Rust SDK - Stream Method - Edgee documentation

The stream() method is used to make streaming chat completion requests to the Edgee AI Gateway. It returns a Result containing a Stream that yields Result<StreamChunk> objects as they arrive from the API.

Arguments

Parameter	Type	Description
`model`	`impl Into<String>`	The model identifier to use (e.g., `"gpt-5.2"`)
`input`	`impl Into<Input>`	The input for the completion. Can be a string (`&str` or `String`), `Vec<Message>`, or `InputObject`

Input Types

The stream() method accepts the same input types as send():

String Input

When input is a string, it’s automatically converted to a user message:

use tokio_stream::StreamExt;

let mut stream = client.stream("gpt-5.2", "Tell me a story").await?;

while let Some(result) = stream.next().await {
    match result {
        Ok(chunk) => {
            if let Some(text) = chunk.text() {
                print!("{}", text);
            }
            
            if let Some(reason) = chunk.finish_reason() {
                println!("\nFinished: {}", reason);
            }
        }
        Err(e) => eprintln!("Stream error: {}", e),
    }
}
// Equivalent to: input: InputObject::new(vec![Message::user("Tell me a story")])

`Vec<Message>` or `InputObject`

When input is a Vec<Message> or InputObject, you have full control over the conversation:

Property	Type	Description
`messages`	`Vec<Message>`	Array of conversation messages
`tools`	`Option<Vec<Tool>>`	Array of function tools available to the model
`tool_choice`	`Option<serde_json::Value>`	Controls which tool (if any) the model should call. See Tools documentation for details
`tags`	`Option<Vec<String>>`	Optional tags to categorize and label the request for analytics and filtering. Can also be sent via the `x-edgee-tags` header (comma-separated)
`compression_model`	`Option<String>`	Compression model for this request: `"claude"`, `"codex"`, `"opencode"`, `"cursor"`. Each model is a bundle of compression strategies. Overrides API key settings when present.

For details about Message type, see the Send Method documentation. For details about Tool and ToolChoice types, see the Tools documentation. Example - Streaming with Messages:

use edgee::Message;
use tokio_stream::StreamExt;

let messages = vec![
    Message::system("You are a helpful assistant."),
    Message::user("Write a poem about coding"),
];

let mut stream = client.stream("gpt-5.2", messages).await?;

while let Some(result) = stream.next().await {
    if let Ok(chunk) = result {
        if let Some(text) = chunk.text() {
            print!("{}", text);
        }
    }
}

Return Value

The stream() method returns a Result containing a Stream that yields Result<StreamChunk>. Each chunk contains incremental updates to the response.

StreamChunk Object

Each chunk yielded by the stream has the following structure:

Property	Type	Description
`id`	`String`	Unique identifier for the completion
`object`	`String`	Object type (typically `"chat.completion.chunk"`)
`created`	`u64`	Unix timestamp of when the chunk was created
`model`	`String`	Model identifier used for the completion
`choices`	`Vec<StreamChoice>`	Array of streaming choices (typically one)
`compression`	`Option<Compression>`	Token compression metrics (if compression was applied)

StreamChoice Object

Each choice in the choices array contains:

Property	Type	Description
`index`	`u32`	The index of this choice in the array
`delta`	`StreamDelta`	The incremental update to the message
`finish_reason`	`Option<String>`	Reason why the generation stopped. Only present in the final chunk. Possible values: `"stop"`, `"length"`, `"tool_calls"`, `"content_filter"`, or `None`

Example - Handling Multiple Choices:

use tokio_stream::StreamExt;

let mut stream = client.stream("gpt-5.2", "Give me creative ideas").await?;

while let Some(result) = stream.next().await {
    if let Ok(chunk) = result {
        for choice in &chunk.choices {
            if let Some(content) = &choice.delta.content {
                println!("Choice {}: {}", choice.index, content);
            }
        }
    }
}

StreamDelta Object

The delta object contains incremental updates:

Property	Type	Description
`role`	`Option<Role>`	The role of the message (typically `Role::Assistant`). Only present in the first chunk
`content`	`Option<String>`	Incremental text content. Each chunk contains a portion of the full response
`tool_calls`	`Option<Vec<ToolCall>>`	Array of tool calls (if any). See Tools documentation for details

