add ENABLE_SELF_KNOWLEDGE
This commit is contained in:
朱潮
parent
dfc1c003c6
commit
f13f208900
@ -24,6 +24,7 @@ class AgentConfig:
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mcp_settings: Optional[List[Dict]] = field(default_factory=list)
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generate_cfg: Optional[Dict] = None
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enable_thinking: bool = False
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enable_self_knowledge: bool = False
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# 上下文参数
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project_dir: Optional[str] = None
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@ -64,6 +65,7 @@ class AgentConfig:
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'mcp_settings': self.mcp_settings,
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'generate_cfg': self.generate_cfg,
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'enable_thinking': self.enable_thinking,
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'enable_self_knowledge': self.enable_self_knowledge,
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'project_dir': self.project_dir,
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'user_identifier': self.user_identifier,
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'session_id': self.session_id,
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@ -122,6 +124,7 @@ class AgentConfig:
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user_identifier=request.user_identifier,
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session_id=request.session_id,
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enable_thinking=request.enable_thinking,
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enable_self_knowledge=request.enable_self_knowledge,
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project_dir=project_dir,
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stream=request.stream,
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tool_response=request.tool_response,
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@ -179,6 +182,7 @@ class AgentConfig:
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enable_thinking = bot_config.get("enable_thinking", False)
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enable_memori = bot_config.get("enable_memory", False)
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enable_self_knowledge = bot_config.get("enable_self_knowledge", False)
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config = cls(
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bot_id=request.bot_id,
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@ -191,6 +195,7 @@ class AgentConfig:
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user_identifier=request.user_identifier,
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session_id=request.session_id,
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enable_thinking=enable_thinking,
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enable_self_knowledge=enable_self_knowledge,
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project_dir=project_dir,
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stream=request.stream,
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tool_response=request.tool_response,
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@ -246,6 +251,7 @@ class AgentConfig:
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'language': self.language,
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'generate_cfg': self.generate_cfg,
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'enable_thinking': self.enable_thinking,
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'enable_self_knowledge': self.enable_self_knowledge,
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'user_identifier': self.user_identifier,
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'session_id': self.session_id,
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'dataset_ids': self.dataset_ids, # 添加dataset_ids到缓存键生成
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@ -306,6 +306,7 @@ async def init_agent(config: AgentConfig):
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"ASSISTANT_ID": config.bot_id,
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"USER_IDENTIFIER": config.user_identifier,
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"TRACE_ID": config.trace_id,
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"ENABLE_SELF_KNOWLEDGE": str(config.enable_self_knowledge).lower(),
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**(config.shell_env or {}),
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}
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)
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@ -214,6 +214,7 @@ async def _execute_command(skill_path: str, command: str, hook_type: str, config
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env['ASSISTANT_ID'] = str(getattr(config, 'bot_id', ''))
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env['USER_IDENTIFIER'] = str(getattr(config, 'user_identifier', ''))
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env['TRACE_ID'] = str(getattr(config, 'trace_id', ''))
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env['ENABLE_SELF_KNOWLEDGE'] = str(getattr(config, 'enable_self_knowledge', False)).lower()
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env['SESSION_ID'] = str(getattr(config, 'session_id', ''))
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env['LANGUAGE'] = str(getattr(config, 'language', ''))
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env['HOOK_TYPE'] = hook_type
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@ -512,7 +512,7 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
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project_dir = create_project_directory(request.dataset_ids, bot_id, request.skills)
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'n', 'shell_env', 'max_tokens'}
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exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'enable_self_knowledge', 'n', 'shell_env', 'max_tokens'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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logger.info("chat_completions generate_cfg_keys=%s model=%s", list(generate_cfg.keys()), request.model)
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# 处理消息
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@ -563,7 +563,7 @@ async def chat_warmup_v1(request: ChatRequest, authorization: Optional[str] = He
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project_dir = create_project_directory(request.dataset_ids, bot_id, request.skills)
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'n', 'shell_env'}
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exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'enable_self_knowledge', 'n', 'shell_env'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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# 创建一个空的消息列表用于预热（实际消息不会在warmup中处理）
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@ -666,6 +666,7 @@ async def chat_warmup_v2(request: ChatRequestV2, authorization: Optional[str] =
