Refactored AI Service components - ready for UI workflow testing
This commit is contained in:
ValueOn AG
parent
ac755681b3
commit
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15 changed files with 2874 additions and 3320 deletions
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# Development Environment Configuration
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# System Configuration
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APP_ENV_TYPE = dev
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APP_ENV_LABEL = Development Instance Patrick
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APP_API_URL = http://localhost:8000
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APP_KEY_SYSVAR = D:/Athi/Local/Web/poweron/local/key.txt
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APP_INIT_PASS_ADMIN_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEeFFtRGtQeVUtcjlrU3dab1ZxUm9WSks0MlJVYUtERFlqUElHemZrOGNENk1tcmJNX3Vxc01UMDhlNU40VzZZRVBpUGNmT3podzZrOGhOeEJIUEt4eVlSWG5UYXA3d09DVXlLT21Kb1JYSUU9
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APP_INIT_PASS_EVENT_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpERzZjNm56WGVBdjJTeG5Udjd6OGQwUVotYXUzQjJ1YVNyVXVBa3NZVml3ODU0MVNkZjhWWmJwNUFkc19BcHlHMTU1Q3BRcHU0cDBoZkFlR2l6UEZQU3d2U3MtMDh5UDZteGFoQ0EyMUE1ckE9
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# PostgreSQL Storage (new)
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DB_APP_HOST=localhost
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DB_APP_DATABASE=poweron_app
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DB_APP_USER=poweron_dev
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DB_APP_PASSWORD_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEcUIxNEFfQ2xnS0RrSC1KNnUxTlVvTGZoMHgzaEI4Z3NlVzVROTVLak5Ubi1vaEZubFZaMTFKMGd6MXAxekN2d2NvMy1hRjg2UVhybktlcFA5anZ1WjFlQmZhcXdwaGhWdzRDc3ExeUhzWTg9
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DB_APP_PORT=5432
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# PostgreSQL Storage (new)
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DB_CHAT_HOST=localhost
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DB_CHAT_DATABASE=poweron_chat
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DB_CHAT_USER=poweron_dev
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DB_CHAT_PASSWORD_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpERFNzNVhoalpCR0QxYXAwdEpXWXVVOTdZdWtqWW5FNXFGcFl2amNYLWYwYl9STXltRlFxLWNzVWlMVnNYdXk0RklnRExFT0FaQjg2aGswNnhhSGhCN29KN2VEb2FlUV9NTlV3b0tLelplSVU9
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DB_CHAT_PORT=5432
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# PostgreSQL Storage (new)
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DB_MANAGEMENT_HOST=localhost
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DB_MANAGEMENT_DATABASE=poweron_management
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DB_MANAGEMENT_USER=poweron_dev
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DB_MANAGEMENT_PASSWORD_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEUldqSTVpUnFqdGhITDYzT3RScGlMYVdTMmZhOXdudDRCc3dhdllOd3l6MS1vWHY2MjVsTUF1Sk9saEJOSk9ONUlBZjQwb2c2T1gtWWJhcXFzVVVXd01xc0U0b0lJX0JyVDRxaDhNS01JcWs9
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DB_MANAGEMENT_PORT=5432
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# Security Configuration
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APP_JWT_KEY_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpERjlrSktmZHVuQnJ1VVJDdndLaUcxZGJsT2ZlUFRlcFdOZ001RnlzM2FhLWhRV2tjWWFhaWQwQ3hkcUFvbThMcndxSjFpYTdfRV9OZGhTcksxbXFTZWg5MDZvOHpCVXBHcDJYaHlJM0tyNWRZckZsVHpQcmxTZHJoZUs1M3lfU2ljRnJaTmNSQ0w0X085OXI0QW80M2xfQnJqZmZ6VEh3TUltX0xzeE42SGtZPQ==
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APP_TOKEN_EXPIRY=300
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# CORS Configuration
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APP_ALLOWED_ORIGINS=http://localhost:8080,https://playground.poweron-center.net
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# Logging configuration
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APP_LOGGING_LOG_LEVEL = DEBUG
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APP_LOGGING_LOG_DIR = D:/Athi/Local/Web/poweron/local/logs
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APP_LOGGING_FORMAT = %(asctime)s - %(levelname)s - %(name)s - %(message)s
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APP_LOGGING_DATE_FORMAT = %Y-%m-%d %H:%M:%S
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APP_LOGGING_CONSOLE_ENABLED = True
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APP_LOGGING_FILE_ENABLED = True
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APP_LOGGING_ROTATION_SIZE = 10485760
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APP_LOGGING_BACKUP_COUNT = 5
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# Service Redirects
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Service_MSFT_REDIRECT_URI = http://localhost:8000/api/msft/auth/callback
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Service_GOOGLE_REDIRECT_URI = http://localhost:8000/api/google/auth/callback
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# OpenAI configuration
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Connector_AiOpenai_API_URL = https://api.openai.com/v1/chat/completions
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Connector_AiOpenai_API_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEajBuZmtYTVdqLTBpQm9KZ2pCXzRCV3VhZzlYTEhKb1FqWXNrV3lyb25uZUN1WVVQUEY3dGYtejludV9MNGlKeVREanZGOGloV09mY2ttQ3k5SjBFOGFac2ZQTkNKNUZWVnRINVQyeWhsR2wyYnVrRDNzV2NqSHB0ajQ4UWtGeGZtbmR0Q3VvS0hDZlphVmpSc2Z6RG5nPT0=
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Connector_AiOpenai_MODEL_NAME = gpt-4o
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Connector_AiOpenai_TEMPERATURE = 0.2
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Connector_AiOpenai_MAX_TOKENS = 2000
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# Anthropic configuration
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Connector_AiAnthropic_API_URL = https://api.anthropic.com/v1/messages
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Connector_AiAnthropic_API_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpENmFBWG16STFQUVZxNzZZRzRLYTA4X3lRanF1VkF4cU45OExNMzlsQmdISGFxTUxud1dXODBKcFhMVG9KNjdWVnlTTFFROVc3NDlsdlNHLUJXeG41NDBHaXhHR0VHVWl5UW9RNkVWbmlhakRKVW5pM0R4VHk0LUw0TV9LdkljNHdBLXJua21NQkl2b3l4UkVkMGN1YjBrMmJEeWtMay1jbmxrYWJNbUV0aktCXzU1djR2d2RSQXZORTNwcG92ZUVvVGMtQzQzTTVncEZTRGRtZUFIZWQ0dz09
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Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
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Connector_AiAnthropic_TEMPERATURE = 0.2
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Connector_AiAnthropic_MAX_TOKENS = 2000
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# Perplexity AI configuration
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Connector_AiPerplexity_API_URL = https://api.perplexity.ai/chat/completions
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Connector_AiPerplexity_API_SECRET = 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
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Connector_AiPerplexity_MODEL_NAME = sonar
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Connector_AiPerplexity_TEMPERATURE = 0.2
