Update app/main.py

app/main.py

@@ -1,29 +1,64 @@
 import os
 import re
-from langchain.llms import OpenAI
-from langchain.chat_models import ChatOpenAI
+from openai import OpenAI
+from langchain_openai import ChatOpenAI
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain.prompts.chat import ChatPromptTemplate
-from langchain.chains import RetrievalQA
-from langchain.vectorstores import FAISS
-from langchain.document_loaders import UnstructuredWordDocumentLoader as DocxLoader
+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 UnstructuredWordDocumentLoader as DocxLoader
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi import FastAPI
 from pydantic import BaseModel
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-import nltk
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+import nltk  # Importing NLTK
 import time
 
-# Ensure necessary directories are writable
-cache_dir = "/app/cache"  # Update this to a writable directory path
-os.makedirs(cache_dir, exist_ok=True)
-os.environ["TRANSFORMERS_CACHE"] = cache_dir
+# Set writable paths for cache and data
+cache_dir = '/tmp'
+nltk_data_path = os.path.join(cache_dir, 'nltk_data')
+
+# Configure NLTK and other library paths
+os.environ['TRANSFORMERS_CACHE'] = os.path.join(cache_dir, 'transformers_cache')
+os.environ['HF_HOME'] = os.path.join(cache_dir, 'huggingface')
+os.environ['XDG_CACHE_HOME'] = cache_dir
+
+# Add NLTK data path
+nltk.data.path.append(nltk_data_path)
+
+# Ensure the directory exists
+try:
+    os.makedirs(nltk_data_path, exist_ok=True)
+except OSError as e:
+    print(f"Error creating directory {nltk_data_path}: {e}")
+    raise
+
+# Download required NLTK resources
+try:
+    nltk.download('punkt', download_dir=nltk_data_path)
+    print("NLTK 'punkt' resource downloaded successfully.")
+except Exception as e:
+    print(f"Error downloading NLTK resources: {e}")
+    raise
+
+def clean_response(response):
+    # Remove any leading/trailing whitespace, including newlines
+    cleaned = response.strip()
+    
+    # Remove any enclosing quotation marks
+    cleaned = re.sub(r'^["\']+|["\']+$', '', cleaned)
+    
+    # Replace multiple newlines with a single newline
+    cleaned = re.sub(r'\n+', '\n', cleaned)
+    
+    # Remove any remaining '\n' characters
+    cleaned = cleaned.replace('\\n', '')
+    
+    return cleaned
 
-# Initialize FastAPI app
 app = FastAPI()
 
-# Configure CORS (if required)
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -32,68 +67,104 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Get OpenAI API key from environment
-openai_api_key = os.environ.get("OPENAI_API_KEY")
-if not openai_api_key:
-    raise ValueError("Please set the OPENAI_API_KEY environment variable.")
-
-# Initialize LLM
+openai_api_key = os.environ.get('OPENAI_API_KEY')
 llm = ChatOpenAI(
     api_key=openai_api_key,
-    model_name="gpt-4-turbo-preview",
+    model_name="gpt-4-turbo-preview",  # or "gpt-3.5-turbo" for a more economical option
     temperature=0.7,
+    max_tokens=200
 )
 
 @app.get("/")
 def read_root():
     return {"Hello": "World"}
 
-# Define Pydantic model for query input
 class Query(BaseModel):
     query_text: str
 
-# Utility function to clean responses
-def clean_response(response):
-    cleaned = response.strip()
-    cleaned = re.sub(r'^["\']+|["\']+$', '', cleaned)
-    cleaned = re.sub(r'\n+', '\n', cleaned)
-    cleaned = cleaned.replace('\\n', '')
-    return cleaned
-
-# Define the prompt for the chatbot
 prompt = ChatPromptTemplate.from_template(
-    """
-    You are a helpful assistant designed specifically for the Thapar Institute of Engineering and Technology (TIET), 
-    a renowned technical college. Your task is to answer all queries related to TIET. 
-    If a question falls outside of this context, please decline by stating, 'Sorry, I cannot help with that.' 
-    For more information, please contact our toll-free number: 18002024100 or email us at admissions@thapar.edu.
-    <context>
-    {context}
-    </context>
-    Question: {query}
-    Answer:
-    """
+"""
+You are a helpful assistant designed specifically for the Thapar Institute of Engineering and Technology (TIET), a renowned technical college. Your task is to answer all queries related to TIET in concised manner. Every response you provide should be relevant to the context of TIET. If a question falls outside of this context, please decline by stating, 'Sorry, I cannot help with that.' If you do not know the answer to a question, do not attempt to fabricate a response; instead, politely decline.
+but avoid sounding boastful or exaggerating. Stay focused on the context provided.
+If the query is not related to TIET or falls outside the context of education, respond with:
+        "Sorry, I cannot help with that. I'm specifically designed to answer questions about the Thapar Institute of Engineering and Technology. 
+        For more information, please contact at our toll-free number: 18002024100 or E-mail us at [email protected]
+<context>
+{context}
+</context>
+Question: {input}  
+"""
 )
 
-# Load embeddings
-try:
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",  # Ensure this model is valid
-        cache_folder=cache_dir,
-    )
-except Exception as e:
-    raise RuntimeError(f"Failed to initialize embeddings: {e}")
+def vector_embedding():
+    try:
+        file_path = "./data/Data.docx"
+        if not os.path.exists(file_path):
+            print(f"The file {file_path} does not exist.")
+            return {"response": "Error: Data file not found"}
+
+        loader = DocxLoader(file_path)
+        documents = loader.load()
 
-# Example endpoint for handling queries
-@app.post("/chat")
-async def chat(query: Query):
-    context = "Thapar Institute of Engineering and Technology information."
+        print(f"Loaded document: {file_path}")
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+        chunks = text_splitter.split_documents(documents)
+        
+        print(f"Created {len(chunks)} chunks.")
+
+        model_name = "BAAI/bge-base-en"
+        encode_kwargs = {'normalize_embeddings': True}
+        model_norm = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
+        
+        db = FAISS.from_documents(chunks, model_norm)
+        db.save_local("./vectors_db")
+        
+        print("Vector store created and saved successfully.")
+        return {"response": "Vector Store DB Is Ready"}
+
+    except Exception as e:
+        print(f"An error occurred: {str(e)}")
+        return {"response": f"Error: {str(e)}"}
+
+def get_embeddings():
+    model_name = "BAAI/bge-base-en"
+    encode_kwargs = {'normalize_embeddings': True}
+    model_norm = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
+    return model_norm
+
+@app.post("/chat")  # Changed from /anthropic to /chat
+def read_item(query: Query):
     try:
-        # Use the LLM to generate a response
-        response = llm.generate(
-            prompt.format(context=context, query=query.query_text)
-        )
-        cleaned_response = clean_response(response)
-        return {"response": cleaned_response}
+        embeddings = get_embeddings()
+        vectors = FAISS.load_local("./vectors_db", embeddings, allow_dangerous_deserialization=True)
     except Exception as e:
-        return {"error": str(e)}
+        print(f"Error loading vector store: {str(e)}")
+        return {"response": "Vector Store Not Found or Error Loading. Please run /setup first."}
+    
+    prompt1 = query.query_text
+    if prompt1:
+        start = time.process_time()
+        document_chain = create_stuff_documents_chain(llm, prompt)
+        retriever = vectors.as_retriever()
+        retrieval_chain = create_retrieval_chain(retriever, document_chain)
+        response = retrieval_chain.invoke({'input': prompt1})
+        print("Response time:", time.process_time() - start)
+        
+        # Apply the cleaning function to the response
+        cleaned_response = clean_response(response['answer'])
+        
+        # For debugging, print the cleaned response
+        print("Cleaned response:", repr(cleaned_response))
+        
+        return cleaned_response
+    else:
+        return "No Query Found"
+
+@app.get("/setup")
+def setup():
+    return vector_embedding()
+
+if _name_ == "_main_":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)