Fix update_embeddings function in FAISSDocumentStore and add retriever fixture in tests (#481)

* 1. Prevent update_embeddings function in FAISSDocumentStore to set faiss_index as None when document store does not have any docs.

2. cleaning up tests by adding fixture for retriever.

* TfidfRetriever need document store with documents during initialization as it call fit() function in constructor so fixing it by checking self.paragraphs of None

* Fix naming of retriever's fixture (embedded to embedding and tfid to tfidf)

haystack/document_store/faiss.py

@@ -128,17 +128,19 @@ class FAISSDocumentStore(SQLDocumentStore):
         :param index: (SQL) index name for storing the docs and metadata
         :return: None
         """
-        # To clear out the FAISS index contents and frees all memory immediately that is in use by the index
-        self.faiss_index.reset()
+        if not self.faiss_index:
+            raise ValueError("Couldn't find a FAISS index. Try to init the FAISSDocumentStore() again ...")
 
         index = index or self.index
         documents = self.get_all_documents(index=index)
 
         if len(documents) == 0:
             logger.warning("Calling DocumentStore.update_embeddings() on an empty index")
-            self.faiss_index = None
             return
 
+        # To clear out the FAISS index contents and frees all memory immediately that is in use by the index
+        self.faiss_index.reset()
+
         logger.info(f"Updating embeddings for {len(documents)} docs...")
         embeddings = retriever.embed_passages(documents)  # type: ignore
         assert len(documents) == len(embeddings)

haystack/retriever/sparse.py

@@ -167,6 +167,12 @@ class TfidfRetriever(BaseRetriever):
         return documents
 
     def fit(self):
+        if not self.paragraphs or len(self.paragraphs) == 0:
+            self.paragraphs = self._get_all_paragraphs()
+            if not self.paragraphs or len(self.paragraphs) == 0:
+                logger.warning("Fit method called with empty document store")
+                return
+
         self.df = pd.DataFrame.from_dict(self.paragraphs)
         self.df["text"] = self.df["text"].apply(lambda x: " ".join(x))
         self.tfidf_matrix = self.vectorizer.fit_transform(self.df["text"])

test/conftest.py

@@ -7,6 +7,9 @@ from sys import platform
 import pytest
 import requests
 from elasticsearch import Elasticsearch
+from haystack.retriever.sparse import ElasticsearchFilterOnlyRetriever, ElasticsearchRetriever, TfidfRetriever
+
+from haystack.retriever.dense import DensePassageRetriever, EmbeddingRetriever
 
 from haystack import Document
 from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
@@ -157,6 +160,16 @@ def document_store(request, test_docs_xs, elasticsearch_fixture):
     return get_document_store(request.param)
 
 
+@pytest.fixture(params=["es_filter_only", "elsticsearch", "dpr", "embedding", "tfidf"])
+def retriever(request, document_store):
+    return get_retriever(request.param, document_store)
+
+
+@pytest.fixture(params=["es_filter_only", "elsticsearch", "dpr", "embedding", "tfidf"])
+def retriever_with_docs(request, document_store_with_docs):
+    return get_retriever(request.param, document_store_with_docs)
+
+
 def get_document_store(document_store_type):
     if document_store_type == "sql":
         if os.path.exists("haystack_test.db"):
@@ -177,3 +190,27 @@ def get_document_store(document_store_type):
         raise Exception(f"No document store fixture for '{document_store_type}'")
 
     return document_store
+
+
+def get_retriever(retriever_type, document_store):
+
+    if retriever_type == "dpr":
+        retriever = DensePassageRetriever(document_store=document_store,
+                                          query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
+                                          passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
+                                          use_gpu=False, embed_title=True,
+                                          remove_sep_tok_from_untitled_passages=True)
+    elif retriever_type == "tfidf":
+        return TfidfRetriever(document_store=document_store)
+    elif retriever_type == "embedding":
+        retriever = EmbeddingRetriever(document_store=document_store,
+                                       embedding_model="deepset/sentence_bert",
+                                       use_gpu=False)
+    elif retriever_type == "elsticsearch":
+        retriever = ElasticsearchRetriever(document_store=document_store)
+    elif retriever_type == "es_filter_only":
+        retriever = ElasticsearchFilterOnlyRetriever(document_store=document_store)
+    else:
+        raise Exception(f"No retriever fixture for '{retriever_type}'")
+
+    return retriever

test/test_dpr_retriever.py

@@ -1,13 +1,13 @@
 import pytest
 import time
 
-from haystack.retriever.dense import DensePassageRetriever
 from haystack import Document
 from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
 
