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Revert: broken agent completion by #9631 (#9760)

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### What problem does this PR solve?

Revert broken agent completion by #9631.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Yongteng Lei
2025-08-27 17:16:55 +08:00
committed by GitHub
parent 986b9cbb1a
commit 6cb3e08381
2 changed files with 71 additions and 73 deletions
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103
api/apps/sdk/session.py
View File

@ -16,8 +16,10 @@
import json
import re
import time
import tiktoken
from flask import Response, jsonify, request
from agent.canvas import Canvas
from api import settings
from api.db import LLMType, StatusEnum
@ -27,7 +29,8 @@ from api.db.services.canvas_service import UserCanvasService, completionOpenAI
from api.db.services.canvas_service import completion as agent_completion
from api.db.services.conversation_service import ConversationService, iframe_completion
from api.db.services.conversation_service import completion as rag_completion
from api.db.services.dialog_service import DialogService, ask, chat, gen_mindmap
from api.db.services.dialog_service import DialogService, ask, chat, gen_mindmap, meta_filter
from api.db.services.document_service import DocumentService
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMBundle
from api.db.services.search_service import SearchService
@ -37,7 +40,7 @@ from api.utils.api_utils import check_duplicate_ids, get_data_openai, get_error_
from rag.app.tag import label_question
from rag.prompts import chunks_format
from rag.prompts.prompt_template import load_prompt
from rag.prompts.prompts import cross_languages, keyword_extraction
from rag.prompts.prompts import cross_languages, gen_meta_filter, keyword_extraction
@manager.route("/chats/<chat_id>/sessions", methods=["POST"]) # noqa: F821
@ -81,7 +84,7 @@ def create_agent_session(tenant_id, agent_id):
if not isinstance(cvs.dsl, str):
cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
session_id=get_uuid()
session_id = get_uuid()
canvas = Canvas(cvs.dsl, tenant_id, agent_id)
canvas.reset()
@ -442,26 +445,46 @@ def agents_completion_openai_compatibility(tenant_id, agent_id):
def agent_completions(tenant_id, agent_id):
req = request.json
ans = {}
if req.get("stream", True):
def generate():
for answer in agent_completion(tenant_id=tenant_id, agent_id=agent_id, **req):
if isinstance(answer, str):
try:
ans = json.loads(answer[5:]) # remove "data:"
except Exception:
continue
if ans.get("event") != "message" or not ans.get("data", {}).get("reference", None):
continue
yield answer
yield "data:[DONE]\n\n"
if req.get("stream", True):
resp = Response(agent_completion(tenant_id=tenant_id, agent_id=agent_id, **req), mimetype="text/event-stream")
resp = Response(generate(), mimetype="text/event-stream")
resp.headers.add_header("Cache-control", "no-cache")
resp.headers.add_header("Connection", "keep-alive")
resp.headers.add_header("X-Accel-Buffering", "no")
resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
return resp
result = {}
full_content = ""
for answer in agent_completion(tenant_id=tenant_id, agent_id=agent_id, **req):
try:
ans = json.loads(answer[5:]) # remove "data:"
if not result:
result = ans.copy()
else:
result["data"]["answer"] += ans["data"]["answer"]
result["data"]["reference"] = ans["data"].get("reference", [])
ans = json.loads(answer[5:])
if ans["event"] == "message":
full_content += ans["data"]["content"]
if ans.get("data", {}).get("reference", None):
ans["data"]["content"] = full_content
return get_result(data=ans)
except Exception as e:
return get_error_data_result(str(e))
return result
return get_result(data=f"**ERROR**: {str(e)}")
return get_result(data=ans)
@manager.route("/chats/<chat_id>/sessions", methods=["GET"]) # noqa: F821
@ -556,10 +579,7 @@ def list_agent_session(tenant_id, agent_id):
if message_num != 0 and messages[message_num]["role"] != "user":
chunk_list = []
# Add boundary and type checks to prevent KeyError
if (chunk_num < len(conv["reference"]) and
conv["reference"][chunk_num] is not None and
isinstance(conv["reference"][chunk_num], dict) and
"chunks" in conv["reference"][chunk_num]):
if chunk_num < len(conv["reference"]) and conv["reference"][chunk_num] is not None and isinstance(conv["reference"][chunk_num], dict) and "chunks" in conv["reference"][chunk_num]:
chunks = conv["reference"][chunk_num]["chunks"]
for chunk in chunks:
# Ensure chunk is a dictionary before calling get method
@ -860,15 +880,7 @@ def begin_inputs(agent_id):
return get_error_data_result(f"Can't find agent by ID: {agent_id}")
canvas = Canvas(json.dumps(cvs.dsl), objs[0].tenant_id)
return get_result(
data={
"title": cvs.title,
"avatar": cvs.avatar,
"inputs": canvas.get_component_input_form("begin"),
"prologue": canvas.get_prologue(),
"mode": canvas.get_mode()
}
)
return get_result(data={"title": cvs.title, "avatar": cvs.avatar, "inputs": canvas.get_component_input_form("begin"), "prologue": canvas.get_prologue(), "mode": canvas.get_mode()})
@manager.route("/searchbots/ask", methods=["POST"]) # noqa: F821
@ -908,7 +920,7 @@ def ask_about_embedded():
return resp
@manager.route("/searchbots/retrieval_test", methods=['POST']) # noqa: F821
@manager.route("/searchbots/retrieval_test", methods=["POST"]) # noqa: F821
@validate_request("kb_id", "question")
def retrieval_test_embedded():
token = request.headers.get("Authorization").split()
@ -938,18 +950,30 @@ def retrieval_test_embedded():
if not tenant_id:
return get_error_data_result(message="permission denined.")
if req.get("search_id", ""):
search_config = SearchService.get_detail(req.get("search_id", "")).get("search_config", {})
meta_data_filter = search_config.get("meta_data_filter", {})
metas = DocumentService.get_meta_by_kbs(kb_ids)
if meta_data_filter.get("method") == "auto":
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", ""))
filters = gen_meta_filter(chat_mdl, metas, question)
doc_ids.extend(meta_filter(metas, filters))
if not doc_ids:
doc_ids = None
elif meta_data_filter.get("method") == "manual":
doc_ids.extend(meta_filter(metas, meta_data_filter["manual"]))
if not doc_ids:
doc_ids = None
try:
tenants = UserTenantService.query(user_id=tenant_id)
for kb_id in kb_ids:
for tenant in tenants:
if KnowledgebaseService.query(
tenant_id=tenant.tenant_id, id=kb_id):
if KnowledgebaseService.query(tenant_id=tenant.tenant_id, id=kb_id):
tenant_ids.append(tenant.tenant_id)
break
else:
return get_json_result(
data=False, message='Only owner of knowledgebase authorized for this operation.',
code=settings.RetCode.OPERATING_ERROR)
return get_json_result(data=False, message="Only owner of knowledgebase authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
if not e:
@ -969,17 +993,11 @@ def retrieval_test_embedded():
question += keyword_extraction(chat_mdl, question)
labels = label_question(question, [kb])
ranks = settings.retrievaler.retrieval(question, embd_mdl, tenant_ids, kb_ids, page, size,
similarity_threshold, vector_similarity_weight, top,
doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"),
rank_feature=labels
ranks = settings.retrievaler.retrieval(
question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top, doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels
)
if use_kg:
ck = settings.kg_retrievaler.retrieval(question,
tenant_ids,
kb_ids,
embd_mdl,
LLMBundle(kb.tenant_id, LLMType.CHAT))
ck = settings.kg_retrievaler.retrieval(question, tenant_ids, kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT))
if ck["content_with_weight"]:
ranks["chunks"].insert(0, ck)
@ -990,8 +1008,7 @@ def retrieval_test_embedded():
return get_json_result(data=ranks)
except Exception as e:
if str(e).find("not_found") > 0:
return get_json_result(data=False, message='No chunk found! Check the chunk status please!',
code=settings.RetCode.DATA_ERROR)
return get_json_result(data=False, message="No chunk found! Check the chunk status please!", code=settings.RetCode.DATA_ERROR)
return server_error_response(e)