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Connector_AiPerplexity_MAX_TOKENS = 2000
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# Agent Mail configuration
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Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
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Service_MSFT_CLIENT_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEQk4xYnpmbnItUEU3dHU4eHB5dzVYay1WT012RTRLUWJDTlBILVY5dC1FX3VMNjZmLThrbDRFNWFSNGprY3RRTlpYNGlubVBpNnY3MjNJcGtzVk9PMzRacl9LUlM2RU5vTVVZWHJvaUhWSHVfc1pNR0pfQmI5SEprOG5KdlB1QnQ=
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Service_MSFT_TENANT_ID = common
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# Google Service configuration
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Service_GOOGLE_CLIENT_ID = 354925410565-aqs2b2qaiqmm73qpjnel6al8eid78uvg.apps.googleusercontent.com
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Service_GOOGLE_CLIENT_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpETDJhbGVQMHlFQzNPVFI1ZzBMa3pNMGlQUHhaQm10eVl1bFlSeTBybzlTOWE2MURXQ0hkRlo0NlNGbHQxWEl1OVkxQnVKYlhhOXR1cUF4T3k0WDdscktkY1oyYllRTmdDTWpfbUdwWGtSd1JvNlYxeTBJdEtaaS1vYnItcW0yaFM=
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# Tavily Web Search configuration
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Connector_WebTavily_API_KEY_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEQTdnUHMwd2pIaXNtMmtCTFREd0pyQXRKb1F5eGtHSnkyOGZiUnlBOFc0b3Vzcndrc3ViRm1nMDJIOEZKYWxqdWNkZGh5N0Z4R0JlQmxXSG5pVnJUR2VYckZhMWNMZ1FNeXJ3enJLVlpiblhOZTNleUg3ZzZyUzRZanFSeDlVMkI=
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# Google Cloud Speech Services configuration
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Connector_GoogleSpeech_API_KEY_SECRET = DEV_ENC: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# Feature SyncDelta JIRA configuration
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Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEbm0yRUJ6VUJKbUwyRW5kMnRaNW4wM2YxMkJUTXVXZUdmdVRCaUZIVHU2TTV2RWZLRmUtZkcwZE4yRUNlNDQ0aUJWYjNfdVg5YjV5c2JwMHhoUUYxZWdkeS11bXR0eGxRLWRVaVU3cUVQZWJlNDRtY1lWUDdqeDVFSlpXS0VFX21WajlRS3lHQjc0bS11akkybWV3QUFlR2hNWUNYLUdiRjZuN2dQODdDSExXWG1Dd2ZGclI2aUhlSWhETVZuY3hYdnhkb2c2LU1JTFBvWFpTNmZtMkNVOTZTejJwbDI2eGE0OS1xUlIwQnlCSmFxRFNCeVJNVzlOMDhTR1VUamx4RDRyV3p6Tk9qVHBrWWdySUM3TVRaYjd3N0JHMFhpdzFhZTNDLTFkRVQ2RVE4U19COXRhRWtNc0NVOHRqUS1CRDFpZ19xQmtFLU9YSDU3TXBZQXpVcld3PT0=
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@ -1,94 +0,0 @@
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# Development Environment Configuration
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# System Configuration
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APP_ENV_TYPE = dev
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APP_ENV_LABEL = Development Instance Patrick
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APP_API_URL = http://localhost:8000
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APP_KEY_SYSVAR = D:/Athi/Local/Web/poweron/local/key.txt
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APP_INIT_PASS_ADMIN_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEeFFtRGtQeVUtcjlrU3dab1ZxUm9WSks0MlJVYUtERFlqUElHemZrOGNENk1tcmJNX3Vxc01UMDhlNU40VzZZRVBpUGNmT3podzZrOGhOeEJIUEt4eVlSWG5UYXA3d09DVXlLT21Kb1JYSUU9
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APP_INIT_PASS_EVENT_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpERzZjNm56WGVBdjJTeG5Udjd6OGQwUVotYXUzQjJ1YVNyVXVBa3NZVml3ODU0MVNkZjhWWmJwNUFkc19BcHlHMTU1Q3BRcHU0cDBoZkFlR2l6UEZQU3d2U3MtMDh5UDZteGFoQ0EyMUE1ckE9
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# PostgreSQL Storage (new)
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DB_APP_HOST=localhost
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DB_APP_DATABASE=poweron_app
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DB_APP_USER=poweron_dev
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DB_APP_PASSWORD_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEcUIxNEFfQ2xnS0RrSC1KNnUxTlVvTGZoMHgzaEI4Z3NlVzVROTVLak5Ubi1vaEZubFZaMTFKMGd6MXAxekN2d2NvMy1hRjg2UVhybktlcFA5anZ1WjFlQmZhcXdwaGhWdzRDc3ExeUhzWTg9
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DB_APP_PORT=5432
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# PostgreSQL Storage (new)
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DB_CHAT_HOST=localhost
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DB_CHAT_DATABASE=poweron_chat
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DB_CHAT_USER=poweron_dev
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DB_CHAT_PASSWORD_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpERFNzNVhoalpCR0QxYXAwdEpXWXVVOTdZdWtqWW5FNXFGcFl2amNYLWYwYl9STXltRlFxLWNzVWlMVnNYdXk0RklnRExFT0FaQjg2aGswNnhhSGhCN29KN2VEb2FlUV9NTlV3b0tLelplSVU9
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DB_CHAT_PORT=5432
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# PostgreSQL Storage (new)
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DB_MANAGEMENT_HOST=localhost
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DB_MANAGEMENT_DATABASE=poweron_management
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DB_MANAGEMENT_USER=poweron_dev
|
||||
DB_MANAGEMENT_PASSWORD_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEUldqSTVpUnFqdGhITDYzT3RScGlMYVdTMmZhOXdudDRCc3dhdllOd3l6MS1vWHY2MjVsTUF1Sk9saEJOSk9ONUlBZjQwb2c2T1gtWWJhcXFzVVVXd01xc0U0b0lJX0JyVDRxaDhNS01JcWs9
|
||||
DB_MANAGEMENT_PORT=5432
|
||||
|
||||
# Security Configuration
|
||||
APP_JWT_KEY_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpERjlrSktmZHVuQnJ1VVJDdndLaUcxZGJsT2ZlUFRlcFdOZ001RnlzM2FhLWhRV2tjWWFhaWQwQ3hkcUFvbThMcndxSjFpYTdfRV9OZGhTcksxbXFTZWg5MDZvOHpCVXBHcDJYaHlJM0tyNWRZckZsVHpQcmxTZHJoZUs1M3lfU2ljRnJaTmNSQ0w0X085OXI0QW80M2xfQnJqZmZ6VEh3TUltX0xzeE42SGtZPQ==
|
||||
APP_TOKEN_EXPIRY=300
|
||||
|
||||
# CORS Configuration
|
||||
APP_ALLOWED_ORIGINS=http://localhost:8080,https://playground.poweron-center.net
|
||||
|
||||
# Logging configuration
|
||||
APP_LOGGING_LOG_LEVEL = DEBUG
|
||||
APP_LOGGING_LOG_DIR = D:/Athi/Local/Web/poweron/local/logs
|
||||
APP_LOGGING_FORMAT = %(asctime)s - %(levelname)s - %(name)s - %(message)s
|
||||
APP_LOGGING_DATE_FORMAT = %Y-%m-%d %H:%M:%S
|
||||
APP_LOGGING_CONSOLE_ENABLED = True
|
||||
APP_LOGGING_FILE_ENABLED = True
|
||||
APP_LOGGING_ROTATION_SIZE = 10485760
|
||||
APP_LOGGING_BACKUP_COUNT = 5
|
||||
|
||||
# Service Redirects
|
||||
Service_MSFT_REDIRECT_URI = http://localhost:8000/api/msft/auth/callback
|
||||
Service_GOOGLE_REDIRECT_URI = http://localhost:8000/api/google/auth/callback
|
||||
|
||||
# OpenAI configuration
|
||||
Connector_AiOpenai_API_URL = https://api.openai.com/v1/chat/completions
|
||||
Connector_AiOpenai_API_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEajBuZmtYTVdqLTBpQm9KZ2pCXzRCV3VhZzlYTEhKb1FqWXNrV3lyb25uZUN1WVVQUEY3dGYtejludV9MNGlKeVREanZGOGloV09mY2ttQ3k5SjBFOGFac2ZQTkNKNUZWVnRINVQyeWhsR2wyYnVrRDNzV2NqSHB0ajQ4UWtGeGZtbmR0Q3VvS0hDZlphVmpSc2Z6RG5nPT0=
|
||||
Connector_AiOpenai_MODEL_NAME = gpt-4o
|
||||
Connector_AiOpenai_TEMPERATURE = 0.2
|
||||
Connector_AiOpenai_MAX_TOKENS = 2000
|
||||
|
||||
# Anthropic configuration
|
||||
Connector_AiAnthropic_API_URL = https://api.anthropic.com/v1/messages
|
||||
Connector_AiAnthropic_API_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpENmFBWG16STFQUVZxNzZZRzRLYTA4X3lRanF1VkF4cU45OExNMzlsQmdISGFxTUxud1dXODBKcFhMVG9KNjdWVnlTTFFROVc3NDlsdlNHLUJXeG41NDBHaXhHR0VHVWl5UW9RNkVWbmlhakRKVW5pM0R4VHk0LUw0TV9LdkljNHdBLXJua21NQkl2b3l4UkVkMGN1YjBrMmJEeWtMay1jbmxrYWJNbUV0aktCXzU1djR2d2RSQXZORTNwcG92ZUVvVGMtQzQzTTVncEZTRGRtZUFIZWQ0dz09
|
||||
Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
|
||||
Connector_AiAnthropic_TEMPERATURE = 0.2
|
||||
Connector_AiAnthropic_MAX_TOKENS = 2000
|
||||
|
||||
# Perplexity AI configuration
|
||||
Connector_AiPerplexity_API_URL = https://api.perplexity.ai/chat/completions
|
||||
Connector_AiPerplexity_API_SECRET = DEV_ENC:Z0FBQUFBQm82Mzk2Q1MwZ0dNcUVBcUtuRDJIcTZkMXVvYnpjM3JEMzJiT1NKSHljX282ZDIyZTJYc09VSTdVNXAtOWU2UXp5S193NTk5dHJsWlFjRjhWektFOG1DVGY4ZUhHTXMzS0RPN1lNcF9nSlVWbW5BZ1hkZDVTejl6bVZNRFVvX29xamJidWRFMmtjQmkyRUQ2RUh6UTN1aWNPSUJBPT0=
|
||||
Connector_AiPerplexity_MODEL_NAME = sonar
|
||||
Connector_AiPerplexity_TEMPERATURE = 0.2
|
||||
Connector_AiPerplexity_MAX_TOKENS = 2000
|
||||
|
||||
# Agent Mail configuration
|
||||
Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
|
||||
Service_MSFT_CLIENT_SECRET = KDH8Q~H2OCtdvYy5yx6HOCYEbdnJCq90G21vTcPw
|
||||
Service_MSFT_TENANT_ID = common
|
||||
|
||||
# Google Service configuration
|
||||
Service_GOOGLE_CLIENT_ID = 354925410565-aqs2b2qaiqmm73qpjnel6al8eid78uvg.apps.googleusercontent.com
|
||||
Service_GOOGLE_CLIENT_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpETDJhbGVQMHlFQzNPVFI1ZzBMa3pNMGlQUHhaQm10eVl1bFlSeTBybzlTOWE2MURXQ0hkRlo0NlNGbHQxWEl1OVkxQnVKYlhhOXR1cUF4T3k0WDdscktkY1oyYllRTmdDTWpfbUdwWGtSd1JvNlYxeTBJdEtaaS1vYnItcW0yaFM=
|
||||
|
||||
# Tavily Web Search configuration
|
||||
Connector_WebTavily_API_KEY_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEQTdnUHMwd2pIaXNtMmtCTFREd0pyQXRKb1F5eGtHSnkyOGZiUnlBOFc0b3Vzcndrc3ViRm1nMDJIOEZKYWxqdWNkZGh5N0Z4R0JlQmxXSG5pVnJUR2VYckZhMWNMZ1FNeXJ3enJLVlpiblhOZTNleUg3ZzZyUzRZanFSeDlVMkI=
|
||||
|
||||
# Google Cloud Speech Services configuration
|
||||
Connector_GoogleSpeech_API_KEY_SECRET = DEV_ENC: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
|
||||
|
||||
# Feature SyncDelta JIRA configuration
|
||||
Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET = DEV_ENC:Z0FBQUFBQm8xSUpEbm0yRUJ6VUJKbUwyRW5kMnRaNW4wM2YxMkJUTXVXZUdmdVRCaUZIVHU2TTV2RWZLRmUtZkcwZE4yRUNlNDQ0aUJWYjNfdVg5YjV5c2JwMHhoUUYxZWdkeS11bXR0eGxRLWRVaVU3cUVQZWJlNDRtY1lWUDdqeDVFSlpXS0VFX21WajlRS3lHQjc0bS11akkybWV3QUFlR2hNWUNYLUdiRjZuN2dQODdDSExXWG1Dd2ZGclI2aUhlSWhETVZuY3hYdnhkb2c2LU1JTFBvWFpTNmZtMkNVOTZTejJwbDI2eGE0OS1xUlIwQnlCSmFxRFNCeVJNVzlOMDhTR1VUamx4RDRyV3p6Tk9qVHBrWWdySUM3TVRaYjd3N0JHMFhpdzFhZTNDLTFkRVQ2RVE4U19COXRhRWtNc0NVOHRqUS1CRDFpZ19xQmtFLU9YSDU3TXBZQXpVcld3PT0=
|
||||
|
||||
# Debug Configuration
|
||||
APP_DEBUG_CHAT_WORKFLOW_ENABLED = True
|
||||
APP_DEBUG_CHAT_WORKFLOW_DIR = ./test-chat
|
||||
|
|
@ -1,90 +0,0 @@
|
|||
# Integration Environment Configuration
|
||||
|
||||
# System Configuration
|
||||
APP_ENV_TYPE = int
|
||||
APP_ENV_LABEL = Integration Instance
|
||||
APP_API_URL = https://gateway-int.poweron-center.net
|
||||
APP_KEY_SYSVAR = CONFIG_KEY
|
||||
APP_INIT_PASS_ADMIN_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjWm41MWZ4TUZGaVlrX3pWZWNwakJsY3Facm0wLVZDd1VKeTFoZEVZQnItcEdUUnVJS1NXeDBpM2xKbGRsYmxOSmRhc29PZjJSU2txQjdLbUVrTTE1NEJjUXBHbV9NOVJWZUR3QlJkQnJvTEU9
|
||||
APP_INIT_PASS_EVENT_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjdmtrakgxa0djekZVNGtTZV8wM2I5UUpCZllveVBMWXROYk5yS3BiV3JEelJSM09VYTRONHpnY3VtMGxDRk5JTEZSRFhtcDZ0RVRmZ1RicTFhb3c5dVZRQ1o4SmlkLVpPTW5MMTU2eTQ0Vkk9
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_APP_HOST=gateway-int-server.postgres.database.azure.com
|
||||
DB_APP_DATABASE=poweron_app
|
||||
DB_APP_USER=heeshkdlby
|
||||
DB_APP_PASSWORD_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjb2dka2pnN0tUbW1EU0w1Rk1jNERKQ0Z1U3JkVDhuZWZDM0g5M0kwVDE5VHdubkZna3gtZVAxTnl4MDdrR1c1ZXJ3ejJHYkZvcGUwbHJaajBGOWJob0EzRXVHc0JnZkJyNGhHZTZHOXBxd2c9
|
||||
DB_APP_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_CHAT_HOST=gateway-int-server.postgres.database.azure.com
|
||||
DB_CHAT_DATABASE=poweron_chat
|
||||
DB_CHAT_USER=heeshkdlby
|
||||
DB_CHAT_PASSWORD_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjczYzOUtTa21MMGJVTUQ5UmFfdWc3YlhCbWZOeXFaNEE1QzdJV3BLVjhnalBkLVVCMm5BZzdxdlFXQXc2RHYzLWtPSFZkZE1iWG9rQ1NkVWlpRnF5TURVbnl1cm9iYXlSMGYxd1BGYVc0VDA9
|
||||
DB_CHAT_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_MANAGEMENT_HOST=gateway-int-server.postgres.database.azure.com
|
||||
DB_MANAGEMENT_DATABASE=poweron_management
|
||||
DB_MANAGEMENT_USER=heeshkdlby
|
||||
DB_MANAGEMENT_PASSWORD_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjTnJKNlJMNmEwQ0Y5dVNrR3pkZk9SQXVvLTRTNW9lQ1g3TTE5cFhBNTd5UENqWW9qdWd3NWNseWhnUHJveDJyd1Z3X1czS3VuZnAwZHBXYVNQWlZsRy12ME42NndEVlR5X3ZPdFBNNmhLYm89
|
||||
DB_MANAGEMENT_PORT=5432
|
||||
|
||||
# Security Configuration
|
||||
APP_JWT_KEY_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjNUctb2RwU25iR3ZnanBOdHZhWUtIajZ1RnZzTEp4aDR0MktWRjNoeVBrY1Npd1R0VE9YVHp3M2w1cXRzbUxNaU82QUJvaDNFeVQyN05KblRWblBvbWtoT0VXbkNBbDQ5OHhwSUFnaDZGRG10Vmgtdm1YUkRsYUhFMzRVZURmSFlDTFIzVWg4MXNueDZyMGc5aVpFdWRxY3dkTExGM093ZTVUZVl5LUhGWnlRPQ==
|
||||
APP_TOKEN_EXPIRY=300
|
||||
|
||||
# CORS Configuration
|
||||
APP_ALLOWED_ORIGINS=http://localhost:8080,https://playground.poweron-center.net,https://playground-int.poweron-center.net,http://localhost:5176,https://nyla.poweron-center.net, https://nyla-int.poweron-center.net
|
||||
|
||||
# Logging configuration
|
||||
APP_LOGGING_LOG_LEVEL = DEBUG
|
||||
APP_LOGGING_LOG_DIR = /home/site/wwwroot/
|
||||
APP_LOGGING_FORMAT = %(asctime)s - %(levelname)s - %(name)s - %(message)s
|
||||
APP_LOGGING_DATE_FORMAT = %Y-%m-%d %H:%M:%S
|
||||
APP_LOGGING_CONSOLE_ENABLED = True
|
||||
APP_LOGGING_FILE_ENABLED = True
|
||||
APP_LOGGING_ROTATION_SIZE = 10485760
|
||||
APP_LOGGING_BACKUP_COUNT = 5
|
||||
|
||||
# Service Redirects
|
||||
Service_MSFT_REDIRECT_URI = https://gateway-int.poweron-center.net/api/msft/auth/callback
|
||||
Service_GOOGLE_REDIRECT_URI = https://gateway-int.poweron-center.net/api/google/auth/callback
|
||||
|
||||
# OpenAI configuration
|
||||
Connector_AiOpenai_API_URL = https://api.openai.com/v1/chat/completions
|
||||
Connector_AiOpenai_API_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjSDBNYkptSkQxTUotYVVpZVNZc0dxNGNwSEtkOEE0T3RZWjROTEhSRlRXdlZmQUxxZ0w3Y0xOV2JNV19LNF9yTUZiU1pUNG15U2VDUDdSVlI4VlpnR3JXVFFtcXBaTEZiaUtSclVFd0lCZG1rWVhra1dfWTVQOTBEYUU0MjByYVNEMTFmeXNOcmpUT216MmJKdlVPeW5nPT0=
|
||||
Connector_AiOpenai_MODEL_NAME = gpt-4o
|
||||
Connector_AiOpenai_TEMPERATURE = 0.2
|
||||
Connector_AiOpenai_MAX_TOKENS = 2000
|
||||
|
||||
# Anthropic configuration
|
||||
Connector_AiAnthropic_API_URL = https://api.anthropic.com/v1/messages
|
||||
Connector_AiAnthropic_API_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjT1ZlRWVJdVZMT3ljSFJDcFdxRFBRVkZhS204NnN5RDBlQ0tpenhTM0FFVktuWW9mWHNwRWx2dHB0eDBSZ0JFQnZKWlp6c01pVGREWHd1eGpERnU0Q2xhaks1clQ1ZXVsdnd2ZzhpNXNQS1BhY3FjSkdkVEhHalNaRGR4emhpakZncnpDQUVxOHVXQzVUWmtQc0FsYmFwTF9TSG5FOUFtWk5Ick1NcHFvY2s1T1c2WXlRUFFJZnh6TWhuaVpMYmppcDR0QUx0a0R6RXlwbGRYb1R4dzJkUT09
|
||||
Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
|
||||
Connector_AiAnthropic_TEMPERATURE = 0.2
|
||||
Connector_AiAnthropic_MAX_TOKENS = 2000
|
||||
|
||||
# Perplexity AI configuration
|
||||
Connector_AiPerplexity_API_URL = https://api.perplexity.ai/chat/completions
|
||||
Connector_AiPerplexity_API_SECRET = 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
|
||||
Connector_AiPerplexity_MODEL_NAME = sonar
|
||||
Connector_AiPerplexity_TEMPERATURE = 0.2
|
||||
Connector_AiPerplexity_MAX_TOKENS = 2000
|
||||
|
||||
# Agent Mail configuration
|
||||
Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
|
||||
Service_MSFT_CLIENT_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjNzB2M3ZjaE1SVE9ON2FKam9yVURxcHl1Ym5VNVUtS0MyWUpNVXVlaWpWS2U3VVd3em9vQl9lcnVYay03bS04YjNBbDZZNTB4eUtjT3ppQjJjY3dOT0FNLW9LeDhIUU5iaTNqNURUWE5La3kzaHNGcU9yNVI0YjhWZTZRRFktcTk=
|
||||
Service_MSFT_TENANT_ID = common
|
||||
|
||||
# Google Service configuration
|
||||
Service_GOOGLE_CLIENT_ID = 354925410565-aqs2b2qaiqmm73qpjnel6al8eid78uvg.apps.googleusercontent.com
|
||||
Service_GOOGLE_CLIENT_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjNThGeVRNd3hacThtRnE0bzlDa0JPUWQyaEd6QjlFckdsMGZjRlRfUks2bXV3aDdVRTF3LVRlZVY5WjVzSXV4ZGNnX002RDl3dkNYdGFzZkxVUW01My1wTHRCanVCLUozZEx4TlduQlB5MnpvNTR2SGlvbFl1YkhzTEtsSi1SOEo=
|
||||
|
||||
# Tavily Web Search configuration
|
||||
Connector_WebTavily_API_KEY_SECRET = INT_ENC:Z0FBQUFBQm8xSVRkdkJMTDY0akhXNzZDWHVYSEt1cDZoOWEzSktneHZEV2JndTNmWlNSMV9KbFNIZmQzeVlrNE5qUEIwcUlBSGM1a0hOZ3J6djIyOVhnZzI3M1dIUkdicl9FVXF3RGktMmlEYmhnaHJfWTdGUkktSXVUSGdQMC1vSEV6VE8zR2F1SVk=
|
||||
|
||||
# Google Cloud Speech Services configuration
|
||||
Connector_GoogleSpeech_API_KEY_SECRET = INT_ENC: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
|
||||
|
||||
# Feature SyncDelta JIRA configuration
|
||||
Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET = INT_ENC:Z0FBQUFBQm8xSVRkTUNsWm4wX0p6eXFDZmJ4dFdHNEs1MV9MUzdrb3RzeC1jVWVYZ0REWHRyZkFiaGZLcUQtTXFBZzZkNzRmQ0gxbEhGbUNlVVFfR1JEQTc0aldkZkgyWnBOcjdlUlZxR0tDTEdKRExULXAyUEtsVmNTMkRKU1BJNnFiM0hlMXo4YndMcHlRMExtZDQ3Zm9vNFhMcEZCcHpBPT0=
|
||||
|
|
@ -1,94 +0,0 @@
|
|||
# Integration Environment Configuration
|
||||
|
||||
# System Configuration
|
||||
APP_ENV_TYPE = int
|
||||
APP_ENV_LABEL = Integration Instance
|
||||
APP_API_URL = https://gateway-int.poweron-center.net
|
||||
APP_KEY_SYSVAR = CONFIG_KEY
|
||||
APP_INIT_PASS_ADMIN_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjWm41MWZ4TUZGaVlrX3pWZWNwakJsY3Facm0wLVZDd1VKeTFoZEVZQnItcEdUUnVJS1NXeDBpM2xKbGRsYmxOSmRhc29PZjJSU2txQjdLbUVrTTE1NEJjUXBHbV9NOVJWZUR3QlJkQnJvTEU9
|
||||
APP_INIT_PASS_EVENT_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjdmtrakgxa0djekZVNGtTZV8wM2I5UUpCZllveVBMWXROYk5yS3BiV3JEelJSM09VYTRONHpnY3VtMGxDRk5JTEZSRFhtcDZ0RVRmZ1RicTFhb3c5dVZRQ1o4SmlkLVpPTW5MMTU2eTQ0Vkk9
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_APP_HOST=gateway-int-server.postgres.database.azure.com
|
||||
DB_APP_DATABASE=poweron_app
|
||||
DB_APP_USER=heeshkdlby
|
||||
DB_APP_PASSWORD_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjb2dka2pnN0tUbW1EU0w1Rk1jNERKQ0Z1U3JkVDhuZWZDM0g5M0kwVDE5VHdubkZna3gtZVAxTnl4MDdrR1c1ZXJ3ejJHYkZvcGUwbHJaajBGOWJob0EzRXVHc0JnZkJyNGhHZTZHOXBxd2c9
|
||||
DB_APP_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_CHAT_HOST=gateway-int-server.postgres.database.azure.com
|
||||
DB_CHAT_DATABASE=poweron_chat
|
||||
DB_CHAT_USER=heeshkdlby
|
||||
DB_CHAT_PASSWORD_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjczYzOUtTa21MMGJVTUQ5UmFfdWc3YlhCbWZOeXFaNEE1QzdJV3BLVjhnalBkLVVCMm5BZzdxdlFXQXc2RHYzLWtPSFZkZE1iWG9rQ1NkVWlpRnF5TURVbnl1cm9iYXlSMGYxd1BGYVc0VDA9
|
||||
DB_CHAT_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_MANAGEMENT_HOST=gateway-int-server.postgres.database.azure.com
|
||||
DB_MANAGEMENT_DATABASE=poweron_management
|
||||
DB_MANAGEMENT_USER=heeshkdlby
|
||||
DB_MANAGEMENT_PASSWORD_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjTnJKNlJMNmEwQ0Y5dVNrR3pkZk9SQXVvLTRTNW9lQ1g3TTE5cFhBNTd5UENqWW9qdWd3NWNseWhnUHJveDJyd1Z3X1czS3VuZnAwZHBXYVNQWlZsRy12ME42NndEVlR5X3ZPdFBNNmhLYm89
|
||||
DB_MANAGEMENT_PORT=5432
|
||||
|
||||
# Security Configuration
|
||||
APP_JWT_KEY_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjNUctb2RwU25iR3ZnanBOdHZhWUtIajZ1RnZzTEp4aDR0MktWRjNoeVBrY1Npd1R0VE9YVHp3M2w1cXRzbUxNaU82QUJvaDNFeVQyN05KblRWblBvbWtoT0VXbkNBbDQ5OHhwSUFnaDZGRG10Vmgtdm1YUkRsYUhFMzRVZURmSFlDTFIzVWg4MXNueDZyMGc5aVpFdWRxY3dkTExGM093ZTVUZVl5LUhGWnlRPQ==
|
||||
APP_TOKEN_EXPIRY=300
|
||||
|
||||
# CORS Configuration
|
||||
APP_ALLOWED_ORIGINS=http://localhost:8080,https://playground.poweron-center.net,https://playground-int.poweron-center.net,http://localhost:5176,https://nyla.poweron-center.net, https://nyla-int.poweron-center.net
|
||||
|
||||
# Logging configuration
|
||||
APP_LOGGING_LOG_LEVEL = DEBUG
|
||||
APP_LOGGING_LOG_DIR = /home/site/wwwroot/
|
||||
APP_LOGGING_FORMAT = %(asctime)s - %(levelname)s - %(name)s - %(message)s
|
||||
APP_LOGGING_DATE_FORMAT = %Y-%m-%d %H:%M:%S
|
||||
APP_LOGGING_CONSOLE_ENABLED = True
|
||||
APP_LOGGING_FILE_ENABLED = True
|
||||
APP_LOGGING_ROTATION_SIZE = 10485760
|
||||
APP_LOGGING_BACKUP_COUNT = 5
|
||||
|
||||
# Service Redirects
|
||||
Service_MSFT_REDIRECT_URI = https://gateway-int.poweron-center.net/api/msft/auth/callback
|
||||
Service_GOOGLE_REDIRECT_URI = https://gateway-int.poweron-center.net/api/google/auth/callback
|
||||
|
||||
# OpenAI configuration
|
||||
Connector_AiOpenai_API_URL = https://api.openai.com/v1/chat/completions
|
||||
Connector_AiOpenai_API_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjSDBNYkptSkQxTUotYVVpZVNZc0dxNGNwSEtkOEE0T3RZWjROTEhSRlRXdlZmQUxxZ0w3Y0xOV2JNV19LNF9yTUZiU1pUNG15U2VDUDdSVlI4VlpnR3JXVFFtcXBaTEZiaUtSclVFd0lCZG1rWVhra1dfWTVQOTBEYUU0MjByYVNEMTFmeXNOcmpUT216MmJKdlVPeW5nPT0=
|
||||
Connector_AiOpenai_MODEL_NAME = gpt-4o
|
||||
Connector_AiOpenai_TEMPERATURE = 0.2
|
||||
Connector_AiOpenai_MAX_TOKENS = 2000
|
||||
|
||||
# Anthropic configuration
|
||||
Connector_AiAnthropic_API_URL = https://api.anthropic.com/v1/messages
|
||||
Connector_AiAnthropic_API_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjT1ZlRWVJdVZMT3ljSFJDcFdxRFBRVkZhS204NnN5RDBlQ0tpenhTM0FFVktuWW9mWHNwRWx2dHB0eDBSZ0JFQnZKWlp6c01pVGREWHd1eGpERnU0Q2xhaks1clQ1ZXVsdnd2ZzhpNXNQS1BhY3FjSkdkVEhHalNaRGR4emhpakZncnpDQUVxOHVXQzVUWmtQc0FsYmFwTF9TSG5FOUFtWk5Ick1NcHFvY2s1T1c2WXlRUFFJZnh6TWhuaVpMYmppcDR0QUx0a0R6RXlwbGRYb1R4dzJkUT09
|
||||
Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
|
||||
Connector_AiAnthropic_TEMPERATURE = 0.2
|
||||
Connector_AiAnthropic_MAX_TOKENS = 2000
|
||||
|
||||
# Perplexity AI configuration
|
||||
Connector_AiPerplexity_API_URL = https://api.perplexity.ai/chat/completions
|
||||
Connector_AiPerplexity_API_SECRET = INT_ENC:Z0FBQUFBQm82Mzk2UWZJdUFhSW8yc3RKc0tKRXphd0xWMkZOVlFpSGZ4SGhFWnk0cTF5VjlKQVZjdS1QSWdkS0pUSWw4OFU5MjUxdTVQel9aeWVIZTZ5TXRuVmFkZG0zWEdTOGdHMHpsTzI0TGlWYURKU1Q0VVpKTlhxUk5FTmN6SUJScDZ3ZldIaUJZcWpaQVRiSEpyQm9tRTNDWk9KTnZBPT0=
|
||||
Connector_AiPerplexity_MODEL_NAME = sonar
|
||||
Connector_AiPerplexity_TEMPERATURE = 0.2
|
||||
Connector_AiPerplexity_MAX_TOKENS = 2000
|
||||
|
||||
# Agent Mail configuration
|
||||
Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
|
||||
Service_MSFT_CLIENT_SECRET = KDH8Q~H2OCtdvYy5yx6HOCYEbdnJCq90G21vTcPw
|
||||
Service_MSFT_TENANT_ID = common
|
||||
|
||||
# Google Service configuration
|
||||
Service_GOOGLE_CLIENT_ID = 354925410565-aqs2b2qaiqmm73qpjnel6al8eid78uvg.apps.googleusercontent.com
|
||||
Service_GOOGLE_CLIENT_SECRET = INT_ENC:Z0FBQUFBQm8xSVRjNThGeVRNd3hacThtRnE0bzlDa0JPUWQyaEd6QjlFckdsMGZjRlRfUks2bXV3aDdVRTF3LVRlZVY5WjVzSXV4ZGNnX002RDl3dkNYdGFzZkxVUW01My1wTHRCanVCLUozZEx4TlduQlB5MnpvNTR2SGlvbFl1YkhzTEtsSi1SOEo=
