Merge pull request #28 from stanford-oval/auto-sync-2024-08-23-12-52-43

Add troubleshooting notes to README

README.md

@@ -114,6 +114,8 @@ conda activate wikichat
 python -m spacy download en_core_web_sm # Spacy is only needed for certain WikiChat configurations
 ```
 
+If you see `Error: Redis lookup failed` after running the chatbot, it probably means Redis is not properly installed. You can try reinstalling it by following its [official documentation](https://redis.io/docs/latest/operate/oss_and_stack/install/install-redis/).
+
 Keep this environment activated for all subsequent commands.
 
 Install Docker for your operating system by following the instructions at https://docs.docker.com/engine/install/. WikiChat uses Docker primarily for creating and serving vector databases for retrieval, specifically [🤗 Text Embedding Inference](https://github.com/huggingface/text-embeddings-inference) and [Qdrant](https://github.com/qdrant/qdrant). On recent Ubuntu versions, you can try running `inv install-docker`. For other operating systems, follow the instructions on the docker website.
@@ -203,6 +205,8 @@ See `wikipedia_preprocessing/preprocess_html_dump.py` for details on how this is
 inv index-collection --collection-path <path to preprocessed JSONL> --collection-name <name>
 ```
 
+This command starts docker containers for [🤗 Text Embedding Inference](https://github.com/huggingface/text-embeddings-inference) (one per available GPU). By default, it uses the docker image compatible with NVIDIA GPUs with Ampere 80 architecture, e.g. A100. Support for some other GPUs is also available, but you would need to choose the right docker image from [available docker images](https://github.com/huggingface/text-embeddings-inference?tab=readme-ov-file#docker-images).
+
 
 #### To upload a Qdrant index to 🤗 Hub:
 1. Split the index into smaller parts:
@@ -268,7 +272,7 @@ This script reads the topic (i.e., a Wikipedia title and article) from the corre
 ```bash
 inv simulate-users --num-dialogues 1 --num-turns 2 --simulation-mode passage --language en --subset head
 ```
-Depending on the engine you are using, this might take some time. The simulated dialogues and log files will be saved in `benchmark/simulated_dialogs/`.
+Depending on the engine you are using, this might take some time. The simulated dialogues and log files will be saved in `benchmark/simulated_dialogues/`.
 You can also provide any of the pipeline parameters from above.
 You can experiment with different user characteristics by modifying `user_characteristics` in `benchmark/user_simulator.py`.
 

backend_server.py

@@ -12,8 +12,8 @@
  add_pipeline_arguments,
  check_pipeline_arguments,
 )
-from tasks.defaults import CHATBOT_DEFAULT_CONFIG
 from pipelines.utils import dict_to_command_line, get_logger
+from tasks.defaults import CHATBOT_DEFAULT_CONFIG
 
 logger = get_logger(__name__)
 

benchmark/user_simulator.py

@@ -154,7 +154,9 @@ def main(args):
  elif args.mode == "multihop":
  with open(args.input_file) as input_file:
  dialogue_inputs = json.load(input_file)
- dialogue_inputs = repeat_dialogue_inputs(dialogue_inputs, args.num_dialogues)
+ dialogue_inputs = repeat_dialogue_inputs(
+ dialogue_inputs, args.num_dialogues
+ )
  topics = [m["title_1"] + " and " + m["title_2"] for m in dialogue_inputs]
  else:
  raise ValueError("Unknown mode: %s" % args.mode)

pipelines/pipeline_arguments.py

@@ -3,6 +3,7 @@
 """
 
 from chainlite.llm_config import GlobalVars
+
 from pipelines.chatbot_config import PipelineEnum
 from tasks.defaults import CHATBOT_DEFAULT_CONFIG
 

retrieval/add_payload_index.py

@@ -1,4 +1,5 @@
 import argparse
+
 from qdrant_client import QdrantClient
 from qdrant_client.models import PayloadSchemaType
 

retrieval/create_index.py

@@ -24,11 +24,10 @@
 )
 from tqdm import tqdm
 
-
 sys.path.insert(0, "./")
-from tasks.defaults import DEFAULT_QDRANT_COLLECTION_NAME
 from pipelines.utils import get_logger
 from retrieval.qdrant_index import QdrantIndex
+from tasks.defaults import DEFAULT_QDRANT_COLLECTION_NAME
 
 logger = get_logger(__name__)
 
