Zilliz修复 (#1874)

* 修复zilliz的问题

README_en.md

@@ -27,7 +27,7 @@ LLM API.
 of [langchain](https://github.com/hwchase17/langchain). The goal is to build a KBQA(Knowledge based Q&A) solution that
 is friendly to Chinese scenarios and open source models and can run both offline and online.
 
-💡 Inspried by [document.ai](https://github.com/GanymedeNil/document.ai)
+💡 Inspired by [document.ai](https://github.com/GanymedeNil/document.ai)
 and [ChatGLM-6B Pull Request](https://github.com/THUDM/ChatGLM-6B/pull/216) , we build a local knowledge base question
 answering application that can be implemented using an open source model or remote LLM api throughout the process. In
 the latest version of this project, [FastChat](https://github.com/lm-sys/FastChat) is used to access Vicuna, Alpaca,
@@ -41,10 +41,10 @@ to expand the access to various models and remote APIs in the future.
 
 ⛓️ The implementation principle of this project is shown in the graph below. The main process includes: loading files ->
 reading text -> text segmentation -> text vectorization -> question vectorization -> matching the `top-k` most similar
-to the question vector in the text vector -> The matched text is added to `prompt `as context and question -> submitted
+to the question vector in the text vector -> The matched text is added to `prompt `as context and question -> submitte
 to `LLM` to generate an answer.
 
-📺[video introdution](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514)
+📺[video introduction](https://www.bilibili.com/video/BV13M4y1e7cN/?share_source=copy_web&vd_source=e6c5aafe684f30fbe41925d61ca6d514)
 
 ![实现原理图](img/langchain+chatglm.png)
 
@@ -52,7 +52,7 @@ The main process analysis from the aspect of document process:
 
 ![实现原理图2](img/langchain+chatglm2.png)
 
-🚩 The training or fined-tuning are not involved in the project, but still, one always can improve performance by do
+🚩 The training or fine-tuning are not involved in the project, but still, one always can improve performance by do
 these.
 
 🌐 [AutoDL image](registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.5) is supported, and in v9 the codes are update

configs/server_config.py.example

@@ -117,6 +117,9 @@ FSCHAT_MODEL_WORKERS = {
     # "baichuan-api": {
     #     "port": 21007,
     # },
+    # "azure-api": {
+    #    "port": 21008,
+    # },
 }
 
 # fastchat multi model worker server

server/knowledge_base/kb_service/base.py

@@ -32,6 +32,7 @@ class SupportedVSType:
     FAISS = 'faiss'
     MILVUS = 'milvus'
     DEFAULT = 'default'
+    ZILLIZ = 'zilliz'
     PG = 'pg'
 
 
@@ -246,14 +247,16 @@ class KBServiceFactory:
         if SupportedVSType.FAISS == vector_store_type:
             from server.knowledge_base.kb_service.faiss_kb_service import FaissKBService
             return FaissKBService(kb_name, embed_model=embed_model)
-        if SupportedVSType.PG == vector_store_type:
+        elif SupportedVSType.PG == vector_store_type:
             from server.knowledge_base.kb_service.pg_kb_service import PGKBService
             return PGKBService(kb_name, embed_model=embed_model)
         elif SupportedVSType.MILVUS == vector_store_type:
             from server.knowledge_base.kb_service.milvus_kb_service import MilvusKBService
-            return MilvusKBService(kb_name,
-                                   embed_model=embed_model)  # other milvus parameters are set in model_config.kbs_config
-        elif SupportedVSType.DEFAULT == vector_store_type:  # kb_exists of default kbservice is False, to make validation easier.
+            return MilvusKBService(kb_name,embed_model=embed_model)
+        elif SupportedVSType.ZILLIZ == vector_store_type:
+            from server.knowledge_base.kb_service.zilliz_kb_service import ZillizKBService
+            return ZillizKBService(kb_name, embed_model=embed_model)
+        elif SupportedVSType.DEFAULT == vector_store_type:
             from server.knowledge_base.kb_service.default_kb_service import DefaultKBService
             return DefaultKBService(kb_name)