mirror of
https://github.com/hwchase17/langchain.git
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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),
}
```
91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
import os
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import sys
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from typing import Any, List
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel
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class JohnSnowLabsEmbeddings(BaseModel, Embeddings):
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"""JohnSnowLabs embedding models
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To use, you should have the ``johnsnowlabs`` python package installed.
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Example:
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.. code-block:: python
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from langchain_community.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings
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embedding = JohnSnowLabsEmbeddings(model='embed_sentence.bert')
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output = embedding.embed_query("foo bar")
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""" # noqa: E501
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model: Any = "embed_sentence.bert"
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def __init__(
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self,
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model: Any = "embed_sentence.bert",
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hardware_target: str = "cpu",
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**kwargs: Any,
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):
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"""Initialize the johnsnowlabs model."""
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super().__init__(**kwargs)
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# 1) Check imports
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try:
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from johnsnowlabs import nlp
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from nlu.pipe.pipeline import NLUPipeline
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except ImportError as exc:
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raise ImportError(
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"Could not import johnsnowlabs python package. "
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"Please install it with `pip install johnsnowlabs`."
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) from exc
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# 2) Start a Spark Session
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try:
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os.environ["PYSPARK_PYTHON"] = sys.executable
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os.environ["PYSPARK_DRIVER_PYTHON"] = sys.executable
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nlp.start(hardware_target=hardware_target)
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except Exception as exc:
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raise Exception("Failure starting Spark Session") from exc
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# 3) Load the model
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try:
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if isinstance(model, str):
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self.model = nlp.load(model)
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elif isinstance(model, NLUPipeline):
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self.model = model
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else:
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self.model = nlp.to_nlu_pipe(model)
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except Exception as exc:
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raise Exception("Failure loading model") from exc
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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 JohnSnowLabs transformer model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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df = self.model.predict(texts, output_level="document")
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emb_col = None
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for c in df.columns:
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if "embedding" in c:
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emb_col = c
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return [vec.tolist() for vec in df[emb_col].tolist()]
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a JohnSnowLabs 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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return self.embed_documents([text])[0]
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