dev分支解决pydantic版本冲突问题，增加ollama配置，支持ollama会话和向量接口 (#3508)

* dev分支解决pydantic版本冲突问题，增加ollama配置，支持ollama会话和向量接口
1、因dev版本的pydantic升级到了v2版本，由于在class History(BaseModel)中使用了from server.pydantic_v1，而fastapi的引用已变为pydantic的v2版本，所以fastapi用v2版本去校验用v1版本定义的对象，当会话历史histtory不为空的时候，会报错：TypeError: BaseModel.validate() takes 2 positional arguments but 3 were given。经测试，解方法为在class History(BaseModel)中也使用v2版本即可;
2、配置文件参照其它平台配置，增加了ollama平台相关配置，会话模型用户可根据实际情况自行添加，向量模型目前支持nomic-embed-text（必须升级ollama到0.1.29以上）。
3、因ollama官方只在会话部分对openai api做了兼容，向量api暂未适配，好在langchain官方库支持OllamaEmbeddings，因而在get_Embeddings方法中添加了相关支持代码。

* 修复 pydantic 升级到 v2 后 DocumentWithVsID 和 /v1/embeddings 兼容性问题

---------

Co-authored-by: srszzw <srszzw@163.com>
Co-authored-by: liunux4odoo <liunux@qq.com>

configs/model_config.py.example

@@ -151,7 +151,23 @@ MODEL_PLATFORMS = [
         "image_models": [],
         "multimodal_models": [],
     },
-
+    {
+        "platform_name": "ollama",
+        "platform_type": "ollama",
+        "api_base_url": "http://{host}:{port}/v1",
+        "api_key": "sk-",
+        "api_concurrencies": 5,
+        "llm_models": [
+            # Qwen API,其它更多模型请参考https://ollama.com/library
+            "qwen:7b",
+        ],
+        "embed_models": [
+            # 必须升级ollama到0.1.29以上，低版本向量服务有问题
+            "nomic-embed-text"
+        ],
+        "image_models": [],
+        "multimodal_models": [],
+    },
     # {
     #     "platform_name": "loom",
     #     "platform_type": "loom",

server/api_server/api_schemas.py

@@ -24,6 +24,9 @@ class OpenAIBaseInput(BaseModel):
     extra_body: Optional[Dict] = None
     timeout: Optional[float] = None
 
+    class Config:
+        extra = "allow"
+
 
 class OpenAIChatInput(OpenAIBaseInput):
     messages: List[ChatCompletionMessageParam]

server/api_server/kb_routes.py

@@ -11,7 +11,6 @@ from server.knowledge_base.kb_doc_api import (list_files, upload_docs, delete_do
                                             search_docs, update_info)
 from server.knowledge_base.kb_summary_api import (summary_file_to_vector_store, recreate_summary_vector_store,
                                                     summary_doc_ids_to_vector_store)
-from server.knowledge_base.model.kb_document_model import DocumentWithVSId
 from server.utils import BaseResponse, ListResponse
 
 
@@ -38,7 +37,7 @@ kb_router.get("/list_files",
         )(list_files)
 
 kb_router.post("/search_docs",
-            response_model=List[DocumentWithVSId],
+            response_model=List[dict],
             summary="搜索知识库"
             )(search_docs)
 

server/api_server/openai_routes.py

@@ -98,7 +98,12 @@ async def create_chat_completions(
     body: OpenAIChatInput,
 ):
     async with acquire_model_client(body.model) as client:
-        return await openai_request(client.chat.completions.create, body)
+        result = await openai_request(client.chat.completions.create, body)
+        # result["related_docs"] = ["doc1"]
+        # result["choices"][0]["message"]["related_docs"] = ["doc1"]
+        # print(result)
+        # breakpoint()
+        return result
 
 
 @openai_router.post("/completions")
@@ -115,9 +120,9 @@ async def create_embeddings(
     request: Request,
     body: OpenAIEmbeddingsInput,
 ):
-    params = body.dict(exclude_unset=True)
+    params = body.model_dump(exclude_unset=True)
     client = get_OpenAIClient(model_name=body.model)
-    return (await client.embeddings.create(**params)).dict()
+    return (await client.embeddings.create(**params)).model_dump()
 
 
 @openai_router.post("/images/generations")

server/chat/utils.py

@@ -1,5 +1,5 @@
 from functools import lru_cache
-from server.pydantic_v1 import BaseModel, Field
+from server.pydantic_v2 import BaseModel, Field
 from langchain.prompts.chat import ChatMessagePromptTemplate
 from configs import logger, log_verbose
 from typing import List, Tuple, Dict, Union

server/knowledge_base/kb_doc_api.py

@@ -29,7 +29,7 @@ def search_docs(
                                       ge=0, le=1),
         file_name: str = Body("", description="文件名称，支持 sql 通配符"),
         metadata: dict = Body({}, description="根据 metadata 进行过滤，仅支持一级键"),
-) -> List[DocumentWithVSId]:
+) -> List[Dict]:
     kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
     data = []
     if kb is not None:
@@ -41,7 +41,7 @@ def search_docs(
             for d in data:
                 if "vector" in d.metadata:
                     del d.metadata["vector"]
-    return data
+    return [x.dict() for x in data]
 
 
 def list_files(

server/utils.py

@@ -202,6 +202,7 @@ def get_Embeddings(
     local_wrap: bool = False, # use local wrapped api
 ) -> Embeddings:
     from langchain_community.embeddings.openai import OpenAIEmbeddings
+    from langchain_community.embeddings import OllamaEmbeddings
     from server.localai_embeddings import LocalAIEmbeddings # TODO: fork of lc pr #17154
 
     model_info = get_model_info(model_name=embed_model)
@@ -220,6 +221,10 @@ def get_Embeddings(
             )
         if model_info.get("platform_type") == "openai":
             return OpenAIEmbeddings(**params)
+        elif model_info.get("platform_type") == "ollama":
+            return OllamaEmbeddings(base_url=model_info.get("api_base_url").replace('/v1',''),
+                                    model=embed_model,
+                                    )
         else:
             return LocalAIEmbeddings(**params)
     except Exception as e: