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Feat: add OpenAI compatible API for agent (#6329)
### What problem does this PR solve? add openai agent _Briefly describe what this PR aims to solve. Include background context that will help reviewers understand the purpose of the PR._ ### Type of change - [ ] Bug Fix (non-breaking change which fixes an issue) - [x] New Feature (non-breaking change which adds functionality) - [ ] Documentation Update - [ ] Refactoring - [ ] Performance Improvement - [ ] Other (please describe): --------- Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
This commit is contained in:
parent
2acb02366e
commit
cded812b97
@ -17,22 +17,23 @@ import json
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import re
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import re
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import time
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import time
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import tiktoken
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from flask import Response, jsonify, request
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from flask import Response, jsonify, request
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from api.db.services.conversation_service import ConversationService, iframe_completion
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from api.db.services.conversation_service import completion as rag_completion
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from api.db.services.canvas_service import completion as agent_completion, completionOpenAI
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from agent.canvas import Canvas
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from agent.canvas import Canvas
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from api.db import LLMType, StatusEnum
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from api.db import LLMType, StatusEnum
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from api.db.db_models import APIToken
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from api.db.db_models import APIToken
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from api.db.services.api_service import API4ConversationService
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from api.db.services.api_service import API4ConversationService
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from api.db.services.canvas_service import UserCanvasService
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from api.db.services.canvas_service import UserCanvasService
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from api.db.services.canvas_service import completion as agent_completion
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from api.db.services.conversation_service import ConversationService, iframe_completion
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from api.db.services.conversation_service import completion as rag_completion
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from api.db.services.dialog_service import DialogService, ask, chat
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from api.db.services.dialog_service import DialogService, ask, chat
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from api.db.services.file_service import FileService
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from api.db.services.file_service import FileService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import LLMBundle
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from api.utils import get_uuid
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from api.utils import get_uuid
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from api.utils.api_utils import get_error_data_result, get_result, token_required, validate_request
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from api.utils.api_utils import get_result, token_required, get_data_openai, get_error_data_result, validate_request
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from api.db.services.llm_service import LLMBundle
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@manager.route("/chats/<chat_id>/sessions", methods=["POST"]) # noqa: F821
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@manager.route("/chats/<chat_id>/sessions", methods=["POST"]) # noqa: F821
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@ -71,14 +72,11 @@ def create_agent_session(tenant_id, agent_id):
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req = request.form
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req = request.form
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files = request.files
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files = request.files
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user_id = request.args.get("user_id", "")
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user_id = request.args.get("user_id", "")
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e, cvs = UserCanvasService.get_by_id(agent_id)
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e, cvs = UserCanvasService.get_by_id(agent_id)
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if not e:
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if not e:
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return get_error_data_result("Agent not found.")
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return get_error_data_result("Agent not found.")
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if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
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if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
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return get_error_data_result("You cannot access the agent.")
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return get_error_data_result("You cannot access the agent.")
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if not isinstance(cvs.dsl, str):
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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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cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
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@ -352,6 +350,40 @@ def chat_completion_openai_like(tenant_id, chat_id):
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}
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}
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return jsonify(response)
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return jsonify(response)
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@manager.route('/agents_openai/<agent_id>/chat/completions', methods=['POST']) # noqa: F821
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@validate_request("model", "messages") # noqa: F821
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@token_required
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def agents_completion_openai_compatibility (tenant_id, agent_id):
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req = request.json
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tiktokenenc = tiktoken.get_encoding("cl100k_base")
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messages = req.get("messages", [])
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if not messages:
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return get_error_data_result("You must provide at least one message.")