 
 @pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory"], indirect=True)
-def test_dpr_inmemory_retrieval(document_store):
+@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
+def test_dpr_inmemory_retrieval(document_store, retriever):
 
     documents = [
         Document(
@@ -33,11 +33,6 @@ def test_dpr_inmemory_retrieval(document_store):
 
     document_store.delete_all_documents(index="test_dpr")
     document_store.write_documents(documents, index="test_dpr")
-    retriever = DensePassageRetriever(document_store=document_store,
-                                      query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-                                      passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-                                      use_gpu=True, embed_title=True,
-                                      remove_sep_tok_from_untitled_passages=True)
     document_store.update_embeddings(retriever=retriever, index="test_dpr")
     time.sleep(2)
 

test/test_dummy_retriever.py

@@ -3,21 +3,20 @@ import pytest
 
 
 @pytest.mark.parametrize("document_store_with_docs", [("elasticsearch")], indirect=True)
-def test_dummy_retriever(document_store_with_docs):
-    from haystack.retriever.sparse import ElasticsearchFilterOnlyRetriever
-    retriever = ElasticsearchFilterOnlyRetriever(document_store_with_docs)
+@pytest.mark.parametrize("retriever_with_docs", ["es_filter_only"], indirect=True)
+def test_dummy_retriever(retriever_with_docs, document_store_with_docs):
 
-    result = retriever.retrieve(query="godzilla", filters={"name": ["filename1"]}, top_k=1)
+    result = retriever_with_docs.retrieve(query="godzilla", filters={"name": ["filename1"]}, top_k=1)
     assert type(result[0]) == Document
     assert result[0].text == "My name is Carla and I live in Berlin"
     assert result[0].meta["name"] == "filename1"
 
-    result = retriever.retrieve(query="godzilla", filters={"name": ["filename1"]}, top_k=5)
+    result = retriever_with_docs.retrieve(query="godzilla", filters={"name": ["filename1"]}, top_k=5)
     assert type(result[0]) == Document
     assert result[0].text == "My name is Carla and I live in Berlin"
     assert result[0].meta["name"] == "filename1"
 
-    result = retriever.retrieve(query="godzilla", filters={"name": ["filename3"]}, top_k=5)
+    result = retriever_with_docs.retrieve(query="godzilla", filters={"name": ["filename3"]}, top_k=5)
     assert type(result[0]) == Document
     assert result[0].text == "My name is Christelle and I live in Paris"
     assert result[0].meta["name"] == "filename3"

test/test_elastic_retriever.py

@@ -1,35 +1,33 @@
-from haystack.retriever.sparse import ElasticsearchRetriever
 import pytest
 
 
 @pytest.mark.parametrize("document_store_with_docs", [("elasticsearch")], indirect=True)
-def test_elasticsearch_retrieval(document_store_with_docs):
-    retriever = ElasticsearchRetriever(document_store=document_store_with_docs)
-    res = retriever.retrieve(query="Who lives in Berlin?")
+@pytest.mark.parametrize("retriever_with_docs", ["elsticsearch"], indirect=True)
+def test_elasticsearch_retrieval(retriever_with_docs, document_store_with_docs):
+    res = retriever_with_docs.retrieve(query="Who lives in Berlin?")
     assert res[0].text == "My name is Carla and I live in Berlin"
     assert len(res) == 3
     assert res[0].meta["name"] == "filename1"
 
+
 @pytest.mark.parametrize("document_store_with_docs", [("elasticsearch")], indirect=True)
-def test_elasticsearch_retrieval_filters(document_store_with_docs):
-    retriever = ElasticsearchRetriever(document_store=document_store_with_docs)
-    res = retriever.retrieve(query="Who lives in Berlin?", filters={"name": ["filename1"]})
+@pytest.mark.parametrize("retriever_with_docs", ["elsticsearch"], indirect=True)
+def test_elasticsearch_retrieval_filters(retriever_with_docs, document_store_with_docs):
+    res = retriever_with_docs.retrieve(query="Who lives in Berlin?", filters={"name": ["filename1"]})
     assert res[0].text == "My name is Carla and I live in Berlin"
     assert len(res) == 1
     assert res[0].meta["name"] == "filename1"
 
-    res = retriever.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "meta_field": ["not_existing_value"]})
+    res = retriever_with_docs.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "meta_field": ["not_existing_value"]})
     assert len(res) == 0
 
-    res = retriever.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "not_existing_field": ["not_existing_value"]})
+    res = retriever_with_docs.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "not_existing_field": ["not_existing_value"]})
     assert len(res) == 0
 
-    retriever = ElasticsearchRetriever(document_store=document_store_with_docs)
-    res = retriever.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "meta_field": ["test1","test2"]})
+    res = retriever_with_docs.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "meta_field": ["test1","test2"]})
     assert res[0].text == "My name is Carla and I live in Berlin"
     assert len(res) == 1
     assert res[0].meta["name"] == "filename1"
 
-    retriever = ElasticsearchRetriever(document_store=document_store_with_docs)
-    res = retriever.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "meta_field":["test2"]})
+    res = retriever_with_docs.retrieve(query="Who lives in Berlin?", filters={"name":["filename1"], "meta_field":["test2"]})
     assert len(res) == 0

test/test_embedding_retriever.py

@@ -1,10 +1,10 @@
 import pytest
 from haystack import Finder
-from haystack.retriever.dense import EmbeddingRetriever
 
 
 @pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory"], indirect=True)
-def test_embedding_retriever(document_store):
+@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
+def test_embedding_retriever(retriever, document_store):
 
     documents = [
         {'text': 'By running tox in the command line!', 'meta': {'name': 'How to test this library?', 'question': 'How to test this library?'}},
@@ -20,8 +20,6 @@ def test_embedding_retriever(document_store):
         {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
     ]
 
-    retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False)
-
     embedded = []
     for doc in documents:
         doc['embedding'] = retriever.embed([doc['meta']['question']])[0]

test/test_eval.py

@@ -1,6 +1,5 @@
 import pytest
 from haystack.document_store.base import BaseDocumentStore
-from haystack.retriever.sparse import ElasticsearchRetriever
 from haystack.finder import Finder
 
 
@@ -62,9 +61,8 @@ def test_eval_reader(reader, document_store: BaseDocumentStore):
 
 @pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
 @pytest.mark.parametrize("open_domain", [True, False])
-def test_eval_elastic_retriever(document_store: BaseDocumentStore, open_domain):
-    retriever = ElasticsearchRetriever(document_store=document_store)
-
+@pytest.mark.parametrize("retriever", ["elsticsearch"], indirect=True)
+def test_eval_elastic_retriever(document_store: BaseDocumentStore, open_domain, retriever):
     # add eval data (SQUAD format)
     document_store.delete_all_documents(index="test_eval_document")
     document_store.delete_all_documents(index="test_feedback")
@@ -83,8 +81,8 @@ def test_eval_elastic_retriever(document_store: BaseDocumentStore, open_domain):
 
 @pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
 @pytest.mark.parametrize("reader", ["farm"], indirect=True)
-def test_eval_finder(document_store: BaseDocumentStore, reader):
-    retriever = ElasticsearchRetriever(document_store=document_store)
+@pytest.mark.parametrize("retriever", ["elsticsearch"], indirect=True)
+def test_eval_finder(document_store: BaseDocumentStore, reader, retriever):
     finder = Finder(reader=reader, retriever=retriever)
 
     # add eval data (SQUAD format)

test/test_faiss.py

@@ -4,8 +4,6 @@ from haystack import Document
 import faiss
 
 from haystack.document_store.faiss import FAISSDocumentStore
-from haystack.retriever.dense import DensePassageRetriever
-from haystack.retriever.dense import EmbeddingRetriever
 from haystack import Finder
 
 DOCUMENTS = [
@@ -47,7 +45,8 @@ def test_faiss_index_save_and_load(document_store):
     assert document_store.faiss_index.ntotal == 0
 
     # test loading the index
-    new_document_store = document_store.load(sql_url="sqlite:///haystack_test.db", faiss_file_path="haystack_test_faiss")
+    new_document_store = document_store.load(sql_url="sqlite:///haystack_test.db",
+                                             faiss_file_path="haystack_test_faiss")
 
     # check faiss index is restored
     assert new_document_store.faiss_index.ntotal == len(DOCUMENTS)
@@ -78,21 +77,15 @@ def test_faiss_write_docs(document_store, index_buffer_size, batch_size):
 
 
 @pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
 @pytest.mark.parametrize("index_buffer_size", [10_000, 2])
-def test_faiss_update_docs(document_store, index_buffer_size):
+def test_faiss_update_docs(document_store, index_buffer_size, retriever):
     # adjust buffer size
     document_store.index_buffer_size = index_buffer_size
 
     # initial write
     document_store.write_documents(DOCUMENTS)
 
-    # do the update
-    retriever = DensePassageRetriever(document_store=document_store,
-                                      query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
-                                      passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
-                                      use_gpu=False, embed_title=True,
-                                      remove_sep_tok_from_untitled_passages=True)
-
     document_store.update_embeddings(retriever=retriever)
     documents_indexed = document_store.get_all_documents()
 
@@ -109,28 +102,40 @@ def test_faiss_update_docs(document_store, index_buffer_size):
 
