mirror of
https://github.com/hwchase17/langchain
synced 2024-11-11 19:11:02 +00:00
34edfe4a16
**Description:** this PR adds Volcengine Rerank capability to Langchain, you can find Volcengine Rerank API from [here](https://www.volcengine.com/docs/84313/1254474) & [here](https://www.volcengine.com/docs/84313/1254605). [Volcengine](https://www.volcengine.com/) is a cloud service platform developed by ByteDance, the parent company of TikTok. You can obtain Volcengine API AK/SK from [here](https://www.volcengine.com/docs/84313/1254553). **Dependencies:** VolcengineRerank depends on `volcengine` python package. **Twitter handle:** my twitter/x account is https://x.com/LastMonopoly and I'd like a mention, thank you! **Tests and docs** 1. integration test: `test_volcengine_rerank.py` 2. example notebook: `volcengine_rerank.ipynb` **Lint and test**: I have run `make format`, `make lint` and `make test` from the root of the package I've modified.
135 lines
4.4 KiB
Python
135 lines
4.4 KiB
Python
from __future__ import annotations
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from copy import deepcopy
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from typing import Any, Dict, List, Optional, Sequence, Union
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from langchain_core.callbacks.base import Callbacks
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from langchain_core.documents import BaseDocumentCompressor, Document
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from langchain_core.pydantic_v1 import Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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class VolcengineRerank(BaseDocumentCompressor):
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"""Document compressor that uses `Volcengine Rerank API`."""
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client: Any = None
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"""Volcengine client to use for compressing documents."""
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ak: Optional[str] = None
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"""Access Key ID.
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https://www.volcengine.com/docs/84313/1254553"""
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sk: Optional[str] = None
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"""Secret Access Key.
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https://www.volcengine.com/docs/84313/1254553"""
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region: str = "api-vikingdb.volces.com"
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"""https://www.volcengine.com/docs/84313/1254488. """
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host: str = "cn-beijing"
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"""https://www.volcengine.com/docs/84313/1254488. """
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top_n: Optional[int] = 3
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"""Number of documents to return."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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allow_population_by_field_name = True
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if not values.get("client"):
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try:
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from volcengine.viking_db import VikingDBService
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except ImportError:
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raise ImportError(
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"Could not import volcengine python package. "
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"Please install it with `pip install volcengine` "
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"or `pip install --user volcengine`."
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)
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values["ak"] = get_from_dict_or_env(values, "ak", "VOLC_API_AK")
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values["sk"] = get_from_dict_or_env(values, "sk", "VOLC_API_SK")
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values["client"] = VikingDBService(
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host="api-vikingdb.volces.com",
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region="cn-beijing",
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scheme="https",
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connection_timeout=30,
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socket_timeout=30,
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ak=values["ak"],
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sk=values["sk"],
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)
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return values
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def rerank(
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self,
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documents: Sequence[Union[str, Document, dict]],
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query: str,
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*,
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top_n: Optional[int] = -1,
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) -> List[Dict[str, Any]]:
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"""Returns an ordered list of documents ordered by their relevance to the provided query.
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Args:
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query: The query to use for reranking.
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documents: A sequence of documents to rerank.
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top_n : The number of results to return. If None returns all results.
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Defaults to self.top_n.
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""" # noqa: E501
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if len(documents) == 0: # to avoid empty api call
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return []
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docs = [
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{
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"query": query,
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"content": doc.page_content if isinstance(doc, Document) else doc,
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}
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for doc in documents
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]
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from volcengine.viking_db import VikingDBService
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client: VikingDBService = self.client
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results = client.batch_rerank(docs)
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result_dicts = []
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for index, score in enumerate(results):
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result_dicts.append({"index": index, "relevance_score": score})
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result_dicts.sort(key=lambda x: x["relevance_score"], reverse=True)
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top_n = top_n if (top_n is None or top_n > 0) else self.top_n
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return result_dicts[:top_n]
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def compress_documents(
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self,
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documents: Sequence[Document],
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query: str,
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callbacks: Optional[Callbacks] = None,
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) -> Sequence[Document]:
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"""
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Compress documents using Volcengine's rerank API.
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Args:
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documents: A sequence of documents to compress.
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query: The query to use for compressing the documents.
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callbacks: Callbacks to run during the compression process.
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Returns:
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A sequence of compressed documents.
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"""
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compressed = []
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for res in self.rerank(documents, query):
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doc = documents[res["index"]]
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doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
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doc_copy.metadata["relevance_score"] = res["relevance_score"]
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compressed.append(doc_copy)
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return compressed
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