Upload main.py

app/main.py

@@ -0,0 +1,144 @@
+import os
+import re
+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_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_community.embeddings import HuggingFaceBgeEmbeddings
+import time
+
+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
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+openai_api_key = os.environ.get('OPENAI_API_KEY')
+llm = ChatOpenAI(
+    api_key=openai_api_key,
+    model_name="gpt-4-turbo-preview",  # or "gpt-3.5-turbo" for a more economical option
+    temperature=0.7
+)
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+class Query(BaseModel):
+    query_text: str
+
+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. 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.
+You may elaborate on your answers slightly to provide more information, 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}  
+"""
+)
+
+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()
+
+        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:
+        embeddings = get_embeddings()
+        vectors = FAISS.load_local("./vectors_db", embeddings, allow_dangerous_deserialization=True)
+    except Exception as 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()
+
+# Uncomment this to check if the API key is set
+# print(f"API key set: {'Yes' if os.environ.get('OPENAI_API_KEY') else 'No'}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)