index.py

index.py

@@ -1,94 +0,0 @@
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from pydantic import BaseModel
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
-from llama_index.llms.huggingface import HuggingFaceInferenceAPI
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.core import Settings
-import os
-from dotenv import load_dotenv
-import shutil
-
-# Load environment variables
-load_dotenv()
-
-app = FastAPI()
-
-# Configure the Llama index settings
-Settings.llm = HuggingFaceInferenceAPI(
-    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    context_window=3900,
-    token=os.getenv("HF_TOKEN"),
-    max_new_tokens=1000,
-    generate_kwargs={"temperature": 0.5},
-)
-Settings.embed_model = HuggingFaceEmbedding(
-    model_name="BAAI/bge-small-en-v1.5"
-)
-
-# Define the directory for persistent storage and data
-PERSIST_DIR = "./db"
-DATA_DIR = "data"
-
-# Ensure data directory exists
-os.makedirs(DATA_DIR, exist_ok=True)
-os.makedirs(PERSIST_DIR, exist_ok=True)
-
-class Query(BaseModel):
-    question: str
-
-def data_ingestion():
-    documents = SimpleDirectoryReader(DATA_DIR).load_data()
-    storage_context = StorageContext.from_defaults()
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
-
-def handle_query(query):
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-    chat_text_qa_msgs = [
-        (
-            "user",
-            """You are Q&A assistant named CHAT-DOC. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
-            Context:
-            {context_str}
-            Question:
-            {query_str}
-            """
-        )
-    ]
-    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
-    answer = query_engine.query(query)
-    
-    if hasattr(answer, 'response'):
-        return answer.response
-    elif isinstance(answer, dict) and 'response' in answer:
-        return answer['response']
-    else:
-        return "Sorry, I couldn't find an answer."
-
-@app.post("/upload")
-async def upload_file(file: UploadFile = File(...)):
-    file_extension = os.path.splitext(file.filename)[1].lower()
-    if file_extension not in [".pdf", ".docx", ".txt"]:
-        raise HTTPException(status_code=400, detail="Invalid file type. Only PDF, DOCX, and TXT are allowed.")
-    
-    file_path = os.path.join(DATA_DIR, file.filename)
-    with open(file_path, "wb") as buffer:
-        shutil.copyfileobj(file.file, buffer)
-    
-    data_ingestion()
-    return {"message": "File uploaded and processed successfully"}
-
-@app.post("/query")
-async def query_document(query: Query):
-    if not os.listdir(DATA_DIR):
-        raise HTTPException(status_code=400, detail="No document has been uploaded yet.")
-    
-    response = handle_query(query.question)
-    return {"response": response}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)