Source code for langchain_box.retrievers.box

from typing import Any, Dict, List, Optional

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import root_validator
from langchain_core.retrievers import BaseRetriever

from langchain_box.utilities import BoxAuth, _BoxAPIWrapper


[docs]class BoxRetriever(BaseRetriever): """Box retriever. `BoxRetriever` provides the ability to retrieve content from your Box instance in a couple of ways. 1. You can use the Box full-text search to retrieve the complete document(s) that match your search query, as `List[Document]` 2. You can use the Box AI Platform API to retrieve the results from a Box AI prompt. This can be a `Document` containing the result of the prompt, or you can retrieve the citations used to generate the prompt to include in your vectorstore. Setup: Install ``langchain-box``: .. code-block:: bash pip install -U langchain-box Instantiate: To use search: .. code-block:: python from langchain_box.retrievers import BoxRetriever retriever = BoxRetriever() To use Box AI: .. code-block:: python from langchain_box.retrievers import BoxRetriever file_ids=["12345","67890"] retriever = BoxRetriever(file_ids) Usage: .. code-block:: python retriever = BoxRetriever() retriever.invoke("victor") print(docs[0].page_content[:100]) .. code-block:: none [ Document( metadata={ 'source': 'url', 'title': 'FIVE_FEET_AND_RISING_by_Peter_Sollett_pdf' }, page_content='\\n3/20/23, 5:31 PM F...' ) ] Use within a chain: .. code-block:: python from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_openai import ChatOpenAI retriever = BoxRetriever(box_developer_token=box_developer_token, character_limit=10000) context="You are an actor reading scripts to learn about your role in an upcoming movie." question="describe the character Victor" prompt = ChatPromptTemplate.from_template( \"""Answer the question based only on the context provided. Context: {context} Question: {question}\""" ) def format_docs(docs): return "\\n\\n".join(doc.page_content for doc in docs) chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | llm | StrOutputParser() ) chain.invoke("Victor") # search query to find files in Box ) .. code-block:: none 'Victor is a skinny 12-year-old with sloppy hair who is seen sleeping on his fire escape in the sun. He is hesitant to go to the pool with his friend Carlos because he is afraid of getting in trouble for not letting his mother cut his hair. Ultimately, he decides to go to the pool with Carlos.' """ # noqa: E501 box_developer_token: Optional[str] = None """String containing the Box Developer Token generated in the developer console""" box_auth: Optional[BoxAuth] = None """Configured `BoxAuth <https://python.langchain.com/v0.2/api_reference/box/utilities/langchain_box.utilities.box.BoxAuth.html>`_ object""" box_file_ids: Optional[List[str]] = None """List[str] containing Box file ids""" character_limit: Optional[int] = -1 """character_limit is an int that caps the number of characters to return per document.""" _box: Optional[_BoxAPIWrapper] class Config: arbitrary_types_allowed = True extra = "allow" @root_validator(allow_reuse=True) def validate_box_loader_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: _box = None """Validate that we have either a box_developer_token or box_auth.""" if not values.get("box_auth") and not values.get("box_developer_token"): raise ValueError( "you must provide box_developer_token or a box_auth " "generated with langchain_box.utilities.BoxAuth" ) _box = _BoxAPIWrapper( # type: ignore[call-arg] box_developer_token=values.get("box_developer_token"), box_auth=values.get("box_auth"), character_limit=values.get("character_limit"), ) values["_box"] = _box return values def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: if self.box_file_ids: # If using Box AI return self._box.ask_box_ai(query=query, box_file_ids=self.box_file_ids) # type: ignore[union-attr] else: # If using Search return self._box.search_box(query=query) # type: ignore[union-attr]