Update llm_retrieval_conversation_rerank.py

llm_retrieval_conversation_rerank.py

@@ -1,239 +1,239 @@
-import json
-import os
-from dotenv import load_dotenv
-import yaml
-from together import Together
-from langchain.llms.together import Together as TogetherLLM
-from langchain.prompts import PromptTemplate
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.output_parser import StrOutputParser
-from pinecone import Pinecone
-from typing import List, Dict
-import cohere
-load_dotenv()
-
-
-API_FILE_PATH = r"C:\Users\abhay\Analytics Vidhya\API.yml"
-COURSES_FILE_PATH = r"C:\Users\abhay\Analytics Vidhya\courses.json"
-
-# Global list to store conversation history
-conversation_history: List[Dict[str, str]] = []
-
-def load_api_keys(api_file_path):
-    """Loads API keys from a YAML file."""
-    with open(api_file_path, 'r') as f:
-        api_keys = yaml.safe_load(f)
-    return api_keys
-
-def generate_query_embedding(query, together_api_key):
-    """Generates embedding for the user query."""
-    client = Together(api_key=together_api_key)
-    response = client.embeddings.create(
-        model="WhereIsAI/UAE-Large-V1", input=query
-    )
-    return response.data[0].embedding
-
-def initialize_pinecone(pinecone_api_key):
-    """Initializes Pinecone with API key."""
-    return Pinecone(api_key=pinecone_api_key)
-
-def pinecone_similarity_search(pinecone_instance, index_name, query_embedding, top_k=10):
-    """Performs a similarity search in Pinecone and increase top k for reranking."""
-    try:
-        index = pinecone_instance.Index(index_name)
-        results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
-        if not results.matches:
-            return None
-        return results
-    except Exception as e:
-        print(f"Error during similarity search: {e}")
-        return None
-
-def create_prompt_template():
-    """Creates a prompt template for LLM."""
-    template = """You are a helpful AI assistant that provides information on courses. 
-    Based on the following context, conversation history, and new user query, 
-    suggest relevant courses and explain why they might be useful, or respond accordingly if the user query is unrelated. 
-    If no relevant courses are found, please indicate that.
-
-    Conversation History:
-    {conversation_history}
-
-    Context: {context}
-    User Query: {query}
-
-    Response: Let me help you find relevant courses based on your query.
-    """
-    return PromptTemplate(template=template, input_variables=["context", "query", "conversation_history"])
-
-def initialize_llm(together_api_key):
-    """Initializes Together LLM."""
-    return TogetherLLM(
-        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
-        together_api_key=together_api_key,
-        temperature=0,
-        max_tokens=250
-    )
-
-def create_chain(llm, prompt):
-    """Creates a chain using the new RunnableSequence approach."""
-    chain = (
-        {"context": RunnablePassthrough(), "query": RunnablePassthrough(), "conversation_history": RunnablePassthrough()}
-        | prompt
-        | llm
-        | StrOutputParser()
-    )
-    return chain
-
-
-def initialize_cohere_client(cohere_api_key):
-    """Initializes the Cohere client."""
-    return cohere.ClientV2(api_key=cohere_api_key)
-
-
-def rerank_results(cohere_client, query, documents, top_n=3):
-    """Reranks documents using Cohere."""
-    try:
-        results = cohere_client.rerank(
-            query=query,
-            documents=documents,
-            top_n=top_n,
-            model="rerank-english-v3.0",
-        )
-        return results
-    except Exception as e:
-        print(f"Error reranking results: {e}")
-        return None
-
-def generate_llm_response(chain, query, retrieved_data, history, cohere_client):
-    """Generates an LLM response based on context and conversation history."""
-    try:
-        if not retrieved_data or not retrieved_data.matches:
-            return "I couldn't find any relevant courses matching your query. Please try a different search term."
-
-        # Prepare documents for reranking
-        documents = []
-        for match in retrieved_data.matches:
-            metadata = match.metadata
-            if metadata:
-                documents.append(
-                    { "text" :f"Title: {metadata.get('title', 'No title')}\nDescription: {metadata.get('text', 'No description')}\nLink: {metadata.get('course_link', 'No link')}"
-                    }
-                )
-
-        if not documents:
-            return "I found some matches but couldn't extract course information. Please try again."
-        
-         # Rerank the documents
-        reranked_results = rerank_results(cohere_client, query, documents)
-
-        if not reranked_results:
-              return "I couldn't rerank the results, please try again."
-        
-          # Prepare context from reranked results
-        context_parts = []
-        for result in reranked_results.results:
-            context_parts.append(documents[result.index]["text"])
-            
-        context = "\n\n".join(context_parts)
-            
-        # Format conversation history
-        formatted_history = "\n".join(f"User: {item['user']}\nAssistant: {item['assistant']}" for item in history) if history else "No previous conversation."
-
-        response = chain.invoke({"context": context, "query": query, "conversation_history":formatted_history})
-        return response
-
-    except Exception as e:
-        print(f"Error generating response: {e}")
-        return "I encountered an error while generating the response. Please try again."
-    
-
-def check_context_similarity(query_embedding, previous_query_embedding, threshold=0.7):
-    """Checks if the new query is related to the previous one."""
-    if not previous_query_embedding:
-        return False  # First query, no previous embedding to compare
-
-    from numpy import dot
-    from numpy.linalg import norm
-
-    cos_sim = dot(query_embedding, previous_query_embedding) / (norm(query_embedding) * norm(previous_query_embedding))
-    return cos_sim > threshold
-
-def main():
-    global conversation_history
-    previous_query_embedding = None
-
-    try:
-        
-        api_keys = load_api_keys(API_FILE_PATH)
-        together_api_key = api_keys["together_ai_api_key"]
-        pinecone_api_key = api_keys["pinecone_api_key"]
-        index_name = api_keys["pinecone_index_name"]
-        cohere_api_key = api_keys["cohere_api_key"]
-        print("Initializing services...")
-        
-        # Initialize Pinecone
-        pinecone_instance = initialize_pinecone(pinecone_api_key)
-
-        # Initialize Together LLM
-        llm = initialize_llm(together_api_key)
-
-         # Initialize Cohere client
-        cohere_client = initialize_cohere_client(cohere_api_key)
-
-
-        
-        prompt = create_prompt_template()
-
-        # Create chain
-        chain = create_chain(llm, prompt)
-
-        print("Ready to process queries!")
-
-        while True:
-           
-            user_query = input("\nEnter your query (or 'quit' to exit): ").strip()
-            
-            if user_query.lower() == 'quit':
-                break
-
-            if not user_query:
-                print("Please enter a valid query.")
-                continue
-
-            try:
-                print("Generating query embedding...")
-                query_embedding = generate_query_embedding(user_query, together_api_key)
-
-                # Check context similarity
-                if previous_query_embedding and check_context_similarity(query_embedding, previous_query_embedding):
-                     print("Continuing the previous conversation...")
-                else:
-                    print("Starting a new conversation...")
-                    conversation_history = []  # Clear history for a new conversation
-                
-                print("Searching for relevant courses...")
-                pinecone_results = pinecone_similarity_search(
-                    pinecone_instance, index_name, query_embedding
-                )
-
-                print("Generating response...")
-                llm_response = generate_llm_response(chain, user_query, pinecone_results, conversation_history, cohere_client)
-
-                print("\nResponse:")
-                print(llm_response)
-                print("\n" + "="*50)
-
-                # Update conversation history
-                conversation_history.append({"user": user_query, "assistant": llm_response})
-                previous_query_embedding = query_embedding  # Save for next turn
-               
-            except Exception as e:
-                print(f"Error processing query: {e}")
-                print("Please try again with a different query.")
-
-    except Exception as e:
-        print(f"An error occurred during initialization: {str(e)}")
-
-if __name__ == "__main__":
+import json
+import os
+from dotenv import load_dotenv
+import yaml
+from together import Together
+from langchain.llms.together import Together as TogetherLLM
+from langchain.prompts import PromptTemplate
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from pinecone import Pinecone
+from typing import List, Dict
+import cohere
+load_dotenv()
+
+
+API_FILE_PATH = r".\API.yml"
+COURSES_FILE_PATH = r".\courses.json"
+
+# Global list to store conversation history
+conversation_history: List[Dict[str, str]] = []
+
+def load_api_keys(api_file_path):
+    """Loads API keys from a YAML file."""
+    with open(api_file_path, 'r') as f:
+        api_keys = yaml.safe_load(f)
+    return api_keys
+
+def generate_query_embedding(query, together_api_key):
+    """Generates embedding for the user query."""
+    client = Together(api_key=together_api_key)
+    response = client.embeddings.create(
+        model="WhereIsAI/UAE-Large-V1", input=query
+    )
+    return response.data[0].embedding
+
+def initialize_pinecone(pinecone_api_key):
+    """Initializes Pinecone with API key."""
+    return Pinecone(api_key=pinecone_api_key)
+
+def pinecone_similarity_search(pinecone_instance, index_name, query_embedding, top_k=10):
+    """Performs a similarity search in Pinecone and increase top k for reranking."""
+    try:
+        index = pinecone_instance.Index(index_name)
+        results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
+        if not results.matches:
+            return None
+        return results
+    except Exception as e:
+        print(f"Error during similarity search: {e}")
+        return None
+
+def create_prompt_template():
+    """Creates a prompt template for LLM."""
+    template = """You are a helpful AI assistant that provides information on courses. 
+    Based on the following context, conversation history, and new user query, 
+    suggest relevant courses and explain why they might be useful, or respond accordingly if the user query is unrelated. 
+    If no relevant courses are found, please indicate that.
+
+    Conversation History:
+    {conversation_history}
+
+    Context: {context}
+    User Query: {query}
+
+    Response: Let me help you find relevant courses based on your query.
+    """
+    return PromptTemplate(template=template, input_variables=["context", "query", "conversation_history"])
+
+def initialize_llm(together_api_key):
+    """Initializes Together LLM."""
+    return TogetherLLM(
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        together_api_key=together_api_key,
+        temperature=0,
+        max_tokens=250
+    )
+
+def create_chain(llm, prompt):
+    """Creates a chain using the new RunnableSequence approach."""