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# 处理消息
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messages = process_messages(empty_messages, request.language or "ja")
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exclude_fields = {'messages', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings', 'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'enable_self_knowledge', 'n', 'model', 'model_server', 'api_key', 'shell_env', 'max_tokens'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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logger.info("chat_warmup_v2 generate_cfg_keys=%s requested_model=%s", list(generate_cfg.keys()), request.model)
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# 从请求中提取 model/model_server/api_key，优先级高于 bot_config（排除 "whatever" 和空值）
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@ -773,7 +774,7 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
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# 处理消息
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messages = process_messages(request.messages, request.language)
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# 收集额外参数作为 generate_cfg
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exclude_fields = {'messages', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings', 'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'n', 'model', 'model_server', 'api_key', 'shell_env', 'max_tokens'}
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exclude_fields = {'messages', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings', 'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking', 'skills', 'enable_memory', 'enable_self_knowledge', 'n', 'model', 'model_server', 'api_key', 'shell_env', 'max_tokens'}
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generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
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logger.info("chat_completions_v2 generate_cfg_keys=%s requested_model=%s", list(generate_cfg.keys()), request.model)
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# 从请求中提取 model/model_server/api_key，优先级高于 bot_config（排除 "whatever" 和空值）
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@ -1,55 +0,0 @@
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# Retrieval Policy
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### 1. Retrieval Order and Tool Selection
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- Follow this section for source choice, tool choice, query rewrite, `top_k`, fallback, result handling, and citations.
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- Use this default retrieval order and execute it sequentially: skill-enabled knowledge retrieval tools > `rag_retrieve` / `table_rag_retrieve`.
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- Do NOT answer from model knowledge first.
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- Do NOT bypass the retrieval flow and inspect local filesystem documents on your own.
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- Do NOT use local filesystem retrieval as a fallback knowledge source.
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- Local filesystem documents are not a recommended retrieval source here because file formats are inconsistent and have not been normalized or parsed for reliable knowledge lookup.
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- Knowledge must be retrieved through the supported knowledge tools only: skill-enabled retrieval scripts, `table_rag_retrieve`, and `rag_retrieve`.
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- When a suitable skill-enabled knowledge retrieval tool is available, use it first.
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- If no suitable skill-enabled retrieval tool is available, or if its result is insufficient, continue with `rag_retrieve` or `table_rag_retrieve`.
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- Use `table_rag_retrieve` first for values, prices, quantities, inventory, specifications, rankings, comparisons, summaries, extraction, lists, tables, name lookup, historical coverage, mixed questions, and unclear cases.
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- Use `rag_retrieve` first only for clearly pure concept, definition, workflow, policy, or explanation questions without structured data needs.
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- After each retrieval step, evaluate sufficiency before moving to the next source. Do NOT run these retrieval sources in parallel.
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### 2. Query Preparation
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- Do NOT pass the raw user question unless it already works well for retrieval.
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- Rewrite for recall: extract entity, time scope, attributes, and intent.
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- Add useful variants: synonyms, aliases, abbreviations, related titles, historical names, and category terms.
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- Expand list-style, extraction, overview, historical, roster, timeline, and archive queries more aggressively.
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- Preserve meaning. Do NOT introduce unrelated topics.
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### 3. Retrieval Breadth (`top_k`)
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- Apply `top_k` only to `rag_retrieve`. Use the smallest sufficient value, then expand only if coverage is insufficient.
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- Use `30` for simple fact lookup.
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- Use `50` for moderate synthesis, comparison, summarization, or disambiguation.
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- Use `100` for broad recall, such as comprehensive analysis, scattered knowledge, multiple entities or periods, or list / catalog / timeline / roster / overview requests.
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- Raise `top_k` when keyword branches are many or results are too few, repetitive, incomplete, sparse, or too narrow.
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- Use this expansion order: `30 -> 50 -> 100`. If unsure, use `100`.
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### 4. Result Evaluation
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- Treat results as insufficient if they are empty, start with `Error:`, say `no excel files found`, are off-topic, miss the core entity or scope, or provide no usable evidence.
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- Also treat results as insufficient when they cover only part of the request, or when full-list, historical, comparison, or mixed data + explanation requests return only partial or truncated coverage.
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### 5. Fallback and Sequential Retry
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- If the first retrieval result is insufficient, call the next supported retrieval source in the default order before replying.
