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import logging
import time
from datetime import timedelta
from typing import Dict, List
import streamlit as st
from llm_guard.input_scanners import get_scanner_by_name
from llm_guard.input_scanners.anonymize import default_entity_types
from llm_guard.input_scanners.code import SUPPORTED_LANGUAGES as SUPPORTED_CODE_LANGUAGES
from llm_guard.input_scanners.gibberish import MatchType as GibberishMatchType
from llm_guard.input_scanners.language import MatchType as LanguageMatchType
from llm_guard.input_scanners.prompt_injection import MatchType as PromptInjectionMatchType
from llm_guard.input_scanners.toxicity import MatchType as ToxicityMatchType
from llm_guard.vault import Vault
from streamlit_tags import st_tags
logger = logging.getLogger("llm-guard-playground")
def init_settings() -> (List, Dict):
all_scanners = [
"Anonymize",
"BanCode",
"BanCompetitors",
"BanSubstrings",
"BanTopics",
"Code",
"Gibberish",
"Language",
"PromptInjection",
"Regex",
"Secrets",
"Sentiment",
"TokenLimit",
"Toxicity",
]
st_enabled_scanners = st.sidebar.multiselect(
"Select scanners",
options=all_scanners,
default=all_scanners,
help="The list can be found here: https://llm-guard.com/input_scanners/anonymize/",
)
settings = {}
if "Anonymize" in st_enabled_scanners:
st_anon_expander = st.sidebar.expander(
"Anonymize",
expanded=False,
)
with st_anon_expander:
st_anon_entity_types = st_tags(
label="Anonymize entities",
text="Type and press enter",
value=default_entity_types,
suggestions=default_entity_types
+ ["DATE_TIME", "NRP", "LOCATION", "MEDICAL_LICENSE", "US_PASSPORT"],
maxtags=30,
key="anon_entity_types",
)
st.caption(
"Check all supported entities: https://llm-guard.com/input_scanners/anonymize/"
)
st_anon_hidden_names = st_tags(
label="Hidden names to be anonymized",
text="Type and press enter",
value=[],
suggestions=[],
maxtags=30,
key="anon_hidden_names",
)
st.caption("These names will be hidden e.g. [REDACTED_CUSTOM1].")
st_anon_allowed_names = st_tags(
label="Allowed names to ignore",
text="Type and press enter",
value=[],
suggestions=[],
maxtags=30,
key="anon_allowed_names",
)
st.caption("These names will be ignored even if flagged by the detector.")
st_anon_preamble = st.text_input(
"Preamble", value="Text to prepend to sanitized prompt: "
)
st_anon_use_faker = st.checkbox(
"Use Faker",
value=False,
help="Use Faker library to generate fake data",
key="anon_use_faker",
)
st_anon_threshold = st.slider(
label="Threshold",
value=0.0,
min_value=0.0,
max_value=1.0,
step=0.1,
key="anon_threshold",
)
settings["Anonymize"] = {
"entity_types": st_anon_entity_types,
"hidden_names": st_anon_hidden_names,
"allowed_names": st_anon_allowed_names,
"preamble": st_anon_preamble,
"use_faker": st_anon_use_faker,
"threshold": st_anon_threshold,
}
if "BanCode" in st_enabled_scanners:
st_bc_expander = st.sidebar.expander(
"Ban Code",
expanded=False,
)
with st_bc_expander:
st_bc_threshold = st.slider(
label="Threshold",
value=0.95,
min_value=0.0,
max_value=1.0,
step=0.05,
key="ban_code_threshold",
)
settings["BanCode"] = {
"threshold": st_bc_threshold,
}
if "BanCompetitors" in st_enabled_scanners:
st_bc_expander = st.sidebar.expander(
"Ban Competitors",
expanded=False,
)
with st_bc_expander:
st_bc_competitors = st_tags(
label="List of competitors",
text="Type and press enter",
value=["openai", "anthropic", "deepmind", "google"],
suggestions=[],
maxtags=30,
key="bc_competitors",
)
st_bc_threshold = st.slider(
label="Threshold",
value=0.5,
