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Add assistant urls and views
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‎server/api/views/assistant/urls.py‎

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from django.urls import path
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from .views import Assistant
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urlpatterns = [path("v1/api/assistant", Assistant.as_view(), name="assistant")]

‎server/api/views/assistant/views.py‎

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import os
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import json
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import logging
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from typing import Callable
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from rest_framework.views import APIView
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from rest_framework.response import Response
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from rest_framework import status
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from rest_framework.permissions import IsAuthenticated
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from django.utils.decorators import method_decorator
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from django.views.decorators.csrf import csrf_exempt
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from openai import OpenAI
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from ...services.embedding_services import get_closest_embeddings
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from ...services.conversions_services import convert_uuids
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# Configure logging
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logger = logging.getLogger(__name__)
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# Open AI Cookbook: Handling Function Calls with Reasoning Models
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# https://cookbook.openai.com/examples/reasoning_function_calls
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def invoke_functions_from_response(
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response, tool_mapping: dict[str, Callable]
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) -> list[dict]:
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"""Extract all function calls from the response, look up the corresponding tool function(s) and execute them.
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(This would be a good place to handle asynchroneous tool calls, or ones that take a while to execute.)
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This returns a list of messages to be added to the conversation history.
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Parameters
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----------
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response : OpenAI Response
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The response object from OpenAI containing output items that may include function calls
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tool_mapping : dict[str, Callable]
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A dictionary mapping function names (as strings) to their corresponding Python functions.
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Keys should match the function names defined in the tools schema.
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Returns
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-------
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list[dict]
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List of function call output messages formatted for the OpenAI conversation.
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Each message contains:
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- type: "function_call_output"
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- call_id: The unique identifier for the function call
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- output: The result returned by the executed function (string or error message)
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"""
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intermediate_messages = []
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for response_item in response.output:
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if response_item.type == "function_call":
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target_tool = tool_mapping.get(response_item.name)
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if target_tool:
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try:
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arguments = json.loads(response_item.arguments)
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logger.info(
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f"Invoking tool: {response_item.name} with arguments: {arguments}"
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)
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tool_output = target_tool(**arguments)
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logger.debug(f"Tool {response_item.name} completed successfully")
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except Exception as e:
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msg = f"Error executing function call: {response_item.name}: {e}"
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tool_output = msg
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logger.error(msg, exc_info=True)
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else:
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msg = f"ERROR - No tool registered for function call: {response_item.name}"
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tool_output = msg
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logger.error(msg)
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intermediate_messages.append(
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{
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"type": "function_call_output",
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"call_id": response_item.call_id,
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"output": tool_output,
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}
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)
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elif response_item.type == "reasoning":
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logger.debug("Reasoning step")
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return intermediate_messages
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@method_decorator(csrf_exempt, name="dispatch")
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class Assistant(APIView):
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permission_classes = [IsAuthenticated]
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def post(self, request):
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try:
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user = request.user
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client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
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TOOL_DESCRIPTION = """
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Search through the user's uploaded documents using semantic similarity matching.
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This function finds the most relevant document chunks based on the input query and
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returns contextual information including page numbers, chunk locations, and similarity scores.
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Use this to answer the user's questions.
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"""
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TOOL_PROPERTY_DESCRIPTION = """
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The search query to find semantically similar content in uploaded documents.
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Should be a natural language question or keyword phrase.
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"""
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tools = [
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{
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"type": "function",
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"name": "search_documents",
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"description": TOOL_DESCRIPTION,
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": TOOL_PROPERTY_DESCRIPTION,
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}
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},
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"required": ["query"],
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},
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}
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]
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def search_documents(query: str, user=user) -> str:
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"""
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Search through user's uploaded documents using semantic similarity.
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This function performs vector similarity search against the user's document corpus
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and returns formatted results with context information for the LLM to use.
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Parameters
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----------
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query : str
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The search query string
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user : User
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The authenticated user whose documents to search
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Returns
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-------
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str
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Formatted search results containing document excerpts with metadata
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Raises
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------
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Exception
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If embedding search fails
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"""
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try:
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embeddings_results = get_closest_embeddings(
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user=user, message_data=query.strip()
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)
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embeddings_results = convert_uuids(embeddings_results)
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if not embeddings_results:
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return "No relevant documents found for your query. Please try different search terms or upload documents first."
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# Format results with clear structure and metadata
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prompt_texts = [
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f"[Document {i + 1} - File: {obj['file_id']}, Page: {obj['page_number']}, Chunk: {obj['chunk_number']}, Similarity: {1 - obj['distance']:.3f}]\n{obj['text']}\n[End Document {i + 1}]"
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for i, obj in enumerate(embeddings_results)
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]
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return "\n\n".join(prompt_texts)
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except Exception as e:
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return f"Error searching documents: {str(e)}. Please try again if the issue persists."
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MODEL_DEFAULTS = {
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"model": "gpt-5-nano", # 400,000 token context window
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"reasoning": {"effort": "medium"},
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"tools": tools,
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}
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# We fetch a response and then kick off a loop to handle the response
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request_data = request.data.get("message", None)
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if not request_data:
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return Response(
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{"error": "Message data is required."},
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status=status.HTTP_400_BAD_REQUEST,
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)
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message = str(request_data)
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response = client.responses.create(
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input=[{"type": "text", "text": message}], **MODEL_DEFAULTS
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)
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# Open AI Cookbook: Handling Function Calls with Reasoning Models
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# https://cookbook.openai.com/examples/reasoning_function_calls
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while True:
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# Mapping of the tool names we tell the model about and the functions that implement them
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function_responses = invoke_functions_from_response(
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response, tool_mapping={"search_documents": search_documents}
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)
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if len(function_responses) == 0: # We're done reasoning
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logger.info(f"Reasoning completed for user {user.id}")
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final_response = response.output_text
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logger.debug(
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f"Final response length: {len(final_response)} characters"
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)
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break
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else:
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logger.debug("More reasoning required, continuing...")
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response = client.responses.create(
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input=function_responses,
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previous_response_id=response.id,
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**MODEL_DEFAULTS,
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)
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return Response({"response": final_response}, status=status.HTTP_200_OK)
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except Exception as e:
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logger.error(
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f"Unexpected error in Assistant view for user {request.user.id if hasattr(request, 'user') else 'unknown'}: {e}",
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exc_info=True,
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)
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return Response(
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{"error": "An unexpected error occurred. Please try again later."},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)

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