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- `table_rag_retrieve` now performs an internal fallback to `rag_retrieve` when it returns `no excel files found`, but this does NOT change the higher-level retrieval order.
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- If `table_rag_retrieve` is insufficient or empty, continue with `rag_retrieve`.
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- If `rag_retrieve` is insufficient or empty, continue with `table_rag_retrieve`.
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- Say no relevant information was found only after all applicable skill-enabled retrieval tools, `rag_retrieve`, and `table_rag_retrieve` have been tried and still do not provide enough evidence.
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- Do NOT reply that no relevant information was found before the supported knowledge retrieval flow has been exhausted.
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### 6. Table RAG Result Handling
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- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` content in `table_rag_retrieve` results.
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- If results are truncated, explicitly tell the user total matches (`N+M`), displayed count (`N`), and omitted count (`M`).
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- Cite data sources using filenames from `file_ref_table`.
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### 7. Citation Requirements for Retrieved Knowledge
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- When using knowledge from `rag_retrieve` or `table_rag_retrieve`, you MUST generate `<CITATION ... />` tags.
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- Follow the citation format returned by each tool.
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- Place citations immediately after the paragraph or bullet list that uses the knowledge.
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- Do NOT collect citations at the end.
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- Use 1-2 citations per paragraph or bullet list when possible.
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- If learned knowledge is used, include at least 1 `<CITATION ... />`.
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@ -3,16 +3,24 @@
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PreMemoryPrompt Hook - 用户上下文加载器示例
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在记忆提取提示词（FACT_RETRIEVAL_PROMPT）加载时执行，
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读取同目录下的 memory_prompt.md 作为自定义记忆提取提示词模板。
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根据环境变量决定是否启用禁止使用模型自身知识的 retrieval policy。
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"""
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import os
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import sys
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from pathlib import Path
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def main():
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prompt_file = Path(__file__).parent / "retrieval-policy.md"
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if prompt_file.exists():
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print(prompt_file.read_text(encoding="utf-8"))
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enable_self_knowledge = (
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os.getenv("ENABLE_SELF_KNOWLEDGE", "false").lower() == "true"
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)
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policy_name = (
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"retrieval-policy.md"
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if enable_self_knowledge
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else "retrieval-policy-forbidden-self-knowledge.md"
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)
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prompt_file = Path(__file__).parent / policy_name
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print(prompt_file.read_text(encoding="utf-8"))
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return 0
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@ -0,0 +1,113 @@
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# Retrieval Policy (Forbidden Self-Knowledge)
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## 0. Task Classification
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Classify the request before acting:
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- **Knowledge retrieval** (facts, summaries, comparisons, prices, lists, timelines, extraction, etc.): follow this policy strictly.
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- **Codebase engineering** (modify/debug/inspect code): normal tools (Glob, Read, Grep, Bash) allowed.
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- **Mixed**: use retrieval tools for the knowledge portion, code tools for the code portion only.
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- **Uncertain**: default to knowledge retrieval.
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## 1. Critical Enforcement
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For knowledge retrieval tasks, **this policy overrides all generic assistant behavior**.
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- **Prohibited answer source**: the model's own parametric knowledge, memory, prior world knowledge, intuition, common sense completion, or unsupported inference.
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- **Prohibited tools**: `Glob`, `Read`, `LS`, Bash (`ls`, `find`, `cat`, `head`, `tail`, `grep`, etc.) — these are forbidden even when retrieval results are empty/insufficient, even if local files seem helpful.
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- **Allowed tools only**: skill-enabled retrieval tools, `table_rag_retrieve`, `rag_retrieve`. No other source for factual answering.
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- Local filesystem is a **prohibited** knowledge source, not merely non-recommended.
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- Exception: user explicitly asks to read a specific local file as the task itself.
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- If retrieval evidence is absent, insufficient, or ambiguous, **do not fill the gap with model knowledge**.
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## 2. Core Answering Rule
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For any knowledge retrieval task:
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- Answer **only** from retrieved evidence.
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- Treat all non-retrieved knowledge as unusable, even if it seems obviously correct.
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- Do NOT answer from memory first.
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- Do NOT "helpfully complete" missing facts.
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- Do NOT convert weak hints into confident statements.
