@@ -242,6 +242,16 @@ class Chatgpt(SimpleService):
                         {'type': 'input_text', 'text': input_message.content},
                         {'type': 'input_image', 'image_url': image_data['image_url']['url']}
                     ]
+                elif file:
+                    if sum([len(chunk.content) for chunk in chunks]) > 20_000:
+                        document_name = chunks[0].content.partition(f':{chr(10)}')[2].split(f'{chr(10)}')[0][:100]
+                        embedding_tokens, file_data = self.get_large_file_data(chunks, proxy, input_message.content)
+                        messages[-1]['content'] = self.make_embeddings_prompt(
+                            document_name=document_name, section_texts=file_data, question=input_message.content
+                        )
+                    else:
+                        messages[-1]['content'] = (f'Используй системный промпт. Содержание файла: '
+                                                   f'{chunks}. Вопрос: {input_message.content}')
                 json_data = {
                     'model': model_name,
                     'input': messages,
@@ -305,6 +315,16 @@ class Chatgpt(SimpleService):
                         {'type': 'text', 'text': input_message.content},
                         image_data,
                     ]
+                elif file:
+                    if sum([len(chunk.content) for chunk in chunks]) > 20_000:
+                        document_name = chunks[0].content.partition(f':{chr(10)}')[2].split(f'{chr(10)}')[0][:100]
+                        embedding_tokens, file_data = self.get_large_file_data(chunks, proxy, input_message.content)
+                        messages[-1]['content'] = self.make_embeddings_prompt(
+                            document_name=document_name, section_texts=file_data, question=input_message.content
+                        )
+                    else:
+                        messages[-1]['content'] = (f'Используй системный промпт. Содержание файла: '
+                                                   f'{chunks}. Вопрос: {input_message.content}')
                 json_data = {
                     'model': model_name,
                     'messages': messages
@@ -326,6 +346,16 @@ class Chatgpt(SimpleService):
                     messages
                 )
                 info['web_search'] = search_context_size
+                if file:
+                    if sum([len(chunk.content) for chunk in chunks]) > 20_000:
+                        document_name = chunks[0].content.partition(f':{chr(10)}')[2].split(f'{chr(10)}')[0][:100]
+                        embedding_tokens, file_data = self.get_large_file_data(chunks, proxy, input_message.content)
+                        messages[-1]['content'] = self.make_embeddings_prompt(
+                            document_name=document_name, section_texts=file_data, question=input_message.content
+                        )
+                    else:
+                        messages[-1]['content'] = (f'Используй системный промпт. Содержание файла: '
+                                                   f'{chunks}. Вопрос: {input_message.content}')
                 input_tokens, output_tokens, response = self.call_openai_api(proxy=proxy, endpoint='responses',json_data=json_data)
             elif image:
                 response = self.llm.invoke(llm_input)
@@ -333,46 +363,19 @@ class Chatgpt(SimpleService):
             elif file:
                 input_tokens = self.count_text_tokens([*chat_history.messages])
                 if sum([len(chunk.content) for chunk in chunks]) > 20_000:
-                    redis_client = redis.Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=0)
                     document_name = chunks[0].content.partition(f':{chr(10)}')[2].split(f'{chr(10)}')[0][:100]
-                    message_uid = str(self.store.messages.first().pk).replace('-', '_')
-                    with httpx.Client(
-                        base_url='https://api.openai.com/v1/',
-                        proxy=f'{proxy.protocol}://{proxy.address}',
-                        headers={'Authorization': f'Bearer {settings.OPENAI_API_KEY}'},
-                        timeout=600,
-                    ) as client:
-                        threads = []
-                        with ThreadPoolExecutor(max_workers=settings.MAX_THREADS) as executor:
-                            for chunk_id, chunk in enumerate(chunks):
-                                threads.append(
-                                    executor.submit(self.process_chunk, client, chunk, redis_client, message_uid, chunk_id)
-                                )
-                        for thread in as_completed(threads):
-                            embedding_tokens += thread.result()
-                        query_embedding, e_total_tokens = self.get_embedding(client=client, content=input_message.content)
-                        embedding_tokens += e_total_tokens
-                        result = [
-                            s['section_text']
-                            for s in self.search_via_embeddings(
-                                redis_client=redis_client,
-                                message_uid=message_uid,
-                                user_query_embeddings=query_embedding
-                            )
-                        ]
-                        user_input = [
-                            SystemMessage(content=user_system_prompt),
-                            HumanMessage(self.make_embeddings_prompt(
-                                document_name=document_name, section_texts=result, question=input_message.content
-                            ))
-                        ]
-                        input_tokens += self.count_text_tokens(user_input)
-                        response = conversation.invoke(
-                            {'input': user_input},
-                            config={'configurable': {'session_id': 'default'}},
-                        )
-                        drop_redis_vectors.delay(message_uid)
-                        redis_client.close()
+                    embedding_tokens, file_data = self.get_large_file_data(chunks, proxy, input_message.content)
+                    user_input = [
+                        SystemMessage(content=user_system_prompt),
+                        HumanMessage(self.make_embeddings_prompt(
+                            document_name=document_name, section_texts=file_data, question=input_message.content
+                        ))
+                    ]
+                    input_tokens += self.count_text_tokens(user_input)
+                    response = conversation.invoke(
+                        {'input': user_input},
+                        config={'configurable': {'session_id': 'default'}},
+                    )
                 else:
                     input = [
                         SystemMessage(content=user_system_prompt),
@@ -562,6 +565,37 @@ class Chatgpt(SimpleService):
 
         return total_tokens
 
+    def get_large_file_data(self, chunks, proxy, user_content):
+        redis_client = redis.Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=0)
+        embedding_tokens = 0
+        message_uid = str(self.store.messages.first().pk).replace('-', '_')
+        with httpx.Client(
+            base_url='https://api.openai.com/v1/',
+            proxy=f'{proxy.protocol}://{proxy.address}',
+            headers={'Authorization': f'Bearer {settings.OPENAI_API_KEY}'},
+            timeout=600,
+        ) as client:
+            threads = []
+            with ThreadPoolExecutor(max_workers=settings.MAX_THREADS) as executor:
+                for chunk_id, chunk in enumerate(chunks):
+                    threads.append(
+                        executor.submit(self.process_chunk, client, chunk, redis_client, message_uid, chunk_id)
+                    )
+            for thread in as_completed(threads):
+                embedding_tokens += thread.result()
+            query_embedding, e_total_tokens = self.get_embedding(client=client, content=user_content)
+            embedding_tokens += e_total_tokens
+            result = [
+                s['section_text']
+                for s in self.search_via_embeddings(
+                    redis_client=redis_client,
+                    message_uid=message_uid,
+                    user_query_embeddings=query_embedding
+                )
+            ]
+            drop_redis_vectors.delay(message_uid)
+            redis_client.close()
+            return embedding_tokens, result
 
     def get_web_search_data(self, search_size: str, model_name: str, messages: List[Dict[str, any]]):
         search_context_sizes = {
@@ -14,7 +14,7 @@ services:
         python manage.py collectstatic --no-input
         python manage.py compilemessages
         (python manage.py createsuperuser --no-input || true)
-        python -m uvicorn backend.asgi:application --host 0.0.0.0 --ws wsproto --http httptools --lifespan off --log-level info
+        python -m uvicorn backend.asgi:application --host 0.0.0.0 --ws wsproto --http httptools --lifespan off --log-level info --reload
     volumes:
       - .:/code
     ports: