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https://github.com/kvcache-ai/ktransformers.git
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更改token注入逻辑，减少token注入量，防止遗忘
Update chat.py Update chat.py Update chat.py
This commit is contained in:
Creeper-MZ
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1 changed files with 92 additions and 109 deletions
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@ -72,7 +72,6 @@ def getTools(buffer):
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extracted_tools = []
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working_buffer = buffer
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# Iterate over all function calls
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while tool_call_begin_marker in working_buffer and tool_call_end_marker in working_buffer:
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# Find a complete function call
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@ -115,36 +114,56 @@ def getTools(buffer):
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logger.info(f"Get Function: {function_name}")
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else:
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logger.warning(f"Unable to get function，function_name: {function_name}")
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logger.warning(f"Unable to get function, function_name: {function_name}")
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logger.info(f"Total {len(extracted_tools)} Functions")
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return extracted_tools
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def get_tool_instructions():
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"""Return concise tool calling instructions in English"""
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return """When you need real-time information or specialized operations, use function calls with this format:
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<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>function_name
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```json
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{"param1": "value1", "param2": "value2"}
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```<｜tool▁call▁end｜><｜tool▁calls▁end｜>
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Only use functions when needed. Ensure proper JSON formatting with appropriate parameters."""
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@router.post('/chat/completions', tags=['openai'])
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async def chat_completion(request: Request, create: ChatCompletionCreate):
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id = str(uuid4().hex)
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# 1. Use system prompts to let models know how to use tools
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# Process messages with tool functionality if needed
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enhanced_messages = list(create.messages)
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# If there is a tool and the first message is system, add instructions on how to use the tool in the system tip
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if create.tools and len(create.tools) > 0 and (enhanced_messages[0].role == Role.system or enhanced_messages[0].role == Role.user):
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tool_instructions = "你可以使用function_call，函数调用功能，目前，你可以使用以下工具\n\n"
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# Check if tools are present
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has_tools = create.tools and len(create.tools) > 0
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if has_tools:
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# Find the most recent user message to append tool information
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latest_user_msg_idx = -1
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for i in range(len(enhanced_messages) - 1, -1, -1):
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if enhanced_messages[i].role == Role.user:
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latest_user_msg_idx = i
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break
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# Build the tool descriptions
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tools_description = ""
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for tool in create.tools:
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tool_instructions += f" \"function\":{{\"name\" : {tool.function.name},\"description\" : {tool.function.description} , \"parameters\" : {tool.function.parameters}}}\n"
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tools_description += f"Function: {tool.function.name}\nDescription: {tool.function.description}\nParameters: {tool.function.parameters}\n\n"
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# Modify tool usage guidelines to encourage JSON output
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tool_instructions += "name为函数名称，description为函数功能的描述，parameters中含有函数需要使用的参数和参数的描述, 其中required为必要参数\n"
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tool_instructions += "工具仅在用户明确提出，或者你认为需要调用工具的时候调用，注意，当需要高度实时性的信息比如时间或者最近的事情等，优先调用工具来获取！。当确实调用工具的关键信息时，你可以先向用户索取关键信息再调用工具\n"
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tool_instructions += "\n当你需要使用工具时，请以下列格式输出，格式为：\n"
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tool_instructions += '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>name\n```json {"参数名": "参数值","参数名2": "参数值2"...}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>\n'
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tool_instructions += '示例: \n<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>the_functnion_name_will_be_called\n```json {"arg1": "value1","arg2": "value2"}\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>\n'
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tool_instructions += "这样可以调用名为\"the_functnion_name_will_be_called\",并将value1和value2传入参数arg1,arg2\n"
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tool_instructions += "不要尝试解释你在做什么，直接输出工具函数调用即可。确保函数调用语句格式正确且完整。"
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# If first message is system, add concise tool instructions
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if enhanced_messages[0].role == Role.system:
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if "function calls" not in enhanced_messages[0].content.lower():
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enhanced_messages[0].content += "\n\n" + get_tool_instructions()
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enhanced_messages[0].content = enhanced_messages[0].content + "\n\n" + tool_instructions
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# For the latest user message, append tool information
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if latest_user_msg_idx >= 0:
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# Add tool descriptions to the latest user message
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enhanced_messages[latest_user_msg_idx].content += f"\n\nAvailable tools:\n{tools_description}"
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# Requests processed
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# Process request
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interface: BackendInterfaceBase = get_interface()
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input_message = [json.loads(m.model_dump_json()) for m in enhanced_messages]
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@ -162,19 +181,20 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
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system_fingerprint=f"fp_{uuid4().hex[:12]}",
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)
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# Collect the full output of the model, but specialize in processing tool calls
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# Collect the full output of the model
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full_content = ""
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buffer = "" # Used to temporarily store the current block of text
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tool_call_mode = False # Mark if a tool call is being processed
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tool_calls = [] # Store all detected tool calls
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# Customize model special tokens
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# Tool call markers
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tool_calls_begin_marker = "<｜tool▁calls▁begin｜>"
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tool_call_begin_marker = "<｜tool▁call▁begin｜>"
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tool_sep_marker = "<｜tool▁sep｜>"
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tool_call_end_marker = "<｜tool▁call▁end｜>"
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tool_calls_end_marker = "<｜tool▁calls▁end｜>"
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# Use check_client_connected for early stopping
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async for res in interface.inference(input_message, id, create.temperature, create.top_p):
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if isinstance(res, RawUsage):
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# Final return on utilization
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@ -225,8 +245,7 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
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# If the tool call end marker is found
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if tool_calls_end_marker in buffer:
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try:
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# Parsing Calling Text Extraction Tool Calling Information
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# Parse and extract tool calling information
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tool_calls = getTools(buffer)
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if len(tool_calls):
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# reset state
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@ -370,48 +389,12 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
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# If the tool call end marker is found
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if tool_calls_end_marker in buffer:
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try:
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# Parsing Calling Text Extraction Tool Calling Information
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full_tool_call = buffer
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# Extract function name
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function_name_start = full_tool_call.find(tool_sep_marker) + len(tool_sep_marker)
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function_name_end = full_tool_call.find("\n", function_name_start)
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function_name = full_tool_call[function_name_start:function_name_end].strip()
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# Extract JSON Parameters - Extracts the content between ```json and ```.
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json_pattern = r'```json\s*(.*?)\s*```'
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json_match = re.search(json_pattern, full_tool_call, re.DOTALL)
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if json_match:
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arguments_str = json_match.group(1).strip()
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# Generate tool call IDs
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tool_call_id = f"call_{uuid4().hex[:24]}"
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# Add to tool call list
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tool_calls.append({
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"id": tool_call_id,
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"index": 0,
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"type": "function",
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"function": {
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"name": function_name,
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"arguments": arguments_str
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}
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})
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# If the tool call is successfully parsed, set the reason for completion
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# Extract tool calls
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tool_calls = getTools(buffer)
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if tool_calls:
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finish_reason = "tool_calls"
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# reset state
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tool_call_mode = False
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buffer = ""
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else:
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# JSON extraction failed, probably incomplete formatting
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logger.warning("Failed to extract JSON from tool call")
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tool_call_mode = False
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buffer = ""
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except Exception as e:
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logger.error(f"Error processing tool call: {e}")
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# Reset state
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tool_call_mode = False
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buffer = ""
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@ -430,7 +413,7 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
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},
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"finish_reason": finish_reason or "stop"
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}],
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"usage": usage.__dict__,
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"usage": usage.__dict__ if 'usage' in locals() else None,
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"system_fingerprint": f"fp_{uuid4().hex[:12]}"
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}
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