Chat With Documents Using ChainLit, LangChain, Ollama & Mistral 🧠
In this video, I am demonstrating how you can create a simple Retrieval Augmented Generation UI locally in your computer. You can follow along with me by cloning the repo locally. You can also use LangSmith for tracing the LLM calls and use LangChain Hub for using already available prompt template for different models, for this case, mistral.
Open Source in Action 🚀
Mistral is used as Large Language model.
LangChain is used as a Framework for LLM
Mistral model is downloaded locally using Ollama
Chainlit is used for deploying.
👉🏼 Links:
Chainlit: https://docs.chainlit.io/get-started/...
LangChain: https://www.langchain.com/
Ollama: https://ollama.ai/
💻 GitHub repo for code: https://github.com/sudarshan-koirala/...
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✌️Patreon: / datasciencebasics
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🔗 🎥 Other videos you might find helpful:
🔥 Databricks playlist: • 30 Days Of DataBricks
⛓️ Langflow: • ⛓️ langflow | UI For 🦜️🔗 LangChain
⛓️ Flowise: • Flowise | UI For 🦜️🔗 LangChain
🔥Chainlit playlist: • Chainlit
🦜️🔗 LangChain playlist: • LangChain
🦙 LlamaIndex Playlist: • LlamaIndex
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🤝 Connect with me:
📺 Youtube: https://www.youtube.com/@datascienceb...
👔 LinkedIn: / sudarshan-koirala
🐦 Twitter: / mesudarshan
🔉Medium: / sudarshan-koirala
💼 Consulting: https://topmate.io/sudarshan_koirala
#langchain #chainlit #ollama #mistral #rag #retrievalaugmentedgeneration #chatgpt #datasciencebasics