Otel: llm.request client spans + traceparent on LLM calls (#130)

Add OpenTelemetry tracing to every LLM client call so an insight/chat
turn is a single trace from HTTP entry to each downstream inference.

- LlmClient trait: thread a &opentelemetry::Context through all 5
  methods (generate, chat_with_tools, chat_with_tools_stream,
  generate_embeddings, describe_image), matching the DAO convention of
  explicit context threading rather than ambient Context::current().
- otel::start_llm_request_span: single shared helper that every
  provider (openrouter / llamacpp / ollama) funnels through, so the
  span shape is identical: name llm.request, SpanKind::Client,
  llm.provider / llm.operation / llm.model.
- Per-method spans in all three clients with the spec's operation enum
  (chat | chat_stream | embeddings | describe_image | generate); stream
  spans are owned and moved into the async_stream generator so they end
  when the response drains, not when headers arrive.
- llm.tokens.input / llm.tokens.output recorded on non-stream and
  stream completion; llm.cost_usd from usage.cost (OpenRouter);
  http.status_code + error status on failure.
- W3C traceparent injected on outgoing LLM HTTP requests (llama-swap,
  Ollama, OpenRouter) gated on span_context.is_valid() so debug
  no-op-tracer builds don't send an all-zero parent.
- Turn-level spans (ai.chat.turn.execute, ai.persona_chat.turn.execute)
  now carry llm.backend + llm.model; with_entry streaming paths thread
  the turn context (turn_cx) to child describe_image / agentic-loop
  calls instead of the ambient context.

clippy clean; 690 lib + 775 integration + 4 doc tests green.

src/ai/mod.rs

@@ -115,24 +115,26 @@ fn embed_prefix(key: &str) -> String {
 /// `Instruct: <task>\nQuery: `. Must pair with the document prefix the
 /// corpus was embedded with or similarity degrades.
 pub async fn embed_query(
+    cx: &opentelemetry::Context,
     ollama: &OllamaClient,
     llamacpp: Option<&LlamaCppClient>,
     text: &str,
 ) -> anyhow::Result<Vec<f32>> {
     let prefixed = format!("{}{}", embed_prefix("EMBED_QUERY_PREFIX"), text);
-    embed_one(ollama, llamacpp, &prefixed).await
+    embed_one(cx, ollama, llamacpp, &prefixed).await
 }
 
 /// Embed corpus text (the stored side of retrieval). Applies
 /// `EMBED_DOCUMENT_PREFIX` (default empty; nomic v1.5 wants
 /// `search_document: `, Qwen3-Embedding wants none).
 pub async fn embed_document(
+    cx: &opentelemetry::Context,
     ollama: &OllamaClient,
     llamacpp: Option<&LlamaCppClient>,
     text: &str,
 ) -> anyhow::Result<Vec<f32>> {
     let prefixed = format!("{}{}", embed_prefix("EMBED_DOCUMENT_PREFIX"), text);
-    embed_one(ollama, llamacpp, &prefixed).await
+    embed_one(cx, ollama, llamacpp, &prefixed).await
 }
 
 /// Embed a batch of strings via the configured local backend. Routes
@@ -140,30 +142,32 @@ pub async fn embed_document(
 /// configured), else Ollama. See [`local_backend_is_llamacpp`] for the
 /// rationale on consistency.
 pub async fn embed_many(
+    cx: &opentelemetry::Context,
     ollama: &OllamaClient,
     llamacpp: Option<&LlamaCppClient>,
     texts: &[&str],
 ) -> anyhow::Result<Vec<Vec<f32>>> {
     if local_backend_is_llamacpp() {
         if let Some(lc) = llamacpp {
-            return <LlamaCppClient as LlmClient>::generate_embeddings(lc, texts).await;
+            return <LlamaCppClient as LlmClient>::generate_embeddings(lc, cx, texts).await;
         }
         anyhow::bail!(
             "LLM_BACKEND=llamacpp but LlamaCppClient is unconfigured — \
              set LLAMA_SWAP_URL or switch to LLM_BACKEND=ollama"
         );
     }
-    ollama.generate_embeddings(texts).await
+    ollama.generate_embeddings(cx, texts).await
 }
 
 /// Embed one string via the configured local backend. Single-text
 /// convenience over [`embed_many`].
 pub async fn embed_one(
+    cx: &opentelemetry::Context,
     ollama: &OllamaClient,
     llamacpp: Option<&LlamaCppClient>,
     text: &str,
 ) -> anyhow::Result<Vec<f32>> {
-    let mut vecs = embed_many(ollama, llamacpp, &[text]).await?;
+    let mut vecs = embed_many(cx, ollama, llamacpp, &[text]).await?;
     vecs.pop()
         .ok_or_else(|| anyhow::anyhow!("embedding backend returned no embeddings"))
 }