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from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
import pinecone
import asyncio
from langchain.document_loaders.sitemap import SitemapLoader
# Function to fetch data from website
# https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/sitemap
def get_website_data(sitemap_url):
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
loader = SitemapLoader(
sitemap_url
)
docs = loader.load()
return docs
# Function to split data into smaller chunks
def split_data(docs):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len,
)
docs_chunks = text_splitter.split_documents(docs)
return docs_chunks
# Function to create embeddings instance
def create_embeddings():
embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
return embeddings
# Function to push data to Pinecone
def push_to_pinecone(pinecone_apikey, pinecone_environment, pinecone_index_name, embeddings, docs):
pinecone.init(
api_key=pinecone_apikey,
environment=pinecone_environment
)
index_name = pinecone_index_name
index = Pinecone.from_documents(docs, embeddings, index_name=index_name)
return index
# Function to pull index data from Pinecone
def pull_from_pinecone(pinecone_apikey, pinecone_environment, pinecone_index_name, embeddings):
pinecone.init(
api_key=pinecone_apikey,
environment=pinecone_environment
)
index_name = pinecone_index_name
index = Pinecone.from_existing_index(index_name, embeddings)
return index
# This function will help us in fetching the top relevent documents from our vector store - Pinecone Index
def get_similar_docs(index, query, k=2):
similar_docs = index.similarity_search(query, k=k)
return similar_docs
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