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https://github.com/infiniflow/ragflow.git
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Fix: update broken agent OpenAI-Compatible completion due to v0.20.0 changes (#9241)
### What problem does this PR solve? Update broken agent OpenAI-Compatible completion due to v0.20.0. #9199 Usage example: **Referring the input is important, otherwise, will result in empty output.** <img width="1273" height="711" alt="Image" src="https://github.com/user-attachments/assets/30740be8-f4d6-400d-9fda-d2616f89063f" /> <img width="622" height="247" alt="Image" src="https://github.com/user-attachments/assets/0a2ca57a-9600-4cec-9362-0cafd0ab3aee" /> ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Yongteng Lei
GitHub
parent
0a303d9ae1
commit
e6bad45c6d
@ -436,14 +436,38 @@ def agents_completion_openai_compatibility(tenant_id, agent_id):
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)
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)
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# Get the last user message as the question
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question = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "")
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if req.get("stream", True):
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return Response(completionOpenAI(tenant_id, agent_id, question, session_id=req.get("id", req.get("metadata", {}).get("id", "")), stream=True), mimetype="text/event-stream")
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stream = req.pop("stream", False)
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if stream:
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resp = Response(
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completionOpenAI(
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tenant_id,
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agent_id,
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question,
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session_id=req.get("id", req.get("metadata", {}).get("id", "")),
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stream=True,
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**req,
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),
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mimetype="text/event-stream",
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)
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resp.headers.add_header("Cache-control", "no-cache")
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resp.headers.add_header("Connection", "keep-alive")
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resp.headers.add_header("X-Accel-Buffering", "no")
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resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
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return resp
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else:
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# For non-streaming, just return the response directly
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response = next(completionOpenAI(tenant_id, agent_id, question, session_id=req.get("id", req.get("metadata", {}).get("id", "")), stream=False))
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response = next(
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completionOpenAI(
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tenant_id,
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agent_id,
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question,
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session_id=req.get("id", req.get("metadata", {}).get("id", "")),
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stream=False,
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**req,
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)
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)
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return jsonify(response)
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@ -850,10 +874,10 @@ def begin_inputs(agent_id):
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return get_error_data_result(f"Can't find agent by ID: {agent_id}")
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canvas = Canvas(json.dumps(cvs.dsl), objs[0].tenant_id)
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return get_result(data={
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"title": cvs.title,
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"avatar": cvs.avatar,
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"inputs": canvas.get_component_input_form("begin")
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})
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return get_result(
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data={
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"title": cvs.title,
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"avatar": cvs.avatar,
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"inputs": canvas.get_component_input_form("begin"),
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}
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)
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@ -16,7 +16,6 @@
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import json
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import logging
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import time
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import traceback
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from uuid import uuid4
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from agent.canvas import Canvas
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from api.db import TenantPermission
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@ -54,12 +53,12 @@ class UserCanvasService(CommonService):
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agents = agents.paginate(page_number, items_per_page)
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return list(agents.dicts())
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@classmethod
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@DB.connection_context()
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def get_by_tenant_id(cls, pid):
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try:
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fields = [
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cls.model.id,
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cls.model.avatar,
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@ -83,7 +82,7 @@ class UserCanvasService(CommonService):
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except Exception as e:
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logging.exception(e)
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return False, None
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@classmethod
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@DB.connection_context()
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def get_by_tenant_ids(cls, joined_tenant_ids, user_id,
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@ -103,14 +102,14 @@ class UserCanvasService(CommonService):
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]
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if keywords:
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agents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
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((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission ==
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((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission ==
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TenantPermission.TEAM.value)) | (
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cls.model.user_id == user_id)),
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(fn.LOWER(cls.model.title).contains(keywords.lower()))
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)
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else:
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agents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
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((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission ==
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((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission ==
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TenantPermission.TEAM.value)) | (
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cls.model.user_id == user_id))
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)
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@ -178,219 +177,99 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs):
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def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs):
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"""Main function for OpenAI-compatible completions, structured similarly to the completion function."""
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tiktokenenc = tiktoken.get_encoding("cl100k_base")
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e, cvs = UserCanvasService.get_by_id(agent_id)
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if not e:
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yield get_data_openai(
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id=session_id,
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model=agent_id,
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content="**ERROR**: Agent not found."
