zzstoatzz.io
semantic bufo search
find-bufo.com
bufo
improve logfire observability with structured spans and attributes
- add top-level 'bufo_search' span to group all search operations
- rename spans to use service.operation pattern (voyage.embed_text, turbopuffer.query)
- add comprehensive attributes to each span and log:
* query text visible in all operations
* top_k parameter tracking
* embedding dimensions
* vector search distance metrics (min/max)
* result quality metrics (count, top result, scores)
- remove noisy turbopuffer response debug log
- all child spans now properly nested under parent search span
this creates a clear hierarchy in logfire:
bufo_search (parent)
├─ voyage.embed_text
└─ turbopuffer.query
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
+76
-11
+76
-10
src/search.rs
+76
-10
src/search.rs
···
55
55
query: web::Json<SearchQuery>,
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config: web::Data<Config>,
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) -> ActixResult<HttpResponse> {
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+
let query_text = &query.query;
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+
let top_k_val = query.top_k;
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+
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+
let _search_span = logfire::span!(
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+
"bufo_search",
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+
query = query_text,
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+
top_k = top_k_val as i64
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+
);
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+
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+
logfire::info!(
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+
"search request received",
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+
query = query_text,
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+
top_k = top_k_val as i64
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+
);
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+
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let embedding_client = EmbeddingClient::new(config.voyage_api_key.clone());
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let tpuf_client = TurbopufferClient::new(
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config.turbopuffer_api_key.clone(),
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config.turbopuffer_namespace.clone(),
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);
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64
-
// run vector search
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+
// generate embedding for user query
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let query_embedding = {
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-
let _span = logfire::span!("generate_embedding", query = &query.query);
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+
let _span = logfire::span!(
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+
"voyage.embed_text",
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+
query = query_text,
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+
model = "voyage-3-lite"
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+
);
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embedding_client
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-
.embed_text(&query.query)
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+
.embed_text(query_text)
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.await
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.map_err(|e| {
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-
logfire::error!("failed to generate embedding", error = e.to_string());
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+
let error_msg = e.to_string();
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+
logfire::error!(
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+
"embedding generation failed",
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+
error = error_msg,
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+
query = query_text
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+
);
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actix_web::error::ErrorInternalServerError(format!(
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"failed to generate embedding: {}",
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98
e
···
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100
})?
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};
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+
logfire::info!(
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+
"embedding generated",
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+
query = query_text,
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+
embedding_dim = query_embedding.len() as i64
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+
);
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+
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let vector_request = QueryRequest {
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rank_by: vec![
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serde_json::json!("vector"),
···
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include_attributes: Some(vec!["url".to_string(), "name".to_string(), "filename".to_string()]),
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};
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+
let namespace = &config.turbopuffer_namespace;
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let vector_results = {
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-
let _span = logfire::span!("vector_search", top_k = query.top_k);
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+
let _span = logfire::span!(
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+
"turbopuffer.query",
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+
query = query_text,
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+
top_k = top_k_val as i64,
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+
namespace = namespace
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+
);
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tpuf_client.query(vector_request).await.map_err(|e| {
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-
logfire::error!("vector search failed", error = e.to_string());
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+
let error_msg = e.to_string();
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+
logfire::error!(
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"vector search failed",
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+
error = error_msg,
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+
query = query_text,
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+
top_k = top_k_val as i64
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+
);
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actix_web::error::ErrorInternalServerError(format!(
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"failed to query turbopuffer (vector): {}",
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137
e
···
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})?
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};
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+
let min_dist = vector_results.iter().map(|r| r.dist).min_by(|a, b| a.partial_cmp(b).unwrap()).unwrap_or(0.0) as f64;
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+
let max_dist = vector_results.iter().map(|r| r.dist).max_by(|a, b| a.partial_cmp(b).unwrap()).unwrap_or(0.0) as f64;
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+
let results_found = vector_results.len() as i64;
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+
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+
logfire::info!(
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"vector search completed",
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+
query = query_text,
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+
results_found = results_found,
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+
min_dist = min_dist,
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+
max_dist = max_dist
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+
);
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+
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// convert vector search results to bufo results
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// turbopuffer returns cosine distance (0 = identical, 2 = opposite)
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// convert to similarity score: 1 - (distance / 2) to get 0-1 range
···
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183
})
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.collect();
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-
logfire::info!("search completed",
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-
query = &query.query,
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-
results_count = results.len() as i64,
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-
top_score = results.first().map(|r| r.score as f64).unwrap_or(0.0)
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+
let results_count = results.len() as i64;
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+
let top_result_name = results.first().map(|r| r.name.clone()).unwrap_or_else(|| "none".to_string());
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+
let top_score_val = results.first().map(|r| r.score as f64).unwrap_or(0.0);
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+
let avg_score_val = if !results.is_empty() {
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+
results.iter().map(|r| r.score as f64).sum::<f64>() / results.len() as f64
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+
} else {
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+
0.0
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+
};
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+
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+
logfire::info!(
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+
"search completed successfully",
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+
query = query_text,
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+
results_count = results_count,
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+
top_result = &top_result_name,
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+
top_score = top_score_val,
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+
avg_score = avg_score_val
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);
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Ok(HttpResponse::Ok().json(SearchResponse { results }))