Add doc meta data. (#4442)

### What problem does this PR solve?

#3690

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

api/apps/chunk_app.py

@@ -116,8 +116,7 @@ def get():
 
 @manager.route('/set', methods=['POST'])  # noqa: F821
 @login_required
-@validate_request("doc_id", "chunk_id", "content_with_weight",
-                  "important_kwd", "question_kwd")
+@validate_request("doc_id", "chunk_id", "content_with_weight")
 def set():
     req = request.json
     d = {
@@ -125,14 +124,16 @@ def set():
         "content_with_weight": req["content_with_weight"]}
     d["content_ltks"] = rag_tokenizer.tokenize(req["content_with_weight"])
     d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    if req.get("important_kwd"):
+    if "important_kwd" in req:
         d["important_kwd"] = req["important_kwd"]
         d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
-    if req.get("question_kwd"):
+    if "question_kwd" in req:
         d["question_kwd"] = req["question_kwd"]
         d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["question_kwd"]))
-    if req.get("tag_kwd"):
+    if "tag_kwd" in req:
         d["tag_kwd"] = req["tag_kwd"]
+    if "tag_feas" in req:
+        d["tag_feas"] = req["tag_feas"]
     if "available_int" in req:
         d["available_int"] = req["available_int"]
 
@@ -157,7 +158,7 @@ def set():
             d = beAdoc(d, arr[0], arr[1], not any(
                 [rag_tokenizer.is_chinese(t) for t in q + a]))
 
-        v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
+        v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])])
         v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
         settings.docStoreConn.update({"id": req["chunk_id"]}, d, search.index_name(tenant_id), doc.kb_id)

api/apps/conversation_app.py

@@ -27,12 +27,13 @@ from flask_login import login_required, current_user
 from api.db import LLMType
 from api.db.services.dialog_service import DialogService, chat, ask, label_question
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import LLMBundle, TenantService, TenantLLMService
+from api.db.services.llm_service import LLMBundle, TenantService
 from api import settings
 from api.utils.api_utils import get_json_result
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from graphrag.mind_map_extractor import MindMapExtractor
 
+
 @manager.route('/set', methods=['POST'])  # noqa: F821
 @login_required
 def set_conversation():
@@ -376,8 +377,7 @@ def mindmap():
     if not e:
         return get_data_error_result(message="Knowledgebase not found!")
 
-    embd_mdl = TenantLLMService.model_instance(
-        kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
+    embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
     chat_mdl = LLMBundle(current_user.id, LLMType.CHAT)
     question = req["question"]
     ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12,

api/apps/document_app.py

@@ -13,6 +13,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License
 #
+import json
 import os.path
 import pathlib
 import re
@@ -593,3 +594,34 @@ def parse():
     txt = FileService.parse_docs(file_objs, current_user.id)
 
     return get_json_result(data=txt)
+
+
+@manager.route('/set_meta', methods=['POST'])  # noqa: F821
+@login_required
+@validate_request("doc_id", "meta")
+def set_meta():
+    req = request.json
+    if not DocumentService.accessible(req["doc_id"], current_user.id):
+        return get_json_result(
+            data=False,
+            message='No authorization.',
+            code=settings.RetCode.AUTHENTICATION_ERROR
+        )
+    try:
+        meta = json.loads(req["meta"])
+    except Exception as e:
+        return get_json_result(
+            data=False, message=f'Json syntax error: {e}', code=settings.RetCode.ARGUMENT_ERROR)
+    try:
+        e, doc = DocumentService.get_by_id(req["doc_id"])
+        if not e:
+            return get_data_error_result(message="Document not found!")
+
+        if not DocumentService.update_by_id(
+                req["doc_id"], {"meta_fields": meta}):
+            return get_data_error_result(
+                message="Database error (meta updates)!")
+
+        return get_json_result(data=True)
+    except Exception as e:
+        return server_error_response(e)

api/db/db_models.py

@@ -760,6 +760,7 @@ class Document(DataBaseModel):
         default="")
     process_begin_at = DateTimeField(null=True, index=True)
     process_duation = FloatField(default=0)
+    meta_fields = JSONField(null=True, default={})
 
     run = CharField(
         max_length=1,
@@ -1112,3 +1113,10 @@ def migrate_db():
             )
         except Exception:
             pass
+        try:
+            migrate(
+                migrator.add_column("document", "meta_fields",
+                                    JSONField(null=True, default={}))
+            )
+        except Exception:
+            pass

api/db/services/dialog_service.py

@@ -122,15 +122,17 @@ def kb_prompt(kbinfos, max_tokens):
             knowledges = knowledges[:i]
             break
 
+    #docs = DocumentService.get_by_ids([ck["doc_id"] for ck in kbinfos["chunks"][:chunks_num]])
+    #docs = {d.id: d.meta_fields for d in docs}
+
     doc2chunks = defaultdict(list)
-    for i, ck in enumerate(kbinfos["chunks"]):
-        if i >= chunks_num:
-            break
+    for ck in kbinfos["chunks"][:chunks_num]:
         doc2chunks[ck["docnm_kwd"]].append(ck["content_with_weight"])
 
     knowledges = []
     for nm, chunks in doc2chunks.items():
-        txt = f"Document: {nm} \nContains the following relevant fragments:\n"
+        txt = f"Document: {nm} \n"
+        txt += "Contains the following relevant fragments:\n"
         for i, chunk in enumerate(chunks, 1):
             txt += f"{i}. {chunk}\n"
         knowledges.append(txt)