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https://github.com/geeks-of-data/knowledge-gpt

Extract knowledge from all information sources using gpt and other language models. Index and make Q&A session with information sources.
https://github.com/geeks-of-data/knowledge-gpt

context embedding embedding-vectors gpt gpt3-turbo gpt4 huggingface huggingface-transformers information-extraction language-model llama llm natural-language-processing openai python question-answering scraper sentence-embeddings sentence-similarity vector-search

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Extract knowledge from all information sources using gpt and other language models. Index and make Q&A session with information sources.

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README 

        

![knowledgegpt](static_files/logo.png)



# 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.



## Pypi Link: https://pypi.org/project/knowledgegpt/



# Installation



1. PyPI installation, run in terminal:  `pip install knowledgegpt`



2. Or you can use the latest version from the repository: `pip install -r requirements.txt` and then `pip install .`



3. Download needed language model for parsing: `python3 -m spacy download en_core_web_sm`



## How to use



#### Restful API



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



#### Set Your API Key



1. Go to [OpenAI > Account > Api Keys](https://platform.openai.com/account/api-keys)

2. Create new screet key and copy

3. Enter the key to [example_config.py](./examples/example_config.py)



#### How to use the library



```python

# 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



# 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:



```python

# Basic Usage

basic_extractor = BaseExtractor(df)

answer, prompt, messages = basic_extractor.extract("What is the title of this PDF?", max_tokens=300)

```



```python

# 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)

```



```python

# 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)

```



```python

# 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)

```



```python

# 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)



# 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)

```

## Docker Usage



```bash

docker build -t knowledgegptimage .

docker run -p 8888:8888 knowledgegptimage

```



## 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] Extract knowledge from whole youtube playlist



## TODO



- [x] FAISS support 

- [ ] Add a vector database (Pinecone, Milvus, Qdrant etc.)

- [x] Add Whisper Model

- [x] Add Whisper Local Support (not over openai API)

- [ ] Add Whisper for audio longer than 25MB

- [ ] Add a web interface

- [ ] Migrate to Promptify for prompt generation

- [x] Add ChatGPT support

- [ ] 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

- [ ] Advanced web scraping

- [ ] Prompt-Answer storage (the odds are that this will be done in a separate project)

- [ ] Add a better documentation 

- [ ] Add a better logging system

- [ ] Add a better error handling system

- [ ] Add a better testing system

- [ ] Add a better CI/CD system

- [x] Dockerize the project

- [ ] Add search engine support, such as Google, Bing, etc.

- [ ] Add support for opensource OpenAI alternatives (for answer generation)

- [ ] Evaluating dependencies and removing unnecessary ones

- [ ] Providing prompt flexibility for using with whatever model



( To be extended...)



## System Architecture



(To be updated with a better image)