Langchain chatbot with chroma vector storage memory

[ossirytk]

This is an upgrade to my previous chatbot. It adds a vector storage memory using ChromaDB. The main chatbot is built using llama-cpp-python, langchain and chainlit. It supports json, yaml, V2 and Tavern character card formats.

This version uses langchain llamacpp embeddings to parse documents into chroma vector storage collections. There is also a test script to query and test the collections Everything is local and in python. Runs in a virtual env with minimum installations.

The purpose of this project is to give out a more fleshed out character based chatbot as a foundation for testing and prototyping chroma features.

https://github.com/ossirytk/llama-cpp-chat-memory

[ossirytk]

I've added support for json lorebooks and metadata filtering. It's a bit hacky currently, but I'll see about improving the filtering when Chroma supports more complicated actions.

[ossirytk]

The documentation for this was kind of lacking, so I've updated the instructions. Also added a collection of resources for beginners. https://github.com/ossirytk/llm_resources

[ossirytk]

• Added basic csv parsing
• Improved ner parsing
• Improved web scraping
• Better document fetching

[ossirytk]

Updated Ner parsing to use multiprocess for improved performance.

[ossirytk]

Updated metadata key processing for text chunks to use multiprocess also. Checking all text chunks for relevant metadata keys was slow when there were many text chunks and a large amount of keys. The performance has been improved significantly.

[ossirytk]

Reworked collections. There is now a context collection and a message example collection available.
Run settings and settings panel. Collection and prompt template defaults are now in run settings.
Settings can be changed runtime with settings panel.