Convenience Methods

The StreamChunk struct provides convenience methods for easier access:

Method	Return Type	Description
`text()`	`Option<&str>`	Shortcut to `choices[0].delta.content.as_deref()` - the incremental text content
`role()`	`Option<&Role>`	Shortcut to `choices[0].delta.role.as_ref()` - the message role (first chunk only)
`finish_reason()`	`Option<&str>`	Shortcut to `choices[0].finish_reason.as_deref()` - the finish reason (final chunk only)

Example - Using Convenience Methods:

use tokio_stream::StreamExt;

let mut stream = client.stream("gpt-5.2", "Explain quantum computing").await?;

while let Some(result) = stream.next().await {
    match result {
        Ok(chunk) => {
            // Content chunks
            if let Some(text) = chunk.text() {
                print!("{}", text);
            }

            // First chunk contains the role
            if let Some(role) = chunk.role() {
                println!("\nRole: {:?}", role);
            }

            // Last chunk contains finish reason
            if let Some(reason) = chunk.finish_reason() {
                println!("\nFinish reason: {}", reason);
            }
        }
        Err(e) => eprintln!("Stream error: {}", e),
    }
}

Understanding Streaming Behavior

Chunk Structure

First chunk: Contains role (typically Role::Assistant) and may contain initial content
Content chunks: Contain incremental content updates
Final chunk: Contains finish_reason indicating why generation stopped

Example - Collecting Full Response:

use tokio_stream::StreamExt;

let mut stream = client.stream("gpt-5.2", "Tell me a story").await?;
let mut full_text = String::new();

while let Some(result) = stream.next().await {
    match result {
        Ok(chunk) => {
            if let Some(text) = chunk.text() {
                full_text.push_str(text);
                print!("{}", text); // Also display as it streams
            }
        }
        Err(e) => eprintln!("Stream error: {}", e),
    }
}

println!("\n\nFull response ({} characters):", full_text.len());
println!("{}", full_text);

Finish Reasons

Value	Description
`"stop"`	Model generated a complete response and stopped naturally
`"length"`	Response was cut off due to token limit
`"tool_calls"`	Model requested tool/function calls
`"content_filter"`	Content was filtered by safety systems
`None`	Generation is still in progress (not the final chunk)

Empty Chunks

Some chunks may not contain content. This is normal and can happen when:

The chunk only contains metadata (role, finish_reason)
The chunk is part of tool call processing
Network buffering creates empty chunks

Always check for chunk.text() before using it:

use tokio_stream::StreamExt;

let mut stream = client.stream("gpt-5.2", "Hello").await?;

while let Some(result) = stream.next().await {
    if let Ok(chunk) = result {
        if let Some(text) = chunk.text() {  // ✅ Good: Check before using
            println!("{}", text);
        }
        // ❌ Bad: println!("{:?}", chunk.text()) - may print None
    }
}

Error Handling

The stream() method can return errors at two levels:

Initial error: When creating the stream (returns Result<Stream>)
Stream errors: Individual chunks may contain errors (returns Result<StreamChunk>)

use edgee::Error;
use tokio_stream::StreamExt;

// Handle initial error
let mut stream = match client.stream("gpt-5.2", "Hello!").await {
    Ok(stream) => stream,
    Err(Error::Api { status, message }) => {
        eprintln!("API error {}: {}", status, message);
        return;
    }
    Err(e) => {
        eprintln!("Error creating stream: {}", e);
        return;
    }
};

// Handle stream errors
while let Some(result) = stream.next().await {
    match result {
        Ok(chunk) => {
            if let Some(text) = chunk.text() {
                print!("{}", text);
            }
        }
        Err(e) => {
            eprintln!("Stream error: {}", e);
        }
    }
}

​Arguments

​Input Types

​String Input

​Vec<Message> or InputObject

​Return Value

​StreamChunk Object

​StreamChoice Object

​StreamDelta Object

​Convenience Methods

​Understanding Streaming Behavior

​Chunk Structure

​Finish Reasons

​Empty Chunks

​Error Handling