# 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)