Update app/core/llm_engine.py

app/core/llm_engine.py

@@ -1,77 +1,8 @@
-# # llm_engine.py
-
-# import google.generativeai as genai
-# from app.core.config import GEMINI_API_KEY
-# from langchain_core.prompts import PromptTemplate
-# from langchain_core.output_parsers import StrOutputParser
-# from langchain_google_genai import ChatGoogleGenerativeAI
-
-# # ✅ Configure Gemini client
-# genai.configure(api_key=GEMINI_API_KEY)
-
-
-# def ask_gemini(context: str, question: str) -> str:
-#     """
-#     Ask Gemini a question based on document context using LangChain for better formatting and control.
-#     """
-
-#     try:
-#         # ✅ Initialize Gemini LLM via LangChain
-#         llm = ChatGoogleGenerativeAI(
-#             model="gemini-2.5-flash",
-#             google_api_key=GEMINI_API_KEY,
-#             temperature=0.4,
-#             max_output_tokens=2048,
-#             convert_system_message_to_human=True
-#         )
-
-#         # ✅ Define a structured, formatting-rich prompt
-#         prompt = PromptTemplate(
-#             input_variables=["context", "question"],
-#             template=(
-#                 "You are an intelligent document assistant.\n"
-#                 "Answer the user's question strictly using the provided context.\n"
-#                 "Respond in **clean Markdown formatting** with:\n"
-#                 "- Headings (##)\n"
-#                 "- Bullet points and numbered lists\n"
-#                 "- **Bold keywords**\n"
-#                 "- Tables (if useful)\n"
-#                 "- Code blocks when necessary\n"
-#                 "- Proper spacing and paragraphs for readability\n\n"
-#                 "### 📄 Document Context:\n{context}\n\n"
-#                 "### 💬 User Question:\n{question}\n\n"
-#                 "### 🧠 Answer:"
-#             )
-#         )
-
-#         # ✅ Combine the prompt, model, and parser (modern LCEL chain)
-#         chain = prompt | llm | StrOutputParser()
-
-#         # ✅ Run the chain
-#         response = chain.invoke({"context": context, "question": question})
-
-#         return response.strip() if response else "⚠️ No response from Gemini."
-
-#     except Exception as e:
-#         return f"⚠️ Gemini (LangChain) error: {str(e)}"
-
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-
-# # llm_engine.py
+# llm_engine.py
 
 import google.generativeai as genai
 from app.core.config import GEMINI_API_KEY
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import StrOutputParser
+
 from langchain_google_genai import ChatGoogleGenerativeAI
 
 # ✅ Configure Gemini client
@@ -81,72 +12,8 @@ genai.configure(api_key=GEMINI_API_KEY)
 llm = ChatGoogleGenerativeAI(
     model="gemini-2.5-flash",
     google_api_key=GEMINI_API_KEY,
-    temperature=0.4,
+    temperature=0.2,
     max_output_tokens=500,
     convert_system_message_to_human=True
 )
 
-
-# def ask_gemini(context: str, question: str) -> str:
-#     """
-#     Ask Gemini a question based on document context using LangChain for better formatting and control.
-#     """
-
-#     try:
-#         # ✅ Initialize Gemini LLM via LangChain
-#         # llm = ChatGoogleGenerativeAI(
-#         #     model="gemini-2.5-flash",
-#         #     google_api_key=GEMINI_API_KEY,
-#         #     temperature=0.4,
-#         #     max_output_tokens=2048,
-#         #     convert_system_message_to_human=True
-#         # )
-
-#         # ✅ Define a structured, formatting-rich prompt
-#         prompt = PromptTemplate(
-#             input_variables=["context", "question"],
-#             template=(
-#                 "You are an intelligent document assistant.\n"
-#                 "Answer the user's question strictly using the provided context.\n"
-#                 "Respond in **clean Markdown formatting** with:\n"
-#                 "- Headings (##)\n"
-#                 "- Bullet points and numbered lists\n"
-#                 "- **Bold keywords**\n"
-#                 "- Tables (if useful)\n"
-#                 "- Code blocks when necessary\n"
-#                 "- Proper spacing and paragraphs for readability\n\n"
-#                 "### 📄 Document Context:\n{context}\n\n"
-#                 "### 💬 User Question:\n{question}\n\n"
-#                 "### 🧠 Answer:"
-#             )
-#         )
-
-#         # ✅ Combine the prompt, model, and parser (modern LCEL chain)
-#         chain = prompt | llm | StrOutputParser()
-
-#         # ✅ Run the chain
-#         response = chain.invoke({"context": context, "question": question})
-
-#         return response.strip() if response else "⚠️ No response from Gemini."
-
-#     except Exception as e:
-#         return f"⚠️ Gemini (LangChain) error: {str(e)}"
-
-
-
-
-
-# app/core/llm_engine.py
-
-
-# def run_llm(prompt, inputs: dict):
-#     try:
-#         chain = prompt | llm | StrOutputParser()
-#         return chain.invoke(inputs)
-#     except Exception as e:
-#         return f"⚠️ LLM error: {str(e)}"
-
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-