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https://github.com/skywalkerdarren/chatweb
ChatWeb can crawl web pages, read PDF, DOCX, TXT, and extract the main content, then answer your questions based on the content, or summarize the key points.
https://github.com/skywalkerdarren/chatweb
ai chatgpt crawler docx embedding faiss gpt gpt-35-turbo news-extractor newspaper openai pdf pgvector postgresql vector-database
Last synced: 3 months ago
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ChatWeb can crawl web pages, read PDF, DOCX, TXT, and extract the main content, then answer your questions based on the content, or summarize the key points.
- Host: GitHub
- URL: https://github.com/skywalkerdarren/chatweb
- Owner: SkywalkerDarren
- License: mit
- Created: 2023-03-09T12:15:07.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-06-25T14:26:25.000Z (4 months ago)
- Last Synced: 2024-08-01T02:17:05.786Z (3 months ago)
- Topics: ai, chatgpt, crawler, docx, embedding, faiss, gpt, gpt-35-turbo, news-extractor, newspaper, openai, pdf, pgvector, postgresql, vector-database
- Language: Python
- Homepage:
- Size: 98.6 KB
- Stars: 867
- Watchers: 20
- Forks: 136
- Open Issues: 6
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Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - chatWeb - ChatWeb can crawl web pages, read PDF, DOCX, TXT, and extract the main content, then answer your questions based on the content, or summarize the key points. (NLP)
README
# ChatWeb
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SkywalkerDarren/chatWeb/blob/master/example.ipynb)
[English Doc](readme.md)
[中文文档](readme.zh.md)ChatWeb can crawl any webpage or extract text from PDF, DOCX, TXT files, and generate an embedded summary.
It can also answer your questions based on the content of the text.
It is implemented using the chatAPI and embeddingAPI based on gpt3.5, as well as a vector database.# Basic Principle
The basic principle is similar to existing projects such as chatPDF and automated customer service AI.Crawl web pages
Extract text content
Use GPT3.5's embedding API to generate vectors for each paragraph
Calculate the similarity score between each paragraph's vector and the entire text's vector to generate a summary
Store the vector-text mapping in a vector database
Generate keywords from user input
Generate a vector from the keywords
Use the vector database to perform a nearest neighbor search and return a list of the most similar texts
Use GPT3.5's chat API to design a prompt that answers the user's question based on the most similar texts in the list.
The idea is to extract relevant content from a large amount of text and then answer questions based on that content, which can achieve a similar effect to breaking through token limits.An improvement was made to generate vectors based on keywords rather than the user's question, which increases the accuracy of searching for relevant texts.
# Getting Started
## Manual installation:
- Install Python3
- Download this repository by running `git clone https://github.com/SkywalkerDarren/chatWeb.git`
- Navigate to the directory by running `cd chatWeb`
- Copy `config.example.json` to `config.json`
- Edit `config.json` and set `open_ai_key` to your OpenAI API key
- Install dependencies by running `pip3 install -r requirements.txt`
- Start the application by running `python3 main.py`## Docker:
if you prefer, you can also run this project using docker:- build the container using `docker-compose build` (only needed once when you are not planning to contibute to this repo)
- copy `config.example.json` to `config.json` and set all the needed stuff. The example config is already fine for running with docker, no need to change anything there, if you don't have the OPEN_AI_KEY in your env variables you can set it here too, or later if you run this app.
- run the container: `docker-compose up"
- open the application in browser: `http://localhost:7860`## Set language
- Edit `config.json`, set `language` to `English` or other language
## Mode Selection
- Edit `config.json` and set `mode` to `console`, `api`, or `webui` to choose the startup mode.
- In `console` mode, type `/help` to view commands.
- In `api` mode, an API service can be provided to the outside world. `api_port` and `api_host` can be set in `config.json`.
- In `webui` mode, a web user interface service can be provided. `webui_port` can be set in `config.json`, defaulting to `http://127.0.0.1:7860`.## Stream Mode
- Edit `config.json` and set `use_stream` to `true`.
## Setting the Temperature
- Edit `config.json` and set `temperature` to a value between 0 and 1.
- The smaller the value, the more conservative and stable the response will be. The larger the value, the more daring the response may be, possibly resulting in "hallucinations."## OpenAI Proxy Settings
- Edit `config.json` and add `open_ai_proxy` for your proxy address, for example:
```
"open_ai_proxy": {
"http": "socks5://127.0.0.1:1081",
"https": "socks5://127.0.0.1:1081"
}
```## Install PostgreSQL (Optional)
- Edit `config.json` and set `use_postgres` to `true`.
- Install PostgreSQL.
- The default SQL address is `postgresql://localhost:5432/mydb`, or you can set it in `config.json`.
- Install the pgvector plugin.Compile and install the extension (support Postgres 11+).
```bash
git clone --branch v0.4.0 https://github.com/pgvector/pgvector.git
cd pgvector
make
make install # may need sudo
```
Then load it in the database you want to use it in```postgresql
CREATE EXTENSION vector;
```- Install dependency with pip: `pip3 install psycopg2`
# Example
```txt
Please enter the link to the article or the file path of the PDF/TXT/DOCX document: https://gutenberg.ca/ebooks/hemingwaye-oldmanandthesea/hemingwaye-oldmanandthesea-00-e.html
Please wait for 10 seconds until the webpage finishes loading.
The article has been retrieved, and the number of text fragments is: 663
...
=====================================
Query fragments used tokens: 7219, cost: $0.0028876
Query fragments used tokens: 7250, cost: $0.0029000000000000002
Query fragments used tokens: 7188, cost: $0.0028752
Query fragments used tokens: 7177, cost: $0.0028708
Query fragments used tokens: 2378, cost: $0.0009512000000000001
Embeddings have been created with 663 embeddings, using 31212 tokens, costing $0.0124848
The embeddings have been saved.
=====================================
Please enter your query (/help to view commands):
```# TODO
- [x] Support for pdf/txt/docx files
- [x] Support for in-memory storage without a database (faiss)
- [x] Support for Stream
- [x] Support for API
- [x] Support for proxies
- [x] Add Colab support
- [x] Add language support
- [x] Support for temperature
- [x] Support for webui
- [ ] Other features that have not been thought of yet# Star History
![](https://api.star-history.com/svg?repos=SkywalkerDarren/chatWeb)