“The most effective debugging tool is still careful thought, coupled with judiciously placed print statements.” — Brian Kernighan, co-creator of Unix
CODE
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LangChain Hub Makes Sharing Agents and Chains Easier
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This should make it easier than ever to share your chains/agents, which we think are much more useful than just prompts! See here for how to upload agents (
https://
github.com/hwchase17/lang
chain-hub/tree/master/agents
…) and here for how to upload chains (
https://
github.com/hwchase17/lang
chain-hub/tree/master/chains
…) -
Loading LangChain Agents with Self-Ask Search Tool
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To load agents: “`
from langchain.agents import initialize_agent agent = initialize_agent(tools, llm, agent="lc://agents/self-ask-with-search/agent.json")
“` Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/agents/examples/load_from_hub.html
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LangChain SQL Agent Variants with Custom Parameters
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This makes it possible upload chains/agents that are variants of existing chains/agents but with different parameters For example, here's an agent specifically instructed to interact with SQL databases that @fpingham came up with: https://
github.com/hwchase17/lang
chain-hub/tree/master/agents/zero-shot-react-sql
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Loading Chains with LangChain Framework
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To load chains: “`
from langchain.chains import load_chain chain = load_chain('lc://chains/path/to/file.json')
“` Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/chains/generic/from_hub.html
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LangChainHub Update: All Chains and Agents Now Available
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Big update to LangChainHub With lots of help from @sjwhitmore
, all chains and agents are now on there, and loadable in a single line of code Including some chains/agents not available in the python package -
RecursiveCharacterTextSplitter Text Processing Tool Overview
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RecursiveCharacterTextSplitter is my fav! https://
langchain.readthedocs.io/en/latest/modu
les/utils/combine_docs_examples/textsplitter.html#recursive-character-text-splitting
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Harvard CS197: AI Research Experience Course and Book
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AI Research Experience – Harvard CS197 AI Research course and book that teaches how to do cutting-edge research, research workflows, and using tools commonly used in AI research(like PyTorch, Lightning, Hugging Face, and more). Course book: https://
cs197.seas.harvard.edu -
Efficient Checkpointing Challenges in Distributed Systems
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Haha don’t give all the answers away! Though efficient and fast checkpointing in a distributed system is a challenge in itself. Would love it if you could share some of your exercises with the rest of the community
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GitHub Actions Custom Model Deployment and AI Automation Features
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Speed and flexibility to more quickly see business value from AI – that’s the goal of this month’s release. Explore new features like: GitHub Actions Custom Model Deployment Quickrun Autopilot Time Series Clustering Experience Improvements