|
||||
|
||||
# Tavily Web Search configuration
|
||||
Connector_WebTavily_API_KEY_SECRET = INT_ENC:Z0FBQUFBQm8xSVRkdkJMTDY0akhXNzZDWHVYSEt1cDZoOWEzSktneHZEV2JndTNmWlNSMV9KbFNIZmQzeVlrNE5qUEIwcUlBSGM1a0hOZ3J6djIyOVhnZzI3M1dIUkdicl9FVXF3RGktMmlEYmhnaHJfWTdGUkktSXVUSGdQMC1vSEV6VE8zR2F1SVk=
|
||||
|
||||
# Google Cloud Speech Services configuration
|
||||
Connector_GoogleSpeech_API_KEY_SECRET = INT_ENC: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
|
||||
|
||||
# Feature SyncDelta JIRA configuration
|
||||
Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET = INT_ENC:Z0FBQUFBQm8xSVRkTUNsWm4wX0p6eXFDZmJ4dFdHNEs1MV9MUzdrb3RzeC1jVWVYZ0REWHRyZkFiaGZLcUQtTXFBZzZkNzRmQ0gxbEhGbUNlVVFfR1JEQTc0aldkZkgyWnBOcjdlUlZxR0tDTEdKRExULXAyUEtsVmNTMkRKU1BJNnFiM0hlMXo4YndMcHlRMExtZDQ3Zm9vNFhMcEZCcHpBPT0=
|
||||
|
||||
# Debug Configuration
|
||||
APP_DEBUG_CHAT_WORKFLOW_ENABLED = FALSE
|
||||
APP_DEBUG_CHAT_WORKFLOW_DIR = ./test-chat
|
||||
|
|
@ -1,90 +0,0 @@
|
|||
# Production Environment Configuration
|
||||
|
||||
# System Configuration
|
||||
APP_ENV_TYPE = prod
|
||||
APP_ENV_LABEL = Production Instance
|
||||
APP_API_URL = https://gateway.poweron-center.net
|
||||
APP_KEY_SYSVAR = CONFIG_KEY
|
||||
APP_INIT_PASS_ADMIN_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pSXoyVEVwNDZ6cmthQTROUkxGUjh1UWF2UU5zaWRuX3p2aHJCVFo2NEstR0RqdnQ5clZmeVliRlhHZGFHTlhZV2dzMmRPZFVEemVlSHd5VHR3cmpNUXRaRlhZSFZ6d1dsX2Y5Zl9lOXdYdEU9
|
||||
APP_INIT_PASS_EVENT_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5peGNMWExjWGZxQ2VndXVOSUVGcWhQTWd0N3d0blU3bGJvNjgzNVVNNktCQnZlTEtVckV5RUtQMjMwRTBkdmxEMlZwX0k1M1hlOFFNY3hjaWsyd2JmRGl2UWxfSXEwenVnQ3NmaTlxckp2VXM9
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_APP_HOST=gateway-prod-server.postgres.database.azure.com
|
||||
DB_APP_DATABASE=poweron_app
|
||||
DB_APP_USER=gzxxmcrdhn
|
||||
DB_APP_PASSWORD_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pVmtwYWZQakdWZnJPamVlRWJPa0tnc3daSVVHejVrQ0x1VFZZbHhVSkk0S2tFWl92T2NwWURBMU9UbFROMHZ2TkNKZFlEWjhJZDZ0bnFndC1oYjhNRW1VLWpEYnlDNEJwcGVKckpUVlp6YTg9
|
||||
DB_APP_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_CHAT_HOST=gateway-prod-server.postgres.database.azure.com
|
||||
DB_CHAT_DATABASE=poweron_chat
|
||||
DB_CHAT_USER=gzxxmcrdhn
|
||||
DB_CHAT_PASSWORD_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pZVZnTzBPTDY1Q3c2U1pDV0lxbXhoWnlYSXRDWVhIeGJwSkdNMzMxR2h5a1FRN00xcWtYUE4ySGpqRllSaGM5SmRZZk9Bd2trVDJNZDdWcEFIbTJtel91MHpsazlTQnRsV2docGdBc0RVeEU9
|
||||
DB_CHAT_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_MANAGEMENT_HOST=gateway-prod-server.postgres.database.azure.com
|
||||
DB_MANAGEMENT_DATABASE=poweron_management
|
||||
DB_MANAGEMENT_USER=gzxxmcrdhn
|
||||
DB_MANAGEMENT_PASSWORD_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pQXdaRnVEQUx2MmU5ck9XZzNfaGVoRXlYMlVjSVM5dWNTekhmR2VYNkd6WVhELUlkLWdFWWRWQ1JJLWZ4WUNwclZVRlg3ZHBCS0xwM1laNklTaEs1czFDRTMxYlV2TWNueEJlTHFyNEt4aVk9
|
||||
DB_MANAGEMENT_PORT=5432
|
||||
|
||||
# Security Configuration
|
||||
APP_JWT_KEY_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pY3JfX1R3cEJhTjAzZGx2amtRSE4yVzZhMmY3a3FHam9BdzBxVWd5R0FRSW1KbmNGS3JDMktKTWptZm4wYmZZZTVDQkh3NVlxSW1MZEdiVWdORng4dm0xV08wZDh0YlBNQTdEbmlnVWduMzNWY1RPX1BqaGtnOTc2ZWNBTnNnd1AtaTNRUExpRThVdzNmdVFHM2hkTjFjcW0ya2szMWNaT3VDeDhXMlJ1NDM4PQ==
|
||||
APP_TOKEN_EXPIRY=300
|
||||
|
||||
# CORS Configuration
|
||||
APP_ALLOWED_ORIGINS=http://localhost:8080,https://playground.poweron-center.net,https://playground-int.poweron-center.net,http://localhost:5176,https://nyla.poweron-center.net,https://nyla-int.poweron-center.net
|
||||
|
||||
# Logging configuration
|
||||
APP_LOGGING_LOG_LEVEL = DEBUG
|
||||
APP_LOGGING_LOG_DIR = /home/site/wwwroot/
|
||||
APP_LOGGING_FORMAT = %(asctime)s - %(levelname)s - %(name)s - %(message)s
|
||||
APP_LOGGING_DATE_FORMAT = %Y-%m-%d %H:%M:%S
|
||||
APP_LOGGING_CONSOLE_ENABLED = True
|
||||
APP_LOGGING_FILE_ENABLED = True
|
||||
APP_LOGGING_ROTATION_SIZE = 10485760
|
||||
APP_LOGGING_BACKUP_COUNT = 5
|
||||
|
||||
# Service Redirects
|
||||
Service_MSFT_REDIRECT_URI = https://gateway-prod.poweron-center.net/api/msft/auth/callback
|
||||
Service_GOOGLE_REDIRECT_URI = https://gateway-prod.poweron-center.net/api/google/auth/callback
|
||||
|
||||
# OpenAI configuration
|
||||
Connector_AiOpenai_API_URL = https://api.openai.com/v1/chat/completions
|
||||
Connector_AiOpenai_API_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pU05XM2hMaExPMnpYeFpwRVhyYl9JZmRITmlmRDlWOUJSSWE4NTFLZUptSkJhNlEycHBLZmh3WFA2ZmU5VmxHZks1UUNVOUZnckZNdXZ2MTY2dFg1Nl8yWDRrcTRlT0tHYkhyRGZINTEzU25iYVFRMzJGeUZIdlc4LU9GbmpQYmtmU3lJT2VVZ1UzLVd3R25ZQ092SUVnPT0=
|
||||
Connector_AiOpenai_MODEL_NAME = gpt-4o
|
||||
Connector_AiOpenai_TEMPERATURE = 0.2
|
||||
Connector_AiOpenai_MAX_TOKENS = 2000
|
||||
|
||||
# Anthropic configuration
|
||||
Connector_AiAnthropic_API_URL = https://api.anthropic.com/v1/messages
|
||||
Connector_AiAnthropic_API_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pNTA1RkZ3UllCOXVsNVZzbkw2Rkl1TWxCZ0wwWEVXUm9ReUhBcVl1cGFUdW9FRVh4elVxR0x3NVRxZkc4SkxHVFdzSU1YNG5Rb0FqSHJhdElwWm1iLWdubTVDcUl3UkVjVHNoU0xLa0ZTSFlfTlJUVXg4cVVwUWdlVDBTSFU5SnBzS0ZnVjlQcmtiNzV2UTNMck1IakZ0OWlubUtlWDZnMk4yX2JsZ1U4Wm1yT29fM2d2NVBNOWNBbWtTRWNyQ2tZNjhwSVF6bG5SU3dTenR2MzA3Z19NUT09
|
||||
Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
|
||||
Connector_AiAnthropic_TEMPERATURE = 0.2
|
||||
Connector_AiAnthropic_MAX_TOKENS = 2000
|
||||
|
||||
# Perplexity AI configuration
|
||||
Connector_AiPerplexity_API_URL = https://api.perplexity.ai/chat/completions
|
||||
Connector_AiPerplexity_API_SECRET = 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
|
||||
Connector_AiPerplexity_MODEL_NAME = sonar
|
||||
Connector_AiPerplexity_TEMPERATURE = 0.2
|
||||
Connector_AiPerplexity_MAX_TOKENS = 2000
|
||||
|
||||
# Agent Mail configuration
|
||||
Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
|
||||
Service_MSFT_CLIENT_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pVEhHdlZHU3FNMmhuRGVwaGc3YzIxSjlZNzBCQjlOV2pSYVNXb0t1ZnVwQzZsQzY4cHMtVlZtNF85OEVaV1BMTzdXMmpzaGZpaG1DalJ0bkNPMHA5ZUcwZjNDdGk1TFdxYTJSZnVrVmhhZ2VRUEZxbjJOOGFhWk9EYlY3dmRVTnI=
|
||||
Service_MSFT_TENANT_ID = common
|
||||
|
||||
# Google Service configuration
|
||||
Service_GOOGLE_CLIENT_ID = 354925410565-aqs2b2qaiqmm73qpjnel6al8eid78uvg.apps.googleusercontent.com
|
||||
Service_GOOGLE_CLIENT_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pV2JEV0lNUXhwa1VTUGh2RWcyYnJHSFQyTmdBOEhwRkJWc3MwOFZlcHJGUmlGOVVFbG1XalNyUXVuaExESy1xeFNIQlRiSFVIWTB6Rm1fNFg0OHZZSkF4ZlBIcFZDMjZHcFRERXJ0WlVFclhHa29Za1BqWGxsM05NZGFRc1BLZnE=
|
||||
|
||||
# Tavily Web Search configuration
|
||||
Connector_WebTavily_API_KEY_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pMjhJNS1CZFJubUlkN3ZrTUoxR0Y1QzJFWEJSMk0wQkI0UndqOW1UelVieWhGaTVBcHoxRXo1VjRzVVRROHFIeHMyS3Q5cDZCeUlEMzE1ZlhVTmNveFk5VmFQMm80NTRyVW1TZHVsR3dUN0RtMnd4LW1VWlpqOXJPeXZBTmg4OEM=
|
||||
|
||||
# Google Cloud Speech Services configuration
|
||||
Connector_GoogleSpeech_API_KEY_SECRET = PROD_ENC: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
|
||||
|
||||
# Feature SyncDelta JIRA configuration
|
||||
Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pTDhnTVNzRUhScU8wYnZsZk52bHFkSWxLc18xQmtCeC1HbnNwTzVBbXRNTmQzRjZYaGE2MVlCNGtnWDk1T2I5VXVKNHpKU1VRbXEyN2tRWUJnU2ltZE5qZ3lmNEF6Z1hMTTEwZkk2NUNBYjhmVTJEcWpRUW9HNEVpSGFWdjBWQXQ3eUtHUTFJS3U5QWpaeno0RFNhMUxnPT0=
