update do_preprocess

lightrag/llm.py

@@ -286,7 +286,9 @@ async def hf_model_if_cache(
     output = hf_model.generate(
         **input_ids, max_new_tokens=512, num_return_sequences=1, early_stopping=True
     )
-    response_text = hf_tokenizer.decode(output[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
+    response_text = hf_tokenizer.decode(
+        output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
+    )
     if hashing_kv is not None:
         await hashing_kv.upsert({args_hash: {"return": response_text, "model": model}})
     return response_text
@@ -323,19 +325,38 @@ async def ollama_model_if_cache(
 
 
 @lru_cache(maxsize=1)
-def initialize_lmdeploy_pipeline(model, tp=1, chat_template=None, log_level='WARNING', model_format='hf', quant_policy=0):
+def initialize_lmdeploy_pipeline(
+    model,
+    tp=1,
+    chat_template=None,
+    log_level="WARNING",
+    model_format="hf",
+    quant_policy=0,
+):
     from lmdeploy import pipeline, ChatTemplateConfig, TurbomindEngineConfig
+
     lmdeploy_pipe = pipeline(
         model_path=model,
-        backend_config=TurbomindEngineConfig(tp=tp, model_format=model_format, quant_policy=quant_policy),
-        chat_template_config=ChatTemplateConfig(model_name=chat_template) if chat_template else None,
-        log_level='WARNING')
+        backend_config=TurbomindEngineConfig(
+            tp=tp, model_format=model_format, quant_policy=quant_policy
+        ),
+        chat_template_config=ChatTemplateConfig(model_name=chat_template)
+        if chat_template
+        else None,
+        log_level="WARNING",
+    )
     return lmdeploy_pipe
 
 
 async def lmdeploy_model_if_cache(
-    model, prompt, system_prompt=None, history_messages=[], 
-    chat_template=None, model_format='hf',quant_policy=0, **kwargs
+    model,
+    prompt,
+    system_prompt=None,
+    history_messages=[],
+    chat_template=None,
+    model_format="hf",
+    quant_policy=0,
+    **kwargs,
 ) -> str:
     """
     Args:
@@ -361,29 +382,30 @@ async def lmdeploy_model_if_cache(
         do_preprocess (bool): whether pre-process the messages. Default to
             True, which means chat_template will be applied.
         skip_special_tokens (bool): Whether or not to remove special tokens
-            in the decoding. Default to be False.
+            in the decoding. Default to be True.
         do_sample (bool): Whether or not to use sampling, use greedy decoding otherwise.
             Default to be False, which means greedy decoding will be applied.
     """
     try:
         import lmdeploy
         from lmdeploy import version_info, GenerationConfig
-    except:
+    except Exception:
         raise ImportError("Please install lmdeploy before intialize lmdeploy backend.")
 
     kwargs.pop("response_format", None)
     max_new_tokens = kwargs.pop("max_tokens", 512)
-    tp = kwargs.pop('tp', 1)
-    skip_special_tokens = kwargs.pop('skip_special_tokens', False)
-    do_preprocess = kwargs.pop('do_preprocess', True)
-    do_sample = kwargs.pop('do_sample', False)
+    tp = kwargs.pop("tp", 1)
+    skip_special_tokens = kwargs.pop("skip_special_tokens", True)
+    do_preprocess = kwargs.pop("do_preprocess", True)
+    do_sample = kwargs.pop("do_sample", False)
     gen_params = kwargs
 
     version = version_info
     if do_sample is not None and version < (0, 6, 0):
         raise RuntimeError(
-            '`do_sample` parameter is not supported by lmdeploy until '
-            f'v0.6.0, but currently using lmdeloy {lmdeploy.__version__}')
+            "`do_sample` parameter is not supported by lmdeploy until "
+            f"v0.6.0, but currently using lmdeloy {lmdeploy.__version__}"
+        )
     else:
         do_sample = True
         gen_params.update(do_sample=do_sample)
@@ -394,7 +416,8 @@ async def lmdeploy_model_if_cache(
         chat_template=chat_template,
         model_format=model_format,
         quant_policy=quant_policy,
-        log_level='WARNING')
+        log_level="WARNING",
+    )
 
     messages = []
     if system_prompt:
@@ -410,11 +433,19 @@ async def lmdeploy_model_if_cache(
             return if_cache_return["return"]
 
     gen_config = GenerationConfig(
-        skip_special_tokens=skip_special_tokens, max_new_tokens=max_new_tokens, **gen_params)
+        skip_special_tokens=skip_special_tokens,
+        max_new_tokens=max_new_tokens,
+        **gen_params,
+    )
 
     response = ""
-    async for res in lmdeploy_pipe.generate(messages, gen_config=gen_config, 
-        do_preprocess=do_preprocess, stream_response=False, session_id=1):
+    async for res in lmdeploy_pipe.generate(
+        messages,
+        gen_config=gen_config,
+        do_preprocess=do_preprocess,
+        stream_response=False,
+        session_id=1,
+    ):
         response += res.response
 
     if hashing_kv is not None: