LangChain v0.0.42 Doc fixes by @YouSearchEngine team, @_abi_ Jinja formatting – @benderville Max Marginal Relevance example selector (based on paper by @xiye_nlp Caching improvements (h/t @devonbrackbill for idea) These are sneaky some awesome updates, lets
@langchain
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LangChain v0.0.41 Release: Multiple Inputs, Bug Fixes, Agent Improvements
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LangChain v0.0.41 Make `run` interface work for multiple inputs (
@ankush_gola11
) Bug fix for text splitter (h/t @prof_reed
) Lots of agent improvements (see ) -
LangChain v0.0.40: SQLAlchemy Cache and LLM Improvements
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LangChain v0.0.40 Add generic SQLAlchemy cache – @benderville Documentation cleanup – h/t @prof_reed Some BIG LLM improvements from @YouSearchEngine team: Support for local @huggingface models Support for streaming tokens from @OpenAI
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OpenAI Releases New Tokenizer for Accurate Token Usage Estimation
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@OpenAI also released a new tokenizer This makes it possible to more accurately (and quickly) estimate your token usage This is actually extremely helpful for the `collapse` step mentioned above!
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LangChain Updates: Tokenizer, Document Reduction, Better Embeddings
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TL;DR: The new tokenizer allows easy estimation of token usage The `collapse` step allows for map-reducing over large number of documents Better embeddings allows for better searching over those documents https://
github.com/hwchase17/lang
chain
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LangChain v0.0.39: Document Analysis with OpenAI Integration
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Analyzing documents just got a lot easier with v0.0.39 (with major assist from @OpenAI
) Added a `collapse` step into the map-reduce chain (
@johnjnay
)
Added support for @OpenAI new embeddings
Added support for @OpenAI new tokenizer Heres how they all work together -
Collapse Step for Map-Reduce Chain Scaling
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Adding a `collapse` step into the map-reduce chain This recursively collapses documents into smaller and smaller chunks, allowing you to scale to arbitrarily many documents This was a @johnjnay special – see his thread for more
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OpenAI Releases New Embedding Model for Semantic Search
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@OpenAI released a new embedding model (`text-embedding-ada-002`) This makes it possible to more easily do semantic search over collections of documents This is now the default OpenAI embedding model in LangChain
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LangChain Enables LLM Caching with Three Lines of Code
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Caching now enabled With 3 lines of code, you can now enable caching for all LLM calls This makes it cheaper and easier to experiment with changing only parts of a chain Supports both a temporary InMemoryCache, as well as a persistent SQLiteCache
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LangChain v0.0.38: New LLM Caching Feature Released
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New in v0.0.38 Read more about this here: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/llm_functionality.html#Caching
… @AkashSamant4 for making LLM configurations serializable, a prerequisite for this @brucehammer and @DannyHabibs for highlighting this as a valuable feature