diff --git a/models/moonshot/.difyignore b/models/moonshot/.difyignore
new file mode 100644
index 000000000..af6c7c786
--- /dev/null
+++ b/models/moonshot/.difyignore
@@ -0,0 +1,7 @@
+.venv/
+__pycache__/
+*.py[cod]
+.pytest_cache/
+.ruff_cache/
+.env
+*.difypkg
diff --git a/models/moonshot/.env.example b/models/moonshot/.env.example
deleted file mode 100644
index dbb75b858..000000000
--- a/models/moonshot/.env.example
+++ /dev/null
@@ -1,4 +0,0 @@
-INSTALL_METHOD=remote
-REMOTE_INSTALL_HOST=debug-plugin.dify.dev
-REMOTE_INSTALL_PORT=5003
-REMOTE_INSTALL_KEY=********-****-****-****-************
diff --git a/models/moonshot/manifest.yaml b/models/moonshot/manifest.yaml
index d9b59bd0b..32abc0e40 100644
--- a/models/moonshot/manifest.yaml
+++ b/models/moonshot/manifest.yaml
@@ -1,8 +1,8 @@
author: langgenius
created_at: '2024-09-20T00:13:50.29298939-04:00'
description:
- en_US: Models provided by Moonshot, such as kimi-k2.6, kimi-k2.5, kimi-k2, moonshot-v1-8k, moonshot-v1-32k, and moonshot-v1-128k.
- zh_Hans: Moonshot 提供的模型，例如 kimi-k2.6, kimi-k2.5, kimi-k2, moonshot-v1-8k、moonshot-v1-32k 和 moonshot-v1-128k。
+ en_US: Models provided by Moonshot, such as kimi-k3, kimi-k2.7-code, kimi-k2.6, kimi-k2.5, kimi-k2, moonshot-v1-8k, moonshot-v1-32k, and moonshot-v1-128k.
+ zh_Hans: Moonshot 提供的模型，例如 kimi-k3, kimi-k2.7-code, kimi-k2.6, kimi-k2.5, kimi-k2, moonshot-v1-8k、moonshot-v1-32k 和 moonshot-v1-128k。
icon: icon_s_en.png
label:
en_US: Moonshot
@@ -34,4 +34,4 @@ resource:
tool:
enabled: true
type: plugin
-version: 0.1.8
+version: 0.1.9
diff --git a/models/moonshot/models/llm/_position.yaml b/models/moonshot/models/llm/_position.yaml
index b15ad85f8..1c3946425 100644
--- a/models/moonshot/models/llm/_position.yaml
+++ b/models/moonshot/models/llm/_position.yaml
@@ -8,3 +8,6 @@
- kimi-k2-thinking-turbo
- kimi-k2.5
- kimi-k2.6
+- kimi-k2.7-code
+- kimi-k2.7-code-highspeed
+- kimi-k3
diff --git a/models/moonshot/models/llm/kimi-k2.7-code-highspeed.yaml b/models/moonshot/models/llm/kimi-k2.7-code-highspeed.yaml
new file mode 100644
index 000000000..20694d112
--- /dev/null
+++ b/models/moonshot/models/llm/kimi-k2.7-code-highspeed.yaml
@@ -0,0 +1,42 @@
+model: kimi-k2.7-code-highspeed
+label:
+ zh_Hans: kimi-k2.7-code-highspeed
+ en_US: kimi-k2.7-code-highspeed
+model_type: llm
+features:
+ - vision
+ - video
+ - structured-output
+ - agent-thought
+ - tool-call
+ - multi-tool-call
+ - stream-tool-call
+model_properties:
+ mode: chat
+ context_size: 262144
+parameter_rules:
+ - name: max_completion_tokens
+ use_template: max_tokens
+ default: 32768
+ min: 1
+ max: 262144
+ - name: response_format
+ label:
+ zh_Hans: 回复格式
+ en_US: Response Format
+ type: string
+ help:
+ zh_Hans: 指定模型必须输出的格式
+ en_US: specifying the format that the model must output
+ required: false
+ options:
+ - text
+ - json_object
+ - json_schema
+ - name: json_schema
+ use_template: json_schema
+pricing:
+ input: '13'
+ output: '54'
+ unit: '0.000001'
+ currency: RMB
diff --git a/models/moonshot/models/llm/kimi-k2.7-code.yaml b/models/moonshot/models/llm/kimi-k2.7-code.yaml
new file mode 100644
index 000000000..e6a8d0aa3
--- /dev/null
+++ b/models/moonshot/models/llm/kimi-k2.7-code.yaml
@@ -0,0 +1,42 @@
+model: kimi-k2.7-code
+label:
+ zh_Hans: kimi-k2.7-code
+ en_US: kimi-k2.7-code
+model_type: llm
+features:
+ - vision
+ - video
+ - structured-output
+ - agent-thought
+ - tool-call
+ - multi-tool-call
+ - stream-tool-call
+model_properties:
+ mode: chat
+ context_size: 262144
