rig_core/providers/

llamafile.rs

//! Llamafile API client and Rig integration
//!
//! [Llamafile](https://github.com/Mozilla-Ocho/llamafile) is a Mozilla Builders project
//! that distributes LLMs as single-file executables. When started, it exposes an
//! OpenAI-compatible API at `http://localhost:8080/v1`.
//!
//! # Example
//! ```rust,ignore
//! use rig_core::providers::llamafile;
//! use rig_core::completion::Prompt;
//!
//! // Create a new Llamafile client (defaults to http://localhost:8080)
//! let client = llamafile::Client::from_url("http://localhost:8080");
//!
//! // Create an agent with a preamble
//! let agent = client
//!     .agent(llamafile::LLAMA_CPP)
//!     .preamble("You are a helpful assistant.")
//!     .build();
//!
//! // Prompt the agent and print the response
//! let response = agent.prompt("Hello!").await?;
//! println!("{response}");
//! ```

use crate::client::{
    self, Capabilities, Capable, DebugExt, Nothing, Provider, ProviderBuilder, ProviderClient,
};
use crate::completion::GetTokenUsage;
use crate::http_client::{self, HttpClientExt};
use crate::providers::internal::openai_chat_completions_compatible::{
    self, CompatibleChoiceData, CompatibleChunk, CompatibleFinishReason, CompatibleStreamProfile,
};
use crate::providers::openai::{self, StreamingToolCall};
use crate::{
    completion::{self, CompletionError, CompletionRequest},
    embeddings::{self, EmbeddingError},
    json_utils,
};
use bytes::Bytes;
use serde::{Deserialize, Serialize};
use serde_json::{Map, Value};
use tracing::{Level, info_span};
use tracing_futures::Instrument;

// ================================================================
// Main Llamafile Client
// ================================================================
const LLAMAFILE_API_BASE_URL: &str = "http://localhost:8080";

/// The default model identifier reported by llamafile.
pub const LLAMA_CPP: &str = "LLaMA_CPP";

#[derive(Debug, Default, Clone, Copy)]
pub struct LlamafileExt;

#[derive(Debug, Default, Clone, Copy)]
pub struct LlamafileBuilder;

impl Provider for LlamafileExt {
    type Builder = LlamafileBuilder;
    const VERIFY_PATH: &'static str = "v1/models";
}

impl<H> Capabilities<H> for LlamafileExt {
    type Completion = Capable<CompletionModel<H>>;
    type Embeddings = Capable<EmbeddingModel<H>>;
    type Transcription = Nothing;
    type ModelListing = Nothing;
    #[cfg(feature = "image")]
    type ImageGeneration = Nothing;
    #[cfg(feature = "audio")]
    type AudioGeneration = Nothing;
}

impl DebugExt for LlamafileExt {}

impl ProviderBuilder for LlamafileBuilder {
    type Extension<H>
        = LlamafileExt
    where
        H: HttpClientExt;
    type ApiKey = Nothing;

    const BASE_URL: &'static str = LLAMAFILE_API_BASE_URL;

    fn build<H>(
        _builder: &client::ClientBuilder<Self, Self::ApiKey, H>,
    ) -> http_client::Result<Self::Extension<H>>
    where
        H: HttpClientExt,
    {
        Ok(LlamafileExt)
    }
}

pub type Client<H = reqwest::Client> = client::Client<LlamafileExt, H>;
pub type ClientBuilder<H = crate::markers::Missing> =
    client::ClientBuilder<LlamafileBuilder, Nothing, H>;

impl Client {
    /// Create a client pointing at the given llamafile base URL
    /// (e.g. `http://localhost:8080`).
    pub fn from_url(base_url: &str) -> crate::client::ProviderClientResult<Self> {
        Self::builder()
            .api_key(Nothing)
            .base_url(base_url)
            .build()
            .map_err(Into::into)
    }
}

impl ProviderClient for Client {
    type Input = Nothing;
    type Error = crate::client::ProviderClientError;

    fn from_env() -> Result<Self, Self::Error> {
        let api_base = crate::client::required_env_var("LLAMAFILE_API_BASE_URL")?;
        Self::from_url(&api_base)
    }

    fn from_val(_: Self::Input) -> Result<Self, Self::Error> {
        Self::builder().api_key(Nothing).build().map_err(Into::into)
    }
}

