mirror of
https://github.com/infiniflow/ragflow.git
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e23f63bd93
## Summary - Treat `max_tokens=0` as unset (`or 8192`) when building model context budgets, fixing agents that silently zeroed prompts when a vLLM model had `max_tokens: 0` in tenant config - Replace trailing same-role canvas history in `LLM._sys_prompt_and_msg` instead of skipping the current user prompt - Add `LLM.fit_messages()` validation after `message_fit_in` on agent paths so empty user content fails fast with a clear error instead of reaching vLLM Fixes #16411 ## Root cause Agent canvas workflow called `message_fit_in` with `int(max_length * 0.97)`. When `max_length` was `0`, both system and user content were trimmed to empty strings. The `[HISTORY STREAMLY]` log showing only `{"role":"user","content":""}` matches this. A secondary bug skipped appending the formatted user prompt when history ended with a `user` role message. ## Test plan - [x] Added `test/unit_test/agent/component/test_llm_prompt.py` for role-replace, validation, and zero-budget fitting - [x] Added `test_message_fit_in_zero_budget_preserves_non_empty_messages` in `test_generator_message_fit_in.py` - [ ] CI unit tests - [ ] Manual: agent canvas `begin → Retrieval → Agent → Message` with vLLM Qwen3; confirm user message reaches LLM Made with [Cursor](https://cursor.com) --------- Co-authored-by: Taranum Wasu <taranumwasu@Taranums-MacBook-Pro.local> Co-authored-by: Cursor <cursoragent@cursor.com>
522 lines
22 KiB
Python
522 lines
22 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import asyncio
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import json
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import logging
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import os
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import re
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from copy import deepcopy
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from typing import Any, AsyncGenerator
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import json_repair
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from functools import partial
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from common.constants import LLMType
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from api.db.services.dialog_service import _stream_with_think_delta
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from api.db.services.llm_service import LLMBundle
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from api.db.joint_services.tenant_model_service import get_model_config_from_provider_instance, get_model_type_by_name
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from agent.component.base import ComponentBase, ComponentParamBase
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from common.connection_utils import timeout
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from rag.prompts.generator import tool_call_summary, message_fit_in, citation_prompt, structured_output_prompt
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class LLMParam(ComponentParamBase):
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"""
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Define the LLM component parameters.
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"""
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def __init__(self):
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super().__init__()
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self.llm_id = ""
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self.sys_prompt = ""
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self.prompts = [{"role": "user", "content": "{sys.query}"}]
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self.max_tokens = 0
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self.temperature = 0
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self.top_p = 0
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self.presence_penalty = 0
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self.frequency_penalty = 0
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self.output_structure = None
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self.cite = True
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self.visual_files_var = None
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def check(self):
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self.check_decimal_float(float(self.temperature), "[Agent] Temperature")
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self.check_decimal_float(float(self.presence_penalty), "[Agent] Presence penalty")
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self.check_decimal_float(float(self.frequency_penalty), "[Agent] Frequency penalty")
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self.check_nonnegative_number(int(self.max_tokens), "[Agent] Max tokens")
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self.check_decimal_float(float(self.top_p), "[Agent] Top P")
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self.check_empty(self.llm_id, "[Agent] LLM")
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self.check_empty(self.prompts, "[Agent] User prompt")
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def gen_conf(self):
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conf = {}
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def get_attr(nm):
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try:
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return getattr(self, nm)
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except Exception:
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pass
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if int(self.max_tokens) > 0 and get_attr("maxTokensEnabled"):
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conf["max_tokens"] = int(self.max_tokens)
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if float(self.temperature) > 0 and get_attr("temperatureEnabled"):
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conf["temperature"] = float(self.temperature)
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if float(self.top_p) > 0 and get_attr("topPEnabled"):
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conf["top_p"] = float(self.top_p)
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if float(self.presence_penalty) > 0 and get_attr("presencePenaltyEnabled"):
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conf["presence_penalty"] = float(self.presence_penalty)
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if float(self.frequency_penalty) > 0 and get_attr("frequencyPenaltyEnabled"):
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conf["frequency_penalty"] = float(self.frequency_penalty)
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return conf
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class LLM(ComponentBase):
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component_name = "LLM"
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def __init__(self, canvas, component_id, param: ComponentParamBase):
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super().__init__(canvas, component_id, param)
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model_types = get_model_type_by_name(self._canvas.get_tenant_id(), self._param.llm_id)
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model_type = "chat" if "chat" in model_types else model_types[0]