+parameter_rules:
+ - name: max_completion_tokens
+ use_template: max_tokens
+ default: 32768
+ min: 1
+ max: 262144
+ - name: response_format
+ label:
+ zh_Hans: 回复格式
+ en_US: Response Format
+ type: string
+ help:
+ zh_Hans: 指定模型必须输出的格式
+ en_US: specifying the format that the model must output
+ required: false
+ options:
+ - text
+ - json_object
+ - json_schema
+ - name: json_schema
+ use_template: json_schema
+pricing:
+ input: '6.5'
+ output: '27'
+ unit: '0.000001'
+ currency: RMB
diff --git a/models/moonshot/models/llm/kimi-k3.yaml b/models/moonshot/models/llm/kimi-k3.yaml
new file mode 100644
index 000000000..d50972157
--- /dev/null
+++ b/models/moonshot/models/llm/kimi-k3.yaml
@@ -0,0 +1,42 @@
+model: kimi-k3
+label:
+ zh_Hans: kimi-k3
+ en_US: kimi-k3
+model_type: llm
+features:
+ - vision
+ - video
+ - structured-output
+ - agent-thought
+ - tool-call
+ - multi-tool-call
+ - stream-tool-call
+model_properties:
+ mode: chat
+ context_size: 1048576
+parameter_rules:
+ - name: max_completion_tokens
+ use_template: max_tokens
+ default: 131072
+ min: 1
+ max: 1048576
+ - name: response_format
+ label:
+ zh_Hans: 回复格式
+ en_US: Response Format
+ type: string
+ help:
+ zh_Hans: 指定模型必须输出的格式
+ en_US: specifying the format that the model must output
+ required: false
+ options:
+ - text
+ - json_object
+ - json_schema
+ - name: json_schema
+ use_template: json_schema
+pricing:
+ input: '20'
+ output: '100'
+ unit: '0.000001'
+ currency: RMB
diff --git a/models/moonshot/models/llm/llm.py b/models/moonshot/models/llm/llm.py
index 4b3ace51a..ad33d0411 100644
--- a/models/moonshot/models/llm/llm.py
+++ b/models/moonshot/models/llm/llm.py
@@ -1,10 +1,7 @@
-
import re
from collections.abc import Generator
-from typing import Optional, Union, cast
from dify_plugin import OAICompatLargeLanguageModel
-
from dify_plugin.entities.model import (
AIModelEntity,
FetchFrom,
@@ -21,15 +18,25 @@
PromptMessage,
PromptMessageTool,
SystemPromptMessage,
+ TextPromptMessageContent,
ToolPromptMessage,
+ UserPromptMessage,
+ VideoPromptMessageContent,
)
-
-
+from requests import Response
class MoonshotLargeLanguageModel(OAICompatLargeLanguageModel):
- # Pattern to match ... blocks (case-insensitive, non-greedy)
_THINK_PATTERN = re.compile(r"(.*?)", re.DOTALL | re.IGNORECASE)
+ _THINKING_MODELS = {
+ "kimi-k2-thinking",
+ "kimi-k2-thinking-turbo",
+ "kimi-k2.5",
+ "kimi-k2.6",
+ "kimi-k2.7-code",
+ "kimi-k2.7-code-highspeed",
+ "kimi-k3",
+ }
def _invoke(
self,
@@ -37,19 +44,17 @@ def _invoke(
credentials: dict,
prompt_messages: list[PromptMessage],
model_parameters: dict,
- tools: Optional[list[PromptMessageTool]] = None,
- stop: Optional[list[str]] = None,
+ tools: list[PromptMessageTool] | None = None,
+ stop: list[str] | None = None,
stream: bool = True,
- user: Optional[str] = None,
- ) -> Union[LLMResult, Generator]:
+ user: str | None = None,
+ ) -> LLMResult | Generator:
self._add_custom_parameters(credentials)
self._add_function_call(model, credentials)
user = user[:32] if user else None
- # Store current model for use in _convert_prompt_message_to_dict
credentials["_current_model"] = model
- # 处理 Kimi K2.5 / K2.6 等思考模型的 thinking 参数
if "thinking" in model_parameters:
thinking = model_parameters.pop("thinking")
if thinking:
@@ -57,35 +62,57 @@ def _invoke(
else:
model_parameters["thinking"] = {"type": "disabled"}
- # Merge consecutive messages with the same role to strictly follow API specs
prompt_messages = self._clean_messages(prompt_messages)
