chat_with_your_docs - GitLite

KO | EN

gitlite — search

#python #react #ai #machine-learning #deep-learning #nodejs #awesome #cli #kubernetes #vue #pytorch #mcp #ios #openai

chat_with_your_docs

(★ 160)

Discover and converse with advanced AI models like Mistral, LLAMA2, and GPT-3.5 from leading sources like OLLAMA, Hugging Face, and OpenAI. Easily extract insights from PDFs, web pages, and YouTube videos with our intuitive interface. Unlock the power of knowledge with seamless chat interactions.

파일 탐색기

최종 버전 다운로드 (.zip)

- chatbot_controller.py
- - cwd_diagram.png
  - cwd_flow.png
  - cwd_interface.png
- chain.py
- chatbot.py
- docs.py
- llms.py
.gitignore
LICENSE
main.py
README.md
requirements.txt
vectorstore.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/jorge-armando-navarro-flores/chat_with_your_docs

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd chat_with_your_docs

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

APT (Debian/Ubuntu 계열) Debian/Ubuntu 계열 리눅스에 기본 내장된 패키지 매니저입니다.

curl https://ollama.ai/install.sh | sh

공식 설치 스크립트를 다운로드해서 바로 실행합니다. 이 한 줄로 필요한 게 자동으로 설치됩니다.

sudo apt-get install ffmpeg

APT 패키지 저장소에서 바로 설치합니다 (Debian/Ubuntu 계열).

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

---
title: Chat With Your Docs
emoji: 📚
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 4.41.0
app_file: app.py
pinned: false
license: mit
---

# Chat With Your Docs

## Introduction

---

The ChatWithYourDocs Chat App is a Python application that allows you to chat with multiple Docs formats like PDF, WEB pages and YouTube videos. You can ask questions about the PDFs using natural language, and the application will provide relevant responses based on the content of the documents. This app utilizes a language model to generate accurate answers to your queries. Please note that the app will only respond to questions related to the loaded Docs.

## How It Works

---

![ChatWithYourDocs Chat App Diagram](https://raw.githubusercontent.com/jorge-armando-navarro-flores/chat_with_your_docs/master/docs/images/cwd_flow.png)

The application follows these steps to provide responses to your questions:

1. Doc Loading: The app reads multiple Docs types and extracts their text content.

2. Text Chunking: The extracted text is divided into smaller chunks that can be processed effectively.

3. Language Model: The application utilizes a language model to generate vector representations (embeddings) of the text chunks.

4. Similarity Matching: When you ask a question, the app compares it with the text chunks and identifies the most semantically similar ones.

5. Response Generation: The selected chunks are passed to the language model, which generates a response based on the relevant content of the Docs.

## Dependencies and Installation

---

To install the Chat With Your Docs App, please follow these steps:

1. Download [Ollama library](https://github.com/jmorganca/ollama)
   ```
   curl https://ollama.ai/install.sh | sh
   ```
2. pull the chat models we will use, in this case we will use [LLAMA2](https://ollama.ai/library/llama2), [MISTRAL](https://ollama.ai/library/mistral) and [GEMMA](https://ollama.ai/library/gemma)
   ```
   ollama pull llama2
   ```
   ```
   ollama pull mistral
   ```
   ```
   ollama pull gemma
   ```
3. Create new environment with python 3.9 and activate it, in this case we will use [conda](https://www.anaconda.com/download)

```
   conda create -n cwd python=3.9
   ```

```
   conda activate cwd
   ```

4. Clone the repository to your local machine.

```
   git clone https://github.com/jorge-armando-navarro-flores/chat_with_your_docs.git
   ```

```
   cd chat_with_your_docs
   ```

5. Install the required dependencies by running the following command:

```
   pip install -r requirements.txt
   ```

6. Install ffmpeg for YouTube videos:
   ```
   sudo apt-get install ffmpeg
   ```

## Usage

---

To use the Chat With Your Docs app, follow these steps:

1. Run the `main.py` file using the Streamlit CLI. Execute the following command:

```
   python3 main.py
   ```

2. The application will launch in your default web browser, displaying the user interface.
   ![ChatWithYourDocs Interface](https://raw.githubusercontent.com/jorge-armando-navarro-flores/chat_with_your_docs/master/docs/images/cwd_interface.png)

## Architecture

![ChatWithYourDocs Interface](https://raw.githubusercontent.com/jorge-armando-navarro-flores/chat_with_your_docs/master/docs/images/cwd_diagram.png)

