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metagpt/utils/cost_manager.py

@@ -6,12 +6,14 @@
 @Desc    : mashenquan, 2023/8/28. Separate the `CostManager` class to support user-level cost accounting.
 """
 
+import re
 from typing import NamedTuple
 
 from pydantic import BaseModel
 
 from metagpt.logs import logger
-from metagpt.utils.token_counter import TOKEN_COSTS
+from metagpt.utils.token_counter import FIREWORKS_GRADE_TOKEN_COSTS, TOKEN_COSTS
+
 
 
 class Costs(NamedTuple):
@@ -103,3 +105,44 @@ class TokenCostManager(CostManager):
         self.total_prompt_tokens += prompt_tokens
         self.total_completion_tokens += completion_tokens
         logger.info(f"prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}")
+
+
+class FireworksCostManager(CostManager):
+    def model_grade_token_costs(self, model: str) -> dict[str, float]:
+        def _get_model_size(model: str) -> float:
+            size = re.findall(".*-([0-9.]+)b", model)
+            size = float(size[0]) if len(size) > 0 else -1
+            return size
+
+        if "mixtral-8x7b" in model:
+            token_costs = FIREWORKS_GRADE_TOKEN_COSTS["mixtral-8x7b"]
+        else:
+            model_size = _get_model_size(model)
+            if 0 < model_size <= 16:
+                token_costs = FIREWORKS_GRADE_TOKEN_COSTS["16"]
+            elif 16 < model_size <= 80:
+                token_costs = FIREWORKS_GRADE_TOKEN_COSTS["80"]
+            else:
+                token_costs = FIREWORKS_GRADE_TOKEN_COSTS["-1"]
+        return token_costs
+
+    def update_cost(self, prompt_tokens: int, completion_tokens: int, model: str):
+        """
+        Refs to `https://app.fireworks.ai/pricing` **Developer pricing**
+        Update the total cost, prompt tokens, and completion tokens.
+
+        Args:
+        prompt_tokens (int): The number of tokens used in the prompt.
+        completion_tokens (int): The number of tokens used in the completion.
+        model (str): The model used for the API call.
+        """
+        self.total_prompt_tokens += prompt_tokens
+        self.total_completion_tokens += completion_tokens
+
+        token_costs = self.model_grade_token_costs(model)
+        cost = (prompt_tokens * token_costs["prompt"] + completion_tokens * token_costs["completion"]) / 1000000
+        self.total_cost += cost
+        logger.info(
+            f"Total running cost: ${self.total_cost:.4f}"
+            f"Current cost: ${cost:.4f}, prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}"
+        )

metagpt/utils/token_counter.py

@@ -35,6 +35,9 @@ TOKEN_COSTS = {
     "glm-3-turbo": {"prompt": 0.0007, "completion": 0.0007},  # 128k version, prompt + completion tokens=0.005￥/k-tokens
     "glm-4": {"prompt": 0.014, "completion": 0.014},  # 128k version, prompt + completion tokens=0.1￥/k-tokens
     "gemini-pro": {"prompt": 0.00025, "completion": 0.0005},
+    "moonshot-v1-8k": {"prompt": 0.012, "completion": 0.012},  # prompt + completion tokens=0.012￥/k-tokens
+    "moonshot-v1-32k": {"prompt": 0.024, "completion": 0.024},
+    "moonshot-v1-128k": {"prompt": 0.06, "completion": 0.06},
 }
 
 
@@ -120,6 +123,14 @@ DASHSCOPE_TOKEN_COSTS = {
 }
 
 
+FIREWORKS_GRADE_TOKEN_COSTS = {
+    "-1": {"prompt": 0.0, "completion": 0.0},  # abnormal condition
+    "16": {"prompt": 0.2, "completion": 0.8},  # 16 means model size <= 16B; 0.2 means $0.2/1M tokens
+    "80": {"prompt": 0.7, "completion": 2.8},  # 80 means 16B < model size <= 80B
+    "mixtral-8x7b": {"prompt": 0.4, "completion": 1.6},
+}
+
+
 TOKEN_MAX = {
     "gpt-3.5-turbo": 4096,
     "gpt-3.5-turbo-0301": 4096,
@@ -143,6 +154,9 @@ TOKEN_MAX = {
     "glm-3-turbo": 128000,
     "glm-4": 128000,
     "gemini-pro": 32768,
+    "moonshot-v1-8k": 8192,
+    "moonshot-v1-32k": 32768,
+    "moonshot-v1-128k": 128000,
 }