DocuBot and DocuSense

DocuBot and DocuSense are command-line tools. DocuBot is a chatbot that answers questions using a knowledge base of documents provided by you. It allows you to interactively get answers to questions with citations from the documents provided. DocuSense summarizes the document provided by you. They are written in Python3.

Supported Document Types

DocuBot and DocuSense supports the following document types:

.pdf: Portable Document Format
.docx: Microsoft Word Document
.md: Markdown Document
.txt: Plain Text Document

DocuBot Features

Session based: DocuBot remembers previous interactions within the current session.
Citations provided: DocuBot generates answers based on information from specific documents. It provides citations to these documents, including page numbers if available.
Answer limitations: In some cases, DocuBot may not be able to provide an appropriate answer based on the given context. In such cases, it will indicate that it cannot answer the question accurately.

Please note that while DocuBot to provide accurate information with proper citations, there might be situations where it may not have access to the required resources or may not be able to generate an answer.

Prerequisites

Before using DocuBot, you need to have the following:

To avoid OpenAI rate-limiting issues, it is recommended to preload funds into your OpenAI account. This ensures that you have sufficient credits to make multiple requests during the document indexing phase.

Note: DocuBot provides an estimated cost of indexing documents at the beginning of the process. This helps you understand the potential cost implications before proceeding. Please review the estimated cost and ensure that you have sufficient funds in your OpenAI account to cover the indexing process.

For using DocuSense, pinecone API and ENVIRONMENT keys are not required.

Installation

To use DocuBot and DocuSense, follow these steps:

Clone the repository:

git clone https://github.com/bshastry/docubot.git

Install the required dependencies:
```
pip install -r requirements.txt
```
Set up your environment variables:
- In the docubot top-level directory, copy the .env.template file to a new file called .env.
- Open the .env file and add the following API keys:
  - PINECONE_API_KEY: Your Pinecone API key
  - PINECONE_ENV: The environment where your Pinecone index is hosted (e.g., "gcp-starter")
  - OPENAI_API_KEY: Your OpenAI API key
Make sure to replace the placeholder values with your actual API keys and ENV variables. If you are only going to use DocuSense, providing an OPENAI_API_KEY is sufficient.
Collect documents you want DocuBot to work with in a local sub-directory:
```
cd docubot && mkdir -p documents && cd documents
<download_or_copy_documents_here>
```
Since this project was internally developed at Ethereum, there is a scripts folder in which you can find an ethereum specific document downloader. To use the ethereum downloader script, do:
```
cd docubot && ./scripts/bash/download_ethereum.sh ethereum-docs
```
You could create a similar script for your specific use-case.

For using DocuSense, you need to provide a single document, so please ignore this step.
Run the script:

To use DocuBot, run
```
python3 docubot.py /path/to/documents/directory
```
Please replace /path/to/documents/directory with the path to the directory that holds documents you want DocuBot to interface with (e.g., ethereum-docs from the previous step)

To use DocuSense, run
```
python3 docusense.py /path/to/document /path/to/summary.txt [--chunk_size <chunk_size>] [--chunk_overlap <chunk_overlap>]
```
--chunk_size and --chunk_overlap are optional arguments that accept the size of chunk of document and the overlap between chunks (both measured in OpenAI tokens). --chunk_size defaults to 3300 tokens, and --chunk_overlap defaults to 100 tokens.

Note: DocuSense splits a large document into smaller chunks if it may not be summarized in one shot. The chunk size and overlap impact of large documents are summarized. For example, smaller chunk sizes and larger chunk overlaps may result in an increased number of OpenAPI calls but offer finer granularity. The defaults have been chosen as a balance between summarization cost and accuracy. The defaults may not work for every document, so you can use these parameters to arrive at a trade-off that is acceptable to you.

Usage

Once DocuBot is running, you can start asking questions. Simply type your question and press Enter. To quit DocuBot, type "quit" or "exit".

DocuBot Examples

Here are some examples of questions you can ask DocuBot:

"What is Ethereum?"
"How does Ethereum work?"
"What is a smart contract?"
"Who created Ethereum?"
"What is the current price of Ether?"

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgements

This project uses the following libraries:

dotenv
langchain
pinecone
tiktoken
validators
urllib

I would like to thank the protocol security team at the Ethereum foundation for giving me the freedom to pursue this project. I would also like to thank Andrei Dumitrescu for a wonderful course on LangChain and OpenAI at Udemy that made prototyping DocuBot a breeze.

Contributing

If you'd like to contribute to this project, please open an issue or submit a pull request. We welcome any feedback or improvements.

Liability Information

DocuBot and DocuSense are released under the MIT license. Please note that while they are designed to provide useful information, it should not be considered a substitute for professional advice. The developers and contributors of DocuBot and DocuSense shall not be held liable for any damages or losses arising from the use of this application.

It is recommended to use DocuBot and DocuSense responsibly and exercise caution when relying on its responses. If in doubt, it is always a good idea to consult with domain experts or refer to trusted sources for accurate information.