fix(runtime,hands): 搜索功能修复 — glm空参数回退+schema简化
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根因: glm-5.1 不理解 oneOf+const 复杂 schema,发送 tool_calls 时
arguments 为空 {}。同时缺少从对话上下文提取用户意图的回退机制。
修复:
1. researcher input_schema 从 oneOf+const 改为扁平化属性 — glm 正确传参
2. loop_runner 增加 empty-input 回退 — 从最近用户消息注入 _fallback_query
3. researcher infer_action 增加 _fallback_query 分支处理
4. 调试日志降级 INFO→DEBUG (openai tool_calls delta, researcher input)
This commit is contained in:
@@ -252,38 +252,32 @@ impl ResearcherHand {
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dependencies: vec!["network".to_string()],
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input_schema: Some(serde_json::json!({
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"type": "object",
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"oneOf": [
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{
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"properties": {
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"action": { "const": "search" },
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"query": {
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"type": "object",
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"properties": {
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"query": { "type": "string" },
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"engine": { "type": "string", "enum": ["searxng", "google", "bing", "duckduckgo", "auto"] },
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"depth": { "type": "string", "enum": ["quick", "standard", "deep"] },
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"maxResults": { "type": "integer" }
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},
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"required": ["query"]
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}
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},
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"required": ["action", "query"]
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"properties": {
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"action": {
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"type": "string",
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"enum": ["search", "fetch", "report", "summarize"],
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"description": "Action to perform: search (web search), fetch (get URL content), report (deep research), summarize (multiple URLs)"
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},
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{
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"properties": {
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"action": { "const": "fetch" },
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"url": { "type": "string" }
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},
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"required": ["action", "url"]
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"query": {
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"type": "string",
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"description": "Search query string for search/report actions"
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},
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{
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"properties": {
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"action": { "const": "report" },
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"query": { "$ref": "#/properties/query" }
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},
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"required": ["action", "query"]
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"url": {
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"type": "string",
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"description": "URL to fetch content from"
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},
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"urls": {
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"type": "array",
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"items": { "type": "string" },
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"description": "List of URLs to summarize"
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},
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"engine": {
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"type": "string",
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"enum": ["auto", "searxng", "google", "bing", "duckduckgo"],
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"description": "Search engine preference"
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}
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]
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},
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"description": "Provide 'query' for search/report, or 'url' for fetch, or 'urls' for summarize"
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})),
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tags: vec!["research".to_string(), "web".to_string(), "search".to_string()],
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enabled: true,
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@@ -310,7 +304,7 @@ impl ResearcherHand {
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let keys: Vec<&str> = input.as_object()
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.map(|obj| obj.keys().map(|k| k.as_str()).collect())
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.unwrap_or_default();
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tracing::warn!(target: "researcher", ?keys, %input, "infer_action examining input");
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tracing::debug!(target: "researcher", ?keys, %input, "infer_action examining input");
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// Check for action field with wrong value
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if let Some(action) = input.get("action").and_then(|v| v.as_str()) {
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@@ -364,12 +358,27 @@ impl ResearcherHand {
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}
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}
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}
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// Check for injected fallback query from loop_runner (when LLM sends empty args)
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if let Some(fallback) = input.get("_fallback_query").and_then(|v| v.as_str()) {
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if !fallback.trim().is_empty() {
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tracing::debug!(target: "researcher", query = %fallback, "Using fallback user message as search query");
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return Ok(ResearcherAction::Search { query: ResearchQuery {
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query: fallback.to_string(),
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engine: SearchEngine::Auto,
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depth: ResearchDepth::Standard,
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max_results: 10,
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include_related: false,
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time_limit_secs: 60,
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}});
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}
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}
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// Last resort: if any string field looks like a search query
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if let Some(obj) = input.as_object() {
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for (key, val) in obj {
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if let Some(s) = val.as_str() {
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if s.len() > 2 && !s.starts_with("http") && key != "action" && key != "engine" {
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tracing::warn!(target: "researcher", key = %key, value = %s, "Using fallback field as query");
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tracing::debug!(target: "researcher", key = %key, value = %s, "Using fallback field as query");
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return Ok(ResearcherAction::Search { query: ResearchQuery {
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query: s.to_string(),
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engine: SearchEngine::Auto,
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@@ -1144,12 +1153,12 @@ impl Hand for ResearcherHand {
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}
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async fn execute(&self, _context: &HandContext, input: Value) -> Result<HandResult> {
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tracing::info!(target: "researcher", input = %input, "Researcher hand received input");
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tracing::debug!(target: "researcher", input = %input, "Researcher hand received input");
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// Try strict deserialization first, then fall back to inference
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let action: ResearcherAction = match serde_json::from_value(input.clone()) {
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Ok(a) => a,
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Err(e) => {
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tracing::warn!(target: "researcher", error = %e, input = %input, "Strict deserialization failed, trying inference");
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tracing::debug!(target: "researcher", error = %e, input = %input, "Strict deserialization failed, trying inference");
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Self::infer_action(&input)?
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}
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};
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@@ -208,7 +208,7 @@ impl LlmDriver for OpenAiDriver {
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tracing::debug!("[OpenAI:stream] SSE #{}: {}", sse_event_count, &data[..data.len().min(300)]);
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}
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if data == "[DONE]" {
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tracing::debug!("[OpenAI:stream] Received [DONE], total SSE events: {}, raw bytes: {}", sse_event_count, raw_bytes_total);
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tracing::debug!("[OpenAI:stream] Received [DONE], total SSE events: {}, raw bytes: {}, tool_calls: {:?}", sse_event_count, raw_bytes_total, accumulated_tool_calls);
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// Emit ToolUseEnd for all accumulated tool calls (skip invalid ones with empty name)
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for (id, (name, args)) in &accumulated_tool_calls {
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@@ -264,7 +264,7 @@ impl LlmDriver for OpenAiDriver {
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// Handle tool calls
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if let Some(tool_calls) = &delta.tool_calls {
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tracing::trace!("[OpenAI] Received tool_calls delta: {:?}", tool_calls);
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tracing::debug!("[OpenAI] Received tool_calls delta: {:?}", tool_calls);
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for tc in tool_calls {
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// Tool call start - has id and name
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if let Some(id) = &tc.id {
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@@ -380,6 +380,26 @@ impl AgentLoop {
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if abort_result.is_some() {
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break;
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}
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// GLM and other models sometimes send tool calls with empty arguments `{}`
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// Inject the last user message as a fallback query so the tool can infer intent.
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let input = if input.as_object().map_or(false, |obj| obj.is_empty()) {
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if let Some(last_user_msg) = messages.iter().rev().find_map(|m| {
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if let Message::User { content } = m {
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Some(content.clone())
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} else {
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None
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}
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}) {
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tracing::info!("[AgentLoop] Tool '{}' received empty input, injecting user message as fallback query", name);
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serde_json::json!({ "_fallback_query": last_user_msg })
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} else {
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input
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}
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} else {
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input
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};
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// Check tool call safety — via middleware chain
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{
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let mw_ctx_ref = middleware::MiddlewareContext {
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