+    chain = (
+        {"context": RunnablePassthrough(), "query": RunnablePassthrough(), "conversation_history": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+    return chain
+
+
+def initialize_cohere_client(cohere_api_key):
+    """Initializes the Cohere client."""
+    return cohere.ClientV2(api_key=cohere_api_key)
+
+
+def rerank_results(cohere_client, query, documents, top_n=3):
+    """Reranks documents using Cohere."""
+    try:
+        results = cohere_client.rerank(
+            query=query,
+            documents=documents,
+            top_n=top_n,
+            model="rerank-english-v3.0",
+        )
+        return results
+    except Exception as e:
+        print(f"Error reranking results: {e}")
+        return None
+
+def generate_llm_response(chain, query, retrieved_data, history, cohere_client):
+    """Generates an LLM response based on context and conversation history."""
+    try:
+        if not retrieved_data or not retrieved_data.matches:
+            return "I couldn't find any relevant courses matching your query. Please try a different search term."
+
+        # Prepare documents for reranking
+        documents = []
+        for match in retrieved_data.matches:
+            metadata = match.metadata
+            if metadata:
+                documents.append(
+                    { "text" :f"Title: {metadata.get('title', 'No title')}\nDescription: {metadata.get('text', 'No description')}\nLink: {metadata.get('course_link', 'No link')}"
+                    }
+                )
+
+        if not documents:
+            return "I found some matches but couldn't extract course information. Please try again."
+        
+         # Rerank the documents
+        reranked_results = rerank_results(cohere_client, query, documents)
+
+        if not reranked_results:
+              return "I couldn't rerank the results, please try again."
+        
+          # Prepare context from reranked results
+        context_parts = []
+        for result in reranked_results.results:
+            context_parts.append(documents[result.index]["text"])
+            
+        context = "\n\n".join(context_parts)
+            
+        # Format conversation history
+        formatted_history = "\n".join(f"User: {item['user']}\nAssistant: {item['assistant']}" for item in history) if history else "No previous conversation."
+
+        response = chain.invoke({"context": context, "query": query, "conversation_history":formatted_history})
+        return response
+
+    except Exception as e:
+        print(f"Error generating response: {e}")
+        return "I encountered an error while generating the response. Please try again."
+    
+
+def check_context_similarity(query_embedding, previous_query_embedding, threshold=0.7):
+    """Checks if the new query is related to the previous one."""
+    if not previous_query_embedding:
+        return False  # First query, no previous embedding to compare
+
+    from numpy import dot
+    from numpy.linalg import norm
+
+    cos_sim = dot(query_embedding, previous_query_embedding) / (norm(query_embedding) * norm(previous_query_embedding))
+    return cos_sim > threshold
+
+def main():
+    global conversation_history
+    previous_query_embedding = None
+
+    try:
+        
+        api_keys = load_api_keys(API_FILE_PATH)
+        together_api_key = api_keys["together_ai_api_key"]
+        pinecone_api_key = api_keys["pinecone_api_key"]
+        index_name = api_keys["pinecone_index_name"]
+        cohere_api_key = api_keys["cohere_api_key"]
+        print("Initializing services...")
+        
+        # Initialize Pinecone
+        pinecone_instance = initialize_pinecone(pinecone_api_key)
+
+        # Initialize Together LLM
+        llm = initialize_llm(together_api_key)
+
+         # Initialize Cohere client
+        cohere_client = initialize_cohere_client(cohere_api_key)
+
+
+        
+        prompt = create_prompt_template()
+
+        # Create chain
+        chain = create_chain(llm, prompt)
+
+        print("Ready to process queries!")
+
+        while True:
+           
+            user_query = input("\nEnter your query (or 'quit' to exit): ").strip()
+            
+            if user_query.lower() == 'quit':
+                break
+
+            if not user_query:
+                print("Please enter a valid query.")
+                continue
+
+            try:
+                print("Generating query embedding...")
+                query_embedding = generate_query_embedding(user_query, together_api_key)
+
+                # Check context similarity
+                if previous_query_embedding and check_context_similarity(query_embedding, previous_query_embedding):
+                     print("Continuing the previous conversation...")
+                else:
+                    print("Starting a new conversation...")
+                    conversation_history = []  # Clear history for a new conversation
+                
+                print("Searching for relevant courses...")
+                pinecone_results = pinecone_similarity_search(
+                    pinecone_instance, index_name, query_embedding
+                )
+
+                print("Generating response...")
+                llm_response = generate_llm_response(chain, user_query, pinecone_results, conversation_history, cohere_client)
+
+                print("\nResponse:")
+                print(llm_response)
+                print("\n" + "="*50)
+
+                # Update conversation history
+                conversation_history.append({"user": user_query, "assistant": llm_response})
+                previous_query_embedding = query_embedding  # Save for next turn
+               
+            except Exception as e:
+                print(f"Error processing query: {e}")
+                print("Please try again with a different query.")
+
+    except Exception as e:
+        print(f"An error occurred during initialization: {str(e)}")
+
+if __name__ == "__main__":
     main()