 
 @pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
-def test_faiss_retrieving(document_store):
+@pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
+def test_faiss_update_with_empty_store(document_store, retriever):
+    # Call update with empty doc store
+    document_store.update_embeddings(retriever=retriever)
+
+    # initial write
     document_store.write_documents(DOCUMENTS)
 
-    retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert",
-                                   use_gpu=False)
+    documents_indexed = document_store.get_all_documents()
+
+    # test document correctness
+    check_data_correctness(documents_indexed, DOCUMENTS)
+
+
+@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
+def test_faiss_retrieving(document_store, retriever):
+    document_store.write_documents(DOCUMENTS)
     result = retriever.retrieve(query="How to test this?")
     assert len(result) == len(DOCUMENTS)
     assert type(result[0]) == Document
 
 
 @pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
-def test_faiss_finding(document_store):
+@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
+def test_faiss_finding(document_store, retriever):
     document_store.write_documents(DOCUMENTS)
-
-    retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert",
-                                   use_gpu=False)
     finder = Finder(reader=None, retriever=retriever)
 
     prediction = finder.get_answers_via_similar_questions(question="How to test this?", top_k_retriever=1)
 
     assert len(prediction.get('answers', [])) == 1
 
+
 def test_faiss_passing_index_from_outside():
     d = 768
     nlist = 2
@@ -147,4 +152,4 @@ def test_faiss_passing_index_from_outside():
     documents_indexed = document_store.get_all_documents(index="document")
 
     # test document correctness
-    check_data_correctness(documents_indexed, DOCUMENTS)
+    check_data_correctness(documents_indexed, DOCUMENTS)

test/test_finder.py

@@ -1,11 +1,10 @@
 from haystack import Finder
-from haystack.retriever.sparse import TfidfRetriever
 import pytest
 
 
-def test_finder_get_answers(reader, document_store_with_docs):
-    retriever = TfidfRetriever(document_store=document_store_with_docs)
-    finder = Finder(reader, retriever)
+@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
+def test_finder_get_answers(reader, retriever_with_docs, document_store_with_docs):
+    finder = Finder(reader, retriever_with_docs)
     prediction = finder.get_answers(question="Who lives in Berlin?", top_k_retriever=10,
                                     top_k_reader=3)
     assert prediction is not None
@@ -19,9 +18,9 @@ def test_finder_get_answers(reader, document_store_with_docs):
     assert len(prediction["answers"]) == 3
 
 
-def test_finder_offsets(reader, document_store_with_docs):
-    retriever = TfidfRetriever(document_store=document_store_with_docs)
-    finder = Finder(reader, retriever)
+@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
+def test_finder_offsets(reader, retriever_with_docs, document_store_with_docs):
+    finder = Finder(reader, retriever_with_docs)
     prediction = finder.get_answers(question="Who lives in Berlin?", top_k_retriever=10,
                                     top_k_reader=5)
 
@@ -32,9 +31,9 @@ def test_finder_offsets(reader, document_store_with_docs):
     assert prediction["answers"][0]["context"][start:end] == prediction["answers"][0]["answer"]
 
 
-def test_finder_get_answers_single_result(reader, document_store_with_docs):
-    retriever = TfidfRetriever(document_store=document_store_with_docs)
-    finder = Finder(reader, retriever)
+@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
+def test_finder_get_answers_single_result(reader, retriever_with_docs, document_store_with_docs):
+    finder = Finder(reader, retriever_with_docs)
     query = "testing finder"
     prediction = finder.get_answers(question=query, top_k_retriever=1,
                                     top_k_reader=1)

test/test_tfidf_retriever.py

@@ -1,5 +1,9 @@
-def test_tfidf_retriever():
-    from haystack.retriever.sparse import TfidfRetriever
+import pytest
+
+
+@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
+@pytest.mark.parametrize("retriever", ["tfidf"], indirect=True)
+def test_tfidf_retriever(document_store, retriever):
 
     test_docs = [
         {"id": "26f84672c6d7aaeb8e2cd53e9c62d62d", "name": "testing the finder 1", "text": "godzilla says hello"},
@@ -7,11 +11,8 @@ def test_tfidf_retriever():
         {"name": "testing the finder 3", "text": "alien says arghh"}
     ]
 
-    from haystack.document_store.memory import InMemoryDocumentStore
-    document_store = InMemoryDocumentStore()
     document_store.write_documents(test_docs)
 
-    retriever = TfidfRetriever(document_store)
     retriever.fit()
     doc = retriever.retrieve("godzilla", top_k=1)[0]
     assert doc.id == "26f84672c6d7aaeb8e2cd53e9c62d62d"