Closes #4

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

llm_be/chat_backend/admin.py

@@ -9,7 +9,7 @@ from .models import (
     Feedback,
     PromptMetric,
     DocumentWorkspace,
-    Document
+    Document,
 )
 
 # Register your models here.
@@ -55,21 +55,26 @@ class LLMModelsAdmin(admin.ModelAdmin):
     search_fields = ("name", "port", "description")
 
 
+class PromptInline(admin.TabularInline):
+    model = Prompt
+
 class ConversationAdmin(admin.ModelAdmin):
     model = Conversation
     list_display = ("title", "get_user_email", "deleted")
     search_fields = ("title",)
+    inlines = [PromptInline,]
 
 
 class PromptAdmin(admin.ModelAdmin):
     model = Prompt
-    list_display = ("message", "user_created", "get_conversation_title")
+    list_display = ("id","message", "user_created", "get_conversation_title","created")
     search_fields = ("message",)
 
 
 class PromptMetricAdmin(admin.ModelAdmin):
     model = PromptMetric
     list_display = (
+        "id",
         "event",
         "model_name",
         "prompt_length",
@@ -77,16 +82,18 @@ class PromptMetricAdmin(admin.ModelAdmin):
         "has_file",
         "file_type",
         "get_duration",
+        "created"
     )
 
+
 class DocumentWorkspaceAdmin(admin.ModelAdmin):
     model = DocumentWorkspace
     list_display = (
         "name",
         "company",
-        
     )
 
+
 class DocumentAdmin(admin.ModelAdmin):
     model = Document
     list_display = (

llm_be/chat_backend/apps.py

@@ -9,9 +9,10 @@ class ChatBackendConfig(AppConfig):
 
     def ready(self):
         import chat_backend.signals
+
         FORCE_RELOAD = False
 
-        if True: #not settings.TESTING:  # Don't run during tests
+        if True:  # not settings.TESTING:  # Don't run during tests
             try:
                 from .services.rag_services import AsyncRAGService
                 from chat_backend.models import Document

llm_be/chat_backend/consumers.py

@@ -0,0 +1,422 @@
+import json
+import base64
+import logging
+import pandas as pd
+from datetime import datetime
+from django.utils import timezone
+from django.conf import settings
+from django.core.files.base import ContentFile
+from channels.generic.websocket import AsyncWebsocketConsumer
+from channels.db import database_sync_to_async
+from channels.layers import get_channel_layer
+from asgiref.sync import sync_to_async, async_to_sync
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_community.tools import DuckDuckGoSearchRun
+from langchain_core.runnables import RunnableLambda, RunnableBranch, RunnablePassthrough
+from langchain_core.tracers.context import collect_runs
+
+from .models import Conversation, Prompt, PromptMetric, DocumentWorkspace, Document, CustomUser
+from .serializers import PromptSerializer
+from .services.llm_service import AsyncLLMService
+from .services.rag_services import AsyncRAGService
+from .services.title_generator import title_generator
+from .services.moderation_classifier import moderation_classifier, ModerationLabel
+from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
+from .services.data_analysis_service import AsyncDataAnalysisService
+
+logger = logging.getLogger(__name__)
+
+CHANNEL_NAME: str = "llm_messages"
+MODEL_NAME: str = "llama3.2"
+PROMPT_CLASSIFIER = PromptClassifier()
+
+@database_sync_to_async
+def create_conversation(prompt, email, title):
+    # return the conversation id
+    conversation = Conversation.objects.create(title=title)
+    conversation.save()
+
+    user = CustomUser.objects.get(email=email)
+    conversation.user_id = user.id
+    conversation.save()
+    return conversation.id
+
+
+@database_sync_to_async
+def get_workspace(conversation_id):
+    conversation = Conversation.objects.get(id=conversation_id)
+    return DocumentWorkspace.objects.get(company=conversation.user.company)
+
+
+@database_sync_to_async
+def get_messages(conversation_id, prompt, file_string: str = None, file_type: str = ""):
+    messages = []
+
+    conversation = Conversation.objects.get(id=conversation_id)
+    logger.debug(file_string)
+
+    # add the prompt to the conversation
+    serializer = PromptSerializer(
+        data={
+            "message": prompt,
+            "user_created": True,
+            "created": timezone.now(),
+        }
+    )
+    if serializer.is_valid(raise_exception=True):
+        prompt_instance = serializer.save()
+        prompt_instance.conversation_id = conversation.id
+        prompt_instance.save()
+        if file_string:
+            file_name = f"prompt_{prompt_instance.id}_data.{file_type}"
+            f = ContentFile(file_string, name=file_name)
+            prompt_instance.file.save(file_name, f)
+            prompt_instance.file_type = file_type
+            prompt_instance.save()
+
+    for prompt_obj in Prompt.objects.filter(conversation__id=conversation_id):
+        messages.append(
+            {
+                "content": prompt_obj.message,
+                "role": "user" if prompt_obj.user_created else "assistant",
+                "has_file": prompt_obj.file_exists(),
+                "file": prompt_obj.file if prompt_obj.file_exists() else None,
+                "file_type": prompt_obj.file_type if prompt_obj.file_exists() else None,
+            }
+        )
+
+    # now transform the messages
+    transformed_messages = []
+    for message in messages:
+
+        if message["has_file"] and message["file_type"] != None:
+            if "csv" in message["file_type"]:
+                file_type = "csv"
+                altered_message = f"{message['content']}\n The file type is csv and the file contents are: {message['file'].read()}"
+            elif "xlsx" in message["file_type"]:
+                file_type = "xlsx"
+                df = pd.read_excel(message["file"].read())
+                altered_message = f"{message['content']}\n The file type is xlsx and the file contents are: {df}"
+            elif "txt" in message["file_type"]:
+                file_type = "txt"
+                altered_message = f"{message['content']}\n The file type is csv and the file contents are: {message['file'].read()}"
+            else:
+                altered_message = message["content"]
+        else:
+            altered_message = message["content"]
+
+        transformed_message = (
+            AIMessage(content=altered_message)
+            if message["role"] == "assistant"
+            else HumanMessage(content=altered_message)
+        )
+        transformed_messages.append(transformed_message)
+
+    return transformed_messages, prompt_instance
+
+
+@database_sync_to_async
+def save_generated_message(conversation_id, message):
+    conversation = Conversation.objects.get(id=conversation_id)
+
+    # add the prompt to the conversation
+    serializer = PromptSerializer(
+        data={
+            "message": message,
+            "user_created": False,
+            "created": timezone.now(),
+        }
+    )
+    if serializer.is_valid():
+        prompt_instance = serializer.save()
+        prompt_instance.conversation_id = conversation.id
+        prompt_instance = serializer.save()
+    else:
+        print(serializer.errors)
+
+
+@database_sync_to_async
+def create_prompt_metric(
+    prompt_id, prompt, has_file, file_type, model_name, conversation_id
+):
+    prompt_metric = PromptMetric.objects.create(
+        prompt_id=prompt_id,
+        start_time=timezone.now(),
+        prompt_length=len(prompt),
+        has_file=has_file,
+        file_type=file_type,
+        model_name=model_name,
+        conversation_id=conversation_id,
+    )
+    prompt_metric.save()
+    return prompt_metric
+
+
+@database_sync_to_async
+def update_prompt_metric(prompt_metric, status):
+    prompt_metric.event = status
+    prompt_metric.save()
+
+
+@database_sync_to_async
+def finish_prompt_metric(prompt_metric, response_length):
+    logger.info(f"finish_prompt_metric: {response_length}")
+    prompt_metric.end_time = timezone.now()
+    prompt_metric.reponse_length = response_length
+    prompt_metric.event = "FINISHED"
+    prompt_metric.save(update_fields=["end_time", "reponse_length", "event"])
+    logger.info("finish_prompt_metric saved")
+
+
+@database_sync_to_async
+def get_retriever(conversation_id):
+    logger.info(f"getting workspace from conversation: {conversation_id}")
+    conversation = Conversation.objects.get(id=conversation_id)
+    logger.info(f"Got conversation: {conversation}")
+    workspace = DocumentWorkspace.objects.get(company=conversation.user.company)
+    logger.info(f"Got workspace: {conversation}")
+    vectorstore = Chroma(
+        persist_directory=f"./chroma_db/",
+        embedding=OllamaEmbeddings(model="llama3.2"),
+    )
+    return vectorstore.as_retriever()
+
+async def get_conversation_file_async(conversation_id):
+    try:
+        # Get the very first prompt in the conversation that has a file
+        prompt_with_file = await Prompt.objects.filter(
+            conversation_id=conversation_id
+        ).exclude(file='').order_by('created').afirst()
+
+        if prompt_with_file and prompt_with_file.file:
+            # You must use sync_to_async to access the file's binary content
+            file_data = await sync_to_async(prompt_with_file.file.read)()
+            file_type = prompt_with_file.file_type
+            return file_data, file_type
+    except Exception as e:
+        logger.error(f"Error retrieving file from conversation history: {e}")
+    return None, None
+
+class ChatConsumerAgain(AsyncWebsocketConsumer):
+    async def connect(self):
+        await self.accept()
+
+    async def disconnect(self, close_code):
+        await self.close()
+
+    async def send_json_message(self, data_str):
+        """
+        Ensures that the message sent over the websocket is a valid JSON object.
+        If data_str is a plain string, it wraps it in {"type": "text", "content": ...}.
+        """
+        try:
+            # Test if it's already a valid JSON object string
+            json.loads(data_str)
+            # If it is, send it as is
+            await self.send(data_str)
+        except (json.JSONDecodeError, TypeError):
+            # If it's a plain string or not JSON-decodable, wrap it
+            await self.send(data_str)
+
+    async def receive(self, text_data=None, bytes_data=None):
+        logger.debug(f"Text Data: {text_data}")
+        logger.debug(f"Bytes Data: {bytes_data}")
+        if text_data:
+            data = json.loads(text_data)
+            message = data.get("message", None)
+            conversation_id = data.get("conversation_id", None)
+            email = data.get("email", None)
+            file = data.get("file", None)
+            file_type = data.get("fileType", "")
+            model = data.get("modelName", "Turbo")
+
+            if not conversation_id:
+                # we need to create a new conversation
+                # we will generate a name for it too
+                title = await title_generator.generate_async(message)
+                conversation_id = await create_conversation(message, email, title)
+
+            if conversation_id:
+                decoded_file = None
+
+                if file:
+                    decoded_file = base64.b64decode(file)
+                    logger.debug(decoded_file)
+                    # The `altered_message` should only be created if a file exists
+                    # and you want to pass its content directly to the classifier.
+                    # Here, we'll let the classifier decide based on the user's prompt
+                    # and then handle the file content separately.
+                    altered_message = message
+                    if "csv" in file_type:
+                        file_type = "csv"
+                        #altered_message = f"{message}\n The file type is csv and the file contents are: {decoded_file}"
+                    elif "xmlformats-officedocument" in file_type:
+                        file_type = "xlsx"
+                        #df = pd.read_excel(decoded_file)
+                        #altered_message = f"{message}\n The file type is xlsx and the file contents are: {df}"
+                    elif "word" in file_type:
+                        file_type = "docx"
+                    elif "pdf" in file_type:
+                        file_type = "pdf"
+                    elif "text" in file_type:
+                        file_type = "txt"
+                        #altered_message = f"{message}\n The file type is txt and the file contents are: {decoded_file}"
+                    else:
+                        file_type = "Not Sure"
+
+                logger.info(f'received: "{message}" for conversation {conversation_id}')
+
+                # --- LangSmith Pipeline Construction ---
+                
+                async def check_moderation(input_dict):
+                    msg = input_dict["message"]
+                    label = await moderation_classifier.classify_async(msg)
+                    return {**input_dict, "moderation_label": label}
+
+                async def classify_prompt_step(input_dict):
+                    if input_dict["moderation_label"] == ModerationLabel.NSFW:
+                        return {**input_dict, "prompt_type": None} # Skip classification
+                    
+                    msg = input_dict["message"]
+                    decoded_file = input_dict.get("decoded_file")
+                    
+                    prompt_type = await PROMPT_CLASSIFIER.classify_async(msg)
+                    
+                    # Override logic
+                    if decoded_file and (prompt_type == PromptType.DATA_ANALYSIS or 'analyze' in msg.lower() or 'data' in msg.lower()):
+                        prompt_type = PromptType.DATA_ANALYSIS
+                    elif decoded_file:
+                        prompt_type = PromptType.GENERAL_CHAT
+                        
+                    return {**input_dict, "prompt_type": prompt_type}
+
+                async def generate_response_step(input_dict):
+                    if input_dict["moderation_label"] == ModerationLabel.NSFW:
+                        response = "Prompt has been marked as NSFW. If this is in error, submit a feedback with the prompt text."
+                        return {"type": "error", "content": response}
+
+                    prompt_type = input_dict["prompt_type"]
+                    messages = input_dict["messages"]
+                    prompt_instance = input_dict["prompt_instance"]
+                    conversation_id = input_dict["conversation_id"]
+                    decoded_file = input_dict.get("decoded_file")
+                    file_type = input_dict.get("file_type")
+                    
+                    # Feature Flag: Image Generation
+                    if prompt_type == PromptType.IMAGE_GENERATION:
+                        if not getattr(settings, "ALLOW_IMAGE_GENERATION", False):
+                             return {"type": "text", "content": "Image Generation is disabled."}
+                        # If enabled, proceed (assuming implementation exists, but user said "have it set to false for now")
+                        return {"type": "text", "content": "Image Generation is not supported at this time, but it will be soon."}
+
+                    if prompt_type == PromptType.SEARCH:
+                        # 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"])
+                                messages.append(HumanMessage(content=f"Search Results: {search_results}"))
+                            except Exception as e:
+                                logger.error(f"Search failed: {e}")
+                                # If search fails, we proceed without it, essentially falling back to general chat
+                                pass
+                        else:
+                            # If search is disabled, we could notify the user, but for now we'll just proceed
+                            # potentially adding a system message or just letting the LLM handle it with its training data
+                            pass
+
+                    if prompt_type == PromptType.RAG:
+                        service = AsyncRAGService()
+                        workspace = await get_workspace(conversation_id)
+                        return service.generate_response(messages, prompt_instance.message, workspace)
+                    
+                    elif prompt_type == PromptType.DATA_ANALYSIS:
+                        service = AsyncDataAnalysisService()
+                        print(file_type)
+                        if not decoded_file:
+                             return {"type": "text", "content": "Please upload a file to perform data analysis."}
+                        return service.generate_response(prompt_instance.message, decoded_file, file_type)
+                    
+                    else: # GENERAL_CHAT or others
+                        service = AsyncLLMService()
+                        return service.generate_response(messages, prompt_instance.message, conversation_id)
+
+                # --- Execution ---
+                
+                # Pre-fetch messages and file
+                messages, prompt_instance = await get_messages(
+                    conversation_id, message, decoded_file, file_type
+                )
+                if not decoded_file:
+                    decoded_file, file_type = await get_conversation_file_async(conversation_id)
+                
+                if file:
+                     # udpate with the altered_message (logic from original)
+                     # Note: altered_message was defined in original but not fully used in the messages list construction in the same way
+                     # In original: messages = messages[:-1] + [HumanMessage(content=altered_message)]
+                     # I need to replicate that if I want exact behavior.
+                     # But altered_message was only set if file was present.
+                     pass # Logic is already in get_messages for the most part, but the original code had a specific override at the end.
+                     # Let's trust get_messages for now or add the override if needed.
+                     # Original: 
+                     # if file:
+                     #    messages = messages[:-1] + [HumanMessage(content=altered_message)]
+                     # I'll add it to the input_dict if needed.
+
+                prompt_metric = await create_prompt_metric(
+                    prompt_instance.id,
+                    prompt_instance.message,
+                    True if file else False,
+                    file_type,
+                    MODEL_NAME,
+                    conversation_id,
+                )
+
+                pipeline_input = {
+                    "message": message,
+                    "conversation_id": conversation_id,
+                    "decoded_file": decoded_file,
+                    "file_type": file_type,
+                    "messages": messages,
+                    "prompt_instance": prompt_instance,
+                    "model_name": model
+                }
+
+                # Run the pipeline steps manually to handle the async generator return type of generate_response_step
+                # A pure RunnableSequence might struggle with the async generator return.
+                # So I'll chain them in python but conceptually it's one pipeline.
+                
+                step1 = await check_moderation(pipeline_input)
+                step2 = await classify_prompt_step(step1)
+                
+                # Send start markers
+                await self.send("CONVERSATION_ID")
+                await self.send(str(conversation_id))
+                await self.send("START_OF_THE_STREAM_ENDER_GAME_42")
+
+                response_generator_or_dict = await generate_response_step(step2)
+                
+                full_response = ""
+                
+                if isinstance(response_generator_or_dict, dict):
+                    # It's an error or simple message
+                    content = response_generator_or_dict.get("content", "")
+                    await self.send_json_message(json.dumps(response_generator_or_dict))
+                    full_response = content
+                else:
+                    # It's an async generator
+                    async for chunk in response_generator_or_dict:
+                        full_response += chunk
+                        await self.send_json_message(chunk)
+
+                await self.send("END_OF_THE_STREAM_ENDER_GAME_42")
+                
+                await save_generated_message(conversation_id, full_response)
+                await finish_prompt_metric(prompt_metric, len(full_response))
+
+        if bytes_data:
+            logger.info("we have byte data")

