Updated Anthropic Client In the newest @langchain release (0.0.225) we've updated our @AnthropicAI wrapper to deal with their new 0.3.* client Should be seamless upgrade experience – just `pip install -U anthropic` and you'll be set! No more changes than that
@hwchase17
-
Team feedback on complex AI logic chain implementation
By
–
Big shout to the team at @exemplaryai for feedback on this front! There's sneaky a lot of logic in this one chain, hopefully this clears some of it up. Anything else we can do to help here?
-
Enhanced Reference Documentation with Better Docstrings Examples
By
–
Improved Reference Docs We beefed up our reference documentation to include better docstrings and a more end-to-end example There's a lot of toggles to play with, hopefully this helps make it more clear what all the parameters are Docs:
-
ConversationalRetrievalChain Gets Quality of Life Improvements
By
–
ConversationalRetrievalChain Upgrades One of our more popular chains is the ConversationalRetrievalChain, which allows you to create a retrieval augmented generation chatbot We've introduced some small but impactful quality of life changes:
-
Building Slack Bots with LangChain and Jina AI
By
–
awesome stuff showing how to build slack bots using langchain and @JinaAI_ !
-
LangChain Airbyte Dagster Integration for Document Processing
By
–
LangChain x @AirbyteHQ x @dagster Load documents from one of @AirbyteHQ
's 300+ sources, split/embed/store it with @langchain
… all orchestrated by @dagster
! Excited to share this end to end guide -
Memory Implementation for Follow-up Questions in AI Systems
By
–
And finally, chat: we explain how to use memory to enable follow up questions, why that is necessary, and methods for doing so Big shout to to @RLanceMartin for helping prep all these materials!!!! Always fun to do these! What topic should we do next?
-
Advanced Text Splitting and Semantic Retrieval in LangChain
By
–
Text splitters: there's a lot of nuance in how you split text!! We cover a few examples of the advanced methods we have in LangChain Retrieval: semantic search can get you 80% of the way there easily, but getting that last bit can be hard. We cover methods to push further
-
Six RAG Modules: Deep Dive on Text Splitters and Retrieval
By
–
There are six modules: – Document loaders
– Text splitters
– Embeddings and vector stores
– Retrieval
– QA generation
– Chat We go deep on each one. The three I think are most interesting/insightful: text splitters, retrieval, chat -
Deep Dive: Using LLMs to Chat with Your Data
By
–
New @DeepLearningAI_ class I had so much fun teaching the last one with @AndrewYNg I had to return for a follow up This one is a deep dive on the most popular applications of LLMs to date: using them to chat with your data What do we cover?