LangChain + OpenAI tutorial: Building a Q&A system w/ own text data

LangChain is a fantastic tool for developers looking to build AI systems using the variety of LLMs (large language models, like GPT-4, Alpaca, Llama etc), as it helps unify and standardize the developer experience in text embeddings, vector stores / databases (like Chroma), and chaining it for downstream applications through agents. In this tutorial we’re using our own custom text / data and training a question and answer agent on it.

Want to learn more about LLMs (large language models)? Here’s my learning path:– Watch PART 2 of the LangChain / LLM series:https://youtu.be/Fz0WJWzfNPI

– Watch PART 3 of the LangChain / LLM seriesLangChain + HuggingFace’s Inference API (no OpenAI credits required!)https://youtu.be/dD_xNmePdd0

– Watch PART 4 of the LangChain / LLM sereis:How Embeddings in LLMs work (a practical tutorial + code demo)

All the code for the LLM (large language models) series featuring GPT-3, ChatGPT, LangChain, LlamaIndex and more are on my github repository so go and star or fork it. Happy Coding!https://github.com/onlyphantom/llm-python

Other links mentioned in the video:– LangChain documentation: https://python.langchain.com/en/latest/index.html– Visualizing embeddings: https://github.com/onlyphantom/elang– Learn about DuckDB: https://youtu.be/6iyuMJeGhZk

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