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Merge branch 'main' into feat/new-login 

* main:
  chore: refurbish Python code by applying refurb linter rules (#8296)
  chore: apply ruff E501 line-too-long linter rule (#8275)
  fix(workflow): missing content in the answer node stream output during iterations (#8292)
  chore: cleanup ruff flake8-simplify linter rules (#8286)
  fix: markdown paragraph margin (#8289)
This commit is contained in:
Joe 2024-09-12 17:05:41 +08:00
parent 753f802d97 40fb4d16ef
commit 619eeec9b1
191 changed files with 801 additions and 748 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
api/commands.py
View File

@ -411,7 +411,8 @@ def migrate_knowledge_vector_database():
try:
click.echo(
click.style(
f"Start to created vector index with {len(documents)} documents of {segments_count} segments for dataset {dataset.id}.",
f"Start to created vector index with {len(documents)} documents of {segments_count}"
f" segments for dataset {dataset.id}.",
fg="green",
)
)

24
api/controllers/console/admin.py
View File

@ -60,23 +60,15 @@ class InsertExploreAppListApi(Resource):
site = app.site
if not site:
desc = args["desc"] if args["desc"] else ""
copy_right = args["copyright"] if args["copyright"] else ""
privacy_policy = args["privacy_policy"] if args["privacy_policy"] else ""
custom_disclaimer = args["custom_disclaimer"] if args["custom_disclaimer"] else ""
desc = args["desc"] or ""
copy_right = args["copyright"] or ""
privacy_policy = args["privacy_policy"] or ""
custom_disclaimer = args["custom_disclaimer"] or ""
else:
desc = site.description if site.description else args["desc"] if args["desc"] else ""
copy_right = site.copyright if site.copyright else args["copyright"] if args["copyright"] else ""
privacy_policy = (
site.privacy_policy if site.privacy_policy else args["privacy_policy"] if args["privacy_policy"] else ""
)
custom_disclaimer = (
site.custom_disclaimer
if site.custom_disclaimer
else args["custom_disclaimer"]
if args["custom_disclaimer"]
else ""
)
desc = site.description or args["desc"] or ""
copy_right = site.copyright or args["copyright"] or ""
privacy_policy = site.privacy_policy or args["privacy_policy"] or ""
custom_disclaimer = site.custom_disclaimer or args["custom_disclaimer"] or ""
recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == args["app_id"]).first()

8
api/controllers/console/app/audio.py
View File

@ -99,14 +99,10 @@ class ChatMessageTextApi(Resource):
and app_model.workflow.features_dict
):
text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
voice = args.get("voice") or text_to_speech.get("voice")
else:
try:
voice = (
args.get("voice")
if args.get("voice")
else app_model.app_model_config.text_to_speech_dict.get("voice")
)
voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
except Exception:
voice = None
response = AudioService.transcript_tts(app_model=app_model, text=text, message_id=message_id, voice=voice)

170
api/controllers/console/app/statistic.py
View File

@ -29,10 +29,13 @@ class DailyMessageStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(*) AS message_count
FROM messages where app_id = :app_id
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(*) AS message_count
FROM
messages
WHERE
app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -45,7 +48,7 @@ class DailyMessageStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -55,10 +58,10 @@ class DailyMessageStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -83,10 +86,13 @@ class DailyConversationStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct messages.conversation_id) AS conversation_count
FROM messages where app_id = :app_id
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(DISTINCT messages.conversation_id) AS conversation_count
FROM
messages
WHERE
app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -99,7 +105,7 @@ class DailyConversationStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -109,10 +115,10 @@ class DailyConversationStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -137,10 +143,13 @@ class DailyTerminalsStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct messages.from_end_user_id) AS terminal_count
FROM messages where app_id = :app_id
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(DISTINCT messages.from_end_user_id) AS terminal_count
FROM
messages
WHERE
app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -153,7 +162,7 @@ class DailyTerminalsStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -163,10 +172,10 @@ class DailyTerminalsStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -191,12 +200,14 @@ class DailyTokenCostStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
(sum(messages.message_tokens) + sum(messages.answer_tokens)) as token_count,
sum(total_price) as total_price
FROM messages where app_id = :app_id
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
(SUM(messages.message_tokens) + SUM(messages.answer_tokens)) AS token_count,
SUM(total_price) AS total_price
FROM
messages
WHERE
app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -209,7 +220,7 @@ class DailyTokenCostStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -219,10 +230,10 @@ class DailyTokenCostStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -249,12 +260,22 @@ class AverageSessionInteractionStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """SELECT date(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
AVG(subquery.message_count) AS interactions
FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
FROM conversations c
JOIN messages m ON c.id = m.conversation_id
WHERE c.override_model_configs IS NULL AND c.app_id = :app_id"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
AVG(subquery.message_count) AS interactions
FROM
(
SELECT
m.conversation_id,
COUNT(m.id) AS message_count
FROM
conversations c
JOIN
messages m
ON c.id = m.conversation_id
WHERE
c.override_model_configs IS NULL
AND c.app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -267,7 +288,7 @@ FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and c.created_at >= :start"
sql_query += " AND c.created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -277,14 +298,19 @@ FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and c.created_at < :end"
sql_query += " AND c.created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += """
GROUP BY m.conversation_id) subquery
LEFT JOIN conversations c on c.id=subquery.conversation_id
GROUP BY date
ORDER BY date"""
GROUP BY m.conversation_id
) subquery
LEFT JOIN
conversations c
ON c.id = subquery.conversation_id
GROUP BY
date
ORDER BY
date"""
response_data = []
@ -311,13 +337,17 @@ class UserSatisfactionRateStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', m.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(m.id) as message_count, COUNT(mf.id) as feedback_count
FROM messages m
LEFT JOIN message_feedbacks mf on mf.message_id=m.id and mf.rating='like'
WHERE m.app_id = :app_id
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', m.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(m.id) AS message_count,
COUNT(mf.id) AS feedback_count
FROM
messages m
LEFT JOIN
message_feedbacks mf
ON mf.message_id=m.id AND mf.rating='like'
WHERE
m.app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -330,7 +360,7 @@ class UserSatisfactionRateStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and m.created_at >= :start"
sql_query += " AND m.created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -340,10 +370,10 @@ class UserSatisfactionRateStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and m.created_at < :end"
sql_query += " AND m.created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -373,12 +403,13 @@ class AverageResponseTimeStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
AVG(provider_response_latency) as latency
FROM messages
WHERE app_id = :app_id
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
AVG(provider_response_latency) AS latency
FROM
messages
WHERE
app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -391,7 +422,7 @@ class AverageResponseTimeStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -401,10 +432,10 @@ class AverageResponseTimeStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -429,13 +460,16 @@ class TokensPerSecondStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
CASE
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
CASE
WHEN SUM(provider_response_latency) = 0 THEN 0
ELSE (SUM(answer_tokens) / SUM(provider_response_latency))
END as tokens_per_second
FROM messages
WHERE app_id = :app_id"""
FROM
messages
WHERE
app_id = :app_id"""
arg_dict = {"tz": account.timezone, "app_id": app_model.id}
timezone = pytz.timezone(account.timezone)
@ -448,7 +482,7 @@ WHERE app_id = :app_id"""
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -458,10 +492,10 @@ WHERE app_id = :app_id"""
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []

102
api/controllers/console/app/workflow_statistic.py
View File

@ -30,12 +30,14 @@ class WorkflowDailyRunsStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(id) AS runs
FROM workflow_runs
WHERE app_id = :app_id
AND triggered_from = :triggered_from
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(id) AS runs
FROM
workflow_runs
WHERE
app_id = :app_id
AND triggered_from = :triggered_from"""
arg_dict = {
"tz": account.timezone,
"app_id": app_model.id,
@ -52,7 +54,7 @@ class WorkflowDailyRunsStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -62,10 +64,10 @@ class WorkflowDailyRunsStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -90,12 +92,14 @@ class WorkflowDailyTerminalsStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct workflow_runs.created_by) AS terminal_count
FROM workflow_runs
WHERE app_id = :app_id
AND triggered_from = :triggered_from
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
COUNT(DISTINCT workflow_runs.created_by) AS terminal_count
FROM
workflow_runs
WHERE
app_id = :app_id
AND triggered_from = :triggered_from"""
arg_dict = {
"tz": account.timezone,
"app_id": app_model.id,
@ -112,7 +116,7 @@ class WorkflowDailyTerminalsStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -122,10 +126,10 @@ class WorkflowDailyTerminalsStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -150,14 +154,14 @@ class WorkflowDailyTokenCostStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT
date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
SUM(workflow_runs.total_tokens) as token_count
FROM workflow_runs
WHERE app_id = :app_id
AND triggered_from = :triggered_from
"""
sql_query = """SELECT
DATE(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
SUM(workflow_runs.total_tokens) AS token_count
FROM
workflow_runs
WHERE
app_id = :app_id
AND triggered_from = :triggered_from"""
arg_dict = {
"tz": account.timezone,
"app_id": app_model.id,
@ -174,7 +178,7 @@ class WorkflowDailyTokenCostStatistic(Resource):
start_datetime_timezone = timezone.localize(start_datetime)
start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at >= :start"
sql_query += " AND created_at >= :start"
arg_dict["start"] = start_datetime_utc
if args["end"]:
@ -184,10 +188,10 @@ class WorkflowDailyTokenCostStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query += " and created_at < :end"
sql_query += " AND created_at < :end"
arg_dict["end"] = end_datetime_utc
sql_query += " GROUP BY date order by date"
sql_query += " GROUP BY date ORDER BY date"
response_data = []
@ -217,23 +221,27 @@ class WorkflowAverageAppInteractionStatistic(Resource):
parser.add_argument("end", type=DatetimeString("%Y-%m-%d %H:%M"), location="args")
args = parser.parse_args()
sql_query = """
SELECT
AVG(sub.interactions) as interactions,
sub.date
FROM
(SELECT
date(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
c.created_by,
COUNT(c.id) AS interactions
FROM workflow_runs c
WHERE c.app_id = :app_id
AND c.triggered_from = :triggered_from
{{start}}
{{end}}
GROUP BY date, c.created_by) sub
GROUP BY sub.date
"""
sql_query = """SELECT
AVG(sub.interactions) AS interactions,
sub.date
FROM
(
SELECT
DATE(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date,
c.created_by,
COUNT(c.id) AS interactions
FROM
workflow_runs c
WHERE
c.app_id = :app_id
AND c.triggered_from = :triggered_from
{{start}}
{{end}}
GROUP BY
date, c.created_by
) sub
GROUP BY
sub.date"""
arg_dict = {
"tz": account.timezone,
"app_id": app_model.id,
@ -262,7 +270,7 @@ class WorkflowAverageAppInteractionStatistic(Resource):
end_datetime_timezone = timezone.localize(end_datetime)
end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
sql_query = sql_query.replace("{{end}}", " and c.created_at < :end")
sql_query = sql_query.replace("{{end}}", " AND c.created_at < :end")
arg_dict["end"] = end_datetime_utc
else:
sql_query = sql_query.replace("{{end}}", "")

3
api/controllers/console/auth/login.py
View File

@ -150,7 +150,8 @@ class EmailCodeLoginApi(Resource):
)
except WorkSpaceNotAllowedCreateError:
return redirect(
f"{dify_config.CONSOLE_WEB_URL}/signin?message=Workspace not found, please contact system admin to invite you to join in a workspace."
f"{dify_config.CONSOLE_WEB_URL}/signin"
"?message=Workspace not found, please contact system admin to invite you to join in a workspace."
)
token = AccountService.login(account, ip_address=get_remote_ip(request))

8
api/controllers/console/auth/oauth.py
View File

@ -96,7 +96,8 @@ class OAuthCallback(Resource):
return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin?message=WorkspaceNotFound")
except WorkSpaceNotAllowedCreateError:
return redirect(
f"{dify_config.CONSOLE_WEB_URL}/signin?message=Workspace not found, please contact system admin to invite you to join in a workspace."
f"{dify_config.CONSOLE_WEB_URL}/signin"
"?message=Workspace not found, please contact system admin to invite you to join in a workspace."
)
# Check account status
@ -114,7 +115,8 @@ class OAuthCallback(Resource):
return redirect(f"{dify_config.CONSOLE_WEB_URL}/signin?message=WorkspaceNotFound")
except WorkSpaceNotAllowedCreateError:
return redirect(
f"{dify_config.CONSOLE_WEB_URL}/signin?message=Workspace not found, please contact system admin to invite you to join in a workspace."
f"{dify_config.CONSOLE_WEB_URL}/signin"
"?message=Workspace not found, please contact system admin to invite you to join in a workspace."
)
token = AccountService.login(account, ip_address=get_remote_ip(request))
@ -149,7 +151,7 @@ def _generate_account(provider: str, user_info: OAuthUserInfo):
if not account:
if not dify_config.ALLOW_REGISTER:
raise AccountNotFoundError()
account_name = user_info.name if user_info.name else "Dify"
account_name = user_info.name or "Dify"
account = RegisterService.register(
email=user_info.email, name=account_name, password=None, open_id=user_info.id, provider=provider
)

7
api/controllers/console/datasets/datasets.py
View File

@ -550,12 +550,7 @@ class DatasetApiBaseUrlApi(Resource):
@login_required
@account_initialization_required
def get(self):
return {
"api_base_url": (
dify_config.SERVICE_API_URL if dify_config.SERVICE_API_URL else request.host_url.rstrip("/")
)
+ "/v1"
}
return {"api_base_url": (dify_config.SERVICE_API_URL or request.host_url.rstrip("/")) + "/v1"}
class DatasetRetrievalSettingApi(Resource):

8
api/controllers/console/explore/audio.py
View File

@ -86,14 +86,10 @@ class ChatTextApi(InstalledAppResource):
and app_model.workflow.features_dict
):
text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
voice = args.get("voice") or text_to_speech.get("voice")
else:
try:
voice = (
args.get("voice")
if args.get("voice")
else app_model.app_model_config.text_to_speech_dict.get("voice")
)
voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
except Exception:
voice = None
response = AudioService.transcript_tts(app_model=app_model, message_id=message_id, voice=voice, text=text)

2
api/controllers/console/workspace/tool_providers.py
View File

@ -327,7 +327,7 @@ class ToolApiProviderPreviousTestApi(Resource):
return ApiToolManageService.test_api_tool_preview(
current_user.current_tenant_id,
args["provider_name"] if args["provider_name"] else "",
args["provider_name"] or "",
args["tool_name"],
args["credentials"],
args["parameters"],

3
api/controllers/console/wraps.py
View File

@ -64,7 +64,8 @@ def cloud_edition_billing_resource_check(resource: str):
elif resource == "vector_space" and 0 < vector_space.limit <= vector_space.size:
abort(403, "The capacity of the vector space has reached the limit of your subscription.")
elif resource == "documents" and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
# The api of file upload is used in the multiple places, so we need to check the source of the request from datasets
# The api of file upload is used in the multiple places,
# so we need to check the source of the request from datasets
source = request.args.get("source")
if source == "datasets":
abort(403, "The number of documents has reached the limit of your subscription.")

8
api/controllers/service_api/app/audio.py
View File

@ -84,14 +84,10 @@ class TextApi(Resource):
and app_model.workflow.features_dict
):
text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
voice = args.get("voice") or text_to_speech.get("voice")
else:
try:
voice = (
args.get("voice")
if args.get("voice")
else app_model.app_model_config.text_to_speech_dict.get("voice")
)
voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
except Exception:
voice = None
response = AudioService.transcript_tts(

8
api/controllers/web/audio.py
View File

@ -83,14 +83,10 @@ class TextApi(WebApiResource):
and app_model.workflow.features_dict
):
text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
voice = args.get("voice") if args.get("voice") else text_to_speech.get("voice")
voice = args.get("voice") or text_to_speech.get("voice")
else:
try:
voice = (
args.get("voice")
if args.get("voice")
else app_model.app_model_config.text_to_speech_dict.get("voice")
)
voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
except Exception:
voice = None

3
api/controllers/web/wraps.py
View File

@ -80,7 +80,8 @@ def _validate_web_sso_token(decoded, system_features, app_code):
if not source or source != "sso":
raise WebSSOAuthRequiredError()
# Check if SSO is not enforced for web, and if the token source is SSO, raise an error and redirect to normal passport login
# Check if SSO is not enforced for web, and if the token source is SSO,
# raise an error and redirect to normal passport login
if not system_features.sso_enforced_for_web or not app_web_sso_enabled:
source = decoded.get("token_source")
if source and source == "sso":

2
api/core/agent/cot_agent_runner.py
View File

@ -256,7 +256,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(content=final_answer),
usage=llm_usage["usage"] if llm_usage["usage"] else LLMUsage.empty_usage(),
usage=llm_usage["usage"] or LLMUsage.empty_usage(),
system_fingerprint="",
)
),

2
api/core/agent/fc_agent_runner.py
View File

@ -298,7 +298,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
model=model_instance.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(content=final_answer),
usage=llm_usage["usage"] if llm_usage["usage"] else LLMUsage.empty_usage(),
usage=llm_usage["usage"] or LLMUsage.empty_usage(),
system_fingerprint="",
)
),

6
api/core/agent/prompt/template.py
View File

@ -41,7 +41,8 @@ Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use
{{historic_messages}}
Question: {{query}}
{{agent_scratchpad}}
Thought:"""
Thought:""" # noqa: E501
ENGLISH_REACT_COMPLETION_AGENT_SCRATCHPAD_TEMPLATES = """Observation: {{observation}}
Thought:"""
@ -86,7 +87,8 @@ Action:
```
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
"""
""" # noqa: E501
ENGLISH_REACT_CHAT_AGENT_SCRATCHPAD_TEMPLATES = ""

8
api/core/app/apps/base_app_runner.py
View File

@ -161,7 +161,7 @@ class AppRunner:
app_mode=AppMode.value_of(app_record.mode),
prompt_template_entity=prompt_template_entity,
inputs=inputs,
query=query if query else "",
query=query or "",
files=files,
context=context,
memory=memory,
@ -189,7 +189,7 @@ class AppRunner:
prompt_messages = prompt_transform.get_prompt(
prompt_template=prompt_template,
inputs=inputs,
query=query if query else "",
query=query or "",
files=files,
context=context,
memory_config=memory_config,
@ -238,7 +238,7 @@ class AppRunner:
model=app_generate_entity.model_conf.model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(content=text),
usage=usage if usage else LLMUsage.empty_usage(),
usage=usage or LLMUsage.empty_usage(),
),
),
PublishFrom.APPLICATION_MANAGER,
@ -351,7 +351,7 @@ class AppRunner:
tenant_id=tenant_id,
app_config=app_generate_entity.app_config,
inputs=inputs,
query=query if query else "",
query=query or "",
message_id=message_id,
trace_manager=app_generate_entity.trace_manager,
)

19
api/core/app/apps/workflow_logging_callback.py
View File

@ -84,10 +84,12 @@ class WorkflowLoggingCallback(WorkflowCallback):
if route_node_state.node_run_result:
node_run_result = route_node_state.node_run_result
self.print_text(
f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}", color="green"
f"Inputs: " f"{jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
color="green",
)
self.print_text(
f"Process Data: {jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
f"Process Data: "
f"{jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
color="green",
)
self.print_text(
@ -114,14 +116,17 @@ class WorkflowLoggingCallback(WorkflowCallback):
node_run_result = route_node_state.node_run_result
self.print_text(f"Error: {node_run_result.error}", color="red")
self.print_text(
f"Inputs: {jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}", color="red"
)
self.print_text(
f"Process Data: {jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
f"Inputs: " f"" f"{jsonable_encoder(node_run_result.inputs) if node_run_result.inputs else ''}",
color="red",
)
self.print_text(
f"Outputs: {jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}", color="red"
f"Process Data: "
f"{jsonable_encoder(node_run_result.process_data) if node_run_result.process_data else ''}",
color="red",
)
self.print_text(
f"Outputs: " f"{jsonable_encoder(node_run_result.outputs) if node_run_result.outputs else ''}",
color="red",
)
def on_node_text_chunk(self, event: NodeRunStreamChunkEvent) -> None:

2
api/core/app/task_pipeline/based_generate_task_pipeline.py
View File

@ -65,7 +65,7 @@ class BasedGenerateTaskPipeline:
if isinstance(e, InvokeAuthorizationError):
err = InvokeAuthorizationError("Incorrect API key provided")
elif isinstance(e, InvokeError) or isinstance(e, ValueError):
elif isinstance(e, InvokeError | ValueError):
err = e
else:
err = Exception(e.description if getattr(e, "description", None) is not None else str(e))

4
api/core/extension/extensible.py
View File

@ -3,6 +3,7 @@ import importlib.util
import json
import logging
import os
from pathlib import Path
from typing import Any, Optional
from pydantic import BaseModel
@ -63,8 +64,7 @@ class Extensible:
builtin_file_path = os.path.join(subdir_path, "__builtin__")
if os.path.exists(builtin_file_path):
with open(builtin_file_path, encoding="utf-8") as f:
position = int(f.read().strip())
position = int(Path(builtin_file_path).read_text(encoding="utf-8").strip())
position_map[extension_name] = position
if (extension_name + ".py") not in file_names:

3
api/core/file/message_file_parser.py
View File

@ -188,7 +188,8 @@ class MessageFileParser:
def _check_image_remote_url(self, url):
try:
headers = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
" Chrome/91.0.4472.124 Safari/537.36"
}
def is_s3_presigned_url(url):

3
api/core/helper/code_executor/code_executor.py
View File

@ -89,7 +89,8 @@ class CodeExecutor:
raise CodeExecutionError("Code execution service is unavailable")
elif response.status_code != 200:
raise Exception(
f"Failed to execute code, got status code {response.status_code}, please check if the sandbox service is running"
f"Failed to execute code, got status code {response.status_code},"
f" please check if the sandbox service is running"
)
except CodeExecutionError as e:
raise e

5
api/core/helper/tool_parameter_cache.py
View File

@ -14,7 +14,10 @@ class ToolParameterCache:
def __init__(
self, tenant_id: str, provider: str, tool_name: str, cache_type: ToolParameterCacheType, identity_id: str
):
self.cache_key = f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}:identity_id:{identity_id}"
self.cache_key = (
f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}"
f":identity_id:{identity_id}"
)
def get(self) -> Optional[dict]:
"""

19
api/core/llm_generator/prompts.py
View File

@ -59,24 +59,27 @@ User Input: yo, 你今天咋样？
}
User Input:
"""
""" # noqa: E501
SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
"Please help me predict the three most likely questions that human would ask, "
"and keeping each question under 20 characters.\n"
"MAKE SURE your output is the SAME language as the Assistant's latest response(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
"MAKE SURE your output is the SAME language as the Assistant's latest response"
"(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
"The output must be an array in JSON format following the specified schema:\n"
'["question1","question2","question3"]\n'
)
GENERATOR_QA_PROMPT = (
"<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge in the long text. Please think step by step."
"<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge"
" in the long text. Please think step by step."
"Step 1: Understand and summarize the main content of this text.\n"
"Step 2: What key information or concepts are mentioned in this text?\n"
"Step 3: Decompose or combine multiple pieces of information and concepts.\n"
"Step 4: Generate questions and answers based on these key information and concepts.\n"
"<Constraints> The questions should be clear and detailed, and the answers should be detailed and complete. "
"You must answer in {language}, in a style that is clear and detailed in {language}. No language other than {language} should be used. \n"
"You must answer in {language}, in a style that is clear and detailed in {language}."
" No language other than {language} should be used. \n"
"<Format> Use the following format: Q1:\nA1:\nQ2:\nA2:...\n"
"<QA Pairs>"
)
@ -94,7 +97,7 @@ Based on task description, please create a well-structured prompt template that
- Use the same language as task description.
- Output in ``` xml ``` and start with <instruction>
Please generate the full prompt template with at least 300 words and output only the prompt template.
"""
""" # noqa: E501
RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """
Here is a task description for which I would like you to create a high-quality prompt template for:
@ -109,7 +112,7 @@ Based on task description, please create a well-structured prompt template that
- Use the same language as task description.
- Output in ``` xml ``` and start with <instruction>
Please generate the full prompt template and output only the prompt template.
"""
""" # noqa: E501
RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE = """
I need to extract the following information from the input text. The <information to be extracted> tag specifies the 'type', 'description' and 'required' of the information to be extracted.
@ -134,7 +137,7 @@ Inside <text></text> XML tags, there is a text that I should extract parameters
### Answer
I should always output a valid list. Output nothing other than the list of variable_name. Output an empty list if there is no variable name in input text.
"""
""" # noqa: E501
RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE = """
<instruction>
@ -150,4 +153,4 @@ Welcome! I'm here to assist you with any questions or issues you might have with
Here is the task description: {{INPUT_TEXT}}
You just need to generate the output
"""
""" # noqa: E501

2
api/core/memory/token_buffer_memory.py
View File

@ -39,7 +39,7 @@ class TokenBufferMemory:
)
if message_limit and message_limit > 0:
message_limit = message_limit if message_limit <= 500 else 500
message_limit = min(message_limit, 500)
else:
message_limit = 500

16
api/core/model_runtime/entities/defaults.py
View File

@ -8,8 +8,11 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
},
"type": "float",
"help": {
"en_US": "Controls randomness. Lower temperature results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. Higher temperature results in more random completions.",
"zh_Hans": "温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零，模型将变得确定性和重复性。较高的温度会导致更多的随机完成。",
"en_US": "Controls randomness. Lower temperature results in less random completions."
" As the temperature approaches zero, the model will become deterministic and repetitive."
" Higher temperature results in more random completions.",
"zh_Hans": "温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零，模型将变得确定性和重复性。"
"较高的温度会导致更多的随机完成。",
},
"required": False,
"default": 0.0,
@ -24,7 +27,8 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
},
"type": "float",
"help": {
"en_US": "Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered.",
"en_US": "Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options"
" are considered.",
"zh_Hans": "通过核心采样控制多样性：0.5表示考虑了一半的所有可能性加权选项。",
},
"required": False,
@ -88,7 +92,8 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
},
"type": "int",
"help": {
"en_US": "Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.",
"en_US": "Specifies the upper limit on the length of generated results."
" If the generated results are truncated, you can increase this parameter.",
"zh_Hans": "指定生成结果长度的上限。如果生成结果截断，可以调大该参数。",
},
"required": False,
@ -104,7 +109,8 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
},
"type": "string",
"help": {
"en_US": "Set a response format, ensure the output from llm is a valid code block as possible, such as JSON, XML, etc.",
"en_US": "Set a response format, ensure the output from llm is a valid code block as possible,"
" such as JSON, XML, etc.",
"zh_Hans": "设置一个返回格式，确保llm的输出尽可能是有效的代码块，如JSON、XML等",
},
"required": False,

4
api/core/model_runtime/model_providers/__base/ai_model.py
View File

@ -72,7 +72,9 @@ class AIModel(ABC):
if isinstance(error, tuple(model_errors)):
if invoke_error == InvokeAuthorizationError:
return invoke_error(
description=f"[{provider_name}] Incorrect model credentials provided, please check and try again. "
description=(
f"[{provider_name}] Incorrect model credentials provided, please check and try again."
)
)
return invoke_error(description=f"[{provider_name}] {invoke_error.description}, {str(error)}")

7
api/core/model_runtime/model_providers/__base/large_language_model.py
View File

@ -187,7 +187,7 @@ if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
"""
""" # noqa: E501
code_block = model_parameters.get("response_format", "")
if not code_block:
@ -449,7 +449,7 @@ if you are not sure about the structure.
model=real_model,
prompt_messages=prompt_messages,
message=prompt_message,
usage=usage if usage else LLMUsage.empty_usage(),
usage=usage or LLMUsage.empty_usage(),
system_fingerprint=system_fingerprint,
),
credentials=credentials,
@ -830,7 +830,8 @@ if you are not sure about the structure.
else:
if parameter_value != round(parameter_value, parameter_rule.precision):
raise ValueError(
f"Model Parameter {parameter_name} should be round to {parameter_rule.precision} decimal places."
f"Model Parameter {parameter_name} should be round to {parameter_rule.precision}"
f" decimal places."
)
# validate parameter value range

4
api/core/model_runtime/model_providers/anthropic/llm/llm.py
View File

@ -51,7 +51,7 @@ if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
"""
""" # noqa: E501
class AnthropicLargeLanguageModel(LargeLanguageModel):
@ -409,7 +409,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
),
)
elif isinstance(chunk, ContentBlockDeltaEvent):
chunk_text = chunk.delta.text if chunk.delta.text else ""
chunk_text = chunk.delta.text or ""
full_assistant_content += chunk_text
# transform assistant message to prompt message

2
api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py
View File

@ -213,7 +213,7 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
model=real_model,
prompt_messages=prompt_messages,
message=prompt_message,
usage=usage if usage else LLMUsage.empty_usage(),
usage=usage or LLMUsage.empty_usage(),
system_fingerprint=system_fingerprint,
),
credentials=credentials,

69
api/core/model_runtime/model_providers/azure_openai/_constant.py
View File

@ -16,6 +16,15 @@ from core.model_runtime.entities.model_entities import (
AZURE_OPENAI_API_VERSION = "2024-02-15-preview"
AZURE_DEFAULT_PARAM_SEED_HELP = I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，"
"您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically,"
" such that repeated requests with the same seed and parameters should return the same result."
" Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter"
" to monitor changes in the backend.",
)
def _get_max_tokens(default: int, min_val: int, max_val: int) -> ParameterRule:
rule = ParameterRule(
@ -229,10 +238,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -297,10 +303,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -365,10 +368,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -433,10 +433,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -502,10 +499,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -571,10 +565,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -650,10 +641,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -719,10 +707,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -788,10 +773,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -867,10 +849,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -936,10 +915,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,
@ -1000,10 +976,7 @@ LLM_BASE_MODELS = [
name="seed",
label=I18nObject(zh_Hans="种子", en_US="Seed"),
type="int",
help=I18nObject(
zh_Hans="如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。",
en_US="If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.",
),
help=AZURE_DEFAULT_PARAM_SEED_HELP,
required=False,
precision=2,
min=0,

10
api/core/model_runtime/model_providers/azure_openai/llm/llm.py
View File

@ -225,7 +225,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
continue
# transform assistant message to prompt message
text = delta.text if delta.text else ""
text = delta.text or ""
assistant_prompt_message = AssistantPromptMessage(content=text)
full_text += text
@ -400,15 +400,13 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
continue
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=tool_calls
)
assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
full_assistant_content += delta.delta.content if delta.delta.content else ""
full_assistant_content += delta.delta.content or ""
real_model = chunk.model
system_fingerprint = chunk.system_fingerprint
completion += delta.delta.content if delta.delta.content else ""
completion += delta.delta.content or ""
yield LLMResultChunk(
model=real_model,

2
api/core/model_runtime/model_providers/azure_openai/tts/tts.py
View File

@ -84,7 +84,7 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
)
for i in range(len(sentences))
]
for index, future in enumerate(futures):
for future in futures:
yield from future.result().__enter__().iter_bytes(1024)
else:

3
api/core/model_runtime/model_providers/baichuan/llm/baichuan_tokenizer.py
View File

@ -15,6 +15,7 @@ class BaichuanTokenizer:
@classmethod
def _get_num_tokens(cls, text: str) -> int:
# tokens = number of Chinese characters + number of English words * 1.3 (for estimation only, subject to actual return)
# tokens = number of Chinese characters + number of English words * 1.3
# (for estimation only, subject to actual return)
# https://platform.baichuan-ai.com/docs/text-Embedding
return int(cls.count_chinese_characters(text) + cls.count_english_vocabularies(text) * 1.3)

4
api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py
View File

@ -45,7 +45,7 @@ class BaichuanModel:
parameters: dict[str, Any],
tools: Optional[list[PromptMessageTool]] = None,
) -> dict[str, Any]:
if model in self._model_mapping.keys():
if model in self._model_mapping:
# the LargeLanguageModel._code_block_mode_wrapper() method will remove the response_format of parameters.
# we need to rename it to res_format to get its value
if parameters.get("res_format") == "json_object":
@ -94,7 +94,7 @@ class BaichuanModel:
timeout: int,
tools: Optional[list[PromptMessageTool]] = None,
) -> Union[Iterator, dict]:
if model in self._model_mapping.keys():
if model in self._model_mapping:
api_base = "https://api.baichuan-ai.com/v1/chat/completions"
else:
raise BadRequestError(f"Unknown model: {model}")

14
api/core/model_runtime/model_providers/bedrock/llm/llm.py
View File

@ -52,7 +52,7 @@ if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
"""
""" # noqa: E501
class BedrockLargeLanguageModel(LargeLanguageModel):
@ -331,10 +331,10 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
elif "contentBlockDelta" in chunk:
delta = chunk["contentBlockDelta"]["delta"]
if "text" in delta:
chunk_text = delta["text"] if delta["text"] else ""
chunk_text = delta["text"] or ""
full_assistant_content += chunk_text
assistant_prompt_message = AssistantPromptMessage(
content=chunk_text if chunk_text else "",
content=chunk_text or "",
)
index = chunk["contentBlockDelta"]["contentBlockIndex"]
yield LLMResultChunk(
@ -541,7 +541,9 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
"max_tokens": 32,
}
elif "ai21" in model:
# ValidationException: Malformed input request: #/temperature: expected type: Number, found: Null#/maxTokens: expected type: Integer, found: Null#/topP: expected type: Number, found: Null, please reformat your input and try again.
# ValidationException: Malformed input request: #/temperature: expected type: Number,
# found: Null#/maxTokens: expected type: Integer, found: Null#/topP: expected type: Number, found: Null,
# please reformat your input and try again.
required_params = {
"temperature": 0.7,
"topP": 0.9,
@ -749,7 +751,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
elif model_prefix == "cohere":
output = response_body.get("generations")[0].get("text")
prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
completion_tokens = self.get_num_tokens(model, credentials, output if output else "")
completion_tokens = self.get_num_tokens(model, credentials, output or "")
else:
raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
@ -826,7 +828,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=content_delta if content_delta else "",
content=content_delta or "",
)
index += 1

4
api/core/model_runtime/model_providers/chatglm/llm/llm.py
View File

@ -302,11 +302,11 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
if delta.delta.function_call:
function_calls = [delta.delta.function_call]
assistant_message_tool_calls = self._extract_response_tool_calls(function_calls if function_calls else [])
assistant_message_tool_calls = self._extract_response_tool_calls(function_calls or [])
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_message_tool_calls
content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
)
if delta.finish_reason is not None:

6
api/core/model_runtime/model_providers/google/llm/llm.py
View File

@ -45,7 +45,7 @@ if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
"""
""" # noqa: E501
class GoogleLargeLanguageModel(LargeLanguageModel):
@ -337,9 +337,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
message_text = f"{human_prompt} {content}"
elif isinstance(message, AssistantPromptMessage):
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemPromptMessage):
message_text = f"{human_prompt} {content}"
elif isinstance(message, ToolPromptMessage):
elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
message_text = f"{human_prompt} {content}"
else:
raise ValueError(f"Got unknown type {message}")

3
api/core/model_runtime/model_providers/huggingface_tei/tei_helper.py
View File

@ -54,7 +54,8 @@ class TeiHelper:
url = str(URL(server_url) / "info")
# this method is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
# this method is surrounded by a lock, and default requests may hang forever,
# so we just set a Adapter with max_retries=3
session = Session()
session.mount("http://", HTTPAdapter(max_retries=3))
session.mount("https://", HTTPAdapter(max_retries=3))

3
api/core/model_runtime/model_providers/hunyuan/llm/llm.py
View File

@ -131,7 +131,8 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
{
"Role": message.role.value,
# fix set content = "" while tool_call request
# fix [hunyuan] None, [TencentCloudSDKException] code:InvalidParameter message:Messages Content and Contents not allowed empty at the same time.
# fix [hunyuan] None, [TencentCloudSDKException] code:InvalidParameter
# message:Messages Content and Contents not allowed empty at the same time.
"Content": " ", # message.content if (message.content is not None) else "",
"ToolCalls": dict_tool_calls,
}

6
api/core/model_runtime/model_providers/localai/llm/llm.py
View File

@ -511,7 +511,7 @@ class LocalAILanguageModel(LargeLanguageModel):
delta = chunk.choices[0]
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(content=delta.text if delta.text else "", tool_calls=[])
assistant_prompt_message = AssistantPromptMessage(content=delta.text or "", tool_calls=[])
if delta.finish_reason is not None:
# temp_assistant_prompt_message is used to calculate usage
@ -578,11 +578,11 @@ class LocalAILanguageModel(LargeLanguageModel):
if delta.delta.function_call:
function_calls = [delta.delta.function_call]
assistant_message_tool_calls = self._extract_response_tool_calls(function_calls if function_calls else [])
assistant_message_tool_calls = self._extract_response_tool_calls(function_calls or [])
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_message_tool_calls
content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
)
if delta.finish_reason is not None:

4
api/core/model_runtime/model_providers/minimax/llm/llm.py
View File

@ -211,7 +211,7 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
index=0,
message=AssistantPromptMessage(content=message.content, tool_calls=[]),
usage=usage,
finish_reason=message.stop_reason if message.stop_reason else None,
finish_reason=message.stop_reason or None,
),
)
elif message.function_call:
@ -244,7 +244,7 @@ class MinimaxLargeLanguageModel(LargeLanguageModel):
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(content=message.content, tool_calls=[]),
finish_reason=message.stop_reason if message.stop_reason else None,
finish_reason=message.stop_reason or None,
),
)

3
api/core/model_runtime/model_providers/nvidia/llm/llm.py
View File

@ -93,7 +93,8 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
def _validate_credentials(self, model: str, credentials: dict) -> None:
"""
Validate model credentials using requests to ensure compatibility with all providers following OpenAI's API standard.
Validate model credentials using requests to ensure compatibility with all providers following
OpenAI's API standard.
:param model: model name
:param credentials: model credentials

7
api/core/model_runtime/model_providers/oci/llm/llm.py
View File

@ -239,7 +239,8 @@ class OCILargeLanguageModel(LargeLanguageModel):
config_items = oci_config_content.split("/")
if len(config_items) != 5:
raise CredentialsValidateFailedError(
"oci_config_content should be base64.b64encode('user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))"
"oci_config_content should be base64.b64encode("
"'user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))"
)
oci_config["user"] = config_items[0]
oci_config["fingerprint"] = config_items[1]
@ -442,9 +443,7 @@ class OCILargeLanguageModel(LargeLanguageModel):
message_text = f"{human_prompt} {content}"
elif isinstance(message, AssistantPromptMessage):
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemPromptMessage):
message_text = f"{human_prompt} {content}"
elif isinstance(message, ToolPromptMessage):
elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
message_text = f"{human_prompt} {content}"
else:
raise ValueError(f"Got unknown type {message}")

3
api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py
View File

@ -146,7 +146,8 @@ class OCITextEmbeddingModel(TextEmbeddingModel):
config_items = oci_config_content.split("/")
if len(config_items) != 5:
raise CredentialsValidateFailedError(
"oci_config_content should be base64.b64encode('user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))"
"oci_config_content should be base64.b64encode("
"'user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))"
)
oci_config["user"] = config_items[0]
oci_config["fingerprint"] = config_items[1]

7
api/core/model_runtime/model_providers/ollama/llm/llm.py
View File

@ -639,9 +639,10 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
type=ParameterType.STRING,
help=I18nObject(
en_US="Sets how long the model is kept in memory after generating a response. "
"This must be a duration string with a unit (e.g., '10m' for 10 minutes or '24h' for 24 hours). "
"A negative number keeps the model loaded indefinitely, and '0' unloads the model immediately after generating a response. "
"Valid time units are 's','m','h'. (Default: 5m)"
"This must be a duration string with a unit (e.g., '10m' for 10 minutes or '24h' for 24 hours)."
" A negative number keeps the model loaded indefinitely, and '0' unloads the model"
" immediately after generating a response."
" Valid time units are 's','m','h'. (Default: 5m)"
),
),
ParameterRule(

2
api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py
View File

@ -65,7 +65,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
inputs = []
used_tokens = 0
for i, text in enumerate(texts):
for text in texts:
# Here token count is only an approximation based on the GPT2 tokenizer
num_tokens = self._get_num_tokens_by_gpt2(text)

10
api/core/model_runtime/model_providers/openai/llm/llm.py
View File

@ -37,7 +37,7 @@ if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
"""
""" # noqa: E501
class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
@ -508,7 +508,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
continue
# transform assistant message to prompt message
text = delta.text if delta.text else ""
text = delta.text or ""
assistant_prompt_message = AssistantPromptMessage(content=text)
full_text += text
@ -760,11 +760,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
final_tool_calls.extend(tool_calls)
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=tool_calls
)
assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
full_assistant_content += delta.delta.content if delta.delta.content else ""
full_assistant_content += delta.delta.content or ""
if has_finish_reason:
final_chunk = LLMResultChunk(

2
api/core/model_runtime/model_providers/openai/tts/tts.py
View File

@ -88,7 +88,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
)
for i in range(len(sentences))
]
for index, future in enumerate(futures):
for future in futures:
yield from future.result().__enter__().iter_bytes(1024)
else:

10
api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
View File

@ -103,7 +103,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
def validate_credentials(self, model: str, credentials: dict) -> None:
"""
Validate model credentials using requests to ensure compatibility with all providers following OpenAI's API standard.
Validate model credentials using requests to ensure compatibility with all providers following
OpenAI's API standard.
:param model: model name
:param credentials: model credentials
@ -178,9 +179,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
features = []
function_calling_type = credentials.get("function_calling_type", "no_call")
if function_calling_type in ["function_call"]:
if function_calling_type == "function_call":
features.append(ModelFeature.TOOL_CALL)
elif function_calling_type in ["tool_call"]:
elif function_calling_type == "tool_call":
features.append(ModelFeature.MULTI_TOOL_CALL)
stream_function_calling = credentials.get("stream_function_calling", "supported")
@ -262,7 +263,8 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
return entity
# validate_credentials method has been rewritten to use the requests library for compatibility with all providers following OpenAI's API standard.
# validate_credentials method has been rewritten to use the requests library for compatibility with all providers
# following OpenAI's API standard.
def _generate(
self,
model: str,

4
api/core/model_runtime/model_providers/openllm/llm/llm.py
View File

@ -179,7 +179,7 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
index=0,
message=AssistantPromptMessage(content=message.content, tool_calls=[]),
usage=usage,
finish_reason=message.stop_reason if message.stop_reason else None,
finish_reason=message.stop_reason or None,
),
)
else:
@ -189,7 +189,7 @@ class OpenLLMLargeLanguageModel(LargeLanguageModel):
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(content=message.content, tool_calls=[]),
finish_reason=message.stop_reason if message.stop_reason else None,
finish_reason=message.stop_reason or None,
),
)

2
api/core/model_runtime/model_providers/openllm/llm/openllm_generate.py
View File

@ -106,7 +106,7 @@ class OpenLLMGenerate:
timeout = 120
data = {
"stop": stop if stop else [],
"stop": stop or [],
"prompt": "\n".join([message.content for message in prompt_messages]),
"llm_config": default_llm_config,
}

2
api/core/model_runtime/model_providers/replicate/llm/llm.py
View File

@ -214,7 +214,7 @@ class ReplicateLargeLanguageModel(_CommonReplicate, LargeLanguageModel):
index += 1
assistant_prompt_message = AssistantPromptMessage(content=output if output else "")
assistant_prompt_message = AssistantPromptMessage(content=output or "")
if index < prediction_output_length:
yield LLMResultChunk(

3
api/core/model_runtime/model_providers/sagemaker/rerank/rerank.py
View File

@ -1,5 +1,6 @@
import json
import logging
import operator
from typing import Any, Optional
import boto3
@ -94,7 +95,7 @@ class SageMakerRerankModel(RerankModel):
for idx in range(len(scores)):
candidate_docs.append({"content": docs[idx], "score": scores[idx]})
sorted(candidate_docs, key=lambda x: x["score"], reverse=True)
sorted(candidate_docs, key=operator.itemgetter("score"), reverse=True)
line = 3
rerank_documents = []

2
api/core/model_runtime/model_providers/sagemaker/tts/tts.py
View File

@ -260,7 +260,7 @@ class SageMakerText2SpeechModel(TTSModel):
for payload in payloads
]
for index, future in enumerate(futures):
for future in futures:
resp = future.result()
audio_bytes = requests.get(resp.get("s3_presign_url")).content
for i in range(0, len(audio_bytes), 1024):

5
api/core/model_runtime/model_providers/spark/llm/_client.py
View File

@ -61,7 +61,10 @@ class SparkLLMClient:
signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding="utf-8")
authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
authorization_origin = (
f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line",'
f' signature="{signature_sha_base64}"'
)
authorization = base64.b64encode(authorization_origin.encode("utf-8")).decode(encoding="utf-8")

2
api/core/model_runtime/model_providers/spark/llm/llm.py
View File

@ -220,7 +220,7 @@ class SparkLargeLanguageModel(LargeLanguageModel):
delta = content
assistant_prompt_message = AssistantPromptMessage(
content=delta if delta else "",
content=delta or "",
)
prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)

3
api/core/model_runtime/model_providers/tencent/speech2text/flash_recognizer.py
View File

@ -1,6 +1,7 @@
import base64
import hashlib
import hmac
import operator
import time
import requests
@ -127,7 +128,7 @@ class FlashRecognizer:
return s
def _build_req_with_signature(self, secret_key, params, header):
query = sorted(params.items(), key=lambda d: d[0])
query = sorted(params.items(), key=operator.itemgetter(0))
signstr = self._format_sign_string(query)
signature = self._sign(signstr, secret_key)
header["Authorization"] = signature

8
api/core/model_runtime/model_providers/tongyi/llm/llm.py
View File

@ -4,6 +4,7 @@ import tempfile
import uuid
from collections.abc import Generator
from http import HTTPStatus
from pathlib import Path
from typing import Optional, Union, cast
from dashscope import Generation, MultiModalConversation, get_tokenizer
@ -350,9 +351,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
break
elif isinstance(message, AssistantPromptMessage):
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemPromptMessage):
message_text = content
elif isinstance(message, ToolPromptMessage):
elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
message_text = content
else:
raise ValueError(f"Got unknown type {message}")
@ -456,8 +455,7 @@ class TongyiLargeLanguageModel(LargeLanguageModel):
file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.{mime_type.split('/')[1]}")
with open(file_path, "wb") as image_file:
image_file.write(base64.b64decode(encoded_string))
Path(file_path).write_bytes(base64.b64decode(encoded_string))
return f"file://{file_path}"

8
api/core/model_runtime/model_providers/upstage/llm/llm.py
View File

@ -34,7 +34,7 @@ if you are not sure about the structure.
<instructions>
{{instructions}}
</instructions>
"""
""" # noqa: E501
class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
@ -368,11 +368,9 @@ class UpstageLargeLanguageModel(_CommonUpstage, LargeLanguageModel):
final_tool_calls.extend(tool_calls)
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=tool_calls
)
assistant_prompt_message = AssistantPromptMessage(content=delta.delta.content or "", tool_calls=tool_calls)
full_assistant_content += delta.delta.content if delta.delta.content else ""
full_assistant_content += delta.delta.content or ""
if has_finish_reason:
final_chunk = LLMResultChunk(

14
api/core/model_runtime/model_providers/vertex_ai/llm/llm.py
View File

@ -114,7 +114,8 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
credentials.refresh(request)
token = credentials.token
# Vertex AI Anthropic Claude3 Opus model available in us-east5 region, Sonnet and Haiku available in us-central1 region
# Vertex AI Anthropic Claude3 Opus model available in us-east5 region, Sonnet and Haiku available
# in us-central1 region
if "opus" in model or "claude-3-5-sonnet" in model:
location = "us-east5"
else:
@ -123,7 +124,8 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
# use access token to authenticate
if token:
client = AnthropicVertex(region=location, project_id=project_id, access_token=token)
# When access token is empty, try to use the Google Cloud VM's built-in service account or the GOOGLE_APPLICATION_CREDENTIALS environment variable
# When access token is empty, try to use the Google Cloud VM's built-in service account
# or the GOOGLE_APPLICATION_CREDENTIALS environment variable
else:
client = AnthropicVertex(
region=location,
@ -229,10 +231,10 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
),
)
elif isinstance(chunk, ContentBlockDeltaEvent):
chunk_text = chunk.delta.text if chunk.delta.text else ""
chunk_text = chunk.delta.text or ""
full_assistant_content += chunk_text
assistant_prompt_message = AssistantPromptMessage(
content=chunk_text if chunk_text else "",
content=chunk_text or "",
)
index = chunk.index
yield LLMResultChunk(
@ -633,9 +635,7 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
message_text = f"{human_prompt} {content}"
elif isinstance(message, AssistantPromptMessage):
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemPromptMessage):
message_text = f"{human_prompt} {content}"
elif isinstance(message, ToolPromptMessage):
elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
message_text = f"{human_prompt} {content}"
else:
raise ValueError(f"Got unknown type {message}")

3
api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/auth.py
View File

@ -1,5 +1,6 @@
# coding : utf-8
import datetime
from itertools import starmap
import pytz
@ -48,7 +49,7 @@ class SignResult:
self.authorization = ""
def __str__(self):
return "\n".join(["{}:{}".format(*item) for item in self.__dict__.items()])
return "\n".join(list(starmap("{}:{}".format, self.__dict__.items())))
class Credentials:

3
api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/base/util.py
View File

@ -1,5 +1,6 @@
import hashlib
import hmac
import operator
from functools import reduce
