Baseline project

src/client/gemini.rs

@@ -0,0 +1,136 @@
+use super::vertexai::*;
+use super::*;
+
+use anyhow::{Context, Result};
+use reqwest::RequestBuilder;
+use serde::Deserialize;
+use serde_json::{json, Value};
+
+const API_BASE: &str = "https://generativelanguage.googleapis.com/v1beta";
+
+#[derive(Debug, Clone, Deserialize, Default)]
+pub struct GeminiConfig {
+    pub name: Option<String>,
+    pub api_key: Option<String>,
+    pub api_base: Option<String>,
+    #[serde(default)]
+    pub models: Vec<ModelData>,
+    pub patch: Option<RequestPatch>,
+    pub extra: Option<ExtraConfig>,
+}
+
+impl GeminiClient {
+    config_get_fn!(api_key, get_api_key);
+    config_get_fn!(api_base, get_api_base);
+
+    pub const PROMPTS: [PromptAction<'static>; 1] = [("api_key", "API Key", None)];
+}
+
+impl_client_trait!(
+    GeminiClient,
+    (
+        prepare_chat_completions,
+        gemini_chat_completions,
+        gemini_chat_completions_streaming
+    ),
+    (prepare_embeddings, embeddings),
+    (noop_prepare_rerank, noop_rerank),
+);
+
+fn prepare_chat_completions(
+    self_: &GeminiClient,
+    data: ChatCompletionsData,
+) -> Result<RequestData> {
+    let api_key = self_.get_api_key()?;
+    let api_base = self_
+        .get_api_base()
+        .unwrap_or_else(|_| API_BASE.to_string());
+
+    let func = match data.stream {
+        true => "streamGenerateContent",
+        false => "generateContent",
+    };
+
+    let url = format!(
+        "{}/models/{}:{}",
+        api_base.trim_end_matches('/'),
+        self_.model.real_name(),
+        func
+    );
+
+    let body = gemini_build_chat_completions_body(data, &self_.model)?;
+
+    let mut request_data = RequestData::new(url, body);
+
+    request_data.header("x-goog-api-key", api_key);
+
+    Ok(request_data)
+}
+
+fn prepare_embeddings(self_: &GeminiClient, data: &EmbeddingsData) -> Result<RequestData> {
+    let api_key = self_.get_api_key()?;
+    let api_base = self_
+        .get_api_base()
+        .unwrap_or_else(|_| API_BASE.to_string());
+
+    let url = format!(
+        "{}/models/{}:batchEmbedContents?key={}",
+        api_base.trim_end_matches('/'),
+        self_.model.real_name(),
+        api_key
+    );
+
+    let model_id = format!("models/{}", self_.model.real_name());
+
+    let requests: Vec<_> = data
+        .texts
+        .iter()
+        .map(|text| {
+            json!({
+                "model": model_id,
+                "content": {
+                    "parts": [
+                        {
+                            "text": text
+                        }
+                    ]
+                },
+            })
+        })
+        .collect();
+
+    let body = json!({
+        "requests": requests,
+    });
+
+    let request_data = RequestData::new(url, body);
+
+    Ok(request_data)
+}
+
+async fn embeddings(builder: RequestBuilder, _model: &Model) -> Result<EmbeddingsOutput> {
+    let res = builder.send().await?;
+    let status = res.status();
+    let data: Value = res.json().await?;
+    if !status.is_success() {
+        catch_error(&data, status.as_u16())?;
+    }
+    let res_body: EmbeddingsResBody =
+        serde_json::from_value(data).context("Invalid embeddings data")?;
+    let output = res_body
+        .embeddings
+        .into_iter()
+        .map(|embedding| embedding.values)
+        .collect();
+    Ok(output)
+}
+
+#[derive(Deserialize)]
+struct EmbeddingsResBody {
+    embeddings: Vec<EmbeddingsResBodyEmbedding>,
+}
+
+#[derive(Deserialize)]
+struct EmbeddingsResBodyEmbedding {
+    values: Vec<f32>,
+}