[goose-llm] add generate tooltip & session name via extract method (#2467)

* extract method in provider to use for structured outputs

* generate session name from msgs

* generate tooltip from msgs

* add provider tests

Cargo.lock

@@ -2406,7 +2406,7 @@ dependencies = [
  "fs2",
  "futures",
  "include_dir",
- "indoc",
+ "indoc 2.0.6",
  "jsonwebtoken",
  "keyring",
  "lazy_static",
@@ -2527,7 +2527,11 @@ dependencies = [
  "base64 0.21.7",
  "chrono",
  "criterion",
+ "ctor",
+ "dotenv",
  "include_dir",
+ "indoc 1.0.9",
+ "lazy_static",
  "minijinja",
  "once_cell",
  "regex",
@@ -2559,7 +2563,7 @@ dependencies = [
  "ignore",
  "image 0.24.9",
  "include_dir",
- "indoc",
+ "indoc 2.0.6",
  "keyring",
  "kill_tree",
  "lazy_static",
@@ -3263,6 +3267,12 @@ dependencies = [
  "web-time",
 ]
 
+[[package]]
+name = "indoc"
+version = "1.0.9"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "bfa799dd5ed20a7e349f3b4639aa80d74549c81716d9ec4f994c9b5815598306"
+
 [[package]]
 name = "indoc"
 version = "2.0.6"

crates/goose-llm/Cargo.toml

@@ -35,10 +35,14 @@ base64 = "0.21"
 regex = "1.11.1"
 tracing = "0.1"
 smallvec = { version = "1.13", features = ["serde"] }
+indoc = "1.0"
 
 [dev-dependencies]
 criterion = "0.5"
 tempfile = "3.15.0"
+dotenv = "0.15"
+lazy_static = "1.5"
+ctor = "0.2.7"
 
 
 [[example]]

crates/goose-llm/src/extractors/mod.rs

@@ -0,0 +1,5 @@
+mod session_name;
+mod tooltip;
+
+pub use session_name::generate_session_name;
+pub use tooltip::generate_tooltip;

crates/goose-llm/src/extractors/session_name.rs

@@ -0,0 +1,107 @@
+use crate::message::Message;
+use crate::model::ModelConfig;
+use crate::providers::base::Provider;
+use crate::providers::databricks::DatabricksProvider;
+use crate::providers::errors::ProviderError;
+use crate::types::core::Role;
+use anyhow::Result;
+use indoc::indoc;
+use serde_json::{json, Value};
+
+const SESSION_NAME_EXAMPLES: &[&str] = &[
+    "Research Synthesis",
+    "Sentiment Analysis",
+    "Performance Report",
+    "Feedback Collector",
+    "Accessibility Check",
+    "Design Reminder",
+    "Project Reminder",
+    "Launch Checklist",
+    "Metrics Monitor",
+    "Incident Response",
+    "Deploy Cabinet App",
+    "Design Reminder Alert",
+    "Generate Monthly Expense Report",
+    "Automate Incident Response Workflow",
+    "Analyze Brand Sentiment Trends",
+    "Monitor Device Health Issues",
+    "Collect UI Feedback Summary",
+    "Schedule Project Deadline Reminders",
+];
+
+fn build_system_prompt() -> String {
+    let examples = SESSION_NAME_EXAMPLES
+        .iter()
+        .map(|e| format!("- {}", e))
+        .collect::<Vec<_>>()
+        .join("\n");
+
+    indoc! {r#"
+    You are an assistant that crafts a concise session title.
+    Given the first couple user messages in the conversation so far, 
+    reply with only a short name (up to 4 words) that best describes 
+    this session’s goal.
+
+    Examples:
+    "#}
+    .to_string()
+        + &examples
+}
+
+/// Generates a short (≤4 words) session name
+pub async fn generate_session_name(messages: &[Message]) -> Result<String, ProviderError> {
+    // Collect up to the first 3 user messages (truncated to 300 chars each)
+    let context: Vec<String> = messages
+        .iter()
+        .filter(|m| m.role == Role::User)
+        .take(3)
+        .map(|m| {
+            let text = m.content.concat_text_str();
+            if text.len() > 300 {
+                text.chars().take(300).collect()
+            } else {
+                text
+            }
+        })
+        .collect();
+
+    if context.is_empty() {
+        return Err(ProviderError::ExecutionError(
+            "No user messages found to generate a session name.".to_string(),
+        ));
+    }
+
+    let system_prompt = build_system_prompt();
+    let user_msg_text = format!("Here are the user messages:\n{}", context.join("\n"));
+
+    // Instantiate DatabricksProvider with goose-gpt-4-1
+    let model_cfg = ModelConfig::new("goose-gpt-4-1".to_string()).with_temperature(Some(0.0));
+    let provider = DatabricksProvider::from_env(model_cfg)?;
+
+    // Use `extract` with a simple string schema
+    let schema = json!({
+        "type": "object",
+        "properties": {
+            "name": { "type": "string" }
+        },
+        "required": ["name"],
+        "additionalProperties": false
+    });
+    let user_msg = Message::user().with_text(&user_msg_text);
+    let resp = provider
+        .extract(&system_prompt, &[user_msg], &schema)
+        .await?;
+
+    let obj = resp
+        .data
+        .as_object()
+        .ok_or_else(|| ProviderError::ResponseParseError("Expected object".into()))?;
+
+    let name = obj
+        .get("name")
+        .and_then(Value::as_str)
+        .ok_or_else(|| ProviderError::ResponseParseError("Missing or non-string name".into()))?
+        .to_string();
+
+    Ok(name)
+}

