Metadata-Version: 2.1
Name: knowledgegpt
Version: 0.0.3b0
Summary: A package for extracting and querying knowledge using GPT models
Home-page: https://github.com/geeks-of-data/knowled-gpt
Author: Eren Akbulut, Kaan Ozbudak
Author-email: erenakbulutwork@gmail.com, kaanozbudakk@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

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![knowledgegpt](./public/logo.png)

# Pip Lib

`https://pypi.org/project/knowledgegpt/`

- To use library
- `pip install knowledgegpt`

## Before run project locally

- Please check config file use own open-ai api-key and your own mongo uri

## To run mongo locally

- docker pull mongo:latest
- `sh sh/docker_mongo_local_run.sh`
- docker ps

# knowledgegpt

***knowledgegpt*** is designed to gather information from various sources, including the internet and local data, which
can be used to create prompts. These prompts can then be utilized by OpenAI's GPT-3 model to generate answers that are
subsequently stored in a database for future reference.

To accomplish this, the text is first transformed into a fixed-size vector using either open source or OpenAI models.
When a query is submitted, the text is also transformed into a vector and compared to the stored knowledge embeddings.
The most relevant information is then selected and used to generate a prompt context.

***knowledgegpt*** supports various information sources including websites, PDFs, PowerPoint files (PPTX), and
documents (Docs). Additionally, it can extract text from YouTube subtitles and audio (using speech-to-text technology)
and use it as a source of information. This allows for a diverse range of information to be gathered and used for
generating prompts and answers.

## How to use

#### Restful API

```uvicorn server:app --reload```

#### How to install the library

```pip install knowledgegpt```
or

```
git clone https://github.com/geeks-of-data/knowledge-gpt.git
pip install .
```

Before running for the first time download the related spacy model by running:

```
# !python3 -m spacy download en_core_web_sm
```

#### How to use the library

```
# Import the library
from knowledgegpt.extractors.web_scrape_extractor import WebScrapeExtractor

# Import OpenAI and Set the API Key
import openai
from example_config import SECRET_KEY 
openai.api_key = SECRET_KEY


# If you want to use mongodb to store the data
from config import MONGO_URI
from pymongo import MongoClient

client  = MongoClient(MONGO_URI)
db = client.openai_test

# Define target website
url = "https://en.wikipedia.org/wiki/Bombard_(weapon)"

# Initialize the WebScrapeExtractor
scrape_website = WebScrapeExtractor( url=url, embedding_extractor="hf", model_lang="en")

# Prompt the OpenAI Model
answer, prompt, messages = scrape_website.extract(query="What is a bombard?",max_tokens=300,  to_save=True, mongo_client=db)

# See the answer
print(answer)

# Output: 'A bombard is a type of large cannon used during the 14th to 15th centuries.'

```

Other examples can be found in the [examples](./examples) folder.
But to give a better idea of how to use the library, here is a simple example:

```
# Basic Usage
basic_extractor = BasicExtractor(df)
answer, prompt, messages = basic_extractor.extract("What is the title of this PDF?", max_tokens=300)
```

```
# PDF Extraction
pdf_extractor = PDFExtractor( pdf_file_path, extraction_type="page", embedding_extractor="hf", model_lang="en", )
answer, prompt, messages = pdf_extractor.extract(query, max_tokens=1500, to_save=True, mongo_client=db)
```

```
# PPTX Extraction
ppt_extractor = PowerpointExtractor(file_path=ppt_file_path, embedding_extractor="hf", model_lang="en",)
answer, prompt, messages = ppt_extractor.extract( query,max_tokens=500, to_save=True, mongo_client=db)
```

```
# DOCX Extraction
docs_extractor = DocsExtractor(file_path="../example.docx", embedding_extractor="hf", model_lang="en", is_turbo=False)
answer, prompt, messages = \
    docs_extractor.extract( query="What is an object detection system?", max_tokens=300, to_save=True, mongo_client=db)
```

```
# Extraction from Youtube video (audio)
scrape_yt_audio = YoutubeAudioExtractor(video_id=url, model_lang='tr', embedding_extractor='hf')
answer, prompt, messages = scrape_yt_audio.extract( query=query, max_tokens=1200, to_save=True, mongo_client=db)

# Extraction from Youtube video (transcript)
scrape_yt_subs = YTSubsExtractor(video_id=url, embedding_extractor='hf', model_lang='en')
answer, prompt, messages = scrape_yt_subs.extract( query=query, max_tokens=1200, to_save=True, mongo_client=db)
```

## How to contribute

0. Open an issue
1. Fork the repo
2. Create a new branch
3. Make your changes
4. Create a pull request

## FEATURES

- [x] Extract knowledge from the internet (i.e. Wikipedia)
- [x] Extract knowledge from local data sources - PDF
- [x] Extract knowledge from local data sources - DOCX
- [x] Extract knowledge from local data sources - PPTX
- [x] Extract knowledge from youtube audio (when caption is not available)
- [x] Extract knowledge from youtube transcripts
- [x] Library implementation (partially done, initial release)

## TODO

- [x] Add a database (partially done)
- [ ] Add a vector database
- [x] Add Whisper Model
- [ ] Add Whisper for audio longer than 25MB
- [ ] Add a web interface
- [ ] Migrate to Promptify
- [x] Add ChatGPT support (only in docs endpoint and experimental)
- [ ] Add ChatGPT support with a better infrastructure and planning
- [ ] Increase the number of prompts
- [ ] Increase the number of supported knowledge sources
- [ ] Increase the number of supported languages
- [ ] Increase the number of open source models
- [ ] Dockerize the project
- [ ] Advanced web scraping
- [ ] Prompt-Answer storage
- [ ] Add a better documentation
- [ ] Check library functions to see if they are working properly
- [ ] Add a better logging system
- [ ] Add a better error handling system
- [ ] Add a better testing system

( To be extended...)

## System Architecture

![System Architecture](./public/Knowledge-ex.png)


