add bindings for llm/generate-embeddings and related types

crates/spin-python-engine/src/lib.rs

@@ -545,13 +545,65 @@ fn llm_infer(
         .map(LLMInferencingResult::from)
 }
 
+#[pyo3::pyfunction]
+fn generate_embeddings(model: &str, text: Vec<String>) -> Result<LLMEmbeddingsResult, Anyhow> {
+    let model = match model {
+        "all-minilm-l6-v2" => llm::EmbeddingModel::AllMiniLmL6V2,
+        _ => llm::EmbeddingModel::Other(model),
+    };
+
+    let text = text.iter().map(|s| s.as_str()).collect::<Vec<_>>();
+
+    llm::generate_embeddings(model, &text)
+        .map_err(Anyhow::from)
+        .map(LLMEmbeddingsResult::from)
+}
+
+#[derive(Clone)]
+#[pyo3::pyclass]
+#[pyo3(name = "LLMEmbeddingsUsage")]
+struct LLMEmbeddingsUsage {
+    #[pyo3(get)]
+    prompt_token_count: u32,
+}
+
+impl From<llm::EmbeddingsUsage> for LLMEmbeddingsUsage {
+    fn from(result: llm::EmbeddingsUsage) -> Self {
+        LLMEmbeddingsUsage {
+            prompt_token_count: result.prompt_token_count,
+        }
+    }
+}
+
+#[derive(Clone)]
+#[pyo3::pyclass]
+#[pyo3(name = "LLMEmbeddingResult")]
+struct LLMEmbeddingsResult {
+    #[pyo3(get)]
+    embeddings: Vec<Vec<f32>>,
+    #[pyo3(get)]
+    usage: LLMEmbeddingsUsage,
+}
+
+impl From<llm::EmbeddingsResult> for LLMEmbeddingsResult {
+    fn from(result: llm::EmbeddingsResult) -> Self {
+        LLMEmbeddingsResult {
+            embeddings: result.embeddings,
+            usage: LLMEmbeddingsUsage::from(result.usage),
+        }
+    }
+}
+
 #[pyo3::pymodule]
 #[pyo3(name = "spin_llm")]
 fn spin_llm_module(_py: Python<'_>, module: &PyModule) -> PyResult<()> {
     module.add_function(pyo3::wrap_pyfunction!(llm_infer, module)?)?;
+    module.add_function(pyo3::wrap_pyfunction!(generate_embeddings, module)?)?;
     module.add_class::<LLMInferencingUsage>()?;
     module.add_class::<LLMInferencingParams>()?;
-    module.add_class::<LLMInferencingResult>()
+    module.add_class::<LLMInferencingResult>()?;
+    module.add_class::<LLMEmbeddingsUsage>()?;
+    module.add_class::<LLMEmbeddingsResult>()
 }
 
 pub fn run_ctors() {

examples/llm/app.py

@@ -1,10 +1,18 @@
+import json
 from spin_http import Response
-from spin_llm import llm_infer
+from spin_llm import llm_infer, generate_embeddings
 
 
 def handle_request(request):
     prompt="You are a stand up comedy writer. Tell me a joke."
     result=llm_infer("llama2-chat", prompt)
+
+    embeddings = generate_embeddings("all-minilm-l6-v2", ["hat", "cat", "bat"])
+
+    body = (f"joke: {result.text}\n\n"
+            f"embeddings: {json.dumps(embeddings.embeddings)}\n"
+            f"prompt token count: {embeddings.usage.prompt_token_count}")
+
     return Response(200,
                     {"content-type": "text/plain"},
-                    bytes(result.text, "utf-8"))
+                    bytes(body, "utf-8"))

examples/llm/spin.toml

@@ -8,7 +8,7 @@ version = "0.1.0"
 [[component]]
 id = "python-sdk-example"
 source = "app.wasm"
-ai_models = ["llama2-chat"]
+ai_models = ["llama2-chat", "all-minilm-l6-v2"]
 [component.trigger]
 route = "/..."
 [component.build]