crates/goose-llm/src/extractors/tooltip.rs

@@ -0,0 +1,165 @@
+use crate::message::{Message, MessageContent};
+use crate::model::ModelConfig;
+use crate::providers::base::Provider;
+use crate::providers::databricks::DatabricksProvider;
+use crate::providers::errors::ProviderError;
+use crate::types::core::{Content, Role};
+use anyhow::Result;
+use indoc::indoc;
+use serde_json::{json, Value};
+
+const TOOLTIP_EXAMPLES: &[&str] = &[
+    "analyzing KPIs",
+    "detecting anomalies",
+    "building artifacts in Buildkite",
+    "categorizing issues",
+    "checking dependencies",
+    "collecting feedback",
+    "deploying changes in AWS",
+    "drafting report in Google Docs",
+    "extracting action items",
+    "generating insights",
+    "logging issues",
+    "monitoring tickets in Zendesk",
+    "notifying design team",
+    "running integration tests",
+    "scanning threads in Figma",
+    "sending reminders in Gmail",
+    "sending surveys",
+    "sharing with stakeholders",
+    "summarizing findings",
+    "transcribing meeting",
+    "tracking resolution",
+    "updating status in Linear",
+];
+
+fn build_system_prompt() -> String {
+    let examples = TOOLTIP_EXAMPLES
+        .iter()
+        .map(|e| format!("- {}", e))
+        .collect::<Vec<_>>()
+        .join("\n");
+
+    indoc! {r#"
+    You are an assistant that summarizes the recent conversation into a tooltip.
+    Given the last two messages, reply with only a short tooltip (up to 4 words)
+    describing what is happening now.
+
+    Examples:
+    "#}
+    .to_string()
+        + &examples
+}
+
+/// Generates a tooltip summarizing the last two messages in the session,
+/// including any tool calls or results.
+pub async fn generate_tooltip(messages: &[Message]) -> Result<String, ProviderError> {
+    // Need at least two messages to summarize
+    if messages.len() < 2 {
+        return Err(ProviderError::ExecutionError(
+            "Need at least two messages to generate a tooltip".to_string(),
+        ));
+    }
+
+    // Helper to render a single message's content
+    fn render_message(m: &Message) -> String {
+        let mut parts = Vec::new();
+        for content in m.content.iter() {
+            match content {
+                MessageContent::Text(text_block) => {
+                    let txt = text_block.text.trim();
+                    if !txt.is_empty() {
+                        parts.push(txt.to_string());
+                    }
+                }
+                MessageContent::ToolRequest(req) => {
+                    if let Ok(tool_call) = &req.tool_call {
+                        parts.push(format!(
+                            "called tool '{}' with args {}",
+                            tool_call.name, tool_call.arguments
+                        ));
+                    } else if let Err(e) = &req.tool_call {
+                        parts.push(format!("tool request error: {}", e));
+                    }
+                }
+                MessageContent::ToolResponse(resp) => match &resp.tool_result {
+                    Ok(contents) => {
+                        let results: Vec<String> = contents
+                            .iter()
+                            .map(|c| match c {
+                                Content::Text(t) => t.text.clone(),
+                                Content::Image(_) => "[image]".to_string(),
+                            })
+                            .collect();
+                        parts.push(format!("tool responded with: {}", results.join(" ")));
+                    }
+                    Err(e) => {
+                        parts.push(format!("tool error: {}", e));
+                    }
+                },
+                _ => {} // ignore other variants
+            }
+        }
+
+        let role = match m.role {
+            Role::User => "User",
+            Role::Assistant => "Assistant",
+        };
+
+        format!("{}: {}", role, parts.join("; "))
+    }
+
+    // Take the last two messages (in correct chronological order)
+    let rendered: Vec<String> = messages
+        .iter()
+        .rev()
+        .take(2)
+        .map(render_message)
+        .collect::<Vec<_>>()
+        .into_iter()
+        .rev()
+        .collect();
+
+    let system_prompt = build_system_prompt();
+
+    let user_msg_text = format!(
+        "Here are the last two messages:\n{}\n\nTooltip:",
+        rendered.join("\n")
+    );
+
+    // Instantiate the provider
+    let model_cfg = ModelConfig::new("goose-gpt-4-1".to_string()).with_temperature(Some(0.0));
+    let provider = DatabricksProvider::from_env(model_cfg)?;
+
+    // Schema wrapping our tooltip string
+    let schema = json!({
+        "type": "object",
+        "properties": {
+            "tooltip": { "type": "string" }
+        },
+        "required": ["tooltip"],
+        "additionalProperties": false
+    });
+
+    // Call extract
+    let user_msg = Message::user().with_text(&user_msg_text);
+    let resp = provider
+        .extract(&system_prompt, &[user_msg], &schema)
+        .await?;
+
+    // Pull out the tooltip field
+    let obj = resp
+        .data
+        .as_object()
+        .ok_or_else(|| ProviderError::ResponseParseError("Expected JSON object".into()))?;
+
+    let tooltip = obj
+        .get("tooltip")
+        .and_then(Value::as_str)
+        .ok_or_else(|| {
+            ProviderError::ResponseParseError("Missing or non-string `tooltip` field".into())
+        })?
+        .to_string();
+
+    Ok(tooltip)
+}

crates/goose-llm/src/lib.rs

@@ -1,8 +1,9 @@
 mod completion;
-mod message;
+pub mod extractors;
+pub mod message;
 mod model;
 mod prompt_template;
-mod providers;
+pub mod providers;
 pub mod types;
 
 pub use completion::completion;

crates/goose-llm/src/providers/base.rs

@@ -43,6 +43,23 @@ impl ProviderCompleteResponse {
     }
 }
 
+/// Response from a structured‐extraction call
+#[derive(Debug, Clone)]
+pub struct ProviderExtractResponse {
+    /// The extracted JSON object
+    pub data: serde_json::Value,
+    /// Which model produced it
+    pub model: String,
+    /// Token usage stats
+    pub usage: Usage,
+}
+
+impl ProviderExtractResponse {
+    pub fn new(data: serde_json::Value, model: String, usage: Usage) -> Self {
+        Self { data, model, usage }
+    }
+}
+
 /// Base trait for AI providers (OpenAI, Anthropic, etc)
 #[async_trait]
 pub trait Provider: Send + Sync {
@@ -65,6 +82,27 @@ pub trait Provider: Send + Sync {
         messages: &[Message],
         tools: &[Tool],
     ) -> Result<ProviderCompleteResponse, ProviderError>;
+
+    /// Structured extraction: always JSON‐Schema
+    ///
+    /// # Arguments
+    /// * `system`   – system prompt guiding the extraction task  
+    /// * `messages` – conversation history  
+    /// * `schema`   – a JSON‐Schema for the expected output.
+    ///                 Will set strict=true for OpenAI & Databricks.
+    ///
+    /// # Returns
+    /// A `ProviderExtractResponse` whose `data` is a JSON object matching `schema`.  
+    ///
+    /// # Errors
+    /// * `ProviderError::ContextLengthExceeded` if the prompt is too large  
+    /// * other `ProviderError` variants for API/network failures
+    async fn extract(
+        &self,
+        system: &str,
+        messages: &[Message],
+        schema: &serde_json::Value,
+    ) -> Result<ProviderExtractResponse, ProviderError>;
 }
 
 #[cfg(test)]

crates/goose-llm/src/providers/databricks.rs

@@ -4,7 +4,7 @@ use anyhow::Result;
 use async_trait::async_trait;
 use reqwest::{Client, StatusCode};
 use serde::{Deserialize, Serialize};
-use serde_json::Value;
+use serde_json::{json, Value};
 use url::Url;
 
 use super::{
@@ -15,17 +15,15 @@ use super::{
 use crate::{
     message::Message,
     model::ModelConfig,
-    providers::{Provider, ProviderCompleteResponse, Usage},
+    providers::{Provider, ProviderCompleteResponse, ProviderExtractResponse, Usage},
     types::core::Tool,
 };
 
-pub const DATABRICKS_DEFAULT_MODEL: &str = "databricks-meta-llama-3-3-70b-instruct";
+pub const DATABRICKS_DEFAULT_MODEL: &str = "databricks-claude-3-7-sonnet";
 // Databricks can passthrough to a wide range of models, we only provide the default
 pub const _DATABRICKS_KNOWN_MODELS: &[&str] = &[
     "databricks-meta-llama-3-3-70b-instruct",
-    "databricks-meta-llama-3-1-405b-instruct",
+    "databricks-claude-3-7-sonnet",
-    "databricks-dbrx-instruct",
-    "databricks-mixtral-8x7b-instruct",
 ];
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -215,4 +213,63 @@ impl Provider for DatabricksProvider {
 
         Ok(ProviderCompleteResponse::new(message, model, usage))
     }
+
+    async fn extract(
+        &self,
+        system: &str,
+        messages: &[Message],
+        schema: &Value,
+    ) -> Result<ProviderExtractResponse, ProviderError> {
+        // 1. Build base payload (no tools)
+        let mut payload = create_request(&self.model, system, messages, &[], &ImageFormat::OpenAi)?;
+
+        // 2. Inject strict JSON‐Schema wrapper
+        payload
+            .as_object_mut()
+            .expect("payload must be an object")
+            .insert(
+                "response_format".to_string(),
+                json!({
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": "extraction",
+                        "schema": schema,
+                        "strict": true
+                    }
+                }),
+            );
+
+        // 3. Call OpenAI
+        let response = self.post(payload.clone()).await?;
+
+        // 4. Extract the assistant’s `content` and parse it into JSON
+        let msg = &response["choices"][0]["message"];
+        let raw = msg.get("content").cloned().ok_or_else(|| {
+            ProviderError::ResponseParseError("Missing content in extract response".into())
+        })?;
+        let data = match raw {
+            Value::String(s) => serde_json::from_str(&s)
+                .map_err(|e| ProviderError::ResponseParseError(format!("Invalid JSON: {}", e)))?,
+            Value::Object(_) | Value::Array(_) => raw,
+            other => {
+                return Err(ProviderError::ResponseParseError(format!(
+                    "Unexpected content type: {:?}",
+                    other
+                )))
+            }
+        };
+
+        // 5. Gather usage & model info
+        let usage = match get_usage(&response) {
+            Ok(u) => u,
+            Err(ProviderError::UsageError(e)) => {
+                tracing::debug!("Failed to get usage in extract: {}", e);
+                Usage::default()
+            }
+            Err(e) => return Err(e),
+        };
+        let model = get_model(&response);
+
+        Ok(ProviderExtractResponse::new(data, model, usage))
+    }
 }

crates/goose-llm/src/providers/errors.rs

@@ -22,6 +22,9 @@ pub enum ProviderError {
 
     #[error("Usage data error: {0}")]
     UsageError(String),
+
+    #[error("Invalid response: {0}")]
+    ResponseParseError(String),
 }
 
 impl From<anyhow::Error> for ProviderError {

crates/goose-llm/src/providers/formats/databricks.rs

@@ -200,6 +200,7 @@ pub fn format_messages(messages: &[Message], image_format: &ImageFormat) -> Vec<
 }
 
 /// Convert internal Tool format to OpenAI's API tool specification
+/// https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/api-reference#functionobject
 pub fn format_tools(tools: &[Tool]) -> anyhow::Result<Vec<Value>> {
     let mut tool_names = std::collections::HashSet::new();
     let mut result = Vec::new();

crates/goose-llm/src/providers/mod.rs

@@ -6,5 +6,5 @@ pub mod formats;
 pub mod openai;
 pub mod utils;
 
-pub use base::{Provider, ProviderCompleteResponse, Usage};
+pub use base::{Provider, ProviderCompleteResponse, ProviderExtractResponse, Usage};
 pub use factory::create;

crates/goose-llm/src/providers/openai.rs

@@ -3,7 +3,7 @@ use std::{collections::HashMap, time::Duration};
 use anyhow::Result;
 use async_trait::async_trait;
 use reqwest::Client;
-use serde_json::Value;
+use serde_json::{json, Value};
 
 use super::{
     errors::ProviderError,
@@ -13,20 +13,12 @@ use super::{
 use crate::{
     message::Message,
     model::ModelConfig,
-    providers::{Provider, ProviderCompleteResponse, Usage},
+    providers::{Provider, ProviderCompleteResponse, ProviderExtractResponse, Usage},
     types::core::Tool,
 };
 
 pub const OPEN_AI_DEFAULT_MODEL: &str = "gpt-4o";
-pub const _OPEN_AI_KNOWN_MODELS: &[&str] = &[
+pub const _OPEN_AI_KNOWN_MODELS: &[&str] = &["gpt-4o", "gpt-4.1", "o1", "o3", "o4-mini"];
-    "gpt-4o",
-    "gpt-4o-mini",
-    "gpt-4-turbo",
-    "gpt-3.5-turbo",
-    "o1",
-    "o3",
-    "o4-mini",
-];
 
 #[derive(Debug)]
 pub struct OpenAiProvider {
@@ -146,6 +138,65 @@ impl Provider for OpenAiProvider {
         emit_debug_trace(&self.model, &payload, &response, &usage);
         Ok(ProviderCompleteResponse::new(message, model, usage))
     }
+
+    async fn extract(
+        &self,
+        system: &str,
+        messages: &[Message],
+        schema: &Value,
+    ) -> Result<ProviderExtractResponse, ProviderError> {
+        // 1. Build base payload (no tools)
+        let mut payload = create_request(&self.model, system, messages, &[], &ImageFormat::OpenAi)?;
+
+        // 2. Inject strict JSON‐Schema wrapper
+        payload
+            .as_object_mut()
+            .expect("payload must be an object")
+            .insert(
+                "response_format".to_string(),
+                json!({
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": "extraction",
+                        "schema": schema,
+                        "strict": true
+                    }
+                }),
+            );
+
+        // 3. Call OpenAI
+        let response = self.post(payload.clone()).await?;
+
+        // 4. Extract the assistant’s `content` and parse it into JSON
+        let msg = &response["choices"][0]["message"];
+        let raw = msg.get("content").cloned().ok_or_else(|| {
+            ProviderError::ResponseParseError("Missing content in extract response".into())
+        })?;
+        let data = match raw {
+            Value::String(s) => serde_json::from_str(&s)
+                .map_err(|e| ProviderError::ResponseParseError(format!("Invalid JSON: {}", e)))?,
+            Value::Object(_) | Value::Array(_) => raw,
+            other => {
+                return Err(ProviderError::ResponseParseError(format!(
+                    "Unexpected content type: {:?}",
+                    other
+                )))
+            }
+        };
+
+        // 5. Gather usage & model info
+        let usage = match get_usage(&response) {
+            Ok(u) => u,
+            Err(ProviderError::UsageError(e)) => {
+                tracing::debug!("Failed to get usage in extract: {}", e);
+                Usage::default()
+            }
+            Err(e) => return Err(e),
+        };
+        let model = get_model(&response);
+
+        Ok(ProviderExtractResponse::new(data, model, usage))
+    }
 }
 
