@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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@langchain
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AstraDB Vector Database Integration with LangChain
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Implementing Reflexion for Your App in LangGraph
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1/ See how to implement Reflexion for your app in LangGraph at the links below: Python: https://
github.com/langchain-ai/l
anggraph/blob/main/examples/reflexion/reflexion.ipynb
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Youtube: https://
youtube.com/watch?v=v5ymBT
XNqtk&t=299s
… H/T @WHinthorn -
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 -
Reflexion: Language Agents Learn via Verbal Reinforcement
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Reflexion: Language Agents with Verbal Reinforcement Learning Reflexion lets LLMs learn without updating its weights. Its actor reflects on each agent decision, using citations and other targeted feedback to generate actionable critique. Reflections are stored in chat history
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HyDE Query Translation Technique for RAG Systems
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RAG From Scratch: Query Translation (HyDE) Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. This is our final video on Query Translation, focused on Hypothetical Document Embeddings (HyDE) from Gao et al. Problem:
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Groq LPU Achieves 500 Tokens Per Second Inference Speed
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🏎️ Incredible Speeds with Groq's LPU-Powered Inference 🕔
— LangChain (@LangChain) 22 février 2024
The langchain-groq package exposes inference capabilities powered by @GroqInc's Language Processing Units (LPUs), soaring to new heights with up to 500 tokens per second on this @MistralAI Mixtral model. Welcome to the… pic.twitter.com/2aBTv7KCfPIncredible Speeds with Groq's LPU-Powered Inference The langchain-groq package exposes inference capabilities powered by @GroqInc
's Language Processing Units (LPUs), soaring to new heights with up to 500 tokens per second on this @MistralAI Mixtral model. Welcome to the -
LangChain Launches People Page to Recognize Community Contributors
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We've added a People page! There are some incredible humans from all over the world who have been instrumental in helping the LangChain community flourish, and we'd like to do more to recognize them and thank them for their efforts. To start, we're highlighting top
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IBM WatsonX Foundation Models LangChain Integration for Business
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@IBM WatsonX Foundation Models for Business Dive into IBM WatsonX's powerful foundation models with our LangChain integration! Leverage WatsonX's AI and data platform, built specifically for business applications. Begin your journey today with the `langchain-ibm` python
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RAG Query Translation Step-Back Prompting Technique
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RAG From Scratch: Query Translation (Step-Back) Our RAG From Scratch video series walks through impt RAG concepts in short / focused videos w/ code. This is the fourth in our videos on Query Translation, focused on step-back prompting from @denny_zhou
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Learn GenAI App Development with JavaScript and LangChain
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Want to learn how to build GenAI apps with JavaScript? @adamcowley will be teaching the "Learn with Jason" (
@LWJShow
) how to build their own custom apps using LangChain JS Happening LIVE tomorrow! Sign up here: https://
youtube.com/watch?v=sMTCGF
rAo08
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