Promptcleaning

app.py

@@ -8,7 +8,7 @@ from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 
 # Load pre-processed vector data from .pkl and .faiss files
-VECTOR_STORE_DIR = "./faiss"
+VECTOR_STORE_DIR = r"C:\Users\cshas\Downloads\Banking_Regulations_Compliance_ChatBOT\faiss"
 PICKLE_PATH = os.path.join(VECTOR_STORE_DIR, "index.pkl")
 
 # Load documents
@@ -20,8 +20,6 @@ else:
 
 # Initialize embeddings
 embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-SEC_KEY = os.getenv("SEC_KEY")
-
 
 # Load or create FAISS vector store
 FAISS_INDEX_PATH = os.path.join(VECTOR_STORE_DIR, "index.faiss")
@@ -35,6 +33,15 @@ else:
 retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
 
 # Define the prompt template
+# prompt_template = """
+# You are an AI assistant that helps answer questions based on the following context:
+
+# {context}
+
+# Question: {question}
+# Answer:
+# """
+
 prompt_template = """
 You are an AI assistant that helps answer questions based on the following context:
 
@@ -44,6 +51,7 @@ Question: {question}
 Answer:
 """
 
+
 prompt = PromptTemplate(
     input_variables=["context", "question"],
     template=prompt_template
@@ -51,6 +59,7 @@ prompt = PromptTemplate(
 
 # Initialize LLM model
 REPO_ID = "mistralai/Mistral-7B-Instruct-v0.2"
+SEC_KEY = os.getenv("SEC_KEY")# Replace with your HuggingFace API key
 
 model = HuggingFaceHub(
     repo_id=REPO_ID,
@@ -62,16 +71,25 @@ model = HuggingFaceHub(
 rag_chain = LLMChain(llm=model, prompt=prompt)
 
 def answer_question(question):
-    """Retrieve relevant documents and generate an answer using the RAG chain."""
     docs = retriever.get_relevant_documents(question)
     context = "\n".join([doc.page_content for doc in docs])
-    answer = rag_chain.run({"context": context, "question": question})
-    return answer
+    raw_output = rag_chain.run({"context": context, "question": question})
+
+    # Attempt to isolate only the answer
+    if "Answer:" in raw_output:
+        final_answer = raw_output.split("Answer:")[-1].strip()
+    else:
+        # If 'Answer:' is not found for some reason, just strip leading/trailing whitespace
+        final_answer = raw_output.strip()
+
+    return final_answer
 
-# Define the Gradio function to handle chat history
+# Define the Gradio function
 def ask_model(history, question):
+    # Simulated response (Replace this with your actual model logic)
+    # response = f"AI Response to: '{question}'"
     response = answer_question(question)
-    history.append((question, response))
+    history.append((question, response))  # Append user input and bot response as a tuple
     return history
 
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