New Flexible GraphRAG now supports RDF-based ontologies for both property graph databases and RDF triple store databases (Graphwise Ontotext GraphDB, Fuseki, and Oxigraph). Document ingestion with KG extraction, auto incremental data source change detection, and UI search (hybrid search, AI query, and AI chat) are all supported with both database types.

New Flexible GraphRAG supports automatic incremental updates (Optional) from most data sources, keeping your Vector, Search and Graph databases synchronized in real-time or near real-time.

New - KG Spaces Integration of Flexible GraphRAG in Alfresco ACA Client

Flexible GraphRAG

Flexible GraphRAG is an open source platform supporting document processing (Docling or LlamaParse), knowledge graph auto-building, schemas, LlamaIndex LLMs, RAG and GraphRAG setup, hybrid search (fulltext, vector, graph), AI query, and AI chat capabilities. The backend uses Python, LlamaIndex, and FastAPI. Has Angular, React, and Vue TypeScript frontends. A MCP Server is also available. Currently supports 13 data sources, 10 vector databases, OpenSearch / Elasticsearch / BM25 search, 8 property graph databases, 3 RDF triple stores (Fuseki, GraphDB, Oxigraph), and Alfresco. These servers and their dashboards can be configured in a provided docker compose.

Flexible GraphRAG data sources, processing tab, auto-sync document states in Postgres, Neo4j

Features

• Hybrid Search: A configurable hybrid search system that combines vector search, full-text search, and graph GraphRAG
• Knowledge Graph GraphRAG: Extracts entities and relationships from documents to auto create graphs in property graph databases for GraphRAG. Configuration for schemas to use or use as a starting point for LLM to expand on is supported.
• RDF/Ontology Support: Load OWL/RDFS ontologies to guide KG extraction into any property graph or RDF store; SPARQL 1.1 queries; RDF 1.2 triple annotations; full UI pipeline (ingest, hybrid search, AI query/chat, incremental auto-sync). See Ontology and RDF Support below.
• 8 Property Graph Databases: Neo4j, ArcadeDB, FalkorDB, Ladybug, MemGraph, NebulaGraph, Amazon Neptune, and Amazon Neptune Analytics — with KG extraction, hybrid search, and AI query/chat
• 3 RDF Triple Stores: Apache Jena Fuseki, Ontotext GraphDB, Oxigraph
• 10 Vector Databases: Qdrant, Elasticsearch, OpenSearch, Neo4j, Chroma, Milvus, Weaviate, Pinecone, PostgreSQL pgvector, LanceDB — for semantic similarity search
• 3 Search Databases: Elasticsearch, OpenSearch, BM25 (built-in) — for full-text search and hybrid ranking
• LLM providers (KG extraction & chat): Ollama, OpenAI, Azure OpenAI, Google Gemini, Anthropic Claude, Google Vertex AI, Amazon Bedrock, Groq, Fireworks AI, OpenAI-compatible endpoints (openai_like), OpenRouter, LiteLLM proxy, and vLLM — configurable via LLM_PROVIDER; see Supported LLM Providers
• Embedding providers: OpenAI, Ollama, Azure OpenAI, Google GenAI, Vertex AI, Bedrock, Fireworks, OpenAI-like (EMBEDDING_KIND=openai_like), and LiteLLM — see LLM Configuration
• Configurable Architecture: LlamaIndex provides abstractions for allowing multiple vector databases, property graph databases, RDF triple stores, search engines, and LLM providers to be supported
• Multi-Source Ingestion: Processes documents from 13 data sources (file upload, cloud storage, enterprise repositories, web sources) with Docling (default) or LlamaParse (cloud API) document parsing.
• Observability: Built-in OpenTelemetry instrumentation with automatic LlamaIndex tracing, Prometheus metrics, Jaeger traces, and Grafana dashboards for production monitoring
• FastAPI Server with REST API: Python based FastAPI server with REST APIs for document ingesting, hybrid search, AI query, and AI chat.
• MCP Server: MCP server providing Claude Desktop and other MCP clients with tools for document/text ingesting (all 13 data sources), hybrid search, and AI query. Uses FastAPI backend REST APIs.
• UI Clients: Angular, React, and Vue UI clients support choosing the data source (filesystem, Alfresco, CMIS, etc.), ingesting documents, performing hybrid searches, AI queries, and AI chat. The UI clients use the REST APIs of the FastAPI backend.
• Docker Deployment Flexibility: Supports both standalone and Docker deployment modes. Docker infrastructure provides modular database selection via docker-compose includes - vector, graph, search engines, and Alfresco can be included or excluded with a single comment. Choose between hybrid deployment (databases in Docker, backend and UIs standalone) or full containerization.

Frontend Screenshots

Angular Frontend - Tabbed Interface

Click to view Angular UI screenshots (Light Theme)

Sources Tab	Processing Tab	Search Tab	Chat Tab

React Frontend - Tabbed Interface

Click to view React UI screenshots (Dark Theme)

Sources Tab	Processing Tab	Search Tab	Chat Tab

Click to view React UI screenshots (Light Theme)

Sources Tab	Processing Tab	Search Tab	Chat Tab

Vue Frontend - Tabbed Interface

Click to view Vue UI screenshots (Light Theme)

Sources Tab	Processing Tab	Search Tab	Chat Tab

System Components

FastAPI Backend (/flexible-graphrag)

• REST API Server: Provides endpoints for document ingestion, search, and AI query/chat
• Hybrid Search Engine: Combines vector similarity (RAG), fulltext (BM25), and graph traversal (GraphRAG)
• Document Processing: Advanced document conversion with Docling and LlamaParse integration
• Configurable Architecture: Environment-based configuration for all components
• Async Processing: Background task processing with real-time progress updates

MCP Server (/flexible-graphrag-mcp)

• MCP Client support: Model Context Protocol server for Claude Desktop and other MCP clients
• Full API Parity: Tools like ingest_documents() support all 13 data sources with source-specific configs: filesystem, repositories (Alfresco, SharePoint, Box, CMIS), cloud storage, web; skip_graph flag for all data sources; paths parameter for filesystem/Alfresco/CMIS; Alfresco also supports nodeDetails list (multi-select for KG Spaces)
• Additional Tools: search_documents(), query_documents(), ingest_text(), system diagnostics, and health checks
• Dual Transport: HTTP mode for debugging, stdio mode for production
• Tool Suite: 9 specialized tools for document processing, search, and system management
• Multiple Installation: pipx system installation or uvx no-install execution

UI Clients (/flexible-graphrag-ui)

• Angular Frontend: Material Design with TypeScript
• React Frontend: Modern React with Vite and TypeScript
• Vue Frontend: Vue 3 Composition API with Vuetify and TypeScript
• Unified Features: All clients support the 4 tab views, async processing, progress tracking, and cancellation

Docker Infrastructure (/docker)

• Modular Database Selection: Include/exclude vector, graph, and search engines, and Alfresco with single-line comments
• Flexible Deployment: Hybrid mode (databases in Docker, apps standalone) or full containerization
• NGINX Reverse Proxy: Unified access to all services with proper routing
• Built-in Database Dashboards: Most server dockers also provide built-in web interface dashboards (Neo4j browser, ArcadeDB, FalkorDB, OpenSearch, etc.)
• Separate Dashboards: Additional dashboard dockers are provided: including Kibana for Elasticsearch and optional Ladybug Explorer (see docker/includes/ladybug-explorer.yaml).

Data Sources

Flexible GraphRAG supports 13 different data sources for ingesting documents into your knowledge base:

File & Upload Sources

1. File Upload - Direct file upload through web interface with drag & drop support

Cloud Storage Sources

2. Amazon S3 - AWS S3 bucket integration
3. Google Cloud Storage (GCS) - Google Cloud storage buckets
4. Azure Blob Storage - Microsoft Azure blob containers
5. OneDrive - Microsoft OneDrive personal/business storage
6. Google Drive - Google Drive file storage

Enterprise Repository Sources

7. Alfresco - Alfresco ECM/content repository with two integration options:
   • KG Spaces ACA Extension - Integrates the Flexible GraphRAG Angular UI as an extension plugin within the Alfresco Content Application (ACA), enabling multi-select document/folder ingestion with nodeIds directly from the Alfresco interface
   • Flexible GraphRAG Alfresco Data Source - Direct integration using Alfresco paths (e.g., /Shared/GraphRAG, /Company Home/Shared/GraphRAG, or /Shared/GraphRAG/cmispress.txt)
8. SharePoint - Microsoft SharePoint document libraries
9. Box - Box.com cloud storage
10. CMIS (Content Management Interoperability Services) - Industry-standard content repository interface

Web Sources

11. Web Pages - Extract content from web URLs
12. Wikipedia - Ingest Wikipedia articles by title or URL
13. YouTube - Process YouTube video transcripts

Each data source includes:

• Configuration Forms: Easy-to-use interfaces for credentials and settings
• Progress Tracking: Real-time per-file progress indicators
• Flexible Authentication: Support for various auth methods (API keys, OAuth, service accounts)

Incremental Updates & Auto-Sync

NEW! Flexible GraphRAG supports automatic incremental updates (Optional) from most data sources, keeping your Vector, Search and Graph databases synchronized in real-time or near real-time:

Data Source	Auto-Sync Support	Detection Method	Status	Notes
Alfresco	✅ Real-time	Community ActiveMQ	Ready	Enterprise Event Gateway planned
Amazon S3	✅ Real-time	SQS event notifications	Ready
Azure Blob Storage	✅ Real-time	Change feed	Ready
Google Cloud Storage	✅ Real-time	Pub/Sub notifications	Ready
Google Drive	✅ Near real-time	Changes API (polling)	Ready
OneDrive	✅ Near real-time	Polling	Ready	Delta query support planned
SharePoint	✅ Near real-time	Polling	Ready	Delta query support planned
Box	✅ Near real-time	Events API (polling)	Ready
Local Filesystem	✅ Real-time	OS events (watchdog)	Ready	REST API and MCP Server only
File Upload UI, CMIS, Web Pages, Wikipedia, YouTube	➖ Not supported	-	-	No support for incremental updates

Features:

• Modification Date Tracking: Uses file modification timestamps (ordinal) to detect changes
• Content Hash Optimization: Skips reprocessing when file modification date changed but content hasn't
• Dual Mechanism: Event-driven streams (real-time) + periodic polling fallback
• LlamaIndex Integration: Uses proper abstractions for all databases
• UI, REST API, MCP Server: Setting up an auto update data source location can be done thru the 3 UIs, with the REST API, or with the MCP server

Setup Requirements:

Enable incremental updates in your .env file:

ENABLE_INCREMENTAL_UPDATES=true

# PostgreSQL database for state management
# By default, uses the pgvector database from docker-compose.yaml
POSTGRES_INCREMENTAL_URL=postgresql://postgres:password@localhost:5433/postgres

Note: The incremental updates system uses PostgreSQL to track document state. The docker-compose.yaml includes a pgvector container that can be used both as a vector database option and for incremental updates state management. The database connection creates the necessary tables automatically on first use.

Usage:

• Check the "Enable auto change sync" checkbox in the Processing tab when configuring your data source
• For S3: Also provide the "SQS Queue URL" for event notifications
• For GCS: Also provide the "Pub/Sub Subscription Name" for real-time updates

PostgreSQL for State Management:

The docker/includes/postgres-pgvector.yaml sets up two databases automatically on first start: flexible_graphrag (for optional pgvector vector storage) and flexible_graphrag_incremental (for incremental update state management, with its schema created automatically). pgAdmin is also configured at http://localhost:5050 with both databases pre-registered — just enter the master password admin when prompted, then use password for the server connection and save it. See docs/DATABASES/POSTGRES-SETUP.md for details.

Documentation:

• System overview: docs/DATA-SOURCES/INCREMENTAL-UPDATE-AUTO-SYNC/README.md
• Quick start: docs/DATA-SOURCES/INCREMENTAL-UPDATE-AUTO-SYNC/QUICKSTART.md
• Detailed setup: docs/DATA-SOURCES/INCREMENTAL-UPDATE-AUTO-SYNC/SETUP-GUIDE.md
• API reference: docs/DATA-SOURCES/INCREMENTAL-UPDATE-AUTO-SYNC/API-REFERENCE.md
• PostgreSQL setup: docs/DATABASES/POSTGRES-SETUP.md

Scripts:

• scripts/incremental/sync-now.sh|.ps1|.bat - Trigger immediate synchronization
• scripts/incremental/set-refresh-interval.sh|.ps1|.bat - Configure polling interval
• scripts/incremental/TIMING-CONFIGURATION.md - Timing configuration details
• scripts/incremental/README.md - Script usage documentation

Document Processing Options

All data sources support two document parser options:

Docling (Default):

• Open-source, local processing
• Free with no API costs
• GPU acceleration supported (CUDA/Apple Silicon) for 5-10x faster processing
• Built-in OCR for images and scanned documents
• Multi-language support (English, German, French, Spanish, Czech, Russian, Chinese, Japanese, etc.)
• Configured via: DOCUMENT_PARSER=docling
• New: DOCLING_DEVICE=auto|cpu|cuda|mps - Control GPU vs CPU processing
• New: SAVE_PARSING_OUTPUT=true - Save intermediate parsing results for inspection (works for both parsers)
• New: PARSER_FORMAT_FOR_EXTRACTION=auto|markdown|plaintext - Control format used for knowledge graph extraction
• See Docling GPU Configuration Guide for setup details | Quick Reference

LlamaParse:

• Cloud-based API service with advanced AI
• Multimodal parsing with Claude Sonnet 3.5
• Three modes available:
  • parse_page_without_llm - 1 credit/page
  • parse_page_with_llm - 3 credits/page (default)
  • parse_page_with_agent - 10-90 credits/page
• Configured via: DOCUMENT_PARSER=llamaparse + LLAMAPARSE_API_KEY
• Get your API key from LlamaCloud
• New: SAVE_PARSING_OUTPUT=true - Save parsed output and metadata for inspection
• New: PARSER_FORMAT_FOR_EXTRACTION=auto|markdown|plaintext - Control format used for knowledge graph extraction

Supported File Formats

Document Formats

• PDF: .pdf
  • Docling: Advanced layout analysis, table extraction, formula recognition, configurable OCR (EasyOCR, Tesseract, RapidOCR)
  • LlamaParse: Automatic OCR within parsing pipeline, multimodal vision processing
• Microsoft Office: .docx, .xlsx, .pptx and legacy formats (.doc, .xls, .ppt)
  • Docling: DOCX, XLSX, PPTX structure preservation and content extraction
  • LlamaParse: Full Office suite support including legacy formats and hundreds of variants
• Web Formats: .html, .htm, .xhtml
  • Docling: HTML/XHTML markup structure analysis
  • LlamaParse: HTML/XHTML content extraction and formatting
• Data Formats: .csv, .tsv, .json, .xml
  • Docling: CSV structured data processing
  • LlamaParse: CSV, TSV, JSON, XML with enhanced table understanding
• Documentation: .md, .markdown, .asciidoc, .adoc, .rtf, .txt, .epub
  • Docling: Markdown, AsciiDoc technical documentation with markup preservation
  • LlamaParse: Extended format support including RTF, EPUB, and hundreds of text format variants

Image Formats

• Standard Images: .png, .jpg, .jpeg, .gif, .bmp, .webp, .tiff, .tif
  • Docling: OCR text extraction with configurable OCR backends (EasyOCR, Tesseract, RapidOCR)
  • LlamaParse: Automatic OCR with multimodal vision processing and context understanding

