GPTBot

Question Answering Bot powered by OpenAI GPT models.

Installation

$ go get -u github.com/go-aie/gptbot

Quick Start

func main() {
    ctx := context.Background()
    apiKey := os.Getenv("OPENAI_API_KEY")
    encoder := gptbot.NewOpenAIEncoder(apiKey, "")
    store := gptbot.NewLocalVectorStore()

    // Feed documents into the vector store.
    feeder := gptbot.NewFeeder(&gptbot.FeederConfig{
        Encoder: encoder,
        Updater: store,
    })
    err := feeder.Feed(ctx, &gptbot.Document{
        ID:   "1",
        Text: "Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model released in 2020 that uses deep learning to produce human-like text. Given an initial text as prompt, it will produce text that continues the prompt.",
    })
    if err != nil {
        fmt.Printf("err: %v", err)
        return
    }

    // Chat with the bot to get answers.
    bot := gptbot.NewBot(&gptbot.BotConfig{
        APIKey:  apiKey,
        Encoder: encoder,
        Querier: store,
    })

    question := "When was GPT-3 released?"
    answer, _, err := bot.Chat(ctx, question)
    if err != nil {
        fmt.Printf("err: %v", err)
        return
    }
    fmt.Printf("Q: %s\n", question)
    fmt.Printf("A: %s\n", answer)

    // Output:
    //
    // Q: When was GPT-3 released?
    // A: GPT-3 was released in 2020.
}

NOTE:

• The above example uses a local vector store. If you have a larger dataset, please consider using a vector search engine (e.g. Milvus).
• With the help of GPTBot Server, you can even upload documents as files and then start chatting via HTTP!

Design

GPTBot is an implementation of the method demonstrated in Question Answering using Embeddings.

Core Concepts

Concepts	Description	Built-in Support
Preprocessor	Preprocess the documents by splitting them into chunks.	✅[customizable] Preprocessor
Encoder	Creates an embedding vector for each chunk.	✅[customizable] OpenAIEncoder
VectorStore	Stores and queries document chunk embeddings.	✅[customizable] LocalVectorStore Milvus
Feeder	Feeds the documents into the vector store.	/
Bot	Question answering bot to chat with.	/

License

MIT