Make Elasticsearch configuration more flexible (#29)

haystack/__init__.py

@@ -1,6 +1,7 @@
 import logging
 
 import pandas as pd
+from haystack.finder import Finder
 
 pd.options.display.max_colwidth = 80
 
@@ -11,44 +12,4 @@ logging.getLogger('farm.infer').setLevel(logging.INFO)
 logging.getLogger('transformers').setLevel(logging.WARNING)
 logging.getLogger('farm.eval').setLevel(logging.INFO)
 
-class Finder:
-    """
-    Finder ties together instances of the Reader and Retriever class.
-
-    It provides an interface to predict top n answers for a given question.
-    """
-
-    def __init__(self, reader, retriever):
-        self.retriever = retriever
-        self.reader = reader
-
-    def get_answers(self, question, top_k_reader=1, top_k_retriever=10, filters=None):
-        """
-        Get top k answers for a given question.
-
-        :param question: the question string
-        :param top_k_reader: number of answers returned by the reader
-        :param top_k_retriever: number of text units to be retrieved
-        :param filters: limit scope to documents having the given tags and their corresponding values.
-            The format for the dict is {"tag-1": "value-1", "tag-2": "value-2" ...}
-        :return:
-        """
-
-        # 1) Optional: reduce the search space via document tags
-        if filters:
-            candidate_doc_ids = self.retriever.document_store.get_document_ids_by_tags(filters)
-        else:
-            candidate_doc_ids = None
-
-        # 2) Apply retriever to get fast candidate paragraphs
-        paragraphs, meta_data = self.retriever.retrieve(question, top_k=top_k_retriever, candidate_doc_ids=candidate_doc_ids)
-
-        # 3) Apply reader to get granular answer(s)
-        logger.info(f"Applying the reader now to look for the answer in detail ...")
-        results = self.reader.predict(question=question,
-                                      paragraphs=paragraphs,
-                                      meta_data_paragraphs=meta_data,
-                                      top_k=top_k_reader)
-
-        return results
 

haystack/database/elasticsearch.py

@@ -1,85 +1,124 @@
 from elasticsearch import Elasticsearch
-from elasticsearch_dsl import Search, Document as ESDoc, Text, connections
+from elasticsearch.helpers import scan
+
 from haystack.database.base import BaseDocumentStore
 
 
-class Document(ESDoc):
-    name = Text()
-    text = Text()
-    tags = Text()
-
-    class Index:
-        name = "document"
-
-
 class ElasticsearchDocumentStore(BaseDocumentStore):
-    def __init__(self, host="localhost", username="", password="", index="document"):
+    def __init__(
+        self,
+        host="localhost",
+        username="",
+        password="",
+        index="document",
+        search_fields="text",
+        text_field="text",
+        name_field="name",
+        doc_id_field="document_id",
+        tag_fields=None,
+        custom_mapping=None,
+    ):
         self.client = Elasticsearch(hosts=[{"host": host}], http_auth=(username, password))
-        self.connections = connections.create_connection(hosts=[{"host": host}], http_auth=(username, password))
-        Document.init()  # create mapping if not exists.
+        # if no custom_mapping is supplied, use the default mapping
+        if not custom_mapping:
+            custom_mapping = {
+                "mappings": {
+                    "properties": {
+                        name_field: {"type": "text"},
+                        text_field: {"type": "text"},
+                        doc_id_field: {"type": "text"},
+                    }
+                }
+            }
+        # create an index if not exists
+        self.client.indices.create(index=index, ignore=400, body=custom_mapping)
         self.index = index
 
+        # configure mappings to ES fields that will be used for querying / displaying results
+        if type(search_fields) == str:
+            search_fields = [search_fields]
+        self.search_fields = search_fields
+        self.text_field = text_field
+        self.name_field = name_field
+        self.tag_fields = tag_fields
+        self.doc_id_field = doc_id_field
+
     def get_document_by_id(self, id):
         query = {"filter": {"term": {"_id": id}}}
         result = self.client.search(index=self.index, body=query)["hits"]["hits"]
         if result:
-            document = {"id": result["_id"], "name": result["name"], "text": result["text"]}
+            document = {
+                "id": result[self.doc_id_field],
+                "name": result[self.name_field],
+                "text": result[self.text_field],
+            }
+        else:
+            document = None
+        return document
+
+    def get_document_by_name(self, name):
+        query = {"filter": {"term": {self.name_field: name}}}
+        result = self.client.search(index=self.index, body=query)["hits"]["hits"]
+        if result:
+            document = {
+                "id": result[self.doc_id_field],
+                "name": result[self.name_field],
+                "text": result[self.text_field],
+            }
         else:
             document = None
         return document
 