Audio Formats

• Audio Files: .wav, .mp3, .mp4, .m4a
  • Docling: Automatic speech recognition (ASR) support
  • LlamaParse: Transcription and content extraction for MP3, MP4, MPEG, MPGA, M4A, WAV, WEBM

Processing Intelligence

• Parser Selection:
  • Docling (default, free): Local processing with specialized CV models (DocLayNet layout analysis, TableFormer for tables), configurable OCR backends (EasyOCR/Tesseract/RapidOCR), optional local VLM support (Granite-Docling, SmolDocling, Qwen2.5-VL, Pixtral)
  • LlamaParse (cloud API, 3 credits/page): Automatic OCR in parsing pipeline, supports hundreds of file formats, fast mode (OCR-only), default mode (proprietary LlamaCloud model), premium mode (proprietary VLM mixture), multimodal mode (bring your own API keys: OpenAI GPT-4o, Anthropic Claude 3.5/4.5 Sonnet, Google Gemini 1.5/2.0, Azure OpenAI)
• Output Formats:
  • Flexible GraphRAG saves both markdown and plaintext, then automatically selects which to use for processing (knowledge graph extraction, vector embeddings, and search indexing) - defaults to markdown for tables, plaintext for text-heavy docs - override with PARSER_FORMAT_FOR_EXTRACTION
  • Docling supports: Markdown, JSON (lossless with bounding boxes and provenance), HTML, plain text, and DocTags (specialized markup preserving multi-column layouts, mathematical formulas, and code blocks)
  • LlamaParse supports: Markdown, plain text, raw JSON, XLSX (extracted tables), PDF, images (extracted separately), and structured output (beta - enforces custom JSON schema for strict data model extraction)
• Format Detection: Automatic routing based on file extension and content analysis

Database Configuration

Flexible GraphRAG uses three types of databases for its hybrid search capabilities. Each can be configured independently via environment variables.

Search Databases (Full-Text Search)

Configuration: Set via SEARCH_DB and SEARCH_DB_CONFIG environment variables

• BM25 (Built-in): Local file-based BM25 full-text search with TF-IDF ranking

  • Dashboard: None (file-based)
  • Configuration:

    SEARCH_DB=bm25
    SEARCH_DB_CONFIG={"persist_dir": "./bm25_index"}
    

• Elasticsearch: Enterprise search engine with advanced analyzers, faceted search, and real-time analytics

  • Dashboard: Kibana (http://localhost:5601) for search analytics, index management, and query debugging
  • Configuration:

    SEARCH_DB=elasticsearch
    SEARCH_DB_CONFIG={"hosts": ["http://localhost:9200"], "index_name": "hybrid_search"}
    

• OpenSearch: AWS-led open-source fork with native hybrid scoring (vector + BM25) and k-NN algorithms

  • Dashboard: OpenSearch Dashboards (http://localhost:5601) for cluster monitoring and search pipeline management
  • Configuration:

    SEARCH_DB=opensearch
    SEARCH_DB_CONFIG={"hosts": ["http://localhost:9201"], "index_name": "hybrid_search"}
    

• None: Disable full-text search (vector search only)

  • Configuration:

    SEARCH_DB=none
    

Vector Databases (Semantic Search)

Configuration: Set via VECTOR_DB and VECTOR_DB_CONFIG environment variables

⚠️ Vector Dimension Compatibility

CRITICAL: When switching between different embedding models (e.g., OpenAI ↔ Ollama), you MUST delete existing vector indexes due to dimension incompatibility:

• OpenAI: 1536 dimensions (text-embedding-3-small) or 3072 dimensions (text-embedding-3-large)
• Ollama: 384 dimensions (all-minilm, default), 768 dimensions (nomic-embed-text), or 1024 dimensions (mxbai-embed-large)
• Azure OpenAI: Same as OpenAI (1536 or 3072 dimensions)

See docs/DATABASES/VECTOR-DATABASES/VECTOR-DIMENSIONS.md for detailed cleanup instructions for each database.

Supported Vector Databases

• Neo4j: Can be used as vector database with separate vector configuration

  • Dashboard: Neo4j Browser (http://localhost:7474) for Cypher queries and graph visualization
  • Configuration:

    VECTOR_DB=neo4j
    VECTOR_DB_CONFIG={"uri": "bolt://localhost:7687", "username": "neo4j", "password": "your_password", "index_name": "hybrid_search_vector"}
    

• Qdrant: Dedicated vector database with advanced filtering

  • Dashboard: Qdrant Web UI (http://localhost:6333/dashboard) for collection management
  • Configuration:

    VECTOR_DB=qdrant
    VECTOR_DB_CONFIG={"host": "localhost", "port": 6333, "collection_name": "hybrid_search"}
    

• Elasticsearch: Can be used as vector database with separate vector configuration

  • Dashboard: Kibana (http://localhost:5601) for index management and data visualization
  • Configuration:

    VECTOR_DB=elasticsearch
    VECTOR_DB_CONFIG={"hosts": ["http://localhost:9200"], "index_name": "hybrid_search_vectors"}
    

• OpenSearch: Can be used as vector database with separate vector configuration

  • Dashboard: OpenSearch Dashboards (http://localhost:5601) for cluster and index management
  • Configuration:

    VECTOR_DB=opensearch
    VECTOR_DB_CONFIG={"hosts": ["http://localhost:9201"], "index_name": "hybrid_search_vectors"}
    

• Chroma: Open-source vector database with dual deployment modes

  • Dashboard: Swagger UI (http://localhost:8001/docs/) for API testing and management (HTTP mode)
  • Configuration (Local Mode):

    VECTOR_DB=chroma
    VECTOR_DB_CONFIG={"persist_directory": "./chroma_db", "collection_name": "hybrid_search"}
    

  • Configuration (HTTP Mode):

    VECTOR_DB=chroma
    VECTOR_DB_CONFIG={"host": "localhost", "port": 8001, "collection_name": "hybrid_search"}
    

• Milvus: Cloud-native, scalable vector database for similarity search

  • Dashboard: Attu (http://localhost:3003) for cluster and collection management
  • Configuration:

    VECTOR_DB=milvus
    VECTOR_DB_CONFIG={"uri": "http://localhost:19530", "collection_name": "hybrid_search"}
    

• Weaviate: Vector search engine with semantic capabilities and data enrichment

  • Dashboard: Weaviate Console (http://localhost:8081/console) for schema and data management
  • Configuration:

    VECTOR_DB=weaviate
    VECTOR_DB_CONFIG={"url": "http://localhost:8081", "index_name": "HybridSearch"}
    

• Pinecone: Managed vector database service optimized for real-time applications

  • Dashboard: Pinecone Console (web-based) for index and namespace management
  • Local Info Dashboard: http://localhost:3004 (when using Docker)
  • Configuration:

    VECTOR_DB=pinecone
    VECTOR_DB_CONFIG={"api_key": "your_api_key", "region": "us-east-1", "cloud": "aws", "index_name": "hybrid-search"}
    

• PostgreSQL: Traditional database with pgvector extension for vector similarity search

  • Dashboard: pgAdmin (http://localhost:5050) for database management, vector queries, and similarity searches
  • Configuration:

    VECTOR_DB=postgres
    VECTOR_DB_CONFIG={"host": "localhost", "port": 5433, "database": "postgres", "username": "postgres", "password": "your_password"}
    

• LanceDB: Modern, lightweight vector database designed for high-performance ML applications

  • Dashboard: LanceDB Viewer (http://localhost:3005) for CRUD operations and data management
  • Configuration:

    VECTOR_DB=lancedb
    VECTOR_DB_CONFIG={"uri": "./lancedb", "table_name": "hybrid_search"}
    

RAG without GraphRAG

For faster document ingest processing (no graph extraction), and hybrid search with only full text + vector, configure:

VECTOR_DB=qdrant  # Any vector store
SEARCH_DB=elasticsearch  # Any search engine
GRAPH_DB=none
ENABLE_KNOWLEDGE_GRAPH=false

Property Graph Databases (Knowledge Graph / GraphRAG)

Configuration: Set via GRAPH_DB and GRAPH_DB_CONFIG environment variables

• Neo4j Property Graph: Primary knowledge graph storage with Cypher querying

  • Dashboard: Neo4j Browser (http://localhost:7474) for graph exploration and query execution
  • Configuration:

    GRAPH_DB=neo4j
    GRAPH_DB_CONFIG={"uri": "bolt://localhost:7687", "username": "neo4j", "password": "your_password"}
    

• ArcadeDB: Multi-model database supporting graph, document, key-value, and search capabilities with SQL and Cypher query support

  • Dashboard: ArcadeDB Studio (http://localhost:2480) for graph visualization, SQL/Cypher queries, and database management
  • Configuration:

    GRAPH_DB=arcadedb
    GRAPH_DB_CONFIG={"host": "localhost", "port": 2480, "username": "root", "password": "password", "database": "flexible_graphrag", "query_language": "sql"}
    

• FalkorDB: "A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG)."