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if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
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return get_error_data_result(f"You don't own the agent {agent_id}")
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filtered_messages = [m for m in messages if m["role"] in ["user", "assistant"]]
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prompt_tokens = sum(len(tiktokenenc.encode(m["content"])) for m in filtered_messages)
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if not filtered_messages:
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return jsonify(get_data_openai(
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id=agent_id,
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content="No valid messages found (user or assistant).",
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finish_reason="stop",
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model=req.get("model", ""),
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completion_tokens=len(tiktokenenc.encode("No valid messages found (user or assistant).")),
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prompt_tokens=prompt_tokens,
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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", ""), stream=True), mimetype="text/event-stream")
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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", ""), stream=False))
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return jsonify(response)
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@manager.route("/agents/<agent_id>/completions", methods=["POST"]) # noqa: F821
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@manager.route("/agents/<agent_id>/completions", methods=["POST"]) # noqa: F821
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@token_required
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@token_required
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@ -364,9 +396,7 @@ def agent_completions(tenant_id, agent_id):
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dsl = cvs[0].dsl
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dsl = cvs[0].dsl
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if not isinstance(dsl, str):
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if not isinstance(dsl, str):
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dsl = json.dumps(dsl)
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dsl = json.dumps(dsl)
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# canvas = Canvas(dsl, tenant_id)
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# if canvas.get_preset_param():
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# req["question"] = ""
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conv = API4ConversationService.query(id=req["session_id"], dialog_id=agent_id)
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conv = API4ConversationService.query(id=req["session_id"], dialog_id=agent_id)
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if not conv:
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if not conv:
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return get_error_data_result(f"You don't own the session {req['session_id']}")
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return get_error_data_result(f"You don't own the session {req['session_id']}")
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@ -24,8 +24,9 @@ from api.db.services.api_service import API4ConversationService
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from api.db.services.common_service import CommonService
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from api.db.services.common_service import CommonService
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from api.db.services.conversation_service import structure_answer
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from api.db.services.conversation_service import structure_answer
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from api.utils import get_uuid
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from api.utils import get_uuid
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from api.utils.api_utils import get_data_openai
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import tiktoken
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from peewee import fn
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from peewee import fn
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class CanvasTemplateService(CommonService):
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class CanvasTemplateService(CommonService):
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model = CanvasTemplate
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model = CanvasTemplate
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@ -100,14 +101,14 @@ class UserCanvasService(CommonService):
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]
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]
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if keywords:
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if keywords:
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angents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
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angents = 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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TenantPermission.TEAM.value)) | (
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cls.model.user_id == user_id)),
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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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(fn.LOWER(cls.model.title).contains(keywords.lower()))
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)
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)
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else:
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else:
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angents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
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angents = 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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TenantPermission.TEAM.value)) | (
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cls.model.user_id == user_id))
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cls.model.user_id == user_id))
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)
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)
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@ -154,8 +155,6 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
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"dsl": cvs.dsl
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"dsl": cvs.dsl
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}
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}
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API4ConversationService.save(**conv)
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API4ConversationService.save(**conv)
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conv = API4Conversation(**conv)