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- If evidence does not support a claim, omit the claim.
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## 3. Retrieval Order and Tool Selection
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Execute **sequentially, one at a time**. Do NOT run in parallel. Do NOT probe filesystem first.
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1. **Skill-enabled retrieval tools** (use first when available)
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2. **`table_rag_retrieve`** or **`rag_retrieve`**:
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- Prefer `table_rag_retrieve` for: values, prices, quantities, specs, rankings, comparisons, lists, tables, name lookup, historical coverage, mixed/unclear cases.
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- Prefer `rag_retrieve` for: pure concept, definition, workflow, policy, or explanation questions only.
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- After each step, evaluate sufficiency before proceeding.
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- Retrieval must happen **before** any factual answer generation.
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## 4. Query Preparation
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- Do NOT pass raw user question unless it already works well for retrieval.
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- Rewrite for recall: extract entity, time scope, attributes, intent. Add synonyms, aliases, abbreviations, historical names, category terms.
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- Expand list/extraction/overview/timeline queries more aggressively. Preserve meaning.
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## 5. Retrieval Breadth (`top_k`)
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- Apply `top_k` only to `rag_retrieve`. Use smallest sufficient value, expand if insufficient.
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- `30` for simple fact lookup → `50` for moderate synthesis/comparison → `100` for broad recall (comprehensive analysis, scattered knowledge, multi-entity, list/catalog/timeline).
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- Expansion order: `30 → 50 → 100`. If unsure, use `100`.
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## 6. Result Evaluation
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Treat as insufficient if: empty, `Error:`, `no excel files found`, off-topic, missing core entity/scope, no usable evidence, partial coverage, truncated results, or claims required by the answer are not explicitly supported.
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## 7. Fallback and Sequential Retry
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On insufficient results, follow this sequence:
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1. Rewrite query, retry same tool (once)
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2. Switch to next retrieval source in default order
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3. For `rag_retrieve`, expand `top_k`: `30 → 50 → 100`
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4. `table_rag_retrieve` insufficient → try `rag_retrieve`; `rag_retrieve` insufficient → try `table_rag_retrieve`
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- `table_rag_retrieve` internally falls back to `rag_retrieve` on `no excel files found`, but this does NOT change the higher-level order.
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- Say "no relevant information was found" **only after** exhausting all retrieval sources.
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- Do NOT switch to local filesystem inspection at any point.
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- Do NOT switch to model self-knowledge at any point.
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## 8. Handling Missing or Partial Evidence
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- If some parts are supported and some are not, answer only the supported parts.
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- Clearly mark unsupported parts as unavailable rather than guessing.
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- Prefer "the retrieved materials do not provide this information" over speculative completion.
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- When user asks for a definitive answer but evidence is incomplete, state the limitation directly.
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## 9. Table RAG Result Handling
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- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` in results.
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- If truncated: tell user total (`N+M`), displayed (`N`), omitted (`M`).
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- Cite sources using filenames from `file_ref_table`.
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## 10. Image Handling
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- The content returned by the `rag_retrieve` tool may include images.
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- Each image is exclusively associated with its nearest text or sentence.
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- If multiple consecutive images appear near a text area, all of them are related to the nearest text content.
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- Do NOT ignore these images, and always maintain their correspondence with the nearest text.
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- Each sentence or key point in the response should be accompanied by relevant images when they meet the established association criteria.
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- Avoid placing all images at the end of the response.
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## 11. Citation Requirements
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- MUST generate `<CITATION ... />` tags when using retrieval results.
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- Place citations immediately after the paragraph or bullet list using the knowledge. Do NOT collect at end.
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- 1-2 citations per paragraph/bullet. At least 1 citation when using retrieved knowledge.
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- Do NOT cite claims that were not supported by retrieval.
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## 12. Pre-Reply Self-Check
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Before replying to a knowledge retrieval task, verify:
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- Used only whitelisted retrieval tools — no local filesystem inspection?
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- Did retrieval happen before any factual answer drafting?
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- Did every factual claim come from retrieved evidence rather than model knowledge?
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- Exhausted retrieval flow before concluding "not found"?
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- Citations placed immediately after each relevant paragraph?
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If any answer is "no", correct the process first.
