with: test
+Respond with: test
--- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py
+++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py
@ -194,6 +194,7 @@ def get_llm_client(raise_api_key_error: bool = True):
        )

        # Get optional local mode parameters (will be None if not set)
+        # TODO: refactor llm_config to include these parameters, currently they cannot be defined and defaults are used
        model_path = getattr(llm_config, "llama_cpp_model_path", None)
        n_ctx = getattr(llm_config, "llama_cpp_n_ctx", 2048)
        n_gpu_layers = getattr(llm_config, "llama_cpp_n_gpu_layers", 0)
--- a/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/copilot_conversations.json
+++ b/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/copilot_conversations.json
@ -105,4 +105,3 @@
    }
  }
 }
-
--- a/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/guido_contributions.json
+++ b/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/guido_contributions.json
@ -973,4 +973,4 @@
    "python_version": null,
    "pep_status": null
  }
-]
+]
--- a/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/my_developer_rules.md
+++ b/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/my_developer_rules.md
@ -76,4 +76,4 @@ Section: Open Questions or TODOs
 Create a checklist of unresolved decisions, logic that needs clarification, or tasks that are still pending.

 Section: Last Updated
-Include the most recent update date and who made the update.
+Include the most recent update date and who made the update.
--- a/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/pep_style_guide.md
+++ b/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/pep_style_guide.md
@ -72,4 +72,3 @@ profile = "black"
 - E501: line too long -> break with parentheses
 - E225: missing whitespace around operator
 - E402: module import not at top of file
-
--- a/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/zen_principles.md
+++ b/cognee/modules/notebooks/tutorials/python-development-with-cognee/data/zen_principles.md
@ -72,4 +72,3 @@ Use modules/packages to separate concerns; avoid wildcard imports.
 - Is this the simplest working solution?
 - Are errors explicit and logged?
 - Are modules/namespaces used appropriately?
-
--- a/cognee/modules/retrieval/init.py
+++ b/cognee/modules/retrieval/init.py
@ -1 +0,0 @@
-
--- a/cognee/modules/users/authentication/get_api_auth_backend.py
+++ b/cognee/modules/users/authentication/get_api_auth_backend.py
@ -16,11 +16,8 @@ def get_api_auth_backend():

    def get_jwt_strategy() -> JWTStrategy[models.UP, models.ID]:
        secret = os.getenv("FASTAPI_USERS_JWT_SECRET", "super_secret")
-        try:
-            lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
-        except ValueError:
-            lifetime_seconds = 3600
-        
+        lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
+
        return APIJWTStrategy(secret, lifetime_seconds=lifetime_seconds)

    auth_backend = AuthenticationBackend(
--- a/cognee/modules/users/authentication/get_client_auth_backend.py
+++ b/cognee/modules/users/authentication/get_client_auth_backend.py
@ -18,10 +18,7 @@ def get_client_auth_backend():
        from .default.default_jwt_strategy import DefaultJWTStrategy

        secret = os.getenv("FASTAPI_USERS_JWT_SECRET", "super_secret")
-        try:
-            lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
-        except ValueError:
-            lifetime_seconds = 3600
+        lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))

        return DefaultJWTStrategy(secret, lifetime_seconds=lifetime_seconds)

--- a/cognee/shared/utils.py
+++ b/cognee/shared/utils.py
@ -8,7 +8,8 @@ import http.server
 import socketserver
 from threading import Thread
 import pathlib
-from uuid import uuid4, uuid5, NAMESPACE_OID
+from typing import Union, Any, Dict, List
+from uuid import uuid4, uuid5, NAMESPACE_OID, UUID

 from cognee.base_config import get_base_config
 from cognee.shared.logging_utils import get_logger
@ -58,7 +59,7 @@ def get_anonymous_id():
    return anonymous_id


-def _sanitize_nested_properties(obj, property_names: list[str]):
+def _sanitize_nested_properties(obj: Any, property_names: list[str]) -> Any:
    """
    Recursively replaces any property whose key matches one of `property_names`
    (e.g., ['url', 'path']) in a nested dict or list with a uuid5 hash
@ -78,7 +79,9 @@ def _sanitize_nested_properties(obj, property_names: list[str]):
        return obj


-def send_telemetry(event_name: str, user_id, additional_properties: dict = {}):
+def send_telemetry(event_name: str, user_id: Union[str, UUID], additional_properties: dict = {}):
+    if additional_properties is None:
+        additional_properties = {}
    if os.getenv("TELEMETRY_DISABLED"):
        return

@ -108,7 +111,7 @@ def send_telemetry(event_name: str, user_id, additional_properties: dict = {}):
        print(f"Error sending telemetry through proxy: {response.status_code}")


