add compress_type to config

metagpt/configs/llm_config.py

@@ -86,6 +86,9 @@ class LLMConfig(YamlModel):
     # Cost Control
     calc_usage: bool = True
 
+    # Compress request messages under token limit
+    compress_type: str = ""
+
     @field_validator("api_key")
     @classmethod
     def check_llm_key(cls, v):

metagpt/provider/base_llm.py

@@ -148,7 +148,7 @@ class BaseLLM(ABC):
         else:
             message.extend(msg)
         logger.debug(message)
-        compressed_message = self.compress_messages(message, compress_type="post_cut_by_token")
+        compressed_message = self.compress_messages(message, compress_type=self.config.compress_type)
         rsp = await self.acompletion_text(compressed_message, stream=stream, timeout=self.get_timeout(timeout))
         # rsp = await self.acompletion_text(message, stream=stream, timeout=self.get_timeout(timeout))
         return rsp

tests/metagpt/provider/test_base_llm.py

@@ -147,6 +147,13 @@ def test_compress_messages_long(compress_type):
     assert compressed[2]["role"] != "system"
 
 
+def test_long_messages_no_compress():
+    base_llm = MockBaseLLM()
+    messages = [{"role": "user", "content": "1" * 10000}] * 10000
+    compressed = base_llm.compress_messages(messages)
+    assert len(compressed) == len(messages)
+
+
 @pytest.mark.parametrize(
     "compress_type", ["post_cut_by_msg", "post_cut_by_token", "pre_cut_by_msg", "pre_cut_by_token"]
 )

tests/metagpt/provider/test_openai.py

@@ -178,7 +178,7 @@ def test_count_tokens():
         llm._assistant_msg("assistant 2"),
     ]
     cnt = llm.count_tokens(messages)
-    print(cnt)
+    assert cnt == 47
 
 
 def test_count_tokens_long():
@@ -190,11 +190,11 @@ def test_count_tokens_long():
         llm._user_msg(test_msg_content + " what's the first number you see?"),
     ]
     cnt = llm.count_tokens(messages)  # 299023, ~300k
-    print(cnt)
+    assert 290000 <= cnt <= 300000
 
     llm.config.model = "test_llm"  # a non-openai model, will use heuristics base count_tokens
     cnt = llm.count_tokens(messages)  # 294474, ~300k, ~2% difference
-    print(cnt)
+    assert 290000 <= cnt <= 300000
 
 
 @pytest.mark.skip
@@ -202,9 +202,25 @@ def test_count_tokens_long():
 async def test_aask_long():
     llm = LLM()
     llm.config.model = "deepseek-ai/DeepSeek-Coder-V2-Instruct"  # deepseek-coder on siliconflow, limit 32k
+    llm.config.compress_type = "post_cut_by_token"
     test_msg_content = " ".join([str(i) for i in range(100000)])  # corresponds to ~300k tokens
     messages = [
         llm._system_msg("You are a helpful assistant"),
         llm._user_msg(test_msg_content + " what's the first number you see?"),
     ]
     await llm.aask(messages)  # should not fail with context truncated
+
+
+@pytest.mark.skip
+@pytest.mark.asyncio
+async def test_aask_long_no_compress():
+    llm = LLM()
+    llm.config.model = "deepseek-ai/DeepSeek-Coder-V2-Instruct"  # deepseek-coder on siliconflow, limit 32k
+    # Not specifying llm.config.compress_type will use default "", no compress
+    test_msg_content = " ".join([str(i) for i in range(100000)])  # corresponds to ~300k tokens
+    messages = [
+        llm._system_msg("You are a helpful assistant"),
+        llm._user_msg(test_msg_content + " what's the first number you see?"),
+    ]
+    with pytest.raises(Exception):
+        await llm.aask(messages)  # should fail