from __future__ import annotations
import importlib
import os
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator
from langchain_core.retrievers import BaseRetriever
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
[docs]class NeuralDBRetriever(BaseRetriever):
"""使用ThirdAI的NeuralDB的文档检索器。"""
thirdai_key: SecretStr
"""ThirdAI API密钥"""
db: Any = None #: :meta private:
"""神经数据库实例"""
class Config:
"""此pydantic对象的配置。"""
extra = Extra.forbid
underscore_attrs_are_private = True
@staticmethod
def _verify_thirdai_library(thirdai_key: Optional[str] = None) -> None:
try:
from thirdai import licensing
importlib.util.find_spec("thirdai.neural_db")
licensing.activate(thirdai_key or os.getenv("THIRDAI_KEY"))
except ImportError:
raise ImportError(
"Could not import thirdai python package and neuraldb dependencies. "
"Please install it with `pip install thirdai[neural_db]`."
)
[docs] @classmethod
def from_scratch(
cls,
thirdai_key: Optional[str] = None,
**model_kwargs: dict,
) -> NeuralDBRetriever:
"""从头开始创建一个NeuralDBRetriever。
要使用，设置``THIRDAI_KEY``环境变量为您的ThirdAI API密钥，或将``thirdai_key``作为命名参数传递。
示例：
.. code-block:: python
from langchain_community.retrievers import NeuralDBRetriever
retriever = NeuralDBRetriever.from_scratch(
thirdai_key="your-thirdai-key",
)
retriever.insert([
"/path/to/doc.pdf",
"/path/to/doc.docx",
"/path/to/doc.csv",
])
documents = retriever.invoke("AI驱动的音乐疗法")
"""
NeuralDBRetriever._verify_thirdai_library(thirdai_key)
from thirdai import neural_db as ndb
return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB(**model_kwargs)) # type: ignore[arg-type]
[docs] @classmethod
def from_checkpoint(
cls,
checkpoint: Union[str, Path],
thirdai_key: Optional[str] = None,
) -> NeuralDBRetriever:
"""使用保存的检查点创建一个带有基础模型的NeuralDBRetriever
要使用，请设置``THIRDAI_KEY``环境变量为您的ThirdAI API密钥，或将``thirdai_key``作为一个命名参数传递。
示例：
.. code-block:: python
from langchain_community.retrievers import NeuralDBRetriever
retriever = NeuralDBRetriever.from_checkpoint(
checkpoint="/path/to/checkpoint.ndb",
thirdai_key="your-thirdai-key",
)
retriever.insert([
"/path/to/doc.pdf",
"/path/to/doc.docx",
"/path/to/doc.csv",
])
documents = retriever.invoke("AI-driven music therapy")
"""
NeuralDBRetriever._verify_thirdai_library(thirdai_key)
from thirdai import neural_db as ndb
return cls(thirdai_key=thirdai_key, db=ndb.NeuralDB.from_checkpoint(checkpoint)) # type: ignore[arg-type]
@root_validator()
def validate_environments(cls, values: Dict) -> Dict:
"""验证 ThirdAI 环境变量。"""
values["thirdai_key"] = convert_to_secret_str(
get_from_dict_or_env(
values,
"thirdai_key",
"THIRDAI_KEY",
)
)
return values
[docs] def insert(
self,
sources: List[Any],
train: bool = True,
fast_mode: bool = True,
**kwargs: dict,
) -> None:
"""将文件/文档源插入检索器中。
参数：
train: 当为True时，意味着NeuralDB中的基础模型将对插入的文件进行无监督预训练。默认为True。
fast_mode: 更快的插入速度，性能略有下降。默认为True。
"""
sources = self._preprocess_sources(sources)
self.db.insert(
sources=sources,
train=train,
fast_approximation=fast_mode,
**kwargs,
)
def _preprocess_sources(self, sources: list) -> list:
"""检查提供的源是否为字符串路径。如果是，则转换为NeuralDB文档对象。
参数:
sources: 字符串路径列表，可以是PDF、DOCX或CSV文件，也可以是NeuralDB文档对象。
"""
from thirdai import neural_db as ndb
if not sources:
return sources
preprocessed_sources = []
for doc in sources:
if not isinstance(doc, str):
preprocessed_sources.append(doc)
else:
if doc.lower().endswith(".pdf"):
preprocessed_sources.append(ndb.PDF(doc))
elif doc.lower().endswith(".docx"):
preprocessed_sources.append(ndb.DOCX(doc))
elif doc.lower().endswith(".csv"):
preprocessed_sources.append(ndb.CSV(doc))
else:
raise RuntimeError(
f"Could not automatically load {doc}. Only files "
"with .pdf, .docx, or .csv extensions can be loaded "
"automatically. For other formats, please use the "
"appropriate document object from the ThirdAI library."
)
return preprocessed_sources
[docs] def upvote(self, query: str, document_id: int) -> None:
"""检索器会提升特定查询的文档得分。这对于调整检索器以适应用户行为非常有用。
参数：
query：与`document_id`相关联的文本
document_id：要与查询相关联的文档的ID。
"""
self.db.text_to_result(query, document_id)
[docs] def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]) -> None:
"""给定一批（查询，文档ID）对，检索器会增加对应查询的文档得分。这对于对检索器进行用户行为微调非常有用。
参数：
query_id_pairs: （查询，文档ID）对的列表。对于列表中的每对，模型将增加查询的文档ID的权重。
"""
self.db.text_to_result_batch(query_id_pairs)
[docs] def associate(self, source: str, target: str) -> None:
"""检索器将源短语与目标短语关联起来。
当检索器看到源短语时，它还会考虑与目标短语相关的结果。
参数：
source：要与“target”关联的文本。
target：要将“source”关联到的文本。
"""
self.db.associate(source, target)
[docs] def associate_batch(self, text_pairs: List[Tuple[str, str]]) -> None:
"""给定一批（源，目标）对，检索器将每个源短语与相应的目标短语关联。
参数：
text_pairs：（源，目标）文本对的列表。对于列表中的每一对，源将与目标相关联。
"""
self.db.associate_batch(text_pairs)
def _get_relevant_documents(
self, query: str, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
) -> List[Document]:
"""使用检索器检索给定查询的{top_k}个上下文
参数：
query: 提交给模型的查询
top_k: 要检索的上下文结果的最大数量。默认为10。
"""
try:
if "top_k" not in kwargs:
kwargs["top_k"] = 10
references = self.db.search(query=query, **kwargs)
return [
Document(
page_content=ref.text,
metadata={
"id": ref.id,
"upvote_ids": ref.upvote_ids,
"source": ref.source,
"metadata": ref.metadata,
"score": ref.score,
"context": ref.context(1),
},
)
for ref in references
]
except Exception as e:
raise ValueError(f"Error while retrieving documents: {e}") from e
[docs] def save(self, path: str) -> None:
"""将NeuralDB实例保存到磁盘。可以通过调用NeuralDB.from_checkpoint(path)将其加载到内存中。
参数:
path: 保存NeuralDB实例的磁盘路径。
"""
self.db.save(path)