init app update

document_quieries

@@ -0,0 +1,39 @@
+from haystack.nodes import BM25Retriever, FARMReader
+from haystack.document_stores import ElasticsearchDocumentStore
+from haystack.pipelines import ExtractiveQAPipeline
+
+import certifi
+ca_certs=certifi.where()
+
+from abc import ABC, abstractmethod
+
+class DocumentQueries(ABC):
+    
+    @abstractmethod
+    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str):
+        pass
+
+class ExtractiveProposalQueries(DocumentQueries):
+    
+    def __init__(self, es_host: str, es_index: str, es_user, es_password, reader_name_or_path: str, use_gpu = False) -> None:
+        reader = FARMReader(model_name_or_path = reader_name_or_path, use_gpu = use_gpu, num_processes=1)
+        self._initialize_pipeline(es_host, es_index, es_user, es_password, reader = reader)
+        
+    
+    def _initialize_pipeline(self, es_host, es_index, es_user, es_password, reader = None):
+        if reader is not None:
+            self.reader = reader
+        self.es_host = es_host
+        self.es_user = es_user
+        self.es_password = es_password
+        self.document_store = ElasticsearchDocumentStore(host = es_host, username=es_user, password=es_password, index = es_index, port = 443, scheme='https', verify_certs=True, ca_certs=ca_certs)        
+        self.retriever = BM25Retriever(document_store = self.document_store)
+        self.pipe = ExtractiveQAPipeline(self.reader, self.retriever)
+
+    def search_by_query(self, query : str, retriever_top_k: int, reader_top_k: int, es_index: str = None) :
+        if es_index is not None:
+            self._initialize_pipeline(self.es_host, es_index, self.es_user, self.es_password)
+        params = {"Retriever": {"top_k": retriever_top_k}, "Reader": {"top_k": reader_top_k}}
+        prediction = self.pipe.run( query = query, params = params)      
+        return prediction["answers"]
+