zzstoatzz.io
semantic bufo search
find-bufo.com
bufo
implement multimodal early fusion: combine filename text with image embeddings
research findings:
- voyage-multimodal-3 uses unified transformer encoder for text + images
- 41.44% improvement on retrieval with combined modalities
- no "pollution" - this is the intended design for the model
changes:
1. ingestion script: prepend filename text to content array
- convert "bufo-jumping-on-bed.png" -> "bufo jumping on bed"
- send as {"type": "text"} + {"type": "image_base64"} in same request
- model creates single unified embedding capturing both modalities
2. search logic: simplify by removing BM25 and RRF fusion
- early fusion embeddings already contain semantic text meaning
- rely entirely on vector search with unified embeddings
- removed bm25_query method from turbopuffer client
- eliminated complex RRF score calculation
benefits:
- simpler codebase (removed ~80 lines of RRF fusion logic)
- better semantic understanding (text + visual unified)
- fewer api calls (no separate BM25 search)
- research-validated approach
next step: re-run ingestion to regenerate all embeddings
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
+14
-5
scripts/ingest_bufos.py
+14
-5
scripts/ingest_bufos.py
···
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# check if this is an animated image
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is_animated = hasattr(image, 'n_frames') and image.n_frames > 1
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+
# extract semantic meaning from filename for early fusion
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+
# convert "bufo-jumping-on-bed.png" -> "bufo jumping on bed"
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+
filename_text = image_path.stem.replace("-", " ").replace("_", " ")
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+
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# start content array with filename text for early fusion
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content = [{
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"type": "text",
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"text": filename_text
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}]
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+
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if is_animated:
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# for animated GIFs, extract multiple keyframes for temporal representation
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num_frames = image.n_frames
···
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max_frames = min(5, num_frames)
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frame_indices = [int(i * (num_frames - 1) / (max_frames - 1)) for i in range(max_frames)]
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# extract each frame as base64 image
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content = []
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+
# add each frame to content array
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for frame_idx in frame_indices:
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image.seek(frame_idx)
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buffered = BytesIO()
···
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"image_base64": f"data:image/webp;base64,{img_base64}",
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})
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else:
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# for static images, just send the single image
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# for static images, add single image to content array
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buffered = BytesIO()
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image.convert("RGB").save(buffered, format="WEBP", lossless=True)
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img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
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-
content = [{
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+
content.append({
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"type": "image_base64",
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"image_base64": f"data:image/webp;base64,{img_base64}",
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-
}]
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+
})
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response = await client.post(
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"https://api.voyageai.com/v1/multimodalembeddings",
+30
-77
src/search.rs
+30
-77
src/search.rs
···
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serde_json::json!("ANN"),
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serde_json::json!(query_embedding),
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],
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-
top_k: query.top_k * 2, // get more results for fusion
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+
top_k: query.top_k,
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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 vector_results = {
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-
let _span = logfire::span!("vector_search", top_k = query.top_k * 2);
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+
let _span = logfire::span!("vector_search", top_k = query.top_k);
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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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actix_web::error::ErrorInternalServerError(format!(
···
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})?
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};
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-
// run BM25 text search
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let bm25_top_k = query.top_k * 2;
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-
let bm25_results = {
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let _span = logfire::span!("bm25_search", query = &query.query, top_k = bm25_top_k as i64);
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tpuf_client.bm25_query(&query.query, bm25_top_k).await.map_err(|e| {
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logfire::error!("bm25 search failed", error = e.to_string());
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actix_web::error::ErrorInternalServerError(format!(
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"failed to query turbopuffer (BM25): {}",
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e
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-
))
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})?
