mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-08-29 10:56:40 +00:00
13510aa753
* Files moved, imports all broken * Fix most imports and docstrings into * Fix the paths to the modules in the API docs * Add latest docstring and tutorial changes * Add a few pipelines that were lost in the inports * Fix a bunch of mypy warnings * Add latest docstring and tutorial changes * Create a file_classifier module * Add docs for file_classifier * Fixed most circular imports, now the REST API can start * Add latest docstring and tutorial changes * Tackling more mypy issues * Reintroduce from FARM and fix last mypy issues hopefully * Re-enable old-style imports * Fix some more import from the top-level package in an attempt to sort out circular imports * Fix some imports in tests to new-style to prevent failed class equalities from breaking tests * Change document_store into document_stores * Update imports in tutorials * Add latest docstring and tutorial changes * Probably fixes summarizer tests * Improve the old-style import allowing module imports (should work) * Try to fix the docs * Remove dedicated KnowledgeGraph page from autodocs * Remove dedicated GraphRetriever page from autodocs * Fix generate_docstrings.sh with an updated list of yaml files to look for * Fix some more modules in the docs * Fix the document stores docs too * Fix a small issue on Tutorial14 * Add latest docstring and tutorial changes * Add deprecation warning to old-style imports * Remove stray folder and import Dict into dense.py * Change import path for MLFlowLogger * Add old loggers path to the import path aliases * Fix debug output of convert_ipynb.py * Fix circular import on BaseRetriever * Missed one merge block * re-run tutorial 5 * Fix imports in tutorial 5 * Re-enable squad_to_dpr CLI from the root package and move get_batches_from_generator into document_stores.base * Add latest docstring and tutorial changes * Fix typo in utils __init__ * Fix a few more imports * Fix benchmarks too * New-style imports in test_knowledge_graph * Rollback setup.py * Rollback squad_to_dpr too Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
325 lines
14 KiB
Python
325 lines
14 KiB
Python
import pandas as pd
|
|
from pathlib import Path
|
|
from time import perf_counter
|
|
from utils import get_document_store, get_retriever, index_to_doc_store, load_config, download_from_url
|
|
from haystack.document_stores.utils import eval_data_from_json
|
|
from haystack.document_stores.faiss import FAISSDocumentStore
|
|
|
|
from haystack.schema import Document
|
|
import pickle
|
|
import time
|
|
from tqdm import tqdm
|
|
import logging
|
|
import datetime
|
|
import random
|
|
import traceback
|
|
import json
|
|
from results_to_json import retriever as retriever_json
|
|
from templates import RETRIEVER_TEMPLATE, RETRIEVER_MAP_TEMPLATE, RETRIEVER_SPEED_TEMPLATE
|
|
from haystack.utils import stop_service
|
|
|
|
logger = logging.getLogger(__name__)
|
|
logging.getLogger("haystack.retriever.base").setLevel(logging.WARN)
|
|
logging.getLogger("elasticsearch").setLevel(logging.WARN)
|
|
|
|
doc_index = "eval_document"
|
|
label_index = "label"
|
|
|
|
index_results_file = "retriever_index_results.csv"
|
|
query_results_file = "retriever_query_results.csv"
|
|
|
|
overview_json = "../../docs/_src/benchmarks/retriever_performance.json"
|
|
map_json = "../../docs/_src/benchmarks/retriever_map.json"
|
|
speed_json = "../../docs/_src/benchmarks/retriever_speed.json"
|
|
|
|
DEVICES = None
|
|
|
|
|
|
seed = 42
|
|
random.seed(42)
|
|
|
|
def benchmark_indexing(n_docs_options, retriever_doc_stores, data_dir, filename_gold, filename_negative, data_s3_url, embeddings_filenames, embeddings_dir, update_json, save_markdown, **kwargs):
|
|
|
|
retriever_results = []
|
|
for n_docs in n_docs_options:
|
|
for retriever_name, doc_store_name in retriever_doc_stores:
|
|
logger.info(f"##### Start indexing run: {retriever_name}, {doc_store_name}, {n_docs} docs ##### ")
|
|
try:
|
|
doc_store = get_document_store(doc_store_name)
|
|
retriever = get_retriever(retriever_name, doc_store, DEVICES)
|
|
docs, _ = prepare_data(data_dir=data_dir,
|
|
filename_gold=filename_gold,
|
|
filename_negative=filename_negative,
|
|
remote_url=data_s3_url,
|
|
embeddings_filenames=embeddings_filenames,
|
|
embeddings_dir=embeddings_dir,
|
|
n_docs=n_docs)
|
|
|
|
tic = perf_counter()
|
|
index_to_doc_store(doc_store, docs, retriever)
|
|
toc = perf_counter()
|
|
indexing_time = toc - tic
|
|
|
|
print(indexing_time)
|
|
|
|
retriever_results.append({
|
|
"retriever": retriever_name,
|
|
"doc_store": doc_store_name,
|
|
"n_docs": n_docs,
|
|
"indexing_time": indexing_time,
|
|
"docs_per_second": n_docs / indexing_time,
|
|
"date_time": datetime.datetime.now(),
|
|
"error": None})
|
|
retriever_df = pd.DataFrame.from_records(retriever_results)
|
|
retriever_df = retriever_df.sort_values(by="retriever").sort_values(by="doc_store")
|
|
retriever_df.to_csv(index_results_file)
|
|
logger.info("Deleting all docs from this run ...")