- return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user=user)
+ return super()._invoke(
+ model,
+ credentials,
+ prompt_messages,
+ model_parameters,
+ tools,
+ stop,
+ stream,
+ user=user,
+ )
def _clean_messages(self, messages: list[PromptMessage]) -> list[PromptMessage]:
cleaned: list[PromptMessage] = []
for m in messages:
- # Keep messages that have content or tool calls
has_tool_calls = isinstance(m, AssistantPromptMessage) and m.tool_calls
if not m.content and not has_tool_calls:
continue
-
- # Tool and system messages should NEVER be merged - each has a unique tool_call_id
+
if isinstance(m, ToolPromptMessage) or isinstance(m, SystemPromptMessage):
cleaned.append(m.model_copy())
continue
-
+
if cleaned and cleaned[-1].role == m.role:
prev = cleaned[-1]
- # Merge content if both are strings
if isinstance(prev.content, str) and isinstance(m.content, str):
if prev.content and m.content:
prev.content += "\n\n" + m.content
else:
prev.content = prev.content or m.content
-
- # Merge tool_calls if both are assistants
- if isinstance(prev, AssistantPromptMessage) and isinstance(m, AssistantPromptMessage):
+ elif isinstance(prev.content, list) and isinstance(m.content, list):
+ prev.content = [*prev.content, *m.content]
+ elif isinstance(prev.content, str) and isinstance(m.content, list):
+ prev.content = [
+ TextPromptMessageContent(data=prev.content),
+ *m.content,
+ ]
+ elif isinstance(prev.content, list) and isinstance(m.content, str):
+ prev.content = [
+ *prev.content,
+ TextPromptMessageContent(data=m.content),
+ ]
+
+ if isinstance(prev, AssistantPromptMessage) and isinstance(
+ m, AssistantPromptMessage
+ ):
+ if isinstance(prev.content, str) and self._THINK_PATTERN.search(
+ prev.content
+ ):
+ prev.opaque_body = None
if m.tool_calls:
if not prev.tool_calls:
prev.tool_calls = []
@@ -98,17 +125,25 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
self._add_custom_parameters(credentials)
super().validate_credentials(model, credentials)
- def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
+ def get_customizable_model_schema(
+ self, model: str, credentials: dict
+ ) -> AIModelEntity | None:
return AIModelEntity(
model=model,
label=I18nObject(en_us=model, zh_hans=model),
model_type=ModelType.LLM,
- features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
+ features=[
+ ModelFeature.TOOL_CALL,
+ ModelFeature.MULTI_TOOL_CALL,
+ ModelFeature.STREAM_TOOL_CALL,
+ ]
if credentials.get("function_calling_type") == "tool_call"
else [],
fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
model_properties={
- ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 4096)),
+ ModelPropertyKey.CONTEXT_SIZE: int(
+ credentials.get("context_size", 4096)
+ ),
ModelPropertyKey.MODE: LLMMode.CHAT.value,
},
parameter_rules=[
@@ -143,83 +178,124 @@ def _add_custom_parameters(self, credentials: dict) -> None:
def _add_function_call(self, model: str, credentials: dict) -> None:
model_schema = self.get_model_schema(model, credentials)
- if model_schema and {ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL}.intersection(
- model_schema.features or []
- ):
+ if model_schema and {
+ ModelFeature.TOOL_CALL,
+ ModelFeature.MULTI_TOOL_CALL,
+ }.intersection(model_schema.features or []):
credentials["function_calling_type"] = "tool_call"
- def _convert_prompt_message_to_dict(self, message: PromptMessage, credentials: Optional[dict] = None) -> dict:
- """
- Convert PromptMessage to dict for OpenAI API format.
