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# GoRag: Go Interface for LLM / RAG development via Multiple Vector Database types 📜
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# GoRag: A Go Library for Retrieval-Augmented Generation (RAG) Development with Multi-Vector Database Support 📜
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[![Go Report Card](https://goreportcard.com/badge/github.com/stackloklabs/gorag)](https://goreportcard.com/report/github.com/stackloklabs/gorag)
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[![License](https://img.shields.io/github/license/stackloklabs/gorag)](LICENSE)
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GoRag is a Go library that provides an easy interface to interact with Large
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Language Model backends including [Ollama](https://ollama.com) and [OpenAI](https://openai.com), along with an embeddings interface for RAG (currently with Postgres pgvector).
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GoRag provides an intuitive Go interface for developing Retrieval-Augmented Generation (RAG) applications. It supports multiple vector database types, enabling efficient data retrieval for enhanced generation augmentation.
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Language Model backends including [Ollama](https://ollama.com) and [OpenAI](https://openai.com), along with an embeddings interface for RAG using a local embeddings model (mxbai-embed-large) or a hosted such type (such as text-embedding-ada-002 from OpenAI)
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## 🌟 Features
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})
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```
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Generate a response
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Call the Generations API
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```go
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response, err := generationBackend.Generate(ctx, prompt)
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})
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```
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Generate a response
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Call the Generations API and get a response
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```go
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response, err := generationBackend.Generate(ctx, prompt)
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2024/10/28 15:08:26 Vector Document generated
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2024/10/28 15:08:26 Vector embeddings generated
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2024/10/28 15:08:26 Retrieved Document: {doc-5630d3f2-bf61-4e13-8ec9-9e863bc1a962 map[content:Mickey mouse is a real human being]}
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2024/10/28 15:08:34 Retrieval-Augmented Generation influenced output from LLM model: In that case, since Mickey Mouse is a real human being (as per your context), I would say... **YES**, Mickey Mouse is indeed a human!
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2024/10/28 15:08:34 Retrieval-Augmented Generation influenced output from LLM model: Mickey Mouse is indeed a human!
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```
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# 📝 Contributing

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