// ================================================================
// API Error Handling
// ================================================================

#[derive(Debug, Deserialize)]
struct ApiErrorResponse {
    message: String,
}

#[derive(Debug, Deserialize)]
#[serde(untagged)]
enum ApiResponse<T> {
    Ok(T),
    Err(ApiErrorResponse),
}

// ================================================================
// Completion Request
// ================================================================

/// Llamafile uses the OpenAI chat completions format.
/// We reuse the OpenAI `Message` type for maximum compatibility.
#[derive(Debug, Serialize)]
struct LlamafileCompletionRequest {
    model: String,
    messages: Vec<Value>,
    #[serde(skip_serializing_if = "Option::is_none")]
    temperature: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none")]
    max_tokens: Option<u64>,
    #[serde(skip_serializing_if = "Vec::is_empty")]
    tools: Vec<openai::ToolDefinition>,
    #[serde(flatten, skip_serializing_if = "Option::is_none")]
    additional_params: Option<serde_json::Value>,
}

fn join_text_segments<I>(segments: I) -> String
where
    I: IntoIterator<Item = String>,
{
    let segments = segments
        .into_iter()
        .filter(|segment| !segment.is_empty())
        .collect::<Vec<_>>();

    if segments.is_empty() {
        String::new()
    } else {
        segments.join("\n\n")
    }
}

fn flatten_system_content(content: &crate::OneOrMany<openai::SystemContent>) -> String {
    join_text_segments(content.iter().map(|item| item.text.clone()))
}

fn flatten_user_content(content: &crate::OneOrMany<openai::UserContent>) -> Option<String> {
    content
        .iter()
        .map(|item| match item {
            openai::UserContent::Text { text } => Some(text.clone()),
            _ => None,
        })
        .collect::<Option<Vec<_>>>()
        .map(join_text_segments)
}

fn flatten_assistant_content(content: &[openai::AssistantContent]) -> String {
    join_text_segments(content.iter().map(|item| match item {
        openai::AssistantContent::Text { text } => text.clone(),
        openai::AssistantContent::Refusal { refusal } => refusal.clone(),
    }))
}

fn optional_value<T>(value: Option<T>) -> Result<Option<Value>, CompletionError>
where
    T: Serialize,
{
    value
        .map(serde_json::to_value)
        .transpose()
        .map_err(Into::into)
}

fn message_content_value<T>(
    flattened: Option<String>,
    original: &T,
) -> Result<Value, CompletionError>
where
    T: Serialize,
{
    match flattened {
        Some(text) => Ok(Value::String(text)),
        None => Ok(serde_json::to_value(original)?),
    }
}

fn llamafile_message_value(message: openai::Message) -> Result<Value, CompletionError> {
    match message {
        openai::Message::System { content, name } => {
            let mut object = Map::new();
            object.insert("role".into(), Value::String("system".into()));
            object.insert(
                "content".into(),
                Value::String(flatten_system_content(&content)),
            );
            if let Some(name) = name {
                object.insert("name".into(), Value::String(name));
            }
            Ok(Value::Object(object))
        }
        openai::Message::User { content, name } => {
            let mut object = Map::new();
            object.insert("role".into(), Value::String("user".into()));
            object.insert(
                "content".into(),
                message_content_value(flatten_user_content(&content), &content)?,
            );
            if let Some(name) = name {
                object.insert("name".into(), Value::String(name));
            }
            Ok(Value::Object(object))
        }
        openai::Message::Assistant {
            content,
            refusal,
            reasoning: _,
            audio,
            name,
            tool_calls,
        } => {
            let mut object = Map::new();
            object.insert("role".into(), Value::String("assistant".into()));
            object.insert(
                "content".into(),
                Value::String(flatten_assistant_content(&content)),
            );
            if let Some(refusal) = refusal {
                object.insert("refusal".into(), Value::String(refusal));
            }
            if let Some(audio) = optional_value(audio)? {
                object.insert("audio".into(), audio);
            }
            if let Some(name) = name {
                object.insert("name".into(), Value::String(name));
            }
            if !tool_calls.is_empty() {
                object.insert("tool_calls".into(), serde_json::to_value(tool_calls)?);
            }
            Ok(Value::Object(object))
        }
        openai::Message::ToolResult {
            tool_call_id,
            content,
        } => {
            let mut object = Map::new();
            object.insert("role".into(), Value::String("tool".into()));
            object.insert("tool_call_id".into(), Value::String(tool_call_id));
            object.insert("content".into(), Value::String(content.as_text()));
            Ok(Value::Object(object))
        }
    }
}