-def embed_logo(p, layout_scale, logo_alpha, position):
+def embed_logo(p: Any, layout_scale: float, logo_alpha: float, position: str):
    """
    Embed a logo into the graph visualization as a watermark.
    """
@ -138,7 +141,11 @@ def embed_logo(p, layout_scale, logo_alpha, position):


 def start_visualization_server(
-    host="0.0.0.0", port=8001, handler_class=http.server.SimpleHTTPRequestHandler
+    host: str = "0.0.0.0",
+    port: int = 8001,
+    handler_class: type[
+        http.server.SimpleHTTPRequestHandler
+    ] = http.server.SimpleHTTPRequestHandler,
 ):
    """
    Spin up a simple HTTP server in a background thread to serve files.
--- a/cognee/tasks/temporal_graph/init.py
+++ b/cognee/tasks/temporal_graph/init.py
@ -1 +0,0 @@
-
--- a/cognee/tests/test_cleanup_unused_data.py
+++ b/cognee/tests/test_cleanup_unused_data.py
@ -46,10 +46,10 @@ async def test_textdocument_cleanup_with_sql():

    # Step 1: Add and cognify a test document
    dataset_name = "test_cleanup_dataset"
-    test_text = """        
-    Machine learning is a subset of artificial intelligence that enables systems to learn        
-    and improve from experience without being explicitly programmed. Deep learning uses        
-    neural networks with multiple layers to process data.        
+    test_text = """
+    Machine learning is a subset of artificial intelligence that enables systems to learn
+    and improve from experience without being explicitly programmed. Deep learning uses
+    neural networks with multiple layers to process data.
    """

    await setup()
--- a/cognee/tests/test_delete_by_id.py
+++ b/cognee/tests/test_delete_by_id.py
@ -47,20 +47,20 @@ async def main():

    # Test data
    text_1 = """
-    Apple Inc. is an American multinational technology company that specializes in consumer electronics, 
-    software, and online services. Apple is the world's largest technology company by revenue and, 
+    Apple Inc. is an American multinational technology company that specializes in consumer electronics,
+    software, and online services. Apple is the world's largest technology company by revenue and,
    since January 2021, the world's most valuable company.
    """

    text_2 = """
-    Microsoft Corporation is an American multinational technology corporation which produces computer software, 
-    consumer electronics, personal computers, and related services. Its best known software products are the 
+    Microsoft Corporation is an American multinational technology corporation which produces computer software,
+    consumer electronics, personal computers, and related services. Its best known software products are the
    Microsoft Windows line of operating systems and the Microsoft Office suite.
    """

    text_3 = """
-    Google LLC is an American multinational technology company that specializes in Internet-related services and products, 
-    which include online advertising technologies, search engine, cloud computing, software, and hardware. Google has been 
+    Google LLC is an American multinational technology company that specializes in Internet-related services and products,
+    which include online advertising technologies, search engine, cloud computing, software, and hardware. Google has been
    referred to as the most powerful company in the world and one of the world's most valuable brands.
    """

--- a/deployment/helm/README.md
+++ b/deployment/helm/README.md
@ -1,6 +1,7 @@

-# cognee-infra-helm
-General infrastructure setup for Cognee on Kubernetes using a Helm chart.
+# Example helm chart
+Example Helm chart fro Cognee with PostgreSQL and pgvector extension
+It is not ready for production usage

 ## Prerequisites
 Before deploying the Helm chart, ensure the following prerequisites are met: 
@ -13,13 +14,22 @@ Before deploying the Helm chart, ensure the following prerequisites are met:

 Clone the Repository Clone this repository to your local machine and navigate to the directory.

-## Deploy Helm Chart:
+## Example deploy Helm Chart:

   ```bash
-   helm install cognee ./cognee-chart
+   helm upgrade --install cognee deployment/helm \
+  --namespace cognee --create-namespace \
+  --set cognee.env.LLM_API_KEY="$YOUR_KEY"
   ```

-**Uninstall Helm Release**:  
+**Uninstall Helm Release**:
   ```bash
   helm uninstall cognee
   ```
+
+## Port forwarding
+To access cognee, run
+```
+kubectl port-forward svc/cognee-service -n cognee 8000
+```
+it will be available at localhost:8000
--- a/deployment/helm/docker-compose-helm.yml
+++ b/deployment/helm/docker-compose-helm.yml
@ -43,4 +43,3 @@ networks:

 volumes:
  postgres_data:
-
--- a/deployment/helm/templates/cognee_deployment.yaml
+++ b/deployment/helm/templates/cognee_deployment.yaml
@ -20,12 +20,35 @@ spec:
          ports:
            - containerPort: {{ .Values.cognee.port }}
          env:
+            - name: ENABLE_BACKEND_ACCESS_CONTROL
+              value: "false"
            - name: HOST
              value: {{ .Values.cognee.env.HOST }}
            - name: ENVIRONMENT
              value: {{ .Values.cognee.env.ENVIRONMENT }}
            - name: PYTHONPATH
              value: {{ .Values.cognee.env.PYTHONPATH }}
+            - name: VECTOR_DB_PROVIDER
+              value: pgvector
+            - name: DB_HOST
+              value: {{ .Release.Name }}-postgres
+            - name: DB_PORT
+              value: "{{ .Values.postgres.port }}"
+            - name: DB_NAME
+              value: {{ .Values.postgres.env.POSTGRES_DB }}
+            - name: DB_USERNAME
+              value: {{ .Values.postgres.env.POSTGRES_USER }}
+            - name: DB_PASSWORD
+              value: {{ .Values.postgres.env.POSTGRES_PASSWORD }}
+            - name: LLM_API_KEY
+              valueFrom:
+                secretKeyRef:
+                  name: {{ .Release.Name }}-llm-api-key
+                  key: LLM_API_KEY
+            - name: LLM_MODEL
+              value: {{ .Values.cognee.env.LLM_MODEL }}
+            - name: LLM_PROVIDER
+              value: {{ .Values.cognee.env.LLM_PROVIDER }}
          resources:
            limits:
              cpu: {{ .Values.cognee.resources.cpu }}
--- a/deployment/helm/templates/cognee_service.yaml
+++ b/deployment/helm/templates/cognee_service.yaml
@ -5,7 +5,7 @@ metadata:
  labels:
    app: {{ .Release.Name }}-cognee
 spec:
-  type: NodePort
+  type: ClusterIP
  ports:
    - port: {{ .Values.cognee.port }}
      targetPort: {{ .Values.cognee.port }}
--- a/deployment/helm/templates/postgres_service.yaml
+++ b/deployment/helm/templates/postgres_service.yaml
@ -11,4 +11,3 @@ spec:
      targetPort: {{ .Values.postgres.port }}
  selector:
    app: {{ .Release.Name }}-postgres
-
--- a/deployment/helm/templates/secrets.yml
+++ b/deployment/helm/templates/secrets.yml
@ -0,0 +1,7 @@
+apiVersion: v1
+kind: Secret
+metadata:
+  name: {{ .Release.Name }}-llm-api-key
+type: Opaque
+data:
+  LLM_API_KEY: {{ .Values.cognee.env.LLM_API_KEY | b64enc | quote }}
--- a/deployment/helm/values.yaml
+++ b/deployment/helm/values.yaml
@ -7,9 +7,11 @@ cognee:
    HOST: "0.0.0.0"
    ENVIRONMENT: "local"
    PYTHONPATH: "."
+    LLM_MODEL: "openai/gpt-4o-mini"
+    LLM_PROVIDER: "openai"
  resources:
    cpu: "4.0"
-    memory: "8Gi"
+    memory: "2Gi"

 # Configuration for the 'postgres' database service
 postgres:
@ -19,4 +21,4 @@ postgres:
    POSTGRES_USER: "cognee"
    POSTGRES_PASSWORD: "cognee"
    POSTGRES_DB: "cognee_db"
-  storage: "8Gi"
+  storage: "2Gi"
--- a/evals/requirements.txt
+++ b/evals/requirements.txt
@ -3,4 +3,4 @@ numpy==1.26.4
 matplotlib==3.10.0
 seaborn==0.13.2
 scipy==1.11.4
-pathlib 
+pathlib
--- a/examples/data/car_and_tech_companies.txt
+++ b/examples/data/car_and_tech_companies.txt
@ -34,4 +34,4 @@ What began as an online bookstore has grown into one of the largest e-commerce p
 Meta, originally known as Facebook, revolutionized social media by connecting billions of people worldwide. Beyond its core social networking service, Meta is investing in the next generation of digital experiences through virtual and augmented reality technologies, with projects like Oculus. The company's efforts signal a commitment to evolving digital interaction and building the metaverse—a shared virtual space where users can connect and collaborate.