from urllib.parse import quote
@ -40,4 +41,4 @@ class Util:
if len(hv) == 1:
hv = "0" + hv
lst.append(hv)
return reduce(lambda x, y: x + y, lst)
return reduce(operator.add, lst)

8
api/core/model_runtime/model_providers/volcengine_maas/llm/llm.py
View File

@ -174,9 +174,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
prompt_messages=prompt_messages,
delta=LLMResultChunkDelta(
index=index,
message=AssistantPromptMessage(
content=message["content"] if message["content"] else "", tool_calls=[]
),
message=AssistantPromptMessage(content=message["content"] or "", tool_calls=[]),
usage=usage,
finish_reason=choice.get("finish_reason"),
),
@ -208,7 +206,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
model=model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(
content=message["content"] if message["content"] else "",
content=message["content"] or "",
tool_calls=tool_calls,
),
usage=self._calc_response_usage(
@ -284,7 +282,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
model=model,
prompt_messages=prompt_messages,
message=AssistantPromptMessage(
content=message.content if message.content else "",
content=message.content or "",
tool_calls=tool_calls,
),
usage=self._calc_response_usage(

8
api/core/model_runtime/model_providers/wenxin/llm/llm.py
View File

@ -28,7 +28,7 @@ if you are not sure about the structure.
</instructions>
You should also complete the text started with ``` but not tell ``` directly.
"""
""" # noqa: E501
class ErnieBotLargeLanguageModel(LargeLanguageModel):
@ -199,7 +199,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
secret_key=credentials["secret_key"],
)
user = user if user else "ErnieBotDefault"
user = user or "ErnieBotDefault"
# convert prompt messages to baichuan messages
messages = [
@ -289,7 +289,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
index=0,
message=AssistantPromptMessage(content=message.content, tool_calls=[]),
usage=usage,
finish_reason=message.stop_reason if message.stop_reason else None,
finish_reason=message.stop_reason or None,
),
)
else:
@ -299,7 +299,7 @@ class ErnieBotLargeLanguageModel(LargeLanguageModel):
delta=LLMResultChunkDelta(
index=0,
message=AssistantPromptMessage(content=message.content, tool_calls=[]),
finish_reason=message.stop_reason if message.stop_reason else None,
finish_reason=message.stop_reason or None,
),
)

2
api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py
View File

@ -85,7 +85,7 @@ class WenxinTextEmbeddingModel(TextEmbeddingModel):
api_key = credentials["api_key"]
secret_key = credentials["secret_key"]
embedding: TextEmbedding = self._create_text_embedding(api_key, secret_key)
user = user if user else "ErnieBotDefault"
user = user or "ErnieBotDefault"
context_size = self._get_context_size(model, credentials)
max_chunks = self._get_max_chunks(model, credentials)

21
api/core/model_runtime/model_providers/xinference/llm/llm.py
View File

@ -130,7 +130,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
credentials["completion_type"] = "completion"
else:
raise ValueError(
f"xinference model ability {extra_param.model_ability} is not supported, check if you have the right model type"
f"xinference model ability {extra_param.model_ability} is not supported,"
f" check if you have the right model type"
)
if extra_param.support_function_call:
@ -272,11 +273,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
"""
text = ""
for item in message:
if isinstance(item, UserPromptMessage):
text += item.content
elif isinstance(item, SystemPromptMessage):
text += item.content
elif isinstance(item, AssistantPromptMessage):
if isinstance(item, UserPromptMessage | SystemPromptMessage | AssistantPromptMessage):
text += item.content
else:
raise NotImplementedError(f"PromptMessage type {type(item)} is not supported")
@ -362,7 +359,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
help=I18nObject(
en_US="Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they "
"appear in the text so far, increasing the model's likelihood to talk about new topics.",
zh_Hans="介于 -2.0 和 2.0 之间的数字。正值会根据新词是否已出现在文本中对其进行惩罚，从而增加模型谈论新话题的可能性。",
zh_Hans="介于 -2.0 和 2.0 之间的数字。正值会根据新词是否已出现在文本中对其进行惩罚，"
"从而增加模型谈论新话题的可能性。",
),
default=0.0,
min=-2.0,
@ -382,7 +380,8 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
en_US="Number between -2.0 and 2.0. Positive values penalize new tokens based on their "
"existing frequency in the text so far, decreasing the model's likelihood to repeat the "
"same line verbatim.",
zh_Hans="介于 -2.0 和 2.0 之间的数字。正值会根据新词在文本中的现有频率对其进行惩罚，从而降低模型逐字重复相同内容的可能性。",
zh_Hans="介于 -2.0 和 2.0 之间的数字。正值会根据新词在文本中的现有频率对其进行惩罚，"
"从而降低模型逐字重复相同内容的可能性。",
),
default=0.0,
min=-2.0,
@ -590,7 +589,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
# convert tool call to assistant message tool call
tool_calls = assistant_message.tool_calls
assistant_prompt_message_tool_calls = self._extract_response_tool_calls(tool_calls if tool_calls else [])
assistant_prompt_message_tool_calls = self._extract_response_tool_calls(tool_calls or [])
function_call = assistant_message.function_call
if function_call:
assistant_prompt_message_tool_calls += [self._extract_response_function_call(function_call)]
@ -653,7 +652,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_message_tool_calls
content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
)
if delta.finish_reason is not None:
@ -750,7 +749,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
delta = chunk.choices[0]
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(content=delta.text if delta.text else "", tool_calls=[])
assistant_prompt_message = AssistantPromptMessage(content=delta.text or "", tool_calls=[])
if delta.finish_reason is not None:
# temp_assistant_prompt_message is used to calculate usage

10
api/core/model_runtime/model_providers/xinference/speech2text/speech2text.py
View File

@ -101,12 +101,16 @@ class XinferenceSpeech2TextModel(Speech2TextModel):
:param model: model name
:param credentials: model credentials
:param file: The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpe g,mpga, m4a, ogg, wav, or webm.
:param file: The audio file object (not file name) to transcribe, in one of these formats: flac, mp3, mp4, mpeg,
mpga, m4a, ogg, wav, or webm.
:param language: The language of the input audio. Supplying the input language in ISO-639-1
:param prompt: An optional text to guide the model's style or continue a previous audio segment.
The prompt should match the audio language.
:param response_format: The format of the transcript output, in one of these options: json, text, srt, verbose _json, or vtt.
:param temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output mor e random,while lower values like 0.2 will make it more focused and deterministic.If set to 0, the model wi ll use log probability to automatically increase the temperature until certain thresholds are hit.
:param response_format: The format of the transcript output, in one of these options: json, text, srt,
verbose_json, or vtt.
:param temperature: The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more
random,while lower values like 0.2 will make it more focused and deterministic.If set to 0, the model will use
log probability to automatically increase the temperature until certain thresholds are hit.
:return: text for given audio file
"""
server_url = credentials["server_url"]

2
api/core/model_runtime/model_providers/xinference/tts/tts.py
View File

@ -215,7 +215,7 @@ class XinferenceText2SpeechModel(TTSModel):
for i in range(len(sentences))
]
for index, future in enumerate(futures):
for future in futures:
response = future.result()
for i in range(0, len(response), 1024):
yield response[i : i + 1024]

6
api/core/model_runtime/model_providers/xinference/xinference_helper.py
View File

@ -76,7 +76,8 @@ class XinferenceHelper:
url = str(URL(server_url) / "v1" / "models" / model_uid)
# this method is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
# this method is surrounded by a lock, and default requests may hang forever,
# so we just set a Adapter with max_retries=3
session = Session()
session.mount("http://", HTTPAdapter(max_retries=3))
session.mount("https://", HTTPAdapter(max_retries=3))
@ -88,7 +89,8 @@ class XinferenceHelper:
raise RuntimeError(f"get xinference model extra parameter failed, url: {url}, error: {e}")
if response.status_code != 200:
raise RuntimeError(
f"get xinference model extra parameter failed, status code: {response.status_code}, response: {response.text}"
f"get xinference model extra parameter failed, status code: {response.status_code},"
f" response: {response.text}"
)
response_json = response.json()

17
api/core/model_runtime/model_providers/zhipuai/llm/llm.py
View File

@ -31,7 +31,7 @@ And you should always end the block with a "```" to indicate the end of the JSON
{{instructions}}
</instructions>
```JSON"""
```JSON""" # noqa: E501
class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
@ -209,9 +209,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
):
new_prompt_messages[-1].content += "\n\n" + copy_prompt_message.content
else:
if copy_prompt_message.role == PromptMessageRole.USER:
new_prompt_messages.append(copy_prompt_message)
elif copy_prompt_message.role == PromptMessageRole.TOOL:
if (
copy_prompt_message.role == PromptMessageRole.USER
or copy_prompt_message.role == PromptMessageRole.TOOL
):
new_prompt_messages.append(copy_prompt_message)
elif copy_prompt_message.role == PromptMessageRole.SYSTEM:
new_prompt_message = SystemPromptMessage(content=copy_prompt_message.content)
@ -413,10 +414,10 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
# transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage(
content=delta.delta.content if delta.delta.content else "", tool_calls=assistant_tool_calls
content=delta.delta.content or "", tool_calls=assistant_tool_calls
)
full_assistant_content += delta.delta.content if delta.delta.content else ""
full_assistant_content += delta.delta.content or ""
if delta.finish_reason is not None and chunk.usage is not None:
completion_tokens = chunk.usage.completion_tokens
@ -461,9 +462,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
message_text = f"{human_prompt} {content}"
elif isinstance(message, AssistantPromptMessage):
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemPromptMessage):
message_text = content
elif isinstance(message, ToolPromptMessage):
elif isinstance(message, SystemPromptMessage | ToolPromptMessage):
message_text = content
else:
raise ValueError(f"Got unknown type {message}")

3
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_type.py
View File

@ -75,7 +75,8 @@ Headers = Mapping[str, Union[str, Omit]]
ResponseT = TypeVar(
"ResponseT",
bound="Union[str, None, BaseModel, list[Any], Dict[str, Any], Response, UnknownResponse, ModelBuilderProtocol, BinaryResponseContent]",
bound="Union[str, None, BaseModel, list[Any], Dict[str, Any], Response, UnknownResponse, ModelBuilderProtocol,"
" BinaryResponseContent]",
)
# for user input files

4
api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py
View File

@ -30,6 +30,8 @@ def _merge_map(map1: Mapping, map2: Mapping) -> Mapping:
return {key: val for key, val in merged.items() if val is not None}
from itertools import starmap
from httpx._config import DEFAULT_TIMEOUT_CONFIG as HTTPX_DEFAULT_TIMEOUT
ZHIPUAI_DEFAULT_TIMEOUT = httpx.Timeout(timeout=300.0, connect=8.0)
@ -159,7 +161,7 @@ class HttpClient:
return [(key, str_data)]
def _make_multipartform(self, data: Mapping[object, object]) -> dict[str, object]:
items = flatten([self._object_to_formdata(k, v) for k, v in data.items()])
items = flatten(list(starmap(self._object_to_formdata, data.items())))
serialized: dict[str, object] = {}
for key, value in items:

3
api/core/model_runtime/schema_validators/common_validator.py
View File

@ -67,7 +67,8 @@ class CommonValidator:
if credential_form_schema.max_length:
if len(value) > credential_form_schema.max_length:
raise ValueError(
f"Variable {credential_form_schema.variable} length should not greater than {credential_form_schema.max_length}"
f"Variable {credential_form_schema.variable} length should not"
f" greater than {credential_form_schema.max_length}"
)
# check the type of value

13
api/core/moderation/keywords/keywords.py
View File

@ -56,14 +56,7 @@ class KeywordsModeration(Moderation):
)
def _is_violated(self, inputs: dict, keywords_list: list) -> bool:
for value in inputs.values():