impl TryFrom<(&str, CompletionRequest)> for LlamafileCompletionRequest {
    type Error = CompletionError;

    fn try_from((model, req): (&str, CompletionRequest)) -> Result<Self, Self::Error> {
        if req.output_schema.is_some() {
            tracing::warn!("Structured outputs may not be supported by llamafile");
        }
        let model = req.model.clone().unwrap_or_else(|| model.to_string());

        // Build message history: preamble -> documents -> chat history
        let mut full_history: Vec<openai::Message> = match &req.preamble {
            Some(preamble) => vec![openai::Message::system(preamble)],
            None => vec![],
        };

        if let Some(docs) = req.normalized_documents() {
            let docs: Vec<openai::Message> = docs.try_into()?;
            full_history.extend(docs);
        }

        let chat_history: Vec<openai::Message> = req
            .chat_history
            .clone()
            .into_iter()
            .map(|msg| msg.try_into())
            .collect::<Result<Vec<Vec<openai::Message>>, _>>()?
            .into_iter()
            .flatten()
            .collect();

        full_history.extend(chat_history);

        Ok(Self {
            model,
            messages: full_history
                .into_iter()
                .map(llamafile_message_value)
                .collect::<Result<Vec<_>, _>>()?,
            temperature: req.temperature,
            max_tokens: req.max_tokens,
            tools: req
                .tools
                .into_iter()
                .map(openai::ToolDefinition::from)
                .collect(),
            additional_params: req.additional_params,
        })
    }
}

// ================================================================
// Completion Model
// ================================================================

/// Llamafile completion model.
#[derive(Clone)]
pub struct CompletionModel<T = reqwest::Client> {
    client: Client<T>,
    /// The model identifier (usually `LLaMA_CPP`).
    pub model: String,
}

impl<T> CompletionModel<T> {
    /// Create a new completion model for the given client and model name.
    pub fn new(client: Client<T>, model: impl Into<String>) -> Self {
        Self {
            client,
            model: model.into(),
        }
    }
}

impl<T> completion::CompletionModel for CompletionModel<T>
where
    T: HttpClientExt + Clone + Default + std::fmt::Debug + Send + 'static,
{
    type Response = openai::CompletionResponse;
    type StreamingResponse = StreamingCompletionResponse;
    type Client = Client<T>;

    fn make(client: &Self::Client, model: impl Into<String>) -> Self {
        Self::new(client.clone(), model)
    }

    async fn completion(
        &self,
        completion_request: CompletionRequest,
    ) -> Result<completion::CompletionResponse<openai::CompletionResponse>, CompletionError> {
        let span = if tracing::Span::current().is_disabled() {
            info_span!(
                target: "rig::completions",
                "chat",
                gen_ai.operation.name = "chat",
                gen_ai.provider.name = "llamafile",
                gen_ai.request.model = self.model,
                gen_ai.system_instructions = completion_request.preamble,
                gen_ai.response.id = tracing::field::Empty,
                gen_ai.response.model = tracing::field::Empty,
                gen_ai.usage.output_tokens = tracing::field::Empty,
                gen_ai.usage.input_tokens = tracing::field::Empty,
                gen_ai.usage.cache_read.input_tokens = tracing::field::Empty,
            )
        } else {
            tracing::Span::current()
        };

        let request =
            LlamafileCompletionRequest::try_from((self.model.as_ref(), completion_request))?;

        if tracing::enabled!(Level::TRACE) {
            tracing::trace!(target: "rig::completions",
                "Llamafile completion request: {}",
                serde_json::to_string_pretty(&request)?
            );
        }

        let body = serde_json::to_vec(&request)?;
        let req = self
            .client
            .post("v1/chat/completions")?
            .body(body)
            .map_err(|e| CompletionError::HttpError(e.into()))?;

        async move {
            let response = self.client.send::<_, Bytes>(req).await?;
            let status = response.status();
            let response_body = response.into_body().into_future().await?.to_vec();

            if status.is_success() {
                match serde_json::from_slice::<ApiResponse<openai::CompletionResponse>>(
                    &response_body,
                )? {
                    ApiResponse::Ok(response) => {
                        let span = tracing::Span::current();
                        span.record("gen_ai.response.id", response.id.clone());
                        span.record("gen_ai.response.model", response.model.clone());
                        if let Some(ref usage) = response.usage {
                            span.record("gen_ai.usage.input_tokens", usage.prompt_tokens);
                            span.record(
                                "gen_ai.usage.output_tokens",
                                usage.total_tokens - usage.prompt_tokens,
                            );
                        }

                        if tracing::enabled!(Level::TRACE) {
                            tracing::trace!(target: "rig::completions",
                                "Llamafile completion response: {}",
                                serde_json::to_string_pretty(&response)?
                            );
                        }

                        response.try_into()
                    }
                    ApiResponse::Err(err) => Err(CompletionError::ProviderError(err.message)),
                }
            } else {
                Err(CompletionError::ProviderError(
                    String::from_utf8_lossy(&response_body).to_string(),
                ))
            }
        }
        .instrument(span)
        .await
    }

    async fn stream(
        &self,
        completion_request: CompletionRequest,
    ) -> Result<
        crate::streaming::StreamingCompletionResponse<Self::StreamingResponse>,
        CompletionError,
    > {
        let span = if tracing::Span::current().is_disabled() {
            info_span!(
                target: "rig::completions",
                "chat_streaming",
                gen_ai.operation.name = "chat_streaming",
                gen_ai.provider.name = "llamafile",
                gen_ai.request.model = self.model,
                gen_ai.system_instructions = completion_request.preamble,
                gen_ai.response.id = tracing::field::Empty,
                gen_ai.response.model = tracing::field::Empty,
                gen_ai.usage.output_tokens = tracing::field::Empty,
                gen_ai.usage.input_tokens = tracing::field::Empty,
            )
        } else {
            tracing::Span::current()
        };

        let mut request =
            LlamafileCompletionRequest::try_from((self.model.as_ref(), completion_request))?;

        let params = json_utils::merge(
            request.additional_params.unwrap_or(serde_json::json!({})),
            serde_json::json!({"stream": true}),
        );
        request.additional_params = Some(params);

        if tracing::enabled!(Level::TRACE) {
            tracing::trace!(target: "rig::completions",
                "Llamafile streaming completion request: {}",
                serde_json::to_string_pretty(&request)?
            );
        }

        let body = serde_json::to_vec(&request)?;
        let req = self
            .client
            .post("v1/chat/completions")?
            .body(body)
            .map_err(|e| CompletionError::HttpError(e.into()))?;

        send_streaming_request(self.client.clone(), req, span).await
    }
}

// ================================================================
// Streaming Support
// ================================================================

#[derive(Deserialize, Debug)]
struct StreamingDelta {
    #[serde(default)]
    content: Option<String>,
    #[serde(default, deserialize_with = "json_utils::null_or_vec")]
    tool_calls: Vec<StreamingToolCall>,
}

#[derive(Deserialize, Debug)]
struct StreamingChoice {
    delta: StreamingDelta,
    #[serde(default)]
    finish_reason: Option<openai::completion::streaming::FinishReason>,
}

#[derive(Deserialize, Debug)]
struct StreamingCompletionChunk {
    id: Option<String>,
    model: Option<String>,
    choices: Vec<StreamingChoice>,
    usage: Option<openai::Usage>,
}

/// Final streaming response containing usage information.
#[derive(Clone, Deserialize, Serialize, Debug)]
pub struct StreamingCompletionResponse {
    /// Token usage from the streaming response.
    pub usage: openai::Usage,
}

impl GetTokenUsage for StreamingCompletionResponse {
    fn token_usage(&self) -> Option<crate::completion::Usage> {
        self.usage.token_usage()
    }
}

#[derive(Clone, Copy)]
struct LlamafileCompatibleProfile;

impl CompatibleStreamProfile for LlamafileCompatibleProfile {
    type Usage = openai::Usage;
    type Detail = ();
    type FinalResponse = StreamingCompletionResponse;

    fn normalize_chunk(
        &self,
        data: &str,
    ) -> Result<Option<CompatibleChunk<Self::Usage, Self::Detail>>, CompletionError> {
        let data = match serde_json::from_str::<StreamingCompletionChunk>(data) {
            Ok(data) => data,
            Err(error) => {
                tracing::debug!(
                    ?error,
                    "Couldn't parse SSE payload as StreamingCompletionChunk"
                );
                return Ok(None);
            }
        };

        Ok(Some(
            openai_chat_completions_compatible::normalize_first_choice_chunk(
                data.id,
                data.model,
                data.usage,
                &data.choices,
                |choice| CompatibleChoiceData {
                    finish_reason: if choice.finish_reason
                        == Some(openai::completion::streaming::FinishReason::ToolCalls)
                    {
                        CompatibleFinishReason::ToolCalls
                    } else {
                        CompatibleFinishReason::Other
                    },
                    text: choice.delta.content.clone(),
                    reasoning: None,
                    tool_calls: openai_chat_completions_compatible::tool_call_chunks(
                        &choice.delta.tool_calls,
                    ),
                    details: Vec::new(),
                },
            ),
        ))
    }

    fn build_final_response(&self, usage: Self::Usage) -> Self::FinalResponse {
        StreamingCompletionResponse { usage }
    }

    fn uses_distinct_tool_call_eviction(&self) -> bool {
        true
    }

    fn emits_complete_single_chunk_tool_calls(&self) -> bool {
        true
    }
}

async fn send_streaming_request<T>(
    client: T,
    req: http::Request<Vec<u8>>,
    span: tracing::Span,
) -> Result<
    crate::streaming::StreamingCompletionResponse<StreamingCompletionResponse>,
    CompletionError,
>
where
    T: HttpClientExt + Clone + 'static,
{
    tracing::Instrument::instrument(
        openai_chat_completions_compatible::send_compatible_streaming_request(
            client,
            req,
            LlamafileCompatibleProfile,
        ),
        span,
    )
    .await
}

// ================================================================
// Embedding Model
// ================================================================

/// Llamafile embedding model.
///
/// Llamafile supports the OpenAI-compatible `/v1/embeddings` endpoint.
#[derive(Clone)]
pub struct EmbeddingModel<T = reqwest::Client> {
    client: Client<T>,
    /// The model identifier.
    pub model: String,
    ndims: usize,
}

impl<T> EmbeddingModel<T> {
    /// Create a new embedding model for the given client, model name, and dimensions.
    pub fn new(client: Client<T>, model: impl Into<String>, ndims: usize) -> Self {
        Self {
            client,
            model: model.into(),
            ndims,
        }
    }
}

impl<T> embeddings::EmbeddingModel for EmbeddingModel<T>
where
    T: HttpClientExt + Clone + std::fmt::Debug + Default + Send + 'static,
{
    const MAX_DOCUMENTS: usize = 1024;

    type Client = Client<T>;

    fn make(client: &Self::Client, model: impl Into<String>, ndims: Option<usize>) -> Self {
        Self::new(client.clone(), model, ndims.unwrap_or_default())
    }

    fn ndims(&self) -> usize {
        self.ndims
    }

    async fn embed_texts(
        &self,
        documents: impl IntoIterator<Item = String>,
    ) -> Result<Vec<embeddings::Embedding>, EmbeddingError> {
        let documents = documents.into_iter().collect::<Vec<_>>();

        let body = serde_json::json!({
            "model": self.model,
            "input": documents,
        });

        let body = serde_json::to_vec(&body)?;

        let req = self
            .client
            .post("v1/embeddings")?
            .body(body)
            .map_err(|e| EmbeddingError::HttpError(e.into()))?;

        let response = self.client.send(req).await?;

        if response.status().is_success() {
            let body: Vec<u8> = response.into_body().await?;
            let body: ApiResponse<openai::EmbeddingResponse> = serde_json::from_slice(&body)?;

            match body {
                ApiResponse::Ok(response) => {
                    tracing::info!(target: "rig",
                        "Llamafile embedding token usage: {:?}",
                        response.usage
                    );

                    if response.data.len() != documents.len() {
                        return Err(EmbeddingError::ResponseError(
                            "Response data length does not match input length".into(),
                        ));
                    }

                    Ok(response
                        .data
                        .into_iter()
                        .zip(documents.into_iter())
                        .map(|(embedding, document)| embeddings::Embedding {
                            document,
                            vec: embedding
                                .embedding
                                .into_iter()
                                .filter_map(|n| n.as_f64())
                                .collect(),
                        })
                        .collect())
                }
                ApiResponse::Err(err) => Err(EmbeddingError::ProviderError(err.message)),
            }
        } else {
            let text = http_client::text(response).await?;
            Err(EmbeddingError::ProviderError(text))
        }
    }
}

// ================================================================
// Tests
// ================================================================
#[cfg(test)]
mod tests {
    use super::*;
    use crate::client::Nothing;
    use crate::completion::Document;
    use std::collections::HashMap;

    #[test]
    fn test_client_initialization() {
        let _client =
            crate::providers::llamafile::Client::new(Nothing).expect("Client::new() failed");
        let _client_from_builder = crate::providers::llamafile::Client::builder()
            .api_key(Nothing)
            .build()
            .expect("Client::builder() failed");
    }

    #[test]
    fn test_client_from_url() {
        let _client = crate::providers::llamafile::Client::from_url("http://localhost:8080");
    }

    #[test]
    fn test_completion_request_conversion() {
        use crate::OneOrMany;
        use crate::completion::Message as CompletionMessage;
        use crate::message::{Text, UserContent};

        let completion_request = CompletionRequest {
            model: None,
            preamble: Some("You are a helpful assistant.".to_string()),
            chat_history: OneOrMany::one(CompletionMessage::User {
                content: OneOrMany::one(UserContent::Text(Text {
                    text: "Hello!".to_string(),
                })),
            }),
            documents: vec![],
            tools: vec![],
            temperature: Some(0.7),
            max_tokens: Some(256),
            tool_choice: None,
            additional_params: None,
            output_schema: None,
        };

        let request = LlamafileCompletionRequest::try_from((LLAMA_CPP, completion_request))
            .expect("Failed to create request");

        assert_eq!(request.model, LLAMA_CPP);
        assert_eq!(request.messages.len(), 2); // system + user
        assert_eq!(
            request.messages[0]["content"],
            "You are a helpful assistant."
        );
        assert_eq!(request.messages[1]["content"], "Hello!");
        assert_eq!(request.temperature, Some(0.7));
        assert_eq!(request.max_tokens, Some(256));
    }

    #[test]
    fn test_completion_request_flattens_text_only_document_arrays() {
        use crate::OneOrMany;
        use crate::completion::Message as CompletionMessage;
        use crate::message::{Text, UserContent};

        let completion_request = CompletionRequest {
            model: None,
            preamble: None,
            chat_history: OneOrMany::one(CompletionMessage::User {
                content: OneOrMany::one(UserContent::Text(Text {
                    text: "What does glarb-glarb mean?".to_string(),
                })),
            }),
            documents: vec![
                Document {
                    id: "doc-1".into(),
                    text: "Definition of flurbo: a green alien.".into(),
                    additional_props: HashMap::new(),
                },
                Document {
                    id: "doc-2".into(),
                    text: "Definition of glarb-glarb: an ancient farming tool.".into(),
                    additional_props: HashMap::new(),
                },
            ],
            tools: vec![],
            temperature: None,
            max_tokens: None,
            tool_choice: None,
            additional_params: None,
            output_schema: None,
        };

        let request = LlamafileCompletionRequest::try_from((LLAMA_CPP, completion_request))
            .expect("Failed to create request");

        assert_eq!(request.messages.len(), 2);
        assert!(request.messages[0]["content"].is_string());
        let documents = request.messages[0]["content"]
            .as_str()
            .expect("documents should serialize as a string");
        assert!(documents.contains("Definition of flurbo"));
        assert!(documents.contains("Definition of glarb-glarb"));
    }

    #[test]
    fn test_llamafile_message_value_flattens_assistant_text_content() {
        let message = openai::Message::Assistant {
            content: vec![openai::AssistantContent::Text {
                text: "Tool returned the answer.".into(),
            }],
            reasoning: None,
            refusal: None,
            audio: None,
            name: None,
            tool_calls: vec![openai::ToolCall {
                id: "call_1".into(),
                r#type: openai::ToolType::Function,
                function: openai::Function {
                    name: "weather".into(),
                    arguments: serde_json::json!({"city": "London"}),
                },
            }],
        };

        let value = llamafile_message_value(message).expect("message conversion should succeed");

        assert_eq!(value["role"], "assistant");
        assert_eq!(value["content"], "Tool returned the answer.");
        assert!(value["tool_calls"].is_array());
    }
}