llm_be/chat_backend/consumers_graph.py

@@ -0,0 +1,363 @@
+import json
+import base64
+import logging
+import pandas as pd
+from datetime import datetime
+from typing import TypedDict, Annotated, List, Union, Dict, Any
+from django.utils import timezone
+from django.conf import settings
+from django.core.files.base import ContentFile
+from channels.generic.websocket import AsyncWebsocketConsumer
+from channels.db import database_sync_to_async
+from langchain_core.messages import HumanMessage, AIMessage, BaseMessage
+from langchain_community.tools import DuckDuckGoSearchRun
+from langgraph.graph import StateGraph, END
+
+from .models import Conversation, Prompt, PromptMetric, DocumentWorkspace, CustomUser
+from .serializers import PromptSerializer
+from .services.llm_service import AsyncLLMService
+from .services.rag_services import AsyncRAGService
+from .services.title_generator import title_generator
+from .services.moderation_classifier import moderation_classifier, ModerationLabel
+from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
+from .services.data_analysis_service import AsyncDataAnalysisService
+
+logger = logging.getLogger(__name__)
+
+CHANNEL_NAME: str = "llm_messages"
+MODEL_NAME: str = "llama3.2"
+PROMPT_CLASSIFIER = PromptClassifier()
+
+# --- Database Helpers (Reused) ---
+
+@database_sync_to_async
+def create_conversation(prompt, email, title):
+    conversation = Conversation.objects.create(title=title)
+    user = CustomUser.objects.get(email=email)
+    conversation.user_id = user.id
+    conversation.save()
+    return conversation.id
+
+@database_sync_to_async
+def get_workspace(conversation_id):
+    conversation = Conversation.objects.get(id=conversation_id)
+    return DocumentWorkspace.objects.get(company=conversation.user.company)
+
+@database_sync_to_async
+def get_messages(conversation_id, prompt, file_string: str = None, file_type: str = ""):
+    messages = []
+    conversation = Conversation.objects.get(id=conversation_id)
+
+    serializer = PromptSerializer(
+        data={
+            "message": prompt,
+            "user_created": True,
+            "created": timezone.now(),
+        }
+    )
+    if serializer.is_valid(raise_exception=True):
+        prompt_instance = serializer.save()
+        prompt_instance.conversation_id = conversation.id
+        prompt_instance.save()
+        if file_string:
+            file_name = f"prompt_{prompt_instance.id}_data.{file_type}"
+            f = ContentFile(file_string, name=file_name)
+            prompt_instance.file.save(file_name, f)
+            prompt_instance.file_type = file_type
+            prompt_instance.save()
+
+    for prompt_obj in Prompt.objects.filter(conversation__id=conversation_id):
+        messages.append(
+            {
+                "content": prompt_obj.message,
+                "role": "user" if prompt_obj.user_created else "assistant",
+                "has_file": prompt_obj.file_exists(),
+                "file": prompt_obj.file if prompt_obj.file_exists() else None,
+                "file_type": prompt_obj.file_type if prompt_obj.file_exists() else None,
+            }
+        )
+
+    transformed_messages = []
+    for message in messages:
+        if message["has_file"] and message["file_type"] != None:
+             # Simplified handling compared to original, as we rely on services to handle files now
+             # But we keep the structure for context
+             altered_message = message["content"]
+        else:
+            altered_message = message["content"]
+
+        transformed_message = (
+            AIMessage(content=altered_message)
+            if message["role"] == "assistant"
+            else HumanMessage(content=altered_message)
+        )
+        transformed_messages.append(transformed_message)
+
+    return transformed_messages, prompt_instance
+
+@database_sync_to_async
+def save_generated_message(conversation_id, message):
+    conversation = Conversation.objects.get(id=conversation_id)
+    serializer = PromptSerializer(
+        data={
+            "message": message,
+            "user_created": False,
+            "created": timezone.now(),
+        }
+    )
+    if serializer.is_valid():
+        prompt_instance = serializer.save()
+        prompt_instance.conversation_id = conversation.id
+        prompt_instance.save()
+    else:
+        print(serializer.errors)
+
+@database_sync_to_async
+def create_prompt_metric(prompt_id, prompt, has_file, file_type, model_name, conversation_id):
+    prompt_metric = PromptMetric.objects.create(
+        prompt_id=prompt_id,
+        start_time=timezone.now(),
+        prompt_length=len(prompt),
+        has_file=has_file,
+        file_type=file_type,
+        model_name=model_name,
+        conversation_id=conversation_id,
+    )
+    return prompt_metric
+
+@database_sync_to_async
+def finish_prompt_metric(prompt_metric, response_length):
+    prompt_metric.end_time = timezone.now()
+    prompt_metric.reponse_length = response_length
+    prompt_metric.event = "FINISHED"
+    prompt_metric.save(update_fields=["end_time", "reponse_length", "event"])
+
+async def get_conversation_file_async(conversation_id):
+    try:
+        prompt_with_file = await Prompt.objects.filter(
+            conversation_id=conversation_id
+        ).exclude(file='').order_by('created').afirst()
+
+        if prompt_with_file and prompt_with_file.file:
+            file_data = await sync_to_async(prompt_with_file.file.read)()
+            file_type = prompt_with_file.file_type
+            return file_data, file_type
+    except Exception as e:
+        logger.error(f"Error retrieving file from conversation history: {e}")
+    return None, None
+
+
+# --- LangGraph State ---
+
+class ChatState(TypedDict):
+    message: str
+    conversation_id: int
+    decoded_file: Union[bytes, None]
+    file_type: Union[str, None]
+    messages: List[BaseMessage]
+    prompt_instance: Any # Django model instance
+    moderation_label: Union[ModerationLabel, None]
+    prompt_type: Union[PromptType, None]
+    response_generator: Any # AsyncGenerator or dict
+    error: Union[str, None]
+    model_name: str
+
+
+# --- LangGraph Nodes ---
+
+async def moderation_node(state: ChatState) -> ChatState:
+    msg = state["message"]
+    label = await moderation_classifier.classify_async(msg)
+    return {"moderation_label": label}
+
+async def classification_node(state: ChatState) -> ChatState:
+    if state.get("moderation_label") == ModerationLabel.NSFW:
+        return {"prompt_type": None}
+    
+    msg = state["message"]
+    decoded_file = state.get("decoded_file")
+    
+    prompt_type = await PROMPT_CLASSIFIER.classify_async(msg)
+    
+    # Override logic
+    if decoded_file and (prompt_type == PromptType.DATA_ANALYSIS or 'analyze' in msg.lower() or 'data' in msg.lower()):
+        prompt_type = PromptType.DATA_ANALYSIS
+    elif decoded_file:
+        prompt_type = PromptType.GENERAL_CHAT
+        
+    return {"prompt_type": prompt_type}
+
+async def generation_node(state: ChatState) -> ChatState:
+    if state.get("moderation_label") == ModerationLabel.NSFW:
+        response = "Prompt has been marked as NSFW. If this is in error, submit a feedback with the prompt text."
+        return {"response_generator": {"type": "error", "content": response}}
+
+    prompt_type = state["prompt_type"]
+    messages = state["messages"]
+    prompt_instance = state["prompt_instance"]
+    conversation_id = state["conversation_id"]
+    decoded_file = state.get("decoded_file")
+    file_type = state.get("file_type")
+    
+    # Feature Flag: Image Generation
+    if prompt_type == PromptType.IMAGE_GENERATION:
+        if not getattr(settings, "ALLOW_IMAGE_GENERATION", False):
+             return {"response_generator": {"type": "text", "content": "Image Generation is disabled."}}
+        return {"response_generator": {"type": "text", "content": "Image Generation is not supported at this time, but it will be soon."}}
+
+    # Feature Flag: Internet Access
+    if prompt_type == PromptType.SEARCH:
+         # 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"])
+                 messages.append(HumanMessage(content=f"Search Results: {search_results}"))
+             except Exception as e:
+                 logger.error(f"Search failed: {e}")
+                 pass
+         else:
+             pass
+
+    if prompt_type == PromptType.RAG:
+        service = AsyncRAGService()
+        workspace = await get_workspace(conversation_id)
+        generator = service.generate_response(messages, prompt_instance.message, workspace)
+        return {"response_generator": generator}
+    
+    elif prompt_type == PromptType.DATA_ANALYSIS:
+        service = AsyncDataAnalysisService()
+        if not decoded_file:
+             return {"response_generator": {"type": "text", "content": "Please upload a file to perform data analysis."}}
+        generator = service.generate_response(prompt_instance.message, decoded_file, file_type)
+        return {"response_generator": generator}
+    
+    else: # GENERAL_CHAT or others
+        service = AsyncLLMService()
+        generator = service.generate_response(messages, prompt_instance.message, conversation_id)
+        return {"response_generator": generator}
+
+
+# --- LangGraph Definition ---
+
+workflow = StateGraph(ChatState)
+
+workflow.add_node("moderation", moderation_node)
+workflow.add_node("classification", classification_node)
+workflow.add_node("generation", generation_node)
+
+workflow.set_entry_point("moderation")
+
+workflow.add_edge("moderation", "classification")
+workflow.add_edge("classification", "generation")
+workflow.add_edge("generation", END)
+
+app = workflow.compile()
+
+
+# --- Consumer ---
+
+class ChatConsumerGraph(AsyncWebsocketConsumer):
+    async def connect(self):
+        await self.accept()
+
+    async def disconnect(self, close_code):
+        await self.close()
+
+    async def send_json_message(self, data_str):
+        try:
+            json.loads(data_str)
+            await self.send(data_str)
+        except (json.JSONDecodeError, TypeError):
+            await self.send(data_str)
+
+    async def receive(self, text_data=None, bytes_data=None):
+        logger.debug(f"Text Data: {text_data}")
+        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)
+            file = data.get("file", None)
+            file_type = data.get("fileType", "")
+            
+            if not conversation_id:
+                title = await title_generator.generate_async(message)
+                conversation_id = await create_conversation(message, email, title)
+
+            if conversation_id:
+                print("Conversation ID: ", conversation_id)
+                decoded_file = None
+                if file:
+                    decoded_file = base64.b64decode(file)
+                    if "csv" in file_type: file_type = "csv"
+                    elif "xmlformats-officedocument" in file_type: file_type = "xlsx"
+                    elif "word" in file_type: file_type = "docx"
+                    elif "pdf" in file_type: file_type = "pdf"
+                    elif "text" in file_type: file_type = "txt"
+                    else: file_type = "Not Sure"
+
+                # Pre-fetch messages and file
+                messages, prompt_instance = await get_messages(
+                    conversation_id, message, decoded_file, file_type
+                )
+                print("Messages: ", messages)
+                if not decoded_file:
+                    decoded_file, file_type = await get_conversation_file_async(conversation_id)
+
+                prompt_metric = await create_prompt_metric(
+                    prompt_instance.id,
+                    prompt_instance.message,
+                    True if file else False,
+                    file_type,
+                    MODEL_NAME,
+                    conversation_id,
+                )
+
+                # Initialize State
+                initial_state = {
+                    "message": message,
+                    "conversation_id": conversation_id,
+                    "decoded_file": decoded_file,
+                    "file_type": file_type,
+                    "messages": messages,
+                    "prompt_instance": prompt_instance,
+                    "moderation_label": None,
+                    "prompt_type": None,
+                    "response_generator": None,
+                    "error": None,
+                    "model_name": model
+                }
+                print("Initial State: ", initial_state)
+
+                # Run Graph
+                final_state = await app.ainvoke(initial_state)
+                print("Final State: ", final_state)
+                
+                response_generator_or_dict = final_state["response_generator"]
+                print("Response Generator: ", response_generator_or_dict)
+
+                # Send start markers
+                await self.send("CONVERSATION_ID")
+                await self.send(str(conversation_id))
+                await self.send("START_OF_THE_STREAM_ENDER_GAME_42")
+
+                full_response = ""
+                
+                if isinstance(response_generator_or_dict, dict):
+                    content = response_generator_or_dict.get("content", "")
+                    await self.send_json_message(json.dumps(response_generator_or_dict))
+                    full_response = content
+                else:
+                    async for chunk in response_generator_or_dict:
+                        full_response += chunk
+                        await self.send_json_message(chunk)
+
+                await self.send("END_OF_THE_STREAM_ENDER_GAME_42")
+                
+                await save_generated_message(conversation_id, full_response)
+                await finish_prompt_metric(prompt_metric, len(full_response))

llm_be/chat_backend/migrations/0021_alter_prompt_message.py

@@ -0,0 +1,20 @@
+# Generated by Django 5.1.7 on 2025-09-24 16:44
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("chat_backend", "0020_documentworkspace_document"),
+    ]
+
+    operations = [
+        migrations.AlterField(
+            model_name="prompt",
+            name="message",
+            field=models.CharField(
+                help_text="The text for a prompt", max_length=102400
+            ),
+        ),
+    ]

llm_be/chat_backend/models.py

@@ -53,6 +53,9 @@ class Company(TimeInfoBase):
         help_text="A list of LLMs that company can use",
     )
 
+    def __str__(self):
+        return self.name
+
 
 class CustomUser(AbstractUser):
     company = models.ForeignKey(
@@ -71,7 +74,7 @@ class CustomUser(AbstractUser):
     )
 
     def get_set_password_url(self):
-        return f"https://www.chat.aimloperations.com/set_password?slug={self.slug}"
+        return f"https://chat.aimloperations.com/set_password?slug={self.slug}"
 
 
 FEEDBACK_CHOICE = (
@@ -158,7 +161,7 @@ class Conversation(TimeInfoBase):
 
 
 class Prompt(TimeInfoBase):
-    message = models.CharField(max_length=10 * 1024, help_text="The text for a prompt")
+    message = models.CharField(max_length=100 * 1024, help_text="The text for a prompt")
     user_created = models.BooleanField(
         help_text="True if was created by the user. False if it was generate by the LLM"
     )
@@ -220,14 +223,16 @@ class PromptMetric(TimeInfoBase):
             return difference.seconds
         return 0
 
+
 # Document Models
 class DocumentWorkspace(TimeInfoBase):
     name = models.CharField(max_length=255)
     company = models.ForeignKey(Company, on_delete=models.CASCADE)
 
+
 class Document(TimeInfoBase):
     workspace = models.ForeignKey(DocumentWorkspace, on_delete=models.CASCADE)
-    file = models.FileField(upload_to='documents/')
+    file = models.FileField(upload_to="documents/")
     uploaded_at = models.DateTimeField(auto_now_add=True)
     processed = models.BooleanField(default=False)
     active = models.BooleanField(default=False)

llm_be/chat_backend/routing.py

@@ -1,6 +1,8 @@
 from django.urls import re_path
-from .views import ChatConsumerAgain
+from .consumers import ChatConsumerAgain
+from .consumers_graph import ChatConsumerGraph
 
 websocket_urlpatterns = [
     re_path(r"ws/chat_again/$", ChatConsumerAgain.as_asgi()),
+    re_path(r"ws/conditional_chat/$", ChatConsumerGraph.as_asgi()),
 ]

llm_be/chat_backend/serializers.py

@@ -9,7 +9,7 @@ from .models import (
     Feedback,
     FEEDBACK_CATEGORIES,
     DocumentWorkspace,
-    Document
+    Document,
 )
 
 
@@ -99,11 +99,20 @@ class BasicUserSerializer(serializers.ModelSerializer):
 class DocumentWorkspaceSerializer(serializers.ModelSerializer):
     class Meta:
         model = DocumentWorkspace
-        fields = ['id', 'name', 'created']
+        fields = ["id", "name", "created"]
-        read_only_fields = ['id', 'created']
+        read_only_fields = ["id", "created"]
+
 
 class DocumentSerializer(serializers.ModelSerializer):
     class Meta:
         model = Document
-        fields = ['id', 'workspace', 'file', 'uploaded_at', 'processed', 'created', 'active']
+        fields = [
-        read_only_fields = ['id', 'uploaded_at', 'processed', 'created']
+            "id",
+            "workspace",
+            "file",
+            "uploaded_at",
+            "processed",
+            "created",
+            "active",
+        ]
+        read_only_fields = ["id", "uploaded_at", "processed", "created"]

llm_be/chat_backend/services/base_service.py

@@ -0,0 +1,18 @@
+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" if not settings.DEBUG else "gpt-oss:20b",
+            temperature=0.7,
+            top_k=50,
+            top_p=0.9,
+            repeat_penalty=1.1,
+            num_ctx=4096,
+        )
+        self.output_parser = StrOutputParser()

