Add rerank config

Signed-off-by: shiyu22 <shiyu.chen@zilliz.com>

README.md

@@ -26,8 +26,8 @@ config.collection_name = 'chatbot'
 config.top_k = 5
 
 # If using zilliz cloud
-config.user = [zilliz-cloud-username]
-config.password = [zilliz-cloud-password]
+# config.user = [zilliz-cloud-username]
+# config.password = [zilliz-cloud-password]
 
 # OpenAI api key
 config.openai_api_key = [your-openai-api-key]
@@ -36,8 +36,8 @@ config.embedding_model = 'all-MiniLM-L6-v2'
 # Embedding model device
 config.embedding_device = -1
 
-# The threshold to filter milvus search result
-config.threshold = 0.5
+# Rerank the docs searched from knowledge base
+config.rerank = True
 
 # The llm model source, openai or dolly
 config.llm_src = 'openai'
@@ -99,11 +99,19 @@ The user name for [Cloud user](https://zilliz.com/cloud), defaults to `None`.
 
 The user password for [Cloud user](https://zilliz.com/cloud), defaults to `None`.
 
-### Configuration for Similarity Evaluation
+### Configuration for Rerank
 
-***threshold (Union[float, int]):***
+***rerank***: bool
 
-The threshold to filter the milvus search result.
+Whether to rerank the docs searched from knowledge base, defaults to False. If set it to True it will using the [rerank](https://towhee.io/towhee/rerank) operator.
+
+***rerank_model***: str
+
+The name of rerank model, you can set it according to the [rerank](https://towhee.io/towhee/rerank) operator.
+
+***threshold:*** Union[float, int]
+
+The threshold for rerank, defaults to 0.6. If the `rerank` is `False`, it will filter the milvus search result, otherwise it will be filtered with the [rerank](https://towhee.io/towhee/rerank) operator.
 
 
 ### Configuration for LLM

eqa_search.py

@@ -33,8 +33,6 @@ class EnhancedQASearchConfig(BaseModel):
     top_k: Optional[int] = 5
     user: Optional[str] = None
     password: Optional[str] = None
-    # config for similarity evaluation
-    threshold: Optional[Union[float, int]] = 0.6
     # config for llm
     llm_src: Optional[str] = 'openai'
     openai_model: Optional[str] = 'gpt-3.5-turbo'
@@ -44,7 +42,11 @@ class EnhancedQASearchConfig(BaseModel):
     ernie_secret_key: Optional[str] = None
 
     customize_llm: Optional[Any] = None
-    customize_prompt: Optional[Any] = None    
+    customize_prompt: Optional[Any] = None
+    # config for rerank
+    rerank: Optional[bool] = False
+    rerank_model: Optional[str] = 'cross-encoder/ms-marco-MiniLM-L-6-v2'
+    threshold: Optional[Union[float, int]] = 0.6
 
 
 _hf_models = ops.sentence_embedding.transformers().get_op().supported_model_names()
@@ -131,15 +133,23 @@ def enhanced_qa_search_pipe(config):
             .map('embedding', 'result', search_milvus_op)
     )
 
-    # if config.similarity_evaluation:
-    if config.threshold:
+    if config.rerank:
+        p = (
+            p.map('result', 'docs', lambda x: [i[2] for i in x])
+             .map(('question', 'docs'), ('docs', 'score'), ops.rerank(config.rerank_model, config.threshold))
+             .map('docs', 'docs', lambda x: '\n'.join([i for i in x]))
+        )
+    elif config.threshold:
         sim_eval_op = _get_similarity_evaluation_op(config)
-        p = p.map('result', 'result', sim_eval_op)
+        p = (p.map('result', 'result', sim_eval_op)
+             .map('result', 'docs', lambda x: '\n'.join([i[2] for i in x]))
+             )
+    else:
+        p = p.map('result', 'docs', lambda x: '\n'.join([i[2] for i in x]))
 
     p = (
-        p.map('result', 'docs', lambda x: '\n'.join([i[2] for i in x]))
-            .map(('question', 'docs', 'history'), 'prompt', _get_prompt(config))
-            .map('prompt', 'answer', llm_op)
+        p.map(('question', 'docs', 'history'), 'prompt', _get_prompt(config))
+         .map('prompt', 'answer', llm_op)
     )
 
     return p.output('answer')