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
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LangChain Updates: Tokenizer, Document Reduction, Better Embeddings
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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
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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 -
LLM Configuration Serialization in JSON and YAML Formats
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Serialization of LLM Configurations Have a favorite temperature/parameter setting for your LLM that you want store/version not in code? Now you can! @AkashSamant4 made it super easy to read/write LLM configurations to/from disk in json and yaml format
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LangChain Documentation Updates: New Functionality and Serialization
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Check out these docs for a walkthrough of this new functionality: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/llm_functionality.html
… Check out these docs for an overview of serialization: https://
langchain.readthedocs.io/en/latest/exam
ples/prompts/llm_serialization.html
… And of course, check out the GitHub if you want to flag an issue or open a PR! https://
github.com/hwchase17/lang
chain
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LLM Interface Enhancement: Extended Output with Top Results and Token Counts
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Returning extra information Previously, the LLM only returned the most likely string Now there is a new interface to return more information: the top n strings for each input, as well as LLM specific information (h/t @0xAwill for adding the token counts you see below)
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New abstraction enables returning generation metadata like logprobs
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This abstraction also opens up the door to returning more information about each generation For example, the logprobs associated with a generation (something I know @alexgraveley would love)