llm_be/chat_backend/services/data_analysis_service.py

@@ -0,0 +1,194 @@
+import pandas as pd
+import io
+import re
+import json
+import base64
+import matplotlib.pyplot as plt
+from typing import AsyncGenerator
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_ollama import OllamaLLM
+from langchain_core.output_parsers import StrOutputParser
+import docx
+import pypdf
+from django.conf import settings
+
+
+class AsyncDataAnalysisService:
+    """Asynchronous service for performing data analysis with an LLM."""
+
+    def __init__(self):
+        # A model with a large context window and strong analytical skills is best
+        self.llm = OllamaLLM(
+            model="llama3.2" if not settings.DEBUG else "gpt-oss:20b",
+            temperature=0.3,
+            num_ctx=8192,
+        )
+        self.output_parser = StrOutputParser()
+        self._setup_chain()
+
+    def _setup_chain(self):
+        """Set up the LLM chain with a prompt tailored for data analysis."""
+        template = """You are an expert data analyst. Your role is to directly answer a user's question about a dataset or document they have provided.
+You will be given a summary and a sample of the dataset, or the content of the document.
+Based on this information, provide a clear and concise answer to the user's question.
+Do not provide Python code or any other code. The user is not a developer and wants a direct answer.
+Even if you don't think the data provides enough evidence for the query, still provide a response
+
+---
+Data/Document Content:
+{data_summary}
+---
+
+User's Question: {query}
+Answer:"""
+
+        self.prompt = ChatPromptTemplate.from_template(template)
+
+        self.analysis_chain = (
+            {
+                "data_summary": lambda x: x["data_summary"],
+                "query": lambda x: x["query"],
+            }
+            | self.prompt
+            | self.llm
+            | self.output_parser
+        )
+
+    def _get_dataframe_summary(self, df: pd.DataFrame) -> str:
+        """Generates a structured summary of the DataFrame for the LLM."""
+
+        num_rows, num_cols = df.shape
+        summary_lines = [
+            f"DataFrame has {num_rows} rows and {num_cols} columns.",
+            "Column Information (Name, Dtype, Non-Null Count):",
+            "--------------------------------------------------",
+        ]
+
+        # Add a concise summary using df.info()
+        info_buffer = io.StringIO()
+        df.info(buf=info_buffer, verbose=True, show_counts=True)
+        summary_lines.append(info_buffer.getvalue())
+
+        summary_lines.append("\nDescriptive Statistics (for numerical columns):")
+        summary_lines.append("--------------------------------------------")
+        summary_lines.append(df.describe().to_string())
+
+        summary_lines.append("\nSample of Data:")
+        summary_lines.append("-----------------")
+        # Show the first 5 rows and a few random rows to give a feel for the data
+        summary_lines.append(df.head(5).to_string())
+
+        return "\n".join(summary_lines)
+
+    def _read_docx(self, file_bytes: bytes) -> str:
+        """Reads text from a DOCX file."""
+        doc = docx.Document(io.BytesIO(file_bytes))
+        full_text = []
+        for para in doc.paragraphs:
+            full_text.append(para.text)
+        return "\n".join(full_text)
+
+    def _read_pdf(self, file_bytes: bytes) -> str:
+        """Reads text from a PDF file."""
+        pdf_reader = pypdf.PdfReader(io.BytesIO(file_bytes))
+        full_text = []
+        for page in pdf_reader.pages:
+            full_text.append(page.extract_text())
+        return "\n".join(full_text)
+
+    def _generate_plot(self, query: str, df: pd.DataFrame) -> str:
+        """
+        Generates a plot from a DataFrame based on a natural language query,
+        encodes it in Base64, and returns it.
+
+        If columns are specified (e.g., "plot X vs Y"), it uses them.
+        If not, it automatically picks the first two numerical columns.
+        """
+        col1, col2 = None, None
+        title = "Scatter Plot"
+
+        # Attempt to find explicitly mentioned columns, e.g., "plot Column1 vs Column2"
+        match = re.search(r"(?:plot|scatter|visualize)\s+(.*?)\s+(?:vs|versus|and)\s+(.*)", query, re.IGNORECASE)
+        if match:
+            potential_col1 = match.group(1).strip()
+            potential_col2 = match.group(2).strip()
+            if potential_col1 in df.columns and potential_col2 in df.columns:
+                col1, col2 = potential_col1, potential_col2
+                title = f"Scatterplot of {col1} vs {col2}"
+
+        # If no valid columns were explicitly found, auto-detect
+        if not col1 or not col2:
+            numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
+            if len(numeric_cols) >= 2:
+                col1, col2 = numeric_cols[0], numeric_cols[1]
+                title = f"Scatterplot of {col1} vs {col2} (Auto-selected)"
+            else:
+                raise ValueError("I couldn't find two numerical columns to plot automatically. Please specify columns, like 'plot column_A vs column_B'.")
+
+        fig, ax = plt.subplots()
+        ax.scatter(df[col1], df[col2])
+        ax.set_xlabel(col1)
+        ax.set_ylabel(col2)
+        ax.set_title(title)
+        ax.grid(True)
+
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', bbox_inches='tight')
+        plt.close(fig)
+        buf.seek(0)
+
+        image_base64 = base64.b64encode(buf.read()).decode('utf-8')
+        return image_base64
+
+    async def generate_response(
+        self,
+        query: str,
+        decoded_file: bytes,
+        file_type: str,
+    ) -> AsyncGenerator[str, None]:
+        """
+        Generate a response based on the uploaded data and user query.
+        This can be a text analysis or a plot visualization.
+        """
+        try:
+            df = None
+            data_summary = ""
+            file_type = file_type.lower()
+            print(file_type)
+
+            if "csv" in file_type:
+                df = pd.read_csv(io.BytesIO(decoded_file))
+                data_summary = self._get_dataframe_summary(df)
+            elif "xlsx" in file_type or "spreadsheet" in file_type:
+                df = pd.read_excel(io.BytesIO(decoded_file))
+                data_summary = self._get_dataframe_summary(df)
+            elif "word" in file_type or "docx" in file_type:
+                data_summary = self._read_docx(decoded_file)
+            elif "pdf" in file_type:
+                data_summary = self._read_pdf(decoded_file)
+            else:
+                yield json.dumps({"type": "error", "content": f"Unsupported file type: {file_type}. I can analyze CSV, XLSX, DOCX, and PDF files."})
+                return
+
+            # Only attempt plotting if we have a DataFrame
+            if df is not None:
+                plot_keywords = ["plot", "graph", "scatter", "visualize"]
+                if any(keyword in query.lower() for keyword in plot_keywords):
+                    try:
+                        image_base64 = self._generate_plot(query, df)
+                        yield json.dumps({
+                            "type": "plot",
+                            "format": "png",
+                            "image": image_base64
+                        })
+                    except ValueError as e:
+                        yield json.dumps({"type": "error", "content": str(e)})
+                    return
+
+            chain_input = {"data_summary": data_summary, "query": query}
+
+            async for chunk in self.analysis_chain.astream(chain_input):
+                yield chunk #json.dumps({"type": "text", "content": chunk})
+
+        except Exception as e:
+            yield json.dumps({"type": "error", "content": f"An error occurred: {e}"})

llm_be/chat_backend/services/image_generation.py

@@ -7,6 +7,7 @@ from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler
 
 logger = logging.getLogger(__name__)
 
+
 class ImageGenerationService:
     """
     Service for text-to-image generation using Stable Diffusion.
@@ -69,7 +70,7 @@ class ImageGenerationService:
         prompt: str,
         negative_prompt: Optional[str] = None,
         output_path: Optional[str] = None,
-        **kwargs
+        **kwargs,
     ) -> Tuple[Image.Image, dict]:
         """
         Generate image from text prompt.
@@ -94,9 +95,7 @@ class ImageGenerationService:
 
             with torch.inference_mode():
                 result = self.pipeline(
-                    prompt=prompt,
+                    prompt=prompt, negative_prompt=negative_prompt, **params
-                    negative_prompt=negative_prompt,
-                    **params
                 )
 
             image = result.images[0]
@@ -127,7 +126,7 @@ class AsyncImageGenerationService:
         prompt: str,
         negative_prompt: Optional[str] = None,
         output_path: Optional[str] = None,
-        **kwargs
+        **kwargs,
     ) -> Tuple[Image.Image, dict]:
         """Async version of generate_image"""
         import asyncio
@@ -139,7 +138,7 @@ class AsyncImageGenerationService:
             prompt=prompt,
             negative_prompt=negative_prompt,
             output_path=output_path,
-            **kwargs
+            **kwargs,
         )
 
         return await loop.run_in_executor(None, func)

llm_be/chat_backend/services/llm_service.py

@@ -1,23 +1,26 @@
 from abc import ABC, abstractmethod
 from typing import AsyncGenerator, Generator, Optional
 
-from langchain_community.llms import Ollama
+# from langchain_community.llms import Ollama
+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
 
+
 class LLMService(ABC):
     """Abstract base class for LLM conversation services."""
 
     def __init__(self):
-        self.llm = Ollama(
+        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,
             repeat_penalty=1.1,
-            num_ctx=4096
+            num_ctx=4096,
         )
         self.output_parser = StrOutputParser()
 
@@ -28,10 +31,11 @@ class LLMService(ABC):
 
     def _format_history(self, conversation: Conversation) -> str:
         """Format conversation history for the prompt."""
-        prompts = Prompt.objects.filter(conversation=conversation).order_by('created_at')
+        prompts = Prompt.objects.filter(conversation=conversation).order_by(
+            "created_at"
+        )
         return "\n".join(
-            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}"
+            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
-            for prompt in prompts
         )
 
 
@@ -56,19 +60,18 @@ class SyncLLMService(LLMService):
         self.conversation_chain = (
             {
                 "history": lambda x: self._format_history(x["conversation"]),
-                "query": lambda x: x["query"]
+                "query": lambda x: x["query"],
             }
             | self.prompt
             | self.llm
             | self.output_parser
         )
 
-    def generate_response(self, conversation: Conversation, query: str, **kwargs) -> Generator[str, None, None]:
+    def generate_response(
+        self, conversation: Conversation, query: str, **kwargs
+    ) -> Generator[str, None, None]:
         """Generate response with streaming support."""
-        chain_input = {
+        chain_input = {"query": query, "conversation": conversation}
-            "query": query,
-            "conversation": conversation
-        }
 
         for chunk in self.conversation_chain.stream(chain_input):
             yield chunk
@@ -102,37 +105,48 @@ class AsyncLLMService(LLMService):
 
         self.conversation_chain = (
             {
-                "context": lambda x: self._format_history(x["conversation"]),
+                "context":lambda x: x["conversation"],
-                "recent_history": lambda x: self._get_recent_messages(x["conversation"]),
+                "recent_history":lambda x: x['recent_conversation'],
-                "query": lambda x: x["query"]
+                "query": lambda x: x["query"],
             }
             | self.prompt
             | self.llm
             | self.output_parser
         )
 
-    async def _format_history(self, conversation: Conversation) -> str:
+    async def _format_history(self, conversation: list) -> str:
         """Async version of format conversation history."""
-        prompts = await Prompt.objects.filter(conversation=conversation).order_by('created_at').alist()
+        # prompts = list(
-        return "\n".join(
+        #     await Prompt.objects.filter(conversation_id=conversation_id)
-            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}"
+        #     .order_by("created")
-            for prompt in prompts
-        )
             
-    async def _get_recent_messages(self, conversation: Conversation) -> str:
+        # )
+        # return "\n".join(
+        #     f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
+        # )
+        return "\n".join([f"{"User" if prompt.type=="human" else "AI"}: {prompt.text()}" for prompt in conversation])
+
+    async def _get_recent_messages(self, conversation: list) -> str:
         """Async version of format conversation history."""
-        prompts = await Prompt.objects.filter(conversation=conversation).order_by('created_at').alist()[-3:]
-        return "\n".join(
-            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}"
-            for prompt in prompts
-        )
 
-    async def generate_response(self, conversation: Conversation, query: str, **kwargs) -> AsyncGenerator[str, None]:
+        # prompts = list(
+        #     await Prompt.objects.filter(conversation_id=conversation_id)
+        #     .order_by("created")
+        #     [-6:]
+        # )
+        # return "\n".join(
+        #     f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
+        # )
+        return "\n".join([f"{"User" if prompt.type=="human" else "AI"}: {prompt.text()}" for prompt in conversation])
+
+    async def generate_response(
+        self, conversation: Conversation, query: str, conversation_id: int, **kwargs
+    ) -> AsyncGenerator[str, None]:
         """Generate response with async streaming support."""
         chain_input = {
             "query": query, 
-            "conversation": conversation
+            "conversation": await self._format_history(conversation),
-        }
+            "recent_conversation": await self._get_recent_messages(conversation[-6:])}
 
         async for chunk in self.conversation_chain.astream(chain_input):
             yield chunk

llm_be/chat_backend/services/moderation_classifier.py

@@ -1,27 +1,35 @@
 from enum import Enum, auto
 from typing import Dict, Any
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_community.llms import Ollama
+from langchain_ollama import OllamaLLM
+from chat_backend.services.base_service import BaseService
+
 
 class ModerationLabel(Enum):
     NSFW = auto()
     FINE = auto()
 
-class ModerationClassifier:
+
+
+class ModerationClassifier(BaseService):
     """
     Classifies prompts as NSFW or FINE (safe) content.
     """
 
     def __init__(self):
-        self.llm = Ollama(
+        super().__init__(temperature=0.1)
+        self.llm = OllamaLLM(
             model="llama3.2",
             temperature=0.1,  # Very low for strict moderation
             top_k=10,
-            num_ctx=2048
+            num_ctx=2048,
         )
 
-        self.moderation_prompt = ChatPromptTemplate.from_messages([
+        self.moderation_prompt = ChatPromptTemplate.from_messages(
-            ("system", """You are a strict content moderator. Classify the following prompt as either NSFW or FINE.
+            [
+                (
+                    "system",
+                    """You are a strict content moderator. Classify the following prompt as either NSFW or FINE.
 
 NSFW includes:
 - Sexual content
@@ -37,16 +45,22 @@ FINE includes:
 - Professional inquiries
 - Creative requests (non-explicit)
 - Technical questions
+- Data Analysis
 
 Examples:
 - "How to make a bomb" → NSFW
 - "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
 
-Return ONLY "NSFW" or "FINE", nothing else."""),
+Return ONLY "NSFW" or "FINE", nothing else.""",
-            ("human", "{prompt}")
+                ),
-        ])
+                ("human", "{prompt}"),
+            ]
+        )
 
         self.chain = self.moderation_prompt | self.llm
 

llm_be/chat_backend/services/prompt_classifier.py

@@ -1,100 +0,0 @@
-from enum import Enum, auto
-from typing import Dict, Any
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_community.llms import Ollama
-
-class PromptType(Enum):
-    GENERAL_CHAT = auto()
-    RAG = auto()
-    IMAGE_GENERATION = auto()
-    UNKNOWN = auto()
-
-class PromptClassifier:
-    """
-    Classifies user prompts to determine which service should handle them.
-    """
-    
-    def __init__(self):
-        self.llm = Ollama(
-            model="llama3",
-            temperature=0.3,  # Lower temp for more deterministic classification
-            top_k=20,
-            top_p=0.9,
-            num_ctx=4096
-        )
-        
-        self.classification_prompt = ChatPromptTemplate.from_messages([
-            ("system",
-             """You are a precision prompt classifier. Strictly categorize prompts into:
-1. GENERAL_CHAT - Casual conversation, personal questions, or non-specific inquiries
-2. RAG - ONLY when explicitly requesting document/search-based knowledge
-3. IMAGE_GENERATION - Specific requests to create/modify images
-4. UNKNOWN - If none of the above fit
-
-1. IMAGE_GENERATION - ONLY if:
-   - Explicitly contains: "generate/create/draw/make an image/picture/photo/art/illustration"
-   - Requests visual content creation
-   - Example: "Make a picture of a castle" → IMAGE_GENERATION
-
-2. RAG - ONLY if:
-   - Explicitly mentions documents/files/data
-   - Uses search terms: "find/search/lookup in [source]"
-   - Example: "What does contracts.pdf say?" → RAG
-
-3. GENERAL_CHAT - DEFAULT category when:
-   - Doesn't meet above criteria
-   - Conversational/general knowledge
-   - Uncertain cases
-   - Example: "Tell me a joke" → GENERAL_CHAT
-
-Examples:
-[Definitely RAG]
-- "What does the uploaded PDF say about quarterly results?"
-- "Search our documents for the 2023 marketing strategy"
-- "Find the contract clause about termination"
-
-[Definitely GENERAL_CHAT]
-- "How does photosynthesis work?" (General knowledge)
-- "Tell me a joke"
-- "What's your opinion on AI?"
-
-[Borderline → GENERAL_CHAT]
-- "What's our company policy on X?" (No doc reference → general)
-- "Explain quantum computing" (General knowledge)
-- "Summarize the meeting" (No doc reference)
-
-Return ONLY the label, no explanations."""),
-            ("human", "{prompt}")
-        ])
-        
-        self.chain = self.classification_prompt | self.llm
-        
-    async def classify_async(self, prompt: str) -> PromptType:
-        """Asynchronously classify the prompt"""
-        try:
-            response = await self.chain.ainvoke({"prompt": prompt})
-            return self._parse_response(response.strip())
-        except Exception as e:
-            print(f"Classification error: {e}")
-            return PromptType.UNKNOWN
-    
-    def classify(self, prompt: str) -> PromptType:
-        """Synchronously classify the prompt"""
-        try:
-            response = self.chain.invoke({"prompt": prompt})
-            return self._parse_response(response.strip())
-        except Exception as e:
-            print(f"Classification error: {e}")
-            return PromptType.UNKNOWN
-    
-    def _parse_response(self, response: str) -> PromptType:
-        """Convert string response to PromptType enum"""
-        response = response.upper()
-        for prompt_type in PromptType:
-            if prompt_type.name in response:
-                return prompt_type
-        return PromptType.UNKNOWN
-
-
-# Singleton instance for easy access
-prompt_classifier = PromptClassifier()

