Check out example notebooks: https://
langchain.readthedocs.io/en/latest/modu
les/chains/combine_docs_examples/question_answering.html
… Shout out to @johnjnay
, @BruceHammer
, and others in the discord for helping to ideate + test John's already started to use it in some more awesome work on llm-lobbyist:
@langchain
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LangChain Question Answering Examples and LLM Applications
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Recursive Text Splitter for Improved Chunk Processing
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Recursive Text Splitter The previous text splitter only split on a single character This one recursively splits chunks on different types of characters, until all chunks are below the desired size
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LangChain 0.0.59 Adds Map-Rerank Chain and Text Splitting
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New LangChain version makes it easier than ever to combine LLMs with your own data Brand new map-rerank chain
Recursive Text Splitter
Customize summarization and question answering `pip install langchain==0.0.59` -
Map-Rerank Chain for Enhanced Question Answering Systems
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Brand new map-rerank chain This chain can be used for question answering. It maps over documents, trying to both (a) answer a question, (b) assign a score to how good the answer is It then picks the answer with the highest score
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OCR and QA Library Enables Intelligent PDF Document Interaction
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Lots of older docs are in PDFs. That makes them hard to search and work with.
— LangChain (@LangChain) 8 janvier 2023
This library combines OCR with Question/Answering to allow you to talk with any PDF file
Awesome stuff https://t.co/qqhinBsV8bLots of older docs are in PDFs. That makes them hard to search and work with. This library combines OCR with Question/Answering to allow you to talk with any PDF file Awesome stuff
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LangChain Notebook Replicates ChatGPT Functionality
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@cyberfilmai we even have a notebook replicating ChatGPT! https://
langchain.readthedocs.io/en/latest/modu
les/memory/examples/chatgpt_clone.html
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Vector Search and Map-Reduce Chains for Document Question Answering
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exactly! if doc is larger you can still prune with the vector search, so you may not need to chain. but you could also load more documents and do a map-reduce chain over them. See these docs for QA in general: https://
langchain.readthedocs.io/en/latest/use_
cases/question_answering.html
… and let us know if any questions! -
Chains Generate Long Original Content: Screenplay Example
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a great example of this is https://
arxiv.org/abs/2209.14958 – it uses chains to create long and original generations (whole screenplays) -
Data Augmentation and Chaining for Enhanced AI Responses
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+1, data augmented generation will be key for insightful responses also chaining can help generate a more complex response in multiple steps
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Code Differences Analysis Awaiting Release Details
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very similar stuff in there already! waiting to see the code released to understand where exactly the differences lie