4/ Monte-Carlo Tree What? Monte-Carlo Tree Search (MCTS) is a heuristic search algorithm used in decision-making processes. In LATS, MCTS selects the best candidate using an Upper Confidence Bound for Trees (UCT).
This balances exploration and exploitation of a policy.
@langchain
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Monte-Carlo Tree Search: UCT Algorithm for Decision-Making
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Language Agent Tree Search: Self-Optimizing AI Algorithm
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1/ Language Agent Tree Search (LATS) LATS is a unified algorithm that uses self-reflection (and additional external feedback) to self-optimize and efficiently adapt to new tasks and environments. It performs better than similar algorithms like Tree of Thoughts, ReAct, and
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LangGraph LATS Implementation Code and Video Tutorial Released
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2/ Links For those who are eager, you can check out the code and video tutorial at the links below: Youtube: https://
youtube.com/watch?t=625
Python: https://
github.com/langchain-ai/l
anggraph/blob/main/examples/lats/lats.ipynb
… Thanks to @hinthornw for the implementation. -
LATS: Monte-Carlo Tree Search for AI Decision Making
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3/ What is LATS? 1. Select: the best scoring action thus far
2. Expand & act: Generate candidate actions and execute
3. Reflect & evaluate: Observe outcomes and score decisions
4. Backpropagate: update applicable trajectory scores So… Monte-Carlo Tree Search! -
LangSmith Improves Data Filtering for LLM Pipeline Analysis
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Better Filtering Experience in LangSmith If you’re logging a good portion of your production traces to LangSmith, you probably have a lot of data to sift through. Filtering through data in a systematic way is important for understanding how your LLM pipeline is
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LangSmith Tracing and Evaluation Tools Now Available for TypeScript
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LangSmith Tracing and Evaluation in TypeScript It's now easier than ever to trace non-LangChain TS code and run evaluations in LangSmith! We've added a cookbook here: https://
github.com/langchain-ai/l
angsmith-cookbook/blob/main/typescript-testing-examples/traceable-example/LangSmith_TS_Demo-Traceable.ipynb
… For tracing, we've added – wrapOpenAI(new OpenAI()) allows you to call @OpenAI -
Natural Language to SQL with Local LLM Models
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natural language -> sql, visualizations, analytics impressive stuff. uses a local LLM
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FireFunction V1: Open-Weights Function Calling Model from Fireworks
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FireFunction V1 – a new, open-weights function calling model from @FireworksAI_HQ Returning structured outputs is (a) hard, (b) incredibly useful Excited to highlight **4** different ways to use this model with LangChain #1: Function Calling with LangChain JS Cookbook:
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AstraDB Vector Database Integration with LangChain
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@AstraDB Cassandra-based Vector DB Explore the langchain-astradb integration package! Harness the serverless, vector-capable database built on Apache Cassandra for scalable NoSQL solutions enhanced with the power of vector similarity search. https://
python.langchain.com/docs/integrati
ons/vectorstores/astradb
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Reflexion: Agents Learn to Reflect and Self-Correct
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2/ Paper: Reflexion, by Shinn, @ashwingop
, @ShunyuYao12 , et. al. https://
arxiv.org/abs/2303.11366