Experiment with langchain based embeddings

1. Create a virtual environment python3 -m venv venv
2. Activate the virtual environment source venv/bin/activate
3. Install the requirements pip install -r requirements.txt
4. Create nltk_data directory mkdir nltk_data
5. Create chroma directory mkdir chroma
6. Fill .env file with your appropriate values for LLM and embeddings connections

Run streamlit run ui.py and open the link in your browser Some depencencies might be missing, install them with pip install <dependency>

Loader Params:

{
  "path": "./data",
  "use_multithreading": false,
  "loader_cls": "TextLoader",
  "table_raw_content": true,
  "docs_page_split": true
}

Load params:

{}

Loader Params:

{
    "url": "https://yoursite.atlassian.com/wiki",
    "token": "Your API Token for confluence",
    "cloud": false | true
}

Load params:

{
    "space_key": "SOMESPACE",
    "limit": 50,
    "max_pages": 1000,
    "content_format": "view" | "storage" | "anonymous" | "editor"
}

Loader Params:

{
    "source": "https://github.com/arozumenko/langanswer.git",
    "branch": "main",
    "username": "your git username",
    "password": "your git api token",
    "use_multithreading": false,
    "loader_cls": "TextLoader",
    "index_file_exts": ".py, .txt"
}

loader_cls is a fallback class in case document was not qualified for any of the available classes.

Load params:

{}