- For Kimi K2.5 / K2.6 thinking mode, extract content to reasoning_content field.
- """
- credentials = credentials or {}
- model_name = credentials.get("_current_model", "").lower()
+ def _handle_generate_response(
+ self,
+ model: str,
+ credentials: dict,
+ response: Response,
+ prompt_messages: list[PromptMessage],
+ ) -> LLMResult:
+ result = super()._handle_generate_response(
+ model,
+ credentials,
+ response,
+ prompt_messages,
+ )
+ if LLMMode.value_of(credentials["mode"]) is not LLMMode.CHAT:
+ return result
- # 判断是否为支持深度思考模式的模型（K2.5、K2.6 以及 K2-thinking 系列）
- is_thinking_model = any(x in model_name for x in ["k2.5", "k2.6", "k2-thinking"])
+ response_message = response.json()["choices"][0].get("message", {})
+ reasoning_content = response_message.get("reasoning_content")
+ if not isinstance(reasoning_content, str):
+ return result
- # Use base implementation for standard conversion
+ opaque_body = (
+ result.message.opaque_body
+ if isinstance(result.message.opaque_body, dict)
+ else {}
+ )
+ result.message.opaque_body = {
+ **opaque_body,
+ "reasoning_content": reasoning_content,
+ }
+ if reasoning_content:
+ result.message.content = (
+ f"{reasoning_content}{result.message.content or ''}"
+ )
+ return result
+
+ def _convert_prompt_message_to_dict(
+ self,
+ message: PromptMessage,
+ credentials: dict | None = None,
+ ) -> dict:
+ credentials = credentials or {}
+ model_name = credentials.get("_current_model", "").lower()
+ is_thinking_model = model_name in self._THINKING_MODELS
message_dict = super()._convert_prompt_message_to_dict(message, credentials)
+ if isinstance(message, UserPromptMessage) and isinstance(message.content, list):
+ for index, content in enumerate(message.content):
+ if isinstance(content, VideoPromptMessageContent):
+ message_dict["content"].insert(
+ index,
+ {
+ "type": "video_url",
+ "video_url": {"url": content.data},
+ },
+ )
+
if isinstance(message, AssistantPromptMessage):
content = message.content or ""
reasoning_content = None
+ if isinstance(message.opaque_body, dict):
+ raw_reasoning_content = message.opaque_body.get("reasoning_content")
+ if isinstance(raw_reasoning_content, str):
+ reasoning_content = raw_reasoning_content
+
if isinstance(content, str):
- # Extract content from text
- clean_content, extracted_reasoning = self._extract_reasoning_content(content)
- if extracted_reasoning:
+ clean_content, extracted_reasoning = self._extract_reasoning_content(
+ content
+ )
+ if reasoning_content is None and extracted_reasoning is not None:
reasoning_content = extracted_reasoning
+ if extracted_reasoning is not None:
content = clean_content
- # For Kimi K2.5 thinking mode, assistant messages MUST include reasoning_content
- # when thinking is enabled (even if empty string) - especially for tool call messages
if is_thinking_model or reasoning_content is not None:
message_dict["reasoning_content"] = reasoning_content or ""
- # Update content if it was cleaned
message_dict["content"] = content
return message_dict
- def _extract_reasoning_content(self, text: str) -> tuple[str, Optional[str]]:
+ def _extract_reasoning_content(self, text: str) -> tuple[str, str | None]:
if not text:
return text, None
-
+
matches = self._THINK_PATTERN.findall(text)
- reasoning_content = "\n".join(match.strip() for match in matches) if matches else None
-
- # Remove all blocks
- clean_text = self._THINK_PATTERN.sub("", text)
- clean_text = re.sub(r"\n\s*\n", "\n\n", clean_text).strip()
-
- return clean_text, reasoning_content
-
- def _wrap_thinking_by_reasoning_content(self, delta: dict, is_reasoning: bool) -> tuple[str, bool]:
- """
- If the reasoning response is from delta.get("reasoning_content"), we wrap
- it with HTML think tag.
