secure

.env

@@ -4,6 +4,6 @@ LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
 LANGCHAIN_API_KEY="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"
 LANGCHAIN_PROJECT="langchain_udemy_k"
 INDEX_NAME="langchain_udemy_k-index"
-PINECONE_API_KEY="37d0996a-8a8a-4792-b29b-f2f78e7f1df2"
+PINECONE_API_KEY="pcsk_6vVrG6_2ZUYuJwQcrKefGPodrfDuYAXB4AKD2ZENoXF7qmAahuSAYWijPTaBhkkb8DoB4M"
 HF_TOKEN="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"
 GROQ_API_KEY="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"

tools/retrive.py

@@ -8,10 +8,11 @@ from langchain_community.tools import WikipediaQueryRun
 from langchain_community.utilities import WikipediaAPIWrapper
 import os
 OPENAI_API_KEY= os.getenv("OPENAI_API_KEY")
+PINECONE_API_KEY= os.getenv("PINECONE_API_KEY")
 
 
 # Initialize Pinecone client
-pc = Pinecone(api_key="pcsk_6vVrG6_2ZUYuJwQcrKefGPodrfDuYAXB4AKD2ZENoXF7qmAahuSAYWijPTaBhkkb8DoB4M")
+pc = Pinecone(api_key=PINECONE_API_KEY)
 index = "mage-x-embeddings-all"
 index = pc.Index(index)
 docsearch = PineconeVectorStore(index=index, embedding=OpenAIEmbeddings())