@@ -243,7 +242,9 @@ def batch_generator(collection_file, embedding_batch_size):
  default=48,
  help="The size of each request sent to GPU. The actual batch size is `embedding_batch_size * num_embedding_workers`",
  )
- parser.add_argument("--collection_name", default=DEFAULT_QDRANT_COLLECTION_NAME, type=str)
+ parser.add_argument(
+ "--collection_name", default=DEFAULT_QDRANT_COLLECTION_NAME, type=str
+ )
  parser.add_argument(
  "--index",
  action="store_true",
@@ -258,12 +259,12 @@ def batch_generator(collection_file, embedding_batch_size):
  args = parser.parse_args()
 
  model_port = args.model_port
- embedding_size = QdrantIndex.get_embedding_model_parameters(args.embedding_model_name)[
- "embedding_dimension"
- ]
- query_prefix = QdrantIndex.get_embedding_model_parameters(args.embedding_model_name)[
- "query_prefix"
- ]
+ embedding_size = QdrantIndex.get_embedding_model_parameters(
+ args.embedding_model_name
+ )["embedding_dimension"]
+ query_prefix = QdrantIndex.get_embedding_model_parameters(
+ args.embedding_model_name
+ )["query_prefix"]
 
  if args.index:
  collection_size = 0

retrieval/qdrant_index.py

@@ -2,11 +2,12 @@
 import math
 from time import time
 from typing import Any
-from pydantic import BaseModel, Field
+
 import numpy as np
 import onnxruntime as ort
 import torch
 from huggingface_hub import hf_hub_download
+from pydantic import BaseModel, Field
 from qdrant_client import AsyncQdrantClient
 from qdrant_client.models import (
  FieldCondition,

retrieval/qdrant_snapshot.py

@@ -47,7 +47,9 @@ def main():
 
  args = parser.parse_args()
 
- qdrant_client = QdrantClient(url="http://localhost", port=6333, timeout=60, prefer_grpc=False)
+ qdrant_client = QdrantClient(
+ url="http://localhost", port=6333, timeout=60, prefer_grpc=False
+ )
 
  if args.action == "save":
  qdrant_client.create_snapshot(collection_name=args.collection_name, wait=False)

retrieval/retriever_server.py

@@ -8,7 +8,7 @@
 
 from async_lru import alru_cache
 from fastapi import FastAPI, Request, Response
-from pydantic import BaseModel, Field, field_validator, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field, field_validator
 from slowapi import Limiter, _rate_limit_exceeded_handler
 from slowapi.errors import RateLimitExceeded
 from slowapi.util import get_remote_address

retrieval/upload_folder_to_hf_hub.py

@@ -1,6 +1,8 @@
 import argparse
+
 from huggingface_hub import upload_folder
 
+
 def main(repo_id, folder_path):
  upload_folder(
  folder_path=folder_path,
@@ -10,11 +12,16 @@ def main(repo_id, folder_path):
  multi_commits_verbose=True,
  )
 
+
 if __name__ == "__main__":
  parser = argparse.ArgumentParser(description="Upload a folder to HuggingFace Hub")
- parser.add_argument("--folder_path", type=str, help="The path to the folder to upload")
- parser.add_argument("--repo_id", type=str, help="The repository ID on HuggingFace Hub")
-
+ parser.add_argument(
+ "--folder_path", type=str, help="The path to the folder to upload"
+ )
+ parser.add_argument(
+ "--repo_id", type=str, help="The repository ID on HuggingFace Hub"
+ )
+
  args = parser.parse_args()
- 
- main(args.repo_id, args.folder_path)
+
+ main(args.repo_id, args.folder_path)

tasks/benchmark.py

@@ -48,7 +48,7 @@ def simulate_users(
 
  Accepts all parameters that `inv demo` accepts, plus a few additional parameters for the user simulator.
  """
- 
+
  pipeline_flags = (
  f"--pipeline {pipeline} "
  f"--engine {engine} "
@@ -95,7 +95,7 @@ def simulate_users(
  f"--mode {simulation_mode} "
  f"--input_file benchmark/topics/{input_file} "
  f"--num_turns {num_turns} "
- f"--output_file benchmark/simulated_dialogs/{pipeline}_{subset}_{language}_{engine}.txt "
+ f"--output_file benchmark/simulated_dialogues/{pipeline}_{subset}_{language}_{engine}.txt "
  f"--language {language} "
  f"--no_logging"
  )

tasks/defaults.py

@@ -12,7 +12,7 @@
 DEFAULT_EMBEDDING_MODEL_NAME = "BAAI/bge-m3"
 DEFAULT_WIKIPEDIA_DUMP_LANGUAGE = "en"
 DEFAULT_WORKDIR = "workdir"
-DEFAULT_QDRANT_COLLECTION_NAME="wikipedia"
+DEFAULT_QDRANT_COLLECTION_NAME = "wikipedia"
 