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conv = API4Conversation(**conv)
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else:
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else:
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e, conv = API4ConversationService.get_by_id(session_id)
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e, conv = API4ConversationService.get_by_id(session_id)
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@ -221,3 +220,206 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
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API4ConversationService.append_message(conv.id, conv.to_dict())
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API4ConversationService.append_message(conv.id, conv.to_dict())
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yield result
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yield result
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break
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break
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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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API4ConversationService.save(**conv)
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conv = API4Conversation(**conv)
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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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if stream:
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try:
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completion_tokens = 0
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for ans in canvas.run(stream=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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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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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=final_ans["content"],
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object="chat.completion.chunk",
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finish_reason="stop",
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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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# 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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model=agent_id,
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content="**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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),
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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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try:
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all_answer_content = ""
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for answer in canvas.run(stream=False):
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if answer.get("running_status"):
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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
|
||||||
|
yield get_data_openai(
|
||||||
|
id=session_id,
|
||||||
|
model=agent_id,
|
||||||
|
content=final_ans["content"],
|
||||||
|
finish_reason="stop",
|
||||||
|
completion_tokens=len(tiktokenenc.encode(final_ans["content"])),
|
||||||
|
prompt_tokens=prompt_tokens,
|
||||||
|
param=canvas.get_preset_param() # Added param info like in completion
|
||||||
|
)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
traceback.print_exc()
|
||||||
|
conv.dsl = json.loads(str(canvas))
|
||||||
|
API4ConversationService.append_message(conv.id, conv.to_dict())
|
||||||
|
yield get_data_openai(
|
||||||
|
id=session_id,
|
||||||
|
model=agent_id,
|
||||||
|
content="**ERROR**: " + str(e),
|
||||||
|
finish_reason="stop",
|
||||||
|
completion_tokens=len(tiktokenenc.encode("**ERROR**: " + str(e))),
|
||||||
|
prompt_tokens=prompt_tokens
|
||||||
|
)
|
||||||
|
|
||||||
|
@ -378,6 +378,46 @@ def get_parser_config(chunk_method, parser_config):
|
|||||||
return parser_config
|
return parser_config
|
||||||
|
|
||||||
|
|
||||||
|
def get_data_openai(id=None,
|
||||||
|
created=None,
|
||||||
|
model=None,
|
||||||
|
prompt_tokens= 0,
|
||||||
|
completion_tokens=0,
|
||||||
|
content = None,
|
||||||
|
finish_reason= None,
|
||||||
|
object="chat.completion",
|
||||||
|
param=None,
|
||||||
|
):
|
||||||
|
|
||||||
|
total_tokens= prompt_tokens + completion_tokens
|
||||||
|
return {
|
||||||
|
"id":f"{id}",
|
||||||
|
"object": object,
|
||||||
|
"created": int(time.time()) if created else None,
|
||||||
|
"model": model,
|
||||||
|
"param":param,
|
||||||
|
"usage": {
|
||||||
|
"prompt_tokens": prompt_tokens,
|
||||||
|
"completion_tokens": completion_tokens,
|
||||||
|
"total_tokens": total_tokens,
|
||||||
|
"completion_tokens_details": {
|
||||||
|
"reasoning_tokens": 0,
|
||||||
|
"accepted_prediction_tokens": 0,
|
||||||
|
"rejected_prediction_tokens": 0
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"choices": [
|
||||||
|
{
|
||||||
|
"message": {
|
||||||
|
"role": "assistant",
|
||||||
|
"content": content
|
||||||
|
},
|
||||||
|
"logprobs": None,
|
||||||
|
"finish_reason": finish_reason,
|
||||||
|
"index": 0
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
def valid_parser_config(parser_config):
|
def valid_parser_config(parser_config):
|
||||||
if not parser_config:
|
if not parser_config:
|
||||||
return
|
return
|
||||||
|
@ -166,6 +166,150 @@ Non-stream:
|
|||||||
|
|
||||||
Failure:
|
Failure:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"code": 102,
|
||||||
|
"message": "The last content of this conversation is not from user."
|
||||||
|
}
|
||||||
|
```
|
||||||
|
---
|
||||||
|
### Create agent completion
|
||||||
|
|
||||||
|
**POST** `/api/v1/agents_openai/{agent_id}/chat/completions`
|
||||||
|
|
||||||
|
Creates a model response for a given chat conversation.
|
||||||
|
|
||||||
|
This API follows the same request and response format as OpenAI's API. It allows you to interact with the model in a manner similar to how you would with [OpenAI's API](https://platform.openai.com/docs/api-reference/chat/create).
|
||||||
|
|
||||||
|
#### Request
|
||||||
|
|
||||||
|
- Method: POST
|
||||||
|
- URL: `/api/v1/agents_openai/{agent_id}/chat/completions`
|
||||||
|
- Headers:
|
||||||
|
- `'content-Type: application/json'`
|
||||||
|
- `'Authorization: Bearer <YOUR_API_KEY>'`
|
||||||
|
- Body:
|
||||||
|
- `"model"`: `string`
|
||||||
|
- `"messages"`: `object list`
|
||||||
|
- `"stream"`: `boolean`
|
||||||
|
|
||||||
|
##### Request example
|
||||||
|
|
||||||
|
```bash
|
||||||
|
curl --request POST \
|
||||||
|
--url http://{address}/api/v1/agents_openai/{agent_id}/chat/completions \
|
||||||
|
--header 'Content-Type: application/json' \
|
||||||
|
--header 'Authorization: Bearer <YOUR_API_KEY>' \
|
||||||
|
--data '{
|
||||||
|
"model": "model",
|
||||||
|
"messages": [{"role": "user", "content": "Say this is a test!"}],
|
||||||
|
"stream": true
|
||||||
|
}'
|
||||||
|
```
|
||||||
|
|
||||||
|
##### Request Parameters
|
||||||
|
|
||||||
|
- `model` (*Body parameter*) `string`, *Required*
|
||||||
|
The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.
