ZIm/crates/collab/src/ai.rs
Kyle Kelley 68a1ad89bb
New revision of the Assistant Panel (#10870)
This is a crate only addition of a new version of the AssistantPanel.
We'll be putting this behind a feature flag while we iron out the new
experience.

Release Notes:

- N/A

---------

Co-authored-by: Nathan Sobo <nathan@zed.dev>
Co-authored-by: Antonio Scandurra <me@as-cii.com>
Co-authored-by: Conrad Irwin <conrad@zed.dev>
Co-authored-by: Marshall Bowers <elliott.codes@gmail.com>
Co-authored-by: Antonio Scandurra <antonio@zed.dev>
Co-authored-by: Nate Butler <nate@zed.dev>
Co-authored-by: Nate Butler <iamnbutler@gmail.com>
Co-authored-by: Max Brunsfeld <maxbrunsfeld@gmail.com>
Co-authored-by: Max <max@zed.dev>
2024-04-23 16:23:26 -07:00

138 lines
5.7 KiB
Rust

use anyhow::{anyhow, Context as _, Result};
use rpc::proto;
use util::ResultExt as _;
pub fn language_model_request_to_open_ai(
request: proto::CompleteWithLanguageModel,
) -> Result<open_ai::Request> {
Ok(open_ai::Request {
model: open_ai::Model::from_id(&request.model).unwrap_or(open_ai::Model::FourTurbo),
messages: request
.messages
.into_iter()
.map(|message: proto::LanguageModelRequestMessage| {
let role = proto::LanguageModelRole::from_i32(message.role)
.ok_or_else(|| anyhow!("invalid role {}", message.role))?;
let openai_message = match role {
proto::LanguageModelRole::LanguageModelUser => open_ai::RequestMessage::User {
content: message.content,
},
proto::LanguageModelRole::LanguageModelAssistant => {
open_ai::RequestMessage::Assistant {
content: Some(message.content),
tool_calls: message
.tool_calls
.into_iter()
.filter_map(|call| {
Some(open_ai::ToolCall {
id: call.id,
content: match call.variant? {
proto::tool_call::Variant::Function(f) => {
open_ai::ToolCallContent::Function {
function: open_ai::FunctionContent {
name: f.name,
arguments: f.arguments,
},
}
}
},
})
})
.collect(),
}
}
proto::LanguageModelRole::LanguageModelSystem => {
open_ai::RequestMessage::System {
content: message.content,
}
}
proto::LanguageModelRole::LanguageModelTool => open_ai::RequestMessage::Tool {
tool_call_id: message
.tool_call_id
.ok_or_else(|| anyhow!("tool message is missing tool call id"))?,
content: message.content,
},
};
Ok(openai_message)
})
.collect::<Result<Vec<open_ai::RequestMessage>>>()?,
stream: true,
stop: request.stop,
temperature: request.temperature,
tools: request
.tools
.into_iter()
.filter_map(|tool| {
Some(match tool.variant? {
proto::chat_completion_tool::Variant::Function(f) => {
open_ai::ToolDefinition::Function {
function: open_ai::FunctionDefinition {
name: f.name,
description: f.description,
parameters: if let Some(params) = &f.parameters {
Some(
serde_json::from_str(params)
.context("failed to deserialize tool parameters")
.log_err()?,
)
} else {
None
},
},
}
}
})
})
.collect(),
tool_choice: request.tool_choice,
})
}
pub fn language_model_request_to_google_ai(
request: proto::CompleteWithLanguageModel,
) -> Result<google_ai::GenerateContentRequest> {
Ok(google_ai::GenerateContentRequest {
contents: request
.messages
.into_iter()
.map(language_model_request_message_to_google_ai)
.collect::<Result<Vec<_>>>()?,
generation_config: None,
safety_settings: None,
})
}
pub fn language_model_request_message_to_google_ai(
message: proto::LanguageModelRequestMessage,
) -> Result<google_ai::Content> {
let role = proto::LanguageModelRole::from_i32(message.role)
.ok_or_else(|| anyhow!("invalid role {}", message.role))?;
Ok(google_ai::Content {
parts: vec![google_ai::Part::TextPart(google_ai::TextPart {
text: message.content,
})],
role: match role {
proto::LanguageModelRole::LanguageModelUser => google_ai::Role::User,
proto::LanguageModelRole::LanguageModelAssistant => google_ai::Role::Model,
proto::LanguageModelRole::LanguageModelSystem => google_ai::Role::User,
proto::LanguageModelRole::LanguageModelTool => {
Err(anyhow!("we don't handle tool calls with google ai yet"))?
}
},
})
}
pub fn count_tokens_request_to_google_ai(
request: proto::CountTokensWithLanguageModel,
) -> Result<google_ai::CountTokensRequest> {
Ok(google_ai::CountTokensRequest {
contents: request
.messages
.into_iter()
.map(language_model_request_message_to_google_ai)
.collect::<Result<Vec<_>>>()?,
})
}