RAG for long context LLMs: Will long context LLMs really kill RAG? This is a talk @RLanceMartin gave at a few recent meetups that pulls together threads from a few different projects related to this question. Multi-needle in a haystack shows limitations in long-context
@langchain
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MongoDB Integration Advanced RAG and LLM Cost Optimization
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We recently added a deep integration with MongoDB in a few key areas… AND we released some awesome references for getting started! A blog on advanced RAG https://
mongodb.com/developer/prod
ucts/atlas/advanced-rag-langchain-mongodb/
… Cutting LLM costs with a semantic cache https://
youtube.com/watch?v=kAgSPz
lgnDM
… Which one is your favorite? -
Deploying Coding Agents Without Pain
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Deploying coding agents without the agonizing pain Do you want to build a coding agent inspired by Devin (
@cognition_labs
) or AlphaCodium (
@CodiumAI
)? You are not alone. OpenDevin repo had ~1k stars before any code was submitted! But they are hard to build, deploy, & secure. -
Optimizing LLM Systems with DSPy and LangChain
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Optimization of LLM Systems with DSPy and LangChain/LangSmith There are a lot of novel and interesting ideas in DSPy around optimization of LLM systems Super excited to host a webinar with @lateinteraction and @hwchase17 diving into this topic! https://
us06web.zoom.us/webinar/regist
er/WN_n4HUNqhFQFa4ax3kYrZW8g#/registration
… We've -
Memory Hackathon: Long-term Memory for LLM Applications
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Memory Hackathon with LangChain, http://
new.computer, MongoDB and Anthropic Sign up here: https://
lu.ma/taa6ijxt Long-term memory for LLM applications is one of the most interesting and important – yet also under explored – areas out there We are THRILLED to be -
Few-Shot Learning: Logging, Feedback, and Self-Learning GPTs
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Great example of the importance of few shot examples How do you get these examples? One way is to have a proper logging + feedback collection service set up You can even automate the "feedback" -> "few shot example" loop – see "self learning GPTs" https://
youtube.com/watch?v=OnQQeW
Ewzyw
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MongoDB Atlas LangChain Launch Semantic Caching Vector Search
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MongoDB Atlas LangChain We have some exciting announcements in tandem with our friends at @MongoDB
! We're launching: Support for semantic caching powered by Atlas Vector Search in Python Practical examples: advanced RAG, a fullstack JS app (s/o -
Self-Learning GPTs: Improving Applications Through Feedback
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YouTube Video: Self Learning GPTs Using Feedback to Improve Your Application Concrete example: easily build a chatbot to generate tweets in a particular style without direct prompting!
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Is RAG Dead With Million Token Context Windows?
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LangChain x @TimescaleDB Webinar: Is RAG Really Dead?! With the advent of LLMs with context windows of 1 million tokens (like Google Gemini), many have declared the utility of Retrieval Augmented Generation dead and no longer necessary. But is this actually true? Or simply
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Self Learning GPTs: Improving Applications Through User Feedback
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Self Learning GPTs: Using Feedback to Improve Your Application We built and hosted a simple demo app to show how applications can learn and improve from feedback over time: …
https://self-
learning-gpts-vz4y4ooboq-uw.a.run.app The app is called "Self Learning GPTs" and it uses LangSmith to gather feedback