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>

api/apps/sdk/session.py

@@ -17,22 +17,23 @@ import json
 import re
 import time
 
+import tiktoken
 from flask import Response, jsonify, request
-
+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.canvas_service import completion as agent_completion, completionOpenAI
 from agent.canvas import Canvas
 from api.db import LLMType, StatusEnum
 from api.db.db_models import APIToken
 from api.db.services.api_service import API4ConversationService
 from api.db.services.canvas_service import UserCanvasService
-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
 from api.db.services.file_service import FileService
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.llm_service import LLMBundle
 from api.utils import get_uuid
-from api.utils.api_utils import get_error_data_result, get_result, token_required, validate_request
+from api.utils.api_utils import get_result, token_required, get_data_openai, get_error_data_result, validate_request
+from api.db.services.llm_service import LLMBundle
+
 
 
 @manager.route("/chats/<chat_id>/sessions", methods=["POST"])  # noqa: F821
@@ -71,14 +72,11 @@ def create_agent_session(tenant_id, agent_id):
         req = request.form
     files = request.files
     user_id = request.args.get("user_id", "")
-
     e, cvs = UserCanvasService.get_by_id(agent_id)
     if not e:
         return get_error_data_result("Agent not found.")
-
     if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
         return get_error_data_result("You cannot access the agent.")
-
     if not isinstance(cvs.dsl, str):
         cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
 
@@ -352,6 +350,40 @@ def chat_completion_openai_like(tenant_id, chat_id):
         }
         return jsonify(response)
 
+@manager.route('/agents_openai/<agent_id>/chat/completions', methods=['POST'])  # noqa: F821
+@validate_request("model", "messages")  # noqa: F821
+@token_required
+def agents_completion_openai_compatibility (tenant_id, agent_id):
+    req = request.json
+    tiktokenenc = tiktoken.get_encoding("cl100k_base")
+    messages = req.get("messages", [])
+    if not messages:
+        return get_error_data_result("You must provide at least one message.")
+    if not UserCanvasService.query(user_id=tenant_id, id=agent_id):
+        return get_error_data_result(f"You don't own the agent {agent_id}")
+  
+    filtered_messages = [m for m in messages if m["role"] in ["user", "assistant"]]
+    prompt_tokens = sum(len(tiktokenenc.encode(m["content"])) for m in filtered_messages)
+    if not filtered_messages:
+        return jsonify(get_data_openai( 
+            id=agent_id,
+            content="No valid messages found (user or assistant).",
+            finish_reason="stop",
+            model=req.get("model", ""), 
+            completion_tokens=len(tiktokenenc.encode("No valid messages found (user or assistant).")),
+            prompt_tokens=prompt_tokens, 
+        ))
+    
+    # Get the last user message as the question
+    question = next((m["content"] for m in reversed(messages) if m["role"] == "user"), "")
+    
+    if req.get("stream", True):
+        return Response(completionOpenAI(tenant_id, agent_id, question, session_id=req.get("id", ""), stream=True), mimetype="text/event-stream")
+    else:
+        # For non-streaming, just return the response directly
+        response = next(completionOpenAI(tenant_id, agent_id, question, session_id=req.get("id", ""), stream=False))
+        return jsonify(response)
+    
 
 @manager.route("/agents/<agent_id>/completions", methods=["POST"])  # noqa: F821
 @token_required
@@ -364,9 +396,7 @@ def agent_completions(tenant_id, agent_id):
         dsl = cvs[0].dsl
         if not isinstance(dsl, str):
             dsl = json.dumps(dsl)
-        # canvas = Canvas(dsl, tenant_id)
-        # if canvas.get_preset_param():
-        #    req["question"] = ""
+
         conv = API4ConversationService.query(id=req["session_id"], dialog_id=agent_id)
         if not conv:
             return get_error_data_result(f"You don't own the session {req['session_id']}")

api/db/services/canvas_service.py

@@ -24,8 +24,9 @@ from api.db.services.api_service import API4ConversationService
 from api.db.services.common_service import CommonService
 from api.db.services.conversation_service import structure_answer
 from api.utils import get_uuid
+from api.utils.api_utils import get_data_openai
+import tiktoken
 from peewee import fn
-
 class CanvasTemplateService(CommonService):
     model = CanvasTemplate
 
