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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LLMS
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Customizing LangChain with Neo4j for Advanced RAG Systems
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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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AutoTrain Now Supports Chat Templates in UI and CLI
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Now you can apply chat template directly in the AutoTrain UI or CLI and train on datasets like H4's no_robots without any extra effort. Just `pip install autotrain-advanced` to run it locally or deploy on huggingface spaces.
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AI-powered web browsing and chat summarization ideas
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From top of my head – Real headless web browsing with AI instead of access to search index – to let AI to understand how a certain UI looks like
– Access to all recent messages in discord channel or any other chat – to aggregate them into a summary -
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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New LLM Playground for Testing Multiple Models
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“It’s hard to experiment across LLMs, prompting strategies, and temperature settings, and to keep track of them to see which ones perform best. Our new #LLM playground makes it easy for you to finally test and track experiments across a wide range of LLMs including Azure OpenAI,… pic.twitter.com/imve72OMPP
— DataRobot (@DataRobot) 26 décembre 2023“It’s hard to experiment across LLMs, prompting strategies, and temperature settings, and to keep track of them to see which ones perform best. Our new #LLM playground makes it easy for you to finally test and track experiments across a wide range of LLMs including Azure OpenAI,
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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
… Comes down to two steps: Does the -
OpenAI’s 6 Strategies for Better GPT-4 Results
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OpenAI released their own Prompt Engineering Guide. The guide is very helpful for anyone trying to maximize LLMs. Here's the 6 strategies they shared for getting better results from GPT-4: