Chat with any Data App

Chatbot app to chat with any source of data (doc, url, ...) leveraging LLMs, LangChain, and Gradio. Current version has the following features:

• LLM: llama3

• Data source: "folder", "csv", "doc", "docx", "epub", "html", "md", "pdf", "ppt", "pptx", "txt", "ipynb", "py", and "url".

This app has been inspired by a few DeepLearningAI courses about LangChain.

Code: https://github.com/aslansd/chat-with-any-data-app 

Startup Brainstorming

A multi-agent system app using open source LLMs of Ollama, CrewAI, LangChain, and Gradio to simulate brainstorming part of a startup.

The multi-agent system simulates the environment of a startup composed of one idea generator and three main staffs: senior technical staff, senior product manager, and senior business intelligence analyst. The idea generator comes up with three ideas relevant to the topic of your choice and the three main members of the startup each one does their portion of works.

This app has been inspired by a DeepLearningAI course about CrewAI.

Code: https://github.com/aslansd/startup-brainstorming 

A Dungeon Game Using Together API

A Dungeon game, powered by one of LLMs provided by Together API. In the notebook, there is a simple Dungeon game built using Together API and Gradio simulating a fantasy world comprised of kingdoms, towns, characters, and inventories.

This app has been inspired by a DeepLearningAI course about Dungeon and Together frameworks. 

Code: https://github.com/aslansd/dungeon_game_using_together_api  

Modified Ollama Deep Researcher

Modified Ollama Deep Researcher is an extension of LangChain’s Ollama Deep Researcher, a local web research assistant built upon the multi-agent framework of LangGraph, that uses any LLM hosted by Ollama. Give it a topic and it will generate a web search query, gather web search results (via seven different search APIs such as DuckDuckGo by default), summarise the results of web search, reflect on the summary to examine knowledge gaps, generate a new search query to address the gaps, search, and improve the summary for a user-defined number of cycles. It will provide the user a final markdown summary with all sources used.

This app has been inspired by two LangChain Academy courses about LangGraph and LangSmith frameworks. 

Code: https://github.com/aslansd/ollama-deep-researcher-modified   

Future Projects:

1) Developing open source tools based on large language models. (short-term)

2) Fine-tuning an open source large language model for a specialised purpose. (mid-term)

3) Building and training a biologically-inspired large language model from scratch. (long-term)