add base url for OpenAI (#166)

README.md

@@ -20,7 +20,7 @@
     <img height="21" src="https://img.shields.io/badge/License-Apache--2.0-ffffff?style=flat-square&labelColor=d4eaf7&color=7d09f1" alt="license">
   </a>
 </p>
-[RagFlow](http://demo.ragflow.io) is a knowledge management platform built on custom-build document understanding engine and LLM, with reasoned and well-founded answers to your question. Clone this repository, you can deploy your own knowledge management platform to empower your business with AI.
+[RagFlow](https://demo.ragflow.io) is a knowledge management platform built on custom-build document understanding engine and LLM, with reasoned and well-founded answers to your question. Clone this repository, you can deploy your own knowledge management platform to empower your business with AI.
     
 
 <div align="center" style="margin-top:20px;margin-bottom:20px;">
@@ -56,12 +56,12 @@
 
 Then, you need to check the following command:
 ```bash
-121:/ragflow# sysctl vm.max_map_count
+$ sysctl vm.max_map_count
 vm.max_map_count = 262144
 ```
 If **vm.max_map_count** is not greater  than 65535:
 ```bash
-121:/ragflow# sudo sysctl -w vm.max_map_count=262144
+$ sudo sysctl -w vm.max_map_count=262144
 ```
 Note that this change is reset after a system reboot. To render your change permanent, add or update the following line in **/etc/sysctl.conf**:
 
@@ -126,6 +126,7 @@ Open your browser, enter the IP address of your server, _**Hallelujah**_ again!
 <div align="center" style="margin-top:20px;margin-bottom:20px;">
 <img src="https://github.com/infiniflow/ragflow/assets/12318111/d6ac5664-c237-4200-a7c2-a4a00691b485" width="1000"/>
 </div>
+
 ## 🔧 Configurations
 
 If you need to change the default setting of the system when you deploy it. There several ways to configure it. 

api/apps/llm_app.py

@@ -45,7 +45,7 @@ def set_api_key():
     for llm in LLMService.query(fid=factory):
         if llm.model_type == LLMType.EMBEDDING.value:
             mdl = EmbeddingModel[factory](
-                req["api_key"], llm.llm_name)
+                req["api_key"], llm.llm_name, req.get("base_url"))
             try:
                 arr, tc = mdl.encode(["Test if the api key is available"])
                 if len(arr[0]) == 0 or tc == 0:
@@ -54,7 +54,7 @@ def set_api_key():
                 msg += f"\nFail to access embedding model({llm.llm_name}) using this api key." + str(e)
         elif not chat_passed and llm.model_type == LLMType.CHAT.value:
             mdl = ChatModel[factory](
-                req["api_key"], llm.llm_name)
+                req["api_key"], llm.llm_name, req.get("base_url"))
             try:
                 m, tc = mdl.chat(None, [{"role": "user", "content": "Hello! How are you doing!"}], {
                                  "temperature": 0.9})
@@ -83,7 +83,9 @@ def set_api_key():
                 llm_factory=factory,
                 llm_name=llm.llm_name,
                 model_type=llm.model_type,
-                api_key=req["api_key"])
+                api_key=req["api_key"],
+                api_base=req.get("base_url", "")
+            )
 
     return get_json_result(data=True)
 

api/db/services/llm_service.py

@@ -84,19 +84,21 @@ class TenantLLMService(CommonService):
             if model_config["llm_factory"] not in EmbeddingModel:
                 return
             return EmbeddingModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"])
+                model_config["api_key"], model_config["llm_name"], model_config["api_base"])
 
         if llm_type == LLMType.IMAGE2TEXT.value:
             if model_config["llm_factory"] not in CvModel:
                 return
             return CvModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"], lang)
+                model_config["api_key"], model_config["llm_name"], lang,
+                base_url=model_config["api_base"]
+            )
 
         if llm_type == LLMType.CHAT.value:
             if model_config["llm_factory"] not in ChatModel:
                 return
             return ChatModel[model_config["llm_factory"]](
-                model_config["api_key"], model_config["llm_name"])
+                model_config["api_key"], model_config["llm_name"], model_config["api_base"])
 
