Refactor: Drop the vector fetch for ES (#14970)

## Summary
- Stop pulling chunk vectors (`q_*_vec`) back from Elasticsearch in the
main retrieval path. ES already knows them; shipping them was pure
bandwidth/memory overhead.
- Recover the per-chunk cosine similarity via a second KNN-only ES call
filtered by the candidate chunk ids. The new `_score` is merged with
locally computed term similarity using the user-configured
`vector_similarity_weight`.
- Lazily fetch the chunk embedding only for the chunks
`insert_citations` actually needs.

## Details
**`rag/nlp/search.py`**
- `Dealer.search`: no longer appends `q_*_vec` to the ES select list.
OceanBase still gets it (its rerank path is unchanged).
- New `Dealer._knn_scores(sres, idx_names, kb_ids)`: a `MatchDenseExpr`
over the cached query vector filtered by `id IN sres.ids`, returning
`{chunk_id: cosine_score}` via ES `_score`.
- New `Dealer.rerank_with_knn(...)`: term similarity from
`qryr.token_similarity` plus the ES-supplied KNN score, combined with
`tkweight`/`vtweight` and the existing rank-feature bonus.
- New `Dealer.fetch_chunk_vectors(chunk_ids, tenant_ids, kb_ids, dim)`:
on-demand vector fetch for citation use.
- `Dealer.retrieval` routes Infinity → unchanged, OceanBase → existing
local `rerank`, ES → new KNN-score path.

**`common/doc_store/es_conn_base.py`**
- New `get_scores(res)` helper returning `{_id: _score}` directly from
hit headers (ES doesn't surface `_score` through `get_fields`).

**`api/db/services/dialog_service.py`**
- New top-level `_hydrate_chunk_vectors(...)` helper. On ES it
back-fills `ck["vector"]` from `fetch_chunk_vectors` right before
`insert_citations`. No-op on Infinity / OB (their chunks already carry
vectors).
- Both `decorate_answer` closures became `async` and are `await`-ed at
all call sites in `async_chat` and `async_ask`.

## Backend behavior
| Backend | Returns chunk vec in main search | Sim source | Vectors for
citations |
|---|---|---|---|
| ES | No | second KNN call (`_score`) merged with term sim | fetched on
demand |
| Infinity | No (unchanged) | normalized `_score` | already on chunks |
| OceanBase | Yes (kept) | local hybrid rerank | already on chunks |

## Test plan

api/db/services/dialog_service.py

@@ -65,6 +65,53 @@ def _chunk_kb_id_for_doc(row_dict, kb_ids, doc_id):
         return kb_ids[0]
     return row_dict.get("kb_id") or row_dict.get("kb_id_kwd")
 
+
+async def _hydrate_chunk_vectors(retriever, chunks, tenant_ids, kb_ids):
+    """
+    Citation prep: on the ES backend the main retrieval call deliberately
+    skips fetching the chunk embedding. insert_citations needs it, so we
+    pull the vectors for just the candidate chunks right before computing
+    answer-vs-chunk similarity. Chunks without an ES chunk_id (e.g. web
+    search results) keep whatever placeholder they were given. Other
+    backends still carry vectors in the chunk, so we skip the round-trip.
+    """
+    if settings.DOC_ENGINE_INFINITY or settings.DOC_ENGINE_OCEANBASE:
+        return
+    if not chunks:
+        return
+    dim = 0
+    for ck in chunks:
+        v = ck.get("vector")
+        if isinstance(v, list) and v:
+            dim = len(v)
+            break
+    if not dim:
+        return
+    # Skip chunks that already have a non-zero vector (e.g. parent chunks
+    # produced by retrieval_by_children copy the child vector inline).
+    chunk_ids = []
+    for ck in chunks:
+        cid = ck.get("chunk_id")
+        if not cid:
+            continue
+        v = ck.get("vector") or []
+        if any(x for x in v):
+            continue
+        chunk_ids.append(cid)
+    if not chunk_ids:
+        return
+    try:
+        vectors = await retriever.fetch_chunk_vectors(chunk_ids, tenant_ids, kb_ids, dim)
+    except Exception as e:  # noqa: BLE001 - degrade gracefully on hydrate failure
+        logger.warning("fetch_chunk_vectors failed; citations will use placeholders: %s", e)
+        return
+    if not vectors:
+        return
+    for ck in chunks:
+        cid = ck.get("chunk_id")
+        if cid and cid in vectors:
+            ck["vector"] = vectors[cid]
+
 def _normalize_internet_flag(value):
     if isinstance(value, bool):
         return value
@@ -735,7 +782,7 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
     if "max_tokens" in gen_conf:
         gen_conf["max_tokens"] = min(gen_conf["max_tokens"], max_tokens - used_token_count)
 
-    def decorate_answer(answer):
+    async def decorate_answer(answer):
         nonlocal embd_mdl, prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts, questions, langfuse_tracer
 
         refs = []
@@ -749,6 +796,9 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
             idx = set([])
             normalized_answer = normalize_arabic_digits(answer) or ""
             if embd_mdl and not CITATION_MARKER_PATTERN.search(normalized_answer):
+                # Main retrieval no longer ships chunk vectors back from ES.
+                # Pull them on demand for the chunks we are about to cite.
+                await _hydrate_chunk_vectors(retriever, kbinfos.get("chunks", []), tenant_ids, dialog.kb_ids)
                 answer, idx = retriever.insert_citations(
                     answer,
                     [ck["content_ltks"] for ck in kbinfos["chunks"]],
@@ -841,7 +891,7 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
             yield {"answer": value, "reference": {}, "audio_binary": tts(tts_mdl, value), "final": False}
         full_answer = last_state.full_text if last_state else ""
         if full_answer:
-            final = decorate_answer(_extract_visible_answer(thought + full_answer))
+            final = await decorate_answer(_extract_visible_answer(thought + full_answer))
             final["final"] = True
             final["audio_binary"] = None
             yield final
@@ -852,7 +902,7 @@ async def async_chat(dialog, messages, stream=True, **kwargs):
             answer = await chat_mdl.async_chat(prompt + prompt4citation, msg[1:], gen_conf, images=image_files)
         user_content = msg[-1].get("content", "[content not available]")
         logging.debug("User: {}|Assistant: {}".format(user_content, answer))
-        res = decorate_answer(answer)
+        res = await decorate_answer(answer)
         res["audio_binary"] = tts(tts_mdl, answer)
         yield res
 
@@ -1542,8 +1592,11 @@ async def async_ask(question, kb_ids, tenant_id, chat_llm_name=None, search_conf
 
     msg = [{"role": "user", "content": question}]
 
-    def decorate_answer(answer):
+    async def decorate_answer(answer):
         nonlocal knowledges, kbinfos, sys_prompt
+        # Main retrieval no longer ships chunk vectors back from ES. Pull
+        # them on demand for the chunks we are about to cite.
+        await _hydrate_chunk_vectors(retriever, kbinfos.get("chunks", []), tenant_ids, kb_ids)
         answer, idx = retriever.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=0.7, vtweight=0.3)
         idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
         recall_docs = [d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
@@ -1570,7 +1623,7 @@ async def async_ask(question, kb_ids, tenant_id, chat_llm_name=None, search_conf
             continue
         yield {"answer": value, "reference": {}, "final": False}
     full_answer = last_state.full_text if last_state else ""
-    final = decorate_answer(_extract_visible_answer(full_answer))
+    final = await decorate_answer(_extract_visible_answer(full_answer))
     final["final"] = True
     yield final