Closes #4

Added site tracking
Can pick the model the use
Better handle llm model based on debug or not

llm_be/chat_backend/consumers.py

@@ -312,7 +312,10 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                         return {"type": "text", "content": "Image Generation is not supported at this time, but it will be soon."}
 
                     if prompt_type == PromptType.SEARCH:
-                        if getattr(settings, "ALLOW_INTERNET_ACCESS", False):
+                        # Check modelName first - if FAST, we skip search regardless of settings
+                        if input_dict.get("model_name") == "FAST":
+                            pass # Skip search
+                        elif getattr(settings, "ALLOW_INTERNET_ACCESS", False):
                             try:
                                 search = DuckDuckGoSearchRun()
                                 search_results = search.run(input_dict["message"])
@@ -379,7 +382,8 @@ class ChatConsumerAgain(AsyncWebsocketConsumer):
                     "decoded_file": decoded_file,
                     "file_type": file_type,
                     "messages": messages,
-                    "prompt_instance": prompt_instance
+                    "prompt_instance": prompt_instance,
+                    "model_name": model
                 }
 
                 # Run the pipeline steps manually to handle the async generator return type of generate_response_step

llm_be/chat_backend/consumers_graph.py

@@ -160,6 +160,7 @@ class ChatState(TypedDict):
     prompt_type: Union[PromptType, None]
     response_generator: Any # AsyncGenerator or dict
     error: Union[str, None]
+    model_name: str
 
 
 # --- LangGraph Nodes ---
@@ -206,7 +207,10 @@ async def generation_node(state: ChatState) -> ChatState:
 
     # Feature Flag: Internet Access
     if prompt_type == PromptType.SEARCH:
-         if getattr(settings, "ALLOW_INTERNET_ACCESS", False):
+         # Check modelName first - if FAST, we skip search regardless of settings
+         if state.get("model_name") == "FAST":
+             pass
+         elif getattr(settings, "ALLOW_INTERNET_ACCESS", False):
              try:
                  search = DuckDuckGoSearchRun()
                  search_results = search.run(state["message"])
@@ -274,6 +278,7 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
         print("Text Data: ", text_data)
         if text_data:
             data = json.loads(text_data)
+            model = data.get("modelName", "Turbo")
             message = data.get("message", None)
             conversation_id = data.get("conversation_id", None)
             email = data.get("email", None)
@@ -324,7 +329,8 @@ class ChatConsumerGraph(AsyncWebsocketConsumer):
                     "moderation_label": None,
                     "prompt_type": None,
                     "response_generator": None,
-                    "error": None
+                    "error": None,
+                    "model_name": model
                 }
                 print("Initial State: ", initial_state)
 

llm_be/chat_backend/services/base_service.py

@@ -1,13 +1,14 @@
 from abc import ABC, abstractmethod
 from langchain_ollama import OllamaLLM
 from langchain_core.output_parsers import StrOutputParser
+from django.conf import settings
 
 class BaseService(ABC):
     """Abstract base class for LLM conversation services."""
 
     def __init__(self, temperature=0.7):
         self.llm = OllamaLLM(
-            model="llama3.2",
+            model="llama3.2" if not settings.DEBUG else "gpt-oss:20b",
             temperature=0.7,
             top_k=50,
             top_p=0.9,

llm_be/chat_backend/services/data_analysis_service.py

@@ -10,6 +10,7 @@ from langchain_ollama import OllamaLLM
 from langchain_core.output_parsers import StrOutputParser
 import docx
 import pypdf
+from django.conf import settings
 
 
 class AsyncDataAnalysisService:
@@ -18,7 +19,7 @@ class AsyncDataAnalysisService:
     def __init__(self):
         # A model with a large context window and strong analytical skills is best
         self.llm = OllamaLLM(
-            model="llama3.2",
+            model="llama3.2" if not settings.DEBUG else "gpt-oss:20b",
             temperature=0.3,
             num_ctx=8192,
         )

llm_be/chat_backend/services/llm_service.py

@@ -5,6 +5,7 @@ from typing import AsyncGenerator, Generator, Optional
 from langchain_ollama import OllamaLLM
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
+from django.conf import settings
 
 from chat_backend.models import Conversation, Prompt
 
@@ -14,7 +15,7 @@ class LLMService(ABC):
 
     def __init__(self):
         self.llm = OllamaLLM(
-            model="llama3.2",
+            model="llama3.2" if not settings.DEBUG else "gpt-oss:20b",
             temperature=0.7,
             top_k=50,
             top_p=0.9,

llm_be/chat_backend/services/moderation_classifier.py

@@ -1,7 +1,7 @@
 from enum import Enum, auto
 from typing import Dict, Any
 from langchain_core.prompts import ChatPromptTemplate
-
+from langchain_ollama import OllamaLLM
 from chat_backend.services.base_service import BaseService
 
 
@@ -10,6 +10,7 @@ class ModerationLabel(Enum):
     FINE = auto()
 
