2023-07-21 19:37:21 +00:00
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[tool.poetry]
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name = "langchain-monorepo"
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version = "0.0.1"
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description = "LangChain mono-repo"
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authors = []
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license = "MIT"
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readme = "README.md"
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2023-08-04 14:31:39 +00:00
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repository = "https://www.github.com/langchain-ai/langchain"
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2023-07-21 19:37:21 +00:00
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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[tool.poetry.group.docs.dependencies]
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langchain = { path = "libs/langchain/", develop = true }
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autodoc_pydantic = "^1.8.0"
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myst_parser = "^0.18.1"
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nbsphinx = "^0.8.9"
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sphinx = "^4.5.0"
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sphinx-autobuild = "^2021.3.14"
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sphinx_book_theme = "^0.3.3"
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sphinx_rtd_theme = "^1.0.0"
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sphinx-typlog-theme = "^0.8.0"
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sphinx-panels = "^0.6.0"
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toml = "^0.10.2"
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myst-nb = "^0.17.1"
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linkchecker = "^10.2.1"
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sphinx-copybutton = "^0.5.1"
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nbdoc = "^0.0.82"
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2023-07-12 20:20:08 +00:00
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[tool.poetry.group.codespell.dependencies]
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codespell = "^2.2.0"
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[tool.codespell]
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skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples'
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# Ignore latin etc
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ignore-regex = '.*(Stati Uniti|Tense=Pres).*'
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# whats is a typo but used frequently in queries so kept as is
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# aapply - async apply
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# unsecure - typo but part of API, decided to not bother for now
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FEAT: Integrate Xinference LLMs and Embeddings (#8171)
- [Xorbits
Inference(Xinference)](https://github.com/xorbitsai/inference) is a
powerful and versatile library designed to serve language, speech
recognition, and multimodal models. Xinference supports a variety of
GGML-compatible models including chatglm, whisper, and vicuna, and
utilizes heterogeneous hardware and a distributed architecture for
seamless cross-device and cross-server model deployment.
- This PR integrates Xinference models and Xinference embeddings into
LangChain.
- Dependencies: To install the depenedencies for this integration, run
`pip install "xinference[all]"`
- Example Usage:
To start a local instance of Xinference, run `xinference`.
To deploy Xinference in a distributed cluster, first start an Xinference
supervisor using `xinference-supervisor`:
`xinference-supervisor -H "${supervisor_host}"`
Then, start the Xinference workers using `xinference-worker` on each
server you want to run them on.
`xinference-worker -e "http://${supervisor_host}:9997"`
To use Xinference with LangChain, you also need to launch a model. You
can use command line interface (CLI) to do so. Fo example: `xinference
launch -n vicuna-v1.3 -f ggmlv3 -q q4_0`. This launches a model named
vicuna-v1.3 with `model_format="ggmlv3"` and `quantization="q4_0"`. A
model UID is returned for you to use.
Now you can use Xinference with LangChain:
```python
from langchain.llms import Xinference
llm = Xinference(
server_url="http://0.0.0.0:9997", # suppose the supervisor_host is "0.0.0.0"
model_uid = {model_uid} # model UID returned from launching a model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024},
)
```
You can also use RESTful client to launch a model:
```python
from xinference.client import RESTfulClient
client = RESTfulClient("http://0.0.0.0:9997")
model_uid = client.launch_model(model_name="vicuna-v1.3", model_size_in_billions=7, quantization="q4_0")
```
The following code block demonstrates how to use Xinference embeddings
with LangChain:
```python
from langchain.embeddings import XinferenceEmbeddings
xinference = XinferenceEmbeddings(
server_url="http://0.0.0.0:9997",
model_uid = model_uid
)
```
```python
query_result = xinference.embed_query("This is a test query")
```
```python
doc_result = xinference.embed_documents(["text A", "text B"])
```
Xinference is still under rapid development. Feel free to [join our
Slack
community](https://xorbitsio.slack.com/join/shared_invite/zt-1z3zsm9ep-87yI9YZ_B79HLB2ccTq4WA)
to get the latest updates!
- Request for review: @hwchase17, @baskaryan
- Twitter handle: https://twitter.com/Xorbitsio
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-07-28 04:23:19 +00:00
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ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon'
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