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LangChain From 0 To 1: Unveiling the Power of LLM Programming

The talk

https://video.fosdem.org/2024/ub2252a/fosdem-2024-2384-langchain-from-0-to-1-unveiling-the-power-of-llm-programming.av1.webm

Install

(optional) create virtualenv

python -m venv fosdemvenv && source fosdemvenv/bin/activate

Install requirements

pip install -r requirements.txt

API Key

export OPENAI_API_KEY=xx-xXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxXxX

run

python src/rag/rag.py

Talk Abstract

Unlocking the realm of Artificial Intelligence has never been more accessible than with LangChain and its seamless integration of external APIs or locally hosted OS Language Model Models (LLMs). In this talk, we embark on a journey from zero to Retrieval Augmented Generation (RAG).

Our hands-on exploration will guide you through the basics of LangChain and LLMs, demonstrating how just a few lines of code can transform any application into an intelligent powerhouse. We'll delve into the construction of a simple Python application, serving as a springboard for grasping more intricate functions and concepts within the LangChain framework.

Key Takeaways:

  • Querying LLMs via APIs: Witness the simplicity of tapping into the vast capabilities of Language Models through straightforward API calls.

  • Textual Data Handling: Learn to load text from a diverse array of documents, enabling your application to process information from various sources.

  • Text Splitting Techniques: Explore the world of text splitting, understanding different methods to break down textual data into meaningful units.

  • Introduction to Embeddings: Gain insights into the fundamental concept of embeddings, unraveling why they are pivotal in enhancing the intelligence of applications.

  • Vector Databases: Navigate the landscape of vector databases and understand their role in optimizing data retrieval and manipulation.

  • RAG (Retrieval Augmented Generation): Witness the transformative power of RAG as we leverage it to query LLMs over your dataset, showcasing a synergy between retrieval and generation.

  • Chains and other notable use cases

Join us in this concise yet comprehensive session, where we demystify LangChain and empower you to harness the full potential of LLM programming. Whether you're a novice or an experienced developer, this talk is your gateway to building intelligent applications with ease.

Useful links

Presentation

https://docs.google.com/presentation/d/1frjlNBY0Et7xpNizhKVtdtTLe09DXuQv8BjGmbRZyNk/

Rag in production

LangChain chatbot

https://chat.langchain.com/

https://github.com/langchain-ai/chat-langchain

LangChain documentation

https://python.langchain.com/docs/get_started/introduction

LangChain GitHub

https://github.com/langchain-ai/langchain

python src/rag/rag.py

Links, tools, credits

LangChain on X is worth following https://x.com/LangChainAI

Visualize different text splitters https://chunkviz.up.railway.app/

Text splitting tutorial https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/5_Levels_Of_Text_Splitting.ipynb

LangChain State of AI 2023 https://blog.langchain.dev/langchain-state-of-ai-2023/

Code screenshot generated with Carbon https://carbon.now.sh/

Contacts

https://twitter.com/Stll00