mirror of
https://github.com/hwchase17/langchain
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60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
import os
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import uuid
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from langchain.document_loaders import PyPDFLoader
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.vectorstores import MongoDBAtlasVectorSearch
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from pymongo import MongoClient
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PARENT_DOC_ID_KEY = "parent_doc_id"
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def parent_child_splitter(data, id_key=PARENT_DOC_ID_KEY):
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parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
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# This text splitter is used to create the child documents
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# It should create documents smaller than the parent
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child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
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documents = parent_splitter.split_documents(data)
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doc_ids = [str(uuid.uuid4()) for _ in documents]
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docs = []
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for i, doc in enumerate(documents):
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_id = doc_ids[i]
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sub_docs = child_splitter.split_documents([doc])
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for _doc in sub_docs:
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_doc.metadata[id_key] = _id
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_doc.metadata["doc_level"] = "child"
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docs.extend(sub_docs)
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doc.metadata[id_key] = _id
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doc.metadata["doc_level"] = "parent"
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return documents, docs
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MONGO_URI = os.environ["MONGO_URI"]
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# Note that if you change this, you also need to change it in `rag_mongo/chain.py`
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DB_NAME = "langchain-test-2"
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COLLECTION_NAME = "test"
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ATLAS_VECTOR_SEARCH_INDEX_NAME = "default"
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EMBEDDING_FIELD_NAME = "embedding"
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client = MongoClient(MONGO_URI)
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db = client[DB_NAME]
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MONGODB_COLLECTION = db[COLLECTION_NAME]
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if __name__ == "__main__":
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# Load docs
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loader = PyPDFLoader("https://arxiv.org/pdf/2303.08774.pdf")
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data = loader.load()
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# Split docs
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parent_docs, child_docs = parent_child_splitter(data)
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# Insert the documents in MongoDB Atlas Vector Search
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_ = MongoDBAtlasVectorSearch.from_documents(
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documents=parent_docs + child_docs,
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embedding=OpenAIEmbeddings(disallowed_special=()),
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collection=MONGODB_COLLECTION,
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index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
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)
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