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fix: flatten tool messages for NEAR AI cloud-api compatibility (#41)
* fix: flatten tool messages for NEAR AI cloud-api compatibility NEAR AI cloud-api does not support the OpenAI multi-turn tool-calling protocol (role:"tool" messages cause HTTP 400). This adds a flatten_tool_messages() pass in NearAiChatProvider that rewrites assistant tool_call messages and tool result messages into plain assistant/user text before sending to the API. The model still sees the tool execution history, just in a text format it can process. Also includes a minor fix to telegram channel send_pairing_reply for updated WASM host function signature. Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: resolve CI failures in fmt, rate limiting, and test configuration - Apply cargo fmt to nearai_chat.rs formatting violations - Fix truncate(true) bug in record_failed_approve that cleared the attempts file before reading, preventing rate limit from ever triggering - Skip bundled channel test when WASM build artifacts are unavailable (CI lacks wasm32-wasip2 target) - Split CI test workflow to exclude workspace_integration tests that require PostgreSQL Co-Authored-By: Claude Opus 4.6 <[email protected]> * fix: resolve clippy unnecessary_unwrap lint (Rust 1.93) Replace is_some() + unwrap() pattern with if-let binding to satisfy clippy::unnecessary_unwrap which is now deny-by-default. Co-Authored-By: Claude Opus 4.6 <[email protected]> --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: firat.sertgoz <[email protected]> Co-authored-by: Illia Polosukhin <[email protected]>
This commit is contained in:
Zaki Manian
co-authored by
Claude Opus 4.6
firat.sertgoz
Illia Polosukhin
Claude Opus 4.6
firat.sertgoz
Illia Polosukhin
parent
33ef0a6ea5
commit
b3dee13954
@@ -222,6 +222,12 @@ impl LlmProvider for NearAiChatProvider {
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let messages: Vec<ChatCompletionMessage> =
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req.messages.into_iter().map(|m| m.into()).collect();
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// NEAR AI cloud-api does not support multi-turn tool calling (rejects
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// any request containing role:"tool" messages with HTTP 400). Rewrite
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// tool-call / tool-result pairs into plain text so the conversation
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// history is preserved without using unsupported message roles.
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let messages = flatten_tool_messages(messages);
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let tools: Vec<ChatCompletionTool> = req
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.tools
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.into_iter()
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@@ -367,6 +373,64 @@ struct ChatCompletionMessage {
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tool_calls: Option<Vec<ChatCompletionToolCall>>,
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}
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/// Rewrite tool-call / tool-result messages into plain assistant/user text.
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///
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/// NEAR AI cloud-api does not support the OpenAI multi-turn tool-calling
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/// protocol (`role: "tool"` messages). This function converts:
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/// - Assistant messages with `tool_calls` → assistant text describing the calls
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/// - Tool result messages (`role: "tool"`) → user messages with the result
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///
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/// Non-tool messages pass through unchanged.