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chat_model_config = get_model_config_from_provider_instance(self._canvas.get_tenant_id(), model_type, self._param.llm_id)
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self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), chat_model_config, max_retries=self._param.max_retries, retry_interval=self._param.delay_after_error)
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self.imgs = []
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def get_input_form(self) -> dict[str, dict]:
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res = {}
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for k, v in self.get_input_elements().items():
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res[k] = {"type": "line", "name": v["name"]}
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return res
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def get_input_elements(self) -> dict[str, Any]:
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res = self.get_input_elements_from_text(self._param.sys_prompt)
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if isinstance(self._param.prompts, str):
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self._param.prompts = [{"role": "user", "content": self._param.prompts}]
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for prompt in self._param.prompts:
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d = self.get_input_elements_from_text(prompt["content"])
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res.update(d)
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return res
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def set_debug_inputs(self, inputs: dict[str, dict]):
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self._param.debug_inputs = inputs
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def add2system_prompt(self, txt):
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self._param.sys_prompt += txt
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def _sys_prompt_and_msg(self, msg, args):
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if isinstance(self._param.prompts, str):
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self._param.prompts = [{"role": "user", "content": self._param.prompts}]
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history_size = len(msg)
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for p in self._param.prompts:
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formatted = deepcopy(p)
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formatted["content"] = self.string_format(formatted["content"], args)
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if len(msg) == history_size and msg and msg[-1]["role"] == formatted["role"]:
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msg[-1] = formatted
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else:
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msg.append(formatted)
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return msg, self.string_format(self._param.sys_prompt, args)
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@staticmethod
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def effective_context_length(max_length) -> int:
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return max_length or 8192
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@classmethod
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def context_fit_budget(cls, max_length) -> int:
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return int(cls.effective_context_length(max_length) * 0.97)
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@staticmethod
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def validate_fitted_messages(msg_fit: list[dict]) -> str | None:
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if len(msg_fit) < 2:
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return "**ERROR**: message_fit_in produced insufficient messages for LLM"
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last = msg_fit[-1]
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if last.get("role") != "user" or not str(last.get("content") or "").strip():
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return "**ERROR**: LLM user message is empty after prompt fitting; check model max_tokens context setting"
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return None
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@classmethod
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def fit_messages(cls, system_prompt: str, msg: list[dict], max_length) -> tuple[list[dict], str | None]:
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_, msg_fit = message_fit_in(
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[{"role": "system", "content": system_prompt}, *deepcopy(msg)],
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cls.context_fit_budget(max_length),
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)
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return msg_fit, cls.validate_fitted_messages(msg_fit)
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@staticmethod
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def _extract_data_images(value) -> list[str]:
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imgs = []
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def walk(v):
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if v is None:
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return
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if isinstance(v, str):
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v = v.strip()
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if v.startswith("data:image/"):
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imgs.append(v)
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return
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if isinstance(v, (list, tuple, set)):
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for item in v:
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walk(item)
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return
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if isinstance(v, dict):
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if "content" in v:
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walk(v.get("content"))
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else:
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for item in v.values():
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walk(item)
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walk(value)
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return imgs
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@staticmethod
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def _uniq_images(images: list[str]) -> list[str]:
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seen = set()
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uniq = []
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for img in images:
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if not isinstance(img, str):
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continue
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if not img.startswith("data:image/"):
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continue
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if img in seen:
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continue
