Show details for query tokens (#1392)

embedchain/llm/cohere.py

@@ -1,8 +1,8 @@
 import importlib
 import os
-from typing import Optional
+from typing import Any, Optional
 
-from langchain_community.llms.cohere import Cohere
+from langchain_cohere import ChatCohere
 
 from embedchain.config import BaseLlmConfig
 from embedchain.helpers.json_serializable import register_deserializable
@@ -17,27 +17,50 @@ class CohereLlm(BaseLlm):
         except ModuleNotFoundError:
             raise ModuleNotFoundError(
                 "The required dependencies for Cohere are not installed."
-                'Please install with `pip install --upgrade "embedchain[cohere]"`'
+                "Please install with `pip install langchain_cohere==1.16.0`"
             ) from None
 
         super().__init__(config=config)
         if not self.config.api_key and "COHERE_API_KEY" not in os.environ:
             raise ValueError("Please set the COHERE_API_KEY environment variable or pass it in the config.")
 
-    def get_llm_model_answer(self, prompt):
+    def get_llm_model_answer(self, prompt) -> tuple[str, Optional[dict[str, Any]]]:
         if self.config.system_prompt:
             raise ValueError("CohereLlm does not support `system_prompt`")
-        return CohereLlm._get_answer(prompt=prompt, config=self.config)
+
+        if self.config.token_usage:
+            response, token_info = self._get_answer(prompt, self.config)
+            model_name = "cohere/" + 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["input_tokens"]
+            ) + self.config.model_pricing_map[model_name]["output_cost_per_token"] * token_info["output_tokens"]
+            response_token_info = {
+                "prompt_tokens": token_info["input_tokens"],
+                "completion_tokens": token_info["output_tokens"],
+                "total_tokens": token_info["input_tokens"] + token_info["output_tokens"],
+                "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:
-        api_key = config.api_key or os.getenv("COHERE_API_KEY")
-        llm = Cohere(
-            cohere_api_key=api_key,
-            model=config.model,
-            max_tokens=config.max_tokens,
-            temperature=config.temperature,
-            p=config.top_p,
-        )
+        api_key = config.api_key or os.environ["COHERE_API_KEY"]
+        kwargs = {
+            "model_name": config.model or "command-r",
+            "temperature": config.temperature,
+            "max_tokens": config.max_tokens,
+            "together_api_key": api_key,
+        }
 
-        return llm.invoke(prompt)
+        chat = ChatCohere(**kwargs)
+        chat_response = chat.invoke(prompt)
+        if config.token_usage:
+            return chat_response.content, chat_response.response_metadata["token_count"]
+        return chat_response.content