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),
}
```
113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
from typing import Any, Dict, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import (
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BaseModel,
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Field,
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SecretStr,
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)
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
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from langchain_community.utilities.requests import Requests
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class EdenAiEmbeddings(BaseModel, Embeddings):
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"""EdenAI embedding.
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environment variable ``EDENAI_API_KEY`` set with your API key, or pass
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it as a named parameter.
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"""
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edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token")
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provider: str = "openai"
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"""embedding provider to use (eg: openai,google etc.)"""
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model: Optional[str] = None
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"""
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model name for above provider (eg: 'gpt-3.5-turbo-instruct' for openai)
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available models are shown on https://docs.edenai.co/ under 'available providers'
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"""
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class Config:
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extra = "forbid"
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@pre_init
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key exists in environment."""
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values["edenai_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "edenai_api_key", "EDENAI_API_KEY")
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)
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return values
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@staticmethod
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def get_user_agent() -> str:
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from langchain_community import __version__
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return f"langchain/{__version__}"
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def _generate_embeddings(self, texts: List[str]) -> List[List[float]]:
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"""Compute embeddings using EdenAi api."""
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url = "https://api.edenai.run/v2/text/embeddings"
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headers = {
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"accept": "application/json",
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"content-type": "application/json",
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"authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr]
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"User-Agent": self.get_user_agent(),
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}
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payload: Dict[str, Any] = {"texts": texts, "providers": self.provider}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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request = Requests(headers=headers)
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response = request.post(url=url, data=payload)
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if response.status_code >= 500:
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raise Exception(f"EdenAI Server: Error {response.status_code}")
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elif response.status_code >= 400:
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raise ValueError(f"EdenAI received an invalid payload: {response.text}")
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elif response.status_code != 200:
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raise Exception(
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f"EdenAI returned an unexpected response with status "
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f"{response.status_code}: {response.text}"
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)
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temp = response.json()
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provider_response = temp[self.provider]
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if provider_response.get("status") == "fail":
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err_msg = provider_response.get("error", {}).get("message")
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raise Exception(err_msg)
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embeddings = []
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for embed_item in temp[self.provider]["items"]:
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embedding = embed_item["embedding"]
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embeddings.append(embedding)
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return embeddings
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed a list of documents using EdenAI.
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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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return self._generate_embeddings(texts)
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using EdenAI.
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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._generate_embeddings([text])[0]
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