# Embeddings + Retrieval through Spark Control (v0.15.0)

Spark Control now fronts a local RAG stack so your agent/CRM system can do
dense embeddings, reranking, and hybrid vector search against one trusted host
— same TLS cert and allowlist as the LLM and audio endpoints.

## What runs where

| Component | Host | Port | Role |
|---|---|---|---|
| **spark-embed** | Spark 2 (GPU) | 8088 | `BAAI/bge-m3` dense embeddings (1024-d) + `BAAI/bge-reranker-v2-m3` cross-encoder rerank |
| **Qdrant** | Spark 2 (CPU) | 6333/6334 | Vector storage, hybrid dense+sparse retrieval, RRF fusion, payload filtering |
| **Spark Control** | Start9 | (your LAN URL) | Proxies all of the above behind one host |

`spark-embed` is a small FastAPI server built **from the NGC PyTorch image**
(the torch we've proven runs on the GB10's sm_121 GPU). We did *not* use HF Text
Embeddings Inference because as of 2026 it ships no arm64 CUDA image. No
torchaudio, no flash-attn — bge-m3 + the reranker are plain XLM-RoBERTa
encoders.

## Endpoints (all on the Spark Control host)

### `POST /v1/embeddings` — dense embeddings (OpenAI-compatible)

```json
// request
{ "input": "text to embed", "model": "BAAI/bge-m3" }
// or  { "input": ["batch", "of", "texts"] }

// response (OpenAI shape)
{
  "object": "list",
  "data": [ { "object": "embedding", "index": 0, "embedding": [0.01, ...1024 floats] } ],
  "model": "BAAI/bge-m3",
  "usage": { "prompt_tokens": 0, "total_tokens": 0 }
}
```

Vectors are L2-normalized by default (cosine == dot product). Works with the
stock OpenAI Python/JS client by pointing `base_url` at Spark Control.

### `POST /v1/rerank` — cross-encoder rerank

```json
// request
{ "query": "did Brightwater commit?", "documents": ["chunk a", "chunk b", ...],
  "top_n": 5, "return_documents": false }

// response (Cohere-ish)
{ "object": "rerank.result", "model": "BAAI/bge-reranker-v2-m3",
  "results": [ { "index": 3, "relevance_score": 5.21 }, { "index": 0, "relevance_score": 1.04 }, ... ] }
```

`relevance_score` is the reranker's raw logit (higher = more relevant; unbounded,
roughly −10..+10). Sort desc. Send a candidate set (≤200), not your whole corpus.

### `POST /api/search` — orchestrated hybrid retrieval

One call: embeds the query (dense), retrieves from Qdrant (hybrid dense+sparse
with RRF **when you supply a sparse vector**, else dense-only), optionally
cross-encoder reranks, returns top_k.

```json
// request
{
  "query": "Did Brightwater commit to the Fund III close in Q1?",
  "collection": "crm_chunks",
  "top_k": 8,
  "retrieve_n": 80,                 // first-stage candidates (default max(50, top_k*10))
  "sparse": { "indices": [12, 904], "values": [0.7, 1.2] },  // optional BM25 vector for hybrid
  "fusion": "rrf",                  // or "dbsf"
  "filter": {                       // raw Qdrant filter (pre-filter, see below)
    "must": [ { "key": "lp_id", "match": { "value": "lp_0427" } } ]
  },
  "rerank": true,
  "text_field": "text",             // payload field holding the chunk text
  "with_payload": true
}

// response
{
  "object": "search.result_list",
  "model": "BAAI/bge-m3+bge-reranker-v2-m3",
  "query": "...",
  "collection": "crm_chunks",
  "reranked": true,
  "data": [
    { "object": "search.result", "index": 0, "id": "chunk_99c1_3",
      "score": 5.21, "fused_score": 0.41, "rerank_score": 5.21,
      "text": "...Brightwater confirmed verbal commitment...",
      "payload": { "lp_id": "lp_0427", "doc_type": "email", "date_ts": 1771027200, ... } }
  ],
  "usage": { "embed_ms": 21, "qdrant_ms": 9, "rerank_ms": 140, "candidates": 80 }
}
```

`score` is the rerank score when reranked, else the fused/dense score.

## The sparse (BM25) story — important for entity-heavy data

bge-m3's dense vectors carry semantic meaning, but exact entity matches
(fund names, tickers, people) are a **lexical** signal. For that you want hybrid
dense + sparse. Two design facts:

1. **spark-embed serves dense only.** TEI/Infinity can't emit bge-m3's learned
   sparse either, and bge-m3's pretrained sparse weights underweight novel
   entity tokens (brand-new fund names). So we don't use bge-m3 sparse.
2. **Use Qdrant BM25 with its built-in IDF**, generated client-side. This learns
   IDF over *your* corpus, so novel entity strings get correct weight.

