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),
}
```
93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
from typing import Any, Dict, List
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import requests
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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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DEFAULT_MODEL_NAME = "@cf/baai/bge-base-en-v1.5"
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class CloudflareWorkersAIEmbeddings(BaseModel, Embeddings):
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"""Cloudflare Workers AI embedding model.
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To use, you need to provide an API token and
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account ID to access Cloudflare Workers AI.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import CloudflareWorkersAIEmbeddings
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account_id = "my_account_id"
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api_token = "my_secret_api_token"
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model_name = "@cf/baai/bge-small-en-v1.5"
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cf = CloudflareWorkersAIEmbeddings(
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account_id=account_id,
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api_token=api_token,
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model_name=model_name
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)
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"""
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api_base_url: str = "https://api.cloudflare.com/client/v4/accounts"
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account_id: str
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api_token: str
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model_name: str = DEFAULT_MODEL_NAME
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batch_size: int = 50
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strip_new_lines: bool = True
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headers: Dict[str, str] = {"Authorization": "Bearer "}
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def __init__(self, **kwargs: Any):
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"""Initialize the Cloudflare Workers AI client."""
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super().__init__(**kwargs)
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self.headers = {"Authorization": f"Bearer {self.api_token}"}
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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 Cloudflare Workers AI.
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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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if self.strip_new_lines:
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texts = [text.replace("\n", " ") for text in texts]
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batches = [
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texts[i : i + self.batch_size]
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for i in range(0, len(texts), self.batch_size)
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]
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embeddings = []
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for batch in batches:
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response = requests.post(
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f"{self.api_base_url}/{self.account_id}/ai/run/{self.model_name}",
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headers=self.headers,
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json={"text": batch},
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)
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embeddings.extend(response.json()["result"]["data"])
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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 Cloudflare Workers AI.
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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", " ") if self.strip_new_lines else text
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response = requests.post(
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f"{self.api_base_url}/{self.account_id}/ai/run/{self.model_name}",
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headers=self.headers,
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json={"text": [text]},
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)
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return response.json()["result"]["data"][0]
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