min_value=0.0,
max_value=1.0,
step=0.05,
key="ban_competitors_threshold",
)
settings["BanCompetitors"] = {
"competitors": st_bc_competitors,
"threshold": st_bc_threshold,
}
if "BanSubstrings" in st_enabled_scanners:
st_bs_expander = st.sidebar.expander(
"Ban Substrings",
expanded=False,
)
with st_bs_expander:
st_bs_substrings = st.text_area(
"Enter substrings to ban (one per line)",
value="test\nhello\nworld",
height=200,
).split("\n")
st_bs_match_type = st.selectbox(
"Match type", ["str", "word"], index=0, key="bs_match_type"
)
st_bs_case_sensitive = st.checkbox(
"Case sensitive", value=False, key="bs_case_sensitive"
)
st_bs_redact = st.checkbox("Redact", value=False, key="bs_redact")
st_bs_contains_all = st.checkbox("Contains all", value=False, key="bs_contains_all")
settings["BanSubstrings"] = {
"substrings": st_bs_substrings,
"match_type": st_bs_match_type,
"case_sensitive": st_bs_case_sensitive,
"redact": st_bs_redact,
"contains_all": st_bs_contains_all,
}
if "BanTopics" in st_enabled_scanners:
st_bt_expander = st.sidebar.expander(
"Ban Topics",
expanded=False,
)
with st_bt_expander:
st_bt_topics = st_tags(
label="List of topics",
text="Type and press enter",
value=["violence"],
suggestions=[],
maxtags=30,
key="bt_topics",
)
st_bt_threshold = st.slider(
label="Threshold",
value=0.6,
min_value=0.0,
max_value=1.0,
step=0.05,
key="ban_topics_threshold",
)
settings["BanTopics"] = {
"topics": st_bt_topics,
"threshold": st_bt_threshold,
}
if "Code" in st_enabled_scanners:
st_cd_expander = st.sidebar.expander(
"Code",
expanded=False,
)
with st_cd_expander:
st_cd_languages = st.multiselect(
"Programming languages",
SUPPORTED_CODE_LANGUAGES,
default=["Python"],
)
st_cd_is_blocked = st.checkbox("Is blocked", value=False, key="code_is_blocked")
settings["Code"] = {
"languages": st_cd_languages,
"is_blocked": st_cd_is_blocked,
}
if "Gibberish" in st_enabled_scanners:
st_gib_expander = st.sidebar.expander(
"Gibberish",
expanded=False,
)
with st_gib_expander:
st_gib_threshold = st.slider(
label="Threshold",
value=0.7,
min_value=0.0,
max_value=1.0,
step=0.1,
key="gibberish_threshold",
)
st_gib_match_type = st.selectbox(
"Match type",
[e.value for e in GibberishMatchType],
index=1,
key="gibberish_match_type",
)
settings["Gibberish"] = {
"threshold": st_gib_threshold,
"match_type": st_gib_match_type,
}
if "Language" in st_enabled_scanners:
st_lan_expander = st.sidebar.expander(
"Language",
expanded=False,
)
with st_lan_expander:
st_lan_valid_language = st.multiselect(
"Languages",
[
"ar",
"bg",
"de",
"el",
"en",
"es",
"fr",
"hi",
"it",
"ja",
"nl",
"pl",
"pt",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
],
default=["en"],
)
st_lan_match_type = st.selectbox(
"Match type",
[e.value for e in LanguageMatchType],
index=1,
key="language_match_type",
)
settings["Language"] = {
"valid_languages": st_lan_valid_language,
"match_type": st_lan_match_type,
}
if "PromptInjection" in st_enabled_scanners:
st_pi_expander = st.sidebar.expander(
"Prompt Injection",
expanded=False,
)
with st_pi_expander:
st_pi_threshold = st.slider(
label="Threshold",
value=0.75,
min_value=0.0,
max_value=1.0,
step=0.05,
key="prompt_injection_threshold",
)
st_pi_match_type = st.selectbox(
"Match type",
[e.value for e in PromptInjectionMatchType],
index=1,
key="prompt_injection_match_type",
)
settings["PromptInjection"] = {
"threshold": st_pi_threshold,
"match_type": st_pi_match_type,
}
if "Regex" in st_enabled_scanners:
st_regex_expander = st.sidebar.expander(
"Regex",
expanded=False,
)