|
|
|
|
if isinstance(doc_store, FAISSDocumentStore):
|
|
doc_store.session.close()
|
|
else:
|
|
doc_store.delete_all_documents(index=doc_index)
|
|
doc_store.delete_all_documents(index=label_index)
|
|
|
|
if save_markdown:
|
|
md_file = index_results_file.replace(".csv", ".md")
|
|
with open(md_file, "w") as f:
|
|
f.write(str(retriever_df.to_markdown()))
|
|
time.sleep(10)
|
|
stop_service(doc_store)
|
|
del doc_store
|
|
del retriever
|
|
|
|
except Exception:
|
|
tb = traceback.format_exc()
|
|
logging.error(f"##### The following Error was raised while running indexing run: {retriever_name}, {doc_store_name}, {n_docs} docs #####")
|
|
logging.error(tb)
|
|
retriever_results.append({
|
|
"retriever": retriever_name,
|
|
"doc_store": doc_store_name,
|
|
"n_docs": n_docs,
|
|
"indexing_time": 0,
|
|
"docs_per_second": 0,
|
|
"date_time": datetime.datetime.now(),
|
|
"error": str(tb)})
|
|
logger.info("Deleting all docs from this run ...")
|
|
if isinstance(doc_store, FAISSDocumentStore):
|
|
doc_store.session.close()
|
|
else:
|
|
doc_store.delete_all_documents(index=doc_index)
|
|
doc_store.delete_all_documents(index=label_index)
|
|
time.sleep(10)
|
|
stop_service(doc_store)
|
|
del doc_store
|
|
del retriever
|
|
if update_json:
|
|
populate_retriever_json()
|
|
|
|
|
|
|
|
def benchmark_querying(n_docs_options,
|
|
retriever_doc_stores,
|
|
data_dir,
|
|
data_s3_url,
|
|
filename_gold,
|
|
filename_negative,
|
|
n_queries,
|
|
embeddings_filenames,
|
|
embeddings_dir,
|
|
update_json,
|
|
save_markdown,
|
|
wait_write_limit=100,
|
|
**kwargs):
|
|
""" Benchmark the time it takes to perform querying. Doc embeddings are loaded from file."""
|
|
retriever_results = []
|
|
|
|
for n_docs in n_docs_options:
|
|
for retriever_name, doc_store_name in retriever_doc_stores:
|
|
try:
|
|
logger.info(f"##### Start querying run: {retriever_name}, {doc_store_name}, {n_docs} docs ##### ")
|
|
if retriever_name in ["elastic", "sentence_transformers"]:
|
|
similarity = "cosine"
|
|
else:
|
|
similarity = "dot_product"
|
|
doc_store = get_document_store(doc_store_name, similarity=similarity)
|
|
retriever = get_retriever(retriever_name, doc_store, DEVICES)
|
|
add_precomputed = retriever_name in ["dpr"]
|
|
# For DPR, precomputed embeddings are loaded from file
|
|
docs, labels = prepare_data(data_dir=data_dir,
|
|
filename_gold=filename_gold,
|
|
filename_negative=filename_negative,
|
|
remote_url=data_s3_url,
|
|
embeddings_filenames=embeddings_filenames,
|
|
embeddings_dir=embeddings_dir,
|
|
n_docs=n_docs,
|
|
n_queries=n_queries,
|
|
add_precomputed=add_precomputed)
|
|
logger.info("Start indexing...")
