Fix: avoid empty doc filter in knowledge retrieval (#13484)

## Summary
Fix knowledge-base chat retrieval when no individual document IDs are
selected.

## Root Cause
`async_chat()` initialized `doc_ids` as an empty list when the request
did not explicitly select documents. That empty list was then forwarded
into retrieval as an active `doc_id` filter, effectively becoming
`doc_id IN []` and suppressing all chunk matches.

## Changes
- treat missing selected document IDs as `None` instead of `[]`
- keep explicit document filtering when IDs are actually provided
- add regression coverage for the shared chat retrieval path

## Validation
- `python3 -m py_compile api/db/services/dialog_service.py
test/unit_test/api/db/services/test_dialog_service_use_sql_source_columns.py`
- `.venv/bin/python -m pytest
test/unit_test/api/db/services/test_dialog_service_use_sql_source_columns.py`
- manually verified that chat completions again inject retrieved
knowledge into the prompt

---------

Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>

test/unit_test/api/db/services/test_dialog_service_use_sql_source_columns.py

@@ -17,6 +17,7 @@ import asyncio
 import sys
 import types
 import warnings
+from types import SimpleNamespace
 
 import pytest
 
@@ -101,6 +102,19 @@ class _StubRetriever:
         return self._results[idx]
 
 
+class _StubAsyncRetriever:
+    def __init__(self, result):
+        self.result = result
+        self.calls = []
+
+    async def retrieval(self, *args, **kwargs):
+        self.calls.append({"args": args, "kwargs": kwargs})
+        return self.result
+
+    def retrieval_by_children(self, chunks, tenant_ids):
+        return chunks
+
+
 @pytest.fixture
 def force_es_engine(monkeypatch):
     monkeypatch.setattr(dialog_service.settings, "DOC_ENGINE_INFINITY", False)
@@ -219,3 +233,84 @@ def test_use_sql_source_repair_is_bounded_to_single_retry(monkeypatch, force_es_
     assert "Source" not in result["answer"]
     assert len(chat_model.calls) == 2
     assert len(retriever.sql_calls) == 2
+
+
+@pytest.mark.p2
+def test_async_chat_uses_all_docs_when_no_doc_ids_selected(monkeypatch):
+    retriever = _StubAsyncRetriever(
+        {
+            "total": 1,
+            "chunks": [
+                {
+                    "chunk_id": "chunk-1",
+                    "content_ltks": "chunk text",
+                    "content_with_weight": "Chunk text from dataset.",
+                    "doc_id": "doc-1",
+                    "docnm_kwd": "doc.txt",
+                    "kb_id": "kb-1",
+                    "important_kwd": [],
+                    "positions": [],
+                    "vector": [0.1, 0.2],
+                }
+            ],
+            "doc_aggs": [],
+        }
+    )
+    chat_model = _StubChatModel(["stub answer"])
+    dialog = SimpleNamespace(
+        kb_ids=["kb-1"],
+        llm_id="chat-model",
+        tenant_id="tenant-id",
+        llm_setting={},
+        similarity_threshold=0.1,
+        vector_similarity_weight=0.2,
+        top_n=8,
+        top_k=32,
+        meta_data_filter=None,
+        prompt_config={
+            "quote": False,
+            "keyword": False,
+            "tts": False,
+            "empty_response": "",
+            "system": "Use only this knowledge: {knowledge}",
+            "parameters": [{"key": "knowledge", "optional": False}],
+            "reasoning": False,
+            "toc_enhance": False,
+            "use_kg": False,
+        },
+    )
+
+    monkeypatch.setattr(dialog_service.settings, "retriever", retriever, raising=False)
+    monkeypatch.setattr(dialog_service.TenantLLMService, "llm_id2llm_type", lambda _llm_id: "chat")
+    monkeypatch.setattr(
+        dialog_service.TenantLLMService,
+        "get_model_config",
+        lambda *_args, **_kwargs: {"llm_factory": "unit", "max_tokens": 4096},
+    )
+    monkeypatch.setattr(dialog_service.TenantLangfuseService, "filter_by_tenant", lambda **_kwargs: None)
+    monkeypatch.setattr(
+        dialog_service,
+        "get_models",
+        lambda _dialog: ([SimpleNamespace(tenant_id="tenant-id")], object(), None, chat_model, None),
+    )
+    monkeypatch.setattr(dialog_service.KnowledgebaseService, "get_field_map", lambda _kb_ids: {})
+    monkeypatch.setattr(dialog_service, "label_question", lambda _question, _kbs: None)
+    monkeypatch.setattr(
+        dialog_service,
+        "kb_prompt",
+        lambda kbinfos, _max_tokens: ["Chunk text from dataset."] if kbinfos["chunks"] else [],
+    )
+    monkeypatch.setattr(dialog_service, "message_fit_in", lambda msg, _max_tokens: (0, msg))
+
+    async def _collect():
+        items = []
+        async for item in dialog_service.async_chat(dialog, [{"role": "user", "content": "What does the dataset say?"}], stream=False):
+            items.append(item)
+        return items
+
+    result = asyncio.run(_collect())
+
+    assert len(retriever.calls) == 1
+    assert retriever.calls[0]["kwargs"]["doc_ids"] is None
+    assert "Chunk text from dataset." in chat_model.calls[0]["system_prompt"]
+    assert result[0]["answer"] == "stub answer"