diff --git a/fastdeploy/entrypoints/llm.py b/fastdeploy/entrypoints/llm.py
index 8365c6985..3e150abf2 100644
--- a/fastdeploy/entrypoints/llm.py
+++ b/fastdeploy/entrypoints/llm.py
@@ -285,6 +285,10 @@ class LLM:
             self.llm_engine.add_requests(tasks, current_sampling_params, enable_thinking=enable_thinking)
         return req_ids
 
+    def _decode_token(self, token_id: int) -> str:
+        """Decodes a single token ID into its string representation."""
+        return self.llm_engine.data_processor.process_logprob_response([token_id], clean_up_tokenization_spaces=False)
+
     def _build_sample_logprobs(self, logprobs_lists: LogprobsLists, topk_logprobs: int) -> list[dict[int, Logprob]]:
         """
         Constructs a list of dictionaries mapping token IDs to Logprob objects,
@@ -318,8 +322,9 @@ class LLM:
             sliced_logprobs_lists = logprobs_lists.slice_columns(1, 1 + effective_topk_logprobs)
             result = []
             for token_ids, logprobs in zip(sliced_logprobs_lists.logprob_token_ids, sliced_logprobs_lists.logprobs):
+
                 logprob_dict = {
-                    token_id: Logprob(logprob=logprob, rank=i + 1, decoded_token=None)
+                    token_id: Logprob(logprob=logprob, rank=i + 1, decoded_token=self._decode_token(token_id))
                     for i, (token_id, logprob) in enumerate(zip(token_ids, logprobs))
                 }
                 result.append(logprob_dict)
diff --git a/test/ce/server/test_base_chat.py b/test/ce/server/test_base_chat.py
new file mode 100644
index 000000000..12be895fe
--- /dev/null
+++ b/test/ce/server/test_base_chat.py
@@ -0,0 +1,221 @@
+#!/bin/env python3
+# -*- coding: utf-8 -*-
+# @author DDDivano
+# encoding=utf-8 vi:ts=4:sw=4:expandtab:ft=python
+
+"""
+some basic check for fd web api
+"""
+
+import json
+
+from core import TEMPLATE, URL, build_request_payload, send_request
+
+
+def test_stream_response():
+    data = {
+        "stream": True,
+        "messages": [
+            {"role": "system", "content": "你是一个知识渊博的 AI 助手"},
+            {"role": "user", "content": "讲讲爱因斯坦的相对论"},
+        ],
+        "max_tokens": 10,
+    }
+    payload = build_request_payload(TEMPLATE, data)
+    resp = send_request(URL, payload, stream=True)
+
+    output = ""
+    for line in resp.iter_lines(decode_unicode=True):
+        if line.strip() == "" or not line.startswith("data: "):
+            continue
+        line = line[len("data: ") :]
+        if line.strip() == "[DONE]":
+            break
+        chunk = json.loads(line)
+        delta = chunk.get("choices", [{}])[0].get("delta", {})
+        output += delta.get("content", "")
+
+    print("Stream输出:", output)
+    assert "相对论" in output or len(output) > 0
+
+
+def test_system_prompt_effect():
+    data = {
+        "stream": False,
+        "messages": [
+            {"role": "system", "content": "请用一句话回答"},
+            {"role": "user", "content": "什么是人工智能？"},
+        ],
+        "max_tokens": 30,
+    }
+    payload = build_request_payload(TEMPLATE, data)
+    resp = send_request(URL, payload).json()
+    content = resp["choices"][0]["message"]["content"]
+    print("内容输出:", content)
+    assert len(content) < 50
+
+
+def test_logprobs_enabled():
+    data = {
+        "stream": False,
+        "logprobs": True,
+        "top_logprobs": 5,
+        "messages": [{"role": "user", "content": "非洲的首都是？"}],
+        "max_tokens": 3,
+    }
+    payload = build_request_payload(TEMPLATE, data)
+    resp = send_request(URL, payload).json()
+    logprob_data = resp["choices"][0].get("logprobs")
+    print("LogProbs:", logprob_data)
+    assert logprob_data is not None