llm_be/chat_backend/services/prompt_classifier/prompt_classifier.py

@@ -0,0 +1,195 @@
+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
+
+
+class PromptType(Enum):
+    GENERAL_CHAT = auto()
+    RAG = auto()
+    IMAGE_GENERATION = auto()
+    DATA_ANALYSIS = auto()
+    SEARCH = auto()
+    UNKNOWN = auto()
+
+
+class PromptClassifier(BaseService):
+    """
+    Classifies user prompts to determine which service should handle them.
+    """
+
+    def __init__(self):
+        super().__init__(temperature=0.1)
+        # self.llm = OllamaLLM(
+        #     model="llama3.2",
+        #     temperature=0.3,  # Lower temp for more deterministic classification
+        #     top_k=20,
+        #     top_p=0.9,
+        #     num_ctx=4096,
+        # )
+
+        self.classification_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a precision prompt classifier. Strictly categorize prompts into:
+1. GENERAL_CHAT - Casual conversation, personal questions, or non-specific inquiries
+2. RAG - ONLY when explicitly requesting document/search-based knowledge
+3. IMAGE_GENERATION - Specific requests to create/modify images
+4. DATA_ANALYSIS - When a user wants to read an uploaded document (PDF, Word, etc.) and generate an index, summary, or extract structured information. Includes prompts like "Please read this document and make me an index for it".
+5. SEARCH - When the user is seeking specific, up-to-date information (e.g., current events, celebrity news, sports scores).
+6. UNKNOWN - If none of the above fit
+
+1. IMAGE_GENERATION - ONLY if:
+    - Explicitly contains: "generate/create/draw/make an image/picture/photo/art/illustration"
+    - Requests visual content creation
+    - Example: "Make a picture of a castle" → IMAGE_GENERATION
+
+2. RAG - ONLY if:
+    - Explicitly mentions documents/files/data
+    - Uses search terms: "find/search/lookup in [source]"
+    - Example: "What does contracts.pdf say?" → RAG
+
+3. DATA_ANALYSIS - ONLY if:
+    - The user provides or references an uploaded document (PDF, Word, etc.) and asks for an index, summary, extraction, or analysis of its contents.
+    - Example: "Please read this document and make me an index for it" → DATA_ANALYSIS
+    - Example: "Here is the file content. What is the sum of all 'Sales'?" → DATA_ANALYSIS
+
+5. SEARCH - ONLY if:
+    - User asks for current information (news, weather, sports, stock prices)
+    - User asks for specific facts that might change or require lookup (e.g. "Who won the 2024 election?")
+    - Example: "What is the latest news on X?" → SEARCH
+    - Example: "Who won the Super Bowl this year?" → SEARCH
+
+6. GENERAL_CHAT - DEFAULT category when:
+    - Doesn't meet above criteria
+    - Conversational/general knowledge (that doesn't require live search)
+    - Creative writing (poems, jokes)
+    - Example: "Tell me a joke" → GENERAL_CHAT
+    - Example: "Write a poem about cats" → GENERAL_CHAT
+
+Examples:
+[Definitely RAG]
+- "What does the uploaded PDF say about quarterly results?"
+- "Search our documents for the 2023 marketing strategy"
+
+[Definitely DATA_ANALYSIS]
+- "Please read this document and make me an index for it"
+- "Here is the file content. What is the sum of all 'Sales'?"
+- "Based on this CSV data, show me the top 5 customers."
+
+[Definitely SEARCH]
+- "Who won the 2024 presidential race?"
+- "What is the latest celebrity news?"
+- "Current stock price of Apple"
+
+[Definitely GENERAL_CHAT]
+- "How does photosynthesis work?" (General knowledge)
+- "Tell me a joke"
+- "What's your opinion on AI?"
+
+[Borderline -> GENERAL_CHAT]
+- "What's our company policy on X?" (No doc reference -> general)
+
+Return ONLY the exact Enum label (e.g. "GENERAL_CHAT"), no explanations."""
+                ),
+                ("human", "{prompt}"),
+            ]
+        )
+
+        self.chain = self.classification_prompt | self.llm
+
+    def _quick_check(self, prompt: str) -> PromptType | None:
+        """
+        Performs a quick, rule-based classification before involving the LLM.
+        Returns a PromptType if a clear match is found, otherwise None.
+        """
+        lower_prompt = prompt.lower()
+
+        # 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"]):
+            return PromptType.DATA_ANALYSIS
+
+        # RAG (explicitly asking to search within provided context/documents)
+        # This might overlap with DATA_ANALYSIS, but RAG is more about retrieval from a knowledge base.
+        # The prompt examples for RAG are "What does the uploaded PDF say about quarterly results?"
+        # "Search our documents for the 2023 marketing strategy"
+        if ("uploaded pdf" in lower_prompt or "our documents" in lower_prompt or "this document" in lower_prompt or "the document" in lower_prompt) and \
+           any(keyword in lower_prompt for keyword in ["say about", "search for", "find in", "lookup in"]):
+            return PromptType.RAG
+
+        # SEARCH
+        if any(keyword in lower_prompt for keyword in ["latest news", "current weather", "stock price", "who won", "what is the current", "breaking news", "real-time information", "up-to-date"]):
+            return PromptType.SEARCH
+
+        return None
+
+    async def classify_async(self, prompt: str) -> PromptType:
+        """Asynchronously classify the prompt"""
+        quick = self._quick_check(prompt)
+        if quick:
+            return quick
+        try:
+            response = await self.chain.ainvoke({"prompt": prompt})
+            return self._parse_response(response.strip())
+        except Exception as e:
+            print(f"Classification error: {e}")
+            return PromptType.UNKNOWN
+
+    def classify(self, prompt: str) -> PromptType:
+        """Synchronously classify the prompt"""
+        quick = self._quick_check(prompt)
+        if quick:
+            return quick
+        try:
+            response = self.chain.invoke({"prompt": prompt})
+            return self._parse_response(response.strip())
+        except Exception as e:
+            print(f"Classification error: {e}")
+            return PromptType.UNKNOWN
+
+    def _parse_response(self, response: str) -> PromptType:
+        """Convert string response to PromptType enum"""
+        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]
+        except KeyError:
+            pass
+            
+        # Handle missing underscores (e.g. GENERALCHAT)
+        normalized_response = response.replace("_", "")
+        for prompt_type in PromptType:
+            if prompt_type.name.replace("_", "") == normalized_response:
+                return prompt_type
+                
+        # Substring match as fallback
+        for prompt_type in PromptType:
+            if prompt_type.name in response:
+                return prompt_type
+                
+        return PromptType.UNKNOWN
+
+
+# Singleton instance for easy access
+prompt_classifier = PromptClassifier()

llm_be/chat_backend/services/prompt_classifier/tests.py

@@ -0,0 +1,31 @@
+import os
+from unittest import TestCase, mock
+from unittest.mock import MagicMock, patch, AsyncMock
+from typing import List, Dict, Any
+
+from django.test import TestCase as DjangoTestCase
+
+from chat_backend.services.rag_services import (
+    RAGService,
+    SyncRAGService,
+    AsyncRAGService,
+)
+from chat_backend.models import Conversation, Prompt, DocumentWorkspace, Document
+from chat_backend.services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
+from parameterized import parameterized
+
+
+
+class PromptClassifierTestCase(TestCase):
+    def setUp(self):
+        self.service = PromptClassifier()
+
+    @parameterized.expand([
+        ["Tell me a joke",PromptType.GENERAL_CHAT],
+        ["Create an image of a dog for me",PromptType.IMAGE_GENERATION],
+        ["highlight the features of the backyard playset if they were to choose us and make the language more long form",PromptType.GENERAL_CHAT],
+        ["Great, can you make it about a duck now", PromptType.IMAGE_GENERATION],
+    ])
+    def test_prompt_classification(self, prompt, expected_output):
+        result = self.service.classify(prompt)
+        self.assertEqual(result, expected_output)

llm_be/chat_backend/services/rag_services.py

@@ -3,7 +3,10 @@ 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 langchain_community.llms import Ollama
+from django.conf import settings
+
+# from langchain_community.llms import Ollama
+from langchain_ollama import OllamaLLM
 from langchain_community.vectorstores import Chroma
 from langchain_core.documents import Document as LangDocument
 from langchain_core.output_parsers import StrOutputParser
@@ -14,11 +17,13 @@ from langchain_community.document_loaders import (
     PyPDFLoader,
     Docx2txtLoader,
     TextLoader,
-    UnstructuredFileLoader
+    UnstructuredFileLoader,
 )
 from django.core.files.uploadedfile import UploadedFile
 from chat_backend.models import Conversation, Prompt, DocumentWorkspace, Document
 from pathlib import Path
+from chat_backend.services.base_service import BaseService
+
 
 @database_sync_to_async
 def get_documents(workspace: DocumentWorkspace | None = None):
@@ -28,9 +33,9 @@ def get_documents(workspace: DocumentWorkspace | None = None):
         return [doc for doc in Document.objects.all()]
 
 
-
+class RAGService(BaseService):
-class RAGService(ABC):
     """Abstract base class for RAG services."""
+
     _instance = None
 
     def __new__(cls):
@@ -40,36 +45,27 @@ class RAGService(ABC):
         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")
-        self.llm = Ollama(
+        super().__init__()
-            model="llama3.2",
-            temperature=0.7,
-            top_k=50,
-            top_p=0.9,
-            repeat_penalty=1.1,
-            num_ctx=4096
-        )
         self.text_splitter = RecursiveCharacterTextSplitter(
-            chunk_size=1000,
+            chunk_size=1000, chunk_overlap=200
-            chunk_overlap=200
         )
         self.vector_store = self._initialize_vector_store()
 
         # Supported file types and their loaders
         self.loader_mapping = {
-            '.pdf': PyPDFLoader,
+            ".pdf": PyPDFLoader,
-            '.docx': Docx2txtLoader,
+            ".docx": Docx2txtLoader,
-            '.txt': TextLoader,
+            ".txt": TextLoader,
             # Fallback for other file types
-            '*': UnstructuredFileLoader,
+            "*": UnstructuredFileLoader,
         }
 
     def _initialize_vector_store(self) -> Chroma:
         """Initialize and return the Chroma vector store."""
-        persist_directory=f"./chroma_db/"
+        persist_directory = f"./chroma_db/"
         vector_store = Chroma(
-            embedding_function=self.embedding_model,
+            embedding_function=self.embedding_model, persist_directory=persist_directory
-            persist_directory=persist_directory
         )
         return vector_store
 
@@ -84,16 +80,14 @@ class RAGService(ABC):
 
         for doc in documents:
             print(f"Processing: {doc.file.name}")
-            loader_class = self._get_file_loader( doc.file.name)
+            loader_class = self._get_file_loader(doc.file.name)
             loader = loader_class(doc.file)
 
-            
             chunks = self._load_and_split_documents(doc.file.path)
             if chunks:
                 self.vector_store.add_documents(chunks)
         self.vector_store.persist()
 
-    
     def ingest_documents(self, workspace: DocumentWorkspace | None = None) -> None:
         """Ingest documents from a workspace into the vector store."""
         print(f"Getting the Document via the workspace: {workspace}")
@@ -105,25 +99,26 @@ class RAGService(ABC):
         print(f"Processing the documents : {documents}")
         self._prepare_documents(documents)
 
+    # @abstractmethod
+    # def generate_response(self, conversation: Conversation, query: str, **kwargs):
+    #     """Generate a response using RAG."""
+    #     pass
 
-    @abstractmethod
+    # @abstractmethod
-    def generate_response(self, conversation: Conversation, query: str, **kwargs):
+    # def search_documents(
-        """Generate a response using RAG."""
+    #     self, query: str, workspace: Optional[DocumentWorkspace] = None, k: int = 4
-        pass
+    # ) -> List[Document]:
-    
+    #     """Search relevant documents from the vector store."""
-    @abstractmethod
+    #     pass
-    def search_documents(self, query: str, workspace: Optional[DocumentWorkspace] = None, k: int = 4) -> List[Document]:
-        """Search relevant documents from the vector store."""
-        pass
 
     def _get_file_loader(self, file_path: str):
         """Get appropriate loader for file type"""
         ext = Path(file_path).suffix.lower()
-        return self.loader_mapping.get(ext, self.loader_mapping['*'])
+        return self.loader_mapping.get(ext, self.loader_mapping["*"])
 
     def _sanitize_filename(self, filename: str) -> str:
         """Sanitize filename for safe storage"""
-        return re.sub(r'[^\w\-_. ]', '_', filename)
+        return re.sub(r"[^\w\-_. ]", "_", filename)
 
     def _save_uploaded_file(self, uploaded_file: UploadedFile, save_dir: str) -> str:
         """Save uploaded file to disk"""
@@ -131,13 +126,15 @@ class RAGService(ABC):
         sanitized_name = self._sanitize_filename(uploaded_file.name)
         file_path = os.path.join(save_dir, sanitized_name)
 
-        with open(file_path, 'wb+') as destination:
+        with open(file_path, "wb+") as destination:
             for chunk in uploaded_file.chunks():
                 destination.write(chunk)
 
         return file_path
 
-    def _load_and_split_documents(self, file_path: str, metadata: dict = None) -> List[Document]:
+    def _load_and_split_documents(
+        self, file_path: str, metadata: dict = None
+    ) -> List[Document]:
         """Load and split documents from file"""
         loader_class = self._get_file_loader(file_path)
         loader = loader_class(file_path)
@@ -154,7 +151,7 @@ class RAGService(ABC):
         file_tupls: List[UploadedFile],  # (file_path, name,workspace_id)
         workspace_id: str,
         source: str = "upload",
-        save_dir: str = "data/uploads"
+        save_dir: str = "data/uploads",
     ) -> Dict[str, Any]:
         """
         Process and add uploaded files to vector store
@@ -168,23 +165,18 @@ class RAGService(ABC):
         Returns:
             Dictionary with processing results
         """
-        results = {
+        results = {"total_added": 0, "failed_files": [], "processed_files": []}
-            'total_added': 0,
-            'failed_files': [],
-            'processed_files': []
-        }
 
         for file_tuple in file_tupls:
             try:
                 # Save file to disk
 
-                
                 # Prepare metadata
                 metadata = {
-                    'source': file_tuple[1],
+                    "source": file_tuple[1],
-                    'workspace_id': file_tuple[2],
+                    "workspace_id": file_tuple[2],
-                    'original_filename': file_tuple[1],
+                    "original_filename": file_tuple[1],
-                    'file_path': file_tuple[0],
+                    "file_path": file_tuple[0],
                 }
 
                 # Load and split documents
@@ -193,17 +185,15 @@ class RAGService(ABC):
                 # Add to vector store
                 if docs:
                     self.vector_store.add_documents(docs)
-                    results['total_added'] += len(docs)
+                    results["total_added"] += len(docs)
-                    results['processed_files'].append({
+                    results["processed_files"].append(
-                        'filename': file_tuple[1],
+                        {"filename": file_tuple[1], "document_count": len(docs)}
-                        'document_count': len(docs)
+                    )
-                    })
 
             except Exception as e:
-                results['failed_files'].append({
+                results["failed_files"].append(
-                    'filename': file_tuple[1],
+                    {"filename": file_tuple[1], "error": str(e)}
-                    'error': str(e)
+                )
-                })
                 continue
 
         # Persist changes
@@ -234,7 +224,7 @@ class SyncRAGService(RAGService):
             {
                 "context": self._retriever_with_history,
                 "history": lambda x: self._format_history(x["conversation"]),
-                "question": lambda x: x["query"]
+                "question": lambda x: x["query"],
             }
             | self.prompt
             | self.llm
@@ -243,10 +233,11 @@ class SyncRAGService(RAGService):
 
     def _format_history(self, conversation: Conversation) -> str:
         """Format conversation history for the prompt."""
-        prompts = Prompt.objects.filter(conversation=conversation).order_by('created_at')
+        prompts = Prompt.objects.filter(conversation=conversation).order_by(
+            "created_at"
+        )
         return "\n".join(
-            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}"
+            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
-            for prompt in prompts
         )
 
     def _retriever_with_history(self, input_dict: Dict[str, Any]) -> str:
@@ -262,8 +253,9 @@ class SyncRAGService(RAGService):
         else:
             return relevant_docs
 
-    
+    def search_documents(
-    def search_documents(self, query: str, workspace: Optional[DocumentWorkspace] = None, k: int = 4) -> List[Document]:
+        self, query: str, workspace: Optional[DocumentWorkspace] = None, k: int = 4
+    ) -> List[Document]:
         """Search relevant documents from the vector store."""
         filter_dict = {}
         if workspace:
@@ -271,19 +263,15 @@ class SyncRAGService(RAGService):
         print(f"search_kwargs: {search_kwargs}")
         retriever = self.vector_store.as_retriever(
             search_type="similarity",
-            search_kwargs={
+            search_kwargs={"k": k, "filter": filter_dict if filter_dict else None},
-                "k": k,
-                "filter": filter_dict if filter_dict else None
-            }
         )
         return retriever.get_relevant_documents(query)
 
-    def generate_response(self, conversation: Conversation, query: str, **kwargs) -> Generator[str, None, None]:
+    def generate_response(
+        self, conversation: Conversation, query: str, **kwargs
+    ) -> Generator[str, None, None]:
         """Generate response with streaming support."""
-        chain_input = {
+        chain_input = {"query": query, "conversation": conversation}
-            "query": query,
-            "conversation": conversation
-        }
 
         for chunk in self.rag_chain.stream(chain_input):
             yield chunk
@@ -311,8 +299,8 @@ class AsyncRAGService(RAGService):
         self.rag_chain = (
             {
                 "context": self._retriever_with_history,
-                "history": lambda x: self._format_history(x["conversation"]),
+                "history": lambda x: x['recent_conversation'], #self._format_history(x["conversation"]),
-                "question": lambda x: x["query"]
+                "question": lambda x: x["query"],
             }
             | self.prompt
             | self.llm
@@ -321,12 +309,16 @@ class AsyncRAGService(RAGService):
 
     async def _format_history(self, conversation: Conversation) -> str:
         """Format conversation history for the prompt."""
-        prompts = await Prompt.objects.filter(conversation=conversation).order_by('created_at').alist()
+        # prompts = (
-        print(f"prompts that we are seeding with are: {prompts}")
+        #     await Prompt.objects.filter(conversation=conversation)
-        return "\n".join(
+        #     .order_by("created_at")
-            f"{'User' if prompt.is_user else 'AI'}: {prompt.text}"
+        #     .alist()
-            for prompt in prompts
+        # )
-        )
+        # print(f"prompts that we are seeding with are: {prompts}")
+        # return "\n".join(
+        #     f"{'User' if prompt.is_user else 'AI'}: {prompt.text}" for prompt in prompts
+        # )
+        return "\n".join([f"{"User" if prompt.type=="human" else "AI"}: {prompt.text()}" for prompt in conversation])
 
     async def _retriever_with_history(self, input_dict: Dict[str, Any]) -> str:
         """Retrieve documents considering conversation history."""
@@ -336,7 +328,7 @@ class AsyncRAGService(RAGService):
         workspace = input_dict["workspace"]
 
         # You could enhance this to consider historical context in retrieval
-        docs= await self.search_documents(query, workspace)
+        docs = await self.search_documents(query, workspace)
 
         if not docs:
             print("Didn't find any relevant docs")
@@ -344,34 +336,36 @@ class AsyncRAGService(RAGService):
         print("\n\n".join(doc.page_content for doc in docs))
         return "\n\n".join(doc.page_content for doc in docs)
 
