Build a RAG system with Llama 3B-Instruct for your PDFs Uses API for partitioning & chunking, FAISS for vector store, huggingface for the model It's a collab notebook so it's easy to get started! Thanks @mariaKhalusova for a great resource! https://
colab.research.google.com/drive/1BJYYyrP
Ve0_9EGyXqeNyzmVZDrCRZwsg?usp=sharing#scrollTo=Y2m2l-vt_RSp
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@langchain
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Build RAG System Llama 3B-Instruct PDFs FAISS
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LangSmith Achieves 30% Accuracy Improvement Without Prompt Engineering
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How Dosu used LangSmith to achieve a 30% accuracy improvement with no prompt engineering One of the goals of LangSmith is to help teams set up a data flywheel. By capturing LLM outputs alongside user feedback, we can help developers automatically use that feedback to improve
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LangSmith Regression Testing Improvements for LLM Applications
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🧮Regression testing your LLM applications
— LangChain (@LangChain) 1 mai 2024
We've made big improvements to the "regression testing" flow in LangSmith to allow you to quickly spot changes in your application's performance:
🟥Automatically highlighting changes
⛪️Filter to changed datapoints
📄Different ways of… pic.twitter.com/ZxnE6pHAyiRegression testing your LLM applications We've made big improvements to the "regression testing" flow in LangSmith to allow you to quickly spot changes in your application's performance: Automatically highlighting changes
Filter to changed datapoints
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LangSmith RAG Evaluation Video Series Launch
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LangSmith Evaluations: RAG Evaluation (Document Retrieval)
— LangChain (@LangChain) 30 avril 2024
Evaluations can accelerate LLM app development, but it can be challenging to get started. We've kicked off a new video series focused on evaluations in LangSmith.
This is the 14th video in our series focusing… pic.twitter.com/P1qVbnJxm8LangSmith Evaluations: RAG Evaluation (Document Retrieval) Evaluations can accelerate LLM app development, but it can be challenging to get started. We've kicked off a new video series focused on evaluations in LangSmith. This is the 14th video in our series focusing
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Perplexica: Open-Source AI-Powered Search Engine Tool
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Perplexica – An AI-powered search engine Perplexica is an open-source AI-powered searching tool or an AI-powered search engine that goes deep into the internet to find answers. Built with LangChain, this is a great resource for getting started! https://
github.com/ItzCrazyKns/Pe
rplexica
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AutoHyDE: Advanced RAG Technique for LLM Enhancement
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AutoHyDE: Making HyDE Better for Advanced LLM RAG A fun article exploring new and creative RAG techniques Builds upon HyDE to add in keyword generation, clustering, etc Implements a LangChain Embedder which makes it easy to try out in your pipeline https://
towardsdatascience.com/autohyde-makin
g-hyde-better-for-advanced-llm-rag-619e58cdbd8e
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LangChain Tool Calling Enables Cross-Model Agent Development
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"LangChain Tool Calling feature just changed everything" We recently added a common interface for tool calling across model providers This makes it easy to build agents that work across models Watch @EdenEmarco177 explain why this is a big deal https://
youtube.com/watch?v=dj8Yqi
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Upstage releases LangChain integration package with Solar models
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Upstage integration package @upstageai has released their `langchain-upstage` integration package, including their Solar chat model, embedding model, a task-specific groundedness check model, and a layout analysis document loader. Check them out here:
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Advanced Vector Retrieval with Graph-Based Metadata Using LangChain Neo4j
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Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Text embeddings and vector similarity search help us find documents by understanding their meanings and how similar they are to each other. However, text embeddings aren’t
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LangGraph: Language Models Solve Olympiad Programming Benchmark
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LangGraph: Can Language Models Solve Olympiad Programming? Last week, Princeton researchers released the USACO benchmark dataset and showed that a zero-shot GPT-4 agent only passes 8.7% of the questions. We've implemented this paper in LangGraph and created a tutorial