<div _ngcontent-ng-c1219956002="" class="markdown markdown-main-panel" dir="ltr" style="--animation-duration: 600ms; --fade-animation-function: linear;"><p data-sourcepos="1:1-1:212"></p><p data-sourcepos="3:1-3:12"><strong>Classes:</strong></p><ul data-sourcepos="5:1-11:0">
<li data-sourcepos="5:1-5:225"><strong>LLM:</strong> This abstract class serves as a blueprint for different LLM implementations. It has subclasses like <code>OllamaModel</code>, <code>OpenAIModel</code>, and <code>HFModel</code> that handle specific LLM providers (Ollama, OpenAI, and Hugging Face).</li>
<li data-sourcepos="6:1-6:110"><strong>Docs:</strong> This class manages document loading from various sources like PDFs, web pages, and YouTube videos.</li>
<li data-sourcepos="7:1-7:124"><strong>VectorStore:</strong> This class creates a vector representation of the documents using LLM embeddings for efficient retrieval.</li>
<li data-sourcepos="8:1-8:206"><strong>Chain:</strong> This class defines the processing pipeline for the chatbot. It can be a simple chain for basic question answering or a retrieval chain that searches documents based on the conversation history.</li>
<li data-sourcepos="9:1-9:145"><strong>ChatBot:</strong> This class handles user interaction, maintains conversation history, and calls the appropriate chain method to generate responses.</li>
<li data-sourcepos="10:1-11:0"><strong>ChatBotController:</strong> This class is the main interface for the user. It allows setting the LLM model, document source, and retrieval options. It also handles user queries and interacts with the ChatBot instance.</li>
</ul><p data-sourcepos="12:1-12:18"><strong>Functionality:</strong></p><ol data-sourcepos="14:1-31:0">
<li data-sourcepos="14:1-17:0">
<p data-sourcepos="14:4-14:18"><strong>Setting Up:</strong></p>
<ul data-sourcepos="15:5-17:0">
<li data-sourcepos="15:5-15:166">The user selects the LLM provider (Ollama, OpenAI, or HuggingFace) and model from a dropdown menu. Optionally, an API key might be needed for certain providers.</li>
<li data-sourcepos="16:5-17:0">The user can choose to add documents (PDF, web page URL, or YouTube video URL) for retrieval tasks.</li>
</ul>
</li>
<li data-sourcepos="18:1-26:0">
<p data-sourcepos="18:4-18:18"><strong>Processing:</strong></p>
<ul data-sourcepos="19:5-26:0">
<li data-sourcepos="19:5-19:101">Based on the chosen LLM and documents (if any), the controller configures the processing chain.</li>
<li data-sourcepos="20:5-20:85">A simple chain is used for basic question answering without document retrieval.</li>
<li data-sourcepos="21:5-26:0">A retrieval chain involves:
<ul data-sourcepos="22:9-26:0">
<li data-sourcepos="22:9-22:87">Creating a vector representation of the documents using the LLM's embeddings.</li>
<li data-sourcepos="23:9-23:88">Defining prompts to generate search queries based on the conversation history.</li>
<li data-sourcepos="24:9-24:71">Retrieving relevant documents based on the generated queries.</li>
<li data-sourcepos="25:9-26:0">Using the retrieved documents to answer the user's question.</li>
</ul>
</li>
</ul>
</li>
<li data-sourcepos="27:1-31:0">
<p data-sourcepos="27:4-27:19"><strong>Interaction:</strong></p>
<ul data-sourcepos="28:5-31:0">
<li data-sourcepos="28:5-28:59">The user types their questions in the chat interface.</li>
<li data-sourcepos="29:5-29:140">The controller calls the <code>predict</code> method of the ChatBot, passing the user's query and conversation history (if retrieval is enabled).</li>
<li data-sourcepos="30:5-31:0">The ChatBot retrieves the appropriate response based on the chosen chain configuration.</li>
</ul>
</li>
</ol><p data-sourcepos="32:1-32:24"><strong>Additional Features:</strong></p><ul data-sourcepos="34:1-35:0">
<li data-sourcepos="34:1-35:0">The interface includes buttons for "Undo" and "Clear" conversation history.</li>
</ul><p data-sourcepos="36:1-36:171">Overall, this code demonstrates a well-structured architecture for a chatbot that can leverage different LLMs and incorporate document retrieval for enhanced capabilities.</p></div>