Upload chatbot.py

chatbot.py

@@ -0,0 +1,158 @@
+import os
+import asyncio
+from fastapi import FastAPI, HTTPException, Depends
+from fastapi.security import OAuth2PasswordBearer
+from pydantic import BaseModel
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.llms import HuggingFacePipeline
+from langchain.document_loaders import TextLoader, DataFrameLoader
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.document_loaders import TextLoader, DataFrameLoader
+from langchain_community.llms import HuggingFacePipeline
+from huggingface_hub import login
+
+# Log in using the token
+login(token=os.getenv("HUGGINGFACE_HUB_TOKEN"))
+
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+# Step 1: Configure LangSmith Observability
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
+os.environ["LANGCHAIN_API_KEY"] = "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"
+os.environ["LANGCHAIN_PROJECT"] = "yotta-vm-chatbot"
+
+# Step 2: Llama 2 Chat Model Setup
+def load_llama2_chat_model():
+    model_name = "meta-llama/Llama-2-7b-chat-hf"
+    print("Loading Llama 2 Chat model...")
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        device_map="auto",
+        torch_dtype="float16"
+    )
+    pipeline_model = pipeline("text-generation", model=model, tokenizer=tokenizer)
+    return HuggingFacePipeline(pipeline=pipeline_model)
+
+llama_model = load_llama2_chat_model()
+
+# Step 3: Load Excel Data
+def fetch_excel_data(file_path):
+    """
+    Fetch data from a local Excel file and prepare documents for vector store.
+    """
+    print("Loading data from Excel file...")
+    df = pd.read_excel(file_path)
+    loader = DataFrameLoader(df, page_content_column="Description", metadata_columns=["Title"])
+    documents = loader.load()
+    return documents
+
+# Step 4: Vector Store with Excel Data
+def update_vector_store(file_path):
+    """
+    Load data from Excel and update the vector store.
+    """
+    documents = fetch_excel_data(file_path)
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_documents(documents, embeddings)
+    return vector_store
+
+# Path to the Excel file
+excel_file_path = "certificate_details_chatbot_2.xlsx"
+vector_store = update_vector_store(excel_file_path)
+
+# Step 5: RAG Workflow with Memory
+retriever = vector_store.as_retriever()
+memory = ConversationBufferMemory()
+
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llama_model,
+    retriever=retriever,
+    memory=memory,
+    return_source_documents=True,
+    chain_type_kwargs={
+        "prompt": PromptTemplate(
+            input_variables=["context", "question"],
+            template="Use the following context to answer the question:\n{context}\n\nQuestion: {question}\nAnswer:"
+        )
+    }
+)
+
+
+# Step 6: Periodic Sync with Excel
+async def periodic_sync(interval: int = 3600):
+    """
+    Periodically fetch new data from the Excel file and update the vector store.
+    """
+    while True:
+        try:
+            update_vector_store(excel_file_path)
+            print("Vector store updated with the latest Excel data.")
+        except Exception as e:
+            print(f"Error updating vector store: {str(e)}")
+        await asyncio.sleep(interval)
+
+
+# Step 8: FastAPI Deployment
+app = FastAPI()
+
+class QueryRequest(BaseModel):
+    query: str
+
+@app.get("/")
+def root():
+    return {"message": "Welcome to the Excel-based Chatbot with RAG and Llama Integration!"}
+
+@app.post("/query")
+async def query(request: QueryRequest):
+    try:
+        response = qa_chain({"query": request.query})
+        return {
+            "answer": response['result'],
+            "source_documents": [
+                {"page_content": doc.page_content, "metadata": doc.metadata} 
+                for doc in response["source_documents"]
+            ]
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error processing the query: {str(e)}")
+
+@app.post("/token")
+async def token():
+    return {"access_token": "secure_token_123", "token_type": "bearer"}
+
+@app.on_event("startup")
+async def start_background_tasks():
+    asyncio.create_task(periodic_sync())
+
+# Step 9: Interface and Deployment
+@app.get("/interface")
+def interface():
+    """Return a simple HTML interface for interacting with the chatbot."""
+    return {
+        "html": """
+        <html>
+        <head><title>Chatbot Interface</title></head>
+        <body>
+            <h1>Chat with the Bot</h1>
+            <form method="post" action="/query">
+                <label for="query">Enter your query:</label><br>
+                <input type="text" id="query" name="query"/><br><br>
+                <button type="submit">Submit</button>
+            </form>
+        </body>
+        </html>
+        """
+    }
+
+if __name__ == "__main__":
+    import uvicorn
+    print("Starting the chatbot server...")
+    uvicorn.run(app, host="0.0.0.0", port=8000)