feat: add knowledge retrieval service

backend/apps/knowledge/api.py

@@ -9,8 +9,10 @@ from apps.knowledge.schemas import (
     KnowledgeDocumentOut,
     KnowledgeItemIn,
     KnowledgeItemOut,
+    KnowledgeSearchIn,
+    KnowledgeSearchResultOut,
 )
-from apps.knowledge.services import ingest_document_text, rebuild_item_chunks
+from apps.knowledge.services import ingest_document_text, rebuild_item_chunks, retrieve_knowledge
 
 router = Router(tags=["knowledge"])
 
@@ -81,3 +83,13 @@ def create_knowledge_document(request, payload: KnowledgeDocumentIn):
         content=payload.content,
         operator=request.user if request.user.is_authenticated else None,
     )
+
+
+@router.post("/search", response=list[KnowledgeSearchResultOut])
+def search_knowledge(request, payload: KnowledgeSearchIn):
+    require_permission(request, "knowledge.view")
+    return retrieve_knowledge(
+        query=payload.query,
+        knowledge_base_id=payload.knowledge_base_id,
+        limit=min(max(payload.limit, 1), 20),
+    )

backend/apps/knowledge/schemas.py

@@ -65,3 +65,19 @@ class KnowledgeItemOut(Schema):
     source_type: str
     created_at: datetime
     updated_at: datetime
+
+
+class KnowledgeSearchIn(Schema):
+    query: str
+    knowledge_base_id: int | None = None
+    limit: int = 5
+
+
+class KnowledgeSearchResultOut(Schema):
+    id: int
+    knowledge_base_id: int
+    title: str
+    source_type: str
+    content: str
+    score: float
+    metadata: dict

backend/apps/knowledge/services.py

@@ -1,7 +1,8 @@
 import hashlib
 import math
 
-from apps.knowledge.models import KnowledgeChunk, KnowledgeDocument, KnowledgeItem
+from apps.knowledge.models import KnowledgeBase, KnowledgeChunk, KnowledgeDocument, KnowledgeItem
+from apps.solutions.models import Solution
 
 DEFAULT_CHUNK_SIZE = 600
 
@@ -23,6 +24,18 @@ def embed_text(content: str, dimensions: int = 16) -> list[float]:
     return [round(value / length, 6) for value in values]
 
 
+def keyword_score(query: str, content: str) -> float:
+    normalized_query = query.lower()
+    terms = [term for term in normalized_query.split() if term]
+    terms.extend(character for character in normalized_query if not character.isspace())
+    lowered = content.lower()
+    return sum(1 for term in terms if term in lowered)
+
+
+def vector_score(left: list[float], right: list[float]) -> float:
+    return sum(a * b for a, b in zip(left, right, strict=False))
+
+
 def rebuild_item_chunks(item: KnowledgeItem) -> list[KnowledgeChunk]:
     item.chunks.all().delete()
     content = "\n".join(
@@ -85,3 +98,70 @@ def ingest_document_text(
             },
         )
     return document
+
+
+def index_published_solution(solution: Solution) -> list[KnowledgeChunk]:
+    base, _ = KnowledgeBase.objects.get_or_create(
+        name="解决方案知识库",
+        defaults={"description": "从已发布解决方案生成的检索知识", "scope": "solutions"},
+    )
+    KnowledgeChunk.objects.filter(metadata__solution_id=solution.id).delete()
+    content = "\n".join(
+        [
+            solution.title,
+            solution.problem_signature,
+            solution.related_system,
+            solution.environment_scope,
+            solution.root_cause,
+            solution.solution,
+            solution.risk_note,
+        ]
+    )
+    chunks = []
+    for index, chunk_text in enumerate(split_text(content)):
+        chunks.append(
+            KnowledgeChunk.objects.create(
+                knowledge_base=base,
+                chunk_index=index,
+                content=chunk_text,
+                embedding=embed_text(chunk_text),
+                metadata={
+                    "source_type": "solution",
+                    "title": solution.title,
+                    "solution_id": solution.id,
+                },
+            )
+        )
+    return chunks
+
+
+def retrieve_knowledge(
+    query: str,
+    knowledge_base_id: int | None = None,
+    limit: int = 5,
+) -> list[dict]:
+    query_embedding = embed_text(query)
+    queryset = KnowledgeChunk.objects.select_related("knowledge_base", "item", "document")
+    if knowledge_base_id:
+        queryset = queryset.filter(knowledge_base_id=knowledge_base_id)
+    scored = []
+    for chunk in queryset[:1000]:
+        score = keyword_score(query, chunk.content) * 10 + vector_score(
+            query_embedding,
+            chunk.embedding,
+        )
+        if score > 0:
+            scored.append((score, chunk))
+    scored.sort(key=lambda pair: pair[0], reverse=True)
+    return [
+        {
+            "id": chunk.id,
+            "knowledge_base_id": chunk.knowledge_base_id,
+            "title": chunk.metadata.get("title", chunk.knowledge_base.name),
+            "source_type": chunk.metadata.get("source_type", "chunk"),
+            "content": chunk.content,
+            "score": round(score, 4),
+            "metadata": chunk.metadata,
+        }
+        for score, chunk in scored[:limit]
+    ]