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seen.add(img)
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uniq.append(img)
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return uniq
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@classmethod
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def _remove_data_images(cls, value):
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if value is None:
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return None
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if isinstance(value, str):
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return None if value.strip().startswith("data:image/") else value
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if isinstance(value, list):
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cleaned = []
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for item in value:
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v = cls._remove_data_images(item)
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if v is None:
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continue
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if isinstance(v, (list, tuple, set, dict)) and not v:
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continue
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cleaned.append(v)
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return cleaned
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if isinstance(value, tuple):
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cleaned = []
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for item in value:
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v = cls._remove_data_images(item)
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if v is None:
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continue
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if isinstance(v, (list, tuple, set, dict)) and not v:
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continue
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cleaned.append(v)
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return tuple(cleaned)
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if isinstance(value, set):
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cleaned = []
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for item in value:
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v = cls._remove_data_images(item)
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if v is None:
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continue
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if isinstance(v, (list, tuple, set, dict)) and not v:
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continue
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cleaned.append(v)
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return cleaned
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if isinstance(value, dict):
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if value.get("type") in {"image_url", "input_image", "image"} and cls._extract_data_images(value):
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return None
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cleaned = {}
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for k, item in value.items():
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v = cls._remove_data_images(item)
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if v is None:
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continue
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if isinstance(v, (list, tuple, set, dict)) and not v:
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continue
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cleaned[k] = v
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return cleaned
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return value
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def _collect_sys_files(self) -> tuple[list[str], list[str]]:
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files = self._canvas.globals.get("sys.files") or []
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if not files:
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logging.debug("[LLM] sys.files empty; skipping attachment injection")
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return [], []
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logging.info("[LLM] sys.files present: count=%d", len(files))
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explicit = "{sys.files}" in (self._param.sys_prompt or "")
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if not explicit and isinstance(self._param.prompts, list):
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for p in self._param.prompts:
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if isinstance(p, dict) and "{sys.files}" in (p.get("content") or ""):
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explicit = True
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break
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if explicit:
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logging.info("[LLM] prompt template references {sys.files}; skipping auto-injection (explicit=%s)", explicit)
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return [], []
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text_parts: list[str] = []
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image_data_uris: list[str] = []
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for f in files:
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if not isinstance(f, str):
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logging.debug("[LLM] skipping non-str sys.files entry: type=%s", type(f).__name__)
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continue
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if f.startswith("data:image/"):
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image_data_uris.append(f)
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else:
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text_parts.append(f)
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logging.info(
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"[LLM] sys.files split: text_parts=%d image_data_uris=%d (explicit=%s)",
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len(text_parts),
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len(image_data_uris),
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explicit,
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)
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return text_parts, image_data_uris
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def _prepare_prompt_variables(self):
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self.imgs = []
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if self._param.visual_files_var:
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visual_val = self._canvas.get_variable_value(self._param.visual_files_var)
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self.imgs.extend(self._extract_data_images(visual_val))
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args = {}
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vars = self.get_input_elements() if not self._param.debug_inputs else self._param.debug_inputs
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extracted_imgs = []