+    content_logprobs = logprob_data.get("content", [])
+    assert isinstance(content_logprobs, list)
+    assert all("token" in item for item in content_logprobs)
+
+
+def test_stop_sequence():
+    data = {
+        "stream": False,
+        "stop": ["果冻"],
+        "messages": [
+            {
+                "role": "user",
+                "content": "你要严格按照我接下来的话输出，输出冒号后面的内容，请输出：这是第一段。果冻这是第二段啦啦啦啦啦。",
+            },
+        ],
+        "max_tokens": 20,
+        "top_p": 0,
+    }
+    payload = build_request_payload(TEMPLATE, data)
+    resp = send_request(URL, payload).json()
+    content = resp["choices"][0]["message"]["content"]
+    print("截断输出:", content)
+    assert "第二段" not in content
+
+
+def test_sampling_parameters():
+    data = {
+        "stream": False,
+        "temperature": 0,
+        "top_p": 0,
+        "messages": [
+            {"role": "user", "content": "1+1=？,直接回答答案"},
+        ],
+        "max_tokens": 50,
+    }
+    payload = build_request_payload(TEMPLATE, data)
+    resp = send_request(URL, payload).json()
+    answer = resp["choices"][0]["message"]["content"]
+    print("Sampling输出:", answer)
+    assert any(ans in answer for ans in ["2", "二"])
+
+
+def test_multi_turn_conversation():
+    data = {
+        "stream": False,
+        "messages": [
+            {"role": "user", "content": "牛顿是谁？"},
+            {"role": "assistant", "content": "牛顿是一位物理学家。"},
+            {"role": "user", "content": "他提出了什么理论？"},
+        ],
+        "max_tokens": 30,
+    }
+    payload = build_request_payload(TEMPLATE, data)
+    resp = send_request(URL, payload).json()
+    content = resp["choices"][0]["message"]["content"]
+    print("多轮记忆:", content)
+    assert "三大运动定律" in content or "万有引力" in content
+
+
+def test_bad_words_filtering():
+    banned_tokens = ["和", "呀"]
+
+    data = {
+        "stream": False,
+        "messages": [
+            {"role": "system", "content": "你是一个助手，回答简洁清楚"},
+            {"role": "user", "content": "请输出冒号后面的字: 我爱吃果冻，和苹果，香蕉，和荔枝"},
+        ],
+        "top_p": 0,
+        "max_tokens": 69,
+        "bad_words": banned_tokens,
+    }
+
+    payload = build_request_payload(TEMPLATE, data)
+    response = send_request(URL, payload).json()
+
+    content = response["choices"][0]["message"]["content"]
+    print("生成内容:", content)
+
+    for word in banned_tokens:
+        assert word not in content, f"bad_word '{word}' 不应出现在生成结果中"
+
+    print("test_bad_words_filtering 通过：生成结果未包含被禁词")
+
+    data = {
+        "stream": False,
+        "messages": [
+            {"role": "system", "content": "你是一个助手，回答简洁清楚"},
+            {"role": "user", "content": "请输出冒号后面的字，一模一样: 我爱吃果冻，苹果，香蕉，和荔枝呀呀呀"},
+        ],
+        "top_p": 0,
+        "max_tokens": 69,
+        # "bad_words": banned_tokens,
+    }
+
+    payload = build_request_payload(TEMPLATE, data)
+    response = send_request(URL, payload).json()
+
+    content = response["choices"][0]["message"]["content"]
+    print("生成内容:", content)
+
+    for word in banned_tokens:
+        assert word not in content, f"bad_word '{word}' 不应出现在生成结果中"
+
+    print("test_bad_words_filtering 通过：生成结果未包含被禁词")
+
+
+def test_bad_words_filtering1():
+    banned_tokens = ["和", "呀"]
+
+    data = {
+        "stream": False,
+        "messages": [
+            {"role": "system", "content": "你是一个助手，回答简洁清楚"},
+            {"role": "user", "content": "请输出冒号后面的字: 我爱吃果冻，和苹果，香蕉，和荔枝"},
+        ],
+        "top_p": 0,
+        "max_tokens": 69,
+        "bad_words": banned_tokens,
+    }
+
+    payload = build_request_payload(TEMPLATE, data)
+    response = send_request(URL, payload).json()
+
+    content = response["choices"][0]["message"]["content"]
+    print("生成内容:", content)
+
