langchain/poetry.lock
Ankush Gola bc7e56e8df
Add asyncio support for LLM (OpenAI), Chain (LLMChain, LLMMathChain), and Agent (#841)
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)
2023-02-07 21:21:57 -08:00

6589 lines
437 KiB
TOML

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[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[[package]]
name = "zipp"
version = "3.12.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
category = "dev"
optional = false
python-versions = ">=3.7"
files = [
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]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["flake8 (<5)", "func-timeout", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
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all = ["manifest-ml", "elasticsearch", "faiss-cpu", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text"]
llms = ["manifest-ml", "torch", "transformers"]
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lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "d39158bd758a0665d7a214297ef3b30efee553aaf9f50160d79cd405853d0450"