[NeuralChat] Add bm25 into enabled retrievers and add Uts (intel#1313)

 Add bm25 into enabled retrievers and add Uts

Signed-off-by: XuhuiRen <[email protected]>

Author: XuhuiRen

intel_extension_for_transformers/langchain/retrievers/bge_reranker.py

@@ -26,14 +26,15 @@
 from FlagEmbedding import FlagReranker
 
 class BgeReranker(BaseDocumentCompressor):
-    model_name:str = 'bge_reranker_model_path'
     top_n: int = 3   # Number of documents to return.
-    model:FlagReranker = FlagReranker(model_name)
+    model:FlagReranker
     """CrossEncoder instance to use for reranking."""
 
     def bge_rerank(self, query, docs):
         model_inputs =  [[query, doc] for doc in docs]
         scores = self.model.compute_score(model_inputs)
+        if len(docs) == 1:
+            return [(0, scores)]
         results = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
         return results[:self.top_n]
 

intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/retrieval_agent.py

@@ -87,6 +87,7 @@ def __init__(self,
         allowed_retrieval_type: ClassVar[Collection[str]] = (
             "default",
             "child_parent",
+            'bm25',
         )
         allowed_generation_mode: ClassVar[Collection[str]] = (
             "accuracy",

intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/retriever_adapter.py

@@ -36,13 +36,15 @@ def __init__(self, retrieval_type='default', document_store=None, child_document
         self.retrieval_type = retrieval_type
         if enable_rerank:
             from intel_extension_for_transformers.langchain.retrievers.bge_reranker import BgeReranker
-            self.reranker = BgeReranker(model_name = reranker_model, top_n=top_n)
+            from FlagEmbedding import FlagReranker
+            reranker = FlagReranker(reranker_model)
+            self.reranker = BgeReranker(model = reranker, top_n=top_n)
         else:
             self.reranker = None
 
         if self.retrieval_type == "default":
             self.retriever = VectorStoreRetriever(vectorstore=document_store, **kwargs)
-        if self.retrieval_type == "bm25":
+        elif self.retrieval_type == "bm25":
             self.retriever = BM25Retriever.from_documents(docs, **kwargs)
         elif self.retrieval_type == "child_parent":
             self.retriever = ChildParentRetriever(parentstore=document_store, \

intel_extension_for_transformers/neural_chat/tests/ci/plugins/retrieval/test_parameters.py

@@ -79,6 +79,61 @@ def test_accuracy_mode(self):
         plugins.retrieval.args = {}
         plugins.retrieval.enable = False
 
+class TestBM25Retriever(unittest.TestCase):
+    def setUp(self):
+        if os.path.exists("./bm25"):
+            shutil.rmtree("./bm25", ignore_errors=True)
+        return super().setUp()
+
+    def tearDown(self) -> None:
+        if os.path.exists("./bm25"):
+            shutil.rmtree("./bm25", ignore_errors=True)
+        return super().tearDown()
+
+    def test_accuracy_mode(self):
+        plugins.retrieval.args = {}
+        plugins.retrieval.enable = True
+        plugins.retrieval.args["input_path"] = "../assets/docs/sample.txt"
+        plugins.retrieval.args["persist_directory"] = "./bm25"
+        plugins.retrieval.args["retrieval_type"] = 'bm25'
+        plugins.retrieval.args["mode"] = 'accuracy'
+        config = PipelineConfig(model_name_or_path="facebook/opt-125m",
+                        plugins=plugins)
+        chatbot = build_chatbot(config)
+        response = chatbot.predict("How many cores does the Intel Xeon Platinum 8480+ Processor have in total?")
+        print(response)
+        self.assertIsNotNone(response)
+        plugins.retrieval.args = {}
+        plugins.retrieval.enable = False
+
+class TestRerank(unittest.TestCase):
+    def setUp(self):
+        if os.path.exists("./rerank"):
+            shutil.rmtree("./rerank", ignore_errors=True)
+        return super().setUp()
+
+    def tearDown(self) -> None:
+        if os.path.exists("./rerank"):
+            shutil.rmtree("./rerank", ignore_errors=True)
+        return super().tearDown()
+
+    def test_general_mode(self):
+        plugins.retrieval.args = {}
+        plugins.retrieval.enable = True
+        plugins.retrieval.args["input_path"] = "../assets/docs/sample.txt"
+        plugins.retrieval.args["persist_directory"] = "./rerank"
+        plugins.retrieval.args["retrieval_type"] = 'default'
+        plugins.retrieval.args['enable_rerank'] = True
+        plugins.retrieval.args['reranker_model'] = 'BAAI/bge-reranker-base'
+        config = PipelineConfig(model_name_or_path="facebook/opt-125m",
+                        plugins=plugins)
+        chatbot = build_chatbot(config)
+        response = chatbot.predict("How many cores does the Intel Xeon Platinum 8480+ Processor have in total?")
+        print(response)
+        self.assertIsNotNone(response)
+        plugins.retrieval.args = {}
+        plugins.retrieval.enable = False
+
 class TestGeneralMode(unittest.TestCase):
     def setUp(self):
         if os.path.exists("./general_mode"):