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)
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return
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if cvs.user_id != tenant_id:
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yield get_data_openai(
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id=session_id,
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model=agent_id,
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content="**ERROR**: You do not own the agent"
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)
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return
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if not isinstance(cvs.dsl, str):
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cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
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canvas = Canvas(cvs.dsl, tenant_id)
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canvas.reset()
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message_id = str(uuid4())
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# Handle new session creation
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if not session_id:
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query = canvas.get_preset_param()
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if query:
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for ele in query:
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if not ele["optional"]:
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if not kwargs.get(ele["key"]):
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yield get_data_openai(
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id=None,
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model=agent_id,
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content=f"`{ele['key']}` is required",
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completion_tokens=len(tiktokenenc.encode(f"`{ele['key']}` is required")),
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prompt_tokens=len(tiktokenenc.encode(question if question else ""))
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)
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return
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ele["value"] = kwargs[ele["key"]]
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if ele["optional"]:
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if kwargs.get(ele["key"]):
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ele["value"] = kwargs[ele['key']]
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else:
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if "value" in ele:
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ele.pop("value")
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cvs.dsl = json.loads(str(canvas))
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session_id = get_uuid()
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conv = {
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"id": session_id,
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"dialog_id": cvs.id,
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"user_id": kwargs.get("user_id", "") if isinstance(kwargs, dict) else "",
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"message": [{"role": "assistant", "content": canvas.get_prologue(), "created_at": time.time()}],
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"source": "agent",
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"dsl": cvs.dsl
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}
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canvas.messages.append({"role": "user", "content": question, "id": message_id})
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canvas.add_user_input(question)
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API4ConversationService.save(**conv)
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conv = API4Conversation(**conv)
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if not conv.message:
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conv.message = []
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conv.message.append({
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"role": "user",
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"content": question,
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"id": message_id
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})
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if not conv.reference:
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conv.reference = []
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conv.reference.append({"chunks": [], "doc_aggs": []})
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# Handle existing session
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else:
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e, conv = API4ConversationService.get_by_id(session_id)
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if not e:
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yield get_data_openai(
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id=session_id,
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model=agent_id,
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content="**ERROR**: Session not found!"
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)
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return
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canvas = Canvas(json.dumps(conv.dsl), tenant_id)
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canvas.messages.append({"role": "user", "content": question, "id": message_id})
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canvas.add_user_input(question)
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if not conv.message:
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conv.message = []
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conv.message.append({
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"role": "user",
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"content": question,
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"id": message_id
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})
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if not conv.reference:
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conv.reference = []
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conv.reference.append({"chunks": [], "doc_aggs": []})
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# Process request based on stream mode
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final_ans = {"reference": [], "content": ""}
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prompt_tokens = len(tiktokenenc.encode(str(question)))
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user_id = kwargs.get("user_id", "")
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if stream:
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completion_tokens = 0
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try:
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completion_tokens = 0
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for ans in canvas.run(stream=True, bypass_begin=True):
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if ans.get("running_status"):
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completion_tokens += len(tiktokenenc.encode(ans.get("content", "")))
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yield "data: " + json.dumps(
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get_data_openai(
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id=session_id,
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model=agent_id,
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content=ans["content"],
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object="chat.completion.chunk",
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completion_tokens=completion_tokens,
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prompt_tokens=prompt_tokens
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),
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ensure_ascii=False
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) + "\n\n"
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for ans in completion(
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tenant_id=tenant_id,
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agent_id=agent_id,
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session_id=session_id,
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query=question,
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user_id=user_id,
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**kwargs
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):
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if isinstance(ans, str):
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try:
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ans = json.loads(ans[5:]) # remove "data:"
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except Exception as e:
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logging.exception(f"Agent OpenAI-Compatible completionOpenAI parse answer failed: {e}")
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continue
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if ans.get("event") != "message":
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continue
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for k in ans.keys():
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final_ans[k] = ans[k]
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completion_tokens += len(tiktokenenc.encode(final_ans.get("content", "")))
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content_piece = ans["data"]["content"]
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completion_tokens += len(tiktokenenc.encode(content_piece))
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yield "data: " + json.dumps(
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get_data_openai(
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id=session_id,
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id=session_id or str(uuid4()),
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model=agent_id,
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content=final_ans["content"],
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object="chat.completion.chunk",
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finish_reason="stop",
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content=content_piece,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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prompt_tokens=prompt_tokens
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stream=True
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),
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ensure_ascii=False
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) + "\n\n"
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# Update conversation
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canvas.messages.append({"role": "assistant", "content": final_ans["content"], "created_at": time.time(), "id": message_id})
