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https://github.com/hwchase17/langchain.git
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24605bcdb6
- [x] **PR message**:
- **Description:** Fixes warning messages raised due to missing
`protected_namespaces` parameter in `ConfigDict`.
- **Issue:** https://github.com/langchain-ai/langchain/issues/27609
- **Dependencies:** No dependencies
- **Twitter handle:** @gawbul
93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
"""Wrapper around Bookend AI embedding models."""
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import json
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from typing import Any, List
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import requests
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from langchain_core.embeddings import Embeddings
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from pydantic import BaseModel, ConfigDict, Field
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API_URL = "https://api.bookend.ai/"
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DEFAULT_TASK = "embeddings"
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PATH = "/models/predict"
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class BookendEmbeddings(BaseModel, Embeddings):
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"""Bookend AI sentence_transformers embedding models.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import BookendEmbeddings
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bookend = BookendEmbeddings(
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domain={domain}
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api_token={api_token}
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model_id={model_id}
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)
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bookend.embed_documents([
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"Please put on these earmuffs because I can't you hear.",
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"Baby wipes are made of chocolate stardust.",
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])
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bookend.embed_query(
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"She only paints with bold colors; she does not like pastels."
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)
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"""
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domain: str
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"""Request for a domain at https://bookend.ai/ to use this embeddings module."""
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api_token: str
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"""Request for an API token at https://bookend.ai/ to use this embeddings module."""
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model_id: str
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"""Embeddings model ID to use."""
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auth_header: dict = Field(default_factory=dict)
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model_config = ConfigDict(protected_namespaces=())
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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self.auth_header = {"Authorization": "Basic {}".format(self.api_token)}
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed documents using a Bookend deployed embeddings 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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result = []
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headers = self.auth_header
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headers["Content-Type"] = "application/json; charset=utf-8"
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params = {
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"model_id": self.model_id,
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"task": DEFAULT_TASK,
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}
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for text in texts:
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data = json.dumps(
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{"text": text, "question": None, "context": None, "instruction": None}
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)
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r = requests.request(
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"POST",
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API_URL + self.domain + PATH,
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headers=headers,
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params=params,
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data=data,
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)
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result.append(r.json()[0]["data"])
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return result
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def embed_query(self, text: str) -> List[float]:
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"""Embed a query using a Bookend deployed embeddings 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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