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Raggo

Raggo is an Apache-2.0-licensed Go library for building retrieval-augmented generation applications that process documents, search indexed content and provide context-aware responses.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Raggo — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Raggo when implementing a RAG workflow in Go, particularly when an application needs to search its own documents and use retrieved context in responses.

Who it is for

  • Go developers adding retrieval-backed search or question answering to an application.
  • Teams building document-grounded chat or contextual response features.

Documented uses

  • Index a directory of documents for later search.
  • Retrieve relevant passages for natural-language questions.
  • Build document-grounded question-answering or context-aware chat flows.
  • Configure chunking, embedding model, vector-store collection and search thresholds.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the Apache-2.0 license.

  • Raggo is a developer library rather than a ready-made end-user chat application.
  • The quick start specifies an OpenAI API key; configuration examples also identify Milvus as a vector-store provider.
  • The README includes environment-variable, JSON-file and programmatic configuration options.
  • Search behavior can be tuned through settings such as chunk size, overlap, TopK and minimum score.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up a simple document Q&A flow

Install Raggo, configure its API key, add a local document directory and submit a natural-language search.

Before you start

  • Go environment
  • OpenAI API key, as shown in the quick-start prerequisites
  • A local directory of documents

Steps

  1. Install the library with the command below.

    Documented command
    go get github.com/teilomillet/raggo

  2. Set the OPENAI_API_KEY environment variable.

  3. Initialize a RAG instance with raggo.NewSimpleRAG(raggo.DefaultConfig()).

  4. Add documents from a directory with AddDocuments, then call Search with a natural-language question.

Expected outcome

The example prints a response to the search query after documents are added.

Guide limitations

  • The README’s example uses a local directory; it does not specify a complete document-format support list.
  • The guide follows the documented example and does not verify execution.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
RaggoIndex a directory of documents for later search.ServerThe repository is public and identifies the project as open source under the Apache-2.0 license.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
teilomillet/raggo · Official homepage title · retained in its source language
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