Update app/core/llm_engine.py

app/core/llm_engine.py

@@ -1,56 +1,152 @@
-# 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)}"
+# # 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)}"
+
+
+
+
+
+
+
+
+
+
+
+
+# # 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)
+
+
+llm = ChatGoogleGenerativeAI(
+    model="gemini-2.5-flash",
+    google_api_key=GEMINI_API_KEY,
+    temperature=0.4,
+    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)}"
+
+
+
+
+