graphsearch-rag 0.2.1

GraphSearch: a GraphQL API server for Retrieval-Augmented Generation (RAG) over your documents

GraphSearch

A GraphQL API server for Retrieval-Augmented Generation (RAG) over your documents.

Upload documents through a GraphQL mutation, then ask questions through a GraphQL query. GraphSearch chunks and embeds your documents, retrieves the most relevant passages with vector similarity search, and feeds them to an LLM to generate a grounded answer — all behind a single, typed, introspectable GraphQL endpoint.

query {
  answer(question: "How do I reset my password?") {
    text                                # cites sources as [1], [2], ...
    sources { documentTitle text score }
  }
}

Semantic retrieval with the offline local backend: "How do I get my money back?" finds the returns policy — no shared keywords needed.

Why GraphQL for RAG?

• One endpoint, typed schema — clients ask for exactly the fields they need (answer text, source chunks, scores) instead of juggling REST routes.
• Introspection & tooling for free — GraphiQL, codegen'd TypeScript clients, and schema docs come with the ecosystem.
• Composable — answer, search, and document management live in one schema and can be combined in a single request.

Quickstart

Install

pip install graphsearch-rag        # from PyPI (imports as `graphsearch`)
# or run the prebuilt image:
docker run -p 8000:8000 ghcr.io/mohithgowdak/graphsearch:latest
# or from source:
git clone https://github.com/mohithgowdak/graphsearch && cd graphsearch
pip install -e ".[dev]"

Run locally (no API keys needed)

The default configuration is fully offline: a hashing-trick embedder plus an extractive answer mode. It exercises the entire pipeline and is perfect for kicking the tires, tests, and CI.

# Ingest some documents (bundled examples shown; any .md/.txt works)
graphsearch-ingest data/example_docs

# Start the server
graphsearch

Open http://localhost:8000/graphql for the GraphiQL playground and try:

query {
  answer(question: "What is the return policy?") {
    text
    sources { text score }
  }
}

Run with Docker

docker compose up --build

Semantic search without any API key

The local backend runs sentence-transformers on your CPU — real semantic retrieval ("How do I get my money back?" finds the refund policy), still zero keys and zero external services:

pip install -e ".[local]"
export GRAPHSEARCH_EMBEDDINGS=local   # $env:GRAPHSEARCH_EMBEDDINGS='local' on Windows
graphsearch-ingest data/example_docs  # re-ingest: embeddings are created at ingest time
graphsearch

The default model (all-MiniLM-L6-v2, ~80 MB) downloads on first use.

Generated answers with citations

Point the LLM stage at OpenAI or Anthropic and answers become generated text that cites its sources — [1], [2], … map 1:1 to the sources list returned alongside the answer:

export GRAPHSEARCH_LLM=anthropic          # or openai
export ANTHROPIC_API_KEY=sk-ant-...
pip install -e ".[anthropic]"
graphsearch

Setting	Options	Default
GRAPHSEARCH_EMBEDDINGS	hash (offline), local (offline, semantic), openai	hash
GRAPHSEARCH_LLM	extractive (offline), openai, anthropic	extractive

Note: documents are embedded at ingest time, so re-ingest after switching embedding backends.

GraphQL API

Queries

answer(question: String!, topK: Int): Answer!      # RAG: retrieve + generate
search(query: String!, topK: Int): [Chunk!]!       # raw semantic search
documents(limit: Int = 20, offset: Int = 0): [Document!]!
document(id: ID!): Document

Mutations

uploadDocument(content: String!, title: String, source: String): Document!
deleteDocument(id: ID!): Boolean!

Example: ingest and ask in one session

mutation {
  uploadDocument(
    content: "Support hours are 9am-5pm PST, Monday through Friday."
    title: "support-hours"
  ) { id chunkCount }
}

query {
  answer(question: "When is support available?") {
    text
    sources { documentId score }
  }
}

Architecture

Client ── GraphQL (FastAPI + Strawberry) ── RagService
                                              ├─ chunking       (paragraph-aware splitter)
                                              ├─ Embedder       (hash | sentence-transformers | OpenAI)
                                              ├─ VectorStore    (SQLite-backed, in-memory cosine search)
                                              ├─ Database       (SQLite: documents, chunks, vectors)
                                              └─ LLM            (extractive | OpenAI | Anthropic)

Every pipeline stage is an abstract interface (Embedder, VectorStore, LLM), so new backends are drop-in additions — see Contributing.

Development

pip install -e ".[dev]"
ruff check .          # lint
pytest -v             # tests run fully offline

Roadmap / good first issues

• Additional vector store backends: Qdrant, Weaviate, Redis/Valkey, pgvector, FAISS
• Additional embedding backends: Cohere, Voyage
• Streaming answers via GraphQL subscriptions
• Metadata filters on search (tags, date ranges) and hybrid keyword+vector search
• Auth (API keys / JWT) and rate limiting
• Query/embedding caching
• Auto-generated TypeScript client (GraphQL Code Generator)
• Evaluation harness with known Q&A pairs; Prometheus metrics
• Advanced RAG: query rewriting, multi-hop retrieval, citation spans

Contributing

Contributions are welcome! See CONTRIBUTING.md for setup, style, and PR guidelines, and the issue tracker for good first issue labels.

License

MIT