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@ -3,16 +3,24 @@
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PreMemoryPrompt Hook - 用户上下文加载器示例
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在记忆提取提示词（FACT_RETRIEVAL_PROMPT）加载时执行，
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读取同目录下的 memory_prompt.md 作为自定义记忆提取提示词模板。
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根据环境变量决定是否启用禁止使用模型自身知识的 retrieval policy。
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"""
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import os
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import sys
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from pathlib import Path
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def main():
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prompt_file = Path(__file__).parent / "retrieval-policy.md"
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if prompt_file.exists():
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print(prompt_file.read_text(encoding="utf-8"))
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enable_self_knowledge = (
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os.getenv("ENABLE_SELF_KNOWLEDGE", "false").lower() == "true"
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)
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policy_name = (
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"retrieval-policy.md"
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if enable_self_knowledge
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else "retrieval-policy-forbidden-self-knowledge.md"
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)
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prompt_file = Path(__file__).parent / policy_name
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print(prompt_file.read_text(encoding="utf-8"))
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return 0
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@ -0,0 +1,113 @@
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# Retrieval Policy (Forbidden Self-Knowledge)
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## 0. Task Classification
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Classify the request before acting:
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- **Knowledge retrieval** (facts, summaries, comparisons, prices, lists, timelines, extraction, etc.): follow this policy strictly.
|
||||
- **Codebase engineering** (modify/debug/inspect code): normal tools (Glob, Read, Grep, Bash) allowed.
|
||||
- **Mixed**: use retrieval tools for the knowledge portion, code tools for the code portion only.
|
||||
- **Uncertain**: default to knowledge retrieval.
|
||||
|
||||
## 1. Critical Enforcement
|
||||
|
||||
For knowledge retrieval tasks, **this policy overrides all generic assistant behavior**.
|
||||
|
||||
- **Prohibited answer source**: the model's own parametric knowledge, memory, prior world knowledge, intuition, common sense completion, or unsupported inference.
|
||||
- **Prohibited tools**: `Glob`, `Read`, `LS`, Bash (`ls`, `find`, `cat`, `head`, `tail`, `grep`, etc.) — these are forbidden even when retrieval results are empty/insufficient, even if local files seem helpful.
|
||||
- **Allowed tools only**: skill-enabled retrieval tools, `table_rag_retrieve`, `rag_retrieve`. No other source for factual answering.
|
||||
- Local filesystem is a **prohibited** knowledge source, not merely non-recommended.
|
||||
- Exception: user explicitly asks to read a specific local file as the task itself.
|
||||
- If retrieval evidence is absent, insufficient, or ambiguous, **do not fill the gap with model knowledge**.
|
||||
|
||||
## 2. Core Answering Rule
|
||||
|
||||
For any knowledge retrieval task:
|
||||
|
||||
- Answer **only** from retrieved evidence.
|
||||
- Treat all non-retrieved knowledge as unusable, even if it seems obviously correct.
|
||||
- Do NOT answer from memory first.
|
||||
- Do NOT "helpfully complete" missing facts.
|
||||
- Do NOT convert weak hints into confident statements.
|
||||
- If evidence does not support a claim, omit the claim.
|
||||
|
||||
## 3. Retrieval Order and Tool Selection
|
||||
|
||||
Execute **sequentially, one at a time**. Do NOT run in parallel. Do NOT probe filesystem first.
|
||||
|
||||
1. **Skill-enabled retrieval tools** (use first when available)
|
||||
2. **`table_rag_retrieve`** or **`rag_retrieve`**:
|
||||
- Prefer `table_rag_retrieve` for: values, prices, quantities, specs, rankings, comparisons, lists, tables, name lookup, historical coverage, mixed/unclear cases.
|
||||
- Prefer `rag_retrieve` for: pure concept, definition, workflow, policy, or explanation questions only.
|
||||
|
||||
- After each step, evaluate sufficiency before proceeding.
|
||||
- Retrieval must happen **before** any factual answer generation.
|
||||
|
||||
## 4. Query Preparation
|
||||
|
||||
- Do NOT pass raw user question unless it already works well for retrieval.
|
||||
- Rewrite for recall: extract entity, time scope, attributes, intent. Add synonyms, aliases, abbreviations, historical names, category terms.
|
||||
- Expand list/extraction/overview/timeline queries more aggressively. Preserve meaning.