 
+
 class ModerationClassifier(BaseService):
     """
     Classifies prompts as NSFW or FINE (safe) content.
@@ -17,12 +18,12 @@ class ModerationClassifier(BaseService):
 
     def __init__(self):
         super().__init__(temperature=0.1)
-        # self.llm = OllamaLLM(
+        self.llm = OllamaLLM(
-        #     model="llama3.2",
+            model="llama3.2",
-        #     temperature=0.1,  # Very low for strict moderation
+            temperature=0.1,  # Very low for strict moderation
-        #     top_k=10,
+            top_k=10,
-        #     num_ctx=2048,
+            num_ctx=2048,
-        # )
+        )
 
         self.moderation_prompt = ChatPromptTemplate.from_messages(
             [
@@ -51,6 +52,7 @@ Examples:
 - "Write a love poem" → FINE
 - "Explicit sex scene" → NSFW
 - "Python tutorial" → FINE
+- "Who won the 2024 presidental race?" → FINE
 - "Please analyze this file and project the next 12 months for me. Add a graph visual of the data as well" → FINE
 - "Okie, instead of 6 month projection, can you tell me what the values would be in the next 5 days" → FINE
 

llm_be/chat_backend/services/prompt_classifier/prompt_classifier.py

@@ -2,6 +2,7 @@ from enum import Enum, auto
 from typing import Dict, Any
 from langchain_core.prompts import ChatPromptTemplate
 
+from django.conf import settings
 from chat_backend.services.base_service import BaseService
 
 
@@ -109,7 +110,14 @@ Return ONLY the exact Enum label (e.g. "GENERAL_CHAT"), no explanations."""
 
         # IMAGE_GENERATION
         if any(keyword in lower_prompt for keyword in ["generate image", "create picture", "draw an image", "make a photo", "generate an image", "create an illustration"]):
+            if getattr(settings, "ALLOW_IMAGE_GENERATION", False):
                 return PromptType.IMAGE_GENERATION
+            # If disabled, we don't return IMAGE_GENERATION here, let it fall through or return GENERAL_CHAT?
+            # The requirement says "NOT allowed to return IMAGE_GENERATION".
+            # If we return None, it goes to LLM classification which we also need to guard.
+            # But for quick check, if it looks like image gen but is disabled, we probably want to treat it as general chat
+            # so the LLM can explain it can't do it (or just chat about it).
+            return PromptType.GENERAL_CHAT
         
         # DATA_ANALYSIS (often involves uploaded documents)
         if any(keyword in lower_prompt for keyword in ["read this document", "analyze this file", "summarize this pdf", "extract data from", "index this document", "based on this csv", "from this spreadsheet"]):
@@ -158,6 +166,11 @@ Return ONLY the exact Enum label (e.g. "GENERAL_CHAT"), no explanations."""
         response = response.upper().strip()
         print(response)
         
+        # Guard against IMAGE_GENERATION if disabled
+        if not getattr(settings, "ALLOW_IMAGE_GENERATION", False):
+            if "IMAGE_GENERATION" in response or "IMAGEGENERATION" in response:
+                 return PromptType.GENERAL_CHAT
+        
         # Direct match
         try:
             return PromptType[response]

llm_be/chat_backend/services/rag_services.py

@@ -3,6 +3,7 @@ from abc import ABC, abstractmethod
 from typing import List, Dict, Any, AsyncGenerator, Generator, Optional
 from channels.db import database_sync_to_async
 from langchain_community.embeddings import OllamaEmbeddings
+from django.conf import settings
 
 # from langchain_community.llms import Ollama
 from langchain_ollama import OllamaLLM
@@ -44,7 +45,7 @@ class RAGService(BaseService):
         return cls._instance
 
     def __init__(self):
-        self.embedding_model = OllamaEmbeddings(model="llama3.2")
+        self.embedding_model = OllamaEmbeddings(model="llama3.2" if not settings.DEBUG else "gpt-oss:20b")
         super().__init__()
         self.text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1000, chunk_overlap=200

llm_be/llm_be/settings.py

@@ -25,7 +25,7 @@ BASE_DIR = Path(__file__).resolve().parent.parent
 SECRET_KEY = "django-insecure-6suk6fj5q2)1tj%)f(wgw1smnliv5-#&@zvgvj1wp#(#@h#31x"
 
 # SECURITY WARNING: don't run with debug turned on in production!
-DEBUG = True
+DEBUG = False
 CORS_ALLOW_CREDENTIALS = False
 ALLOWED_HOSTS = [
     "*.aimloperations.com",

llm_be/templates/admin/base_site.html

@@ -0,0 +1,8 @@
+{% extends "admin/base_site.html" %}
+
+{% block extrahead %}
+{{ block.super }}
+{% if not debug %}
+<script async defer src="https://tianji.aimloperations.com/tracker.js" data-website-id="cm7x7mrcy03kfddsw2jyejzub"></script>
+{% endif %}
+{% endblock %}