diff --git a/rag_tutorials/rag_chain/README.md b/rag_tutorials/rag_chain/README.md
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+# PharmaQuery
+
+## Overview
+PharmaQuery is an advanced Pharmaceutical Insight Retrieval System designed to help users gain meaningful insights from research papers and documents in the pharmaceutical domain.
+
+## PharmaQuery Architecture
+
+
+## Features
+- **Natural Language Querying**: Ask complex questions about the pharmaceutical industry and get concise, accurate answers.
+- **Custom Database**: Upload your own research documents to enhance the retrieval system's knowledge base.
+- **Similarity Search**: Retrieves the most relevant documents for your query using AI embeddings.
+- **Streamlit Interface**: User-friendly interface for queries and document uploads.
+
+## Technologies Used
+- **Programming Language**: [Python 3.10+](https://www.python.org/downloads/release/python-31011/)
+- **Framework**: [LangChain](https://www.langchain.com/)
+- **Database**: [ChromaDB](https://www.trychroma.com/)
+- **Models**:
+ - Embeddings: [Google Gemini API (embedding-001)](https://ai.google.dev/gemini-api/docs/embeddings)
+ - Chat: [Google Gemini API (gemini-1.5-pro)](https://ai.google.dev/gemini-api/docs/models/gemini#gemini-1.5-pro)
+- **PDF Processing**: [PyPDFLoader](https://python.langchain.com/docs/integrations/document_loaders/pypdfloader/)
+- **Document Splitter**: [SentenceTransformersTokenTextSplitter](https://python.langchain.com/api_reference/text_splitters/sentence_transformers/langchain_text_splitters.sentence_transformers.SentenceTransformersTokenTextSplitter.html)
+
+## Requirements
+1. **Install Dependencies**:
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+2. **Set Up Environment Variables**:
+ Create a `.env` file in the project root directory with the following variables:
+
+ ```bash
+ GOOGLE_API_KEY="your_google_gemini_api_key"
+ ```
+
+ `Note:` Replace `your_google_gemini_api_key` with actual key.
+
+3. **Run the Application**:
+ ```bash
+ streamlit run app.py
+ ```
+
+4. **Use the Application**:
+ - Enter your query in the main interface.
+ - Optionally, upload research papers in the sidebar to enhance the database.
+
+## :mailbox: Connect With Me
+
+
+
+ 
+ 
+ 
+
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diff --git a/rag_tutorials/rag_chain/app.py b/rag_tutorials/rag_chain/app.py
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+import os
+import streamlit as st
+
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_chroma import Chroma
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters.sentence_transformers import SentenceTransformersTokenTextSplitter
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+
+from dotenv import load_dotenv
+load_dotenv()
+
+# Initialize embedding model
+embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+# Initialize pharma database
+db = Chroma(collection_name="pharma_database",
+ embedding_function=embedding_model,
+ persist_directory='./pharma_db')
+
+def format_docs(docs):
+ return "\n\n".join(doc.page_content for doc in docs)
+
+def add_to_db(uploaded_files):
+ # Check if files are uploaded
+ if not uploaded_files:
+ st.error("No files uploaded!")
+ return
+
+ for uploaded_file in uploaded_files:
+ # Save the uploaded file to a temporary path
+ temp_file_path = os.path.join("./temp", uploaded_file.name)
+ os.makedirs(os.path.dirname(temp_file_path), exist_ok=True)
+
+ with open(temp_file_path, "wb") as temp_file:
+ temp_file.write(uploaded_file.getbuffer())
+
+ # Load the file using PyPDFLoader
+ loader = PyPDFLoader(temp_file_path)
+ data = loader.load()
+
+ # Store metadata and content
+ doc_metadata = [data[i].metadata for i in range(len(data))]
+ doc_content = [data[i].page_content for i in range(len(data))]
+
+ # Split documents into smaller chunks
+ st_text_splitter = SentenceTransformersTokenTextSplitter(
+ model_name="sentence-transformers/all-mpnet-base-v2",
+ chunk_size=100,
+ chunk_overlap=50
+ )
+ st_chunks = st_text_splitter.create_documents(doc_content, doc_metadata)
+
+ # Add chunks to database
+ db.add_documents(st_chunks)
+
+ # Remove the temporary file after processing
+ os.remove(temp_file_path)
+
+def run_rag_chain(query):
+ # Create a Retriever Object and apply Similarity Search
+ retriever = db.as_retriever(search_type="similarity", search_kwargs={'k': 5})
+
+ # Initialize a Chat Prompt Template
+ PROMPT_TEMPLATE = """
+ You are a highly knowledgeable assistant specializing in pharmaceutical sciences.
+ Answer the question based only on the following context:
+ {context}
+
+ Answer the question based on the above context:
+ {question}
+
+ Use the provided context to answer the user's question accurately and concisely.
+ Don't justify your answers.
+ Don't give information not mentioned in the CONTEXT INFORMATION.
+ Do not say "according to the context" or "mentioned in the context" or similar.
+ """
+
+ prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+
+ # Initialize a Generator (i.e. Chat Model)
+ chat_model = ChatGoogleGenerativeAI(
+ model="gemini-1.5-pro",
+ temperature=1
+ )
+
+ # Initialize a Output Parser
+ output_parser = StrOutputParser()
+
+ # RAG Chain
+ rag_chain = {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt_template | chat_model | output_parser
+
+ # Invoke the Chain
+ response = rag_chain.invoke(query)
+
+ return response
+
+def main():
+ st.set_page_config(page_title="PharmaQuery", page_icon=":microscope:")
+ st.header("Pharmaceutical Insight Retrieval System")
+
+ query = st.text_area(
+ ":bulb: Enter your query about the Pharmaceutical Industry:",
+ placeholder="e.g., What are the AI applications in drug discovery?"
+ )
+
+ if st.button("Submit"):
+ if not query:
+ st.warning("Please ask a question")
+
+ else:
+ with st.spinner("Thinking..."):
+ result = run_rag_chain(query=query)
+ st.write(result)
+
+ with st.sidebar:
+ st.title("Upload your research documents (Optional) :memo:")
+ pdf_docs = st.file_uploader("Enhance your query by uploading PDF files related to Pharmaceutical Sciences.",
+ type=["pdf"],
+ accept_multiple_files=True
+ )
+
+ if st.button("Submit & Process"):
+ if not pdf_docs:
+ st.warning("Please upload the file")
+
+ else:
+ with st.spinner("Processing your documents..."):
+ add_to_db(pdf_docs)
+ st.success(":file_folder: Documents successfully added to the database!")
+
+ # Sidebar Footer
+ st.sidebar.write("Built with ❤️ by [Charan](https://www.linkedin.com/in/codewithcharan/)")
+
+if __name__ == "__main__":
+ main()
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diff --git a/rag_tutorials/rag_chain/requirements.txt b/rag_tutorials/rag_chain/requirements.txt
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+streamlit
+langchain-google-genai
+langchain-chroma
+langchain-community
+langchain-core
+chromadb
+sentence-transformers
+PyPDF2
+python-dotenv