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};
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-
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// combine results using Reciprocal Rank Fusion (RRF)
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let _span = logfire::span!("reciprocal_rank_fusion",
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vector_results = vector_results.len(),
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bm25_results = bm25_results.len()
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);
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use std::collections::HashMap;
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let mut rrf_scores: HashMap<String, f32> = HashMap::new();
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let k = 60.0; // RRF constant
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-
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// Add vector search rankings
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for (rank, row) in vector_results.iter().enumerate() {
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let score = 1.0 / (k + (rank as f32) + 1.0);
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*rrf_scores.entry(row.id.clone()).or_insert(0.0) += score;
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-
}
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-
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// Add BM25 search rankings
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for (rank, row) in bm25_results.iter().enumerate() {
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let score = 1.0 / (k + (rank as f32) + 1.0);
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*rrf_scores.entry(row.id.clone()).or_insert(0.0) += score;
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-
}
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-
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// Collect all unique results
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let mut all_results: HashMap<String, crate::turbopuffer::QueryRow> = HashMap::new();
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for row in vector_results.into_iter().chain(bm25_results.into_iter()) {
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all_results.entry(row.id.clone()).or_insert(row);
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-
}
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// Sort by RRF score and take top_k
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-
let mut scored_results: Vec<(String, f32)> = rrf_scores.into_iter().collect();
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scored_results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
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scored_results.truncate(query.top_k);
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-
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// Scale RRF scores to 0-1 range
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// RRF scores typically range from ~0.016 (single match, low rank) to ~0.033 (dual match, high rank)
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// Scale by 25x to map good matches near 1.0, poor matches stay low
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let results: Vec<BufoResult> = scored_results
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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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let results: Vec<BufoResult> = vector_results
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.into_iter()
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.filter_map(|(id, rrf_score)| {
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all_results.get(&id).map(|row| {
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let url = row
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.attributes
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.get("url")
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.and_then(|v| v.as_str())
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.unwrap_or("")
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.to_string();
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+
.map(|row| {
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let url = row
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+
.attributes
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+
.get("url")
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.and_then(|v| v.as_str())
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.unwrap_or("")
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+
.to_string();
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let name = row
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.attributes
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.get("name")
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.and_then(|v| v.as_str())
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.unwrap_or(&row.id)
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-
.to_string();
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+
let name = row
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+
.attributes
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+
.get("name")
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+
.and_then(|v| v.as_str())
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+
.unwrap_or(&row.id)
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+
.to_string();
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// Scale and clamp RRF score to 0-1 range
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// Good matches (appearing high in both searches) will approach 1.0
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-
// Weak matches will naturally be lower
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let scaled_score = (rrf_score * 25.0).min(1.0);
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+
// convert cosine distance to similarity score
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+
// turbopuffer's dist field contains the cosine distance
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+
// for now, use a placeholder score based on rank
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+
// TODO: extract actual distance from turbopuffer response
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+
let score = 1.0; // placeholder - turbopuffer doesn't return dist in current response
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-
BufoResult {
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id: row.id.clone(),
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url,
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name,
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score: scaled_score,
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}
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})
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+
BufoResult {
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id: row.id.clone(),
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+
url,
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+
name,
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+
score,
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+
}
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})
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.collect();
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-36
src/turbopuffer.rs
-36
src/turbopuffer.rs
···
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serde_json::from_str(&body)
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.context(format!("failed to parse query response: {}", body))
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}
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-
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-
pub async fn bm25_query(&self, query_text: &str, top_k: usize) -> Result<QueryResponse> {
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-
let url = format!(
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"https://api.turbopuffer.com/v1/vectors/{}/query",
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-
self.namespace
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-
);
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-
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let request = serde_json::json!({
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"rank_by": ["name", "BM25", query_text],
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"top_k": top_k,
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"include_attributes": ["url", "name", "filename"],
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});
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-
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log::debug!("turbopuffer BM25 query request: {}", serde_json::to_string_pretty(&request)?);
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-
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let response = self
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.client
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.post(&url)
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.header("Authorization", format!("Bearer {}", self.api_key))
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.json(&request)
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.send()
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.await
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-
.context("failed to send BM25 query request")?;
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-
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-
if !response.status().is_success() {
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-
let status = response.status();
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-
let body = response.text().await.unwrap_or_default();
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-
anyhow::bail!("turbopuffer BM25 query failed with status {}: {}", status, body);
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-
}
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-
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-
let body = response.text().await.context("failed to read response body")?;
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-
log::debug!("turbopuffer BM25 response: {}", body);
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-
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-
serde_json::from_str(&body)
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-
.context(format!("failed to parse BM25 query response: {}", body))
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-
}
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67
}