|
||||
|
|
@ -1,94 +0,0 @@
|
|||
# Production Environment Configuration
|
||||
|
||||
# System Configuration
|
||||
APP_ENV_TYPE = prod
|
||||
APP_ENV_LABEL = Production Instance
|
||||
APP_API_URL = https://gateway.poweron-center.net
|
||||
APP_KEY_SYSVAR = CONFIG_KEY
|
||||
APP_INIT_PASS_ADMIN_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pSXoyVEVwNDZ6cmthQTROUkxGUjh1UWF2UU5zaWRuX3p2aHJCVFo2NEstR0RqdnQ5clZmeVliRlhHZGFHTlhZV2dzMmRPZFVEemVlSHd5VHR3cmpNUXRaRlhZSFZ6d1dsX2Y5Zl9lOXdYdEU9
|
||||
APP_INIT_PASS_EVENT_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5peGNMWExjWGZxQ2VndXVOSUVGcWhQTWd0N3d0blU3bGJvNjgzNVVNNktCQnZlTEtVckV5RUtQMjMwRTBkdmxEMlZwX0k1M1hlOFFNY3hjaWsyd2JmRGl2UWxfSXEwenVnQ3NmaTlxckp2VXM9
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_APP_HOST=gateway-prod-server.postgres.database.azure.com
|
||||
DB_APP_DATABASE=poweron_app
|
||||
DB_APP_USER=gzxxmcrdhn
|
||||
DB_APP_PASSWORD_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pVmtwYWZQakdWZnJPamVlRWJPa0tnc3daSVVHejVrQ0x1VFZZbHhVSkk0S2tFWl92T2NwWURBMU9UbFROMHZ2TkNKZFlEWjhJZDZ0bnFndC1oYjhNRW1VLWpEYnlDNEJwcGVKckpUVlp6YTg9
|
||||
DB_APP_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_CHAT_HOST=gateway-prod-server.postgres.database.azure.com
|
||||
DB_CHAT_DATABASE=poweron_chat
|
||||
DB_CHAT_USER=gzxxmcrdhn
|
||||
DB_CHAT_PASSWORD_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pZVZnTzBPTDY1Q3c2U1pDV0lxbXhoWnlYSXRDWVhIeGJwSkdNMzMxR2h5a1FRN00xcWtYUE4ySGpqRllSaGM5SmRZZk9Bd2trVDJNZDdWcEFIbTJtel91MHpsazlTQnRsV2docGdBc0RVeEU9
|
||||
DB_CHAT_PORT=5432
|
||||
|
||||
# PostgreSQL Storage (new)
|
||||
DB_MANAGEMENT_HOST=gateway-prod-server.postgres.database.azure.com
|
||||
DB_MANAGEMENT_DATABASE=poweron_management
|
||||
DB_MANAGEMENT_USER=gzxxmcrdhn
|
||||
DB_MANAGEMENT_PASSWORD_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pQXdaRnVEQUx2MmU5ck9XZzNfaGVoRXlYMlVjSVM5dWNTekhmR2VYNkd6WVhELUlkLWdFWWRWQ1JJLWZ4WUNwclZVRlg3ZHBCS0xwM1laNklTaEs1czFDRTMxYlV2TWNueEJlTHFyNEt4aVk9
|
||||
DB_MANAGEMENT_PORT=5432
|
||||
|
||||
# Security Configuration
|
||||
APP_JWT_KEY_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pY3JfX1R3cEJhTjAzZGx2amtRSE4yVzZhMmY3a3FHam9BdzBxVWd5R0FRSW1KbmNGS3JDMktKTWptZm4wYmZZZTVDQkh3NVlxSW1MZEdiVWdORng4dm0xV08wZDh0YlBNQTdEbmlnVWduMzNWY1RPX1BqaGtnOTc2ZWNBTnNnd1AtaTNRUExpRThVdzNmdVFHM2hkTjFjcW0ya2szMWNaT3VDeDhXMlJ1NDM4PQ==
|
||||
APP_TOKEN_EXPIRY=300
|
||||
|
||||
# CORS Configuration
|
||||
APP_ALLOWED_ORIGINS=http://localhost:8080,https://playground.poweron-center.net,https://playground-int.poweron-center.net,http://localhost:5176,https://nyla.poweron-center.net,https://nyla-int.poweron-center.net
|
||||
|
||||
# Logging configuration
|
||||
APP_LOGGING_LOG_LEVEL = DEBUG
|
||||
APP_LOGGING_LOG_DIR = /home/site/wwwroot/
|
||||
APP_LOGGING_FORMAT = %(asctime)s - %(levelname)s - %(name)s - %(message)s
|
||||
APP_LOGGING_DATE_FORMAT = %Y-%m-%d %H:%M:%S
|
||||
APP_LOGGING_CONSOLE_ENABLED = True
|
||||
APP_LOGGING_FILE_ENABLED = True
|
||||
APP_LOGGING_ROTATION_SIZE = 10485760
|
||||
APP_LOGGING_BACKUP_COUNT = 5
|
||||
|
||||
# Service Redirects
|
||||
Service_MSFT_REDIRECT_URI = https://gateway-prod.poweron-center.net/api/msft/auth/callback
|
||||
Service_GOOGLE_REDIRECT_URI = https://gateway-prod.poweron-center.net/api/google/auth/callback
|
||||
|
||||
# OpenAI configuration
|
||||
Connector_AiOpenai_API_URL = https://api.openai.com/v1/chat/completions
|
||||
Connector_AiOpenai_API_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pU05XM2hMaExPMnpYeFpwRVhyYl9JZmRITmlmRDlWOUJSSWE4NTFLZUptSkJhNlEycHBLZmh3WFA2ZmU5VmxHZks1UUNVOUZnckZNdXZ2MTY2dFg1Nl8yWDRrcTRlT0tHYkhyRGZINTEzU25iYVFRMzJGeUZIdlc4LU9GbmpQYmtmU3lJT2VVZ1UzLVd3R25ZQ092SUVnPT0=
|
||||
Connector_AiOpenai_MODEL_NAME = gpt-4o
|
||||
Connector_AiOpenai_TEMPERATURE = 0.2
|
||||
Connector_AiOpenai_MAX_TOKENS = 2000
|
||||
|
||||
# Anthropic configuration
|
||||
Connector_AiAnthropic_API_URL = https://api.anthropic.com/v1/messages
|
||||
Connector_AiAnthropic_API_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pNTA1RkZ3UllCOXVsNVZzbkw2Rkl1TWxCZ0wwWEVXUm9ReUhBcVl1cGFUdW9FRVh4elVxR0x3NVRxZkc4SkxHVFdzSU1YNG5Rb0FqSHJhdElwWm1iLWdubTVDcUl3UkVjVHNoU0xLa0ZTSFlfTlJUVXg4cVVwUWdlVDBTSFU5SnBzS0ZnVjlQcmtiNzV2UTNMck1IakZ0OWlubUtlWDZnMk4yX2JsZ1U4Wm1yT29fM2d2NVBNOWNBbWtTRWNyQ2tZNjhwSVF6bG5SU3dTenR2MzA3Z19NUT09
|
||||
Connector_AiAnthropic_MODEL_NAME = claude-3-5-sonnet-20241022
|
||||
Connector_AiAnthropic_TEMPERATURE = 0.2
|
||||
Connector_AiAnthropic_MAX_TOKENS = 2000
|
||||
|
||||
# Perplexity AI configuration
|
||||
Connector_AiPerplexity_API_URL = https://api.perplexity.ai/chat/completions
|
||||
Connector_AiPerplexity_API_SECRET = PROD_ENC:Z0FBQUFBQm82Mzk2Q1FGRkJEUkI4LXlQbHYzT2RkdVJEcmM4WGdZTWpJTEhoeUF1NW5LUVpJdDBYN3k1WFN4a2FQSWJSQmd0U0xJbzZDTmFFN05FcXl0Z3V1OEpsZjYydV94TXVjVjVXRTRYSWdLMkd5XzZIbFV6emRCZHpuOUpQeThadE5xcDNDVGV1RHJrUEN0c1BBYXctZFNWcFRuVXhRPT0=
|
||||
Connector_AiPerplexity_MODEL_NAME = sonar
|
||||
Connector_AiPerplexity_TEMPERATURE = 0.2
|
||||
Connector_AiPerplexity_MAX_TOKENS = 2000
|
||||
|
||||
# Agent Mail configuration
|
||||
Service_MSFT_CLIENT_ID = c7e7112d-61dc-4f3a-8cd3-08cc4cd7504c
|
||||
Service_MSFT_CLIENT_SECRET = KDH8Q~H2OCtdvYy5yx6HOCYEbdnJCq90G21vTcPw
|
||||
Service_MSFT_TENANT_ID = common
|
||||
|
||||
# Google Service configuration
|
||||
Service_GOOGLE_CLIENT_ID = 354925410565-aqs2b2qaiqmm73qpjnel6al8eid78uvg.apps.googleusercontent.com
|
||||
Service_GOOGLE_CLIENT_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pV2JEV0lNUXhwa1VTUGh2RWcyYnJHSFQyTmdBOEhwRkJWc3MwOFZlcHJGUmlGOVVFbG1XalNyUXVuaExESy1xeFNIQlRiSFVIWTB6Rm1fNFg0OHZZSkF4ZlBIcFZDMjZHcFRERXJ0WlVFclhHa29Za1BqWGxsM05NZGFRc1BLZnE=
|
||||
|
||||
# Tavily Web Search configuration
|
||||
Connector_WebTavily_API_KEY_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pMjhJNS1CZFJubUlkN3ZrTUoxR0Y1QzJFWEJSMk0wQkI0UndqOW1UelVieWhGaTVBcHoxRXo1VjRzVVRROHFIeHMyS3Q5cDZCeUlEMzE1ZlhVTmNveFk5VmFQMm80NTRyVW1TZHVsR3dUN0RtMnd4LW1VWlpqOXJPeXZBTmg4OEM=
|
||||
|
||||
# Google Cloud Speech Services configuration
|
||||
Connector_GoogleSpeech_API_KEY_SECRET = PROD_ENC: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
|
||||
|
||||
# Feature SyncDelta JIRA configuration
|
||||
Feature_SyncDelta_JIRA_DELTA_TOKEN_SECRET = PROD_ENC:Z0FBQUFBQm8xSU5pTDhnTVNzRUhScU8wYnZsZk52bHFkSWxLc18xQmtCeC1HbnNwTzVBbXRNTmQzRjZYaGE2MVlCNGtnWDk1T2I5VXVKNHpKU1VRbXEyN2tRWUJnU2ltZE5qZ3lmNEF6Z1hMTTEwZkk2NUNBYjhmVTJEcWpRUW9HNEVpSGFWdjBWQXQ3eUtHUTFJS3U5QWpaeno0RFNhMUxnPT0=
|
||||
|
||||
# Debug Configuration
|
||||
APP_DEBUG_CHAT_WORKFLOW_ENABLED = FALSE
|
||||
APP_DEBUG_CHAT_WORKFLOW_DIR = ./test-chat
|
||||
File diff suppressed because it is too large
Load diff
596
modules/services/serviceAi/subCoreAi.py
Normal file
596
modules/services/serviceAi/subCoreAi.py
Normal file
|
|
@ -0,0 +1,596 @@
|
|||
import logging
|
||||
from typing import Dict, Any, List, Optional, Tuple, Union
|
||||
from modules.datamodels.datamodelChat import PromptPlaceholder, ChatDocument
|
||||
from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, ModelCapabilities, OperationType, Priority
|
||||
from modules.interfaces.interfaceAiObjects import AiObjects
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SubCoreAi:
|
||||
"""Core AI operations including image analysis, text generation, and planning calls."""
|
||||
|
||||
def __init__(self, services, aiObjects):
|
||||
"""Initialize core AI operations.
