3 Levels of Building Agents A great article by @LucasEnkrateia goes over how to build agents in three levels of abstraction: From Scratch
OpenAI function calling
LangChain Check it out here:
@langchain
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Three Levels of Building AI Agents Explained
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Customizing LangChain with Neo4j for Advanced RAG Systems
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Customized LangChain @neo4j Integration If you want to build advanced RAG, you probably need to know how to customize any off-the-shelf guide to your use case @tb_tomaz dives deep on how to do this with Graph DBs like Neo4j Blog: https://
github.com/langchain-ai/l
angchain/pull/15084
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ResearchPlot: AI-Powered Scenario Analysis and Flowchart Generation
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ResearchPlot This project aims to understand the user's scenario, ask relevant questions, and generate a flowchart for clear and concise representation of the findings Helpful for rubber-ducking with yourself! Code: https://
github.com/PratikSingh121
/ResearchPlot/blob/main/readme.md
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Create Customized Chat Bots with LangChain Framework
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[Community Spotlight] Create Your Customized Chat Bot with LangChain The most prevalent UX for LLM applications is chat. This blog walks through all the steps needed to create a customized Chat Bot Blog: https://
medium.com/@akturkrehsan/
create-your-customized-chat-bot-with-langchain-41be6b928428
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RAG Inference with Local Models on CPUs
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RAG on CPUs (with Intel Developer Cloud) Retrieval Augmented Generation is a great way to combine YOUR data with LLMs… which is why it's so nice to be able to do this with local models This great goes over how to do that (with CPUs no less!) Blog: https://
towardsdatascience.com/retrieval-augm
ented-generation-rag-inference-engines-with-langchain-on-cpus-d5d55f398502
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Hands-on RAG Meetup: Combining Your Data with LLMs
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Hands-on RAG meetup Want to learn how to combine YOUR data with LLMs? Retrieval Augmented Generation (RAG) is the dominant way to do do The Austin community group will be doing a hands-on RAG meetup – check it out
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LangChain Transforms Data Analysis with AI Agents
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(Community Spotlight) How LangChain Transforms Data Analysis and More Want to learn how to build an agent for data analysis? This blog walks through that and more, covering a few different use cases for LangChain h/t Ankush Singal Blog: https://
ai.gopubby.com/unlocking-the-
power-of-language-how-langchain-transforms-data-analysis-and-more-3c4f327d520d
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Per-User RAG Retrieval Guide with LangChain Pinecone
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Per-User Retrieval Guide Most production RAG apps need some sort of per-user (or per-org) retrieval We've recently added a guide showing how to do this with @pinecone
! s/o @cyrusofeden for the nudge Guide: https://
python.langchain.com/docs/use_cases
/question_answering/per_user
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RAG Pipeline Setup Tutorial for PDF Processing
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RAG With PDFs Want to easily set up a RAG pipeline over PDFs? This great tutorial by our friends at @clarifai goes over how to do exactly that Blog: https://
clarifai.com/blog/ai-in-5-r
ag-with-pdfs
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Connecting LLMs to Your Data: Options Overview
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If you’re looking to connect LLMs to your data… @pjausovec will be going through some of the options we have tmrw!