37
api/db/services/canvas_service.py
View File

@ -135,24 +135,6 @@ class UserCanvasService(CommonService):
return True
def structure_answer(conv, ans, message_id, session_id):
if not conv:
return ans
content = ""
if ans["event"] == "message":
if ans["data"].get("start_to_think") is True:
content = "<think>"
elif ans["data"].get("end_to_think") is True:
content = "</think>"
else:
content = ans["data"]["content"]
reference = ans["data"].get("reference")
result = {"id": message_id, "session_id": session_id, "answer": content}
if reference:
result["reference"] = [reference]
return result
def completion(tenant_id, agent_id, session_id=None, **kwargs):
query = kwargs.get("query", "") or kwargs.get("question", "")
files = kwargs.get("files", [])
@ -196,14 +178,13 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs):
})
txt = ""
for ans in canvas.run(query=query, files=files, user_id=user_id, inputs=inputs):
ans = structure_answer(conv, ans, message_id, session_id)
txt += ans["answer"]
if ans.get("answer") or ans.get("reference"):
yield "data:" + json.dumps({"code": 0, "data": ans},
ensure_ascii=False) + "\n\n"
ans["session_id"] = session_id
if ans["event"] == "message":
txt += ans["data"]["content"]
yield "data:" + json.dumps(ans, ensure_ascii=False) + "\n\n"
conv.message.append({"role": "assistant", "content": txt, "created_at": time.time(), "id": message_id})
conv.reference.append(canvas.get_reference())
conv.reference = canvas.get_reference()
conv.errors = canvas.error
conv.dsl = str(canvas)
conv = conv.to_dict()
@ -232,9 +213,9 @@ def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True
except Exception as e:
logging.exception(f"Agent OpenAI-Compatible completionOpenAI parse answer failed: {e}")
continue
if not ans["data"]["answer"]:
if ans.get("event") != "message" or not ans.get("data", {}).get("reference", None):
continue
content_piece = ans["data"]["answer"]
content_piece = ans["data"]["content"]
completion_tokens += len(tiktokenenc.encode(content_piece))
yield "data: " + json.dumps(
@ -279,9 +260,9 @@ def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True
):
if isinstance(ans, str):
ans = json.loads(ans[5:])
if not ans["data"]["answer"]:
if ans.get("event") != "message" or not ans.get("data", {}).get("reference", None):
continue
all_content += ans["data"]["answer"]
all_content += ans["data"]["content"]
completion_tokens = len(tiktokenenc.encode(all_content))