Practical step-by-step LangChain guides

The personal assistance chatbot integrates appointment creation, Google Calendar event scheduling with Google Meet, and email notifications using the Langchain and Zapier NLA integration.

This repository hosts a Jupyter notebook that demonstrates the seamless integration of the Bard API with the LangChain library. By leveraging the capabilities of both platforms, we've crafted a custom Language Learning Model (LLM) that allows users to harness the power of Bard within the LangChain ecosystem.

LanchainLLM

A Multi-modal chatbot with LangChain, that supports RAG, Paperswithcode, and Image generation using Dall-E-3

Generative AI demos, an example of a langchain based application that implements Retrieval-Augmented-Generation for an enhanced generation. It uses OpenAI model and OpenAI function tool agent. Able to run Ollama Mistral local LLM too.

FastAPI application with streaming Langchain agents and Tavily search tool

This project leverages the power of OpenAI's Language Model Agents to create an intelligent SQL query assistant.

Explores the implementation of a LangChain Agent using Azure Cosmos DB for MongoDB vCore to handle traveler inquiries and bookings. The project provides detailed instructions for setting up the environment and loading travel data, aiming to empower developers to integrate similar agents into their solutions.

This Project Contains Gemini Based RAG Application for Better Interaction and Insights on Online Data

Use a Langchain Wikipedia Agent that uses the LLMChain function that for a given input, first writes a short summary and then gives information about the physiological effects.

A foundation for building conversational AI applications that leverage OpenAI's API and the langchain library for seamless interaction with users.

Use a Langchain GPT-agent to search the web with Tavily and answer the prompt including the target-URL.

Use a RAG Langchain agent that connects to your supermarket's inventory database and get information about product stocks and locations.

A detailed notebook tutorial about LangChain Chains, Memory, Agents, Models, and Prompts.

Agentic RAG with LangChain and Pinecone

For the purposes of familiarization and learning. Consists of utilizing LangChain framework, LangSmith for tracing, OpenAI LLM models, Pinecone serverless vectorDB using Jupyter Notebook and Python.