if self._check_keywords_in_value(keywords_list, value):
return True
return any(self._check_keywords_in_value(keywords_list, value) for value in inputs.values())
return False
def _check_keywords_in_value(self, keywords_list, value):
for keyword in keywords_list:
if keyword.lower() in value.lower():
return True
return False
def _check_keywords_in_value(self, keywords_list, value) -> bool:
return any(keyword.lower() in value.lower() for keyword in keywords_list)

22
api/core/ops/langfuse_trace/langfuse_trace.py
View File

@ -65,7 +65,7 @@ class LangFuseDataTrace(BaseTraceInstance):
self.generate_name_trace(trace_info)
def workflow_trace(self, trace_info: WorkflowTraceInfo):
trace_id = trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
trace_id = trace_info.workflow_app_log_id or trace_info.workflow_run_id
user_id = trace_info.metadata.get("user_id")
if trace_info.message_id:
trace_id = trace_info.message_id
@ -84,7 +84,7 @@ class LangFuseDataTrace(BaseTraceInstance):
)
self.add_trace(langfuse_trace_data=trace_data)
workflow_span_data = LangfuseSpan(
id=(trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id),
id=(trace_info.workflow_app_log_id or trace_info.workflow_run_id),
name=TraceTaskName.WORKFLOW_TRACE.value,
input=trace_info.workflow_run_inputs,
output=trace_info.workflow_run_outputs,
@ -93,7 +93,7 @@ class LangFuseDataTrace(BaseTraceInstance):
end_time=trace_info.end_time,
metadata=trace_info.metadata,
level=LevelEnum.DEFAULT if trace_info.error == "" else LevelEnum.ERROR,
status_message=trace_info.error if trace_info.error else "",
status_message=trace_info.error or "",
)
self.add_span(langfuse_span_data=workflow_span_data)
else:
@ -143,7 +143,7 @@ class LangFuseDataTrace(BaseTraceInstance):
else:
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
created_at = node_execution.created_at if node_execution.created_at else datetime.now()
created_at = node_execution.created_at or datetime.now()
elapsed_time = node_execution.elapsed_time
finished_at = created_at + timedelta(seconds=elapsed_time)
@ -172,10 +172,8 @@ class LangFuseDataTrace(BaseTraceInstance):
end_time=finished_at,
metadata=metadata,
level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
status_message=trace_info.error if trace_info.error else "",
parent_observation_id=(
trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id
),
status_message=trace_info.error or "",
parent_observation_id=(trace_info.workflow_app_log_id or trace_info.workflow_run_id),
)
else:
span_data = LangfuseSpan(
@ -188,7 +186,7 @@ class LangFuseDataTrace(BaseTraceInstance):
end_time=finished_at,
metadata=metadata,
level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
status_message=trace_info.error if trace_info.error else "",
status_message=trace_info.error or "",
)
self.add_span(langfuse_span_data=span_data)
@ -212,7 +210,7 @@ class LangFuseDataTrace(BaseTraceInstance):
output=outputs,
metadata=metadata,
level=(LevelEnum.DEFAULT if status == "succeeded" else LevelEnum.ERROR),
status_message=trace_info.error if trace_info.error else "",
status_message=trace_info.error or "",
usage=generation_usage,
)
@ -277,7 +275,7 @@ class LangFuseDataTrace(BaseTraceInstance):
output=message_data.answer,
metadata=metadata,
level=(LevelEnum.DEFAULT if message_data.status != "error" else LevelEnum.ERROR),
status_message=message_data.error if message_data.error else "",
status_message=message_data.error or "",
usage=generation_usage,
)
@ -319,7 +317,7 @@ class LangFuseDataTrace(BaseTraceInstance):
end_time=trace_info.end_time,
metadata=trace_info.metadata,
level=(LevelEnum.DEFAULT if message_data.status != "error" else LevelEnum.ERROR),
status_message=message_data.error if message_data.error else "",
status_message=message_data.error or "",
usage=generation_usage,
)

10
api/core/ops/langsmith_trace/langsmith_trace.py
View File

@ -82,7 +82,7 @@ class LangSmithDataTrace(BaseTraceInstance):
langsmith_run = LangSmithRunModel(
file_list=trace_info.file_list,
total_tokens=trace_info.total_tokens,
id=trace_info.workflow_app_log_id if trace_info.workflow_app_log_id else trace_info.workflow_run_id,
id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
name=TraceTaskName.WORKFLOW_TRACE.value,
inputs=trace_info.workflow_run_inputs,
run_type=LangSmithRunType.tool,
@ -94,7 +94,7 @@ class LangSmithDataTrace(BaseTraceInstance):
},
error=trace_info.error,
tags=["workflow"],
parent_run_id=trace_info.message_id if trace_info.message_id else None,
parent_run_id=trace_info.message_id or None,
)
self.add_run(langsmith_run)
@ -133,7 +133,7 @@ class LangSmithDataTrace(BaseTraceInstance):
else:
inputs = json.loads(node_execution.inputs) if node_execution.inputs else {}
outputs = json.loads(node_execution.outputs) if node_execution.outputs else {}
created_at = node_execution.created_at if node_execution.created_at else datetime.now()
created_at = node_execution.created_at or datetime.now()
elapsed_time = node_execution.elapsed_time
finished_at = created_at + timedelta(seconds=elapsed_time)
@ -180,9 +180,7 @@ class LangSmithDataTrace(BaseTraceInstance):
extra={
"metadata": metadata,
},
parent_run_id=trace_info.workflow_app_log_id
if trace_info.workflow_app_log_id
else trace_info.workflow_run_id,
parent_run_id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
tags=["node_execution"],
)

17
api/core/ops/ops_trace_manager.py
View File

@ -223,7 +223,7 @@ class OpsTraceManager:
:return:
"""
# auth check
if tracing_provider not in provider_config_map.keys() and tracing_provider is not None:
if tracing_provider not in provider_config_map and tracing_provider is not None:
raise ValueError(f"Invalid tracing provider: {tracing_provider}")
app_config: App = db.session.query(App).filter(App.id == app_id).first()
@ -354,11 +354,11 @@ class TraceTask:
workflow_run_inputs = json.loads(workflow_run.inputs) if workflow_run.inputs else {}
workflow_run_outputs = json.loads(workflow_run.outputs) if workflow_run.outputs else {}
workflow_run_version = workflow_run.version
error = workflow_run.error if workflow_run.error else ""
error = workflow_run.error or ""
total_tokens = workflow_run.total_tokens
file_list = workflow_run_inputs.get("sys.file") if workflow_run_inputs.get("sys.file") else []
file_list = workflow_run_inputs.get("sys.file") or []
query = workflow_run_inputs.get("query") or workflow_run_inputs.get("sys.query") or ""
# get workflow_app_log_id
@ -452,7 +452,7 @@ class TraceTask:
message_tokens=message_tokens,
answer_tokens=message_data.answer_tokens,
total_tokens=message_tokens + message_data.answer_tokens,
error=message_data.error if message_data.error else "",
error=message_data.error or "",
inputs=inputs,
outputs=message_data.answer,
file_list=file_list,
@ -487,7 +487,7 @@ class TraceTask:
workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
moderation_trace_info = ModerationTraceInfo(
message_id=workflow_app_log_id if workflow_app_log_id else message_id,
message_id=workflow_app_log_id or message_id,
inputs=inputs,
message_data=message_data.to_dict(),
flagged=moderation_result.flagged,
@ -527,7 +527,7 @@ class TraceTask:
workflow_app_log_id = str(workflow_app_log_data.id) if workflow_app_log_data else None
suggested_question_trace_info = SuggestedQuestionTraceInfo(
message_id=workflow_app_log_id if workflow_app_log_id else message_id,
message_id=workflow_app_log_id or message_id,
message_data=message_data.to_dict(),
inputs=message_data.message,
outputs=message_data.answer,
@ -569,7 +569,7 @@ class TraceTask:
dataset_retrieval_trace_info = DatasetRetrievalTraceInfo(
message_id=message_id,
inputs=message_data.query if message_data.query else message_data.inputs,
inputs=message_data.query or message_data.inputs,
documents=[doc.model_dump() for doc in documents],
start_time=timer.get("start"),
end_time=timer.get("end"),
@ -695,8 +695,7 @@ class TraceQueueManager:
self.start_timer()
def add_trace_task(self, trace_task: TraceTask):
global trace_manager_timer
global trace_manager_queue
global trace_manager_timer, trace_manager_queue
try:
if self.trace_instance:
trace_task.app_id = self.app_id

8
api/core/prompt/prompt_templates/advanced_prompt_templates.py
View File

@ -1,11 +1,11 @@
CONTEXT = "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n"
CONTEXT = "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n" # noqa: E501
BAICHUAN_CONTEXT = "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n\n"
BAICHUAN_CONTEXT = "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n\n" # noqa: E501
CHAT_APP_COMPLETION_PROMPT_CONFIG = {
"completion_prompt_config": {
"prompt": {
"text": "{{#pre_prompt#}}\nHere is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: "
"text": "{{#pre_prompt#}}\nHere is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: " # noqa: E501
},
"conversation_histories_role": {"user_prefix": "Human", "assistant_prefix": "Assistant"},
},
@ -24,7 +24,7 @@ COMPLETION_APP_COMPLETION_PROMPT_CONFIG = {
BAICHUAN_CHAT_APP_COMPLETION_PROMPT_CONFIG = {
"completion_prompt_config": {
"prompt": {
"text": "{{#pre_prompt#}}\n\n用户和助手的历史对话内容如下：\n```\n{{#histories#}}\n```\n\n\n\n用户：{{#query#}}"
"text": "{{#pre_prompt#}}\n\n用户和助手的历史对话内容如下：\n```\n{{#histories#}}\n```\n\n\n\n用户：{{#query#}}" # noqa: E501
},
"conversation_histories_role": {"user_prefix": "用户", "assistant_prefix": "助手"},
},

6
api/core/prompt/simple_prompt_transform.py
View File

@ -112,11 +112,11 @@ class SimplePromptTransform(PromptTransform):
for v in prompt_template_config["special_variable_keys"]:
# support #context#, #query# and #histories#
if v == "#context#":
variables["#context#"] = context if context else ""
variables["#context#"] = context or ""
elif v == "#query#":
variables["#query#"] = query if query else ""
variables["#query#"] = query or ""
elif v == "#histories#":
variables["#histories#"] = histories if histories else ""
variables["#histories#"] = histories or ""
prompt_template = prompt_template_config["prompt_template"]
prompt = prompt_template.format(variables)

2
api/core/rag/datasource/keyword/keyword_base.py
View File

@ -34,7 +34,7 @@ class BaseKeyword(ABC):
raise NotImplementedError
def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
for text in texts[:]:
for text in texts.copy():
doc_id = text.metadata["doc_id"]
exists_duplicate_node = self.text_exists(doc_id)
if exists_duplicate_node:

4
api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py
View File

@ -239,7 +239,7 @@ class AnalyticdbVector(BaseVector):
def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
request = gpdb_20160503_models.QueryCollectionDataRequest(
dbinstance_id=self.config.instance_id,
region_id=self.config.region_id,
@ -267,7 +267,7 @@ class AnalyticdbVector(BaseVector):
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
request = gpdb_20160503_models.QueryCollectionDataRequest(
dbinstance_id=self.config.instance_id,
region_id=self.config.region_id,

2
api/core/rag/datasource/vdb/chroma/chroma_vector.py
View File

@ -92,7 +92,7 @@ class ChromaVector(BaseVector):
def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
collection = self._client.get_or_create_collection(self._collection_name)
results: QueryResult = collection.query(query_embeddings=query_vector, n_results=kwargs.get("top_k", 4))
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
ids: list[str] = results["ids"][0]
documents: list[str] = results["documents"][0]

6
api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py
View File

@ -86,8 +86,8 @@ class ElasticSearchVector(BaseVector):
id=uuids[i],
document={
Field.CONTENT_KEY.value: documents[i].page_content,
Field.VECTOR.value: embeddings[i] if embeddings[i] else None,
Field.METADATA_KEY.value: documents[i].metadata if documents[i].metadata else {},
Field.VECTOR.value: embeddings[i] or None,
Field.METADATA_KEY.value: documents[i].metadata or {},
},
)
self._client.indices.refresh(index=self._collection_name)
@ -131,7 +131,7 @@ class ElasticSearchVector(BaseVector):
docs = []
for doc, score in docs_and_scores:
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if score > score_threshold:
doc.metadata["score"] = score
docs.append(doc)

2
api/core/rag/datasource/vdb/milvus/milvus_vector.py
View File

@ -141,7 +141,7 @@ class MilvusVector(BaseVector):
for result in results[0]:
metadata = result["entity"].get(Field.METADATA_KEY.value)
metadata["score"] = result["distance"]
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if result["distance"] > score_threshold:
doc = Document(page_content=result["entity"].get(Field.CONTENT_KEY.value), metadata=metadata)
docs.append(doc)

2
api/core/rag/datasource/vdb/myscale/myscale_vector.py
View File

@ -122,7 +122,7 @@ class MyScaleVector(BaseVector):
def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
top_k = kwargs.get("top_k", 5)
score_threshold = kwargs.get("score_threshold") or 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
where_str = (
f"WHERE dist < {1 - score_threshold}"
if self._metric.upper() == "COSINE" and order == SortOrder.ASC and score_threshold > 0.0

2
api/core/rag/datasource/vdb/opensearch/opensearch_vector.py
View File

@ -170,7 +170,7 @@ class OpenSearchVector(BaseVector):
metadata = {}
metadata["score"] = hit["_score"]
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if hit["_score"] > score_threshold:
doc = Document(page_content=hit["_source"].get(Field.CONTENT_KEY.value), metadata=metadata)
docs.append(doc)

10
api/core/rag/datasource/vdb/oracle/oraclevector.py
View File

@ -195,11 +195,12 @@ class OracleVector(BaseVector):
top_k = kwargs.get("top_k", 5)
with self._get_cursor() as cur:
cur.execute(
f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name} ORDER BY distance fetch first {top_k} rows only",
f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
f" ORDER BY distance fetch first {top_k} rows only",
[numpy.array(query_vector)],
)
docs = []
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
for record in cur:
metadata, text, distance = record
score = 1 - distance
@ -211,7 +212,7 @@ class OracleVector(BaseVector):
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
top_k = kwargs.get("top_k", 5)
# just not implement fetch by score_threshold now, may be later
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if len(query) > 0:
# Check which language the query is in
zh_pattern = re.compile("[\u4e00-\u9fa5]+")
@ -254,7 +255,8 @@ class OracleVector(BaseVector):
entities.append(token)
with self._get_cursor() as cur:
cur.execute(
f"select meta, text, embedding FROM {self.table_name} WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
f"select meta, text, embedding FROM {self.table_name}"
f" WHERE CONTAINS(text, :1, 1) > 0 order by score(1) desc fetch first {top_k} rows only",
[" ACCUM ".join(entities)],
)
docs = []

2
api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py
View File

@ -198,7 +198,7 @@ class PGVectoRS(BaseVector):
metadata = record.meta
score = 1 - dis
metadata["score"] = score
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if score > score_threshold:
doc = Document(page_content=record.text, metadata=metadata)
docs.append(doc)

5
api/core/rag/datasource/vdb/pgvector/pgvector.py
View File

@ -139,11 +139,12 @@ class PGVector(BaseVector):
with self._get_cursor() as cur:
cur.execute(
f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name} ORDER BY distance LIMIT {top_k}",
f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name}"
f" ORDER BY distance LIMIT {top_k}",
(json.dumps(query_vector),),
)
docs = []
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
for record in cur:
metadata, text, distance = record
score = 1 - distance

2
api/core/rag/datasource/vdb/qdrant/qdrant_vector.py
View File

@ -339,7 +339,7 @@ class QdrantVector(BaseVector):
for result in results:
metadata = result.payload.get(Field.METADATA_KEY.value) or {}
# duplicate check score threshold
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if result.score > score_threshold:
metadata["score"] = result.score
doc = Document(

48
api/core/rag/datasource/vdb/relyt/relyt_vector.py
View File

@ -127,27 +127,26 @@ class RelytVector(BaseVector):
)
chunks_table_data = []
with self.client.connect() as conn:
with conn.begin():
for document, metadata, chunk_id, embedding in zip(texts, metadatas, ids, embeddings):
chunks_table_data.append(
{
"id": chunk_id,
"embedding": embedding,
"document": document,
"metadata": metadata,
}
)
with self.client.connect() as conn, conn.begin():
for document, metadata, chunk_id, embedding in zip(texts, metadatas, ids, embeddings):
chunks_table_data.append(
{
"id": chunk_id,
"embedding": embedding,
"document": document,
"metadata": metadata,
}
)
# Execute the batch insert when the batch size is reached
if len(chunks_table_data) == 500:
conn.execute(insert(chunks_table).values(chunks_table_data))
# Clear the chunks_table_data list for the next batch
chunks_table_data.clear()
# Insert any remaining records that didn't make up a full batch
if chunks_table_data:
# Execute the batch insert when the batch size is reached
if len(chunks_table_data) == 500:
conn.execute(insert(chunks_table).values(chunks_table_data))
# Clear the chunks_table_data list for the next batch
chunks_table_data.clear()
# Insert any remaining records that didn't make up a full batch
if chunks_table_data:
conn.execute(insert(chunks_table).values(chunks_table_data))
return ids
@ -186,11 +185,10 @@ class RelytVector(BaseVector):
)
try:
with self.client.connect() as conn:
with conn.begin():
delete_condition = chunks_table.c.id.in_(ids)
conn.execute(chunks_table.delete().where(delete_condition))
return True
with self.client.connect() as conn, conn.begin():
delete_condition = chunks_table.c.id.in_(ids)
conn.execute(chunks_table.delete().where(delete_condition))
return True
except Exception as e:
print("Delete operation failed:", str(e))
return False
@ -232,7 +230,7 @@ class RelytVector(BaseVector):
# Organize results.
docs = []
for document, score in results:
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
if 1 - score > score_threshold:
docs.append(document)
return docs

7
api/core/rag/datasource/vdb/tencent/tencent_vector.py
View File

@ -63,10 +63,7 @@ class TencentVector(BaseVector):
def _has_collection(self) -> bool:
collections = self._db.list_collections()
for collection in collections:
if collection.collection_name == self._collection_name:
return True
return False
return any(collection.collection_name == self._collection_name for collection in collections)
def _create_collection(self, dimension: int) -> None:
lock_name = "vector_indexing_lock_{}".format(self._collection_name)
@ -156,7 +153,7 @@ class TencentVector(BaseVector):
limit=kwargs.get("top_k", 4),
timeout=self._client_config.timeout,
)
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
return self._get_search_res(res, score_threshold)
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:

34
api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py
View File

@ -124,20 +124,19 @@ class TiDBVector(BaseVector):
texts = [d.page_content for d in documents]
chunks_table_data = []
with self._engine.connect() as conn:
with conn.begin():
for id, text, meta, embedding in zip(ids, texts, metas, embeddings):
chunks_table_data.append({"id": id, "vector": embedding, "text": text, "meta": meta})
with self._engine.connect() as conn, conn.begin():
for id, text, meta, embedding in zip(ids, texts, metas, embeddings):
chunks_table_data.append({"id": id, "vector": embedding, "text": text, "meta": meta})
# Execute the batch insert when the batch size is reached
if len(chunks_table_data) == 500:
conn.execute(insert(table).values(chunks_table_data))
# Clear the chunks_table_data list for the next batch
chunks_table_data.clear()
# Insert any remaining records that didn't make up a full batch
if chunks_table_data:
# Execute the batch insert when the batch size is reached
if len(chunks_table_data) == 500:
conn.execute(insert(table).values(chunks_table_data))
# Clear the chunks_table_data list for the next batch
chunks_table_data.clear()
# Insert any remaining records that didn't make up a full batch
if chunks_table_data:
conn.execute(insert(table).values(chunks_table_data))
return ids
def text_exists(self, id: str) -> bool:
@ -160,11 +159,10 @@ class TiDBVector(BaseVector):
raise ValueError("No ids provided to delete.")
table = self._table(self._dimension)
try:
with self._engine.connect() as conn:
with conn.begin():
delete_condition = table.c.id.in_(ids)
conn.execute(table.delete().where(delete_condition))
return True
with self._engine.connect() as conn, conn.begin():
delete_condition = table.c.id.in_(ids)
conn.execute(table.delete().where(delete_condition))
return True
except Exception as e:
print("Delete operation failed:", str(e))
return False
@ -187,7 +185,7 @@ class TiDBVector(BaseVector):
def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
top_k = kwargs.get("top_k", 5)
score_threshold = kwargs.get("score_threshold") if kwargs.get("score_threshold") else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
filter = kwargs.get("filter")
distance = 1 - score_threshold

2
api/core/rag/datasource/vdb/vector_base.py
View File

@ -49,7 +49,7 @@ class BaseVector(ABC):
raise NotImplementedError
def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
for text in texts[:]:
for text in texts.copy():
doc_id = text.metadata["doc_id"]
exists_duplicate_node = self.text_exists(doc_id)
if exists_duplicate_node:

2
api/core/rag/datasource/vdb/vector_factory.py
View File

@ -153,7 +153,7 @@ class Vector:
return CacheEmbedding(embedding_model)
def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
for text in texts[:]:
for text in texts.copy():
doc_id = text.metadata["doc_id"]
exists_duplicate_node = self.text_exists(doc_id)
if exists_duplicate_node:

2
api/core/rag/datasource/vdb/weaviate/weaviate_vector.py
View File

@ -205,7 +205,7 @@ class WeaviateVector(BaseVector):
docs = []
for doc, score in docs_and_scores:
score_threshold = kwargs.get("score_threshold", 0.0) if kwargs.get("score_threshold", 0.0) else 0.0
score_threshold = kwargs.get("score_threshold", 0.0)
# check score threshold
if score > score_threshold:
doc.metadata["score"] = score

8
api/core/rag/extractor/blob/blob.py
View File

@ -12,7 +12,7 @@ import mimetypes
from abc import ABC, abstractmethod
from collections.abc import Generator, Iterable, Mapping
from io import BufferedReader, BytesIO
from pathlib import PurePath
from pathlib import Path, PurePath
from typing import Any, Optional, Union
from pydantic import BaseModel, ConfigDict, model_validator
@ -56,8 +56,7 @@ class Blob(BaseModel):
def as_string(self) -> str:
"""Read data as a string."""
if self.data is None and self.path:
with open(str(self.path), encoding=self.encoding) as f:
return f.read()
return Path(str(self.path)).read_text(encoding=self.encoding)
elif isinstance(self.data, bytes):
return self.data.decode(self.encoding)
elif isinstance(self.data, str):
@ -72,8 +71,7 @@ class Blob(BaseModel):
elif isinstance(self.data, str):
return self.data.encode(self.encoding)
elif self.data is None and self.path:
with open(str(self.path), "rb") as f:
return f.read()
return Path(str(self.path)).read_bytes()
else:
raise ValueError(f"Unable to get bytes for blob {self}")

12
api/core/rag/extractor/extract_processor.py
View File

@ -30,7 +30,10 @@ from extensions.ext_storage import storage
from models.model import UploadFile
SUPPORT_URL_CONTENT_TYPES = ["application/pdf", "text/plain", "application/json"]
USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
USER_AGENT = (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124"
" Safari/537.36"
)
class ExtractProcessor:
@ -65,8 +68,7 @@ class ExtractProcessor:
suffix = "." + re.search(r"\.(\w+)$", filename).group(1)
file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
with open(file_path, "wb") as file:
file.write(response.content)
Path(file_path).write_bytes(response.content)
extract_setting = ExtractSetting(datasource_type="upload_file", document_model="text_model")
if return_text:
delimiter = "\n"
@ -108,7 +110,7 @@ class ExtractProcessor:
)
elif file_extension in [".htm", ".html"]:
extractor = HtmlExtractor(file_path)
elif file_extension in [".docx"]:
elif file_extension == ".docx":
extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by)
elif file_extension == ".csv":
extractor = CSVExtractor(file_path, autodetect_encoding=True)
@ -140,7 +142,7 @@ class ExtractProcessor:
extractor = MarkdownExtractor(file_path, autodetect_encoding=True)
elif file_extension in [".htm", ".html"]:
extractor = HtmlExtractor(file_path)
elif file_extension in [".docx"]:
elif file_extension == ".docx":
extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by)
elif file_extension == ".csv":
extractor = CSVExtractor(file_path, autodetect_encoding=True)

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