@@ -100,14 +101,14 @@ class UserCanvasService(CommonService):
         ]
         if keywords:
             angents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
-                ((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission ==
+                ((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission == 
                                                                 TenantPermission.TEAM.value)) | (
                     cls.model.user_id == user_id)),
                 (fn.LOWER(cls.model.title).contains(keywords.lower()))
             )
         else:
             angents = cls.model.select(*fields).join(User, on=(cls.model.user_id == User.id)).where(
-                ((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission ==
+                ((cls.model.user_id.in_(joined_tenant_ids) & (cls.model.permission == 
                                                                 TenantPermission.TEAM.value)) | (
                     cls.model.user_id == user_id))
             )
@@ -154,8 +155,6 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
             "dsl": cvs.dsl
         }
         API4ConversationService.save(**conv)
-
-        
         conv = API4Conversation(**conv)
     else:
         e, conv = API4ConversationService.get_by_id(session_id)
@@ -221,3 +220,206 @@ def completion(tenant_id, agent_id, question, session_id=None, stream=True, **kw
             API4ConversationService.append_message(conv.id, conv.to_dict())
             yield result
             break
+def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs):
+    """Main function for OpenAI-compatible completions, structured similarly to the completion function."""
+    tiktokenenc = tiktoken.get_encoding("cl100k_base")
+    e, cvs = UserCanvasService.get_by_id(agent_id)
+    
+    if not e:
+        yield get_data_openai(
+            id=session_id,
+            model=agent_id,
+            content="**ERROR**: Agent not found."
+        )
+        return
+    
+    if cvs.user_id != tenant_id:
+        yield get_data_openai(
+            id=session_id,
+            model=agent_id,
+            content="**ERROR**: You do not own the agent"
+        )
+        return
+    
+    if not isinstance(cvs.dsl, str):
+        cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
+    
+    canvas = Canvas(cvs.dsl, tenant_id)
+    canvas.reset()
+    message_id = str(uuid4())
+    
+    # Handle new session creation
+    if not session_id:
+        query = canvas.get_preset_param()
+        if query:
+            for ele in query:
+                if not ele["optional"]:
+                    if not kwargs.get(ele["key"]):
+                        yield get_data_openai(
+                            id=None,
+                            model=agent_id,
+                            content=f"`{ele['key']}` is required",
+                            completion_tokens=len(tiktokenenc.encode(f"`{ele['key']}` is required")),
+                            prompt_tokens=len(tiktokenenc.encode(question if question else ""))
+                        )
+                        return
+                    ele["value"] = kwargs[ele["key"]]
+                if ele["optional"]:
+                    if kwargs.get(ele["key"]):
+                        ele["value"] = kwargs[ele['key']]
+                    else:
+                        if "value" in ele:
+                            ele.pop("value")
+        
+        cvs.dsl = json.loads(str(canvas))
+        session_id = get_uuid()
+        conv = {
+            "id": session_id,
+            "dialog_id": cvs.id,
+            "user_id": kwargs.get("user_id", "") if isinstance(kwargs, dict) else "",
+            "message": [{"role": "assistant", "content": canvas.get_prologue(), "created_at": time.time()}],
+            "source": "agent",
+            "dsl": cvs.dsl
+        }
+        API4ConversationService.save(**conv)
+        conv = API4Conversation(**conv)
+            
+    # Handle existing session
+    else:
+        e, conv = API4ConversationService.get_by_id(session_id)
+        if not e:
+            yield get_data_openai(
+                id=session_id,
+                model=agent_id,
+                content="**ERROR**: Session not found!"
+            )
+            return
+        
+        canvas = Canvas(json.dumps(conv.dsl), tenant_id)
+        canvas.messages.append({"role": "user", "content": question, "id": message_id})
+        canvas.add_user_input(question)
+        
+        if not conv.message:
+            conv.message = []
+        conv.message.append({
+            "role": "user",
+            "content": question,
+            "id": message_id
+        })
+        
+        if not conv.reference:
+            conv.reference = []
+        conv.reference.append({"chunks": [], "doc_aggs": []})
+    
+    # Process request based on stream mode
+    final_ans = {"reference": [], "content": ""}
+    prompt_tokens = len(tiktokenenc.encode(str(question)))
+    
+    if stream:
+        try:
+            completion_tokens = 0
+            for ans in canvas.run(stream=True):
+                if ans.get("running_status"):
+                    completion_tokens += len(tiktokenenc.encode(ans.get("content", "")))
+                    yield "data: " + json.dumps(
+                        get_data_openai(
+                            id=session_id,
+                            model=agent_id,
+                            content=ans["content"],
+                            object="chat.completion.chunk",
+                            completion_tokens=completion_tokens,
+                            prompt_tokens=prompt_tokens
+                        ),
+                        ensure_ascii=False
+                    ) + "\n\n"
+                    continue
+                
+                for k in ans.keys():
+                    final_ans[k] = ans[k]
+                
+                completion_tokens += len(tiktokenenc.encode(final_ans.get("content", "")))
+                yield "data: " + json.dumps(
+                    get_data_openai(
+                        id=session_id,
+                        model=agent_id,
+                        content=final_ans["content"],
+                        object="chat.completion.chunk",
+                        finish_reason="stop",
+                        completion_tokens=completion_tokens,
+                        prompt_tokens=prompt_tokens
+                    ),
+                    ensure_ascii=False
+                ) + "\n\n"
+            
+            # Update conversation
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "created_at": time.time(), "id": message_id})
+            canvas.history.append(("assistant", final_ans["content"]))
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            
+            yield "data: [DONE]\n\n"
+            
+        except Exception as e:
+            traceback.print_exc()
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            yield "data: " + json.dumps(
+                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
+                ),
+                ensure_ascii=False
+            ) + "\n\n"
+            yield "data: [DONE]\n\n"
+    
+    else:  # Non-streaming mode
+        try:
+            all_answer_content = ""
+            for answer in canvas.run(stream=False):
+                if answer.get("running_status"):
+                    continue
+                
+                final_ans["content"] = "\n".join(answer["content"]) if "content" in answer else ""
+                final_ans["reference"] = answer.get("reference", [])
+                all_answer_content += final_ans["content"]
+            
+            final_ans["content"] = all_answer_content
+            
+            # Update conversation
+            canvas.messages.append({"role": "assistant", "content": final_ans["content"], "created_at": time.time(), "id": message_id})
+            canvas.history.append(("assistant", final_ans["content"]))
+            if final_ans.get("reference"):
+                canvas.reference.append(final_ans["reference"])
+            conv.dsl = json.loads(str(canvas))
+            API4ConversationService.append_message(conv.id, conv.to_dict())
+            
+            # 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
+            )
+

api/utils/api_utils.py

@@ -378,6 +378,46 @@ def get_parser_config(chunk_method, 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):
     if not parser_config:
         return

docs/references/http_api_reference.md

@@ -166,6 +166,150 @@ Non-stream:
 
 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
 {
   "code": 102,