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Supporting asyncio in langchain primitives allows for users to run them concurrently and creates more seamless integration with asyncio-supported frameworks (FastAPI, etc.) Summary of changes: **LLM** * Add `agenerate` and `_agenerate` * Implement in OpenAI by leveraging `client.Completions.acreate` **Chain** * Add `arun`, `acall`, `_acall` * Implement them in `LLMChain` and `LLMMathChain` for now **Agent** * Refactor and leverage async chain and llm methods * Add ability for `Tools` to contain async coroutine * Implement async SerpaPI `arun` Create demo notebook. Open questions: * Should all the async stuff go in separate classes? I've seen both patterns (keeping the same class and having async and sync methods vs. having class separation) |
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| .. | ||
| agents | ||
| chains | ||
| document_loaders | ||
| llms | ||
| memory | ||
| prompts | ||
| utils | ||
| agents.rst | ||
| chains.rst | ||
| document_loaders.rst | ||
| llms.rst | ||
| memory.rst | ||
| prompts.rst | ||
| state_of_the_union.txt | ||
| utils.rst | ||