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https://github.com/hwchase17/langchain.git
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8516a03a02
This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
"""Wrapper for Rememberizer APIs."""
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from typing import Any, Dict, List, Optional, cast
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import requests
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from langchain_core.documents import Document
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from langchain_core.utils import get_from_dict_or_env
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from pydantic import BaseModel, model_validator
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class RememberizerAPIWrapper(BaseModel):
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"""Wrapper for Rememberizer APIs."""
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top_k_results: int = 10
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rememberizer_api_key: Optional[str] = None
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@model_validator(mode="before")
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@classmethod
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def validate_environment(cls, values: Dict) -> Any:
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"""Validate that api key in environment."""
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rememberizer_api_key = get_from_dict_or_env(
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values, "rememberizer_api_key", "REMEMBERIZER_API_KEY"
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)
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values["rememberizer_api_key"] = rememberizer_api_key
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return values
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def search(self, query: str) -> dict:
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"""Search for a query in the Rememberizer API."""
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url = f"https://api.rememberizer.ai/api/v1/documents/search?q={query}&n={self.top_k_results}"
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response = requests.get(
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url, headers={"x-api-key": cast(str, self.rememberizer_api_key)}
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)
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data = response.json()
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if response.status_code != 200:
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raise ValueError(f"API Error: {data}")
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matched_chunks = data.get("matched_chunks", [])
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return matched_chunks
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def load(self, query: str) -> List[Document]:
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matched_chunks = self.search(query)
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docs = []
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for matched_chunk in matched_chunks:
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docs.append(
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Document(
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page_content=matched_chunk["matched_content"],
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metadata=matched_chunk["document"],
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)
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)
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return docs
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