|
||||||
|
|
||||||
|
- `messages` (*Body parameter*) `list[object]`, *Required*
|
||||||
|
A list of historical chat messages used to generate the response. This must contain at least one message with the `user` role.
|
||||||
|
|
||||||
|
- `stream` (*Body parameter*) `boolean`
|
||||||
|
Whether to receive the response as a stream. Set this to `false` explicitly if you prefer to receive the entire response in one go instead of as a stream.
|
||||||
|
|
||||||
|
#### Response
|
||||||
|
|
||||||
|
Stream:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"id": "chatcmpl-3a9c3572f29311efa69751e139332ced",
|
||||||
|
"choices": [
|
||||||
|
{
|
||||||
|
"delta": {
|
||||||
|
"content": "This is a test. If you have any specific questions or need information, feel",
|
||||||
|
"role": "assistant",
|
||||||
|
"function_call": null,
|
||||||
|
"tool_calls": null
|
||||||
|
},
|
||||||
|
"finish_reason": null,
|
||||||
|
"index": 0,
|
||||||
|
"logprobs": null
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"created": 1740543996,
|
||||||
|
"model": "model",
|
||||||
|
"object": "chat.completion.chunk",
|
||||||
|
"system_fingerprint": "",
|
||||||
|
"usage": null
|
||||||
|
}
|
||||||
|
// omit duplicated information
|
||||||
|
{"choices":[{"delta":{"content":" free to ask, and I will do my best to provide an answer based on","role":"assistant"}}]}
|
||||||
|
{"choices":[{"delta":{"content":" the knowledge I have. If your question is unrelated to the provided knowledge base,","role":"assistant"}}]}
|
||||||
|
{"choices":[{"delta":{"content":" I will let you know.","role":"assistant"}}]}
|
||||||
|
// the last chunk
|
||||||
|
{
|
||||||
|
"id": "chatcmpl-3a9c3572f29311efa69751e139332ced",
|
||||||
|
"choices": [
|
||||||
|
{
|
||||||
|
"delta": {
|
||||||
|
"content": null,
|
||||||
|
"role": "assistant",
|
||||||
|
"function_call": null,
|
||||||
|
"tool_calls": null
|
||||||
|
},
|
||||||
|
"finish_reason": "stop",
|
||||||
|
"index": 0,
|
||||||
|
"logprobs": null
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"created": 1740543996,
|
||||||
|
"model": "model",
|
||||||
|
"object": "chat.completion.chunk",
|
||||||
|
"system_fingerprint": "",
|
||||||
|
"usage": {
|
||||||
|
"prompt_tokens": 18,
|
||||||
|
"completion_tokens": 225,
|
||||||
|
"total_tokens": 243
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Non-stream:
|
||||||
|
|
||||||
|
```json
|
||||||
|
{
|
||||||
|
"choices":[
|
||||||
|
{
|
||||||
|
"finish_reason":"stop",
|
||||||
|
"index":0,
|
||||||
|
"logprobs":null,
|
||||||
|
"message":{
|
||||||
|
"content":"This is a test. If you have any specific questions or need information, feel free to ask, and I will do my best to provide an answer based on the knowledge I have. If your question is unrelated to the provided knowledge base, I will let you know.",
|
||||||
|
"role":"assistant"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"created":1740543499,
|
||||||
|
"id":"chatcmpl-3a9c3572f29311efa69751e139332ced",
|
||||||
|
"model":"model",
|
||||||
|
"object":"chat.completion",
|
||||||
|
"usage":{
|
||||||
|
"completion_tokens":246,
|
||||||
|
"completion_tokens_details":{
|
||||||
|
"accepted_prediction_tokens":246,
|
||||||
|
"reasoning_tokens":18,
|
||||||
|
"rejected_prediction_tokens":0
|
||||||
|
},
|
||||||
|
"prompt_tokens":18,
|
||||||
|
"total_tokens":264
|
||||||
|
}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Failure:
|
||||||
|
|
||||||
```json
|
```json
|
||||||
{
|
{
|
||||||
"code": 102,
|
"code": 102,
|
||||||
|
Loading…
x
Reference in New Issue
Block a user