  • Dashboard: FalkorDB Browser (http://localhost:3001) (Was moved from 3000 used by the flexible-graphrag Vue frontend)
  • Configuration:

    GRAPH_DB=falkordb
    GRAPH_DB_CONFIG={"url": "falkor://localhost:6379", "database": "falkor"}
    

• Ladybug: Embedded property graph database (Cypher, single .lbug file) with optional structured schema and HNSW vector index on chunks; Explorer UI available via Docker (port 7003). See docker/includes/ladybug-explorer.yaml.

  • Configuration:

    GRAPH_DB=ladybug
    GRAPH_DB_CONFIG={"db_dir": "./ladybug", "db_file": "database.lbug", "use_vector_index": true, "has_structured_schema": false, "strict_schema": false}
    

  • Or use LADYBUG_DB_DIR, LADYBUG_DB_FILE, LADYBUG_USE_VECTOR_INDEX, LADYBUG_STRUCTURED_SCHEMA, LADYBUG_STRICT_SCHEMA (see env-sample.txt).

• MemGraph: Real-time graph database with native support for streaming data and advanced graph algorithms

  • Dashboard: MemGraph Lab (http://localhost:3002) for graph visualization and Cypher queries
  • Configuration:

    GRAPH_DB=memgraph
    GRAPH_DB_CONFIG={"url": "bolt://localhost:7687", "username": "", "password": ""}
    

• NebulaGraph: Distributed graph database designed for large-scale data with horizontal scalability

  • Dashboard: NebulaGraph Studio (http://localhost:7001) for graph exploration and nGQL queries
  • Configuration:

    GRAPH_DB=nebula
    GRAPH_DB_CONFIG={"space": "flexible_graphrag", "host": "localhost", "port": 9669, "username": "root", "password": "nebula"}
    

• Amazon Neptune: Fully managed graph database service supporting both property graph and RDF models

  • Dashboard: Graph-Explorer (http://localhost:3007) for visual graph exploration, or Neptune Workbench (AWS Console) for Jupyter-based queries
  • Configuration:

    GRAPH_DB=neptune
    GRAPH_DB_CONFIG={"host": "your-cluster.region.neptune.amazonaws.com", "port": 8182}
    

• Amazon Neptune Analytics: Serverless graph analytics engine for large-scale graph analysis with openCypher support

  • Dashboard: Graph-Explorer (http://localhost:3007) or Neptune Workbench (AWS Console)
  • Configuration:

    GRAPH_DB=neptune_analytics
    GRAPH_DB_CONFIG={"graph_identifier": "g-xxxxx", "region": "us-east-1"}
    

• None: Disable knowledge graph extraction for RAG-only mode

  • Configuration:

    GRAPH_DB=none
    ENABLE_KNOWLEDGE_GRAPH=false
    

  • Use when you want vector + full-text search without graph traversal

Ontology and RDF Support

Flexible GraphRAG supports RDF/RDFS/OWL ontologies to guide knowledge graph extraction, with optional RDF triple store backends. Ontology-guided extraction works with any configured store — property graph, RDF store, or both.

• Load OWL/RDFS ontologies (owl:Class, owl:ObjectProperty, owl:DatatypeProperty, rdfs:domain, rdfs:range) to constrain entity/relation extraction; OWL is supported but not required
• Works with all 8 property graph databases (Neo4j, ArcadeDB, FalkorDB, Ladybug, etc.) — no RDF store required to use ontology-guided extraction
• Full pipeline for all 3 RDF triple stores: UI document ingest → KG extraction → RDF storage; auto incremental data source change updates via the auto-sync checkbox; Hybrid Search and AI Query/Chat tabs fuse RDF store results alongside vector, BM25, and graph results
• SPARQL 1.1 queries; RDF 1.2 triple terms and relation annotations ({| |} syntax); XSD-typed literals from OWL DatatypeProperty ranges

Storage Modes (set via INGESTION_STORAGE_MODE):

• property_graph — entities and relations go to the configured property graph (Neo4j, ArcadeDB, FalkorDB, Ladybug, etc.); no RDF store needed
• rdf_only — triples written directly to enabled RDF store(s); native SPARQL 1.1 queries, RDF 1.2 annotations
• both — written to property graph and RDF store(s) simultaneously; all retrievers active in hybrid search and AI query/chat

In all modes, the UI Hybrid Search and AI Query/Chat tabs work with whichever stores are configured. RDF store results are fused into the same retrieval pipeline as vector, BM25, and property graph results when USE_LANGCHAIN_RDF=true.

RDF Store Configuration (all three support RDF 1.2 triple terms):

• Apache Jena Fuseki — SPARQL 1.1 server; dashboard: http://localhost:3030

  FUSEKI_ENABLED=true
  FUSEKI_BASE_URL=http://localhost:3030
  FUSEKI_DATASET=flexible-graphrag
  

• Ontotext GraphDB — enterprise RDF store with OWL reasoning; dashboard: http://localhost:7200

  GRAPHDB_ENABLED=true
  GRAPHDB_BASE_URL=http://localhost:7200
  GRAPHDB_REPOSITORY=flexible-graphrag
  GRAPHDB_USERNAME=admin
  GRAPHDB_PASSWORD=admin
  

• Oxigraph — lightweight local store, native RDF 1.2; dashboard: http://localhost:7878

  OXIGRAPH_ENABLED=true
  OXIGRAPH_URL=http://localhost:7878
  

Docker Setup: Uncomment RDF store includes in docker-compose.yaml:

includes:
  # - includes/jena-fuseki.yaml
  # - includes/ontotext-graphdb.yaml
  # - includes/oxigraph.yaml

Complete Documentation: docs/DATABASES/RDF/RDF-ONTOLOGY-SUPPORT.md | docs/DATABASES/RDF/RDF-STORE-USER-GUIDE.md

LangChain RDF retrieval (USE_LANGCHAIN_RDF=true) fuses SPARQL-based results from the configured RDF store directly into hybrid search and AI query alongside vector and graph results. Supports SynonymExpander for query keyword expansion, GraphEntityVectorRetriever for Neo4j entity vector search, and GraphNeighborhoodRetriever for k-hop graph expansion. See docs/DATABASES/RDF/RDF-STORE-USER-GUIDE.md.

LLM Configuration

Configuration: Set via LLM_PROVIDER and provider-specific environment variables. See docs/LLM/LLM-EMBEDDING-CONFIG.md for detailed examples and all options.