|
|
index_to_doc_store(doc_store, docs, retriever, labels)
|
|
|
|
logger.info("Start queries...")
|
|
|
|
raw_results = retriever.eval()
|
|
results = {
|
|
"retriever": retriever_name,
|
|
"doc_store": doc_store_name,
|
|
"n_docs": n_docs,
|
|
"n_queries": raw_results["n_questions"],
|
|
"retrieve_time": raw_results["retrieve_time"],
|
|
"queries_per_second": raw_results["n_questions"] / raw_results["retrieve_time"],
|
|
"seconds_per_query": raw_results["retrieve_time"]/ raw_results["n_questions"],
|
|
"recall": raw_results["recall"] * 100,
|
|
"map": raw_results["map"] * 100,
|
|
"top_k": raw_results["top_k"],
|
|
"date_time": datetime.datetime.now(),
|
|
"error": None
|
|
}
|
|
|
|
logger.info("Deleting all docs from this run ...")
|
|
if isinstance(doc_store, FAISSDocumentStore):
|
|
doc_store.session.close()
|
|
else:
|
|
doc_store.delete_all_documents(index=doc_index)
|
|
doc_store.delete_all_documents(index=label_index)
|
|
time.sleep(5)
|
|
stop_service(doc_store)
|
|
del doc_store
|
|
del retriever
|
|
except Exception:
|
|
tb = traceback.format_exc()
|
|
logging.error(f"##### The following Error was raised while running querying run: {retriever_name}, {doc_store_name}, {n_docs} docs #####")
|
|
logging.error(tb)
|
|
results = {
|
|
"retriever": retriever_name,
|
|
"doc_store": doc_store_name,
|
|
"n_docs": n_docs,
|
|
"n_queries": 0,
|
|
"retrieve_time": 0.,
|
|
"queries_per_second": 0.,
|
|
"seconds_per_query": 0.,
|
|
"recall": 0.,
|
|
"map": 0.,
|
|
"top_k": 0,
|
|
"date_time": datetime.datetime.now(),
|
|
"error": str(tb)
|
|
}
|
|
logger.info("Deleting all docs from this run ...")
|
|
if isinstance(doc_store, FAISSDocumentStore):
|
|
doc_store.session.close()
|
|
else:
|
|
doc_store.delete_all_documents(index=doc_index)
|
|
doc_store.delete_all_documents(index=label_index)
|
|
time.sleep(5)
|
|
del doc_store
|
|
del retriever
|
|
logger.info(results)
|
|
retriever_results.append(results)
|
|
|
|
retriever_df = pd.DataFrame.from_records(retriever_results)
|
|
retriever_df = retriever_df.sort_values(by="retriever").sort_values(by="doc_store")
|
|
retriever_df.to_csv(query_results_file)
|
|
if save_markdown:
|
|
md_file = query_results_file.replace(".csv", ".md")
|
|
with open(md_file, "w") as f:
|
|
f.write(str(retriever_df.to_markdown()))
|
|
if update_json:
|
|
populate_retriever_json()
|
|
|
|
|
|
def populate_retriever_json():
|
|
retriever_overview_data, retriever_map_data, retriever_speed_data = retriever_json(index_csv=index_results_file,
|
|
query_csv=query_results_file)
|
|
overview = RETRIEVER_TEMPLATE
|
|
overview["data"] = retriever_overview_data
|
|
map = RETRIEVER_MAP_TEMPLATE
|
|
map["data"] = retriever_map_data
|
|
speed = RETRIEVER_SPEED_TEMPLATE
|
|
speed["data"] = retriever_speed_data
|
|
json.dump(overview, open(overview_json, "w"), indent=4)
|
|
json.dump(speed, open(speed_json, "w"), indent=4)
|
|
json.dump(map, open(map_json, "w"), indent=4)
|
|
|
|
|
|
def add_precomputed_embeddings(embeddings_dir, embeddings_filenames, docs):
|
|
ret = []
|
|
id_to_doc = {x.meta["passage_id"]: x for x in docs}
|
|
for ef in embeddings_filenames:
|
|
logger.info(f"Adding precomputed embeddings from {embeddings_dir + ef}")
