Practical step-by-step LangChain guides
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Updated
Apr 9, 2024 - Jupyter Notebook
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.
A Multi-modal chatbot with LangChain, that supports RAG, Paperswithcode, and Image generation using Dall-E-3
LanchainLLM
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
Use a RAG Langchain agent that connects to your supermarket's inventory database and get information about product stocks and locations.
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 flask-based app using OpenAI API , Langchain and SQLAlchemy to query a SQL-based database from natural language.
This Project Contains Gemini Based RAG Application for Better Interaction and Insights on Online Data
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.
A foundation for building conversational AI applications that leverage OpenAI's API and the langchain library for seamless interaction with users.
A detailed notebook tutorial about LangChain Chains, Memory, Agents, Models, and Prompts.
Agentic RAG with LangChain and Pinecone
Use a Langchain GPT-agent to search the web with Tavily and answer the prompt including the target-URL.
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.
FullStack LangChain application using SpringBoot, LangChain4J and React - integrate with internal data and api and upload internal documents for RAG
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