-
- :param delta: delta dictionary from LLM streaming response
- :param is_reasoning: is reasoning
- :return: tuple of (processed_content, is_reasoning)
- """
+ reasoning_content = "\n\n".join(matches) if matches else None
+ return self._THINK_PATTERN.sub("", text), reasoning_content
+ def _wrap_thinking_by_reasoning_content(
+ self,
+ delta: dict,
+ is_reasoning: bool,
+ ) -> tuple[str, bool]:
content = delta.get("content") or ""
reasoning_content = delta.get("reasoning_content")
- output = content
- if reasoning_content:
+ if reasoning_content == "":
+ if content and is_reasoning:
+ return "" + content, False
+ return content, is_reasoning
+
+ if reasoning_content is not None:
+ output = reasoning_content
if not is_reasoning:
- output = "\n" + reasoning_content
+ output = "" + output
is_reasoning = True
- else:
- output = reasoning_content
- else:
- if is_reasoning:
+
+ if content:
+ output += "" + content
is_reasoning = False
- if not reasoning_content:
- output = "\n"
- if content:
- output += content
-
- return output, is_reasoning
+ return output, is_reasoning
+
+ if is_reasoning:
+ return "" + content, False
+ return content, False
diff --git a/models/moonshot/pyproject.toml b/models/moonshot/pyproject.toml
index 602d67021..e8ab44e2a 100644
--- a/models/moonshot/pyproject.toml
+++ b/models/moonshot/pyproject.toml
@@ -7,9 +7,16 @@ requires-python = ">=3.12"
# Managed with uv; refresh the lockfile with `uv lock`.
dependencies = [
- "dify_plugin>=0.9.0",
+ "dify_plugin>=0.9.1",
]
# uv run black . -C -l 100 && uv run ruff check --fix
[dependency-groups]
-dev = []
+dev = [
+ "pytest>=9.1.1",
+]
+
+[tool.pytest.ini_options]
+markers = [
+ "live: sends billable requests to the real Moonshot API",
+]
diff --git a/models/moonshot/tests/test_live.py b/models/moonshot/tests/test_live.py
new file mode 100644
index 000000000..f15227e58
--- /dev/null
+++ b/models/moonshot/tests/test_live.py
@@ -0,0 +1,113 @@
+import json
+import os
+import sys
+from pathlib import Path
+
+import pytest
+import yaml
+from dify_plugin.entities.model import AIModelEntity
+from dify_plugin.entities.model.message import UserPromptMessage
+from dotenv import dotenv_values
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from models.llm.llm import MoonshotLargeLanguageModel # noqa: E402
+
+MODEL = "kimi-k2.7-code"
+
+
+def _api_key() -> str:
+ if value := os.getenv("MOONSHOT_API_KEY", "").strip():
+ return value
+ value = dotenv_values(ROOT / ".env").get("MOONSHOT_API_KEY")
+ return value.strip() if value else ""
+
+
+API_KEY = _api_key()
+LIVE_ENABLED = os.getenv("RUN_MOONSHOT_LIVE") == "1"
+pytestmark = [
+ pytest.mark.live,
+ pytest.mark.skipif(
+ not (API_KEY and LIVE_ENABLED),
+ reason=(
+ "set RUN_MOONSHOT_LIVE=1 and provide MOONSHOT_API_KEY "
+ "in the environment or .env"
+ ),
+ ),
+]
+
+
+class _Credentials(dict[str, str]):
+ def __repr__(self) -> str:
+ return "Moonshot live test credentials"
+
+ __str__ = __repr__
+
+
+schema = AIModelEntity.model_validate(
+ yaml.safe_load((ROOT / "models" / "llm" / f"{MODEL}.yaml").read_text()),
+)