|
|
filename = embeddings_dir + ef
|
|
embeds = pickle.load(open(filename, "rb"))
|
|
for i, vec in embeds:
|
|
if int(i) in id_to_doc:
|
|
curr = id_to_doc[int(i)]
|
|
curr.embedding = vec
|
|
ret.append(curr)
|
|
# In the official DPR repo, there are only 20594995 precomputed embeddings for 21015324 wikipedia passages
|
|
# If there isn't an embedding for a given doc, we remove it here
|
|
ret = [x for x in ret if x.embedding is not None]
|
|
logger.info(f"Embeddings loaded for {len(ret)}/{len(docs)} docs")
|
|
return ret
|
|
|
|
|
|
def prepare_data(data_dir, filename_gold, filename_negative, remote_url, embeddings_filenames, embeddings_dir, n_docs=None, n_queries=None, add_precomputed=False):
|
|
"""
|
|
filename_gold points to a squad format file.
|
|
filename_negative points to a csv file where the first column is doc_id and second is document text.
|
|
If add_precomputed is True, this fn will look in the embeddings files for precomputed embeddings to add to each Document
|
|
"""
|
|
|
|
logging.getLogger("farm").setLevel(logging.INFO)
|
|
download_from_url(remote_url + filename_gold, filepath=data_dir + filename_gold)
|
|
download_from_url(remote_url + filename_negative, filepath=data_dir + filename_negative)
|
|
if add_precomputed:
|
|
for embedding_filename in embeddings_filenames:
|
|
download_from_url(remote_url + str(embeddings_dir) + embedding_filename, filepath=data_dir + str(embeddings_dir) + embedding_filename)
|
|
logging.getLogger("farm").setLevel(logging.WARN)
|
|
|
|
gold_docs, labels = eval_data_from_json(data_dir + filename_gold)
|
|
|
|
# Reduce number of docs
|
|
gold_docs = gold_docs[:n_docs]
|
|
|
|
# Remove labels whose gold docs have been removed
|
|
doc_ids = [x.id for x in gold_docs]
|
|
labels = [x for x in labels if x.document_id in doc_ids]
|
|
|
|
# Filter labels down to n_queries
|
|
selected_queries = list(set(f"{x.document_id} | {x.query}" for x in labels))
|
|
selected_queries = selected_queries[:n_queries]
|
|
labels = [x for x in labels if f"{x.document_id} | {x.query}" in selected_queries]
|
|
|
|
n_neg_docs = max(0, n_docs - len(gold_docs))
|
|
neg_docs = prepare_negative_passages(data_dir, filename_negative, n_neg_docs)
|
|
docs = gold_docs + neg_docs
|
|
|
|
if add_precomputed:
|
|
docs = add_precomputed_embeddings(data_dir + embeddings_dir, embeddings_filenames, docs)
|
|
|
|
return docs, labels
|
|
|
|
def prepare_negative_passages(data_dir, filename_negative, n_docs):
|
|
if n_docs == 0:
|
|
return []
|
|
with open(data_dir + filename_negative) as f:
|
|
lines = []
|
|
_ = f.readline() # Skip column titles line
|
|
for _ in range(n_docs):
|
|
lines.append(f.readline()[:-1])
|
|
|
|
docs = []
|
|
for l in lines[:n_docs]:
|
|
id, text, title = l.split("\t")
|
|
d = {"text": text,
|
|
"meta": {"passage_id": int(id),
|
|
"title": title}}
|
|
d = Document(**d)
|
|
docs.append(d)
|
|
return docs
|
|
|
|
|
|
if __name__ == "__main__":
|
|
params, filenames = load_config(config_filename="config.json", ci=True)
|
|
benchmark_indexing(**params, **filenames, update_json=True, save_markdown=False)
|
|
benchmark_querying(**params, **filenames, update_json=True, save_markdown=False)
|
|
|