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Feat: Add question parameter to edit chunk modal (#3875)

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### What problem does this PR solve?

Close #3873

### Type of change

- [x] New Feature (non-breaking change which adds functionality)
This commit is contained in:
Kevin Hu
2024-12-05 14:51:19 +08:00
committed by GitHub
parent b502dc7399
commit 56f473b680
8 changed files with 55 additions and 24 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
api/apps/chunk_app.py
View File

@ -68,6 +68,7 @@ def list_chunk():
"doc_id": sres.field[id]["doc_id"],
"docnm_kwd": sres.field[id]["docnm_kwd"],
"important_kwd": sres.field[id].get("important_kwd", []),
"question_kwd": sres.field[id].get("question_kwd", []),
"image_id": sres.field[id].get("img_id", ""),
"available_int": sres.field[id].get("available_int", 1),
"positions": json.loads(sres.field[id].get("position_list", "[]")),
@ -115,7 +116,7 @@ def get():
@manager.route('/set', methods=['POST'])
@login_required
@validate_request("doc_id", "chunk_id", "content_with_weight",
"important_kwd")
"important_kwd", "question_kwd")
def set():
req = request.json
d = {
@ -125,6 +126,8 @@ def set():
d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
d["important_kwd"] = req["important_kwd"]
d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_kwd"]))
d["question_kwd"] = req["question_kwd"]
d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["question_kwd"]))
if "available_int" in req:
d["available_int"] = req["available_int"]
@ -152,7 +155,7 @@ 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"]])
v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d["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)
@ -213,6 +216,8 @@ def create():
d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
d["important_kwd"] = req.get("important_kwd", [])
d["important_tks"] = rag_tokenizer.tokenize(" ".join(req.get("important_kwd", [])))
d["question_kwd"] = req.get("question_kwd", [])
d["question_tks"] = rag_tokenizer.tokenize("\n".join(req.get("question_kwd", [])))
d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
@ -237,7 +242,7 @@ def create():
embd_id = DocumentService.get_embd_id(req["doc_id"])
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING.value, embd_id)
v, c = embd_mdl.encode([doc.name, req["content_with_weight"]])
v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
v = 0.1 * v[0] + 0.9 * v[1]
d["q_%d_vec" % len(v)] = v.tolist()
settings.docStoreConn.insert([d], search.index_name(tenant_id), doc.kb_id)

24
api/apps/sdk/doc.py
View File

@ -844,6 +844,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
"doc_id": sres.field[id]["doc_id"],
"docnm_kwd": sres.field[id]["docnm_kwd"],
"important_kwd": sres.field[id].get("important_kwd", []),
"question_kwd": sres.field[id].get("question_kwd", []),
"img_id": sres.field[id].get("img_id", ""),
"available_int": sres.field[id].get("available_int", 1),
"positions": sres.field[id].get("position_int", "").split("\t"),
@ -879,6 +880,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
"content_with_weight": "content",
"doc_id": "document_id",
"important_kwd": "important_keywords",
"question_kwd": "questions",
"img_id": "image_id",
"available_int": "available",
}
@ -978,6 +980,11 @@ def add_chunk(tenant_id, dataset_id, document_id):
return get_error_data_result(
"`important_keywords` is required to be a list"
)
if "questions" in req:
if type(req["questions"]) != list:
return get_error_data_result(
"`questions` is required to be a list"
)
md5 = hashlib.md5()
md5.update((req["content"] + document_id).encode("utf-8"))
@ -992,6 +999,10 @@ def add_chunk(tenant_id, dataset_id, document_id):
d["important_tks"] = rag_tokenizer.tokenize(
" ".join(req.get("important_keywords", []))
)
d["question_kwd"] = req.get("questions", [])
d["question_tks"] = rag_tokenizer.tokenize(
"\n".join(req.get("questions", []))
)
d["create_time"] = str(datetime.datetime.now()).replace("T", " ")[:19]
d["create_timestamp_flt"] = datetime.datetime.now().timestamp()
d["kb_id"] = dataset_id
@ -1001,7 +1012,7 @@ def add_chunk(tenant_id, dataset_id, document_id):
embd_mdl = TenantLLMService.model_instance(
tenant_id, LLMType.EMBEDDING.value, embd_id
)
v, c = embd_mdl.encode([doc.name, req["content"]])
v, c = embd_mdl.encode([doc.name, req["content"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
v = 0.1 * v[0] + 0.9 * v[1]
d["q_%d_vec" % len(v)] = v.tolist()
settings.docStoreConn.insert([d], search.index_name(tenant_id), dataset_id)
@ -1013,6 +1024,7 @@ def add_chunk(tenant_id, dataset_id, document_id):
"content_with_weight": "content",
"doc_id": "document_id",
"important_kwd": "important_keywords",
"question_kwd": "questions",
"kb_id": "dataset_id",
"create_timestamp_flt": "create_timestamp",
"create_time": "create_time",
@ -1166,8 +1178,13 @@ def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
if "important_keywords" in req:
if not isinstance(req["important_keywords"], list):
return get_error_data_result("`important_keywords` should be a list")
d["important_kwd"] = req.get("important_keywords")
d["important_kwd"] = req.get("important_keywords", [])
d["important_tks"] = rag_tokenizer.tokenize(" ".join(req["important_keywords"]))
if "questions" in req:
if not isinstance(req["questions"], list):
return get_error_data_result("`questions` should be a list")
d["question_kwd"] = req.get("questions")
d["question_tks"] = rag_tokenizer.tokenize("\n".join(req["questions"]))
if "available" in req:
d["available_int"] = int(req["available"])
embd_id = DocumentService.get_embd_id(document_id)
@ -1185,7 +1202,7 @@ def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
d, arr[0], arr[1], not any([rag_tokenizer.is_chinese(t) for t in q + a])
)
v, c = embd_mdl.encode([doc.name, d["content_with_weight"]])
v, c = embd_mdl.encode([doc.name, d["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": chunk_id}, d, search.index_name(tenant_id), dataset_id)
@ -1353,6 +1370,7 @@ def retrieval_test(tenant_id):
"content_with_weight": "content",
"doc_id": "document_id",
"important_kwd": "important_keywords",
"question_kwd": "questions",
"docnm_kwd": "document_keyword",
}
rename_chunk = {}