+llm = MoonshotLargeLanguageModel(model_schemas=[schema])
+
+
+def _invoke(*, stream: bool, parameters: dict) -> list:
+ return list(
+ llm.invoke(
+ model=MODEL,
+ credentials=_Credentials(api_key=API_KEY),
+ prompt_messages=[UserPromptMessage(content="Reply with OK.")],
+ model_parameters=parameters,
+ stream=stream,
+ ),
+ )
+
+
+def _text(chunks: list) -> str:
+ return "".join(
+ chunk.delta.message.content
+ for chunk in chunks
+ if isinstance(chunk.delta.message.content, str)
+ )
+
+
+def _assert_usage(chunks: list) -> None:
+ assert any(
+ chunk.delta.usage and chunk.delta.usage.total_tokens > 0 for chunk in chunks
+ )
+
+
+def test_blocking_completion() -> None:
+ chunks = _invoke(
+ stream=False,
+ parameters={"max_completion_tokens": 256},
+ )
+
+ content, _ = llm._extract_reasoning_content(_text(chunks))
+ assert content.strip()
+ _assert_usage(chunks)
+
+
+def test_streaming_structured_output() -> None:
+ output_schema = {
+ "name": "moonshot_live_test",
+ "strict": True,
+ "schema": {
+ "type": "object",
+ "properties": {"answer": {"type": "string", "enum": ["OK"]}},
+ "required": ["answer"],
+ "additionalProperties": False,
+ },
+ }
+ chunks = _invoke(
+ stream=True,
+ parameters={
+ "max_completion_tokens": 512,
+ "response_format": "json_schema",
+ "json_schema": json.dumps(output_schema),
+ },
+ )
+
+ content, _ = llm._extract_reasoning_content(_text(chunks))
+ assert json.loads(content) == {"answer": "OK"}
+ _assert_usage(chunks)
diff --git a/models/moonshot/tests/test_llm.py b/models/moonshot/tests/test_llm.py
new file mode 100644
index 000000000..662dbd7b3
--- /dev/null
+++ b/models/moonshot/tests/test_llm.py
@@ -0,0 +1,319 @@
+import json
+import sys
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import pytest
+import yaml
+from dify_plugin.entities.model import (
+ AIModelEntity,
+ ModelFeature,
+ ModelPropertyKey,
+)
+from dify_plugin.entities.model.message import (
+ AssistantPromptMessage,
+ TextPromptMessageContent,
+ UserPromptMessage,
+ VideoPromptMessageContent,
+)
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from models.llm.llm import MoonshotLargeLanguageModel # noqa: E402
+
+
+def _llm(schemas=None):
+ return MoonshotLargeLanguageModel(model_schemas=schemas or [])
+
+
+def test_non_stream_reasoning_round_trips_exactly():
+ reasoning_content = " must survive exactly\n"
+ response = Mock()
+ response.json.return_value = {
+ "id": "chatcmpl-1",
+ "model": "kimi-k3",
+ "choices": [
+ {
+ "finish_reason": "tool_calls",
+ "message": {
+ "role": "assistant",
+ "content": "",
+ "reasoning_content": reasoning_content,
+ "tool_calls": [
+ {
+ "id": "call_1",
+ "type": "function",
+ "function": {"name": "search", "arguments": "{}"},
+ }
+ ],
+ },
+ }
+ ],
+ "usage": {
+ "prompt_tokens": 1,
+ "completion_tokens": 2,
+ "total_tokens": 3,
+ },
+ }
+ credentials = {
+ "mode": "chat",
+ "function_calling_type": "tool_call",
+ }
+ llm = _llm()
+
+ result = llm._handle_generate_response(
+ "kimi-k3",
+ credentials,
+ response,
+ [UserPromptMessage(content="hi")],
+ )
+ stored_message = AssistantPromptMessage(