-    
+    async def search_documents(
-    async def search_documents(self, query: str, workspace: Optional[DocumentWorkspace] = None, k: int = 4) -> List[Document]:
+        self, query: str, workspace: Optional[DocumentWorkspace] = None, k: int = 4
+    ) -> List[Document]:
         """Search relevant documents from the vector store."""
         filter_dict = {}
         print(f"Do we have a workspace: {workspace}")
         if workspace:
             filter_dict["workspace_id"] = workspace.id
-        search_kwargs={
+        search_kwargs = {"k": k, "filter": filter_dict if filter_dict else None}
-                "k": k,
-                "filter": filter_dict if filter_dict else None
-            }
         print(f"search_kwargs: {search_kwargs}")
 
         retriever = self.vector_store.as_retriever(
             search_type="mmr",
-            search_kwargs={
+            search_kwargs={"k": k, "filter": filter_dict if filter_dict else None},
-                "k": k,
-                "filter": filter_dict if filter_dict else None
-            }
         )
         return await retriever.aget_relevant_documents(query)
 
-    async def generate_response(self, conversation: Conversation, query: str, workspace: DocumentWorkspace, **kwargs) -> AsyncGenerator[str, None]:
+    async def generate_response(
+        self,
+        conversation: Conversation,
+        query: str,
+        workspace: DocumentWorkspace,
+        **kwargs,
+    ) -> AsyncGenerator[str, None]:
         """Generate response with streaming support."""
         chain_input = {
             "query": query,
             "conversation": conversation,
             "workspace": workspace,
+            "recent_conversation":  await self._format_history(conversation),
         }
 
         async for chunk in self.rag_chain.astream(chain_input):

llm_be/chat_backend/services/tests.py

@@ -5,215 +5,247 @@ from typing import List, Dict, Any
 
 from django.test import TestCase as DjangoTestCase
 
-from chat_backend.services.rag_services import RAGService, SyncRAGService, AsyncRAGService
+from chat_backend.services.rag_services import (
+    RAGService,
+    SyncRAGService,
+    AsyncRAGService,
+)
 from chat_backend.models import Conversation, Prompt, DocumentWorkspace, Document
+from chat_backend.services.prompt_classifier import PromptClassifier, PromptType
+from parameterized import parameterized
 
-class TestRAGService(TestCase):
+
+# class TestRAGService(TestCase):
+#     def setUp(self):
+#         self.rag_service = RAGService()
+#         self.rag_service.vector_store = MagicMock()
+#         self.rag_service.embedding_model = MagicMock()
+#         self.rag_service.text_splitter = MagicMock()
+
+#     def test_initialize_vector_store(self):
+#         with patch("os.path.exists", return_value=False), patch(
+#             "os.makedirs"
+#         ) as mock_makedirs, patch(
+#             "langchain_community.vectorstores.Chroma"
+#         ) as mock_chroma:
+
+#             # Reset the vector store to test initialization
+#             self.rag_service.vector_store = None
+#             result = self.rag_service._initialize_vector_store()
+
+#             mock_makedirs.assert_called_once_with("chroma_db")
+#             mock_chroma.assert_called_once_with(
+#                 embedding_function=self.rag_service.embedding_model,
+#                 persist_directory="chroma_db",
+#             )
+#             self.assertIsNotNone(result)
+
+#     def test_prepare_documents(self):
+#         mock_doc1 = MagicMock(spec=Document)
+#         mock_doc1.content = "Test content"
+#         mock_doc1.source = "test_source"
+#         mock_doc1.workspace = MagicMock()
+#         mock_doc1.workspace.id = 1
+#         mock_doc1.id = 1
+
+#         self.rag_service.text_splitter.split_text.return_value = ["chunk1", "chunk2"]
+
+#         result = self.rag_service._prepare_documents([mock_doc1])
+
+#         self.assertEqual(len(result), 2)
+#         self.rag_service.text_splitter.split_text.assert_called_once_with(
+#             "Test content"
+#         )
+#         self.assertEqual(result[0].page_content, "chunk1")
+#         self.assertEqual(result[0].metadata["source"], "test_source")
+
+#     def test_ingest_documents(self):
+#         mock_workspace = MagicMock()
+#         mock_document = MagicMock()
+#         mock_documents = [mock_document]
+
+#         with patch(
+#             "services.rag_services.Document.objects.filter", return_value=mock_documents
+#         ):
+#             self.rag_service._prepare_documents = MagicMock(
+#                 return_value=["processed_doc"]
+#             )
+
+#             self.rag_service.ingest_documents(mock_workspace)
+
+#             self.rag_service.vector_store.add_documents.assert_called_once_with(
+#                 ["processed_doc"]
+#             )
+#             self.rag_service.vector_store.persist.assert_called_once()
+
+
+# class TestSyncRAGService(DjangoTestCase):
+#     def setUp(self):
+#         self.sync_service = SyncRAGService()
+#         self.sync_service.vector_store = MagicMock()
+#         self.sync_service.llm = MagicMock()
+#         self.sync_service.rag_chain = MagicMock()
+
+#         self.mock_conversation = MagicMock(spec=Conversation)
+#         self.mock_conversation.workspace = MagicMock()
+
+#         self.mock_prompt1 = MagicMock(spec=Prompt)
+#         self.mock_prompt1.is_user = True
+#         self.mock_prompt1.text = "User question"
+#         self.mock_prompt1.created_at = "2023-01-01"
+
+#         self.mock_prompt2 = MagicMock(spec=Prompt)
+#         self.mock_prompt2.is_user = False
+#         self.mock_prompt2.text = "AI response"
+#         self.mock_prompt2.created_at = "2023-01-02"
+
+#     def test_format_history(self):
+#         with patch("services.rag_services.Prompt.objects.filter") as mock_filter:
+#             mock_filter.return_value.order_by.return_value = [
+#                 self.mock_prompt1,
+#                 self.mock_prompt2,
+#             ]
+
+#             result = self.sync_service._format_history(self.mock_conversation)
+
+#             expected = "User: User question\nAI: AI response"
+#             self.assertEqual(result, expected)
+#             mock_filter.assert_called_once_with(conversation=self.mock_conversation)
+
+#     def test_retriever_with_history(self):
+#         input_dict = {"query": "test query", "conversation": self.mock_conversation}
+
+#         self.sync_service.search_documents = MagicMock(return_value=["doc1", "doc2"])
+
+#         result = self.sync_service._retriever_with_history(input_dict)
+
+#         self.sync_service.search_documents.assert_called_once_with(
+#             "test query", self.mock_conversation.workspace
+#         )
+#         self.assertEqual(result, ["doc1", "doc2"])
+
+#     def test_search_documents(self):
+#         mock_retriever = MagicMock()
+#         mock_retriever.get_relevant_documents.return_value = ["doc1", "doc2"]
+#         self.sync_service.vector_store.as_retriever.return_value = mock_retriever
+
+#         result = self.sync_service.search_documents(
+#             "test query", self.mock_conversation.workspace
+#         )
+
+#         self.sync_service.vector_store.as_retriever.assert_called_once_with(
+#             search_type="similarity",
+#             search_kwargs={
+#                 "k": 4,
+#                 "filter": {"workspace_id": self.mock_conversation.workspace.id},
+#             },
+#         )
+#         self.assertEqual(result, ["doc1", "doc2"])
+
+#     def test_generate_response(self):
+#         chain_input = {"query": "test query", "conversation": self.mock_conversation}
+
+#         mock_stream = ["chunk1", "chunk2", "chunk3"]
+#         self.sync_service.rag_chain.stream.return_value = mock_stream
+
+#         result = list(
+#             self.sync_service.generate_response(self.mock_conversation, "test query")
+#         )
+
+#         self.sync_service.rag_chain.stream.assert_called_once_with(chain_input)
+#         self.assertEqual(result, mock_stream)
+
+
+# class TestAsyncRAGService(DjangoTestCase):
+#     def setUp(self):
+#         self.async_service = AsyncRAGService()
+#         self.async_service.vector_store = MagicMock()
+#         self.async_service.llm = MagicMock()
+#         self.async_service.rag_chain = AsyncMock()
+
+#         self.mock_conversation = MagicMock(spec=Conversation)
+#         self.mock_conversation.workspace = MagicMock()
+
+#         self.mock_prompt1 = MagicMock(spec=Prompt)
+#         self.mock_prompt1.is_user = True
+#         self.mock_prompt1.text = "User question"
+#         self.mock_prompt1.created_at = "2023-01-01"
+
+#         self.mock_prompt2 = MagicMock(spec=Prompt)
+#         self.mock_prompt2.is_user = False
+#         self.mock_prompt2.text = "AI response"
+#         self.mock_prompt2.created_at = "2023-01-02"
+
+#     async def test_format_history(self):
+#         mock_manager = AsyncMock()
+#         mock_manager.order_by.return_value.alist.return_value = [
+#             self.mock_prompt1,
+#             self.mock_prompt2,
+#         ]
+
+#         with patch(
+#             "services.rag_services.Prompt.objects.filter", return_value=mock_manager
+#         ):
+#             result = await self.async_service._format_history(self.mock_conversation)
+
+#             expected = "User: User question\nAI: AI response"
+#             self.assertEqual(result, expected)
+#             mock_manager.order_by.assert_called_once_with("created_at")
+
+#     async def test_retriever_with_history(self):
+#         input_dict = {"query": "test query", "conversation": self.mock_conversation}
+
+#         self.async_service.search_documents = AsyncMock(return_value=["doc1", "doc2"])
+
+#         result = await self.async_service._retriever_with_history(input_dict)
+
+#         self.async_service.search_documents.assert_awaited_once_with(
+#             "test query", self.mock_conversation.workspace
+#         )
+#         self.assertEqual(result, ["doc1", "doc2"])
+
+#     async def test_search_documents(self):
+#         mock_retriever = AsyncMock()
+#         mock_retriever.aget_relevant_documents.return_value = ["doc1", "doc2"]
+#         self.async_service.vector_store.as_retriever.return_value = mock_retriever
+
+#         result = await self.async_service.search_documents(
+#             "test query", self.mock_conversation.workspace
+#         )
+
+#         self.async_service.vector_store.as_retriever.assert_called_once_with(
+#             search_type="similarity",
+#             search_kwargs={
+#                 "k": 4,
+#                 "filter": {"workspace_id": self.mock_conversation.workspace.id},
+#             },
+#         )
+#         self.assertEqual(result, ["doc1", "doc2"])
+
+#     async def test_generate_response(self):
+#         chain_input = {"query": "test query", "conversation": self.mock_conversation}
+
+#         mock_stream = ["chunk1", "chunk2", "chunk3"]
+#         self.async_service.rag_chain.astream.return_value = mock_stream
+
+#         chunks = []
+#         async for chunk in self.async_service.generate_response(
+#             self.mock_conversation, "test query"
+#         ):
+#             chunks.append(chunk)
+
+#         self.async_service.rag_chain.astream.assert_awaited_once_with(chain_input)
+#         self.assertEqual(chunks, mock_stream)
+
+class PromptClassifierTestCase(TestCase):
     def setUp(self):
-        self.rag_service = RAGService()
+        self.service = PromptClassifier()
-        self.rag_service.vector_store = MagicMock()
-        self.rag_service.embedding_model = MagicMock()
-        self.rag_service.text_splitter = MagicMock()
 
-    def test_initialize_vector_store(self):
+    @parameterized.expand([
-        with patch('os.path.exists', return_value=False), \
+        ["Tell me a joke",PromptType.GENERAL_CHAT],
-             patch('os.makedirs') as mock_makedirs, \
+        ["Create an image of a dog for me",PromptType.IMAGE_GENERATION],
-             patch('langchain_community.vectorstores.Chroma') as mock_chroma:
+        ["highlight the features of the backyard playset if they were to choose us and make the language more long form",PromptType.GENERAL_CHAT],
-            
+    ])
-            # Reset the vector store to test initialization
+    def test_prompt_classification(self, prompt, expected_output):
-            self.rag_service.vector_store = None
+        result = self.service.classify(prompt)
-            result = self.rag_service._initialize_vector_store()
+        self.assertEqual(result, expected_output)
-            
-            mock_makedirs.assert_called_once_with("chroma_db")
-            mock_chroma.assert_called_once_with(
-                embedding_function=self.rag_service.embedding_model,
-                persist_directory="chroma_db"
-            )
-            self.assertIsNotNone(result)
-
-    def test_prepare_documents(self):
-        mock_doc1 = MagicMock(spec=Document)
-        mock_doc1.content = "Test content"
-        mock_doc1.source = "test_source"
-        mock_doc1.workspace = MagicMock()
-        mock_doc1.workspace.id = 1
-        mock_doc1.id = 1
-
-        self.rag_service.text_splitter.split_text.return_value = ["chunk1", "chunk2"]
-        
-        result = self.rag_service._prepare_documents([mock_doc1])
-        
-        self.assertEqual(len(result), 2)
-        self.rag_service.text_splitter.split_text.assert_called_once_with("Test content")
-        self.assertEqual(result[0].page_content, "chunk1")
-        self.assertEqual(result[0].metadata["source"], "test_source")
-
-    def test_ingest_documents(self):
-        mock_workspace = MagicMock()
-        mock_document = MagicMock()
-        mock_documents = [mock_document]
-        
-        with patch('services.rag_services.Document.objects.filter', return_value=mock_documents):
-            self.rag_service._prepare_documents = MagicMock(return_value=["processed_doc"])
-            
-            self.rag_service.ingest_documents(mock_workspace)
-            
-            self.rag_service.vector_store.add_documents.assert_called_once_with(["processed_doc"])
-            self.rag_service.vector_store.persist.assert_called_once()
-
-
-class TestSyncRAGService(DjangoTestCase):
-    def setUp(self):
-        self.sync_service = SyncRAGService()
-        self.sync_service.vector_store = MagicMock()
-        self.sync_service.llm = MagicMock()
-        self.sync_service.rag_chain = MagicMock()
-
-        self.mock_conversation = MagicMock(spec=Conversation)
-        self.mock_conversation.workspace = MagicMock()
-        
-        self.mock_prompt1 = MagicMock(spec=Prompt)
-        self.mock_prompt1.is_user = True
-        self.mock_prompt1.text = "User question"
-        self.mock_prompt1.created_at = "2023-01-01"
-        
-        self.mock_prompt2 = MagicMock(spec=Prompt)
-        self.mock_prompt2.is_user = False
-        self.mock_prompt2.text = "AI response"
-        self.mock_prompt2.created_at = "2023-01-02"
-
-    def test_format_history(self):
-        with patch('services.rag_services.Prompt.objects.filter') as mock_filter:
-            mock_filter.return_value.order_by.return_value = [self.mock_prompt1, self.mock_prompt2]
-            
-            result = self.sync_service._format_history(self.mock_conversation)
-            
-            expected = "User: User question\nAI: AI response"
-            self.assertEqual(result, expected)
-            mock_filter.assert_called_once_with(conversation=self.mock_conversation)
-
-    def test_retriever_with_history(self):
-        input_dict = {
-            "query": "test query",
-            "conversation": self.mock_conversation
-        }
-        
-        self.sync_service.search_documents = MagicMock(return_value=["doc1", "doc2"])
-        
-        result = self.sync_service._retriever_with_history(input_dict)
-        
-        self.sync_service.search_documents.assert_called_once_with(
-            "test query",
-            self.mock_conversation.workspace
-        )
-        self.assertEqual(result, ["doc1", "doc2"])
-
-    def test_search_documents(self):
-        mock_retriever = MagicMock()
-        mock_retriever.get_relevant_documents.return_value = ["doc1", "doc2"]
-        self.sync_service.vector_store.as_retriever.return_value = mock_retriever
-        
-        result = self.sync_service.search_documents("test query", self.mock_conversation.workspace)
-        
-        self.sync_service.vector_store.as_retriever.assert_called_once_with(
-            search_type="similarity",
-            search_kwargs={
-                "k": 4,
-                "filter": {"workspace_id": self.mock_conversation.workspace.id}
-            }
-        )
-        self.assertEqual(result, ["doc1", "doc2"])
-
-    def test_generate_response(self):
-        chain_input = {
-            "query": "test query",
-            "conversation": self.mock_conversation
-        }
-        
-        mock_stream = ["chunk1", "chunk2", "chunk3"]
-        self.sync_service.rag_chain.stream.return_value = mock_stream
-        
-        result = list(self.sync_service.generate_response(self.mock_conversation, "test query"))
-        
-        self.sync_service.rag_chain.stream.assert_called_once_with(chain_input)
-        self.assertEqual(result, mock_stream)
-
-
-class TestAsyncRAGService(DjangoTestCase):
-    def setUp(self):
-        self.async_service = AsyncRAGService()
-        self.async_service.vector_store = MagicMock()
-        self.async_service.llm = MagicMock()
-        self.async_service.rag_chain = AsyncMock()
-
-        self.mock_conversation = MagicMock(spec=Conversation)
-        self.mock_conversation.workspace = MagicMock()
-        
-        self.mock_prompt1 = MagicMock(spec=Prompt)
-        self.mock_prompt1.is_user = True
-        self.mock_prompt1.text = "User question"
-        self.mock_prompt1.created_at = "2023-01-01"
-        
-        self.mock_prompt2 = MagicMock(spec=Prompt)
-        self.mock_prompt2.is_user = False
-        self.mock_prompt2.text = "AI response"
-        self.mock_prompt2.created_at = "2023-01-02"
-
-    async def test_format_history(self):
-        mock_manager = AsyncMock()
-        mock_manager.order_by.return_value.alist.return_value = [self.mock_prompt1, self.mock_prompt2]
-        
-        with patch('services.rag_services.Prompt.objects.filter', return_value=mock_manager):
-            result = await self.async_service._format_history(self.mock_conversation)
-            
-            expected = "User: User question\nAI: AI response"
-            self.assertEqual(result, expected)
-            mock_manager.order_by.assert_called_once_with('created_at')
-
-    async def test_retriever_with_history(self):
-        input_dict = {
-            "query": "test query",
-            "conversation": self.mock_conversation
-        }
-        
-        self.async_service.search_documents = AsyncMock(return_value=["doc1", "doc2"])
-        
-        result = await self.async_service._retriever_with_history(input_dict)
-        
-        self.async_service.search_documents.assert_awaited_once_with(
-            "test query",
-            self.mock_conversation.workspace
-        )
-        self.assertEqual(result, ["doc1", "doc2"])
-
-    async def test_search_documents(self):
-        mock_retriever = AsyncMock()
-        mock_retriever.aget_relevant_documents.return_value = ["doc1", "doc2"]
-        self.async_service.vector_store.as_retriever.return_value = mock_retriever
-        
-        result = await self.async_service.search_documents("test query", self.mock_conversation.workspace)
-        
-        self.async_service.vector_store.as_retriever.assert_called_once_with(
-            search_type="similarity",
-            search_kwargs={
-                "k": 4,
-                "filter": {"workspace_id": self.mock_conversation.workspace.id}
-            }
-        )
-        self.assertEqual(result, ["doc1", "doc2"])
-
-    async def test_generate_response(self):
-        chain_input = {
-            "query": "test query",
-            "conversation": self.mock_conversation
-        }
-        
-        mock_stream = ["chunk1", "chunk2", "chunk3"]
-        self.async_service.rag_chain.astream.return_value = mock_stream
-        
-        chunks = []
-        async for chunk in self.async_service.generate_response(self.mock_conversation, "test query"):
-            chunks.append(chunk)
-        
-        self.async_service.rag_chain.astream.assert_awaited_once_with(chain_input)
-        self.assertEqual(chunks, mock_stream)

llm_be/chat_backend/services/title_generator.py

@@ -1,22 +1,28 @@
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_community.llms import Ollama
+
+# from langchain_community.llms import Ollama
+from langchain_ollama import OllamaLLM
 from typing import Optional
 
+
 class TitleGenerator:
     """
     Generates short, descriptive titles for conversations based on the first prompt.
     """
 
     def __init__(self):
-        self.llm = Ollama(
+        self.llm = OllamaLLM(
-            model="llama3",
+            model="llama3.2",
             temperature=0.5,  # Slightly creative but not too random
             top_k=20,
-            num_ctx=2048  # Shorter context needed for titles
+            num_ctx=2048,  # Shorter context needed for titles
         )
 
-        self.title_prompt = ChatPromptTemplate.from_messages([
+        self.title_prompt = ChatPromptTemplate.from_messages(
-            ("system", """You are a conversation title generator. Create a very short (2-5 word) title based on the user's first message.
+            [
+                (
+                    "system",
+                    """You are a conversation title generator. Create a very short (2-5 word) title based on the user's first message.
              
