Tracing: LLM clients + streaming spans on tracing, drop cx threading (#140)

LlmClient methods no longer take an OpenTelemetry Context. llm.request is
now a tracing span (otel.kind=client, llm.provider/operation/model) that
parents on the caller's ambient span; traceparent injection reads the
span's OTel context via OpenTelemetrySpanExt.

The src/ai feature spans (handlers, insight generation, chat, persona
chat, daily summaries, TTS) move to #[instrument] / info_span!, and every
LLM-bearing tokio::spawn carries its parent span with .instrument(): the
streaming chat turn (chat_turn_stream_inner), async chat / persona turns,
background insight jobs and TTS jobs. Sub-spans in the agentic loops
(ai.chat.loop, ai.agentic.loop) instrument each LLM / tool await.

DAO calls keep their explicit Context (derived from the tracing span)
until the DAO migration (#141).

tests/otel_llm_spans.rs pins nesting + traceparent for the blocking and
streaming paths across a spawn on a multi-thread runtime.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

src/ai/llm_client.rs

@@ -1,7 +1,6 @@
 use anyhow::Result;
 use async_trait::async_trait;
 use futures::stream::BoxStream;
-use opentelemetry::Context;
 use serde::{Deserialize, Serialize};
 
 /// Provider-agnostic surface for LLM backends (Ollama, OpenRouter, …).
@@ -12,11 +11,10 @@ use serde::{Deserialize, Serialize};
 /// stays as base64 strings here and is rewritten into content-parts where
 /// needed.
 ///
-/// Every method that performs an HTTP call takes the caller's tracing
-/// `Context` as its first parameter so the impl can start an `llm.request`
-/// span parented under the caller's feature span — the codebase threads
-/// contexts explicitly rather than relying on ambient `Context::current()`
-/// at deep call sites.
+/// Every method that performs an HTTP call starts an `llm.request` span
+/// parented on the caller's ambient `tracing` span, so callers must run
+/// inside their feature span (`#[instrument]` / `.instrument(span)`) — and
+/// carry it across any `tokio::spawn` — for the LLM span to nest under it.
 // First consumer lands in a later PR (OpenRouter impl + hybrid mode routing).
 #[allow(dead_code)]
 #[async_trait]
@@ -25,7 +23,6 @@ pub trait LlmClient: Send + Sync {
     /// base64 images (ignored by providers without vision support).
     async fn generate(
         &self,
-        cx: &Context,
         prompt: &str,
         system: Option<&str>,
         images: Option<Vec<String>>,
@@ -35,7 +32,6 @@ pub trait LlmClient: Send + Sync {
     /// (which may contain tool_calls) plus optional prompt/eval token counts.
     async fn chat_with_tools(
         &self,
-        cx: &Context,
         messages: Vec<ChatMessage>,
         tools: Vec<Tool>,
     ) -> Result<(ChatMessage, Option<i32>, Option<i32>)>;
@@ -48,17 +44,16 @@ pub trait LlmClient: Send + Sync {
     /// as one `Done` event.
     async fn chat_with_tools_stream(
         &self,
-        cx: &Context,
         messages: Vec<ChatMessage>,
         tools: Vec<Tool>,
     ) -> Result<BoxStream<'static, Result<LlmStreamEvent>>>;
 
     /// Batch embedding generation. Dimensionality is provider/model specific.
-    async fn generate_embeddings(&self, cx: &Context, texts: &[&str]) -> Result<Vec<Vec<f32>>>;
+    async fn generate_embeddings(&self, texts: &[&str]) -> Result<Vec<Vec<f32>>>;
 
     /// One-shot vision description of an image. Used to convert images into
     /// plain text for the hybrid-mode conversation flow.
-    async fn describe_image(&self, cx: &Context, image_base64: &str) -> Result<String>;
+    async fn describe_image(&self, image_base64: &str) -> Result<String>;
 
     /// Enumerate available models with their capabilities.
     async fn list_models(&self) -> Result<Vec<ModelCapabilities>>;