+ content=result.message.content,
+ tool_calls=result.message.tool_calls,
+ )
+
+ for message in (result.message, stored_message):
+ payload = llm._convert_prompt_message_to_dict(
+ message,
+ {
+ "_current_model": "kimi-k3",
+ "function_calling_type": "tool_call",
+ },
+ )
+ assert payload["content"] == ""
+ assert payload["reasoning_content"] == reasoning_content
+ assert payload["tool_calls"][0]["id"] == "call_1"
+
+
+def test_video_content_is_serialized_in_order():
+ llm = _llm()
+ [message] = llm._clean_messages(
+ [
+ UserPromptMessage(content="Watch "),
+ UserPromptMessage(
+ content=[
+ VideoPromptMessageContent(
+ format="base64",
+ base64_data="AAAA",
+ mime_type="video/mp4",
+ ),
+ ],
+ ),
+ UserPromptMessage(
+ content=[TextPromptMessageContent(data=" carefully.")],
+ ),
+ ],
+ )
+
+ assert llm._convert_prompt_message_to_dict(message) == {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Watch "},
+ {
+ "type": "video_url",
+ "video_url": {"url": "data:video/mp4;base64,AAAA"},
+ },
+ {"type": "text", "text": " carefully."},
+ ],
+ }
+
+
+def test_thinking_model_detection_is_exact():
+ message = AssistantPromptMessage(content="answer")
+ llm = _llm()
+
+ for model in (
+ "kimi-k2.5",
+ "kimi-k2.6",
+ "kimi-k2.7-code",
+ "kimi-k2.7-code-highspeed",
+ "kimi-k3",
+ "kimi-k2-thinking",
+ "kimi-k2-thinking-turbo",
+ ):
+ payload = llm._convert_prompt_message_to_dict(
+ message,
+ {"_current_model": model},
+ )
+ assert payload["reasoning_content"] == ""
+
+ for model in (
+ "grok3-custom",
+ "acme-k3-instant",
+ "kimi-k30",
+ "my-k2.7-proxy",
+ ):
+ payload = llm._convert_prompt_message_to_dict(
+ message,
+ {"_current_model": model},
+ )
+ assert "reasoning_content" not in payload
+
+
+def test_adjacent_assistant_reasoning_is_not_lost():
+ llm = _llm()
+ [message] = llm._clean_messages(
+ [
+ AssistantPromptMessage(
+ content="r1a",
+ opaque_body={"reasoning_content": "r1"},
+ ),
+ AssistantPromptMessage(
+ content="r2b",
+ opaque_body={"reasoning_content": "r2"},
+ ),
+ ],
+ )
+
+ assert llm._convert_prompt_message_to_dict(
+ message,
+ {"_current_model": "kimi-k3"},
+ ) == {
+ "role": "assistant",
+ "content": "a\n\nb",
+ "reasoning_content": "r1\n\nr2",
+ }
+
+
+def test_stream_reasoning_ignores_empty_chunks():
+ llm = _llm()
+
+ opening, is_reasoning = llm._wrap_thinking_by_reasoning_content(
+ {"reasoning_content": ""},
+ False,
+ )
+ assert opening == ""
+ assert is_reasoning is False
+
+ reasoning, is_reasoning = llm._wrap_thinking_by_reasoning_content(
+ {"reasoning_content": "reason"},
+ is_reasoning,
+ )
+ empty, is_reasoning = llm._wrap_thinking_by_reasoning_content(
+ {"reasoning_content": "", "tool_calls": [{}]},
+ is_reasoning,
+ )
+ answer, is_reasoning = llm._wrap_thinking_by_reasoning_content(
+ {"content": "answer"},
+ is_reasoning,
+ )
+
+ assert opening + reasoning + empty + answer == "reasonanswer"
+ assert is_reasoning is False
+ assert llm._wrap_thinking_by_reasoning_content(
+ {"reasoning_content": "", "content": "answer"},
+ False,
+ ) == ("answer", False)
+
+
+@pytest.mark.parametrize(
