Building blocks for rapid development of GenAI applications
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Updated
Jun 5, 2025 - Python
Building blocks for rapid development of GenAI applications
Explore LangChain and build powerful chatbots that interact with your own data. Gain insights into document loading, splitting, retrieval, question answering, and more.
A Python package to access different LLMs, embeddings, vector stores etc.
Course LangChain Chat with Your Data
A proof of concept for Question-Answering API on a specific topic using RAG from pre-defined document(s) on the topic
Demonstrate two types of chat interactions with a Mattermost instance leveraging Mattermost OpenAPI v3 spec and Spring AI.
Developed A LLM Powered Recommendation System, Based on Instructor-XL, Google Flan / GPT3.5 and FAISS. Conducted a consumer survey to understand the problems of a consumer, created the problem statement from the insights derived from the survey.
A simple demo of a Flutter application that implements Retrieval-Augmented Generation (RAG) using OpenAI's APIs.
This repository showcases Python scripts demonstrating interactions with various models using the LangChain library. From fine-tuning to custom runnables, explore examples with Gemini, Hugging Face, and Mistral AI models.
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