mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-11 01:56:12 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
from typing import Any, Callable, List
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from langchain_core.embeddings import Embeddings
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from langchain_community.llms.self_hosted import SelfHostedPipeline
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def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
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"""Inference function to send to the remote hardware.
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Accepts a sentence_transformer model_id and
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returns a list of embeddings for each document in the batch.
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"""
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return pipeline(*args, **kwargs)
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class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings):
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"""Custom embedding models on self-hosted remote hardware.
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Supported hardware includes auto-launched instances on AWS, GCP, Azure,
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and Lambda, as well as servers specified
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by IP address and SSH credentials (such as on-prem, or another
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cloud like Paperspace, Coreweave, etc.).
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To use, you should have the ``runhouse`` python package installed.
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Example using a model load function:
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.. code-block:: python
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from langchain_community.embeddings import SelfHostedEmbeddings
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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import runhouse as rh
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gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
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def get_pipeline():
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model_id = "facebook/bart-large"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
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embeddings = SelfHostedEmbeddings(
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model_load_fn=get_pipeline,
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hardware=gpu
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model_reqs=["./", "torch", "transformers"],
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)
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Example passing in a pipeline path:
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.. code-block:: python
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from langchain_community.embeddings import SelfHostedHFEmbeddings
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import runhouse as rh
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from transformers import pipeline
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gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
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pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
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rh.blob(pickle.dumps(pipeline),
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path="models/pipeline.pkl").save().to(gpu, path="models")
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embeddings = SelfHostedHFEmbeddings.from_pipeline(
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pipeline="models/pipeline.pkl",
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hardware=gpu,
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model_reqs=["./", "torch", "transformers"],
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)
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"""
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inference_fn: Callable = _embed_documents
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"""Inference function to extract the embeddings on the remote hardware."""
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inference_kwargs: Any = None
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"""Any kwargs to pass to the model's inference function."""
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class Config:
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extra = "forbid"
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a HuggingFace transformer model.
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Args:
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texts: The list of texts to embed.s
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Returns:
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List of embeddings, one for each text.
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"""
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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embeddings = self.client(self.pipeline_ref, texts)
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if not isinstance(embeddings, list):
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return embeddings.tolist()
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a HuggingFace transformer model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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text = text.replace("\n", " ")
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embeddings = self.client(self.pipeline_ref, text)
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if not isinstance(embeddings, list):
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return embeddings.tolist()
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return embeddings
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