 DEFAULT_BACKEND_PORT = 5001
 DEFAULT_REDIS_PORT = 6379

tasks/docker_utils.py

@@ -1,9 +1,9 @@
 import os
+import sys
 from time import sleep
 
 import docker
 from invoke import task
-import sys
 
 sys.path.insert(0, "./")
 from pipelines.utils import get_logger

tasks/retrieval.py

@@ -5,13 +5,13 @@
 import threading
 from concurrent.futures import ThreadPoolExecutor
 from datetime import datetime
+from typing import Optional
 
 import requests
 from bs4 import BeautifulSoup
 from huggingface_hub import snapshot_download
 from invoke import task
 from tqdm import tqdm
-from typing import Optional
 
 from tasks.docker_utils import (
  start_embedding_docker_container,
@@ -21,19 +21,18 @@
 from tasks.main import start_redis
 
 sys.path.insert(0, "./")
+from pipelines.utils import get_logger
 from tasks.defaults import (
  DEFAULT_EMBEDDING_MODEL_NAME,
  DEFAULT_EMBEDDING_MODEL_PORT,
+ DEFAULT_EMBEDDING_USE_ONNX,
  DEFAULT_NUM_GPUS,
  DEFAULT_QDRANT_COLLECTION_NAME,
  DEFAULT_RETRIEVER_PORT,
- DEFAULT_EMBEDDING_USE_ONNX,
  DEFAULT_WIKIPEDIA_DUMP_LANGUAGE,
  DEFAULT_WORKDIR,
 )
 
-from pipelines.utils import get_logger
-
 logger = get_logger(__name__)
 
 

tasks/setup.py

@@ -14,7 +14,7 @@
 def setup_nvme(c):
  """
  Set up an NVMe drive on the VM by performing the following steps. Only works on certain Linux distributions.
- 
+
  1. Installs the `nvme-cli` package to manage NVMe devices.
  2. Lists available NVMe devices on the system.
  3. Extracts NVMe device names from the listing output.

tests/test_pipelines.py

@@ -6,14 +6,14 @@
 
 sys.path.insert(0, "./")
 from backend_server import chat_profiles_dict
-from tasks.defaults import CHATBOT_DEFAULT_CONFIG
 from pipelines.chatbot import create_chain, run_one_turn
 from pipelines.dialogue_state import DialogueState
 from pipelines.pipeline_arguments import (
  add_pipeline_arguments,
  check_pipeline_arguments,
 )
 from pipelines.utils import dict_to_command_line
+from tasks.defaults import CHATBOT_DEFAULT_CONFIG
 
 test_user_utterances = [
  "Hi", # a turn that doesn't need retrieval

tests/test_retriever.py

@@ -1,6 +1,7 @@
+import sys
+
 import pytest
 from pydantic import ValidationError
-import sys
 
 sys.path.insert(0, "./")
 from retrieval.retriever_server import QueryData

wikipedia_preprocessing/get_all_wiki_sizes.py

@@ -6,7 +6,10 @@
 
 parser = argparse.ArgumentParser()
 parser.add_argument(
- "--wikipedia_date", type=str, required=True, help="Enter the date in the format yyyymmdd"
+ "--wikipedia_date",
+ type=str,
+ required=True,
+ help="Enter the date in the format yyyymmdd",
 )
 args = parser.parse_args()
 

wikipedia_preprocessing/upload_collections_to_hf_hub.py

@@ -1,12 +1,11 @@
 import argparse
-from huggingface_hub import HfApi
 import gzip
-import shutil
 import os
+import shutil
 
+from huggingface_hub import HfApi
 from tqdm import tqdm
 
-
 if __name__ == "__main__":
  parser = argparse.ArgumentParser(
  description="Upload preprocessed Wikipedia collection files to HuggingFace Hub"