Supported LLM Providers

1. OpenAI - gpt-4o-mini (default), gpt-4o, gpt-4.1-mini, gpt-5-mini, etc.
2. Ollama - Local deployment (llama3.2, llama3.1, qwen2.5, gpt-oss, etc.)
3. Azure OpenAI - Azure-hosted OpenAI models
4. Google Gemini - gemini-2.5-flash, gemini-3-flash-preview, gemini-3.1-pro-preview, etc.
5. Anthropic Claude - claude-sonnet-4-5, claude-haiku-4-5, etc.
6. Google Vertex AI - Google Cloud-hosted Vertex AI Platform Gemini models
7. Amazon Bedrock - Amazon Nova, Titan, Anthropic Claude, Meta Llama, Mistral AI, etc.
8. Groq - Fast low-cost LPU inference, low latency: OpenAI GPT-OSS, Meta Llama (4, 3.3, 3.1), Qwen3, Kimi, etc.
9. Fireworks AI - More choices, fine-tuning: Meta, Qwen, Mistral AI, DeepSeek, OpenAI GPT-OSS, Kimi, GLM, MiniMax, etc.
10. OpenAI-Compatible (openai_like) - Any OpenAI-compatible endpoint (LM Studio, LocalAI, Llamafile, vLLM, etc.)
11. OpenRouter - 200+ models via unified API (openai/gpt-4o-mini, anthropic/claude, meta-llama, etc.)
12. LiteLLM Proxy - 100+ providers via LiteLLM proxy; sample config in scripts/litellm_config.yaml
13. vLLM - High-performance local inference server (Linux/macOS; use openai_like on Windows)

Quick Start Examples

OpenAI (recommended):

LLM_PROVIDER=openai
OPENAI_API_KEY=your_api_key
OPENAI_MODEL=gpt-4o-mini

Ollama (local):

LLM_PROVIDER=ollama
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3.2:latest

Azure OpenAI:

LLM_PROVIDER=azure_openai
AZURE_OPENAI_API_KEY=your_key
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_OPENAI_ENGINE=gpt-4o-mini

Embedding Configuration

Embeddings can be configured independently of the LLM provider:

OpenAI:

EMBEDDING_KIND=openai
EMBEDDING_MODEL=text-embedding-3-small
EMBEDDING_DIMENSION=1536  # Auto-detected if not specified

Ollama:

EMBEDDING_KIND=ollama
EMBEDDING_MODEL=all-minilm
EMBEDDING_DIMENSION=384  # Auto-detected if not specified

Common embedding dimensions:

• OpenAI: 1536 (text-embedding-3-small), 3072 (text-embedding-3-large)
• Ollama: 384 (all-minilm, default), 768 (nomic-embed-text), 1024 (mxbai-embed-large)
• Google: 768 (text-embedding-004, configurable with output_dimensionality parameter)

Note: When switching embedding models, you must delete existing vector indexes due to dimension incompatibility. See docs/DATABASES/VECTOR-DATABASES/VECTOR-DIMENSIONS.md for cleanup instructions.

Ollama Configuration

When using Ollama, configure system-wide environment variables before starting the Ollama service:

Key requirements:

• Configure environment variables system-wide (not in Flexible GraphRAG .env file)
• OLLAMA_NUM_PARALLEL=4 for optimal performance (or 1-2 if resource constrained)
• Always restart Ollama service after changing environment variables

See docs/LLM/OLLAMA-CONFIGURATION.md for complete setup instructions including platform-specific steps and performance optimization.

Prerequisites

Required

• Python 3.12, 3.13, or 3.14 (as specified in pyproject.toml)
• UV package manager (for dependency management)
• Node.js 22.x (for UI clients)
• npm (package manager)
• Search database: Elasticsearch or OpenSearch
• Vector database: Qdrant (or other supported vector databases)
• Property graph database: Neo4j (or other supported property graph databases) - unless using vector-only RAG
• OpenAI with API key (recommended) or Ollama (for LLM processing)

Note: The docker/docker-compose.yaml file can provide all these databases via Docker containers.

Optional (depending on data source)

• LangChain integration (uv pip install -e ".[langchain]") — RDF QA fusion retriever and property graph retrievers; installs langchain, langchain-community, langchain-openai, langchain-anthropic, langchain-aws, langchain-ollama, langchain-google-genai, langchain-google-vertexai, langchain-groq, langchain-fireworks, langchain-neo4j; extended graph backends (ArangoDB, Spanner, AGE, Gremlin) via .[langchain,langchain-extras]; see flexible-graphrag/langchain/langchain-requirements.txt for the full list
• ArcadeDB embedded mode (uv pip install arcadedb>=26.3.2) — runs ArcadeDB in-process; includes a bundled JVM, no separate Java install needed; latest release: 26.3.2
• Enterprise Repositories:
  • Alfresco repository - only if using Alfresco data source
  • SharePoint - requires SharePoint access
  • Box - requires Box Business account (3 users minimum), API keys
  • CMIS-compliant repository (e.g., Alfresco) - only if using CMIS data source
• Cloud Storage (requires accounts and API keys/credentials):
  • Amazon S3 - requires AWS account and access keys
  • Google Cloud Storage - requires GCP account and service account credentials
  • Google Drive - requires Google Cloud account and OAuth credentials or service account
  • Azure Blob Storage - requires Azure account and connection string or account keys
  • Microsoft OneDrive - requires OneDrive for Business (not personal OneDrive)
  • Note: SharePoint and OneDrive for Business are also available with a M365 Developer Program sandbox (with full Visual Studio annual subscription, not monthly).
• File Upload (no account required):
  • Web interface with file dialog (drag & drop or click to select)
• Web Sources (no account required):
  • Web pages, Wikipedia, YouTube - no accounts needed

Setup

🐳 Docker Deployment

Docker deployment offers multiple scenarios. Before deploying any scenario, set up your environment files:

Environment File Setup (Required for All Scenarios):

1. Backend Configuration (.env):

   # Navigate to backend directory
   cd flexible-graphrag
   
   # Linux/macOS
   cp env-sample.txt .env
   
   # Windows Command Prompt
   copy env-sample.txt .env
   
   # Edit .env with your database credentials, API keys, and settings
   # Then return to project root
   cd ..
   

2. Docker Configuration (docker.env):

   # Navigate to docker directory
   cd docker
   
   # Linux/macOS
   cp docker-env-sample.txt docker.env
   
   # Windows Command Prompt
   copy docker-env-sample.txt docker.env
   
   # Edit docker.env for Docker-specific overrides (network addresses, service names)
   # Stay in docker directory for next steps
   

---

Scenario A: Databases in Docker, App Standalone (Hybrid)

Configuration Setup:

# If not already in docker directory from previous step:
# cd docker

# Edit docker-compose.yaml to uncomment/comment services as needed
# Scenario A setup in docker-compose.yaml:
# Keep these services uncommented (default setup):
  - includes/neo4j.yaml
  - includes/qdrant.yaml
  - includes/elasticsearch-dev.yaml
  - includes/kibana-simple.yaml

# Keep these services commented out:
# - includes/app-stack.yaml       # Must be commented out for Scenario A
# - includes/proxy.yaml           # Must be commented out for Scenario A
# - All other services remain commented unless you want a different vector database, 
#   graph database, OpenSearch for search, or Alfresco included

Deploy Services:

# From the docker directory
docker-compose -f docker-compose.yaml -p flexible-graphrag up -d

Scenario B: Full Stack in Docker (Complete)

Configuration Setup:

# If not already in docker directory from previous step:
# cd docker

# Edit docker-compose.yaml to uncomment/comment services as needed
# Scenario B setup in docker-compose.yaml:
# Keep these services uncommented:
  - includes/neo4j.yaml
  - includes/qdrant.yaml
  - includes/elasticsearch-dev.yaml
  - includes/kibana-simple.yaml
  - includes/app-stack.yaml       # Backend and UI in Docker
  - includes/proxy.yaml           # NGINX reverse proxy

# Keep other services commented out unless you want a different vector database,
# graph database, OpenSearch for search, or Alfresco included

Deploy Services:

# From the docker directory
docker-compose -f docker-compose.yaml -p flexible-graphrag up -d

Scenario B Service URLs:

• Angular UI: http://localhost:8070/ui/angular/
• React UI: http://localhost:8070/ui/react/
• Vue UI: http://localhost:8070/ui/vue/
• Backend API: http://localhost:8070/api/

