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Question Answering Bot powered by OpenAI GPT models.

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- - endpoint.go
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  - service.go
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  - wikipedia_gpt3.txt
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- milvus.go
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bot.go
bot_test.go
encoder.go
engine.go
entity.go
example_test.go
feeder.go
feeder_test.go
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preprocessor.go
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# GPTBot

[![Go Reference](https://pkg.go.dev/badge/github.com/go-aie/gptbot/vulndb.svg)][1]

Question Answering Bot powered by [OpenAI GPT models][2].

## Installation

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

## Quick Start

```go
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](milvus)).
- With the help of [GPTBot Server](cmd/gptbot), 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][3].

![architecture](https://raw.githubusercontent.com/go-aie/gptbot/main/docs/architecture.png)

## Core Concepts

| Concepts     | Description                                             | Built-in Support                                          |
|--------------|---------------------------------------------------------|-----------------------------------------------------------|
| Preprocessor | Preprocess the documents by splitting them into chunks. | ✅[customizable]<br/>[Preprocessor][4]                     |
| Encoder      | Creates an embedding vector for each chunk.             | ✅[customizable]<br/>[OpenAIEncoder][5]                    |
| VectorStore  | Stores and queries document chunk embeddings.           | ✅[customizable]<br/>[LocalVectorStore][6]<br/>[Milvus][7] |
| Feeder       | Feeds the documents into the vector store.              | /                                                         |
| Bot          | Question answering bot to chat with.                    | /                                                         |

## License

[MIT](LICENSE)

[1]: https://pkg.go.dev/github.com/go-aie/gptbot
[2]: https://platform.openai.com/docs/models
[3]: https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
[4]: https://pkg.go.dev/github.com/go-aie/gptbot#Preprocessor
[5]: https://pkg.go.dev/github.com/go-aie/gptbot#OpenAIEncoder
[6]: https://pkg.go.dev/github.com/go-aie/gptbot#LocalVectorStore
[7]: https://pkg.go.dev/github.com/go-aie/gptbot/milvus#Milvus

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