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fn flatten_tool_messages(messages: Vec<ChatCompletionMessage>) -> Vec<ChatCompletionMessage> {
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let has_tool_msgs = messages.iter().any(|m| m.role == "tool");
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if !has_tool_msgs {
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return messages;
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}
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tracing::debug!("Flattening tool messages for NEAR AI compatibility");
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messages
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.into_iter()
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.map(|msg| {
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if let (true, Some(calls)) = (msg.role == "assistant", &msg.tool_calls) {
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// Convert assistant tool_calls into descriptive text
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let mut parts: Vec<String> = Vec::new();
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if let Some(ref text) = msg.content {
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if !text.is_empty() {
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parts.push(text.clone());
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}
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}
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for tc in calls {
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parts.push(format!(
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"[Called tool `{}` with arguments: {}]",
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tc.function.name, tc.function.arguments
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));
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}
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ChatCompletionMessage {
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role: "assistant".to_string(),
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content: Some(parts.join("\n")),
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tool_call_id: None,
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name: None,
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tool_calls: None,
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}
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} else if msg.role == "tool" {
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// Convert tool result into a user message
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let tool_name = msg.name.as_deref().unwrap_or("unknown");
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let result = msg.content.as_deref().unwrap_or("");
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ChatCompletionMessage {
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role: "user".to_string(),
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content: Some(format!("[Tool `{}` returned: {}]", tool_name, result)),
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tool_call_id: None,
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name: None,
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tool_calls: None,
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}
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} else {
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msg
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}
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})
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.collect()
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}
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impl From<ChatMessage> for ChatCompletionMessage {
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fn from(msg: ChatMessage) -> Self {
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let role = match msg.role {
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@@ -544,4 +608,119 @@ mod tests {
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serde_json::from_str(&calls[0].function.arguments).expect("valid JSON string");
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assert_eq!(parsed["key"], "value");
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}
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#[test]
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fn test_flatten_no_tool_messages_passthrough() {
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let messages = vec![
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ChatCompletionMessage {
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role: "system".to_string(),
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content: Some("You are helpful.".to_string()),
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tool_call_id: None,
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name: None,
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tool_calls: None,
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},
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ChatCompletionMessage {
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role: "user".to_string(),
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content: Some("Hello".to_string()),
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tool_call_id: None,
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name: None,
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tool_calls: None,
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},
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];
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let result = flatten_tool_messages(messages);
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assert_eq!(result.len(), 2);
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assert_eq!(result[0].role, "system");
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assert_eq!(result[1].role, "user");
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}
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#[test]
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fn test_flatten_tool_call_and_result() {
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let messages = vec![
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ChatCompletionMessage {
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role: "user".to_string(),
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content: Some("test".to_string()),
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tool_call_id: None,
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name: None,
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tool_calls: None,
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},
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ChatCompletionMessage {
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role: "assistant".to_string(),
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content: None,
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tool_call_id: None,
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name: None,
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tool_calls: Some(vec![ChatCompletionToolCall {
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id: "call_1".to_string(),
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call_type: "function".to_string(),
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function: ChatCompletionToolCallFunction {
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name: "echo".to_string(),
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arguments: r#"{"message":"hi"}"#.to_string(),
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},
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}]),
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},
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ChatCompletionMessage {
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role: "tool".to_string(),
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content: Some("hi".to_string()),
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tool_call_id: Some("call_1".to_string()),
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name: Some("echo".to_string()),
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tool_calls: None,
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},
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];
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let result = flatten_tool_messages(messages);
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assert_eq!(result.len(), 3);
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// Assistant tool_calls → plain assistant text
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assert_eq!(result[1].role, "assistant");
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assert!(result[1].tool_calls.is_none());
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assert!(
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result[1]
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.content
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.as_ref()
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.unwrap()
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.contains("[Called tool `echo`")
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);
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// Tool result → user message
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assert_eq!(result[2].role, "user");
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assert!(result[2].tool_call_id.is_none());
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assert!(
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result[2]
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.content
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.as_ref()
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.unwrap()
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.contains("[Tool `echo` returned: hi]")
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);
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}
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#[test]
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fn test_flatten_preserves_assistant_text_with_tool_calls() {
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let messages = vec![
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ChatCompletionMessage {
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role: "assistant".to_string(),
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content: Some("Let me check that.".to_string()),
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tool_call_id: None,
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name: None,
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tool_calls: Some(vec![ChatCompletionToolCall {
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id: "call_1".to_string(),
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call_type: "function".to_string(),
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function: ChatCompletionToolCallFunction {
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name: "search".to_string(),
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arguments: r#"{"q":"test"}"#.to_string(),
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},
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}]),
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},
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ChatCompletionMessage {
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role: "tool".to_string(),
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content: Some("found it".to_string()),
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tool_call_id: Some("call_1".to_string()),
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name: Some("search".to_string()),
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tool_calls: None,
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},
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];
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let result = flatten_tool_messages(messages);
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let text = result[0].content.as_ref().unwrap();
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assert!(text.starts_with("Let me check that."));
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assert!(text.contains("[Called tool `search`"));
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}
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}
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