Vector Database Integration¶

Flexible GraphRAG supports 10 vector databases. All are supported with both the LlamaIndex and LangChain backends — set VECTOR_BACKEND=llamaindex or VECTOR_BACKEND=langchain to choose.

Supported Vector Databases¶

`VECTOR_DB`	Store	Port
`qdrant`	Qdrant	6333
`elasticsearch`	Elasticsearch	9200
`opensearch`	OpenSearch	9201
`chroma`	Chroma	8001
`milvus`	Milvus	19530
`weaviate`	Weaviate	8081
`pinecone`	Pinecone	cloud
`postgres`	PostgreSQL + pgvector	5433
`lancedb`	LanceDB	embedded
`neo4j`	Neo4j Vector	7687

Configuration¶

Qdrant¶

Docker: docker/includes/qdrant.yaml

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

Elasticsearch¶

Dashboard: Kibana at http://localhost:5601
Docker: docker/includes/elasticsearch-dev.yaml (dev) or docker/includes/elasticsearch.yaml

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

OpenSearch¶

Dashboard: OpenSearch Dashboards at http://localhost:5601
Docker: docker/includes/opensearch.yaml

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

Chroma¶

Two deployment modes — local file-based or remote HTTP server:

Dashboard: Swagger API at http://localhost:8001/docs (HTTP mode only)
Docker: docker/includes/chroma.yaml

VECTOR_DB=chroma

# Local mode (file-based)
CHROMA_VECTOR_DB_CONFIG={"persist_directory": "./chroma_db", "collection_name": "hybrid_search"}

# HTTP mode (remote server)
CHROMA_VECTOR_DB_CONFIG={"host": "localhost", "port": 8001, "collection_name": "hybrid_search"}

Milvus¶

Dashboard: Attu at http://localhost:3003
Docker: docker/includes/milvus.yaml (includes etcd, MinIO, Attu)
Ports: 19530 (gRPC), 3003 (Attu), 9000/9001 (MinIO)

VECTOR_DB=milvus
MILVUS_VECTOR_DB_CONFIG={"host": "localhost", "port": 19530, "collection_name": "hybrid_search", "username": "root", "password": "milvus"}

Weaviate¶

Dashboard: Weaviate Console at http://localhost:8081/console
Docker: docker/includes/weaviate.yaml

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

Pinecone¶

Dashboard: Pinecone Console (web-based)
Docker: docker/includes/pinecone.yaml
Requirements: API key required

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

PostgreSQL + pgvector¶

Dashboard: pgAdmin at http://localhost:5050
Docker: docker/includes/postgres-pgvector.yaml
Ports: 5433 (PostgreSQL), 5050 (pgAdmin)

VECTOR_DB=postgres
POSTGRES_VECTOR_DB_CONFIG={"host": "localhost", "port": 5433, "database": "flexible_graphrag", "username": "flexible_graphrag", "password": "password"}

LanceDB¶

Storage: Local file-based (embedded)
Docker: docker/includes/lancedb.yaml

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

Neo4j Vector¶

Dashboard: Neo4j Browser at http://localhost:7474
Docker: docker/includes/neo4j.yaml

VECTOR_DB=neo4j
NEO4J_VECTOR_DB_CONFIG={"uri": "bolt://localhost:7687", "username": "neo4j", "password": "your_password", "index_name": "hybrid_search_vector"}

Vector Dimension Compatibility¶

When switching embedding models, delete existing vector indexes — dimensions differ by provider:

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

See Vector Dimensions for cleanup instructions.

Dependencies¶

LlamaIndex vector store packages:

llama-index-vector-stores-qdrant       llama-index-vector-stores-elasticsearch
llama-index-vector-stores-opensearch   llama-index-vector-stores-chroma
llama-index-vector-stores-milvus       llama-index-vector-stores-weaviate
llama-index-vector-stores-pinecone     llama-index-vector-stores-postgres
llama-index-vector-stores-lancedb      llama-index-vector-stores-neo4jvector

LangChain vector store packages (via .[langchain] extras):

langchain-qdrant    langchain-elasticsearch   langchain-chroma
langchain-milvus    langchain-weaviate        langchain-pinecone
langchain-postgres  langchain-community       langchain-neo4j