Merge pull request #296 from sahilds1/278-remove-dead-text-extraction-view

[#278] Remove dead TextExtractionAPIView

Author: taichan03

server/api/views/text_extraction/urls.py

@@ -1,12 +1,9 @@
 from django.urls import path
-from .views import TextExtractionAPIView, RuleExtractionAPIView
+from .views import RuleExtractionAPIView
 
 
 urlpatterns = [
 
-    path('v1/api/text_extraction', TextExtractionAPIView.as_view(),
-         name='text_extraction'),
-
     path('v1/api/rule_extraction', RuleExtractionAPIView.as_view(),
          name='rule_extraction')
 ]

server/api/views/text_extraction/views.py

@@ -6,7 +6,6 @@
 from rest_framework import status
 from django.utils.decorators import method_decorator
 from django.views.decorators.csrf import csrf_exempt
-import openai
 import anthropic
 
 from api.models.model_embeddings import Embeddings
@@ -59,65 +58,6 @@ def anthropic_citations(client, content_chunks, user_prompt):
     return texts, cited_texts
 
 
-@method_decorator(csrf_exempt, name='dispatch')
-class TextExtractionAPIView(APIView):
-
-    permission_classes = [IsAuthenticated]
-
-    def post(self, request):
-        try:
-            guid = request.query_params.get('guid')
-            field = request.query_params.get('field')
-
-            query = Embeddings.objects.filter(upload_file__guid=guid)
-
-            #TODO: Message OpenAI with each chunk separately if there are files that are too large for OpenAI
-
-            query_texts = [
-                f"[Start of INFO === GUID: {obj.upload_file.guid}, Page Number: {obj.page_num}, Chunk Number: {obj.chunk_number}, Text: {obj.text} === End of INFO]" 
-                for obj in query]
-
-            query_text = " ".join(query_texts)
-
-            prompt_text = (
-                f"""You are an AI assistant tasked with providing detailed, well-structured responses based on the information provided in [PROVIDED-INFO]. Follow these guidelines strictly: 
-                1. Content: Use information contained within [PROVIDED-INFO] to answer the question. 
-                2. Organization: Structure your response with clear sections and paragraphs. 
-                3. Citations: After EACH sentence that uses information from [PROVIDED-INFO], include a citation in this exact format:***[{{file_id}}], Page {{page_number}}, Chunk {{chunk_number}}*** . Only use citations that correspond to the information you're presenting. 
-                4. Clarity: Ensure your answer is well-structured and easy to follow. 
-                5. Direct Response: Answer the user's question directly without unnecessary introductions or filler phrases. 
-                Here's an example of the required response format:
-                ________________________________________ 
-                See's Candy in the context of sales during a specific event. The candy counters rang up 2,690 individual sales on a Friday, and an additional 3,931 transactions on a Saturday ***[16s848as-vcc1-85sd-r196-7f820a4s9de1, Page 5, Chunk 26]***.
-                People like the consumption of fudge and peanut brittle the most ***[130714d7-b9c1-4sdf-b146-fdsf854cad4f, Page 9, Chunk 19]***. 
-                Here is the history of See's Candy: the company was purchased in 1972, and its products have not been materially altered in 101 years ***[895sdsae-b7v5-416f-c84v-7f9784dc01e1, Page 2, Chunk 13]***. 
-                Bipolar disorder treatment often involves mood stabilizers. Lithium is a commonly prescribed mood stabilizer effective in reducing manic episodes ***[b99988ac-e3b0-4d22-b978-215e814807f4, Page 29, Chunk 122]***. For acute hypomania or mild to moderate mania, initial treatment with risperidone or olanzapine monotherapy is suggested ***[b99988ac-e3b0-4d22-b978-215e814807f4, Page 24, Chunk 101]***. 
-                ________________________________________ 
-                Please provide your response to the user's question following these guidelines precisely.
-                [PROVIDED-INFO] = {query_text}"""
-            )
-
-
-            openai.api_key = os.getenv("OPENAI_API_KEY")
-            response = openai.ChatCompletion.create(
-                model="gpt-4o-mini",
-                temperature=0.2,
-                messages=[{"role": "system", "content": prompt_text}, 
-                          {"role": "user", "content": f"List out the {field}"}
-                ]
-            )
-
-            answer = response["choices"][0]["message"]["content"]
-
-            return Response({
-                "query":  query_text,
-                "answer": answer
-            }, status=status.HTTP_200_OK)
-
-        except Exception as e:
-            print(f"An error occurred: {e}")
-            return Response({"error": str(e)}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
-        
 @method_decorator(csrf_exempt, name='dispatch')
 class RuleExtractionAPIView(APIView):