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.env

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+'''
+LANGCHAIN_API_KEY="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"
+GROQ_API_KEY="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"
+LANGCHAIN_PROJECT="try"
+'''

Faiss_index/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5de032dd6d0e0f86fddbe8b2feae0c725f9b4d73c788e94a10789adea59205
+size 813439

main.py

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+import gradio as gr
+import os
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+import warnings
+from langchain_groq import ChatGroq
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from typing import Any
+from langchain.agents import AgentExecutor
+from langchain_core.tools import tool
+
+from dotenv import load_dotenv
+from fastapi.middleware.cors import CORSMiddleware
+
+load_dotenv()
+groq_api_key=os.getenv('GROQ_API_KEY')
+
+
+warnings.filterwarnings('ignore')
+
+llm=ChatGroq(groq_api_key=groq_api_key,
+             model_name="mixtral-8x7b-32768")
+
+prompt=ChatPromptTemplate.from_template(
+"""
+You are a Mineral Exploration assistant.You are given a context and an input query. Use the context to answer the query.
+<context>
+{context}
+<context>
+Questions:{input}
+
+"""
+)
+
+def vector_embedding():
+
+    if "vectors" not in st.session_state:
+
+        
+        print("Embeddings defined")
+        loader=PyPDFDirectoryLoader("./rules_regulations") ## Data Ingestion
+        docs=loader.load() ## Document Loading
+        print("PDF loaded")
+        text_splitter=RecursiveCharacterTextSplitter(chunk_size=4000,chunk_overlap=500) ## Chunk Creation
+        print("Chunks created")
+        final_documents=text_splitter.split_documents(docs) #splitting
+        print("Splitting done")
+        vectors=FAISS.from_documents(final_documents,embeddings) #vector embeddings
+        vectors.save_local("Faiss_index")
+        print("Embeddings created")
+    else:
+        print("Faiss index already exist")
+        
+
+
+
+
+embeddings=HuggingFaceEmbeddings()
+vectors = FAISS.load_local("Faiss_index", embeddings, allow_dangerous_deserialization=True)
+
+def Get_Rag_Response(query):
+    if query:
+        print("Got query:")
+        print(query)
+        document_chain=create_stuff_documents_chain(llm,prompt)
+        retriever=vectors.as_retriever()
+        print("Retrieved docs")
+        retrieval_chain=create_retrieval_chain(retriever,document_chain)
+        response=retrieval_chain.invoke({'input':query})
+        return response
+        
+    else:
+        print("Error: No query passed")
+
+iface= gr.Interface(fn = Get_Rag_Response,
+                    inputs = ["text"],
+                    outputs = ['text'],
+                    title = "Rag_Response",
+                    description="Get RAG chatbot response")
+
+iface.launch(inline=False)

requirements.txt

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+langchain_core
+python-dotenv
+streamlit
+langchain_community
+sse_starlette
+bs4
+pypdf
+faiss-cpu
+groq
+cassio
+beautifulsoup4
+langchain-groq
+langchainhub
+langchain
+sentence_transformers
+sentence-transformers
+InstructorEmbedding
+torch
+urllib3<2
+requests
+gradio