 Rules:
 1. Keep it extremely concise
@@ -31,9 +37,11 @@ Examples:
 - "Generate an image of a dragon" → "Dragon Image Generation"
 - "Find our company's privacy policy" → "Privacy Policy Search"
 
-Return ONLY the title, nothing else."""),
+Return ONLY the title, nothing else.""",
-            ("human", "{prompt}")
+                ),
-        ])
+                ("human", "{prompt}"),
+            ]
+        )
 
         self.chain = self.title_prompt | self.llm
 
@@ -58,7 +66,7 @@ Return ONLY the title, nothing else."""),
     def _clean_response(self, response: str) -> str:
         """Clean and format the LLM response"""
         # Remove any quotes or punctuation
-        response = response.strip('"\'.!? \n\t')
+        response = response.strip("\"'.!? \n\t")
         # Ensure title case and trim
         return response.title()[:50]  # Hard limit for safety
 

llm_be/chat_backend/signals.py

@@ -3,14 +3,16 @@ from django.dispatch import receiver
 from chat_backend.models import Document
 from .services.rag_services import AsyncRAGService
 
+
 @receiver(post_save, sender=Document)
 def update_vector_on_save(sender, instance, **kwargs):
     """Update vector store when documents are saved"""
 
-    if kwargs.get('created', False):
+    if kwargs.get("created", False):
         rag_service = AsyncRAGService()
         rag_service.ingest_documents()
 
+
 @receiver(post_delete, sender=Document)
 def delete_vector_on_remove(sender, instance, **kwargs):
     """Handle document deletion by re-indexing the whole workspace"""

llm_be/chat_backend/tests.py

@@ -10,51 +10,51 @@ from .models import DocumentWorkspace, Document, Company
 from django.contrib.auth import get_user_model
 import tempfile
 from django.core.files.uploadedfile import SimpleUploadedFile
+from parameterized import parameterized
+
 
 # Minimal valid PDF bytes
 VALID_PDF_BYTES = (
-    b'%PDF-1.3\n'
+    b"%PDF-1.3\n"
-    b'1 0 obj\n'
+    b"1 0 obj\n"
-    b'<< /Type /Catalog /Pages 2 0 R >>\n'
+    b"<< /Type /Catalog /Pages 2 0 R >>\n"
-    b'endobj\n'
+    b"endobj\n"
-    b'2 0 obj\n'
+    b"2 0 obj\n"
-    b'<< /Type /Pages /Kids [3 0 R] /Count 1 >>\n'
+    b"<< /Type /Pages /Kids [3 0 R] /Count 1 >>\n"
-    b'endobj\n'
+    b"endobj\n"
-    b'3 0 obj\n'
+    b"3 0 obj\n"
-    b'<< /Type /Page /Parent 2 0 R /Resources << >> /MediaBox [0 0 612 792] /Contents 4 0 R >>\n'
+    b"<< /Type /Page /Parent 2 0 R /Resources << >> /MediaBox [0 0 612 792] /Contents 4 0 R >>\n"
-    b'endobj\n'
+    b"endobj\n"
-    b'4 0 obj\n'
+    b"4 0 obj\n"
-    b'<< /Length 44 >>\n'
+    b"<< /Length 44 >>\n"
-    b'stream\n'
+    b"stream\n"
-    b'BT /F1 12 Tf 72 720 Td (Test PDF) Tj ET\n'
+    b"BT /F1 12 Tf 72 720 Td (Test PDF) Tj ET\n"
-    b'endstream\n'
+    b"endstream\n"
-    b'endobj\n'
+    b"endobj\n"
-    b'xref\n'
+    b"xref\n"
-    b'0 5\n'
+    b"0 5\n"
-    b'0000000000 65535 f \n'
+    b"0000000000 65535 f \n"
-    b'0000000009 00000 n \n'
+    b"0000000009 00000 n \n"
-    b'0000000058 00000 n \n'
+    b"0000000058 00000 n \n"
-    b'0000000117 00000 n \n'
+    b"0000000117 00000 n \n"
-    b'0000000223 00000 n \n'
+    b"0000000223 00000 n \n"
-    b'trailer\n'
+    b"trailer\n"
-    b'<< /Size 5 /Root 1 0 R >>\n'
+    b"<< /Size 5 /Root 1 0 R >>\n"
-    b'startxref\n'
+    b"startxref\n"
-    b'317\n'
+    b"317\n"
-    b'%%EOF'
+    b"%%EOF"
 )
 
+
 class DocumentWorkspaceViewsTestCase(APITestCase):
     def setUp(self):
         self.company = Company.objects.create(
-            name="test",
+            name="test", state="IL", zipcode="60189", address="1968 Greensboro Dr"
-            state="IL",
-            zipcode="60189",
-            address="1968 Greensboro Dr"
         )
         self.user = get_user_model().objects.create_user(
             company=self.company,
-            username='testuser',
+            username="testuser",
-            password='testpass123',
+            password="testpass123",
             email="test@test.com",
         )
 
@@ -62,31 +62,28 @@ class DocumentWorkspaceViewsTestCase(APITestCase):
         self.client.force_authenticate(user=self.user)
 
         self.workspace = DocumentWorkspace.objects.create(
-            company = self.user.company,
+            company=self.user.company, name="Test Workspace"
-            name='Test Workspace'
         )
 
     def test_list_workspaces(self):
-        url = reverse('document_workspaces')
+        url = reverse("document_workspaces")
         response = self.client.get(url)
         self.assertEqual(response.status_code, status.HTTP_200_OK)
         self.assertEqual(len(response.data), 1)
-        self.assertEqual(response.data[0]['name'], 'Test Workspace')
+        self.assertEqual(response.data[0]["name"], "Test Workspace")
 
     def test_create_workspace(self):
-        url = reverse('document_workspaces')
+        url = reverse("document_workspaces")
-        data = {
+        data = {"name": "New Workspace"}
-            'name': 'New Workspace'
+        response = self.client.post(url, data, format="json")
-        }
-        response = self.client.post(url, data, format='json')
         self.assertEqual(response.status_code, status.HTTP_201_CREATED)
         self.assertEqual(DocumentWorkspace.objects.count(), 2)
 
     def test_retrieve_workspace(self):
-        url = reverse('document_workspaces')
+        url = reverse("document_workspaces")
         response = self.client.get(url)
         self.assertEqual(response.status_code, status.HTTP_200_OK)
-        self.assertEqual(response.data[0]['name'], 'Test Workspace')
+        self.assertEqual(response.data[0]["name"], "Test Workspace")
 
     # def test_update_workspace(self):
     #     url = reverse('document_workspaces')
@@ -104,18 +101,16 @@ class DocumentWorkspaceViewsTestCase(APITestCase):
     #     self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
     #     self.assertEqual(DocumentWorkspace.objects.count(), 0)
 
+
 class DocumentViewsTestCase(APITestCase):
     def setUp(self):
         self.company = Company.objects.create(
-            name="test",
+            name="test", state="IL", zipcode="60189", address="1968 Greensboro Dr"
-            state="IL",
-            zipcode="60189",
-            address="1968 Greensboro Dr"
         )
         self.user = get_user_model().objects.create_user(
             company=self.company,
-            username='testuser',
+            username="testuser",
-            password='testpass123',
+            password="testpass123",
             email="test@test.com",
         )
 
@@ -123,23 +118,18 @@ class DocumentViewsTestCase(APITestCase):
         self.client.force_authenticate(user=self.user)
 
         self.workspace = DocumentWorkspace.objects.create(
-            company=self.user.company,
+            company=self.user.company, name="Test Workspace"
-            name='Test Workspace'
         )
 
         # Create a test file
         self.test_file = SimpleUploadedFile(
-            "test.pdf",
+            "test.pdf", VALID_PDF_BYTES, content_type="application/pdf"
-            VALID_PDF_BYTES,
-            content_type="application/pdf"
         )
 
     def test_upload_document(self):
-        url = reverse('documents')
+        url = reverse("documents")
-        data = {
+        data = {"file": self.test_file}
-            'file': self.test_file
+        response = self.client.post(url, data, format="multipart")
-        }
-        response = self.client.post(url, data, format='multipart')
         self.assertEqual(response.status_code, status.HTTP_201_CREATED)
         self.assertEqual(Document.objects.count(), 1)
 
@@ -149,17 +139,14 @@ class DocumentViewsTestCase(APITestCase):
 
     def test_list_documents(self):
         # First create a document
-        Document.objects.create(
+        Document.objects.create(workspace=self.workspace, file=self.test_file)
-            workspace=self.workspace,
-            file=self.test_file
-        )
 
-        url = reverse('documents')
+        url = reverse("documents")
         response = self.client.get(url)
         self.assertEqual(response.status_code, status.HTTP_200_OK)
         self.assertEqual(len(response.data), 1)
-        self.assertIn('test', response.data[0]['file'])
+        self.assertIn("test", response.data[0]["file"])
-        self.assertIn('pdf', response.data[0]['file'])
+        self.assertIn("pdf", response.data[0]["file"])
 
     # def test_delete_document(self):
     #     document = Document.objects.create(
@@ -173,38 +160,35 @@ class DocumentViewsTestCase(APITestCase):
     #     self.assertEqual(Document.objects.count(), 0)
 
     def test_upload_invalid_file(self):
-        url = reverse('documents')
+        url = reverse("documents")
-        data = {
+        data = {"file": "not a file"}
-            'file': 'not a file'
+        response = self.client.post(url, data, format="multipart")
-        }
-        response = self.client.post(url, data, format='multipart')
         self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
 
     def test_access_other_users_documents(self):
         # Create another user
         other_company = Company.objects.create(
-            name="test2",
+            name="test2", state="IL", zipcode="60189", address="1968 Greensboro Dr"
-            state="IL",
-            zipcode="60189",
-            address="1968 Greensboro Dr"
         )
         other_user = get_user_model().objects.create_user(
             company=other_company,
-            username='otheruser',
+            username="otheruser",
-            password='otherpass123',
+            password="otherpass123",
-            email="testing2@test.com"
+            email="testing2@test.com",
         )
         other_workspace = DocumentWorkspace.objects.create(
-            company = other_user.company,
+            company=other_user.company, name="Other Workspace"
-            name='Other Workspace'
         )
         other_document = Document.objects.create(
-            workspace=other_workspace,
+            workspace=other_workspace, file=self.test_file
-            file=self.test_file
         )
 
         # Try to access the other user's document
-        url = reverse('documents_details', kwargs={"document_id":other_document.id})
+        url = reverse("documents_details", kwargs={"document_id": other_document.id})
         response = self.client.get(url)
         self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
 
+
+
+
+

llm_be/chat_backend/urls.py

@@ -23,8 +23,7 @@ from .views import (
     reset_password,
     DocumentWorkspaceView,
     DocumentUploadView,
-    DocumentDetailView
+    DocumentDetailView,
-
 )
 from rest_framework.routers import DefaultRouter
 
@@ -80,11 +79,16 @@ urlpatterns = [
         name="analytics_company_usage",
     ),
     path("analytics/admin/", AdminAnalytics.as_view(), name="analytics_admin"),
-
     # document urls
-    path("document_workspaces/", DocumentWorkspaceView.as_view(), name="document_workspaces"),
+    path(
+        "document_workspaces/",
+        DocumentWorkspaceView.as_view(),
+        name="document_workspaces",
+    ),
     path("documents/", DocumentUploadView.as_view(), name="documents"),
-    path("documents_details/<int:document_id>", DocumentDetailView.as_view(), name="documents_details"),
+    path(
-    
+        "documents_details/<int:document_id>",
+        DocumentDetailView.as_view(),
+        name="documents_details",
+    ),
 ]
-

llm_be/chat_backend/views.py

@@ -13,8 +13,9 @@ from .serializers import (
     PromptSerializer,
     FeedbackSerializer,
     DocumentWorkspaceSerializer,
-    DocumentSerializer
+    DocumentSerializer,
 )
+
 from rest_framework.views import APIView
 from rest_framework.response import Response
 from .models import (
@@ -25,7 +26,7 @@ from .models import (
     Feedback,
     PromptMetric,
     DocumentWorkspace,
-    Document
+    Document,
 )
 from django.views.decorators.cache import never_cache
 from django.http import JsonResponse
@@ -35,14 +36,15 @@ from asgiref.sync import sync_to_async, async_to_sync
 from channels.generic.websocket import AsyncWebsocketConsumer
 from langchain_ollama.llms import OllamaLLM
 from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.messages import HumanMessage, AIMessage
-from langchain.chains import RetrievalQA
+from langchain_classic.chains import RetrievalQA
 import re
 import os
 from django.conf import settings
 import json
 import base64
 import pandas as pd
+import io
 
 # For email support
 from django.core.mail import EmailMultiAlternatives
@@ -66,17 +68,23 @@ from .services.llm_service import AsyncLLMService
 from .services.rag_services import AsyncRAGService
 from .services.title_generator import title_generator
 from .services.moderation_classifier import moderation_classifier, ModerationLabel
-from .services.prompt_classifier import prompt_classifier, PromptType
+from .services.prompt_classifier.prompt_classifier import PromptClassifier, PromptType
+from .services.data_analysis_service import AsyncDataAnalysisService
 
 
-from langchain.chains import create_retrieval_chain
+
-from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_classic.chains import create_retrieval_chain
+from langchain_classic.chains.combine_documents import create_stuff_documents_chain
 from langchain_ollama import ChatOllama
 
-CHANNEL_NAME: str = "llm_messages"
+import logging
-MODEL_NAME: str = "llama3"
+logger = logging.getLogger(__name__)
 
 
+
+CHANNEL_NAME: str = "llm_messages"
+MODEL_NAME: str = "llama3.2"
+
 # Create your views here.
 class CustomObtainTokenView(TokenObtainPairView):
     permission_classes = (permissions.AllowAny,)
@@ -98,9 +106,9 @@ class CustomUserCreate(APIView):
 
 
 def send_invite_email(slug, email_to_invite):
-    print("Sending invite email")
+    logger.info("Sending invite email")
-    print(f"url : https://www.chat.aimloperations.com/set_password?slug={slug}")
+    logger.info(f"url : https://chat.aimloperations.com/set_password?slug={slug}")
-    url = f"https://www.chat.aimloperations.com/set_password?slug={slug}"
+    url = f"https://chat.aimloperations.com/set_password?slug={slug}"
     subject = "Welcome to AI ML Operations, LLC Chat Services"
     from_email = "ryan@aimloperations.com"
     to = email_to_invite
@@ -113,8 +121,24 @@ def send_invite_email(slug, email_to_invite):
     msg.send(fail_silently=True)
 
 
+def send_password_reset_email(slug, email_to_invite):
+    logger.info("Sending reset email")
+    logger.info(f"url : https://www.chat.aimloperations.com/set_password?slug={slug}")
+    url = f"https://www.chat.aimloperations.com/set_password?slug={slug}"
+    subject = "Password reset for AI ML Operations, LLC Chat Services"
+    from_email = "ryan@aimloperations.com"
+    to = email_to_invite
+    d = {"url": url}
+    html_content = get_template(r"emails/reset_email.html").render(d)
+    text_content = get_template(r"emails/reset_email.txt").render(d)
+
+    msg = EmailMultiAlternatives(subject, text_content, from_email, [to])
+    msg.attach_alternative(html_content, "text/html")
+    msg.send(fail_silently=True)
+
+
 def send_feedback_email(feedback_obj):
-    print("Sending feedback email")
+    logger.info("Sending feedback email")
     subject = "New Feedback for Chat by AI ML Operations, LLC"
     from_email = "ryan@aimloperations.com"
     to = "ryan@aimloperations.com"
@@ -128,7 +152,7 @@ def send_feedback_email(feedback_obj):
 
 
 def send_password_reset_email(slug, email_to_invite):
-    print("Sending Password reset email")
+    logger.info("Sending Password reset email")
     url = f"https://www.chat.aimloperations.com/set_password?slug={slug}"
     subject = "Password reset for Chat by AI ML Operations, LLC"
     from_email = "ryan@aimloperations.com"
@@ -176,19 +200,22 @@ class CustomUserInvite(APIView):
 
         return Response(status=status.HTTP_201_CREATED)
 
+
 @csrf_exempt
 def reset_password(request):
     if request.method == "POST":
         data = json.loads(request.body)
-        token = data.get('recaptchaToken')
+        token = data.get("recaptchaToken")
         payload = {
-            'secret': settings.CAPTCHA_SECRET_KEY,
+            "secret": settings.CAPTCHA_SECRET_KEY,
-            'response': token,
+            "response": token,
         }
-        response = requests.post('https://www.google.com/recaptcha/api/siteverify', data=payload)
+        response = requests.post(
+            "https://www.google.com/recaptcha/api/siteverify", data=payload
+        )
         result = response.json()
-        if result.get('success') and result.get('score') >= 0.5:
+        if result.get("success") and result.get("score") >= 0.5:
-            email = data.get('email')
+            email = data.get("email")
             user = CustomUser.objects.filter(email=email).first()
             if user:
                 user.set_unusable_password()
@@ -198,9 +225,9 @@ def reset_password(request):
                 send_password_reset_email(user.slug, email)
                 JsonResponse(status=200)
 
-        
     JsonResponse(status=400)
 
+
 class ResetUserPassword(APIView):
     http_method_names = [
         "post",
@@ -213,15 +240,17 @@ class ResetUserPassword(APIView):
         Send an email with a set password link to the set password page
         Also disable the account
         """
-        print(f"Password reset for requests. {request.data}")
+        logger.info(f"Password reset for requests. {request.data}")
-        token = request.data.get('recaptchaToken')
+        token = request.data.get("recaptchaToken")
         payload = {
-            'secret': settings.CAPTCHA_SECRET_KEY,
+            "secret": settings.CAPTCHA_SECRET_KEY,
-            'response': recaptchaToken,
+            "response": recaptchaToken,
         }
-        response = requests.post('https://www.google.com/recaptcha/api/siteverify', data=payload)
+        response = requests.post(
+            "https://www.google.com/recaptcha/api/siteverify", data=payload
+        )
         result = response.json()
-        if result.get('success') and result.get('score') >= 0.5:
+        if result.get("success") and result.get("score") >= 0.5:
             user = CustomUser.objects.filter(email=email).first()
             if user:
                 user.set_unusable_password()
@@ -230,7 +259,7 @@ class ResetUserPassword(APIView):
                 # send the email
                 send_password_reset_email(user.slug, email)
         else:
-            print('Captcha secret failed')
+            logger.error("Captcha secret failed")
 
         return Response(status=status.HTTP_200_OK)
 
@@ -261,10 +290,16 @@ class CustomUserGet(APIView):
 
         email = request.user.email
         username = request.user.username
-        user = CustomUser.objects.get(email=email)
+        user = CustomUser.objects.filter(email=email).last()
+        logger.info(f"Getting the user: {user}")
+        try:
             serializer = CustomUserSerializer(user)
-
+            logger.debug(f"serializer: {serializer}")
+            logger.debug(serializer.data)
             return Response(serializer.data, status=status.HTTP_200_OK)
+        except Exception as e:
+            logger.error(f"Exception: {e}")
+            return Response({}, status=status.HTTP_400_BAD_REQUEST)
 
 
 class FeedbackView(APIView):
@@ -272,7 +307,7 @@ class FeedbackView(APIView):
 
     def post(self, request, format="json"):
         serializer = FeedbackSerializer(data=request.data)
-        print(request.data)
+        logger.debug(request.data)
         if serializer.is_valid():
 
             feedback_obj = serializer.save()
@@ -282,7 +317,7 @@ class FeedbackView(APIView):
             send_feedback_email(feedback_obj)
             return Response(serializer.data, status=status.HTTP_201_CREATED)
         else:
-            print(serializer.errors)
+            logger.error(serializer.errors)
             return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
 
     def get(self, request, format="json"):
@@ -425,7 +460,7 @@ class ConversationDetailView(APIView):
         return Response(serailzer.data, status=status.HTTP_200_OK)
 
     def post(self, request, format="json"):
-        print("In the post")
+        logger.info("In the post")
         # Add the prompt to the database
         # make sure there is a conversation for it
         #   if there is not a conversation create a title for it
@@ -453,7 +488,7 @@ class ConversationDetailView(APIView):
                 prompt_instance = serializer.save()
 
             # set up the streaming response if it is from the user
-            print(f"Do we have a valid user? {is_user}")
+            logger.info(f"Do we have a valid user? {is_user}")
             if is_user:
                 messages = []
                 for prompt_obj in Prompt.objects.filter(
@@ -467,12 +502,12 @@ class ConversationDetailView(APIView):
                     )
 
                 channel_layer = get_channel_layer()
-                print(f"Sending to the channel: {CHANNEL_NAME}")
+                logger.info(f"Sending to the channel: {CHANNEL_NAME}")
                 async_to_sync(channel_layer.group_send)(
                     CHANNEL_NAME, {"type": "receive", "content": messages}
                 )
         except:
-            print(
+            logger.error(
                 f"Error trying to submit to conversation_id: {conversation_id} with request.data: {request.data}"
             )
             pass
@@ -689,274 +724,12 @@ llm = OllamaLLM(model=MODEL_NAME)
 # chain = prompt | llm.with_config({"run_name": "model"}) | output_parser.with_config({"run_name": "Assistant"})
 
 
-@database_sync_to_async
-def create_conversation(prompt, email, title):
-    # return the conversation id
-    conversation = Conversation.objects.create(title=title)
-    conversation.save()
-
-    user = CustomUser.objects.get(email=email)
-    conversation.user_id = user.id
-    conversation.save()
-    return conversation.id
 
 
-@database_sync_to_async
-def get_workspace(conversation_id):
-    conversation = Conversation.objects.get(id=conversation_id)
-    return DocumentWorkspace.objects.get(company=conversation.user.company)
-
-@database_sync_to_async
-def get_messages(conversation_id, prompt, file_string: str = None, file_type: str = ""):
-    messages = []
-
-    conversation = Conversation.objects.get(id=conversation_id)
-    print(file_string)
-
-    # add the prompt to the conversation
-    serializer = PromptSerializer(
-        data={
-            "message": prompt,
-            "user_created": True,
-            "created": timezone.now(),
-        }
-    )
-    if serializer.is_valid(raise_exception=True):
-        prompt_instance = serializer.save()
-        prompt_instance.conversation_id = conversation.id
-        prompt_instance.save()
-        if file_string:
-            file_name = f"prompt_{prompt_instance.id}_data.{file_type}"
-            f = ContentFile(file_string, name=file_name)
-            prompt_instance.file.save(file_name, f)
-            prompt_instance.file_type = file_type
-            prompt_instance.save()
-
-    for prompt_obj in Prompt.objects.filter(conversation__id=conversation_id):
-        messages.append(
-            {
-                "content": prompt_obj.message,
-                "role": "user" if prompt_obj.user_created else "assistant",
-                "has_file": prompt_obj.file_exists(),
-                "file": prompt_obj.file if prompt_obj.file_exists() else None,
-                "file_type": prompt_obj.file_type if prompt_obj.file_exists() else None,
-            }
-        )
-
-    # now transform the messages
-    transformed_messages = []
-    for message in messages:
-
-        if message["has_file"] and message["file_type"] != None:
-            if "csv" in message["file_type"]:
-                file_type = "csv"
-                altered_message = f"{message['content']}\n The file type is csv and the file contents are: {message['file'].read()}"
-            elif "xlsx" in message["file_type"]:
-                file_type = "xlsx"
-                df = pd.read_excel(message["file"].read())
-                altered_message = f"{message['content']}\n The file type is xlsx and the file contents are: {df}"
-            elif "txt" in message["file_type"]:
-                file_type = "txt"
-                altered_message = f"{message['content']}\n The file type is csv and the file contents are: {message['file'].read()}"
-        else:
-            altered_message = message["content"]
-
-        transformed_message = (
-            SystemMessage(content=altered_message)
-            if message["role"] == "assistant"
-            else HumanMessage(content=altered_message)
-        )
-        transformed_messages.append(transformed_message)
-
-    return transformed_messages, prompt_instance
-
-
-@database_sync_to_async
-def save_generated_message(conversation_id, message):
-    conversation = Conversation.objects.get(id=conversation_id)
-
-    # add the prompt to the conversation
-    serializer = PromptSerializer(
-        data={
-            "message": message,
-            "user_created": False,
-            "created": timezone.now(),
-        }
-    )
-    if serializer.is_valid():
-        prompt_instance = serializer.save()
-        prompt_instance.conversation_id = conversation.id
-        prompt_instance = serializer.save()
-
-
-@database_sync_to_async
-def create_prompt_metric(
-    prompt_id, prompt, has_file, file_type, model_name, conversation_id
-):
-    prompt_metric = PromptMetric.objects.create(
-        prompt_id=prompt_id,
-        start_time=timezone.now(),
-        prompt_length=len(prompt),
-        has_file=has_file,
-        file_type=file_type,
-        model_name=model_name,
-        conversation_id=conversation_id,
-    )
-    prompt_metric.save()
-    return prompt_metric
-
-
-@database_sync_to_async
-def update_prompt_metric(prompt_metric, status):
-    prompt_metric.event = status
-    prompt_metric.save()
-
-
-@database_sync_to_async
-def finish_prompt_metric(prompt_metric, response_length):
-    print(f"finish_prompt_metric: {response_length}")
-    prompt_metric.end_time = timezone.now()
-    prompt_metric.reponse_length = response_length
-    prompt_metric.event = "FINISHED"
-    prompt_metric.save(update_fields=["end_time", "reponse_length", "event"])
-    print("finish_prompt_metric saved")
-
-@database_sync_to_async
-def get_retriever(conversation_id):
-    print(f'getting workspace from conversation: {conversation_id}')
-    conversation = Conversation.objects.get(id=conversation_id)
-    print(f'Got conversation: {conversation}')
-    workspace = DocumentWorkspace.objects.get(company=conversation.user.company)
-    print(f'Got workspace: {conversation}')
-    vectorstore = Chroma(
-        persist_directory=f"./chroma_db/",
-        embedding=OllamaEmbeddings(model="llama3.2"),
-    )
-    return vectorstore.as_retriever()
-
-class ChatConsumerAgain(AsyncWebsocketConsumer):
-    async def connect(self):
-        await self.accept()
-
-    async def disconnect(self, close_code):
-        await self.close()
-
-    async def receive(self, text_data=None, bytes_data=None):
-        print(f"Text Data: {text_data}")
-        print(f"Bytes Data: {bytes_data}")
-        if text_data:
-            data = json.loads(text_data)
-            message = data.get("message", None)
-            conversation_id = data.get("conversation_id", None)
-            email = data.get("email", None)
-            file = data.get("file", None)
-            file_type = data.get("fileType", "")
-            model = data.get("modelName", "Turbo")
-
-            if not conversation_id:
-                # we need to create a new conversation
-                # we will generate a name for it too
-                title = await title_generator.generate_async(message)
-                conversation_id = await create_conversation(message, email, title)
-
-            if conversation_id:
-                decoded_file = None
-
-                if file:
-                    decoded_file = base64.b64decode(file)
-                    print(decoded_file)
-                    if "csv" in file_type:
-                        file_type = "csv"
-                        altered_message = f"{message}\n The file type is csv and the file contents are: {decoded_file}"
-                    elif "xmlformats-officedocument" in file_type:
-                        file_type = "xlsx"
-                        df = pd.read_excel(decoded_file)
-                        altered_message = f"{message}\n The file type is xlsx and the file contents are: {df}"
-                    elif "text" in file_type:
-                        file_type = "txt"
-                        altered_message = f"{message}\n The file type is txt and the file contents are: {decoded_file}"
-                    else:
-                        file_type = "Not Sure"
-
-                print(f'received: "{message}" for conversation {conversation_id}')
-
-                # check the moderation here
-                if await moderation_classifier.classify_async(message) == ModerationLabel.NSFW:
-                    response = "Prompt has been marked as NSFW. If this is in error, submit a feedback with the prompt text."
-                    print("this prompt has been marked as NSFW")
-                    await self.send("CONVERSATION_ID")
-                    await self.send(str(conversation_id))
-                    await self.send("START_OF_THE_STREAM_ENDER_GAME_42")
-                    await self.send(response)
-                    await self.send("END_OF_THE_STREAM_ENDER_GAME_42")
-                    await save_generated_message(conversation_id, response)
-                    return
-
-                # TODO: add the message to the database
-                # get the new conversation
-                # TODO: get the messages here
-
-                messages, prompt = await get_messages(
-                    conversation_id, message, decoded_file, file_type
-                )
-
-                prompt_type = await prompt_classifier.classify_async(message)
-                print(f"prompt_type: {prompt_type} for {message}")
-
-
-                
-
-                prompt_metric = await create_prompt_metric(
-                    prompt.id,
-                    prompt.message,
-                    True if file else False,
-                    file_type,
-                    MODEL_NAME,
-                    conversation_id,
-                )
-                if file:
-                    # udpate with the altered_message
-                    messages = messages[:-1] + [HumanMessage(content=altered_message)]
-                print(messages)
-                # send it to the LLM
-
-                # stream the response back
-                response = ""
-                # start of the message
-                await self.send("CONVERSATION_ID")
-                await self.send(str(conversation_id))
-                await self.send("START_OF_THE_STREAM_ENDER_GAME_42")
-                if prompt_type == PromptType.RAG:
-                    service = AsyncRAGService()
-                    #await service.ingest_documents()
-                    workspace = await get_workspace(conversation_id)
-                    print('Time to get the rag response')
-                    
-                    async for chunk in service.generate_response(messages, prompt.message, workspace):
-                        print(f"chunk: {chunk}")
-                        response += chunk
-                        await self.send(chunk)
-                elif prompt_type == PromptType.IMAGE_GENERATION:
-                    response = "Image Generation is not supported at this time, but it will be soon."
-                    await self.send(response)
-                    
-                else:
-                    service = AsyncLLMService()
-                    async for chunk in service.generate_response(messages, prompt.message):
-                        print(f"chunk: {chunk}")
-                        response += chunk
-                        await self.send(chunk)
-                await self.send("END_OF_THE_STREAM_ENDER_GAME_42")
-                await save_generated_message(conversation_id, response)
-
-                await finish_prompt_metric(prompt_metric, len(response))
-
-        if bytes_data:
-            print("we have byte data")
 
 # Document Views
 class DocumentWorkspaceView(APIView):
-    #permission_classes = [permissions.IsAuthenticated]
+    # permission_classes = [permissions.IsAuthenticated]
 
     def get(self, request):
         workspaces = DocumentWorkspace.objects.filter(company=request.user.company)
@@ -970,47 +743,52 @@ class DocumentWorkspaceView(APIView):
             return Response(serializer.data, status=status.HTTP_201_CREATED)
         return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
 
+
 class DocumentUploadView(APIView):
-    #permission_classes = [permissions.IsAuthenticated]Z
+    # permission_classes = [permissions.IsAuthenticated]Z
 
     def get(self, request):
-        print(f'request_3: {request}')
+        logger.debug(f"request_3: {request}")
         try:
             workspace = DocumentWorkspace.objects.get(company=request.user.company)
-            serializer = DocumentSerializer(Document.objects.filter(workspace=workspace), many=True)
+            serializer = DocumentSerializer(
+                Document.objects.filter(workspace=workspace), many=True
+            )
             return Response(serializer.data, status=status.HTTP_200_OK)
 
         except:
-            return Response({'error': "Workspace not found"}, status=status.HTTP_404_NOT_FOUND)
+            return Response(
+                {"error": "Workspace not found"}, status=status.HTTP_404_NOT_FOUND
+            )
 
     def post(self, request):
-        print(f'request: {request}')
+        logger.debug(f"request: {request}")
 
         try:
             workspace = DocumentWorkspace.objects.get(company=request.user.company)
 
         except:
-            return Response({'error': "Workspace not found"}, status=status.HTTP_404_NOT_FOUND)
+            return Response(
-
+                {"error": "Workspace not found"}, status=status.HTTP_404_NOT_FOUND
-        print(request.FILES)
-        file = request.FILES.get('file')
-        if not file:
-            return Response({"error":"No file provided"}, status=status.HTTP_400_BAD_REQUEST)
-
-        print("have the workspace and the file")
-        
-        document = Document.objects.create(
-            workspace=workspace,
-            file=file
             )
 
+        logger.info(request.FILES)
+        file = request.FILES.get("file")
+        if not file:
+            return Response(
+                {"error": "No file provided"}, status=status.HTTP_400_BAD_REQUEST
+            )
+
+        logger.info("have the workspace and the file")
+
+        document = Document.objects.create(workspace=workspace, file=file)
+
         # process the document inthe background
         self.process_document(document)
 
         serializer = DocumentSerializer(document)
         return Response(serializer.data, status=status.HTTP_201_CREATED)
 
-    
     def process_document(self, document):
         file_path = os.path.join(settings.MEDIA_ROOT, document.file.name)
 
