post-graph-rag 0.1.3

Graph RAG library leveraging post-graph and pgvector on PostgreSQL with OpenAI-compatible LLMs.

post-graph-rag

Production-Grade, High-Performance Knowledge Graph RAG Engine Native to PostgreSQL.

post-graph-rag seamlessly combines automated LLM-based entity & triple extraction, vector similarity search via pgvector, and graph relationship traversal directly on PostgreSQL using the post-graph graph database library.

It connects to any OpenAI-compatible API (LiteLLM, vLLM, Ollama, DeepSeek, OpenAI) for zero-shot domain-agnostic knowledge extraction, structured document metadata tracking, and context-aware answer synthesis.

---

🌟 Why post-graph-rag?

Traditional Vector RAG systems suffer from "chunk isolation"—they retrieve isolated text passages based purely on semantic similarity, missing higher-level relationships and cross-document entity connections.

post-graph-rag solves this by building a dual representation inside PostgreSQL:

1. Unstructured Vector Passages: Full document chunks indexed with pgvector HNSW embeddings.
2. Knowledge Graph Triples: Extracted Subject-Predicate-Object entities connected by graph edges.
3. Structured Document Metadata: Rich metadata tracking (source, category, collection, document, page, paragraph).

---

🏗️ Architecture Workflow

graph TD
    subgraph INDEXING ["1. Knowledge Graph & Vector Indexing"]
        A[Document Text + Metadata] --> B[Embedding Service]
        A --> C[LLM GraphExtractor]
        
        B -->|Vectors| D[post-graph Store]
        C -->|Entities & Triples| D
        
        D --> E[(PostgreSQL + pgvector)]
        E -->|Tables| E1[documents]
        E -->|Tables| E2[entities]
        E -->|Edges| E3[relations]
        E -->|Edges| E4[doc_mentions]
    end

    subgraph RETRIEVAL ["2. Hybrid Retrieval & Synthesis"]
        Q[User Question] --> R[GraphRAG Query Engine]
        R -->|Embedding| S[pgvector Similarity Search]
        E1 & E2 -->|Top-K Passages & Entities| S
        S --> T[1-Hop Graph Relationship Traversal]
        E3 -->|Subject-Predicate-Object| T
        
        S & T --> U[LLM Answer Synthesis]
        U --> V[Final Answer + Citations + Graph Triples]
    end

---

📦 Installation

Install post-graph-rag via pip or uv:

pip install post-graph-rag

Or using uv:

uv add post-graph-rag

PostgreSQL Requirements

Ensure PostgreSQL is running with the pgvector extension installed:

CREATE EXTENSION IF NOT EXISTS vector;

---

🚀 Quick Start

1. Basic Indexing & Querying

import asyncio
from post_graph_rag import GraphRAG, RAGConfig, DocumentMetadata

async def main():
    # 1. Configure GraphRAG engine
    config = RAGConfig(
        api_base="http://localhost:4000/v1",       # OpenAI-compatible router endpoint
        api_key="BEVZ-6L81-OZ8Y",                 # Master or OpenAI API Key
        model="DeepSeek-V3.2",                    # LLM model for extraction & synthesis
        embedding_model="text-embedding-3-small", # Embedding model
        embedding_dim=1536,                       # Vector dimensionality
        db_uri="postgresql://user:password@localhost:5432/postgres",
        realm="enterprise_kb"
    )

    rag = GraphRAG(config)
    
    # 2. Connect & initialize PostgreSQL graph schema
    await rag.initialize()

    # 3. Index unstructured documents
    doc_text = (
        "Zeus is the king of the Olympian gods, ruling sky and thunder from Mount Olympus. "
        "He is the son of Cronus and Rhea, and married to Hera. "
        "Zeus defeated the Titans in the Titanomachy to establish his rule."
    )
    
    result = await rag.index_document(doc_text, metadata={"source": "greek_mythology.txt"})
    print(f"Indexed document {result['document_id']}: Extracted {result['entities_extracted']} entities.")