|
||||
|
||||
## 5. Retrieval Breadth (`top_k`)
|
||||
|
||||
- Apply `top_k` only to `rag_retrieve`. Use smallest sufficient value, expand if insufficient.
|
||||
- `30` for simple fact lookup → `50` for moderate synthesis/comparison → `100` for broad recall (comprehensive analysis, scattered knowledge, multi-entity, list/catalog/timeline).
|
||||
- Expansion order: `30 → 50 → 100`. If unsure, use `100`.
|
||||
|
||||
## 6. Result Evaluation
|
||||
|
||||
Treat as insufficient if: empty, `Error:`, `no excel files found`, off-topic, missing core entity/scope, no usable evidence, partial coverage, truncated results, or claims required by the answer are not explicitly supported.
|
||||
|
||||
## 7. Fallback and Sequential Retry
|
||||
|
||||
On insufficient results, follow this sequence:
|
||||
|
||||
1. Rewrite query, retry same tool (once)
|
||||
2. Switch to next retrieval source in default order
|
||||
3. For `rag_retrieve`, expand `top_k`: `30 → 50 → 100`
|
||||
4. `table_rag_retrieve` insufficient → try `rag_retrieve`; `rag_retrieve` insufficient → try `table_rag_retrieve`
|
||||
|
||||
- `table_rag_retrieve` internally falls back to `rag_retrieve` on `no excel files found`, but this does NOT change the higher-level order.
|
||||
- Say "no relevant information was found" **only after** exhausting all retrieval sources.
|
||||
- Do NOT switch to local filesystem inspection at any point.
|
||||
- Do NOT switch to model self-knowledge at any point.
|
||||
|
||||
## 8. Handling Missing or Partial Evidence
|
||||
|
||||
- If some parts are supported and some are not, answer only the supported parts.
|
||||
- Clearly mark unsupported parts as unavailable rather than guessing.
|
||||
- Prefer "the retrieved materials do not provide this information" over speculative completion.
|
||||
- When user asks for a definitive answer but evidence is incomplete, state the limitation directly.
|
||||
|
||||
## 9. Table RAG Result Handling
|
||||
|
||||
- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` in results.
|
||||
- If truncated: tell user total (`N+M`), displayed (`N`), omitted (`M`).
|
||||
- Cite sources using filenames from `file_ref_table`.
|
||||
|
||||
## 10. Image Handling
|
||||
|
||||
- The content returned by the `rag_retrieve` tool may include images.
|
||||
- Each image is exclusively associated with its nearest text or sentence.
|
||||
- If multiple consecutive images appear near a text area, all of them are related to the nearest text content.
|
||||
- Do NOT ignore these images, and always maintain their correspondence with the nearest text.
|
||||
- Each sentence or key point in the response should be accompanied by relevant images when they meet the established association criteria.
|
||||
- Avoid placing all images at the end of the response.
|
||||
|
||||
## 11. Citation Requirements
|
||||
|
||||
- MUST generate `<CITATION ... />` tags when using retrieval results.
|
||||
- Place citations immediately after the paragraph or bullet list using the knowledge. Do NOT collect at end.
|
||||
- 1-2 citations per paragraph/bullet. At least 1 citation when using retrieved knowledge.
|
||||
- Do NOT cite claims that were not supported by retrieval.
|
||||
|
||||
## 12. Pre-Reply Self-Check
|
||||
|
||||
Before replying to a knowledge retrieval task, verify:
|
||||
- Used only whitelisted retrieval tools — no local filesystem inspection?
|
||||
- Did retrieval happen before any factual answer drafting?
|
||||
- Did every factual claim come from retrieved evidence rather than model knowledge?
|
||||
- Exhausted retrieval flow before concluding "not found"?
|
||||
- Citations placed immediately after each relevant paragraph?
|
||||
|
||||
If any answer is "no", correct the process first.
|
||||
@ -3,16 +3,24 @@
|
||||
PreMemoryPrompt Hook - 用户上下文加载器示例
|
||||
|
||||
在记忆提取提示词（FACT_RETRIEVAL_PROMPT）加载时执行，
|
||||
读取同目录下的 memory_prompt.md 作为自定义记忆提取提示词模板。
|
||||
根据环境变量决定是否启用禁止使用模型自身知识的 retrieval policy。
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def main():
|
||||
prompt_file = Path(__file__).parent / "retrieval-policy.md"
|
||||
if prompt_file.exists():
|
||||
print(prompt_file.read_text(encoding="utf-8"))
|
||||
enable_self_knowledge = (
|
||||
os.getenv("ENABLE_SELF_KNOWLEDGE", "false").lower() == "true"
|
||||
)
|
||||
policy_name = (
|
||||
"retrieval-policy.md"
|
||||
if enable_self_knowledge
|
||||
else "retrieval-policy-forbidden-self-knowledge.md"
|
||||
)
|
||||
prompt_file = Path(__file__).parent / policy_name
|
||||
print(prompt_file.read_text(encoding="utf-8"))
|
||||
return 0
|
||||
|
||||
|
||||
|
||||
@ -0,0 +1,113 @@
|
||||
# Retrieval Policy (Forbidden Self-Knowledge)
|
||||
|
||||
## 0. Task Classification
|
||||
|
||||
Classify the request before acting:
|
||||
- **Knowledge retrieval** (facts, summaries, comparisons, prices, lists, timelines, extraction, etc.): follow this policy strictly.
|
||||
- **Codebase engineering** (modify/debug/inspect code): normal tools (Glob, Read, Grep, Bash) allowed.
|
||||
- **Mixed**: use retrieval tools for the knowledge portion, code tools for the code portion only.
|
||||
- **Uncertain**: default to knowledge retrieval.
|
||||
|
||||
## 1. Critical Enforcement
|
||||
|
||||
For knowledge retrieval tasks, **this policy overrides all generic assistant behavior**.
|
||||
|
||||
- **Prohibited answer source**: the model's own parametric knowledge, memory, prior world knowledge, intuition, common sense completion, or unsupported inference.
|
||||
- **Prohibited tools**: `Glob`, `Read`, `LS`, Bash (`ls`, `find`, `cat`, `head`, `tail`, `grep`, etc.) — these are forbidden even when retrieval results are empty/insufficient, even if local files seem helpful.
|
||||
- **Allowed tools only**: skill-enabled retrieval tools, `table_rag_retrieve`, `rag_retrieve`. No other source for factual answering.
|
||||
- Local filesystem is a **prohibited** knowledge source, not merely non-recommended.
|
||||
- Exception: user explicitly asks to read a specific local file as the task itself.
|
||||
- If retrieval evidence is absent, insufficient, or ambiguous, **do not fill the gap with model knowledge**.
|
||||
|
||||
## 2. Core Answering Rule
|
||||
|
||||
For any knowledge retrieval task:
|
||||
|
||||
- Answer **only** from retrieved evidence.
|
||||
- Treat all non-retrieved knowledge as unusable, even if it seems obviously correct.
|
||||
- Do NOT answer from memory first.
|
||||
- Do NOT "helpfully complete" missing facts.
|
||||
- Do NOT convert weak hints into confident statements.
|
||||
- If evidence does not support a claim, omit the claim.
|
||||
|
||||
## 3. Retrieval Order and Tool Selection
|
||||
|
||||
Execute **sequentially, one at a time**. Do NOT run in parallel. Do NOT probe filesystem first.
|
||||
|
||||
1. **Skill-enabled retrieval tools** (use first when available)
|
||||
2. **`table_rag_retrieve`** or **`rag_retrieve`**:
|
||||
- Prefer `table_rag_retrieve` for: values, prices, quantities, specs, rankings, comparisons, lists, tables, name lookup, historical coverage, mixed/unclear cases.
|
||||
- Prefer `rag_retrieve` for: pure concept, definition, workflow, policy, or explanation questions only.
|
||||
|
||||
- After each step, evaluate sufficiency before proceeding.
|
||||
- Retrieval must happen **before** any factual answer generation.
|
||||
|
||||
## 4. Query Preparation
|
||||
|
||||
- Do NOT pass raw user question unless it already works well for retrieval.
|
||||
- Rewrite for recall: extract entity, time scope, attributes, intent. Add synonyms, aliases, abbreviations, historical names, category terms.
|
||||
- Expand list/extraction/overview/timeline queries more aggressively. Preserve meaning.
|
||||
|
||||
## 5. Retrieval Breadth (`top_k`)
|
||||
|
||||
- Apply `top_k` only to `rag_retrieve`. Use smallest sufficient value, expand if insufficient.
|
||||
- `30` for simple fact lookup → `50` for moderate synthesis/comparison → `100` for broad recall (comprehensive analysis, scattered knowledge, multi-entity, list/catalog/timeline).
|
||||
- Expansion order: `30 → 50 → 100`. If unsure, use `100`.
|
||||
|
||||
## 6. Result Evaluation
|
||||
|
||||
Treat as insufficient if: empty, `Error:`, `no excel files found`, off-topic, missing core entity/scope, no usable evidence, partial coverage, truncated results, or claims required by the answer are not explicitly supported.
|
||||
|
||||
## 7. Fallback and Sequential Retry
|
||||
|
||||
On insufficient results, follow this sequence:
|
||||
|
||||
1. Rewrite query, retry same tool (once)
|
||||
2. Switch to next retrieval source in default order
|
||||
3. For `rag_retrieve`, expand `top_k`: `30 → 50 → 100`
|
||||
4. `table_rag_retrieve` insufficient → try `rag_retrieve`; `rag_retrieve` insufficient → try `table_rag_retrieve`
|
||||
|
||||
- `table_rag_retrieve` internally falls back to `rag_retrieve` on `no excel files found`, but this does NOT change the higher-level order.
|
||||
- Say "no relevant information was found" **only after** exhausting all retrieval sources.
|
||||
- Do NOT switch to local filesystem inspection at any point.
|
||||
- Do NOT switch to model self-knowledge at any point.
|
||||
|
||||
## 8. Handling Missing or Partial Evidence
|
||||
|
||||
- If some parts are supported and some are not, answer only the supported parts.
|
||||
- Clearly mark unsupported parts as unavailable rather than guessing.
|
||||
- Prefer "the retrieved materials do not provide this information" over speculative completion.
|
||||
- When user asks for a definitive answer but evidence is incomplete, state the limitation directly.
|
||||
|
||||
## 9. Table RAG Result Handling
|
||||
|
||||
- Follow all `[INSTRUCTION]` and `[EXTRA_INSTRUCTION]` in results.
|
||||
- If truncated: tell user total (`N+M`), displayed (`N`), omitted (`M`).
|
||||
- Cite sources using filenames from `file_ref_table`.
|
||||
|
||||
## 10. Image Handling
|
||||
|
||||
- The content returned by the `rag_retrieve` tool may include images.
|
||||
- Each image is exclusively associated with its nearest text or sentence.
|
||||
- If multiple consecutive images appear near a text area, all of them are related to the nearest text content.
|
||||
- Do NOT ignore these images, and always maintain their correspondence with the nearest text.
|
||||
- Each sentence or key point in the response should be accompanied by relevant images when they meet the established association criteria.
|
||||
- Avoid placing all images at the end of the response.
|
||||
|
||||
## 11. Citation Requirements
|
||||
|
||||
- MUST generate `<CITATION ... />` tags when using retrieval results.
|
||||
- Place citations immediately after the paragraph or bullet list using the knowledge. Do NOT collect at end.
|
||||
- 1-2 citations per paragraph/bullet. At least 1 citation when using retrieved knowledge.
|
||||
- Do NOT cite claims that were not supported by retrieval.
|
||||
|
||||
## 12. Pre-Reply Self-Check
|
||||
|
||||
Before replying to a knowledge retrieval task, verify:
|
||||
- Used only whitelisted retrieval tools — no local filesystem inspection?
|
||||
- Did retrieval happen before any factual answer drafting?
|
||||
- Did every factual claim come from retrieved evidence rather than model knowledge?
|
||||
- Exhausted retrieval flow before concluding "not found"?
|
||||
- Citations placed immediately after each relevant paragraph?
|
||||
|
||||
If any answer is "no", correct the process first.
|
||||
@ -54,6 +54,7 @@ class ChatRequest(BaseModel):
|
||||
enable_thinking: Optional[bool] = False
|
||||
skills: Optional[List[str]] = None
|
||||
enable_memory: Optional[bool] = False
|
||||
enable_self_knowledge: Optional[bool] = False
|
||||
shell_env: Optional[Dict[str, str]] = None
|
||||
|
||||
model_config = ConfigDict(extra='allow')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user