make schema optional argument

server/gpu/modal/reflector_llm.py

@@ -5,9 +5,9 @@ Reflector GPU backend - LLM
 """
 
 import os
+from typing import Optional
 
 from modal import asgi_app, Image, method, Secret, Stub
-from pydantic.typing import Optional
 
 # LLM
 LLM_MODEL: str = "lmsys/vicuna-13b-v1.5"

server/reflector/llm/base.py

@@ -1,10 +1,12 @@
-from reflector.settings import settings
-from reflector.utils.retry import retry
-from reflector.logger import logger as reflector_logger
-from time import monotonic
 import importlib
 import json
 import re
+from time import monotonic
+from typing import Union
+
+from reflector.logger import logger as reflector_logger
+from reflector.settings import settings
+from reflector.utils.retry import retry
 
 
 class LLM:
@@ -44,10 +46,12 @@ class LLM:
     async def _warmup(self, logger: reflector_logger):
         pass
 
-    async def generate(self, prompt: str, logger: reflector_logger, **kwargs) -> dict:
+    async def generate(
+        self, prompt: str, logger: reflector_logger, schema: str = None, **kwargs
+    ) -> dict:
         logger.info("LLM generate", prompt=repr(prompt))
         try:
-            result = await retry(self._generate)(prompt=prompt, **kwargs)
+            result = await retry(self._generate)(prompt=prompt, schema=schema, **kwargs)
         except Exception:
             logger.exception("Failed to call llm after retrying")
             raise
@@ -59,7 +63,7 @@ class LLM:
 
         return result
 
-    async def _generate(self, prompt: str, **kwargs) -> str:
+    async def _generate(self, prompt: str, schema: Union[str | None], **kwargs) -> str:
         raise NotImplementedError
 
     def _parse_json(self, result: str) -> dict:

server/reflector/llm/llm_banana.py

@@ -1,4 +1,5 @@
 import json
+from typing import Union
 
 import httpx
 from reflector.llm.base import LLM
@@ -15,10 +16,10 @@ class BananaLLM(LLM):
             "X-Banana-Model-Key": settings.LLM_BANANA_MODEL_KEY,
         }
 
-    async def _generate(self, prompt: str, **kwargs):
+    async def _generate(self, prompt: str, schema: Union[str | None], **kwargs):
         json_payload = {"prompt": prompt}
-        if "schema" in kwargs:
-            json_payload["schema"] = json.dumps(kwargs["schema"])
+        if schema:
+            json_payload["schema"] = json.dumps(schema)
         async with httpx.AsyncClient() as client:
             response = await retry(client.post)(
                 settings.LLM_URL,
@@ -29,7 +30,7 @@ class BananaLLM(LLM):
             )
             response.raise_for_status()
             text = response.json()["text"]
-            if "schema" not in json_payload:
+            if not schema:
                 text = text[len(prompt) :]
             return text
 

server/reflector/llm/llm_modal.py

@@ -1,4 +1,5 @@
 import json
+from typing import Union
 
 import httpx
 from reflector.llm.base import LLM
@@ -25,10 +26,10 @@ class ModalLLM(LLM):
             )
             response.raise_for_status()
 
-    async def _generate(self, prompt: str, **kwargs):
+    async def _generate(self, prompt: str, schema: Union[str | None], **kwargs):
         json_payload = {"prompt": prompt}
-        if "schema" in kwargs:
-            json_payload["schema"] = json.dumps(kwargs["schema"])
+        if schema:
+            json_payload["schema"] = json.dumps(schema)
         async with httpx.AsyncClient() as client:
             response = await retry(client.post)(
                 self.llm_url,
@@ -39,7 +40,7 @@ class ModalLLM(LLM):
             )
             response.raise_for_status()
             text = response.json()["text"]
-            if "schema" not in json_payload:
+            if not schema:
                 text = text[len(prompt) :]
             return text
 
@@ -54,13 +55,12 @@ if __name__ == "__main__":
         result = await llm.generate("Hello, my name is", logger=logger)
         print(result)
 
