Show details for query tokens (#1392)

embedchain/llm/vertex_ai.py

@@ -1,6 +1,6 @@
 import importlib
 import logging
-from typing import Optional
+from typing import Any, Optional
 
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain_google_vertexai import ChatVertexAI
@@ -24,16 +24,35 @@ class VertexAILlm(BaseLlm):
             ) from None
         super().__init__(config=config)
 
-    def get_llm_model_answer(self, prompt):
-        return VertexAILlm._get_answer(prompt=prompt, config=self.config)
+    def get_llm_model_answer(self, prompt) -> tuple[str, Optional[dict[str, Any]]]:
+        if self.config.token_usage:
+            response, token_info = self._get_answer(prompt, self.config)
+            model_name = "vertexai/" + self.config.model
+            if model_name not in self.config.model_pricing_map:
+                raise ValueError(
+                    f"Model {model_name} not found in `model_prices_and_context_window.json`. \
+                    You can disable token usage by setting `token_usage` to False."
+                )
+            total_cost = (
+                self.config.model_pricing_map[model_name]["input_cost_per_token"] * token_info["prompt_token_count"]
+            ) + self.config.model_pricing_map[model_name]["output_cost_per_token"] * token_info[
+                "candidates_token_count"
+            ]
+            response_token_info = {
+                "prompt_tokens": token_info["prompt_token_count"],
+                "completion_tokens": token_info["candidates_token_count"],
+                "total_tokens": token_info["prompt_token_count"] + token_info["candidates_token_count"],
+                "total_cost": round(total_cost, 10),
+                "cost_currency": "USD",
+            }
+            return response, response_token_info
+        return self._get_answer(prompt, self.config)
 
     @staticmethod
     def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
         if config.top_p and config.top_p != 1:
             logger.warning("Config option `top_p` is not supported by this model.")
 
-        messages = BaseLlm._get_messages(prompt, system_prompt=config.system_prompt)
-
         if config.stream:
             callbacks = config.callbacks if config.callbacks else [StreamingStdOutCallbackHandler()]
             llm = ChatVertexAI(
@@ -42,4 +61,8 @@ class VertexAILlm(BaseLlm):
         else:
             llm = ChatVertexAI(temperature=config.temperature, model=config.model)
 
-        return llm.invoke(messages).content
+        messages = VertexAILlm._get_messages(prompt)
+        chat_response = llm.invoke(messages)
+        if config.token_usage:
+            return chat_response.content, chat_response.response_metadata["usage_metadata"]
+        return chat_response.content