     def get_document_ids_by_tags(self, tags):
-        query = {
-            "query": {
-                "bool": {
-                    "should": [
-                        {
-                            "terms": {
-                                "tags": tags
-                            }
-                        }
-                    ]
-                }
-            }
-        }
-        result = self.client.search(index=self.index, body=query)["hits"]["hits"]
-        documents = []
+        term_queries = [{"terms": {key: value}} for key, value in tags.items()]
+        query = {"query": {"bool": {"must": term_queries}}}
+        result = self.client.search(index=self.index, body=query, size=10000)["hits"]["hits"]
+        doc_ids = []
         for hit in result:
-            documents.append({"id": hit["_id"], "name": hit["name"], "text": hit["text"]})
-        return documents
+            doc_ids.append(hit["_id"])
+        return doc_ids
 
     def write_documents(self, documents):
-        for doc in documents:
-            d = Document(
-                name=doc["name"],
-                text=doc["text"],
-                document_id=doc.get("document_id", None),
-                tags=doc.get("tags", None),
-            )
-            d.save()
+        for d in documents:
+            self.client.index(index=self.index, body=d)
 
     def get_document_count(self):
-        s = Search(using=self.client, index=self.index)
-        return s.count()
+        result = self.client.count()
+        count = result["count"]
+        return count
 
     def get_all_documents(self):
-        search = Search(using=self.client, index=self.index).scan()
+        result = scan(self.client, query={"query": {"match_all": {}}}, index=self.index)
         documents = []
-        for hit in search:
+        for hit in result:
             documents.append(
                 {
-                    "id": hit.meta["id"],
-                    "name": hit["name"],
-                    "text": hit["text"],
+                    "id": hit["_source"][self.doc_id_field],
+                    "name": hit["_source"][self.name_field],
+                    "text": hit["_source"][self.text_field],
                 }
             )
         return documents
 
-    def query(self, query, top_k=10):
-        search = Search(using=self.client, index=self.index).query("match", text=query)[:top_k].execute()
+    def query(self, query, top_k=10, candidate_doc_ids=None):
+        # TODO:
+        # for now: we keep the current structure of candidate_doc_ids for compatibility with SQL documentstores
+        # midterm: get rid of it and do filtering with tags directly in this query
+
+        body = {
+            "size": top_k,
+            "query": {
+                "bool": {
+                    "must": [{"multi_match": {"query": query, "type": "most_fields", "fields": self.search_fields}}]
+                }
+            },
+        }
+        if candidate_doc_ids:
+            body["query"]["bool"]["filter"] = [{"terms": {"_id": candidate_doc_ids}}]
+        result = self.client.search(index=self.index, body=body)["hits"]["hits"]
         paragraphs = []
         meta_data = []
-        for hit in search:
-            paragraphs.append(hit["text"])
-            meta_data.append({"paragraph_id": hit.meta["id"], "document_id": hit["document_id"]})
+        for hit in result:
+            paragraphs.append(hit["_source"][self.text_field])
+            meta_data.append({"paragraph_id": hit["_id"], "document_id": hit["_source"][self.doc_id_field]})
         return paragraphs, meta_data

haystack/finder.py

@@ -0,0 +1,43 @@
+import logging
+logger = logging.getLogger(__name__)
+
+class Finder:
+    """
+    Finder ties together instances of the Reader and Retriever class.
+
+    It provides an interface to predict top n answers for a given question.
+    """
+
+    def __init__(self, reader, retriever):
+        self.retriever = retriever
+        self.reader = reader
+
+    def get_answers(self, question, top_k_reader=1, top_k_retriever=10, filters=None):
+        """
+        Get top k answers for a given question.
+
+        :param question: the question string
+        :param top_k_reader: number of answers returned by the reader
+        :param top_k_retriever: number of text units to be retrieved
+        :param filters: limit scope to documents having the given tags and their corresponding values.
+            The format for the dict is {"tag-1": "value-1", "tag-2": "value-2" ...}
+        :return:
+        """
+
+        # 1) Optional: reduce the search space via document tags
+        if filters:
+            candidate_doc_ids = self.retriever.document_store.get_document_ids_by_tags(filters)
+        else:
+            candidate_doc_ids = None
+
+        # 2) Apply retriever to get fast candidate paragraphs
+        paragraphs, meta_data = self.retriever.retrieve(question, top_k=top_k_retriever, candidate_doc_ids=candidate_doc_ids)
+
+        # 3) Apply reader to get granular answer(s)
+        logger.info(f"Applying the reader now to look for the answer in detail ...")
+        results = self.reader.predict(question=question,
+                                      paragrahps=paragraphs,
+                                      meta_data_paragraphs=meta_data,
+                                      top_k=top_k_reader)
+
+        return results

haystack/retriever/elasticsearch.py

@@ -6,4 +6,5 @@ class ElasticsearchRetriever(BaseRetriever):
         self.document_store = document_store
 
     def retrieve(self, query, candidate_doc_ids=None, top_k=10):
-        return self.document_store.query(query, top_k)
+
+        return self.document_store.query(query, top_k, candidate_doc_ids)

requirements.txt

@@ -7,4 +7,3 @@ pandas
 psycopg2-binary
 sklearn
 elasticsearch
-elasticsearch_dsl