Feat: conversation completion can specify different model (#9485)

### What problem does this PR solve?

Conversation completion can specify different model

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

api/apps/conversation_app.py

@@ -29,7 +29,8 @@ from api.db.services.conversation_service import ConversationService, structure_
 from api.db.services.dialog_service import DialogService, ask, chat
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMBundle
-from api.db.services.user_service import UserTenantService, TenantService
+from api.db.services.tenant_llm_service import TenantLLMService
+from api.db.services.user_service import TenantService, UserTenantService
 from api.utils.api_utils import get_data_error_result, get_json_result, server_error_response, validate_request
 from graphrag.general.mind_map_extractor import MindMapExtractor
 from rag.app.tag import label_question
@@ -66,8 +67,14 @@ def set_conversation():
         e, dia = DialogService.get_by_id(req["dialog_id"])
         if not e:
             return get_data_error_result(message="Dialog not found")
-        conv = {"id": conv_id, "dialog_id": req["dialog_id"], "name": name, "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}],"user_id": current_user.id,
-                "reference":[],}
+        conv = {
+            "id": conv_id,
+            "dialog_id": req["dialog_id"],
+            "name": name,
+            "message": [{"role": "assistant", "content": dia.prompt_config["prologue"]}],
+            "user_id": current_user.id,
+            "reference": [],
+        }
         ConversationService.save(**conv)
         return get_json_result(data=conv)
     except Exception as e:
@@ -174,6 +181,21 @@ def completion():
             continue
         msg.append(m)
     message_id = msg[-1].get("id")
+    chat_model_id = req.get("llm_id", "")
+    req.pop("llm_id", None)
+
+    chat_model_config = {}
+    for model_config in [
+        "temperature",
+        "top_p",
+        "frequency_penalty",
+        "presence_penalty",
+        "max_tokens",
+    ]:
+        config = req.get(model_config)
+        if config:
+            chat_model_config[model_config] = config
+
     try:
         e, conv = ConversationService.get_by_id(req["conversation_id"])
         if not e:
@@ -190,13 +212,23 @@ def completion():
         conv.reference = [r for r in conv.reference if r]
         conv.reference.append({"chunks": [], "doc_aggs": []})
 
+        if chat_model_id:
+            if not TenantLLMService.get_api_key(tenant_id=dia.tenant_id, model_name=chat_model_id):
+                req.pop("chat_model_id", None)
+                req.pop("chat_model_config", None)
+                return get_data_error_result(message=f"Cannot use specified model {chat_model_id}.")
+            dia.llm_id = chat_model_id
+            dia.llm_setting = chat_model_config
+
+        is_embedded = bool(chat_model_id)
         def stream():
             nonlocal dia, msg, req, conv
             try:
                 for ans in chat(dia, msg, True, **req):
                     ans = structure_answer(conv, ans, message_id, conv.id)
                     yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
-                ConversationService.update_by_id(conv.id, conv.to_dict())
+                if not is_embedded:
+                    ConversationService.update_by_id(conv.id, conv.to_dict())
             except Exception as e:
                 traceback.print_exc()
                 yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
@@ -214,7 +246,8 @@ def completion():
             answer = None
             for ans in chat(dia, msg, **req):
                 answer = structure_answer(conv, ans, message_id, conv.id)
-                ConversationService.update_by_id(conv.id, conv.to_dict())
+                if not is_embedded:
+                    ConversationService.update_by_id(conv.id, conv.to_dict())
                 break
             return get_json_result(data=answer)
     except Exception as e:

api/db/db_models.py

@@ -881,11 +881,12 @@ class Search(DataBaseModel):
             # chat settings
             "summary": False,
             "chat_id": "",
+            # Leave it here for reference, don't need to set default values
             "llm_setting": {
-                "temperature": 0.1,
-                "top_p": 0.3,
-                "frequency_penalty": 0.7,
-                "presence_penalty": 0.4,
+                # "temperature": 0.1,
+                # "top_p": 0.3,
+                # "frequency_penalty": 0.7,
+                # "presence_penalty": 0.4,
             },
             "chat_settingcross_languages": [],
             "highlight": False,
@@ -1020,4 +1021,4 @@ def migrate_db():
         migrate(migrator.add_column("dialog", "meta_data_filter", JSONField(null=True, default={})))
     except Exception:
         pass
-    logging.disable(logging.NOTSET)
+    logging.disable(logging.NOTSET)

api/db/services/dialog_service.py

@@ -99,7 +99,6 @@ class DialogService(CommonService):
 
         return list(chats.dicts())
 
-
     @classmethod
     @DB.connection_context()
     def get_by_tenant_ids(cls, joined_tenant_ids, user_id, page_number, items_per_page, orderby, desc, keywords, parser_id=None):
@@ -256,9 +255,10 @@ def repair_bad_citation_formats(answer: str, kbinfos: dict, idx: set):
 
 def meta_filter(metas: dict, filters: list[dict]):
     doc_ids = []
+
     def filter_out(v2docs, operator, value):
         nonlocal doc_ids
-        for input,docids in v2docs.items():
+        for input, docids in v2docs.items():
             try:
                 input = float(input)
                 value = float(value)
@@ -389,7 +389,17 @@ def chat(dialog, messages, stream=True, **kwargs):
             reasoner = DeepResearcher(
                 chat_mdl,
                 prompt_config,
-                partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3, doc_ids=attachments),
+                partial(
+                    retriever.retrieval,
+                    embd_mdl=embd_mdl,
+                    tenant_ids=tenant_ids,
+                    kb_ids=dialog.kb_ids,
+                    page=1,
+                    page_size=dialog.top_n,
+                    similarity_threshold=0.2,
+                    vector_similarity_weight=0.3,
+                    doc_ids=attachments,
+                ),
             )
 
             for think in reasoner.thinking(kbinfos, " ".join(questions)):