|
||||
|
||||
Args:
|
||||
services: Service center instance for accessing other services
|
||||
aiObjects: Initialized AiObjects instance
|
||||
"""
|
||||
self.services = services
|
||||
self.aiObjects = aiObjects
|
||||
|
||||
# AI Processing Call
|
||||
async def callAi(
|
||||
self,
|
||||
prompt: str,
|
||||
documents: Optional[List[ChatDocument]] = None,
|
||||
placeholders: Optional[List[PromptPlaceholder]] = None,
|
||||
options: Optional[AiCallOptions] = None,
|
||||
outputFormat: Optional[str] = None,
|
||||
title: Optional[str] = None,
|
||||
documentProcessor=None,
|
||||
documentGenerator=None
|
||||
) -> Union[str, Dict[str, Any]]:
|
||||
"""
|
||||
Unified AI call interface that automatically routes to appropriate handler.
|
||||
|
||||
Args:
|
||||
prompt: The main prompt for the AI call
|
||||
documents: Optional list of documents to process
|
||||
placeholders: Optional list of placeholder replacements for planning calls
|
||||
options: AI call configuration options
|
||||
outputFormat: Optional output format (html, pdf, docx, txt, md, json, csv, xlsx) for document generation
|
||||
title: Optional title for generated documents
|
||||
documentProcessor: Document processing service instance
|
||||
documentGenerator: Document generation service instance
|
||||
|
||||
Returns:
|
||||
AI response as string, or dict with documents if outputFormat is specified
|
||||
|
||||
Raises:
|
||||
Exception: If all available models fail
|
||||
"""
|
||||
if options is None:
|
||||
options = AiCallOptions()
|
||||
|
||||
# Normalize placeholders from List[PromptPlaceholder]
|
||||
placeholders_dict: Dict[str, str] = {}
|
||||
placeholders_meta: Dict[str, bool] = {}
|
||||
if placeholders:
|
||||
placeholders_dict = {p.label: p.content for p in placeholders}
|
||||
placeholders_meta = {p.label: bool(getattr(p, 'summaryAllowed', False)) for p in placeholders}
|
||||
|
||||
# Auto-determine call type based on documents and operation type
|
||||
call_type = self._determineCallType(documents, options.operationType)
|
||||
options.callType = call_type
|
||||
|
||||
try:
|
||||
# Build the full prompt that will be sent to AI
|
||||
if placeholders:
|
||||
full_prompt = prompt
|
||||
for p in placeholders:
|
||||
placeholder = f"{{{{KEY:{p.label}}}}}"
|
||||
full_prompt = full_prompt.replace(placeholder, p.content)
|
||||
else:
|
||||
full_prompt = prompt
|
||||
|
||||
self._writeAiResponseDebug(
|
||||
label='ai_prompt_debug',
|
||||
content=full_prompt,
|
||||
partIndex=1,
|
||||
modelName=None,
|
||||
continuation=False
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Handle document generation with specific output format
|
||||
if outputFormat and documentGenerator:
|
||||
result = await documentGenerator.callAiWithDocumentGeneration(prompt, documents, options, outputFormat, title)
|
||||
# Log AI response for debugging
|
||||
try:
|
||||
if isinstance(result, dict) and 'content' in result:
|
||||
self._writeAiResponseDebug(
|
||||
label='ai_document_generation',
|
||||
content=result['content'],
|
||||
partIndex=1,
|
||||
modelName=None, # Document generation doesn't return model info
|
||||
continuation=False
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
return result
|
||||
|
||||
if call_type == "planning":
|
||||
result = await self._callAiPlanning(prompt, placeholders_dict, placeholders_meta, options)
|
||||
# Log AI response for debugging
|
||||
try:
|
||||
self._writeAiResponseDebug(
|
||||
label='ai_planning',
|
||||
content=result or "",
|
||||
partIndex=1,
|
||||
modelName=None, # Planning doesn't return model info
|
||||
continuation=False
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
return result
|
||||
else:
|
||||
# Set processDocumentsIndividually from the legacy parameter if not set in options
|
||||
if options.processDocumentsIndividually is None and documents:
|
||||
options.processDocumentsIndividually = False # Default to batch processing
|
||||
|
||||
# For text calls, we need to build the full prompt with placeholders here
|
||||
# since _callAiText doesn't handle placeholders directly
|
||||
if placeholders_dict:
|
||||
full_prompt = self._buildPromptWithPlaceholders(prompt, placeholders_dict)
|
||||
else:
|
||||
full_prompt = prompt
|
||||
|
||||
if documentProcessor:
|
||||
result = await documentProcessor.callAiText(full_prompt, documents, options)
|
||||
else:
|
||||
# Fallback to direct AI call if no document processor available
|
||||
request = AiCallRequest(
|
||||
prompt=full_prompt,
|
||||
context="",
|
||||
options=options
|
||||
)
|
||||
response = await self.aiObjects.call(request)
|
||||
result = response.content
|
||||
|
||||
# Log AI response for debugging (additional logging for text calls)
|
||||
try:
|
||||
self._writeAiResponseDebug(
|
||||
label='ai_text_main',
|
||||
content=result or "",
|
||||
partIndex=1,
|
||||
modelName=None, # Text calls already log internally
|
||||
continuation=False
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
return result
|
||||
|
||||
# AI Image Analysis
|
||||
async def readImage(
|
||||
self,
|
||||
prompt: str,
|
||||
imageData: Union[str, bytes],
|
||||
mimeType: str = None,
|
||||
options: Optional[AiCallOptions] = None,
|
||||
) -> str:
|
||||
"""Call AI for image analysis using interface.callImage()."""
|
||||
try:
|
||||
# Check if imageData is valid
|
||||
if not imageData:
|
||||
error_msg = "No image data provided"
|
||||
self.services.utils.debugLogToFile(f"Error in AI image analysis: {error_msg}", "AI_SERVICE")
|
||||
logger.error(f"Error in AI image analysis: {error_msg}")
|
||||
return f"Error: {error_msg}"
|
||||
|
||||
self.services.utils.debugLogToFile(f"readImage called with prompt, imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}", "AI_SERVICE")
|
||||
logger.info(f"readImage called with prompt, imageData type: {type(imageData)}, length: {len(imageData) if imageData else 0}, mimeType: {mimeType}")
|
||||
|
||||
# Always use IMAGE_ANALYSIS operation type for image processing
|
||||
if options is None:
|
||||
options = AiCallOptions(operationType=OperationType.IMAGE_ANALYSIS)
|
||||
else:
|
||||
# Override the operation type to ensure image analysis
|
||||
options.operationType = OperationType.IMAGE_ANALYSIS
|
||||
|
||||
self.services.utils.debugLogToFile(f"Calling aiObjects.callImage with operationType: {options.operationType}", "AI_SERVICE")
|
||||
logger.info(f"Calling aiObjects.callImage with operationType: {options.operationType}")
|
||||
result = await self.aiObjects.callImage(prompt, imageData, mimeType, options)
|
||||
|
||||
# Debug the result
|
||||
self.services.utils.debugLogToFile(f"Raw AI result type: {type(result)}, value: {repr(result)}", "AI_SERVICE")
|
||||
|
||||
# Check if result is valid
|
||||
if not result or (isinstance(result, str) and not result.strip()):
|
||||
error_msg = f"No response from AI image analysis (result: {repr(result)})"
|
||||
self.services.utils.debugLogToFile(f"Error in AI image analysis: {error_msg}", "AI_SERVICE")
|
||||
logger.error(f"Error in AI image analysis: {error_msg}")
|
||||
return f"Error: {error_msg}"
|
||||
|
||||
self.services.utils.debugLogToFile(f"callImage returned: {result[:200]}..." if len(result) > 200 else result, "AI_SERVICE")
|
||||
logger.info(f"callImage returned: {result[:200]}..." if len(result) > 200 else result)
|
||||
return result
|
||||
except Exception as e:
|
||||
self.services.utils.debugLogToFile(f"Error in AI image analysis: {str(e)}", "AI_SERVICE")
|
||||
logger.error(f"Error in AI image analysis: {str(e)}")
|
||||
return f"Error: {str(e)}"
|
||||
|
||||
# AI Image Generation
|
||||
async def generateImage(
|
||||
self,
|
||||
prompt: str,
|
||||
size: str = "1024x1024",
|
||||
quality: str = "standard",
|
||||
style: str = "vivid",
|
||||
options: Optional[AiCallOptions] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Generate an image using AI using interface.generateImage()."""
|
||||
try:
|
||||
return await self.aiObjects.generateImage(prompt, size, quality, style, options)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in AI image generation: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
def _determineCallType(self, documents: Optional[List[ChatDocument]], operation_type: str) -> str:
|
||||
"""
|
||||
Determine call type based on documents and operation type.
|
||||
|
||||
Criteria: no documents AND operationType is "generate_plan" -> planning
|
||||
All other cases -> text
|
||||
"""
|
||||
has_documents = documents is not None and len(documents) > 0
|
||||
is_planning_operation = operation_type == OperationType.GENERATE_PLAN
|
||||
|
||||
if not has_documents and is_planning_operation:
|
||||
return "planning"
|
||||
else:
|
||||
return "text"
|
||||
|
||||
async def _callAiPlanning(
|
||||
self,
|
||||
prompt: str,
|
||||
placeholders: Optional[Dict[str, str]],
|
||||
placeholdersMeta: Optional[Dict[str, bool]],
|
||||
options: AiCallOptions
|
||||
) -> str:
|
||||
"""
|
||||
Handle planning calls with placeholder system and selective summarization.