backend/apps/knowledge/tests/test_retrieval.py

@@ -0,0 +1,53 @@
+from apps.knowledge.models import KnowledgeBase, KnowledgeChunk, KnowledgeItem
+from apps.knowledge.services import retrieve_knowledge
+from apps.solutions.models import Solution
+
+
+def test_retrieve_knowledge_prefers_relevant_chunk(db):
+    base = KnowledgeBase.objects.create(name="采集知识库", scope="数据采集")
+    item = KnowledgeItem.objects.create(
+        knowledge_base=base,
+        title="税局采集进度",
+        problem="采集进度不更新",
+        solution="查询 Python 日志，确认税局状态。",
+        status="published",
+    )
+    KnowledgeChunk.objects.create(
+        knowledge_base=base,
+        item=item,
+        chunk_index=0,
+        content="税局采集进度不更新时，需要查询 Python 日志。",
+        embedding=[0.1, 0.2],
+        metadata={"source_type": "knowledge_item", "title": item.title},
+    )
+    KnowledgeChunk.objects.create(
+        knowledge_base=base,
+        chunk_index=1,
+        content="登录失败通常需要检查账号密码。",
+        embedding=[0.9, 0.1],
+        metadata={"source_type": "document", "title": "登录问题"},
+    )
+
+    results = retrieve_knowledge("税局采集进度怎么查询", limit=1)
+
+    assert len(results) == 1
+    assert results[0]["title"] == "税局采集进度"
+    assert "Python 日志" in results[0]["content"]
+
+
+def test_published_solution_can_be_indexed_for_retrieval(db):
+    solution = Solution.objects.create(
+        title="税局不稳定采集处理中",
+        problem_signature="采集进度长时间不更新",
+        root_cause="税局服务不稳定",
+        solution="回复客户税局不稳定，任务仍在采集中。",
+        status="published",
+    )
+
+    from apps.knowledge.services import index_published_solution
+
+    chunks = index_published_solution(solution)
+
+    assert len(chunks) == 1
+    assert chunks[0].metadata["source_type"] == "solution"
+    assert chunks[0].metadata["solution_id"] == solution.id

backend/apps/solutions/api.py

@@ -20,7 +20,7 @@ router = Router(tags=["solutions"])
 @router.post("", response=SolutionOut)
 def create_solution(request, payload: SolutionIn):
     require_permission(request, "solution.manage")
-    return Solution.objects.create(
+    solution = Solution.objects.create(
         title=payload.title,
         problem_signature=payload.problem_signature,
         related_system=payload.related_system,
@@ -31,6 +31,11 @@ def create_solution(request, payload: SolutionIn):
         status=payload.status,
         created_by=request.user if request.user.is_authenticated else None,
     )
+    if solution.status == "published":
+        from apps.knowledge.services import index_published_solution
+
+        index_published_solution(solution)
+    return solution
 
 
 @router.get("", response=list[SolutionOut])