Other Deployment Scenarios

Scenario C: Fully Standalone - Not using docker-compose at all

• Standalone backend, standalone UIs, all databases running separately
• Configure all database connections in flexible-graphrag/.env

Scenario D: Backend/UIs in Docker, Databases External

• Using docker-compose for backend and UIs (app-stack + proxy)
• Some or all databases running separately (same docker-compose, other local Docker, cloud/remote servers)
• Configure database connections in docker/docker.env: Backend in Docker reads this file
  • For databases in same docker-compose: Use service names (e.g., neo4j:7687, qdrant:6333)
  • For databases in other local Docker containers: Use host.docker.internal:PORT
  • For remote/cloud databases: Use actual hostnames/IPs

Scenario E: Mixed Docker/Standalone

• Standalone backend and UIs
• Running some databases in Docker (local) and some outside (cloud, external servers)
• Configure all database connections in flexible-graphrag/.env: Use host.docker.internal:PORT for locally-running Docker databases, use actual hostnames/IPs for remote Docker or non-Docker databases

Docker Control and Configuration

Managing Docker services:

# Navigate to docker directory (if not already there)
cd docker

# Create and start services (recreates if configuration changed)
docker-compose -f docker-compose.yaml -p flexible-graphrag up -d

# Stop services (keeps containers)
docker-compose -f docker-compose.yaml -p flexible-graphrag stop

# Start stopped services
docker-compose -f docker-compose.yaml -p flexible-graphrag start

# Stop and remove services
docker-compose -f docker-compose.yaml -p flexible-graphrag down

# View logs
docker-compose -f docker-compose.yaml -p flexible-graphrag logs -f

# Restart after configuration changes
docker-compose -f docker-compose.yaml -p flexible-graphrag down
# Edit docker-compose.yaml, docker.env, or includes/app-stack.yaml as needed
docker-compose -f docker-compose.yaml -p flexible-graphrag up -d

Configuration:

• Modular deployment: Comment/uncomment services in docker/docker-compose.yaml
• Backend configuration (Scenario B): Backend uses flexible-graphrag/.env with docker/docker.env for Docker-specific overrides (like using service names instead of localhost). No configuration needed in app-stack.yaml

See docker/README.md for detailed Docker configuration.

🔧 Local Development Setup (Scenario A)

Note: Skip this entire section if using Scenario B (Full Stack in Docker).

Environment Configuration

Create environment file (cross-platform):

# Linux/macOS
cp flexible-graphrag/env-sample.txt flexible-graphrag/.env

# Windows Command Prompt  
copy flexible-graphrag\env-sample.txt flexible-graphrag\.env

Edit .env with your database credentials and API keys.

Python Backend Setup (Standalone)

Option A — Install from PyPI package (Quickstart)

# 1. Create and activate a virtual environment
uv venv venv-3.13 --python 3.13
venv-3.13\Scripts\Activate   # Windows
source venv-3.13/bin/activate  # Linux/macOS

# 2. Install flexible-graphrag
uv pip install flexible-graphrag

# 3. Optionally install ArcadeDB embedded mode support (includes bundled JVM, no Java install needed)
uv pip install arcadedb>=26.3.2

# 3a. Optionally install LangChain support (multiple packages — RDF QA fusion and property graph retrievers)
uv pip install "flexible-graphrag[langchain]"

# 4. Create .env from the sample (copy from the source repo or download env-sample.txt)
copy env-sample.txt .env   # Windows
cp env-sample.txt .env     # Linux/macOS
# Edit .env with your LLM API keys and database settings

# 5. Start your databases (docker compose or standalone)
docker compose -f docker/docker-compose.yml up -d

# 6. Run the backend
flexible-graphrag
# or: uv run start.py

Option B — Install from source (editable)

1. Navigate to the backend directory:

   cd flexible-graphrag
   

2. Create and activate a virtual environment, then install in editable mode:

   uv venv venv-3.13 --python 3.13
   venv-3.13\Scripts\Activate   # Windows
   source venv-3.13/bin/activate  # Linux/macOS
   uv pip install -e .
   # Optional: LangChain support — installs langchain, langchain-community, langchain-openai,
   #   langchain-anthropic, langchain-aws, langchain-ollama, langchain-google-genai,
   #   langchain-google-vertexai, langchain-groq, langchain-fireworks, langchain-neo4j
   uv pip install -e ".[langchain]"
   # Optional: extended graph backends (ArangoDB, Spanner, AGE, Gremlin)
   uv pip install -e ".[langchain,langchain-extras]"
   # Optional: ArcadeDB embedded mode (includes bundled JVM)
   uv pip install arcadedb>=26.3.2
   

   uv-managed venv (alternative): change managed = false to managed = true in pyproject.toml [tool.uv] section, then just run uv pip install -e ..

   Windows Note: If installation fails with "Microsoft Visual C++ 14.0 or greater is required" error, install Microsoft C++ Build Tools (required for compiling Docling dependencies). Select "Desktop development with C++" during installation.

3. Create a .env file by copying the sample and customizing:

   cp env-sample.txt .env   # Linux/macOS
   copy env-sample.txt .env  # Windows
   

   Edit .env with your specific configuration. See docs/GETTING-STARTED/ENVIRONMENT-CONFIGURATION.md for detailed setup guide.

Note: The system requires Python 3.12, 3.13, or 3.14 as specified in pyproject.toml (requires-python = ">=3.12,<3.15"). Python 3.12 and 3.13 are fully tested. Python 3.14 works with the patches applied automatically in main.py at startup. Virtual environment management is controlled by managed = false in pyproject.toml [tool.uv] section (you control venv creation and naming).

4. Start the backend:

   flexible-graphrag        # after uv pip install flexible-graphrag
   # or: uv run start.py   # with source
   

The backend will be available at http://localhost:8000.

Frontend Setup (Standalone)

Standalone backend and frontend URLs:

• Backend API: http://localhost:8000 (FastAPI server)
• Angular: http://localhost:4200 (npm start)
• React: http://localhost:5173 (npm run dev)
• Vue: http://localhost:3000 (npm run dev)

Choose one of the following frontend options to work with:

React Frontend

1. Navigate to the React frontend directory:

   cd flexible-graphrag-ui/frontend-react
   

2. Install Node.js dependencies (first time only):

   npm install
   

3. Start the development server (uses Vite):

   npm run dev
   

The React frontend will be available at http://localhost:5174.

Angular Frontend

1. Navigate to the Angular frontend directory:

   cd flexible-graphrag-ui/frontend-angular
   

2. Install Node.js dependencies (first time only):

   npm install
   

3. Start the development server (uses Angular CLI):

   npm start
   

The Angular frontend will be available at http://localhost:4200.

Vue Frontend

1. Navigate to the Vue frontend directory:

   cd flexible-graphrag-ui/frontend-vue
   

2. Install Node.js dependencies (first time only):

   npm install
   

3. Start the development server (uses Vite):

   npm run dev
   

The Vue frontend will be available at http://localhost:3000.

UI Usage

The system provides a tabbed interface for document processing and querying. Follow these steps in order:

1. Sources Tab

Configure your data source and select files for processing. The system supports 13 data sources:

Detailed Configuration:

File Upload Data Source

• Select: "File Upload" from the data source dropdown
• Add Files:
  • Drag & Drop: Drag files directly onto the upload area
  • Click to Select: Click the upload area to open file selection dialog (supports multi-select)
  • Note: If you drag & drop new files after selecting via dialog, only the dragged files will be used
• Supported Formats: PDF, DOCX, XLSX, PPTX, TXT, MD, HTML, CSV, PNG, JPG, and more
• Next Step: Click "CONFIGURE PROCESSING →" to proceed to Processing tab

Alfresco Repository

• Select: "Alfresco Repository" from the data source dropdown
• Configure:
  • Alfresco Base URL (e.g., http://localhost:8080/alfresco)
  • Username and password
  • Path (e.g., /Sites/example/documentLibrary)
• Next Step: Click "CONFIGURE PROCESSING →" to proceed to Processing tab

CMIS Repository

• Select: "CMIS Repository" from the data source dropdown
• Configure:
  • CMIS Repository URL (e.g., http://localhost:8080/alfresco/api/-default-/public/cmis/versions/1.1/atom)
  • Username and password
  • Folder path (e.g., /Sites/example/documentLibrary)
• Next Step: Click "CONFIGURE PROCESSING →" to proceed to Processing tab

All Data Sources (13 available):

• Web Sources: Web Page, Wikipedia, YouTube
• Cloud Storage: Amazon S3, Google Cloud Storage, Azure Blob Storage, Google Drive, Microsoft OneDrive
• Enterprise Repositories: Alfresco, Microsoft SharePoint, Box, CMIS

See the Data Sources section for complete details on all 13 sources.

2. Processing Tab

Process your selected documents and monitor progress:

• Start Processing: Click "START PROCESSING" to begin document ingestion
• Monitor Progress: View real-time progress bars for each file
• File Management:
  • Use checkboxes to select files
  • Click "REMOVE SELECTED (N)" to remove selected files from the list
  • Note: This removes files from the processing queue, not from your system
• Processing Pipeline: Documents are processed through Docling conversion, vector indexing, and knowledge graph creation

3. Search Tab

Perform searches on your processed documents:

Hybrid Search

• Purpose: Find and rank the most relevant document excerpts
• Usage: Enter search terms or phrases (e.g., "machine learning algorithms", "financial projections")
• Action: Click "SEARCH" button
• Results: Ranked list of document excerpts with relevance scores and source information
• Best for: Research, fact-checking, finding specific information across documents

Q&A Query

• Purpose: Get AI-generated answers to natural language questions
• Usage: Enter natural language questions (e.g., "What are the main findings in the research papers?")
• Action: Click "ASK" button
• Results: AI-generated narrative answers that synthesize information from multiple documents
• Best for: Summarization, analysis, getting overviews of complex topics

4. Chat Tab

Interactive conversational interface for document Q&A:

• Chat Interface:
  • Your Questions: Displayed on the right side vertically
  • AI Answers: Displayed on the left side vertically
• Usage: Type questions and press Enter or click send
• Conversation History: All questions and answers are preserved in the chat history
• Clear History: Click "CLEAR HISTORY" button to start a new conversation
• Best for: Iterative questioning, follow-up queries, conversational document exploration

Testing Cleanup

Between tests you can clean up data:

• Run cleanup.py: Clears vector, graph, and search indexes in one step — run from the flexible-graphrag directory
• Vector Indexes: See docs/DATABASES/VECTOR-DATABASES/VECTOR-DIMENSIONS.md for vector database cleanup instructions
• Graph Data: See docs/DATABASES/GRAPH-DATABASES/README-neo4j.md for graph-related cleanup commands

MCP Server Setup (Quickstart)

The MCP server (flexible-graphrag-mcp) is a lightweight standalone package that connects MCP clients (Claude Desktop, Cursor, etc.) to the Flexible GraphRAG backend via its REST API.

For full details see flexible-graphrag-mcp/README.md and flexible-graphrag-mcp/QUICK-USAGE-GUIDE.md. For the full list of available MCP tools see MCP Tools for Claude Desktop and Other MCP Clients below.

Steps

1. First terminal — install and run the flexible-graphrag backend (see Python Backend Setup above) — it must be running on http://localhost:8000.

2. Second terminal — install and start the MCP server in HTTP mode:

   uv venv venv-mcp --python 3.13
   venv-mcp\Scripts\Activate   # Windows
   source venv-mcp/bin/activate  # Linux/macOS
   uv pip install flexible-graphrag-mcp
   flexible-graphrag-mcp --http --port 3001
   

3. Third terminal — test with MCP Inspector:

   npx @modelcontextprotocol/inspector
   

   Open the URL printed in the console (token pre-filled), set transport to Streamable HTTP, URL to http://localhost:3001/mcp, then click Connect.

4. Use with Claude Desktop and other MCP clients — see flexible-graphrag-mcp/README.md for stdio transport config and client-specific setup.

MCP Tools for Claude Desktop and Other MCP Clients

The MCP server provides 9 specialized tools for document intelligence workflows:

Tool	Purpose	Usage
get_system_status()	System health and configuration	Verify setup and database connections
ingest_documents()	Bulk document processing	All sources support skip_graph; filesystem/Alfresco/CMIS use paths; Alfresco also supports nodeDetails list (13 sources have their own config: filesystem, repositories (Alfresco, SharePoint, Box, CMIS), cloud storage, web)
ingest_text(content, source_name)	Custom text analysis	Analyze specific text content
search_documents(query, top_k)	Hybrid document retrieval	Find relevant document excerpts
query_documents(query, top_k)	AI-powered Q&A	Generate answers from document corpus
test_with_sample()	System verification	Quick test with sample content
check_processing_status(id)	Async operation monitoring	Track long-running ingestion tasks
get_python_info()	Environment diagnostics	Debug Python environment issues
health_check()	Backend connectivity	Verify API server connection

Client Support

• Claude Desktop and other MCP clients: Native MCP integration with stdio transport
• MCP Inspector: HTTP transport for debugging and development
• Multiple Installation: pipx (system-wide) or uvx (no-install) options

Backend REST API

The FastAPI backend provides the following REST API endpoints:

Base URL: http://localhost:8000/api/

Endpoint	Method	Purpose
/api/health	GET	Health check - verify backend is running
/api/ingest	POST	Ingest documents from configured data sources
/api/upload	POST	Upload files for processing
/api/search	POST	Hybrid search for relevant document excerpts
/api/query	POST	AI-powered Q&A from document corpus
/api/status	GET	Get system status and configuration
/api/processing-status/{id}	GET	Check processing status for async operations
/api/processing-events/{id}	GET	Server-sent events stream for real-time progress
/api/cancel-processing/{id}	POST	Cancel ongoing processing operation
/api/ingest-text	POST	Ingest custom text content
/api/test-sample	POST	Test system with sample content
/api/cleanup-uploads	POST	Clean up uploaded files
/api/info	GET	Get system information and versions
/api/graph	GET	Get graph data for visualization (nodes and relationships)
/api/python-info	GET	Get Python environment diagnostics
/	GET	Root endpoint (basic API info)

Swagger / ReDoc Interactive REST API Documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

See docs/ADVANCED/ARCHITECTURE.md for detailed API workflow and examples.

Full-Stack Debugging (Standalone Mode)

Note: This debugging setup is for standalone backend and frontends (Scenario A or C), not for Full Stack in Docker (Scenario B).

The project includes a sample-launch.json file with VS Code debugging configurations for all three frontend options and the backend. Copy this file to .vscode/launch.json to use these configurations.

Key debugging configurations include:

1. Full Stack with React and Python: Debug both the React frontend and Python backend simultaneously
2. Full Stack with Angular and Python: Debug both the Angular frontend and Python backend simultaneously
3. Full Stack with Vue and Python: Debug both the Vue frontend and Python backend simultaneously
4. Note when ending debugging, you will need to stop the Python backend and the frontend separately.

Each configuration sets up the appropriate ports, source maps, and debugging tools for a seamless development experience. You may need to adjust the ports and paths in the launch.json file to match your specific setup.