    # 4. Perform Hybrid RAG Query
    response = await rag.query("Who are the parents of Zeus and what did he defeat?")
    
    print("\n=== SYNTHESIZED ANSWER ===")
    print(response["answer"])

    print("\n=== RETRIEVED GRAPH TRIPLES ===")
    for triple in response["retrieved_graph_triples"]:
        print(f"  - {triple}")

    # 5. Clean up
    await rag.close()

if __name__ == "__main__":
    asyncio.run(main())

---

📋 Document Metadata (DocumentMetadata)

post-graph-rag includes structured document metadata tracking via the DocumentMetadata model:

from post_graph_rag import DocumentMetadata

metadata = DocumentMetadata(
    source="https://mythology.org/zeus.html",  # Document origin (URL, filepath, API)
    category="greek_mythology",               # Document category/topic
    collection="olympian_deities",             # Collection namespace
    document="zeus_overview.pdf",              # Title or filename
    page=1,                                    # 1-based page number
    paragraph=2,                               # 1-based paragraph index
    extra={"author": "Homer", "year": -700}    # Custom metadata key-value pairs
)

await rag.index_document(chunk_text, metadata=metadata)

Design Rationale: Optional vs. Required

• All metadata fields are optional with default None. This allows seamless indexing of raw strings, short code snippets, webhooks, or unformatted text, while offering rich structural provenance tracking when indexing multi-page PDFs or categorized enterprise documents.

---

⚙️ Configuration Reference (RAGConfig)

RAGConfig can be configured explicitly or automatically loaded from environment variables:

Option	Environment Variable	Default Value	Description
api_base	OPENAI_API_BASE	http://localhost:4000/v1	Base URL for OpenAI-compatible LLM endpoint
api_key	OPENAI_API_KEY	BEVZ-6L81-OZ8Y	API Key for authorization
model	RAG_MODEL	DeepSeek-V3.2	Primary LLM model for triple extraction & synthesis
embedding_model	RAG_EMBEDDING_MODEL	text-embedding-3-small	Model for vector embedding generation
embedding_dim	RAG_EMBEDDING_DIM	1536	Dimensionality of embedding vectors
db_uri	POSTGRES_URI	postgresql://crajah@localhost:5432/postgres	PostgreSQL connection DSN
realm	RAG_REALM	default	Multi-tenant graph namespace

---

📖 API Reference

GraphRAG

The main orchestrator class for indexing and querying.

• await initialize(): Connects to PostgreSQL and creates necessary graph tables (documents, entities, relations, doc_mentions).
• await index_document(text: str, metadata: Optional[Union[Dict[str, Any], DocumentMetadata]] = None) -> Dict[str, Any]: Computes document embeddings, extracts entity/triple structures via LLM, and persists graph nodes/edges into PostgreSQL.
• await query(question: str, top_k: int = 5) -> Dict[str, Any]: Executes hybrid vector similarity search over documents and entities, traverses 1-hop graph relationship edges, and synthesizes a comprehensive answer. Returns dictionary with question, answer, retrieved_documents, retrieved_entities, and retrieved_graph_triples.
• await close(): Closes database connection pools.

DocumentMetadata

Data container for structured document metadata.

• source: Optional[str]: Document URL, path, or origin.
• category: Optional[str]: Document category or domain.
• collection: Optional[str]: Document collection or folder.
• document: Optional[str]: File title or filename.
• page: Optional[int]: 1-based page number.
• paragraph: Optional[int]: 1-based paragraph index.
• extra: Dict[str, Any]: Custom user metadata.
• to_dict() -> Dict[str, Any]: Serializes non-None fields to dictionary representation.
• from_dict(data: Dict[str, Any]) -> DocumentMetadata: Deserializes dictionary data.

RAGGraphStore

Database layer wrapping post-graph.

• add_document(text, embedding, metadata): Inserts a document vertex into the documents table.
• upsert_entity(name, entity_type, description, embedding): Upserts an entity vertex into the entities table.
• add_relation(from_entity, to_entity, relation_type, description): Connects entity vertices with a directed relation edge.
• search_similar_entities(query_vec, top_k): Executes pgvector HNSW similarity search over entities.
• search_similar_documents(query_vec, top_k): Executes pgvector HNSW similarity search over documents.

---

🗄️ PostgreSQL Database Schema

post-graph-rag automatically provisions and manages the following graph schema in PostgreSQL powered by post-graph:

Table Name	Type	Key Columns	Description
{realm}_documents	Vertex Table	id, payload, embedding (vector)	Stores raw text chunks and DocumentMetadata payloads
{realm}_entities	Vertex Table	id, payload, embedding (vector)	Canonical entity nodes (name, type, description)
{realm}_relations	Edge Table	from_id, to_id, relation_type, payload	Directed edges representing entity-to-entity triples
{realm}_doc_mentions	Edge Table	from_id, to_id, relation_type	Directed edges connecting document chunks to mentioned entities
{table}_audit	Audit Table	audit_id, action, changed_by, changed_at	Automatic shadow audit logging for all graph mutations
{table}_data	History Table	data_id, payload, timestamp, embedding	Append-only historical records for vertices and edges

---

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Developed by Chandan Rajah ([e-mail]).