Update the Citation pipeline according to new OpenAI function call in…

…terface (#40)

libs/kotaemon/kotaemon/indices/qa/citation.py

@@ -75,8 +75,8 @@ def prepare_llm(self, context: str, question: str):
             "parameters": schema,
         }
         llm_kwargs = {
-            "functions": [function],
-            "function_call": {"name": function["name"]},
+            "tools": [{"type": "function", "function": function}],
+            "tool_choice": "auto",
         }
         messages = [
             SystemMessage(
@@ -99,14 +99,13 @@ def prepare_llm(self, context: str, question: str):
 
     def invoke(self, context: str, question: str):
         messages, llm_kwargs = self.prepare_llm(context, question)
-
         try:
             print("CitationPipeline: invoking LLM")
             llm_output = self.get_from_path("llm").invoke(messages, **llm_kwargs)
             print("CitationPipeline: finish invoking LLM")
             if not llm_output.messages:
                 return None
-            function_output = llm_output.messages[0].additional_kwargs["function_call"][
+            function_output = llm_output.additional_kwargs["tool_calls"][0]["function"][
                 "arguments"
             ]
             output = QuestionAnswer.parse_raw(function_output)
@@ -123,16 +122,12 @@ async def ainvoke(self, context: str, question: str):
             print("CitationPipeline: async invoking LLM")
             llm_output = await self.get_from_path("llm").ainvoke(messages, **llm_kwargs)
             print("CitationPipeline: finish async invoking LLM")
+            function_output = llm_output.additional_kwargs["tool_calls"][0]["function"][
+                "arguments"
+            ]
+            output = QuestionAnswer.parse_raw(function_output)
         except Exception as e:
             print(e)
             return None
 
-        if not llm_output.messages:
-            return None
-
-        function_output = llm_output.messages[0].additional_kwargs["function_call"][
-            "arguments"
-        ]
-        output = QuestionAnswer.parse_raw(function_output)
-
         return output

libs/kotaemon/kotaemon/llms/chats/openai.py

@@ -152,6 +152,28 @@ def prepare_message(
 
         return output_
 
+    def prepare_output(self, resp: dict) -> LLMInterface:
+        """Convert the OpenAI response into LLMInterface"""
+        additional_kwargs = {}
+        if "tool_calls" in resp["choices"][0]["message"]:
+            additional_kwargs["tool_calls"] = resp["choices"][0]["message"][
+                "tool_calls"
+            ]
+        output = LLMInterface(
+            candidates=[(_["message"]["content"] or "") for _ in resp["choices"]],
+            content=resp["choices"][0]["message"]["content"] or "",
+            total_tokens=resp["usage"]["total_tokens"],
+            prompt_tokens=resp["usage"]["prompt_tokens"],
+            completion_tokens=resp["usage"]["completion_tokens"],
+            additional_kwargs=additional_kwargs,
+            messages=[
+                AIMessage(content=(_["message"]["content"]) or "")
+                for _ in resp["choices"]
+            ],
+        )
+
+        return output
+
     def prepare_client(self, async_version: bool = False):
         """Get the OpenAI client
 
@@ -172,19 +194,7 @@ def invoke(
         resp = self.openai_response(
             client, messages=input_messages, stream=False, **kwargs
         ).dict()
-
-        output = LLMInterface(
-            candidates=[_["message"]["content"] for _ in resp["choices"]],
-            content=resp["choices"][0]["message"]["content"],
-            total_tokens=resp["usage"]["total_tokens"],
-            prompt_tokens=resp["usage"]["prompt_tokens"],
-            completion_tokens=resp["usage"]["completion_tokens"],
-            messages=[
-                AIMessage(content=_["message"]["content"]) for _ in resp["choices"]
-            ],
-        )
-
-        return output
+        return self.prepare_output(resp)
 
     async def ainvoke(
         self, messages: str | BaseMessage | list[BaseMessage], *args, **kwargs
@@ -195,18 +205,7 @@ async def ainvoke(
             client, messages=input_messages, stream=False, **kwargs
         ).dict()
 
-        output = LLMInterface(
-            candidates=[_["message"]["content"] for _ in resp["choices"]],
-            content=resp["choices"][0]["message"]["content"],
-            total_tokens=resp["usage"]["total_tokens"],
-            prompt_tokens=resp["usage"]["prompt_tokens"],
-            completion_tokens=resp["usage"]["completion_tokens"],
-            messages=[
-                AIMessage(content=_["message"]["content"]) for _ in resp["choices"]
-            ],
-        )
-
-        return output
+        return self.prepare_output(resp)
 
     def stream(
         self, messages: str | BaseMessage | list[BaseMessage], *args, **kwargs
@@ -338,7 +337,7 @@ def prepare_client(self, async_version: bool = False):
 
     def openai_response(self, client, **kwargs):
         """Get the openai response"""
-        params = {
+        params_ = {
             "model": self.azure_deployment,
             "temperature": self.temperature,
             "max_tokens": self.max_tokens,
@@ -353,6 +352,7 @@ def openai_response(self, client, **kwargs):
             "top_logprobs": self.top_logprobs,
             "top_p": self.top_p,
         }
+        params = {k: v for k, v in params_.items() if v is not None}
         params.update(kwargs)
 
         return client.chat.completions.create(**params)