-        kwargs = {
-            "schema": {
-                "type": "object",
-                "properties": {"name": {"type": "string"}},
-            }
+        schema = {
+            "type": "object",
+            "properties": {"name": {"type": "string"}},
         }
-        result = await llm.generate("Hello, my name is", kwargs=kwargs, logger=logger)
+
+        result = await llm.generate("Hello, my name is", schema=schema, logger=logger)
         print(result)
 
     import asyncio

server/reflector/llm/llm_oobagooda.py

@@ -1,4 +1,5 @@
 import json
+from typing import Union
 
 import httpx
 from reflector.llm.base import LLM
@@ -6,10 +7,10 @@ from reflector.settings import settings
 
 
 class OobaboogaLLM(LLM):
-    async def _generate(self, prompt: str, **kwargs):
+    async def _generate(self, prompt: str, schema: Union[str | None], **kwargs):
         json_payload = {"prompt": prompt}
-        if "schema" in kwargs:
-            json_payload["schema"] = json.dumps(kwargs["schema"])
+        if schema:
+            json_payload["schema"] = json.dumps(schema)
         async with httpx.AsyncClient() as client:
             response = await client.post(
                 settings.LLM_URL,

server/reflector/llm/llm_openai.py

@@ -1,7 +1,9 @@
+from typing import Union
+
+import httpx
 from reflector.llm.base import LLM
 from reflector.logger import logger
 from reflector.settings import settings
-import httpx
 
 
 class OpenAILLM(LLM):
@@ -15,7 +17,7 @@ class OpenAILLM(LLM):
         self.max_tokens = settings.LLM_MAX_TOKENS
         logger.info(f"LLM use openai backend at {self.openai_url}")
 
-    async def _generate(self, prompt: str, **kwargs) -> str:
+    async def _generate(self, prompt: str, schema: Union[str | None], **kwargs) -> str:
         headers = {
             "Content-Type": "application/json",
             "Authorization": f"Bearer {self.openai_key}",

server/reflector/processors/transcript_topic_detector.py

@@ -14,7 +14,6 @@ class TranscriptTopicDetectorProcessor(Processor):
 
     PROMPT = """
         ### Human:
-        Generate information based on the given schema:
 
         For the title field, generate a short title for the given text.
         For the summary field, summarize the given text in a maximum of
@@ -62,7 +61,7 @@ class TranscriptTopicDetectorProcessor(Processor):
         self.logger.info(f"Topic detector got {len(text)} length transcript")
         prompt = self.PROMPT.format(input_text=text)
         result = await retry(self.llm.generate)(
-            prompt=prompt, kwargs=self.kwargs, logger=self.logger
+            prompt=prompt, schema=self.topic_detector_schema, logger=self.logger
         )
         summary = TitleSummary(
             title=result["title"],

server/tests/test_processors_pipeline.py

@@ -9,13 +9,16 @@ async def test_basic_process(event_loop):
     from reflector.settings import settings
     from reflector.llm.base import LLM
     from pathlib import Path
+    from typing import Union
 
     # use an LLM test backend
     settings.LLM_BACKEND = "test"
     settings.TRANSCRIPT_BACKEND = "whisper"
 
     class LLMTest(LLM):
-        async def _generate(self, prompt: str, **kwargs) -> str:
+        async def _generate(
+            self, prompt: str, schema: Union[str | None], **kwargs
+        ) -> str:
             return {
                 "title": "TITLE",
                 "summary": "SUMMARY",

server/tests/test_transcripts_rtc_ws.py

@@ -3,17 +3,18 @@
 # FIXME test websocket connection after RTC is finished still send the full events
 # FIXME try with locked session, RTC should not work
 
-import pytest
+import asyncio
 import json
+import threading
+from pathlib import Path
+from typing import Union
 from unittest.mock import patch
-from httpx import AsyncClient
 
+import pytest
+from httpx import AsyncClient
+from httpx_ws import aconnect_ws
 from reflector.app import app
 from uvicorn import Config, Server
-import threading
-import asyncio
-from pathlib import Path
-from httpx_ws import aconnect_ws
 
 
 class ThreadedUvicorn:
@@ -61,7 +62,7 @@ async def dummy_llm():
     from reflector.llm.base import LLM
 
     class TestLLM(LLM):
-        async def _generate(self, prompt: str, **kwargs):
+        async def _generate(self, prompt: str, schema: Union[str | None], **kwargs):
             return json.dumps({"title": "LLM TITLE", "summary": "LLM SUMMARY"})
 
     with patch("reflector.llm.base.LLM.get_instance") as mock_llm: