Open Source Extraction Service Have you ever wanted to extract specific structured information from text, HTML, or PDFs? Today we are launching a hosted version of our most recent OSS use-case accelerant: a service for extracting structured data from unstructured sources,
@langchain
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AI Trends Dashboard: Multi-Page Dash App with LangChain
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Trends in AI – Plotly Dash App with LLM A multi-page Python Dash app highlighting the growing role of AI in today's world. It incorporates a LangChain Pandas Agent to empower users with deeper insights into the datasets. YouTube: https://
youtube.com/watch?v=t3O-0m
zLJzI
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LangGraph Agent Supervisor with Anthropic Models Open-Sourced
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Agent Supervisor with Anthropic We've put a lot of work into LangGraph to make it the best run time for agents. Most of the examples are with OpenAI, but @eucheetan just open-sourced an example with @AnthropicAI models https://
github.com/prof-frink-lab
/slangchain/blob/main/docs/modules/graphs/examples/anthropic/agent_supervisor.ipynb
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LangChain SQL Agent for Processing Massive Documents
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LangChain SQL Agent for Massive Documents Interaction Cool blog showing off a complete flow: Extract structured output from raw PDFs with a progressively better approach
Insert into SQL
Query using SQL agent https://
pub.towardsai.net/langchain-sql-
agent-for-massive-documents-interaction-510fc4bc65a4
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LangGraph Financial Agent Application Tutorial for Beginners
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LangGraph: Build Your Own AI Financial Agent Application (Beginners) Walks through creating tools, assigning them to an agent, and coordinating their work through a graph. Plus a Gradio UI! h/t @MervinPraison https://
youtube.com/watch?v=C8wvHK
PlTls
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LangChain Chatchat: Open Source Local Q&A Application
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LangChain Chatchat A local Q&A application. The goal is to build a KBQA(Knowledge based Q&A) solution that is friendly to Chinese scenarios and open source models and can run both offline and online. 25k stars on Github! https://
github.com/chatchat-space
/Langchain-Chatchat/blob/master/README_en.md
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Advanced RAG: Generation and Evaluation Techniques
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Advanced RAG Series: Generation and Evaluation In the fifth part of this series, we look at techniques for: Generation (CRAG, Self-RAG, RRR) Evaluation (RAGAs, Langsmith, DeepEval) Another awesome blog by @divyanshu_van https://
div.beehiiv.com/p/advanced-rag
-series-generation-evaluation
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Building RAG Systems for Powerful Data Access
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How to Make a RAG System to Gain Powerful Access to Your Data This article is a great introduction to RAG, and walks through a few important steps of a RAG pipeline: Retrieve Data
Pre-process Data
Implement RAG
Test https://
towardsdatascience.com/how-to-make-a-
rag-system-to-gain-powerful-access-to-your-data-caf4bb9186ea
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Long Context LLMs and RAG: Exploring Limitations
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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
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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?