update the python slim version to 11, update the packages

Dockerfile

@@ -1,5 +1,5 @@
 # Build stage
-FROM python:3.9-slim as builder
+FROM python:3.11-slim AS builder
 
 WORKDIR /app
 
@@ -13,7 +13,7 @@ COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
 # Runtime stage
-FROM python:3.9-slim
+FROM python:3.11-slim
 
 WORKDIR /app
 
@@ -53,4 +53,4 @@ HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
     CMD curl -f http://localhost:8000/health || exit 1
 
 # Run the application
-CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "8000", "--log-level", "info"]

README.md

@@ -149,7 +149,7 @@ The project includes two special files that help AI models (like LLMs) better un
 ## Prerequisites
 
 - Docker
-- Python 3.9+
+- Python 3.11+
 - NVIDIA GPU (optional)
   - For GPU support:
     1. Install NVIDIA Container Toolkit

src/llm_chain/chain.py

@@ -1,6 +1,7 @@
-from langchain.llms import Ollama
-from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
+from langchain_community.llms import Ollama
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
 from typing import List, Dict
 import logging
 from ..config import Config
@@ -34,7 +35,9 @@ def __init__(self, config: Config):
 Assistant Response:"""
             )
 
-            self.chain = LLMChain(llm=self.llm, prompt=self.prompt)
+            self.chain = (
+                RunnablePassthrough() | self.prompt | self.llm | StrOutputParser()
+            )
             logger.info(f"Initialized PaymentSupportChain with model {config.LLM_MODEL}")
         except Exception as e:
             logger.error(f"Failed to initialize PaymentSupportChain: {str(e)}")
@@ -48,16 +51,14 @@ def generate_response(
     ) -> str:
         try:
             logger.info(f"Generating response for question: {question}")
-            context_str = "\n".join([doc["content"] for doc in context])
-            history_str = "\n".join([
-                f"{msg.role}: {msg.content}" for msg in conversation_history
-            ])
+            context_str = self._format_context(context)
+            history_str = self._format_history(conversation_history)
 
-            response = self.chain.run(
-                context=context_str,
-                conversation_history=history_str,
-                question=question
-            )
+            response = self.chain.invoke({
+                "context": context_str,
+                "conversation_history": history_str,
+                "question": question
+            })
 
             logger.info("Successfully generated response")
             return response