langchain/templates/mongo-parent-document-retrieval/ingest.py
2023-11-04 10:16:02 -07:00

60 lines
2.0 KiB
Python

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