# QuivrHQ/quivr

Opiniated RAG for integrating GenAI in your apps 🧠   Focus on your product rather than the RAG. Easy integration in existing products with customisation!  Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.

Repository: https://github.com/QuivrHQ/quivr
Canonical: https://ross.abutalabs.com/products/quivr
Homepage: https://core.quivr.com
Language: Python
License: NOASSERTION
License Family: other
Topics: ai, llm, api, chatbot, chatgpt, database, docker, frontend, openai, postgresql, privacy, security, typescript, html, javascript, react, rag, vector, groq, framework
Last push: 2025-07-09T12:55:23+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 30, release rhythm 40, longevity 86
- inputs: {"age_days": 1209, "days_push": 420, "days_rel": 575, "gap_med": 1, "n_releases_24m": 42}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 39423, forks 3725 (observed 2026-08-28T04:12:07.849200+00:00)

## What it is
Quivr is an opinionated RAG (Retrieval-Augmented Generation) library, available as the quivr-core Python package, that lets developers ingest files and ask questions over them with a few lines of code. It works with any LLM (OpenAI, Anthropic, Mistral, Groq, Ollama) and any vector store (PGVector, Faiss), and powers the hosted Quivr.com 'second brain' product.

## Use cases
- build a chatbot that answers questions over my pdf documents
- add RAG to my existing app without building retrieval from scratch
- chat with my files locally using Ollama
- ingest markdown and txt files and query them with an LLM
- swap between GPT-4, Mistral, and Groq in a retrieval pipeline
- create a personal 'second brain' knowledge assistant
- customize a RAG workflow with internet search and tools

## When to choose
- you want a batteries-included, opinionated RAG pipeline instead of assembling LangChain pieces yourself
- you need flexible LLM and vector-store backends (OpenAI, Anthropic, Mistral, Ollama, PGVector, Faiss)
- you want quick document Q&A with minimal setup, including PDF parsing via Megaparse integration

## When to avoid
- you need full low-level control over every retrieval and chunking step
- you want a fully managed SaaS without self-hosting or Python integration
- your project is not Python-based and you cannot use the quivr-core package or its API

## Facets
- artifact type: library
- maturity: active
- function: rag, llm-inference, chatbot, agent-framework, nlp, pdf, vector-database
- domain: large-language-models, artificial-intelligence, chatbots, developer-tools
- platform: python, self-hosted, cross-platform
- tags: second-brain, quivr-core, opinionated-rag, document-qa, megaparse, ollama, pgvector, faiss, retrieval-augmented-generation, natural-language-processing, docker

## Member repositories
- QuivrHQ/quivr (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:07.849200+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:24:07.840493+00:00, confidence not recorded.
  - readme: https://github.com/QuivrHQ/quivr (fetched 2026-08-28T04:12:07.849200+00:00, sha c54eda523f75)
  - homepage: https://core.quivr.com (fetched 2026-08-29T07:46:48.145208+00:00, sha 57012d2a6a16)
  - site_page: https://core.quivr.com/en/latest/quickstart (fetched 2026-08-29T07:46:48.154493+00:00, sha c1c7d2a551d8)
- Data as of 2026-08-30T08:39:29.467469+00:00.