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create_es_wildchat_subset_debug.py
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create_es_wildchat_subset_debug.py
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import os
import json
from datasets import load_dataset
from elasticsearch import Elasticsearch, helpers
# Load the dataset
dataset = load_dataset('allenai/WildChat-1M-Full')
# Initialize Elasticsearch client
#es = Elasticsearch('http://localhost:9200')
es = Elasticsearch('https://localhost:9200', basic_auth=('elastic', os.getenv('ES_PASSWD')), ssl_assert_fingerprint=os.getenv('ES_FINGERPRINT'))
LANGUAGES = ['all', 'english', 'russian', 'chinese', 'spanish', 'german', 'french', 'portuguese', 'italian', 'japanese', 'korean']
LANGUAGES = ['english', 'all']
for language in LANGUAGES:
print (language)
index_name = f'wildchat_subset_{language}'
mappings = {
"properties": {
"conversation_hash": {"type": "keyword"},
"model": {"type": "keyword"},
"timestamp": {"type": "date"},
"conversation": {
"type": "nested",
"properties": {
"content": {"type": "text"},
"country": {"type": "keyword"},
"hashed_ip": {"type": "keyword"},
"header": {
"type": "object",
"properties": {
"accept-language": {"type": "keyword"},
"user-agent": {"type": "keyword"}
}
},
"language": {"type": "keyword"},
"redacted": {"type": "boolean"},
"role": {"type": "keyword"},
"state": {"type": "keyword"},
"timestamp": {"type": "date"},
"toxic": {"type": "boolean"},
"turn_identifier": {"type": "integer"}
}
},
"turn": {"type": "integer"},
"language": {"type": "keyword"},
"openai_moderation": {
"type": "nested",
"properties": {
"categories": {
"type": "object",
"properties": {
"harassment": {"type": "boolean"},
"harassment/threatening": {"type": "boolean"},
"harassment_threatening": {"type": "boolean"},
"hate": {"type": "boolean"},
"hate/threatening": {"type": "boolean"},
"hate_threatening": {"type": "boolean"},
"self-harm": {"type": "boolean"},
"self-harm/instructions": {"type": "boolean"},
"self-harm/intent": {"type": "boolean"},
"self_harm": {"type": "boolean"},
"self_harm_instructions": {"type": "boolean"},
"self_harm_intent": {"type": "boolean"},
"sexual": {"type": "boolean"},
"sexual/minors": {"type": "boolean"},
"sexual_minors": {"type": "boolean"},
"violence": {"type": "boolean"},
"violence/graphic": {"type": "boolean"},
"violence_graphic": {"type": "boolean"}
}
},
"category_scores": {
"type": "object",
"properties": {
"harassment": {"type": "float"},
"harassment/threatening": {"type": "float"},
"harassment_threatening": {"type": "float"},
"hate": {"type": "float"},
"hate/threatening": {"type": "float"},
"hate_threatening": {"type": "float"},
"self-harm": {"type": "float"},
"self-harm/instructions": {"type": "float"},
"self-harm/intent": {"type": "float"},
"self_harm": {"type": "float"},
"self_harm_instructions": {"type": "float"},
"self_harm_intent": {"type": "float"},
"sexual": {"type": "float"},
"sexual/minors": {"type": "float"},
"sexual_minors": {"type": "float"},
"violence": {"type": "float"},
"violence/graphic": {"type": "float"},
"violence_graphic": {"type": "float"}
}
},
"flagged": {"type": "boolean"}
}
},
"detoxify_moderation": {
"type": "nested",
"properties": {
"identity_attack": {"type": "float"},
"insult": {"type": "float"},
"obscene": {"type": "float"},
"severe_toxicity": {"type": "float"},
"sexual_explicit": {"type": "float"},
"threat": {"type": "float"},
"toxicity": {"type": "float"}
}
},
"toxic": {"type": "boolean"},
"redacted": {"type": "boolean"},
"state": {"type": "keyword"},
"country": {"type": "keyword"},
"hashed_ip": {"type": "keyword"},
"header": {
"type": "object",
"properties": {
"accept-language": {"type": "keyword"},
"user-agent": {"type": "keyword"}
}
}
}
}
# Create the index with the specified mappings
if es.indices.exists(index=index_name):
es.indices.delete(index=index_name)
assert not es.indices.exists(index=index_name)
if not es.indices.exists(index=index_name):
es.indices.create(index=index_name, mappings=mappings)
import tqdm
# Prepare data for bulk indexing
actions = []
import json
wildchat_embeddings = json.load(open(f'static/{language}/wildchat_embeddings.json'))
conversation_ids = set([item['i'] for item in wildchat_embeddings])
chunk_size = 10000 # Set your desired chunk size
for i, record in enumerate(tqdm.tqdm(dataset['train']), 1):
#if i > 10000:
# break
conversation_id = record['conversation'][0]['turn_identifier']
if conversation_id not in conversation_ids:
continue
action = {
"_index": index_name,
"_source": record
}
actions.append(action)
if i % chunk_size == 0:
helpers.bulk(es, actions)
actions = [] # Reset actions after each chunk is indexed
# Index any remaining actions
if actions:
helpers.bulk(es, actions)
print("Indexing completed.")