with st_regex_expander:
st_regex_patterns = st.text_area(
"Enter patterns to ban (one per line)",
value="Bearer [A-Za-z0-9-._~+/]+",
height=200,
).split("\n")
st_regex_is_blocked = st.checkbox("Is blocked", value=True, key="regex_is_blocked")
st_regex_redact = st.checkbox(
"Redact",
value=False,
help="Replace the matched bad patterns with [REDACTED]",
key="regex_redact",
)
settings["Regex"] = {
"patterns": st_regex_patterns,
"is_blocked": st_regex_is_blocked,
"redact": st_regex_redact,
}
if "Secrets" in st_enabled_scanners:
st_sec_expander = st.sidebar.expander(
"Secrets",
expanded=False,
)
with st_sec_expander:
st_sec_redact_mode = st.selectbox("Redact mode", ["all", "partial", "hash"])
settings["Secrets"] = {
"redact_mode": st_sec_redact_mode,
}
if "Sentiment" in st_enabled_scanners:
st_sent_expander = st.sidebar.expander(
"Sentiment",
expanded=False,
)
with st_sent_expander:
st_sent_threshold = st.slider(
label="Threshold",
value=-0.5,
min_value=-1.0,
max_value=1.0,
step=0.1,
key="sentiment_threshold",
help="Negative values are negative sentiment, positive values are positive sentiment",
)
settings["Sentiment"] = {
"threshold": st_sent_threshold,
}
if "TokenLimit" in st_enabled_scanners:
st_tl_expander = st.sidebar.expander(
"Token Limit",
expanded=False,
)
with st_tl_expander:
st_tl_limit = st.number_input(
"Limit", value=4096, min_value=0, max_value=10000, step=10
)
st_tl_encoding_name = st.selectbox(
"Encoding name",
["cl100k_base", "p50k_base", "r50k_base"],
index=0,
help="Read more: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb",
)
settings["TokenLimit"] = {
"limit": st_tl_limit,
"encoding_name": st_tl_encoding_name,
}
if "Toxicity" in st_enabled_scanners:
st_tox_expander = st.sidebar.expander(
"Toxicity",
expanded=False,
)
with st_tox_expander:
st_tox_threshold = st.slider(
label="Threshold",
value=0.75,
min_value=0.0,
max_value=1.0,
step=0.05,
key="toxicity_threshold",
)
st_tox_match_type = st.selectbox(
"Match type",
[e.value for e in ToxicityMatchType],
index=1,
key="toxicity_match_type",
)
settings["Toxicity"] = {
"threshold": st_tox_threshold,
"match_type": st_tox_match_type,
}
return st_enabled_scanners, settings
def get_scanner(scanner_name: str, vault: Vault, settings: Dict):
logger.debug(f"Initializing {scanner_name} scanner")
if scanner_name == "Anonymize":
settings["vault"] = vault
if scanner_name in [
"Anonymize",
"BanCode",
"BanTopics",
"Code",
"Gibberish",
"PromptInjection",
"Toxicity",
]:
settings["use_onnx"] = True
return get_scanner_by_name(scanner_name, settings)
def scan(
vault: Vault, enabled_scanners: List[str], settings: Dict, text: str, fail_fast: bool = False
) -> (str, List[Dict[str, any]]):
sanitized_prompt = text
results = []
status_text = "Scanning prompt..."
if fail_fast:
status_text = "Scanning prompt (fail fast mode)..."
with st.status(status_text, expanded=True) as status:
for scanner_name in enabled_scanners:
st.write(f"{scanner_name} scanner...")
scanner = get_scanner(scanner_name, vault, settings[scanner_name])
start_time = time.monotonic()
sanitized_prompt, is_valid, risk_score = scanner.scan(sanitized_prompt)
end_time = time.monotonic()
results.append(
{
"scanner": scanner_name,
"is_valid": is_valid,
"risk_score": risk_score,
"took_sec": round(timedelta(seconds=end_time - start_time).total_seconds(), 2),
}
)
if fail_fast and not is_valid:
break
status.update(label="Scanning complete", state="complete", expanded=False)
return sanitized_prompt, results
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