     @classmethod
     @DB.connection_context()

deepdoc/vision/recognizer.py

@@ -43,6 +43,8 @@ class Recognizer(object):
             if not os.path.exists(model_file_path):
                 model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
                 model_file_path = os.path.join(model_dir, task_name + ".onnx")
+        else:
+            model_file_path = os.path.join(model_dir, task_name + ".onnx")
 
         if not os.path.exists(model_file_path):
             raise ValueError("not find model file path {}".format(

rag/llm/chat_model.py

@@ -31,8 +31,9 @@ class Base(ABC):
 
 
 class GptTurbo(Base):
-    def __init__(self, key, model_name="gpt-3.5-turbo"):
-        self.client = OpenAI(api_key=key)
+    def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1"):
+        if not base_url: base_url="https://api.openai.com/v1"
+        self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
 
     def chat(self, system, history, gen_conf):
@@ -53,9 +54,10 @@ class GptTurbo(Base):
 
 
 class MoonshotChat(GptTurbo):
-    def __init__(self, key, model_name="moonshot-v1-8k"):
+    def __init__(self, key, model_name="moonshot-v1-8k", base_url="https://api.moonshot.cn/v1"):
+        if not base_url: base_url="https://api.moonshot.cn/v1"
         self.client = OpenAI(
-            api_key=key, base_url="https://api.moonshot.cn/v1",)
+            api_key=key, base_url=base_url)
         self.model_name = model_name
 
     def chat(self, system, history, gen_conf):
@@ -76,7 +78,7 @@ class MoonshotChat(GptTurbo):
 
 
 class QWenChat(Base):
-    def __init__(self, key, model_name=Generation.Models.qwen_turbo):
+    def __init__(self, key, model_name=Generation.Models.qwen_turbo, **kwargs):
         import dashscope
         dashscope.api_key = key
         self.model_name = model_name
@@ -105,7 +107,7 @@ class QWenChat(Base):
 
 
 class ZhipuChat(Base):
-    def __init__(self, key, model_name="glm-3-turbo"):
+    def __init__(self, key, model_name="glm-3-turbo", **kwargs):
         self.client = ZhipuAI(api_key=key)
         self.model_name = model_name
 
@@ -154,7 +156,7 @@ class LocalLLM(Base):
 
             return do_rpc
 
-    def __init__(self, key, model_name="glm-3-turbo"):
+    def __init__(self, **kwargs):
         self.client = LocalLLM.RPCProxy("127.0.0.1", 7860)
 
     def chat(self, system, history, gen_conf):

rag/llm/cv_model.py

@@ -67,8 +67,9 @@ class Base(ABC):
 
 
 class GptV4(Base):
-    def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese"):
-        self.client = OpenAI(api_key=key)
+    def __init__(self, key, model_name="gpt-4-vision-preview", lang="Chinese", base_url="https://api.openai.com/v1"):
+        if not base_url: base_url="https://api.openai.com/v1"
+        self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
 
@@ -84,7 +85,7 @@ class GptV4(Base):
 
 
 class QWenCV(Base):
-    def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese"):
+    def __init__(self, key, model_name="qwen-vl-chat-v1", lang="Chinese", **kwargs):
         import dashscope
         dashscope.api_key = key
         self.model_name = model_name
@@ -123,7 +124,7 @@ class QWenCV(Base):
 
 
 class Zhipu4V(Base):
-    def __init__(self, key, model_name="glm-4v", lang="Chinese"):
+    def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
         self.client = ZhipuAI(api_key=key)
         self.model_name = model_name
         self.lang = lang
@@ -140,7 +141,7 @@ class Zhipu4V(Base):
 
 
 class LocalCV(Base):
-    def __init__(self, key, model_name="glm-4v", lang="Chinese"):
+    def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
         pass
 
     def describe(self, image, max_tokens=1024):

rag/llm/embedding_model.py

@@ -51,7 +51,7 @@ class Base(ABC):
 
 
 class HuEmbedding(Base):
-    def __init__(self, key="", model_name=""):
+    def __init__(self, **kwargs):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
 
@@ -81,8 +81,9 @@ class HuEmbedding(Base):
 
 
 class OpenAIEmbed(Base):
-    def __init__(self, key, model_name="text-embedding-ada-002"):
-        self.client = OpenAI(api_key=key)
+    def __init__(self, key, model_name="text-embedding-ada-002", base_url="https://api.openai.com/v1"):
+        if not base_url: base_url="https://api.openai.com/v1"
+        self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
 
     def encode(self, texts: list, batch_size=32):
@@ -98,7 +99,7 @@ class OpenAIEmbed(Base):
 
 
 class QWenEmbed(Base):
-    def __init__(self, key, model_name="text_embedding_v2"):
+    def __init__(self, key, model_name="text_embedding_v2", **kwargs):
         dashscope.api_key = key
         self.model_name = model_name
 
@@ -131,7 +132,7 @@ class QWenEmbed(Base):
 
 
 class ZhipuEmbed(Base):
-    def __init__(self, key, model_name="embedding-2"):
+    def __init__(self, key, model_name="embedding-2", **kwargs):
         self.client = ZhipuAI(api_key=key)
         self.model_name = model_name
 

rag/svr/task_executor.py

@@ -280,4 +280,5 @@ if __name__ == "__main__":
     from mpi4py import MPI
 
     comm = MPI.COMM_WORLD
-    main(int(sys.argv[2]), int(sys.argv[1]))
+    while True:
+        main(int(sys.argv[2]), int(sys.argv[1]))