awesome stuff showing how to build slack bots using langchain and @JinaAI_ !
@hwchase17
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LangChain Airbyte Dagster Integration for Document Processing
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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
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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?
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Advanced Text Splitting and Semantic Retrieval in LangChain
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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
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Deep Dive: Using LLMs to Chat with Your Data
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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?
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Six RAG Modules: Deep Dive on Text Splitters and Retrieval
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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 -
Map Rerank Chain: LLM Document Scoring and Ranking
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Map Rerank Finally, the Map Rerank Chain calls an LLM on each document, asking not only for an answer but also a score It then sorts the responses by the score and returns the highest one Docs: https://
api.python.langchain.com/en/latest/chai
ns/langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain.html#langchain.chains.combine_documents.map_rerank.MapRerankDocumentsChain
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Refine Documents Chain: Iterative Document Processing with LangChain
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Refine Documents Chain This chain uses the first document to get an initial response It then loops over the remaining docs, making a call to the language model to combining the response with the next document Docs: https://
api.python.langchain.com/en/latest/chai
ns/langchain.chains.combine_documents.refine.RefineDocumentsChain.html#langchain.chains.combine_documents.refine.RefineDocumentsChain
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MapReduceDocumentsChain: Processing Documents with LLM
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Map Reduce Chain This builds on top of the ReduceDocumentsChain It takes an LLMChain and a ReduceDocumentsChain. It first applies the LLMChain to each document, and then passes all the results to the ReduceDocumentsChain Docs: https://
api.python.langchain.com/en/latest/chai
ns/langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain.html#langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain
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StuffDocumentsChain: Basic Document Combination for LLMs
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Stuff Documents Chain The most basic CombineDocumentsChain, this takes N documents, formats them into a string using a PromptTemplate and `format_document`, and then combines them into a single prompt and passes them to an LLM Docs: https://
api.python.langchain.com/en/latest/chai
ns/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html#langchain.chains.combine_documents.stuff.StuffDocumentsChain
…