mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
import logging
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import uuid
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from typing import Sequence
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from bs4 import BeautifulSoup as Soup
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from langchain_core.documents import Document
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from langchain_core.runnables import Runnable
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from propositional_retrieval.constants import DOCSTORE_ID_KEY
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from propositional_retrieval.proposal_chain import proposition_chain
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from propositional_retrieval.storage import get_multi_vector_retriever
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def add_documents(
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retriever,
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propositions: Sequence[Sequence[str]],
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docs: Sequence[Document],
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id_key: str = DOCSTORE_ID_KEY,
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):
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doc_ids = [
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str(uuid.uuid5(uuid.NAMESPACE_DNS, doc.metadata["source"])) for doc in docs
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]
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prop_docs = [
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Document(page_content=prop, metadata={id_key: doc_ids[i]})
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for i, props in enumerate(propositions)
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for prop in props
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if prop
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]
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retriever.vectorstore.add_documents(prop_docs)
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retriever.docstore.mset(list(zip(doc_ids, docs)))
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def create_index(
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docs: Sequence[Document],
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indexer: Runnable,
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docstore_id_key: str = DOCSTORE_ID_KEY,
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):
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"""
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Create retriever that indexes docs and their propositions
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:param docs: Documents to index
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:param indexer: Runnable creates additional propositions per doc
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:param docstore_id_key: Key to use to store the docstore id
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:return: Retriever
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"""
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logger.info("Creating multi-vector retriever")
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retriever = get_multi_vector_retriever(docstore_id_key)
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propositions = indexer.batch(
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[{"input": doc.page_content} for doc in docs], {"max_concurrency": 10}
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)
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add_documents(
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retriever,
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propositions,
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docs,
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id_key=docstore_id_key,
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)
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return retriever
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if __name__ == "__main__":
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# For our example, we'll load docs from the web
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from langchain.text_splitter import RecursiveCharacterTextSplitter # noqa
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from langchain_community.document_loaders.recursive_url_loader import (
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RecursiveUrlLoader,
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) # noqa
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# The attention is all you need paper
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# Could add more parsing here, as it's very raw.
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loader = RecursiveUrlLoader(
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"https://ar5iv.labs.arxiv.org/html/1706.03762",
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max_depth=2,
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extractor=lambda x: Soup(x, "html.parser").text,
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)
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data = loader.load()
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logger.info(f"Loaded {len(data)} documents")
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# Split
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=8000, chunk_overlap=0)
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all_splits = text_splitter.split_documents(data)
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logger.info(f"Split into {len(all_splits)} documents")
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# Create retriever
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retriever_multi_vector_img = create_index(
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all_splits,
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proposition_chain,
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DOCSTORE_ID_KEY,
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)
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