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for k, o in vars.items():
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raw_value = o["value"]
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extracted_imgs.extend(self._extract_data_images(raw_value))
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args[k] = self._remove_data_images(raw_value)
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if args[k] is None:
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args[k] = ""
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if not isinstance(args[k], str):
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try:
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args[k] = json.dumps(args[k], ensure_ascii=False)
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except Exception:
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args[k] = str(args[k])
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self.set_input_value(k, args[k])
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sys_file_texts, sys_file_imgs = self._collect_sys_files()
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prev_img_count = len(self.imgs) + len(extracted_imgs)
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self.imgs = self._uniq_images(self.imgs + extracted_imgs + sys_file_imgs)
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logging.debug(
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"[LLM] imgs rebuilt: total=%d sys_files_added=%d unique_dropped=%d",
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len(self.imgs),
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len(sys_file_imgs),
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max(0, prev_img_count + len(sys_file_imgs) - len(self.imgs)),
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)
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model_types = get_model_type_by_name(self._canvas.get_tenant_id(), self._param.llm_id)
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if self.imgs and LLMType.IMAGE2TEXT.value in model_types:
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model_type = LLMType.IMAGE2TEXT.value
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elif LLMType.CHAT.value in model_types:
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model_type = LLMType.CHAT.value
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else:
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model_type = model_types[0]
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model_config = get_model_config_from_provider_instance(self._canvas.get_tenant_id(), model_type, self._param.llm_id)
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if self.imgs:
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self.chat_mdl = LLMBundle(self._canvas.get_tenant_id(), model_config, max_retries=self._param.max_retries, retry_interval=self._param.delay_after_error)
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msg, sys_prompt = self._sys_prompt_and_msg(self._canvas.get_history(self._param.message_history_window_size)[:-1], args)
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if sys_file_texts:
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joined = "\n\n".join(sys_file_texts)
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merged_idx = -1
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for i in range(len(msg) - 1, -1, -1):
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if msg[i].get("role") == "user":
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msg[i]["content"] = (msg[i].get("content") or "") + "\n\n" + joined
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merged_idx = i
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break
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else:
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msg.append({"role": "user", "content": joined})
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merged_idx = len(msg) - 1
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logging.info(
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"[LLM] sys.files text merged into msg: parts=%d total_chars=%d msg_index=%d action=%s",
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len(sys_file_texts),
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len(joined),
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merged_idx,
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"merged_into_existing_user" if merged_idx < len(msg) - 1 or msg[merged_idx].get("content", "") != joined else "appended_new_user",
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)
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user_defined_prompt, sys_prompt = self._extract_prompts(sys_prompt)
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if self._param.cite and self._canvas.get_reference()["chunks"]:
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sys_prompt += citation_prompt(user_defined_prompt)
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return sys_prompt, msg, user_defined_prompt
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def _extract_prompts(self, sys_prompt):
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pts = {}
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for tag in ["TASK_ANALYSIS", "PLAN_GENERATION", "REFLECTION", "CONTEXT_SUMMARY", "CONTEXT_RANKING", "CITATION_GUIDELINES"]:
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r = re.search(rf"<{tag}>(.*?)</{tag}>", sys_prompt, flags=re.DOTALL | re.IGNORECASE)
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if not r:
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continue
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pts[tag.lower()] = r.group(1)
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sys_prompt = re.sub(rf"<{tag}>(.*?)</{tag}>", "", sys_prompt, flags=re.DOTALL | re.IGNORECASE)
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return pts, sys_prompt
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async def _generate_async(self, msg: list[dict], **kwargs) -> str:
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if not self.imgs:
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return await self.chat_mdl.async_chat(msg[0]["content"], msg[1:], self._param.gen_conf(), **kwargs)
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return await self.chat_mdl.async_chat(msg[0]["content"], msg[1:], self._param.gen_conf(), images=self.imgs, **kwargs)
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async def _generate_streamly(self, msg: list[dict], **kwargs) -> AsyncGenerator[str, None]:
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stream_kwargs = {"images": self.imgs} if self.imgs else {}
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stream_kwargs.update(kwargs)
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stream = self.chat_mdl.async_chat_streamly_delta(msg[0]["content"], msg[1:], self._param.gen_conf(), **stream_kwargs)
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async for _, value, _ in _stream_with_think_delta(stream, min_tokens=0):
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yield value
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async def _stream_output_async(self, prompt, msg):
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msg_fit, fit_error = self.fit_messages(prompt, msg, self.chat_mdl.max_length)
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if fit_error:
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logging.error("LLM streaming prompt fit error: %s", fit_error)
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if self.get_exception_default_value():
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fallback = self.get_exception_default_value()
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self.set_output("content", fallback)
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yield fallback
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else:
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self.set_output("_ERROR", fit_error)
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return
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answer = ""
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stream_kwargs = {"images": self.imgs} if self.imgs else {}
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extra_chat_kwargs = self._get_chat_template_kwargs()
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stream_kwargs.update(extra_chat_kwargs)
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stream = self.chat_mdl.async_chat_streamly_delta(msg_fit[0]["content"], msg_fit[1:], self._param.gen_conf(), **stream_kwargs)
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async for _, ans, _ in _stream_with_think_delta(stream, min_tokens=0):
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if self.check_if_canceled("LLM streaming"):
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return
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if ans.find("**ERROR**") >= 0:
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if self.get_exception_default_value():
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self.set_output("content", self.get_exception_default_value())
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yield self.get_exception_default_value()
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else:
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self.set_output("_ERROR", ans)
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return
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answer += ans
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yield ans
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self.set_output("content", answer)
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@timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10 * 60)))
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async def _invoke_async(self, **kwargs):
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if self.check_if_canceled("LLM processing"):
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return
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def clean_formated_answer(ans: str) -> str:
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ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
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ans = re.sub(r"^.*```json", "", ans, flags=re.DOTALL)
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return re.sub(r"```\n*$", "", ans, flags=re.DOTALL)
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prompt, msg, _ = self._prepare_prompt_variables()
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extra_chat_kwargs = self._get_chat_template_kwargs()
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error: str = ""
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output_structure = None
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try:
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output_structure = self._param.outputs["structured"]
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except Exception:
|
||
pass
|
||
if output_structure and isinstance(output_structure, dict) and output_structure.get("properties") and len(output_structure["properties"]) > 0:
|
||
schema = json.dumps(output_structure, ensure_ascii=False, indent=2)
|
||
prompt_with_schema = prompt + structured_output_prompt(schema)
|
||
for _ in range(self._param.max_retries + 1):
|
||
if self.check_if_canceled("LLM processing"):
|
||
return
|
||
|
||
msg_fit, fit_error = self.fit_messages(prompt_with_schema, msg, self.chat_mdl.max_length)
|
||
if fit_error:
|
||
logging.error("LLM structured prompt fit error: %s", fit_error)
|
||
self.set_output("_ERROR", fit_error)
|
||
return
|
||
error = ""
|
||
ans = await self._generate_async(msg_fit, **extra_chat_kwargs)
|
||
msg_fit.pop(0)
|
||
if ans.find("**ERROR**") >= 0:
|
||
logging.error(f"LLM response error: {ans}")
|
||
error = ans
|
||
continue
|
||
try:
|
||
self.set_output("structured", json_repair.loads(clean_formated_answer(ans)))
|
||
return
|
||
except Exception:
|
||
msg_fit.append({"role": "user", "content": "The answer can't not be parsed as JSON"})
|
||
error = "The answer can't not be parsed as JSON"
|
||
if error:
|
||
self.set_output("_ERROR", error)
|
||
return
|
||
|
||
downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
|
||
ex = self.exception_handler()
|
||
if any([self._canvas.get_component_obj(cid).component_name.lower() == "message" for cid in downstreams]) and not (ex and ex["goto"]):
|
||
self.set_output("content", partial(self._stream_output_async, prompt, deepcopy(msg)))
|
||
return
|
||
|
||
error = ""
|
||
for _ in range(self._param.max_retries + 1):
|
||
if self.check_if_canceled("LLM processing"):
|
||
return
|
||
|
||
msg_fit, fit_error = self.fit_messages(prompt, msg, self.chat_mdl.max_length)
|
||
if fit_error:
|
||
logging.error("LLM prompt fit error: %s", fit_error)
|
||
error = fit_error
|
||
break
|
||
error = ""
|
||
ans = await self._generate_async(msg_fit, **extra_chat_kwargs)
|
||
msg_fit.pop(0)
|
||
if ans.find("**ERROR**") >= 0:
|
||
logging.error(f"LLM response error: {ans}")
|
||
error = ans
|
||
continue
|
||
self.set_output("content", ans)
|
||
break
|
||
|
||
if error:
|
||
if self.get_exception_default_value():
|
||
self.set_output("content", self.get_exception_default_value())
|
||
else:
|
||
self.set_output("_ERROR", error)
|
||
|
||
@timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10 * 60)))
|
||
def _invoke(self, **kwargs):
|
||
return asyncio.run(self._invoke_async(**kwargs))
|
||
|
||
def _get_chat_template_kwargs(self) -> dict[str, Any]:
|
||
chat_template_kwargs = self._canvas.globals.get("sys.chat_template_kwargs")
|
||
if chat_template_kwargs is None:
|
||
return {}
|
||
|
||
# The API should pass this as a JSON object, but accept a JSON string for compatibility.
|
||
if isinstance(chat_template_kwargs, str):
|
||
try:
|
||
chat_template_kwargs = json_repair.loads(chat_template_kwargs)
|
||
except Exception:
|
||
logging.warning("Ignore invalid sys.chat_template_kwargs: expected JSON object or JSON string object.")
|
||
return {}
|
||
|
||
if not isinstance(chat_template_kwargs, dict):
|
||
logging.warning("Ignore invalid sys.chat_template_kwargs type: %s", type(chat_template_kwargs).__name__)
|
||
return {}
|
||
return {"chat_template_kwargs": chat_template_kwargs}
|
||
|
||
async def add_memory(self, user: str, assist: str, func_name: str, params: dict, results: str, user_defined_prompt: dict = {}):
|
||
summ = await tool_call_summary(self.chat_mdl, func_name, params, results, user_defined_prompt)
|
||
logging.info(f"[MEMORY]: {summ}")
|
||
self._canvas.add_memory(user, assist, summ)
|
||
|
||
def thoughts(self) -> str:
|
||
_, msg, _ = self._prepare_prompt_variables()
|
||
return "⌛Give me a moment—starting from: \n\n" + re.sub(r"(User's query:|[\\]+)", "", msg[-1]["content"], flags=re.DOTALL) + "\n\nI’ll figure out our best next move."
|