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canvas.history.append(("assistant", final_ans["content"]))
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if final_ans.get("reference"):
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canvas.reference.append(final_ans["reference"])
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conv.dsl = json.loads(str(canvas))
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API4ConversationService.append_message(conv.id, conv.to_dict())
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yield "data: [DONE]\n\n"
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except Exception as e:
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traceback.print_exc()
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conv.dsl = json.loads(str(canvas))
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API4ConversationService.append_message(conv.id, conv.to_dict())
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yield "data: " + json.dumps(
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get_data_openai(
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id=session_id,
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id=session_id or str(uuid4()),
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model=agent_id,
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content="**ERROR**: " + str(e),
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content=f"**ERROR**: {str(e)}",
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finish_reason="stop",
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completion_tokens=len(tiktokenenc.encode("**ERROR**: " + str(e))),
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prompt_tokens=prompt_tokens
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prompt_tokens=prompt_tokens,
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completion_tokens=len(tiktokenenc.encode(f"**ERROR**: {str(e)}")),
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stream=True
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),
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ensure_ascii=False
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) + "\n\n"
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yield "data: [DONE]\n\n"
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else: # Non-streaming mode
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else:
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try:
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all_answer_content = ""
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for answer in canvas.run(stream=False, bypass_begin=True):
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if answer.get("running_status"):
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all_content = ""
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for ans in completion(
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tenant_id=tenant_id,
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agent_id=agent_id,
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session_id=session_id,
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query=question,
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user_id=user_id,
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**kwargs
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):
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if isinstance(ans, str):
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ans = json.loads(ans[5:])
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if ans.get("event") != "message":
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continue
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final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
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final_ans["reference"] = answer.get("reference", [])
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all_answer_content += final_ans["content"]
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final_ans["content"] = all_answer_content
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# Update conversation
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canvas.messages.append({"role": "assistant", "content": final_ans["content"], "created_at": time.time(), "id": message_id})
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canvas.history.append(("assistant", final_ans["content"]))
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if final_ans.get("reference"):
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canvas.reference.append(final_ans["reference"])
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conv.dsl = json.loads(str(canvas))
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API4ConversationService.append_message(conv.id, conv.to_dict())
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# Return the response in OpenAI format
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all_content += ans["data"]["content"]
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completion_tokens = len(tiktokenenc.encode(all_content))
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yield get_data_openai(
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id=session_id,
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id=session_id or str(uuid4()),
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model=agent_id,
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content=final_ans["content"],
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finish_reason="stop",
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completion_tokens=len(tiktokenenc.encode(final_ans["content"])),
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prompt_tokens=prompt_tokens,
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param=canvas.get_preset_param() # Added param info like in completion
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)
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except Exception as e:
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traceback.print_exc()
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conv.dsl = json.loads(str(canvas))
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API4ConversationService.append_message(conv.id, conv.to_dict())
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yield get_data_openai(
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id=session_id,
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model=agent_id,
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content="**ERROR**: " + str(e),
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completion_tokens=completion_tokens,
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content=all_content,
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finish_reason="stop",
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completion_tokens=len(tiktokenenc.encode("**ERROR**: " + str(e))),
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prompt_tokens=prompt_tokens
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param=None
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)
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except Exception as e:
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yield get_data_openai(
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id=session_id or str(uuid4()),
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model=agent_id,
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prompt_tokens=prompt_tokens,
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completion_tokens=len(tiktokenenc.encode(f"**ERROR**: {str(e)}")),
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content=f"**ERROR**: {str(e)}",
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finish_reason="stop",
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param=None
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)
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@ -402,8 +402,22 @@ def get_data_openai(
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finish_reason=None,
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object="chat.completion",
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param=None,
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stream=False
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):
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total_tokens = prompt_tokens + completion_tokens
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if stream:
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return {
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"id": f"{id}",
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"object": "chat.completion.chunk",
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"model": model,
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"choices": [{
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"delta": {"content": content},
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"finish_reason": finish_reason,
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"index": 0,
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}],
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}
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return {
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"id": f"{id}",
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"object": object,
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@ -414,9 +428,21 @@ def get_data_openai(
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"prompt_tokens": prompt_tokens,
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"completion_tokens": completion_tokens,
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"total_tokens": total_tokens,
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"completion_tokens_details": {"reasoning_tokens": 0, "accepted_prediction_tokens": 0, "rejected_prediction_tokens": 0},
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"completion_tokens_details": {
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"reasoning_tokens": 0,
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"accepted_prediction_tokens": 0,
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"rejected_prediction_tokens": 0,
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},
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},
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"choices": [{"message": {"role": "assistant", "content": content}, "logprobs": None, "finish_reason": finish_reason, "index": 0}],
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"choices": [{
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"message": {
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"role": "assistant",
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"content": content
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},
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"logprobs": None,
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"finish_reason": finish_reason,
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"index": 0,
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}],
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}
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