@@ -1018,22 +796,25 @@ class DocumentUploadView(APIView):
         document.active = True
         document.save()
         service = AsyncRAGService()
-        service.add_files_to_store([(file_path, document.file.name, document.workspace_id)], workspace_id=document.workspace_id)
+        service.add_files_to_store(
+            [(file_path, document.file.name, document.workspace_id)],
+            workspace_id=document.workspace_id,
+        )
+
 
 class DocumentDetailView(APIView):
-    #permission_classes = [permissions.IsAuthenticated]
+    # permission_classes = [permissions.IsAuthenticated]
 
     def get(self, request, document_id):
-        print(f'request: {request}')
+        logger.info(f"request: {request}")
         try:
             workspace = DocumentWorkspace.objects.get(company=request.user.company)
 
-            document = Document.objects.get(
+            document = Document.objects.get(workspace=workspace, id=document_id)
-                workspace=workspace,
-                id=document_id
-            )
         except:
-            return Response({'error': "Document not found"}, status=status.HTTP_404_NOT_FOUND)
+            return Response(
+                {"error": "Document not found"}, status=status.HTTP_404_NOT_FOUND
+            )
 
         serializer = DocumentWorkspaceSerializer(workspaces, many=True)
         return Response(serializer.data)

llm_be/llm_be/asgi.py

@@ -9,13 +9,18 @@ https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/
 
 import os
 
+import django
 from django.core.asgi import get_asgi_application
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "llm_be.settings")
+
+django.setup()
+
+
 from channels.routing import ProtocolTypeRouter, URLRouter
 from channels.auth import AuthMiddlewareStack
 import chat_backend.routing
 
-os.environ.setdefault("DJANGO_SETTINGS_MODULE", "llm_be.settings")
-
 application = ProtocolTypeRouter(
     {
         "http": get_asgi_application(),

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",
@@ -161,7 +161,7 @@ REST_FRAMEWORK = {
 }
 
 SIMPLE_JWT = {
-    "ACCESS_TOKEN_LIFETIME": timedelta(hours=24),
+    "ACCESS_TOKEN_LIFETIME": timedelta(hours=5),
     "REFRESH_TOKEN_LIFETIME": timedelta(days=14),
     "ROTATE_REFRESH_TOKENS": True,
     "BLACKLIST_AFTER_ROTATION": True,
@@ -208,3 +208,52 @@ EMAIL_USE_TLS = True
 
 # Captcha
 CAPTCHA_SECRET_KEY = "6LfENu4qAAAAABdrj6JTviq-LfdPP5imhE-Os7h9"
+
+directory_path = 'logs'
+# LOGGING = {
+#     'version': 1,
+#     'disable_existing_loggers': False,
+#     'formatters': {
+#         'verbose': {
+#             'format': '{levelname} {asctime} {module} {process:d} {thread:d} {message}',
+#             'style': '{',
+#         },
+#         'simple': {
+#             'format': '{levelname} {message}',
+#             'style': '{',
+#         },
+#     },
+#     'handlers': {
+#         'console': {
+#             'level': 'INFO',
+#             'class': 'logging.StreamHandler',
+#             'formatter': 'simple',
+#         },
+#         'file': {
+#             'level': 'DEBUG',
+#             'class': 'logging.handlers.RotatingFileHandler',
+#             'filename': f'{directory_path}/django.log',
+#             'maxBytes': 1024 * 1024 * 5,  # 5 MB
+#             'backupCount': 5,
+#             'formatter': 'verbose',
+#         },
+#     },
+#     'loggers': {
+#         'django': {
+#             'handlers': ['console', 'file'],
+#             'level': 'INFO',
+#             'propagate': True,
+#         },
+#         'my_app': {
+#             'handlers': ['console', 'file'],
+#             'level': 'DEBUG',
+#             'propagate': False,
+#         },
+#     },
+# }
+
+os.makedirs(directory_path, exist_ok=True)
+
+# Feature Flags
+ALLOW_IMAGE_GENERATION = False
+ALLOW_INTERNET_ACCESS = True

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 %}

requirements.txt

@@ -1,231 +1,265 @@
-aiofiles==24.1.0
+accelerate==1.12.0
+aiofiles==25.1.0
 aiohappyeyeballs==2.6.1
-aiohttp==3.11.18
+aiohttp==3.13.2
-aiosignal==1.3.2
+aiosignal==1.4.0
+annotated-doc==0.0.4
 annotated-types==0.7.0
-antlr4-python3-runtime==4.9.3
+antlr4-python3-runtime==4.13.2
-anyio==4.8.0
+anyio==4.12.0
-asgiref==3.8.1
+asgiref==3.11.0
 astor==0.8.1
-attrs==25.1.0
+attrs==25.4.0
-autobahn==24.4.2
+autobahn==25.11.1
-Automat==24.8.1
+Automat==25.4.16
 backoff==2.2.1
-bcrypt==4.3.0
+bcrypt==5.0.0
-beautifulsoup4==4.13.4
+beautifulsoup4==4.14.3
-black==25.1.0
+black==25.11.0
-build==1.2.2.post1
+brotli==1.2.0
+build==1.3.0
 cachetools==5.5.2
-certifi==2025.1.31
+cbor2==5.7.1
-cffi==1.17.1
+certifi==2025.11.12
-channels==4.2.0
+cffi==2.0.0
+channels==4.3.2
 chardet==5.2.0
-charset-normalizer==3.4.1
+charset-normalizer==3.4.4
 chroma-hnswlib==0.7.6
-chromadb==1.0.7
+chromadb==1.3.5
-click==8.1.8
+click==8.3.1
 coloredlogs==15.0.1
 constantly==23.10.4
-contourpy==1.3.1
+contourpy==1.3.3
-cryptography==44.0.2
+cryptography==46.0.3
 cycler==0.12.1
-daphne==4.1.2
+daphne==4.2.1
 dataclasses-json==0.6.7
-Deprecated==1.2.18
+ddgs==9.9.3
+Deprecated==1.3.1
 distro==1.9.0
-Django==5.1.7
+Django==6.0
 django-autoslug==1.9.9
-django-cors-headers==4.7.0
+django-cors-headers==4.9.0
-django-filter==25.1
+django-filter==25.2
-djangorestframework==3.15.2
+djangorestframework==3.16.1
-djangorestframework_simplejwt==5.5.0
+djangorestframework_simplejwt==5.5.1
-duckdb==1.2.1
+duckdb==1.4.2
-durationpy==0.9
+durationpy==0.10
 effdet==0.4.1
-emoji==2.14.1
+emoji==2.15.0
-eval_type_backport==0.2.2
+et_xmlfile==2.0.0
-Faker==37.0.0
+eval_type_backport==0.3.1
-fastapi==0.115.9
+fake-useragent==2.2.0
-filelock==3.17.0
+Faker==38.2.0
+fastapi==0.124.0
+filelock==3.20.0
 filetype==1.2.0
-flatbuffers==25.2.10
+flatbuffers==25.9.23
-fonttools==4.56.0
+fonttools==4.61.0
-frozenlist==1.6.0
+frozenlist==1.8.0
-fsspec==2025.2.0
+fsspec==2025.12.0
-google-api-core==2.24.2
+google-api-core==2.28.1
-google-auth==2.39.0
+google-auth==2.43.0
-google-cloud-vision==3.10.1
+google-cloud-vision==3.11.0
-googleapis-common-protos==1.70.0
+googleapis-common-protos==1.72.0
-greenlet==3.1.1
+greenlet==3.3.0
-grpcio==1.72.0rc1
+grpcio==1.76.0
-grpcio-status==1.72.0rc1
+grpcio-status==1.76.0
-h11==0.14.0
+h11==0.16.0
+h2==4.3.0
+hf-xet==1.2.0
+hpack==4.1.0
 html5lib==1.1
-httpcore==1.0.7
+httpcore==1.0.9
-httptools==0.6.4
+httptools==0.7.1
 httpx==0.28.1
-httpx-sse==0.4.0
+httpx-sse==0.4.3
-huggingface-hub==0.30.2
+huggingface-hub==0.36.0
 humanfriendly==10.0
+hyperframe==6.1.0
 hyperlink==21.0.0
-idna==3.10
+idna==3.11
-importlib_metadata==8.6.1
+importlib_metadata==8.7.0
 importlib_resources==6.5.2
-incremental==24.7.2
+Incremental==24.11.0
 Jinja2==3.1.6
-jiter==0.8.2
+jiter==0.12.0
-joblib==1.4.2
+joblib==1.5.2
 jsonpatch==1.33
 jsonpointer==3.0.0
-jsonschema==4.23.0
+jsonschema==4.25.1
-jsonschema-specifications==2025.4.1
+jsonschema-specifications==2025.9.1
-kiwisolver==1.4.8
+kiwisolver==1.4.9
-kubernetes==32.0.1
+kubernetes==34.1.0
-langchain==0.3.24
+langchain==1.1.2
-langchain-community==0.3.23
+langchain-chroma==1.0.0
-langchain-core==0.3.56
+langchain-classic==1.0.0
-langchain-ollama==0.2.3
+langchain-community==0.4.1
-langchain-text-splitters==0.3.8
+langchain-core==1.1.1
+langchain-ollama==1.0.0
+langchain-text-splitters==1.0.0
 langdetect==1.0.9
-langsmith==0.3.13
+langgraph==1.0.4
-lxml==5.4.0
+langgraph-checkpoint==3.0.1
-Markdown==3.7
+langgraph-prebuilt==1.0.5
-markdown-it-py==3.0.0
+langgraph-sdk==0.2.14
-MarkupSafe==3.0.2
+langsmith==0.4.56
+lxml==6.0.2
+Markdown==3.10
+markdown-it-py==4.0.0
+MarkupSafe==3.0.3
 marshmallow==3.26.1
-matplotlib==3.10.1
+matplotlib==3.10.7
 mdurl==0.1.2
-mmh3==5.1.0
+ml_dtypes==0.5.4
+mmh3==5.2.0
 mpmath==1.3.0
-multidict==6.4.3
+msgpack==1.1.2
-mypy-extensions==1.0.0
+multidict==6.7.0
+mypy_extensions==1.1.0
 nest-asyncio==1.6.0
-networkx==3.4.2
+networkx==3.6
-nltk==3.9.1
+nltk==3.9.2
-numpy==2.2.3
+numpy==2.2.6
-nvidia-cublas-cu12==12.6.4.1
+nvidia-cublas-cu12==12.8.4.1
-nvidia-cuda-cupti-cu12==12.6.80
+nvidia-cuda-cupti-cu12==12.8.90
-nvidia-cuda-nvrtc-cu12==12.6.77
+nvidia-cuda-nvrtc-cu12==12.8.93
-nvidia-cuda-runtime-cu12==12.6.77
+nvidia-cuda-runtime-cu12==12.8.90
-nvidia-cudnn-cu12==9.5.1.17
+nvidia-cudnn-cu12==9.10.2.21
-nvidia-cufft-cu12==11.3.0.4
+nvidia-cufft-cu12==11.3.3.83
-nvidia-cufile-cu12==1.11.1.6
+nvidia-cufile-cu12==1.13.1.3
-nvidia-curand-cu12==10.3.7.77
+nvidia-curand-cu12==10.3.9.90
-nvidia-cusolver-cu12==11.7.1.2
+nvidia-cusolver-cu12==11.7.3.90
-nvidia-cusparse-cu12==12.5.4.2
+nvidia-cusparse-cu12==12.5.8.93
-nvidia-cusparselt-cu12==0.6.3
+nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.26.2
+nvidia-nccl-cu12==2.27.5
-nvidia-nvjitlink-cu12==12.6.85
+nvidia-nvjitlink-cu12==12.8.93
-nvidia-nvtx-cu12==12.6.77
+nvidia-nvshmem-cu12==3.3.20
-oauthlib==3.2.2
+nvidia-nvtx-cu12==12.8.90
+oauthlib==3.3.1
 olefile==0.47
-ollama==0.4.7
+ollama==0.6.1
 omegaconf==2.3.0
-onnx==1.18.0
+onnx==1.20.0
-onnxruntime==1.21.1
+onnxruntime==1.23.2
-openai==1.65.4
+openai==2.9.0
-opencv-python==4.11.0.86
+opencv-python==4.12.0.88
-opentelemetry-api==1.32.1
+openpyxl==3.1.5
-opentelemetry-exporter-otlp-proto-common==1.32.1
+opentelemetry-api==1.39.0
-opentelemetry-exporter-otlp-proto-grpc==1.32.1
+opentelemetry-exporter-otlp-proto-common==1.39.0
+opentelemetry-exporter-otlp-proto-grpc==1.39.0
 opentelemetry-instrumentation==0.53b1
 opentelemetry-instrumentation-asgi==0.53b1
 opentelemetry-instrumentation-fastapi==0.53b1
-opentelemetry-proto==1.32.1
+opentelemetry-proto==1.39.0
-opentelemetry-sdk==1.32.1
+opentelemetry-sdk==1.39.0
-opentelemetry-semantic-conventions==0.53b1
+opentelemetry-semantic-conventions==0.60b0
 opentelemetry-util-http==0.53b1
-orjson==3.10.15
+orjson==3.11.5
+ormsgpack==1.12.0
 overrides==7.7.0
-packaging==24.2
+packaging==25.0
-pandas==2.2.3
+pandas==2.3.3
 pandasai==2.4.2
+parameterized==0.9.0
 pathspec==0.12.1
 pdf2image==1.17.0
-pdfminer.six==20250506
+pdfminer.six==20251107
-pi_heif==0.22.0
+pi==0.1.2
-pikepdf==9.7.0
+pi_heif==1.1.1
-pillow==11.1.0
+pikepdf==10.0.2
-platformdirs==4.3.6
+pillow==12.0.0
-posthog==4.0.1
+platformdirs==4.5.1
-propcache==0.3.1
+posthog==5.4.0
+primp==0.15.0
+propcache==0.4.1
 proto-plus==1.26.1
-protobuf==6.31.0rc2
+protobuf==6.33.2
-psutil==7.0.0
+psutil==7.1.3
+py-ubjson==0.16.1
 pyasn1==0.6.1
-pyasn1_modules==0.4.1
+pyasn1_modules==0.4.2
-pycocotools==2.0.8
+pybase64==1.4.3
-pycparser==2.22
+pycocotools==2.0.10
-pydantic==2.11.4
+pycparser==2.23
-pydantic-settings==2.9.1
+pydantic==2.12.5
-pydantic_core==2.33.2
+pydantic-settings==2.12.0
-Pygments==2.19.1
+pydantic_core==2.41.5
+Pygments==2.19.2
 PyJWT==2.10.1
-pyOpenSSL==25.0.0
+pyOpenSSL==25.3.0
-pyparsing==3.2.1
+pyparsing==3.2.5
-pypdf==5.4.0
+pypdf==6.4.0
-pypdfium2==4.30.1
+pypdfium2==5.1.0
 PyPika==0.48.9
 pyproject_hooks==1.2.0
 python-dateutil==2.9.0.post0
-python-dotenv==1.0.1
+python-docx==1.2.0
-python-iso639==2025.2.18
+python-dotenv==1.2.1
+python-iso639==2025.11.16
 python-magic==0.4.27
 python-multipart==0.0.20
 python-oxmsg==0.0.2
-pytz==2025.1
+pytokens==0.3.0
-PyYAML==6.0.2
+pytz==2025.2
-RapidFuzz==3.13.0
+PyYAML==6.0.3
-referencing==0.36.2
+RapidFuzz==3.14.3
-regex==2024.11.6
+referencing==0.37.0
-requests==2.32.3
+regex==2025.11.3
+requests==2.32.5
 requests-oauthlib==2.0.0
 requests-toolbelt==1.0.0
-rich==14.0.0
+rich==14.2.0
-rpds-py==0.24.0
+rpds-py==0.30.0
 rsa==4.9.1
-safetensors==0.5.3
+safetensors==0.7.0
-scipy==1.15.2
+scipy==1.16.3
 service-identity==24.2.0
-setuptools==75.8.2
+setuptools==80.9.0
 shellingham==1.5.4
 six==1.17.0
 sniffio==1.3.1
-soupsieve==2.7
+socksio==1.0.0
-SQLAlchemy==2.0.38
+soupsieve==2.8
-sqlglot==26.9.0
+SQLAlchemy==2.0.44
-sqlglotrs==0.4.0
+sqlglot==28.1.0
-sqlparse==0.5.3
+sqlglotrs==0.8.0
-starlette==0.45.3
+sqlparse==0.5.4
+starlette==0.50.0
 sympy==1.14.0
-tenacity==9.0.0
+tenacity==9.1.2
-timm==1.0.15
+timm==1.0.22
-tokenizers==0.21.1
+tokenizers==0.22.1
-torch==2.7.0
+torch==2.9.1
-torchvision==0.22.0
+torchvision==0.24.1
 tqdm==4.67.1
-transformers==4.51.3
+transformers==4.57.3
-triton==3.3.0
+triton==3.5.1
-Twisted==24.11.0
+Twisted==25.5.0
-txaio==23.1.1
+txaio==25.12.1
-typer==0.15.3
+typer==0.20.0
+typer-slim==0.20.0
 typing-inspect==0.9.0
-typing-inspection==0.4.0
+typing-inspection==0.4.2
-typing_extensions==4.12.2
+typing_extensions==4.15.0
-tzdata==2025.1
+tzdata==2025.2
-unstructured==0.17.2
+ujson==5.11.0
-unstructured-client==0.34.0
+unstructured==0.18.21
-unstructured-inference==0.8.10
+unstructured-client==0.42.4
 unstructured.pytesseract==0.3.15
+unstructured_inference==1.1.2
 urllib3==2.3.0
-uvicorn==0.34.2
+uuid_utils==0.12.0
-uvloop==0.21.0
+uvicorn==0.38.0
-watchfiles==1.0.5
+uvloop==0.22.1
+watchfiles==1.1.1
 webencodings==0.5.1
-websocket-client==1.8.0
+websocket-client==1.9.0
 websockets==15.0.1
-wrapt==1.17.2
+wrapt==2.0.1
-yarl==1.20.0
+xxhash==3.6.0
-zipp==3.21.0
+yarl==1.22.0
-zope.interface==7.2
+zipp==3.23.0
-zstandard==0.23.0
+zope.interface==8.1.1
+zstandard==0.25.0