 fn parse_custom_headers(s: String) -> HashMap<String, String> {

crates/goose-llm/tests/extract_session_name.rs

@@ -0,0 +1,56 @@
+use anyhow::Result;
+use dotenv::dotenv;
+use goose_llm::extractors::generate_session_name;
+use goose_llm::message::Message;
+use goose_llm::providers::errors::ProviderError;
+
+#[tokio::test]
+async fn test_generate_session_name_success() -> Result<(), ProviderError> {
+    // Load .env for Databricks credentials
+    dotenv().ok();
+    let has_creds =
+        std::env::var("DATABRICKS_HOST").is_ok() && std::env::var("DATABRICKS_TOKEN").is_ok();
+    if !has_creds {
+        println!("Skipping generate_session_name test – Databricks creds not set");
+        return Ok(());
+    }
+
+    // Build a few messages with at least two user messages
+    let messages = vec![
+        Message::user().with_text("Hello, how are you?"),
+        Message::assistant().with_text("I’m fine, thanks!"),
+        Message::user().with_text("What’s the weather in New York tomorrow?"),
+    ];
+
+    let name = generate_session_name(&messages).await?;
+    println!("Generated session name: {:?}", name);
+
+    // Should be non-empty and at most 4 words
+    let name = name.trim();
+    assert!(!name.is_empty(), "Name must not be empty");
+    let word_count = name.split_whitespace().count();
+    assert!(
+        word_count <= 4,
+        "Name must be 4 words or less, got {}: {}",
+        word_count,
+        name
+    );
+
+    Ok(())
+}
+
+#[tokio::test]
+async fn test_generate_session_name_no_user() {
+    // No user messages → expect ExecutionError
+    let messages = vec![
+        Message::assistant().with_text("System starting…"),
+        Message::assistant().with_text("All systems go."),
+    ];
+
+    let err = generate_session_name(&messages).await;
+    assert!(
+        matches!(err, Err(ProviderError::ExecutionError(_))),
+        "Expected ExecutionError when there are no user messages, got: {:?}",
+        err
+    );
+}

crates/goose-llm/tests/extract_tooltip.rs

@@ -0,0 +1,69 @@
+use anyhow::Result;
+use dotenv::dotenv;
+use goose_llm::extractors::generate_tooltip;
+use goose_llm::message::{Message, MessageContent, ToolRequest};
+use goose_llm::providers::errors::ProviderError;
+use goose_llm::types::core::{Content, ToolCall};
+use serde_json::json;
+
+#[tokio::test]
+async fn test_generate_tooltip_simple() -> Result<(), ProviderError> {
+    // Skip if no Databricks creds
+    dotenv().ok();
+    if std::env::var("DATABRICKS_HOST").is_err() || std::env::var("DATABRICKS_TOKEN").is_err() {
+        println!("Skipping simple tooltip test – Databricks creds not set");
+        return Ok(());
+    }
+
+    // Two plain-text messages
+    let messages = vec![
+        Message::user().with_text("Hello, how are you?"),
+        Message::assistant().with_text("I'm fine, thanks! How can I help?"),
+    ];
+
+    let tooltip = generate_tooltip(&messages).await?;
+    println!("Generated tooltip: {:?}", tooltip);
+
+    assert!(!tooltip.trim().is_empty(), "Tooltip must not be empty");
+    assert!(
+        tooltip.len() < 100,
+        "Tooltip should be reasonably short (<100 chars)"
+    );
+    Ok(())
+}
+
+#[tokio::test]
+async fn test_generate_tooltip_with_tools() -> Result<(), ProviderError> {
+    // Skip if no Databricks creds
+    dotenv().ok();
+    if std::env::var("DATABRICKS_HOST").is_err() || std::env::var("DATABRICKS_TOKEN").is_err() {
+        println!("Skipping tool‐based tooltip test – Databricks creds not set");
+        return Ok(());
+    }
+
+    // 1) Assistant message with a tool request
+    let mut tool_req_msg = Message::assistant();
+    let req = ToolRequest {
+        id: "1".to_string(),
+        tool_call: Ok(ToolCall::new("get_time", json!({"timezone": "UTC"}))),
+    };
+    tool_req_msg.content.push(MessageContent::ToolRequest(req));
+
+    // 2) User message with the tool response
+    let tool_resp_msg = Message::user().with_tool_response(
+        "1",
+        Ok(vec![Content::text("The current time is 12:00 UTC")]),
+    );
+
+    let messages = vec![tool_req_msg, tool_resp_msg];
+
+    let tooltip = generate_tooltip(&messages).await?;
+    println!("Generated tooltip (tools): {:?}", tooltip);
+
+    assert!(!tooltip.trim().is_empty(), "Tooltip must not be empty");
+    assert!(
+        tooltip.len() < 100,
+        "Tooltip should be reasonably short (<100 chars)"
+    );
+    Ok(())
+}

crates/goose-llm/tests/providers_complete.rs

@@ -0,0 +1,379 @@
+use anyhow::Result;
+use dotenv::dotenv;
+use goose_llm::message::{Message, MessageContent};
+use goose_llm::providers::base::Provider;
+use goose_llm::providers::errors::ProviderError;
+use goose_llm::providers::{databricks, openai};
+use goose_llm::types::core::{Content, Tool};
+use std::collections::HashMap;
+use std::sync::Arc;
+use std::sync::Mutex;
+
+#[derive(Debug, Clone, Copy)]
+enum TestStatus {
+    Passed,
+    Skipped,
+    Failed,
+}
+
+impl std::fmt::Display for TestStatus {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        match self {
+            TestStatus::Passed => write!(f, "✅"),
+            TestStatus::Skipped => write!(f, "⏭️"),
+            TestStatus::Failed => write!(f, "❌"),
+        }
+    }
+}
+
+struct TestReport {
+    results: Mutex<HashMap<String, TestStatus>>,
+}
+
+impl TestReport {
+    fn new() -> Arc<Self> {
+        Arc::new(Self {
+            results: Mutex::new(HashMap::new()),
+        })
+    }
+
+    fn record_status(&self, provider: &str, status: TestStatus) {
+        let mut results = self.results.lock().unwrap();
+        results.insert(provider.to_string(), status);
+    }
+
+    fn record_pass(&self, provider: &str) {
+        self.record_status(provider, TestStatus::Passed);
+    }
+
+    fn record_skip(&self, provider: &str) {
+        self.record_status(provider, TestStatus::Skipped);
+    }
+
+    fn record_fail(&self, provider: &str) {
+        self.record_status(provider, TestStatus::Failed);
+    }
+
+    fn print_summary(&self) {
+        println!("\n============== Providers ==============");
+        let results = self.results.lock().unwrap();
+        let mut providers: Vec<_> = results.iter().collect();
+        providers.sort_by(|a, b| a.0.cmp(b.0));
+
+        for (provider, status) in providers {
+            println!("{} {}", status, provider);
+        }
+        println!("=======================================\n");
+    }
+}
+
+lazy_static::lazy_static! {
+    static ref TEST_REPORT: Arc<TestReport> = TestReport::new();
+    static ref ENV_LOCK: Mutex<()> = Mutex::new(());
+}
+
+/// Generic test harness for any Provider implementation
+struct ProviderTester {
+    provider: Arc<dyn Provider>,
+    name: String,
+}
+
+impl ProviderTester {
+    fn new<T: Provider + Send + Sync + 'static>(provider: T, name: String) -> Self {
+        Self {
+            provider: Arc::new(provider),
+            name,
+        }
+    }
+
+    async fn test_basic_response(&self) -> Result<()> {
+        let message = Message::user().with_text("Just say hello!");
+
+        let response = self
+            .provider
+            .complete("You are a helpful assistant.", &[message], &[])
+            .await?;
+
+        // For a basic response, we expect a single text response
+        assert_eq!(
+            response.message.content.len(),
+            1,
+            "Expected single content item in response"
+        );
+
+        // Verify we got a text response
+        assert!(
+            matches!(response.message.content[0], MessageContent::Text(_)),
+            "Expected text response"
+        );
+
+        Ok(())
+    }
+
+    async fn test_tool_usage(&self) -> Result<()> {
+        let weather_tool = Tool::new(
+            "get_weather",
+            "Get the weather for a location",
+            serde_json::json!({
+                "type": "object",
+                "required": ["location"],
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA"
+                    }
+                }
+            }),
+        );
+
+        let message = Message::user().with_text("What's the weather like in San Francisco?");
+
+        let response1 = self
+            .provider
+            .complete(
+                "You are a helpful weather assistant.",
+                &[message.clone()],
+                &[weather_tool.clone()],
+            )
+            .await?;
+
+        println!("=== {}::reponse1 ===", self.name);
+        dbg!(&response1);
+        println!("===================");
+
+        // Verify we got a tool request
+        assert!(
+            response1
+                .message
+                .content
+                .iter()
+                .any(|content| matches!(content, MessageContent::ToolRequest(_))),
+            "Expected tool request in response"
+        );
+
+        let id = &response1
+            .message
+            .content
+            .iter()
+            .filter_map(|message| message.as_tool_request())
+            .last()
+            .expect("got tool request")
+            .id;
+
+        let weather = Message::user().with_tool_response(
+            id,
+            Ok(vec![Content::text(
+                "
+                  50°F°C
+                  Precipitation: 0%
+                  Humidity: 84%
+                  Wind: 2 mph
+                  Weather
+                  Saturday 9:00 PM
+                  Clear",
+            )]),
+        );
+
+        // Verify we construct a valid payload including the request/response pair for the next inference
+        let response2 = self
+            .provider
+            .complete(
+                "You are a helpful weather assistant.",
+                &[message, response1.message, weather],
+                &[weather_tool],
+            )
+            .await?;
+
+        println!("=== {}::reponse2 ===", self.name);
+        dbg!(&response2);
+        println!("===================");
+
+        assert!(
+            response2
+                .message
+                .content
+                .iter()
+                .any(|content| matches!(content, MessageContent::Text(_))),
+            "Expected text for final response"
+        );
+
+        Ok(())
+    }
+
+    async fn test_context_length_exceeded_error(&self) -> Result<()> {
+        // Google Gemini has a really long context window
+        let large_message_content = if self.name.to_lowercase() == "google" {
+            "hello ".repeat(1_300_000)
+        } else {
+            "hello ".repeat(300_000)
+        };
+
+        let messages = vec![
+            Message::user().with_text("hi there. what is 2 + 2?"),
+            Message::assistant().with_text("hey! I think it's 4."),
+            Message::user().with_text(&large_message_content),
+            Message::assistant().with_text("heyy!!"),
+            // Messages before this mark should be truncated
+            Message::user().with_text("what's the meaning of life?"),
+            Message::assistant().with_text("the meaning of life is 42"),
+            Message::user().with_text(
+                "did I ask you what's 2+2 in this message history? just respond with 'yes' or 'no'",
+            ),
+        ];
+
+        // Test that we get ProviderError::ContextLengthExceeded when the context window is exceeded
+        let result = self
+            .provider
+            .complete("You are a helpful assistant.", &messages, &[])
+            .await;
+
+        // Print some debug info
+        println!("=== {}::context_length_exceeded_error ===", self.name);
+        dbg!(&result);
+        println!("===================");
+
+        // Ollama truncates by default even when the context window is exceeded
+        if self.name.to_lowercase() == "ollama" {
+            assert!(
+                result.is_ok(),
+                "Expected to succeed because of default truncation"
+            );
+            return Ok(());
+        }
+
+        assert!(
+            result.is_err(),
+            "Expected error when context window is exceeded"
+        );
+        assert!(
+            matches!(result.unwrap_err(), ProviderError::ContextLengthExceeded(_)),
+            "Expected error to be ContextLengthExceeded"
+        );
+
+        Ok(())
+    }
+
+    /// Run all provider tests
+    async fn run_test_suite(&self) -> Result<()> {
+        self.test_basic_response().await?;
+        self.test_tool_usage().await?;
+        self.test_context_length_exceeded_error().await?;
+        Ok(())
+    }
+}
+
+fn load_env() {
+    if let Ok(path) = dotenv() {
+        println!("Loaded environment from {:?}", path);
+    }
+}
+
+/// Helper function to run a provider test with proper error handling and reporting
+async fn test_provider<F, T>(
+    name: &str,
+    required_vars: &[&str],
+    env_modifications: Option<HashMap<&str, Option<String>>>,
+    provider_fn: F,
+) -> Result<()>
+where
+    F: FnOnce() -> T,
+    T: Provider + Send + Sync + 'static,
+{
+    // We start off as failed, so that if the process panics it is seen as a failure
+    TEST_REPORT.record_fail(name);
+
+    // Take exclusive access to environment modifications
+    let lock = ENV_LOCK.lock().unwrap();
+
+    load_env();
+
+    // Save current environment state for required vars and modified vars
+    let mut original_env = HashMap::new();
+    for &var in required_vars {
+        if let Ok(val) = std::env::var(var) {
+            original_env.insert(var, val);
+        }
+    }
+    if let Some(mods) = &env_modifications {
+        for &var in mods.keys() {
+            if let Ok(val) = std::env::var(var) {
+                original_env.insert(var, val);
+            }
+        }
+    }
+
+    // Apply any environment modifications
+    if let Some(mods) = &env_modifications {
+        for (&var, value) in mods.iter() {
+            match value {
+                Some(val) => std::env::set_var(var, val),
+                None => std::env::remove_var(var),
+            }
+        }
+    }
+
+    // Setup the provider
+    let missing_vars = required_vars.iter().any(|var| std::env::var(var).is_err());
+    if missing_vars {
+        println!("Skipping {} tests - credentials not configured", name);
+        TEST_REPORT.record_skip(name);
+        return Ok(());
+    }
+
+    let provider = provider_fn();
+
+    // Restore original environment
+    for (&var, value) in original_env.iter() {
+        std::env::set_var(var, value);
+    }
+    if let Some(mods) = env_modifications {
+        for &var in mods.keys() {
+            if !original_env.contains_key(var) {
+                std::env::remove_var(var);
+            }
+        }
+    }
+
+    std::mem::drop(lock);
+
+    let tester = ProviderTester::new(provider, name.to_string());
+    match tester.run_test_suite().await {
+        Ok(_) => {
+            TEST_REPORT.record_pass(name);
+            Ok(())
+        }
+        Err(e) => {
+            println!("{} test failed: {}", name, e);
+            TEST_REPORT.record_fail(name);
+            Err(e)
+        }
+    }
+}
+
+#[tokio::test]
+async fn openai_complete() -> Result<()> {
+    test_provider(
+        "OpenAI",
+        &["OPENAI_API_KEY"],
+        None,
+        openai::OpenAiProvider::default,
+    )
+    .await
+}
+
+#[tokio::test]
+async fn databricks_complete() -> Result<()> {
+    test_provider(
+        "Databricks",
+        &["DATABRICKS_HOST", "DATABRICKS_TOKEN"],
+        None,
+        databricks::DatabricksProvider::default,
+    )
+    .await
+}
+
+// Print the final test report
+#[ctor::dtor]
+fn print_test_report() {
+    TEST_REPORT.print_summary();
+}