+ ("model", "context_size", "default_tokens", "max_tokens"),
+ [
+ ("kimi-k3", 1_048_576, 131_072, 1_048_576),
+ ("kimi-k2.7-code", 262_144, 32_768, 262_144),
+ ("kimi-k2.7-code-highspeed", 262_144, 32_768, 262_144),
+ ],
+)
+def test_new_model_schema_and_token_limits(
+ model: str,
+ context_size: int,
+ default_tokens: int,
+ max_tokens: int,
+):
+ raw = yaml.safe_load(
+ (ROOT / "models" / "llm" / f"{model}.yaml").read_text(),
+ )
+ schema = AIModelEntity.model_validate(raw)
+ rules = {rule.name: rule for rule in schema.parameter_rules}
+ token_rule = rules["max_completion_tokens"]
+
+ assert schema.model_properties[ModelPropertyKey.CONTEXT_SIZE] == context_size
+ assert {
+ ModelFeature.VIDEO,
+ ModelFeature.STRUCTURED_OUTPUT,
+ }.issubset(schema.features or [])
+ assert token_rule.use_template == "max_tokens"
+ assert token_rule.default == default_tokens
+ assert token_rule.max == max_tokens
+ assert "json_schema" in rules["response_format"].options
+ assert rules["json_schema"].use_template == "json_schema"
+
+ llm = _llm([schema])
+ assert llm._validate_and_filter_model_parameters(
+ model,
+ {"max_tokens": max_tokens},
+ {},
+ ) == {"max_completion_tokens": max_tokens}
+ with pytest.raises(ValueError, match="less than or equal"):
+ llm._validate_and_filter_model_parameters(
+ model,
+ {"max_completion_tokens": max_tokens + 1},
+ {},
+ )
+
+
+def test_json_schema_is_sent_to_api():
+ model = "kimi-k2.7-code"
+ raw = yaml.safe_load(
+ (ROOT / "models" / "llm" / f"{model}.yaml").read_text(),
+ )
+ llm = _llm([AIModelEntity.model_validate(raw)])
+ output_schema = {
+ "name": "moonshot_test",
+ "strict": True,
+ "schema": {
+ "type": "object",
+ "properties": {"answer": {"type": "string"}},
+ "required": ["answer"],
+ "additionalProperties": False,
+ },
+ }
+ response = Mock()
+ response.status_code = 200
+ response.encoding = "utf-8"
+ response.json.return_value = {
+ "id": "chatcmpl-1",
+ "model": model,
+ "choices": [
+ {
+ "finish_reason": "stop",
+ "message": {
+ "role": "assistant",
+ "content": '{"answer":"ok"}',
+ },
+ }
+ ],
+ "usage": {
+ "prompt_tokens": 1,
+ "completion_tokens": 2,
+ "total_tokens": 3,
+ },
+ }
+
+ with patch(
+ "dify_plugin.interfaces.model.openai_compatible.llm.requests.post",
+ return_value=response,
+ ) as post:
+ chunks = list(
+ llm.invoke(
+ model=model,
+ credentials={"api_key": "test"},
+ prompt_messages=[UserPromptMessage(content="Reply with JSON.")],
+ model_parameters={
+ "max_tokens": 128,
+ "response_format": "json_schema",
+ "json_schema": json.dumps(output_schema),
+ },
+ stream=False,
+ ),
+ )
+
+ request = post.call_args.kwargs["json"]
+ assert request["max_completion_tokens"] == 128
+ assert request["response_format"] == {
+ "type": "json_schema",
+ "json_schema": output_schema,
+ }
+ assert "json_schema" not in request
+ assert chunks[0].delta.message.content == '{"answer":"ok"}'
diff --git a/models/moonshot/uv.lock b/models/moonshot/uv.lock
index 552329be6..bf39a4425 100644
--- a/models/moonshot/uv.lock
+++ b/models/moonshot/uv.lock
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