Updated

main.py

@@ -1,5 +1,5 @@
 # ===============================================
-# Tech Disciples AI Backend — Main.py
+# Tech Disciples AI Backend — Updated for LangChain ≥1.0
 # ===============================================
 
 from fastapi import FastAPI, HTTPException, Header
@@ -7,12 +7,11 @@ from pydantic import BaseModel
 import torch
 import logging
 
-# LangChain ≥1.0 imports
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferMemory
+# LangChain imports (modern >=1.0)
+from langchain.chat_models import HuggingFacePipeline
 from langchain.chains import LLMChain
-from langchain.llms.base import LLM
-from typing import Optional, List
+from langchain.prompts.prompt import PromptTemplate
+from langchain.memory import ConversationBufferMemory
 
 # Transformers pipeline
 from transformers import pipeline
@@ -36,7 +35,7 @@ logger = logging.getLogger("TechDisciplesAI")
 app = FastAPI(title="Tech Disciples AI (LangChain Conversational)", version="3.0")
 
 # ===============================================
-# HUGGING FACE PIPELINE
+# LOAD MODEL USING PIPELINE + LANGCHAIN
 # ===============================================
 try:
     logger.info(f"🚀 Loading model: {MODEL_NAME}")
@@ -51,31 +50,12 @@ try:
         top_p=0.9
     )
 
-    logger.info("✅ Hugging Face pipeline loaded successfully.")
+    llm = HuggingFacePipeline(pipeline=hf_pipeline)
+    logger.info("✅ Model loaded successfully.")
 
 except Exception as e:
-    logger.error(f"❌ Failed to load Hugging Face pipeline: {e}")
-    hf_pipeline = None
-
-# ===============================================
-# HUGGING FACE LLM WRAPPER FOR LANGCHAIN
-# ===============================================
-class HFLLMWrapper(LLM):
-    def __init__(self, pipeline):
-        self.pipeline = pipeline
-
-    @property
-    def _llm_type(self) -> str:
-        return "hf_pipeline"
-
-    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
-        output = self.pipeline(prompt)
-        if isinstance(output, list) and len(output) > 0:
-            return output[0].get("generated_text", str(output[0]))
-        return str(output)
-
-# Initialize LLM wrapper
-llm = HFLLMWrapper(hf_pipeline) if hf_pipeline else None
+    logger.error(f"❌ Failed to load model: {e}")
+    llm = None
 
 # ===============================================
 # MEMORY SYSTEM
@@ -115,7 +95,7 @@ chain = LLMChain(
 # ===============================================
 class QueryInput(BaseModel):
     query: str
-    session_id: str | None = "default"
+    session_id: str | None = "default"  # optional: could be user/session-based
 
 # ===============================================
 # ROUTES

requirements.txt

@@ -3,6 +3,6 @@ uvicorn[standard]
 torch
 transformers
 accelerate
-langchain==0.0.258
+langchain>=1.0
 huggingface-hub
 pydantic