def test_get_index_from_index_path():
index_path_1 = "/something/anserini/foo"
index_path_2 = "/foo/bar"
index_class = Index.get_index_from_index_path(index_path_1)
assert index_class == AnseriniIndex
index_class = Index.get_index_from_index_path(index_path_2)
assert index_class is None
def get(self, request, *args, **kwargs):
index_class = Index.get_index_from_index_path(request.GET["target_index"])
index = index_class(request.GET["target_index"])
doc_id = request.GET["doc_id"]
context = {"doc": index.getdoc(doc_id)}
return render(request, self.template_name, context)
def get_most_relevant_doc_based_on_config(config, query_string, target_index):
"""
1. Instantiate various classes based on config
2. Get the most relevant doc
"""
# We still need to init a pipeline because it pre-processes some config params, and we rely on that to
# construct paths e.t.c.
config = config.copy() # because we end up modifying config
pipeline = Pipeline(config)
pipeline.initialize(config)
path_dict = pipeline.get_paths(config)
index_path = target_index
index_class = Index.get_index_from_index_path(index_path)
index = index_class(pipeline.collection, index_path, None) # TODO: Pass a proper index_key
model_class = Reranker.ALL[config["reranker"]]
tokenizer = NeuralQueryView.get_tokenizer(pipeline, config, index_class.name)
embedding_holder = EmbeddingHolder.get_instance(config.get("embeddings", "glove6b"))
trained_weight_path = path_dict["trained_weight_path"]
config = NeuralQueryView.add_model_required_params_to_config(config, embedding_holder)
return NeuralQueryView.do_query(
config,
query_string,
pipeline,
index,
tokenizer,
embedding_holder,
model_class,
trained_weight_path=trained_weight_path,
)
def get(self, request, *args, **kwargs):
index_class = Index.get_index_from_index_path(
request.GET["target_index"])
# Hack - set the collection and index key as None since we won't need them for the purpose of WSDM demo
index = index_class(None, request.GET["target_index"], None)
doc_id = request.GET["doc_id"]
context = {"doc": index.getdoc(doc_id)}
return render(request, self.template_name, context)
def get_bm25_results(query_string, target_index, b, k1):
index_class = Index.get_index_from_index_path(target_index)
index = index_class(target_index)
bm25_kwargs = {"n": NUM_RESULTS_TO_SHOW}
if b is not None:
bm25_kwargs["b"] = b
if k1 is not None:
bm25_kwargs["k1"] = k1
doc_ids, docs = BM25View.do_query(query_string, index, **bm25_kwargs)
docs = [doc[:250] for doc in docs]
collection = Collection.get_collection_from_index_path(index.index_path)
relevances = collection.get_relevance(query_string, doc_ids) if collection is not None else [0] * len(doc_ids)
result_dicts = NeuralQueryView.construct_result_dicts(doc_ids, docs, relevances)
return result_dicts