Observability and Monitoring

Flexible GraphRAG includes comprehensive observability features for production monitoring:

• OpenTelemetry Integration: Industry-standard instrumentation with automatic LlamaIndex tracing
• Distributed Tracing: Jaeger UI for visualizing complete request flows
• Metrics Collection: Prometheus for RAG-specific metrics (retrieval/LLM latency, token usage, entity/relation counts)
• Visualization: Grafana dashboards with pre-configured RAG metrics panels
• Dual Mode Support: OpenInference (LlamaIndex) + OpenLIT (optional) as dual OTLP producers
• Custom Instrumentation: Decorators for adding tracing to custom code

Quick Start

1. Install observability dependencies (optional):

   cd flexible-graphrag
   uv pip install -e ".[observability-dual]"  # OpenInference + OpenLIT (recommended for complete metrics)
   # Or combine with dev tools: uv pip install -e ".[observability-dual,dev]"
   

   Note: OpenLIT currently requires OpenAI 1.x and will downgrade from 2.x (support for OpenAI 2.x in progress). Flexible GraphRAG works correctly with OpenAI 1.x and has been tested.

2. Enable in .env:

   ENABLE_OBSERVABILITY=true
   OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318
   OBSERVABILITY_BACKEND=both  # openinference, openlit, or both (recommended)
   

3. Start observability stack:

   cd docker
   # Uncomment observability.yaml in docker-compose.yaml first
   docker-compose -f docker-compose.yaml -p flexible-graphrag up -d
   

4. Access dashboards:

   • Grafana: http://localhost:3009 (admin/admin) - RAG metrics dashboards
   • Jaeger: http://localhost:16686 - Distributed tracing
   • Prometheus: http://localhost:9090 - Raw metrics

See docs/DEVELOPER/OBSERVABILITY/OBSERVABILITY.md for complete setup, custom instrumentation, and production best practices.

Project Structure

• /flexible-graphrag: Python FastAPI backend with LlamaIndex

  • main.py: FastAPI REST API server (clean, no MCP)
  • backend.py: Shared business logic core used by both API and MCP
  • config.py: Configurable settings for data sources, databases, and LLM providers
  • hybrid_system.py: Main hybrid search system using LlamaIndex
  • document_processor.py: Document processing with Docling integration
  • factories.py: Factory classes for LLM and database creation
  • post_ingestion_state.py: Post-ingestion document state tracking
  • sources/: Data source connectors (filesystem, CMIS, Alfresco, Azure Blob, S3, GCS, OneDrive, SharePoint, Google Drive, Box, web, etc.)
  • ingest/: Ingestion management and orchestration
  • incremental_updates/: Auto-sync engine — detectors, orchestrator, state manager for real-time/near-real-time source sync
  • observability/: OpenTelemetry instrumentation, Prometheus metrics, tracing setup
  • rdf/: RDF/ontology support — ontology manager, KG-to-RDF converter, SPARQL tools, bundled schemas (rdf/schemas/), unified query engine
    • rdf/store/: RDF store adapters — Fuseki, GraphDB, Oxigraph, store factory
  • langchain/: LangChain integration — LLM factory (all providers), graph retrievers, RDF QA adapters
    • langchain/llm/: LangChain LLM factory supporting all 13 configured providers
    • langchain/graph/: Graph retrievers — RDF QA fusion, synonym expander, Neo4j entity vector, k-hop neighborhood, store adapters (Fuseki, GraphDB, Oxigraph, Neptune)
  • pyproject.toml: Modern Python package definition (PEP 517/518)
  • uv.toml: UV package manager configuration
  • start.py: Startup script (flexible-graphrag console entry point)
  • install.py: Installation helper script

• /flexible-graphrag-mcp: Standalone MCP server

  • main.py: HTTP-based MCP server (calls REST API)
  • pyproject.toml: MCP package definition with minimal dependencies
  • README.md: MCP server setup and installation instructions
  • QUICK-USAGE-GUIDE.md: Quick usage guide
  • Lightweight: Only 4 dependencies (fastmcp, nest-asyncio, httpx, python-dotenv)

• /flexible-graphrag-ui: Frontend applications

  • /frontend-react: React + TypeScript frontend (built with Vite)

    • /src: Source code
    • vite.config.ts: Vite configuration
    • tsconfig.json: TypeScript configuration
    • package.json: Node.js dependencies and scripts

  • /frontend-angular: Angular + TypeScript frontend (built with Angular CLI)

    • /src: Source code
    • angular.json: Angular configuration
    • tsconfig.json: TypeScript configuration
    • package.json: Node.js dependencies and scripts

  • /frontend-vue: Vue + TypeScript frontend (built with Vite)

    • /src: Source code
    • vite.config.ts: Vite configuration
    • tsconfig.json: TypeScript configuration
    • package.json: Node.js dependencies and scripts

• /docker: Docker infrastructure

  • docker-compose.yaml: Main compose file with modular includes
  • /includes: Modular database and service configurations
  • /nginx: Reverse proxy configuration
  • README.md: Docker deployment documentation

• /docs: Documentation

  • ARCHITECTURE.md: System architecture and component relationships
  • DEPLOYMENT-CONFIGURATIONS.md: Standalone, hybrid, and full Docker deployment guides
  • DOCKER-RESOURCE-CONFIGURATION.md: Docker memory/CPU configuration for Windows (WSL2), macOS, and Linux — essential for running the full stack, especially with vLLM
  • ENVIRONMENT-CONFIGURATION.md: Environment setup guide with database switching
  • POSTGRES-SETUP.md: PostgreSQL setup for pgvector and incremental state management
  • SCHEMA-EXAMPLES.md: Knowledge graph schema examples
  • PERFORMANCE.md: Performance benchmarks and optimization guides
  • DEFAULT-USERNAMES-PASSWORDS.md: Database credentials and dashboard access
  • PORT-MAPPINGS.md: Complete port reference for all services
  • DATA-SOURCES/: Data source setup guides (Azure Blob, S3, GCS, Alfresco etc.)
  • DOC-PROCESSING/: Document processing guides (Docling GPU, parser output)
  • GRAPH-DATABASES/: Graph database guides (Neo4j, Neptune, Nebula, ArcadeDB, etc.)
  • INCREMENTAL-UPDATE-AUTO-SYNC/: Incremental updates documentation (README, QUICKSTART, SETUP-GUIDE, API-REFERENCE)
  • LLM/: LLM and embedding configuration guides
  • LANGCHAIN/: LangChain integration guides (RDF QA fusion, graph retriever setup, adapter reference)
  • OBSERVABILITY/: Observability and monitoring guides
  • RDF/: RDF/ontology guides (store setup, ontology config, ingestion modes, SPARQL examples, user guide)
  • VECTOR-DATABASES/: Vector database guides (dimensions, integration, Chroma modes)

• /scripts: Utility scripts

  • create_opensearch_pipeline.py: OpenSearch hybrid search pipeline setup
  • setup-opensearch-pipeline.sh/.bat: Cross-platform pipeline creation
  • rdf_cleanup.py: RDF store CLI tool — list-docs, count, clear-doc, clear-all
  • litellm_config.yaml: Sample LiteLLM proxy config (copy to your LiteLLM install dir)
  • /incremental: Incremental updates control scripts
    • sync-now.sh/.ps1/.bat: Trigger immediate synchronization
    • set-refresh-interval.sh/.ps1/.bat: Configure polling interval
    • README.md: Script usage documentation

• /tests: Test suite

  • test_bm25_*.py: BM25 configuration and integration tests
  • conftest.py: Test configuration and fixtures
  • run_tests.py: Test runner

• /examples: Standalone usage examples (not re-tested)

  • observability_example.py: OpenTelemetry / observability integration example
  • /rdf: RDF/ontology examples
    • sparql_examples.py: Sample SPARQL queries for all three stores
    • unified_query_engine_examples.py: UnifiedQueryEngine usage examples
    • store_index_example.py: Build a LlamaIndex from an RDF store
    • ontology_guided_ingestion_example.py: OntologyAwarePropertyGraphBuilder usage
    • ingest_with_ontology.py: Ontology-guided ingestion example class
    • rdf_export_import_examples.py: RDF export/import patterns
    • config_rdf_stores.py: RDF store config reference snippets

License

This project is licensed under the terms of the Apache License 2.0. See the LICENSE file for details.