cohere-ai/lingoose

cohere-ai/lingoose

Description: 🪿 LinGoose it's a Go framework for developing LLMs-based application using pipelines

License: MIT

Stars: 0

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Created: 2023-12-23T18:34:47Z

Pushed: 2023-12-21T16:11:15Z

Default branch: main

Fork: yes

Parent repository: henomis/lingoose

Archived: no

README:

🪿 LinGoose

LinGoose (_Lingo + Go + Goose_ 🪿) aims to be a complete Go framework for creating LLM apps. 🤖 ⚙️

> Did you know? A goose 🪿 fills its car 🚗 with goose-line ⛽!

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Start learning LinGoose on Replit LinGoose course

Overview

LinGoose is a powerful Go framework for developing Large Language Model (LLM) based applications using pipelines. It is designed to be a complete solution and provides multiple components, including Prompts, Templates, Chat, Output Decoders, LLM, Pipelines, and Memory. With LinGoose, you can interact with LLM AI through prompts and generate complex templates. Additionally, it includes a chat feature, allowing you to create chatbots. The Output Decoders component enables you to extract specific information from the output of the LLM, while the LLM interface allows you to send prompts to various AI, such as the ones provided by OpenAI. You can chain multiple LLM steps together using Pipelines and store the output of each step in Memory for later retrieval. LinGoose also includes a Document component, which is used to store text, and a Loader component, which is used to load Documents from various sources. Finally, it includes TextSplitters, which are used to split text or Documents into multiple parts, Embedders, which are used to embed text or Documents into embeddings, and Indexes, which are used to store embeddings and documents and to perform searches.

Components

LinGoose is composed of multiple components, each one with its own purpose.

| Component | Package | Description | | ----------------- | ----------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Prompt | [prompt](prompt/) | Prompts are the way to interact with LLM AI. They can be simple text, or more complex templates. Supports Prompt Templates and [Whisper](https://openai.com) prompt | | Chat Prompt | [chat](chat/) | Chat is the way to interact with the chat LLM AI. It can be a simple text prompt, or a more complex chatbot. | | Decoders | [decoder](decoder/) | Output decoders are used to decode the output of the LLM. They can be used to extract specific information from the output. Supports JSONDecoder and RegExDecoder | | LLMs | [llm](llm/) | LLM is an interface to various AI such as the ones provided by OpenAI. It is responsible for sending the prompt to the AI and retrieving the output. Supports [OpenAI](https://openai.com), [HuggingFace](https://huggingface.co) and [Llama.cpp](https://github.com/ggerganov/llama.cpp). | | Pipelines | [pipeline](pipeline/) | Pipelines are used to chain multiple LLM steps together. | | Memory | [memory](memory/) | Memory is used to store the output of each step. It can be used to retrieve the output of a previous step. Supports memory in Ram | | Document | [document](document/) | Document is used to store a text | | Loaders | [loader](loader/) | Loaders are used to load Documents from various sources. Supports TextLoader, DirectoryLoader, PDFToTextLoader and PubMedLoader . | | TextSplitters | [textsplitter](textsplitter/) | TextSplitters are used to split text or Documents into multiple parts. Supports RecursiveTextSplitter. | | Embedders | [embedder](embedder/) | Embedders are used to embed text or Documents into embeddings. Supports [OpenAI](https://openai.com) | | Indexes | [index](index/) | Indexes are used to store embeddings and documents and to perform searches. Supports SimpleVectorIndex, [Pinecone](https://pinecone.io) and [Qdrant](https://qdrant.tech) |

Usage

Please refer to the documentation at lingoose.io to understand how to use LinGoose. If you prefer the 👉 [examples directory](examples/) contains a lot of examples 🚀. However, here is a powerful example of what LinGoose is capable of:

_Talk is cheap. Show me the [code](examples/)._ - Linus Torvalds

package main

import (
"context"

openaiembedder "github.com/henomis/lingoose/embedder/openai"
"github.com/henomis/lingoose/index"
"github.com/henomis/lingoose/index/option"
"github.com/henomis/lingoose/index/vectordb/jsondb"
"github.com/henomis/lingoose/llm/openai"
"github.com/henomis/lingoose/loader"
qapipeline "github.com/henomis/lingoose/pipeline/qa"
"github.com/henomis/lingoose/textsplitter"
)

func main() {
docs, _ := loader.NewPDFToTextLoader("./kb").WithTextSplitter(textsplitter.NewRecursiveCharacterTextSplitter(2000, 200)).Load(context.Background())
index := index.New(jsondb.New().WithPersist("db.json"), openaiembedder.New(openaiembedder.AdaEmbeddingV2)).WithIncludeContents(true)
index.LoadFromDocuments(context.Background(), docs)
qapipeline.New(openai.NewChat().WithVerbose(true)).WithIndex(index).Query(context.Background(), "What is the NATO purpose?", option.WithTopK(1))
}

This is the _famous_ 4-lines lingoose knowledge base chatbot. 🤖

Installation

Be sure to have a working Go environment, then run the following command:

go get github.com/henomis/lingoose

License

© Simone Vellei, 2023~time.Now() Released under the [MIT License](LICENSE)