Apply pydantic

Signed-off-by: Kaiyuan Hu <kaiyuan.hu@zilliz.com>

README.md

@@ -41,9 +41,10 @@ config.threshold = 0.5
 
 # The llm model source, openai or dolly
 config.llm_src = 'openai'
-# The llm model name
-config.llm_model = 'gpt-3.5-turbo'
-
+# The openai model name
+config.openai_model = 'gpt-3.5-turbo'
+# The dolly model name
+# config.dolly_model = 'databricks/dolly-v2-12b'
 
 p = AutoPipes.pipeline('eqa-search', config=config)
 res = p('What is towhee?', [])
@@ -111,10 +112,13 @@ The threshold to filter the milvus search result.
 
 The llm model source, `openai` or `dolly`, defaults to `openai`.
 
-***llm_model (str):***
+***openai_model (str):***
+
+The openai model name, defaults to `gpt-3.5-turbo`.
 
-The llm model name, defaults to `gpt-3.5-turbo` for `openai`, `databricks/dolly-v2-12b` for `dolly`.
+***dolly_model (str):***
 
+The dolly model name, defaults to `databricks/dolly-v2-12b`.
 
 <br />
 

eqa_search.py

@@ -11,32 +11,34 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Optional, Union
 
 from towhee import ops, pipe, AutoPipes, AutoConfig
+from pydantic import BaseModel
 
 
 @AutoConfig.register
-class EnhancedQASearchConfig:
+class EnhancedQASearchConfig(BaseModel):
     """
     Config of pipeline
     """
-    def __init__(self):
     # config for sentence_embedding
-        self.embedding_model = 'all-MiniLM-L6-v2'
-        self.openai_api_key = None
-        self.embedding_device = -1
+    embedding_model: Optional[str] = 'all-MiniLM-L6-v2'
+    openai_api_key: Optional[str] = None
+    embedding_device: Optional[int] = -1
     # config for search_milvus
-        self.host = '127.0.0.1'
-        self.port = '19530'
-        self.collection_name = 'chatbot'
-        self.top_k = 5
-        self.user = None
-        self.password = None
+    host: Optional[str] = '127.0.0.1'
+    port: Optional[str] = '19530'
+    collection_name: Optional[str] = 'chatbot'
+    top_k: Optional[int] = 5
+    user: Optional[str] = None
+    password: Optional[str] = None
     # config for similarity evaluation
-        self.threshold = 0.6
+    threshold: Optional[Union[float, int]] = 0.6
     # config for llm
-        self.llm_src = 'openai'
-        self.llm_model = 'gpt-3.5-turbo' if self.llm_src.lower() == 'openai' else 'databricks/dolly-v2-12b'
+    llm_src: Optional[str] = 'openai'
+    openai_model: Optional[str] = 'gpt-3.5-turbo'
+    dolly_model: Optional[str] = 'databricks/dolly-v2-12b'
 
 
 _hf_models = ops.sentence_embedding.transformers().get_op().supported_model_names()
@@ -76,9 +78,9 @@ def _get_similarity_evaluation_op(config):
 
 def _get_llm_op(config):
     if config.llm_src.lower() == 'openai':
-        return ops.LLM.OpenAI(model_name=config.llm_model, api_key=config.openai_api_key)
+        return ops.LLM.OpenAI(model_name=config.openai_model, api_key=config.openai_api_key)
     if config.llm_src.lower() == 'dolly':
-        return ops.LLM.Dolly(model_name=config.llm_model)
+        return ops.LLM.Dolly(model_name=config.dolly_model)
 
     raise RuntimeError('Unknown llm source: [%s], only support \'openai\' and \'dolly\'' % (config.llm_src))