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README.md
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@@ -20,7 +20,7 @@ The Megabots library can be used to create bots that:
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π€ Megabots is backed by some of the most famous tools for productionalising AI. It uses [LangChain](https://docs.langchain.com/docs/) for managing LLM chains, [FastAPI](https://fastapi.tiangolo.com/) to create a production ready API, [Gradio](https://gradio.app/) to create a UI. At the moment it uses [OpenAI](https://openai.com/) to generate answers, but we plan to support other LLMs in the future.
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Note: This is a work in progress. The API might change.
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demo = create_interface(bot("qna-over-docs"))
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```
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The `bot` function should serve as the starting point for creating and customising your bot. Below is a list of the available arguments in `bot`.
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| prompt_variables | A list of variables to be used in the prompt template. These variables are replaced with actual values when the bot processes a query. |
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| sources | When `sources` is `True` the bot will also include sources in the response. A known [issue](https://github.com/hwchase17/langchain/issues/2858) exists, where if you pass a custom prompt with sources the code breaks. |
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Large language models (LLMs) are powerful, but they can't answer questions about documents they haven't seen. If you want to use an LLM to answer questions about documents it was not trained on, you have to give it information about those documents. To solve this, we use "retrieval augmented generation."
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π€ Megabots is backed by some of the most famous tools for productionalising AI. It uses [LangChain](https://docs.langchain.com/docs/) for managing LLM chains, [FastAPI](https://fastapi.tiangolo.com/) to create a production ready API, [Gradio](https://gradio.app/) to create a UI. At the moment it uses [OpenAI](https://openai.com/) to generate answers, but we plan to support other LLMs in the future.
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## Getting started
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Note: This is a work in progress. The API might change.
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demo = create_interface(bot("qna-over-docs"))
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```
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## Customising bot
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The `bot` function should serve as the starting point for creating and customising your bot. Below is a list of the available arguments in `bot`.
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| prompt_variables | A list of variables to be used in the prompt template. These variables are replaced with actual values when the bot processes a query. |
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| sources | When `sources` is `True` the bot will also include sources in the response. A known [issue](https://github.com/hwchase17/langchain/issues/2858) exists, where if you pass a custom prompt with sources the code breaks. |
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## How QnA bot works
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Large language models (LLMs) are powerful, but they can't answer questions about documents they haven't seen. If you want to use an LLM to answer questions about documents it was not trained on, you have to give it information about those documents. To solve this, we use "retrieval augmented generation."
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