 Each of these companies has significantly impacted the technology landscape, driving innovation and transforming everyday life through their groundbreaking products and services.
-"""
+"""
--- a/examples/database_examples/neptune_analytics_example.py
+++ b/examples/database_examples/neptune_analytics_example.py
@ -63,10 +63,10 @@ async def main():
    traversals.
    """

-    sample_text_2 = """Neptune Analytics is an ideal choice for investigatory, exploratory, or data-science workloads 
-    that require fast iteration for data, analytical and algorithmic processing, or vector search on graph data. It 
-    complements Amazon Neptune Database, a popular managed graph database. To perform intensive analysis, you can load 
-    the data from a Neptune Database graph or snapshot into Neptune Analytics. You can also load graph data that's 
+    sample_text_2 = """Neptune Analytics is an ideal choice for investigatory, exploratory, or data-science workloads
+    that require fast iteration for data, analytical and algorithmic processing, or vector search on graph data. It
+    complements Amazon Neptune Database, a popular managed graph database. To perform intensive analysis, you can load
+    the data from a Neptune Database graph or snapshot into Neptune Analytics. You can also load graph data that's
    stored in Amazon S3.
    """

--- a/examples/low_level/product_recommendation.py
+++ b/examples/low_level/product_recommendation.py
@ -165,8 +165,8 @@ async def main():
            // If a stored preference exists and it does not match the new value,
            // raise an error using APOC's utility procedure.
            CALL apoc.util.validate(
-              preference IS NOT NULL AND preference.value <> new_size, 
-              "Conflicting shoe size preference: existing size is " + preference.value + " and new size is " + new_size, 
+              preference IS NOT NULL AND preference.value <> new_size,
+              "Conflicting shoe size preference: existing size is " + preference.value + " and new size is " + new_size,
              []
            )

--- a/examples/python/temporal_example.py
+++ b/examples/python/temporal_example.py
@ -35,16 +35,16 @@ biography_1 = """

 biography_2 = """
    Arnulf Øverland Ole Peter Arnulf Øverland ( 27 April 1889 – 25 March 1968 ) was a Norwegian poet and artist . He is principally known for his poetry which served to inspire the Norwegian resistance movement during the German occupation of Norway during World War II .
-    
+
    Biography .
    Øverland was born in Kristiansund and raised in Bergen . His parents were Peter Anton Øverland ( 1852–1906 ) and Hanna Hage ( 1854–1939 ) . The early death of his father , left the family economically stressed . He was able to attend Bergen Cathedral School and in 1904 Kristiania Cathedral School . He graduated in 1907 and for a time studied philology at University of Kristiania . Øverland published his first collection of poems ( 1911 ) .
-    
+
    Øverland became a communist sympathizer from the early 1920s and became a member of Mot Dag . He also served as chairman of the Norwegian Students Society 1923–28 . He changed his stand in 1937 , partly as an expression of dissent against the ongoing Moscow Trials . He was an avid opponent of Nazism and in 1936 he wrote the poem Du må ikke sove which was printed in the journal Samtiden . It ends with . ( I thought: : Something is imminent . Our era is over – Europe’s on fire! ) . Probably the most famous line of the poem is ( You mustnt endure so well the injustice that doesnt affect you yourself! )
-    
+
    During the German occupation of Norway from 1940 in World War II , he wrote to inspire the Norwegian resistance movement . He wrote a series of poems which were clandestinely distributed , leading to the arrest of both him and his future wife Margrete Aamot Øverland in 1941 . Arnulf Øverland was held first in the prison camp of Grini before being transferred to Sachsenhausen concentration camp in Germany . He spent a four-year imprisonment until the liberation of Norway in 1945 . His poems were later collected in Vi overlever alt and published in 1945 .
-    
+
    Øverland played an important role in the Norwegian language struggle in the post-war era . He became a noted supporter for the conservative written form of Norwegian called Riksmål , he was president of Riksmålsforbundet ( an organization in support of Riksmål ) from 1947 to 1956 . In addition , Øverland adhered to the traditionalist style of writing , criticising modernist poetry on several occasions . His speech Tungetale fra parnasset , published in Arbeiderbladet in 1954 , initiated the so-called Glossolalia debate .
-    
+
    Personal life .
    In 1918 he had married the singer Hildur Arntzen ( 1888–1957 ) . Their marriage was dissolved in 1939 . In 1940 , he married Bartholine Eufemia Leganger ( 1903–1995 ) . They separated shortly after , and were officially divorced in 1945 . Øverland was married to journalist Margrete Aamot Øverland ( 1913–1978 ) during June 1945 . In 1946 , the Norwegian Parliament arranged for Arnulf and Margrete Aamot Øverland to reside at the Grotten . He lived there until his death in 1968 and she lived there for another ten years until her death in 1978 . Arnulf Øverland was buried at Vår Frelsers Gravlund in Oslo . Joseph Grimeland designed the bust of Arnulf Øverland ( bronze , 1970 ) at his grave site .

@ -56,7 +56,7 @@ biography_2 = """
    - Vi overlever alt ( 1945 )
    - Sverdet bak døren ( 1956 )
    - Livets minutter ( 1965 )
-    
+
    Awards .
    - Gyldendals Endowment ( 1935 )
    - Dobloug Prize ( 1951 )
--- a/Show more
+++ b/Show more