### Your ingest pipeline (the part you own)

For each chunk, produce and upsert **both** vectors:

- **dense**: call Spark Control `POST /v1/embeddings` → 1024-d vector.
- **sparse**: run [FastEmbed](https://github.com/qdrant/fastembed) BM25 client-side:
  ```python
  from fastembed import SparseTextEmbedding
  bm25 = SparseTextEmbedding(model_name="Qdrant/bm25")
  sp = next(bm25.embed([chunk_text]))   # -> {indices, values}
  ```

Create the collection with a named dense vector and a named sparse vector that
uses `modifier: idf` (so Qdrant applies IDF server-side):

```json
PUT /collections/crm_chunks
{ "vectors": { "dense": { "size": 1024, "distance": "Cosine" } },
  "sparse_vectors": { "sparse": { "modifier": "idf" } } }
```

Add payload indexes for your filter fields so filtered queries stay fast:

```json
PUT /collections/crm_chunks/index   { "field_name": "lp_id",     "field_schema": "keyword" }
PUT /collections/crm_chunks/index   { "field_name": "doc_type",  "field_schema": "keyword" }
PUT /collections/crm_chunks/index   { "field_name": "date_ts",   "field_schema": "integer" }
```

Upsert points with both vectors + payload:

```json
PUT /collections/crm_chunks/points
{ "points": [ { "id": 1,
    "vector": { "dense": [...1024...], "sparse": { "indices": [...], "values": [...] } },
    "payload": { "lp_id": "lp_0427", "lp_name": "Brightwater Capital",
                 "doc_type": "email", "date_ts": 1771027200, "text": "..." } } ] }
```

### At query time

Generate the query's BM25 sparse vector with the **same** FastEmbed model, and
pass it to `/api/search` as `sparse`. Spark Control fuses dense+sparse with RRF
inside Qdrant, then reranks. If you omit `sparse`, you get dense + rerank
(still good, just no exact-lexical leg).

> You can talk to Qdrant directly (`http://<spark2>:6333`) for collection
> management and upserts — that's the natural home for ingest. `/api/search` is
> the convenience path for the read side so your agents hit one host. If you'd
> rather Spark Control proxy Qdrant admin too, say the word.

## Chunking guidance (entity-heavy CRM)

- One chunk per email / note / transcript-turn; one chunk per memo *section* —
  don't split mid-thought.
- Keep entity + date as **payload fields** (filterable), not buried in embedded text.
- Time-aware: don't merge a 2022 note with a 2026 update in one chunk; store
  `date_ts` so you can pre-filter and recency-boost.
- Resolve entity-name variants ("J. Smith" / "Jonathan Smith" / "JS") to one
  canonical `lp_id` at ingest, or retrieval fragments across variants.

## Pre-filtering

Most agent queries aren't pure semantic — they're "recent emails with Bob about
energy". Pass a Qdrant `filter` to restrict the search space *before* vector
scoring (faster + more precise). Qdrant also supports server-side recency
boosting via Formula/Score-Boosting if you want decay-by-`date_ts` without an
app round-trip — ask and we can expose it through `/api/search`.

## Service discovery + health

- `GET /api/endpoints` includes `embeddings` and `qdrant` entries (base_url +
  ready flag) alongside `vllm`, `parakeet`, `kokoro`.
- The dashboard shows Embeddings + Qdrant health dots and Start/Restart/Stop
  controls in the Always-On Services panel.
- Spark Control will auto-restart a wedged **embedding** container (GPU CUDA
  wedge recovery, like the audio services) but **never** auto-restarts Qdrant —
  it holds your only copy of the index, so a restart is surfaced for manual
  action instead.

## Capacity

At your scale (tens of thousands of chunks now → low hundreds of thousands),
this is trivial: ~0.6–1.2 GB of dense vectors at 300k chunks, spark-embed +
Qdrant together use a few GB of GPU/RAM on Spark 2's 122 GB. Full re-embed of
300k chunks is ~8–15 min, so re-indexing is cheap if you change models.

## Model upgrade path

If dense recall becomes the bottleneck, `Qwen3-Embedding-4B` (Matryoshka-trained,
tops 2026 MTEB) is the A/B candidate — same `/v1/embeddings` contract, swap the
model in spark-embed. bge-m3 is the lower-risk starting point and ships now.