+    for word in banned_tokens:
+        assert word not in content, f"bad_word '{word}' 不应出现在生成结果中"
+
+    print("test_bad_words_filtering 通过：生成结果未包含被禁词")
+    word = "呀呀"
+    data = {
+        "stream": False,
+        "messages": [
+            {"role": "system", "content": "你是一个助手，回答简洁清楚"},
+            {"role": "user", "content": "请输出冒号后面的字，一模一样: 我爱吃果冻，苹果，香蕉，和荔枝呀呀呀"},
+        ],
+        "top_p": 0,
+        "max_tokens": 69,
+    }
+
+    payload = build_request_payload(TEMPLATE, data)
+    response = send_request(URL, payload).json()
+
+    content = response["choices"][0]["message"]["content"]
+    print("生成内容:", content)
+
+    assert word in content, f" '{word}' 应出现在生成结果中"
+
+    print("test_bad_words_filtering 通过：生成结果未包含被禁词")
diff --git a/test/entrypoints/openai/test_build_sample_logprobs.py b/test/entrypoints/openai/test_build_sample_logprobs.py
new file mode 100644
index 000000000..76ff8e87b
--- /dev/null
+++ b/test/entrypoints/openai/test_build_sample_logprobs.py
@@ -0,0 +1,78 @@
+import unittest
+from unittest.mock import MagicMock, patch
+
+from fastdeploy.entrypoints.llm import LLM
+from fastdeploy.worker.output import Logprob, LogprobsLists
+
+
+def get_patch_path(cls, method="__init__"):
+    return f"{cls.__module__}.{cls.__qualname__}.{method}"
+
+
+class TestBuildSampleLogprobs(unittest.TestCase):
+
+    def setUp(self):
+        """
+        Set up the test environment by creating an instance of the LLM class using Mock.
+        """
+        patch_llm = get_patch_path(LLM)
+        with patch(patch_llm, return_value=None):
+            self.llm = LLM()
+            # mock d data_processor
+            self.llm.llm_engine = MagicMock()
+            self.llm.llm_engine.data_processor.process_logprob_response.side_effect = (
+                lambda ids, **kwargs: f"token_{ids[0]}"
+            )
+
+    def test_build_sample_logprobs_basic(self):
+        """
+        Test case for building sample logprobs when `topk_logprobs` is valid.
+        """
+        logprob_token_ids = [[100, 101, 102]]
+        logprobs = [[-0.1, -0.5, -1.0]]
+        sampled_token_ranks = [0]
+
+        logprobs_lists = LogprobsLists(
+            logprob_token_ids=logprob_token_ids, logprobs=logprobs, sampled_token_ranks=sampled_token_ranks
+        )
+
+        result = self.llm._build_sample_logprobs(logprobs_lists, topk_logprobs=2)
+
+        expected = [
+            {
+                101: Logprob(logprob=-0.5, rank=1, decoded_token="token_101"),
+                102: Logprob(logprob=-1.0, rank=2, decoded_token="token_102"),
+            }
+        ]
+
+        self.assertEqual(result, expected)
+
+    def test_build_sample_logprobs_empty_input(self):
+        """
+        Test case where `logprob_token_ids` is empty.
+        """
+        logprobs_lists = MagicMock(spec=LogprobsLists)
+        logprobs_lists.logprob_token_ids = []
+        result = self.llm._build_sample_logprobs(logprobs_lists, topk_logprobs=2)
+        self.assertIsNone(result)
+
+    def test_build_sample_logprobs_invalid_topk(self):
+        """
+        Test case where `topk` value exceeds length of first element in `logprob_token_ids`.
+        """
+        logprobs_lists = MagicMock(spec=LogprobsLists)
+        logprobs_lists.logprob_token_ids = [[100]]
+        result = self.llm._build_sample_logprobs(logprobs_lists, topk_logprobs=2)
+        self.assertIsNone(result)
+
+    def test_decode_token(self):
+        """
+        Test case for decoding a single token ID.
+        """
+        token_id = 123
+        decoded = self.llm._decode_token(token_id)
+        self.assertEqual(decoded, "token_123")
+
+
+if __name__ == "__main__":
+    unittest.main()