mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
import os
|
|
|
|
from langchain_community.document_loaders import PyPDFLoader
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
|
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
|
|
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
|
from pymongo import MongoClient
|
|
|
|
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
|
|
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
|
|
docs = text_splitter.split_documents(data)
|
|
|
|
# Insert the documents in MongoDB Atlas Vector Search
|
|
_ = MongoDBAtlasVectorSearch.from_documents(
|
|
documents=docs,
|
|
embedding=OpenAIEmbeddings(disallowed_special=()),
|
|
collection=MONGODB_COLLECTION,
|
|
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
|
|
)
|