From 6a01a679cec3824ab5cd847143831cf3e5a6dd40 Mon Sep 17 00:00:00 2001
From: didi <84363704+didiforgithub@users.noreply.github.com>
Date: Sun, 25 Aug 2024 22:17:27 +0800
Subject: [PATCH] test for multi llm

---
 test.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 test.py

diff --git a/test.py b/test.py
new file mode 100644
index 000000000..a32c2094f
--- /dev/null
+++ b/test.py
@@ -0,0 +1,51 @@
+import asyncio
+
+from pydantic import BaseModel, Field
+
+from metagpt.actions.action_node import ActionNode
+from metagpt.configs.models_config import ModelsConfig
+from metagpt.provider.llm_provider_registry import create_llm_instance
+from metagpt.utils.cost_manager import CostManager
+
+deepseek_llm_config = ModelsConfig.default().get("deepseek-coder")
+deepseek_llm = create_llm_instance(deepseek_llm_config)
+deepseek_llm.cost_manager = CostManager()
+claude_llm_config = ModelsConfig.default().get("claude-3.5-sonnet")
+claude_llm = create_llm_instance(claude_llm_config)
+
+# TODO 思考一下，如何每次都去创建新实例，从而保证每次计数的一致。
+# llm.cost_manager = data.llm.cost_manager
+
+
+class GenerateCodeSolution(BaseModel):
+    solution: str = Field(default="", description="A description of the solution")
+    thought: str = Field(
+        default="",
+        description="Shortly explain why this solution correctly solves the problem. Be specific and detailed regarding the problem rules and goals.",
+    )
+
+
+GENERATE_ON_CONTEXT_PROMPT = """
+Please generate a solution for the following problem based on the provided context:
+
+### Problem Description
+{problem_description}
+"""
+
+
+async def main():
+    prompt = GENERATE_ON_CONTEXT_PROMPT.format(
+        problem_description="Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?"
+    )
+    node = await ActionNode.from_pydantic(GenerateCodeSolution).fill(
+        context=prompt, llm=deepseek_llm, mode="context_fill"
+    )
+    response = node.instruct_content.model_dump()
+
+    print(deepseek_llm.cost_manager.total_cost)
+
+    return response
+
+
+if __name__ == "__main__":
+    print(asyncio.run(main()))