crates/goose-llm/tests/providers_extract.rs

@@ -0,0 +1,195 @@
+// tests/providers_extract.rs
+
+use anyhow::Result;
+use dotenv::dotenv;
+use goose_llm::message::Message;
+use goose_llm::providers::base::Provider;
+use goose_llm::providers::{databricks::DatabricksProvider, openai::OpenAiProvider};
+use goose_llm::ModelConfig;
+use serde_json::{json, Value};
+use std::sync::Arc;
+
+#[derive(Debug, PartialEq, Copy, Clone)]
+enum ProviderType {
+    OpenAi,
+    Databricks,
+}
+
+impl ProviderType {
+    fn required_env(&self) -> &'static [&'static str] {
+        match self {
+            ProviderType::OpenAi => &["OPENAI_API_KEY"],
+            ProviderType::Databricks => &["DATABRICKS_HOST", "DATABRICKS_TOKEN"],
+        }
+    }
+
+    fn create_provider(&self, cfg: ModelConfig) -> Result<Arc<dyn Provider>> {
+        Ok(match self {
+            ProviderType::OpenAi => Arc::new(OpenAiProvider::from_env(cfg)?),
+            ProviderType::Databricks => Arc::new(DatabricksProvider::from_env(cfg)?),
+        })
+    }
+}
+
+fn check_required_env_vars(required: &[&str]) -> bool {
+    let missing: Vec<_> = required
+        .iter()
+        .filter(|&&v| std::env::var(v).is_err())
+        .cloned()
+        .collect();
+    if !missing.is_empty() {
+        println!("Skipping test; missing env vars: {:?}", missing);
+        false
+    } else {
+        true
+    }
+}
+
+// --- Shared inputs for "paper" task ---
+const PAPER_SYSTEM: &str =
+    "You are an expert at structured data extraction. Extract the metadata of a research paper into JSON.";
+const PAPER_TEXT: &str =
+    "Application of Quantum Algorithms in Interstellar Navigation: A New Frontier \
+     by Dr. Stella Voyager, Dr. Nova Star, Dr. Lyra Hunter. Abstract: This paper \
+     investigates the utilization of quantum algorithms to improve interstellar \
+     navigation systems. Keywords: Quantum algorithms, interstellar navigation, \
+     space-time anomalies, quantum superposition, quantum entanglement, space travel.";
+
+fn paper_schema() -> Value {
+    json!({
+        "type": "object",
+        "properties": {
+            "title":    { "type": "string" },
+            "authors":  { "type": "array",  "items": { "type": "string" } },
+            "abstract": { "type": "string" },
+            "keywords": { "type": "array",  "items": { "type": "string" } }
+        },
+        "required": ["title","authors","abstract","keywords"],
+        "additionalProperties": false
+    })
+}
+
+// --- Shared inputs for "UI" task ---
+const UI_SYSTEM: &str = "You are a UI generator AI. Convert the user input into a JSON-driven UI.";
+const UI_TEXT: &str = "Make a User Profile Form";
+
+fn ui_schema() -> Value {
+    json!({
+        "type": "object",
+        "properties": {
+            "type": {
+                "type": "string",
+                "enum": ["div","button","header","section","field","form"]
+            },
+            "label":   { "type": "string" },
+            "children": {
+                "type": "array",
+                "items": { "$ref": "#" }
+            },
+            "attributes": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "name":  { "type": "string" },
+                        "value": { "type": "string" }
+                    },
+                    "required": ["name","value"],
+                    "additionalProperties": false
+                }
+            }
+        },
+        "required": ["type","label","children","attributes"],
+        "additionalProperties": false
+    })
+}
+
+/// Generic runner for any extract task
+async fn run_extract_test<F>(
+    provider_type: ProviderType,
+    model: &str,
+    system: &'static str,
+    user_text: &'static str,
+    schema: Value,
+    validate: F,
+) -> Result<()>
+where
+    F: Fn(&Value) -> bool,
+{
+    dotenv().ok();
+    if !check_required_env_vars(provider_type.required_env()) {
+        return Ok(());
+    }
+
+    let cfg = ModelConfig::new(model.to_string()).with_temperature(Some(0.0));
+    let provider = provider_type.create_provider(cfg)?;
+
+    let msg = Message::user().with_text(user_text);
+    let resp = provider.extract(system, &[msg], &schema).await?;
+
+    println!("[{:?}] extract => {}", provider_type, resp.data);
+
+    assert!(
+        validate(&resp.data),
+        "{:?} failed validation on {}",
+        provider_type,
+        resp.data
+    );
+    Ok(())
+}
+
+/// Helper for the "paper" task
+async fn run_extract_paper_test(provider: ProviderType, model: &str) -> Result<()> {
+    run_extract_test(
+        provider,
+        model,
+        PAPER_SYSTEM,
+        PAPER_TEXT,
+        paper_schema(),
+        |v| {
+            v.as_object()
+                .map(|o| {
+                    ["title", "authors", "abstract", "keywords"]
+                        .iter()
+                        .all(|k| o.contains_key(*k))
+                })
+                .unwrap_or(false)
+        },
+    )
+    .await
+}
+
+/// Helper for the "UI" task
+async fn run_extract_ui_test(provider: ProviderType, model: &str) -> Result<()> {
+    run_extract_test(provider, model, UI_SYSTEM, UI_TEXT, ui_schema(), |v| {
+        v.as_object()
+            .and_then(|o| o.get("type").and_then(Value::as_str))
+            == Some("form")
+    })
+    .await
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[tokio::test]
+    async fn openai_extract_paper() -> Result<()> {
+        run_extract_paper_test(ProviderType::OpenAi, "gpt-4o").await
+    }
+
+    #[tokio::test]
+    async fn openai_extract_ui() -> Result<()> {
+        run_extract_ui_test(ProviderType::OpenAi, "gpt-4o").await
+    }
+
+    #[tokio::test]
+    async fn databricks_extract_paper() -> Result<()> {
+        run_extract_paper_test(ProviderType::Databricks, "goose-gpt-4-1").await
+    }
+
+    #[tokio::test]
+    async fn databricks_extract_ui() -> Result<()> {
+        run_extract_ui_test(ProviderType::Databricks, "goose-gpt-4-1").await
+    }
+}