Create CDH_chatbot_using_RAG.py

CDH_chatbot_using_RAG.py

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+import streamlit as st
+from langchain_chroma import Chroma
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_google_genai import GoogleGenerativeAI
+from langchain_huggingface import HuggingFaceEmbeddings
+
+# Configuration
+GOOGLE_API_KEY = "AIzaSyB-7cKMdUpA5kTccpNxd72IT5CjeSgSmkc"  # Replace with your API key
+CHROMA_DB_DIR = "./chroma_db_"  # Directory for ChromaDB
+MODEL_NAME = "flax-sentence-embeddings/all_datasets_v4_MiniLM-L6"
+
+# Initialize HuggingFace Embeddings
+embeddings_model = HuggingFaceEmbeddings(model_name=MODEL_NAME)
+
+# Initialize Chroma Database
+db = Chroma(collection_name="vector_database",
+            embedding_function=embeddings_model,
+            persist_directory=CHROMA_DB_DIR)
+
+# Initialize Google Generative AI
+genai_model = GoogleGenerativeAI(api_key=GOOGLE_API_KEY, model="gemini-1.5-flash")
+
+# Streamlit App
+st.title("Question Answering with ChromaDB and Google GenAI")
+st.write("Ask a question based on the context stored in the database.")
+
+# Input Query
+query = st.text_input("Enter your question:")
+
+if query:
+    with st.spinner("Retrieving context and generating an answer..."):
+        # Retrieve Context from ChromaDB
+        docs_chroma = db.similarity_search_with_score(query, k=4)
+        context_text = "\n\n".join([doc.page_content for doc, _score in docs_chroma])
+
+        # Generate Answer
+        PROMPT_TEMPLATE = """
+        Answer the question based only on the following context:
+        {context}
+        Answer the question based on the above context: {question}.
+        Provide a detailed answer.
+        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)
+        prompt = prompt_template.format(context=context_text, question=query)
+
+        response_text = genai_model.invoke(prompt)
+
+    # Display Answer
+    st.subheader("Answer:")
+    st.write(response_text)