|
||||
"""
|
||||
# Build full prompt with placeholders; if too large, summarize summaryAllowed placeholders proportionally
|
||||
effective_placeholders = placeholders or {}
|
||||
full_prompt = self._buildPromptWithPlaceholders(prompt, effective_placeholders)
|
||||
|
||||
if options.compressPrompt and placeholdersMeta:
|
||||
# Determine model capacity
|
||||
try:
|
||||
caps = self._getModelCapabilitiesForContent(full_prompt, None, options)
|
||||
max_bytes = caps.get("maxContextBytes", len(full_prompt.encode("utf-8")))
|
||||
except Exception:
|
||||
max_bytes = len(full_prompt.encode("utf-8"))
|
||||
|
||||
current_bytes = len(full_prompt.encode("utf-8"))
|
||||
if current_bytes > max_bytes:
|
||||
# Compute total bytes contributed by allowed placeholders (approximate by content length)
|
||||
allowed_labels = [l for l, allow in placeholdersMeta.items() if allow]
|
||||
allowed_sizes = {l: len((effective_placeholders.get(l) or "").encode("utf-8")) for l in allowed_labels}
|
||||
total_allowed = sum(allowed_sizes.values())
|
||||
|
||||
overage = current_bytes - max_bytes
|
||||
if total_allowed > 0 and overage > 0:
|
||||
# Target total for allowed after reduction
|
||||
target_allowed = max(total_allowed - overage, 0)
|
||||
# Global ratio to apply across allowed placeholders
|
||||
ratio = target_allowed / total_allowed if total_allowed > 0 else 1.0
|
||||
ratio = max(0.0, min(1.0, ratio))
|
||||
|
||||
reduced: Dict[str, str] = {}
|
||||
for label, content in effective_placeholders.items():
|
||||
if label in allowed_labels and isinstance(content, str) and len(content) > 0:
|
||||
old_len = len(content)
|
||||
# Reduce by proportional ratio on characters (fallback if empty)
|
||||
reduction_factor = ratio if old_len > 0 else 1.0
|
||||
reduced[label] = self._reduceText(content, reduction_factor)
|
||||
else:
|
||||
reduced[label] = content
|
||||
|
||||
effective_placeholders = reduced
|
||||
full_prompt = self._buildPromptWithPlaceholders(prompt, effective_placeholders)
|
||||
|
||||
# If still slightly over, perform a second-pass fine adjustment with updated ratio
|
||||
current_bytes = len(full_prompt.encode("utf-8"))
|
||||
if current_bytes > max_bytes and total_allowed > 0:
|
||||
overage2 = current_bytes - max_bytes
|
||||
# Recompute allowed sizes after first reduction
|
||||
allowed_sizes2 = {l: len((effective_placeholders.get(l) or "").encode("utf-8")) for l in allowed_labels}
|
||||
total_allowed2 = sum(allowed_sizes2.values())
|
||||
if total_allowed2 > 0 and overage2 > 0:
|
||||
target_allowed2 = max(total_allowed2 - overage2, 0)
|
||||
ratio2 = target_allowed2 / total_allowed2
|
||||
ratio2 = max(0.0, min(1.0, ratio2))
|
||||
reduced2: Dict[str, str] = {}
|
||||
for label, content in effective_placeholders.items():
|
||||
if label in allowed_labels and isinstance(content, str) and len(content) > 0:
|
||||
old_len = len(content)
|
||||
reduction_factor = ratio2 if old_len > 0 else 1.0
|
||||
reduced2[label] = self._reduceText(content, reduction_factor)
|
||||
else:
|
||||
reduced2[label] = content
|
||||
effective_placeholders = reduced2
|
||||
full_prompt = self._buildPromptWithPlaceholders(prompt, effective_placeholders)
|
||||
|
||||
|
||||
# Make AI call using AiObjects (let it handle model selection)
|
||||
request = AiCallRequest(
|
||||
prompt=full_prompt,
|
||||
context="", # Context is already included in the prompt
|
||||
options=options
|
||||
)
|
||||
response = await self.aiObjects.call(request)
|
||||
try:
|
||||
logger.debug(f"AI model selected (planning): {getattr(response, 'modelName', 'unknown')}")
|
||||
except Exception:
|
||||
pass
|
||||
return response.content
|
||||
|
||||
async def _callAiDirect(
|
||||
self,
|
||||
prompt: str,
|
||||
documents: Optional[List[ChatDocument]],
|
||||
options: AiCallOptions,
|
||||
documentProcessor=None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Call AI directly with prompt and documents for JSON output.
|
||||
Used for multi-file generation - uses the existing generation pipeline.
|
||||
"""
|
||||
# Use the existing generation pipeline that already works
|
||||
# This ensures proper document processing and content extraction
|
||||
logger.info(f"Using existing generation pipeline for {len(documents) if documents else 0} documents")
|
||||
|
||||
if documentProcessor:
|
||||
# Process documents with JSON merging using the existing pipeline
|
||||
result = await documentProcessor.processDocumentsPerChunkJson(documents, prompt, options)
|
||||
else:
|
||||
# Fallback to simple AI call
|
||||
request = AiCallRequest(
|
||||
prompt=prompt,
|
||||
context="",
|
||||
options=options
|
||||
)
|
||||
response = await self.aiObjects.call(request)
|
||||
result = {"metadata": {"title": "AI Response"}, "sections": [{"id": "section_1", "type": "paragraph", "data": {"text": response.content}}]}
|
||||
|
||||
# Convert single-file result to multi-file format if needed
|
||||
if "sections" in result and "documents" not in result:
|
||||
logger.info("Converting single-file result to multi-file format")
|
||||
# This is a single-file result, convert it to multi-file format
|
||||
return {
|
||||
"metadata": result.get("metadata", {"title": "Converted Document"}),
|
||||
"documents": [{
|
||||
"id": "doc_1",
|
||||
"title": result.get("metadata", {}).get("title", "Document"),
|
||||
"filename": "document.txt",
|
||||
"sections": result.get("sections", [])
|
||||
}]
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
def _getModelCapabilitiesForContent(self, prompt: str, documents: Optional[List[ChatDocument]], options: AiCallOptions) -> Dict[str, int]:
|
||||
"""
|
||||
Get model capabilities for content processing, including appropriate size limits for chunking.
|
||||
"""
|
||||
# Estimate total content size
|
||||
prompt_size = len(prompt.encode('utf-8'))
|
||||
document_size = 0
|
||||
if documents:
|
||||
# Rough estimate of document content size
|
||||
for doc in documents:
|
||||
document_size += doc.fileSize or 0
|
||||
|
||||
total_size = prompt_size + document_size
|
||||
|
||||
# Use AiObjects to select the best model for this content size
|
||||
# We'll simulate the model selection by checking available models
|
||||
from modules.interfaces.interfaceAiObjects import aiModels
|
||||
|
||||
# Find the best model for this content size and operation
|
||||
best_model = None
|
||||
best_context_length = 0
|
||||
|
||||
for model_name, model_info in aiModels.items():
|
||||
context_length = model_info.get("contextLength", 0)
|
||||
|
||||
# Skip models with no context length or too small for content
|
||||
if context_length == 0:
|
||||
continue
|
||||
|
||||
# Check if model supports the operation type
|
||||
capabilities = model_info.get("capabilities", [])
|
||||
if options.operationType == OperationType.IMAGE_ANALYSIS and "image_analysis" not in capabilities:
|
||||
continue
|
||||
elif options.operationType == OperationType.IMAGE_GENERATION and "image_generation" not in capabilities:
|
||||
continue
|
||||
elif options.operationType == OperationType.WEB_RESEARCH and "web_search" not in capabilities:
|
||||
continue
|
||||
elif "text_generation" not in capabilities:
|
||||
continue
|
||||
|
||||
# Prefer models that can handle the content without chunking, but allow chunking if needed
|
||||
if context_length >= total_size * 0.8: # 80% of content size
|
||||
if context_length > best_context_length:
|
||||
best_model = model_info
|
||||
best_context_length = context_length
|
||||
elif best_model is None: # Fallback to largest available model
|
||||
if context_length > best_context_length:
|
||||
best_model = model_info
|
||||
best_context_length = context_length
|
||||
|
||||
# Fallback to a reasonable default if no model found
|
||||
if best_model is None:
|
||||
best_model = {
|
||||
"contextLength": 128000, # GPT-4o default
|
||||
"llmName": "gpt-4o"
|
||||
}
|
||||
|
||||
# Calculate appropriate sizes
|
||||
# Convert tokens to bytes (rough estimate: 1 token ≈ 4 characters)
|
||||
context_length_bytes = int(best_model["contextLength"] * 4)
|
||||
max_context_bytes = int(context_length_bytes * 0.9) # 90% of context length
|
||||
text_chunk_size = int(max_context_bytes * 0.7) # 70% of max context for text chunks
|
||||
image_chunk_size = int(max_context_bytes * 0.8) # 80% of max context for image chunks
|
||||
|
||||
logger.debug(f"Selected model: {best_model.get('llmName', 'unknown')} with context length: {best_model['contextLength']}")
|
||||
logger.debug(f"Content size: {total_size} bytes, Max context: {max_context_bytes} bytes")
|
||||
logger.debug(f"Text chunk size: {text_chunk_size} bytes, Image chunk size: {image_chunk_size} bytes")
|
||||
|
||||
return {
|
||||
"maxContextBytes": max_context_bytes,
|
||||
"textChunkSize": text_chunk_size,
|
||||
"imageChunkSize": image_chunk_size
|
||||
}
|
||||
|
||||
def _getModelsForOperation(self, operation_type: str, options: AiCallOptions) -> List[ModelCapabilities]:
|
||||
"""
|
||||
Get models capable of handling the specific operation with capability filtering.
|
||||
"""
|
||||
# Use the actual AI objects model selection instead of hardcoded default
|
||||
if hasattr(self, 'aiObjects') and self.aiObjects:
|
||||
# Let AiObjects handle the model selection
|
||||
return []
|
||||
else:
|
||||
# Fallback to default model if AiObjects not available
|
||||
default_model = ModelCapabilities(
|
||||
name="default",
|
||||
maxTokens=4000,
|
||||
capabilities=["text", "reasoning"] if operation_type == "planning" else ["text"],
|
||||
costPerToken=0.001,
|
||||
processingTime=1.0,
|
||||
isAvailable=True
|
||||
)
|
||||
return [default_model]
|
||||
|
||||
def _buildPromptWithPlaceholders(self, prompt: str, placeholders: Optional[Dict[str, str]]) -> str:
|
||||
"""
|
||||
Build full prompt by replacing placeholders with their content.
|
||||
Uses the new {{KEY:placeholder}} format.
|
||||
"""
|
||||
if not placeholders:
|
||||
return prompt
|
||||
|
||||
full_prompt = prompt
|
||||
for placeholder, content in placeholders.items():
|
||||
# Replace both old format {{placeholder}} and new format {{KEY:placeholder}}
|
||||
full_prompt = full_prompt.replace(f"{{{{{placeholder}}}}}", content)
|
||||
full_prompt = full_prompt.replace(f"{{{{KEY:{placeholder}}}}}", content)
|
||||
|
||||
return full_prompt
|
||||
|
||||
def _writeAiResponseDebug(self, label: str, content: str, partIndex: int = 1, modelName: str = None, continuation: bool = None) -> None:
|
||||
"""Persist raw AI response parts for debugging under test-chat/ai - only if debug enabled."""
|
||||
try:
|
||||
# Check if debug logging is enabled
|
||||
debug_enabled = self.services.utils.configGet("APP_DEBUG_CHAT_WORKFLOW_ENABLED", False)
|
||||
if not debug_enabled:
|
||||
return
|
||||
|
||||
import os
|
||||
from datetime import datetime, UTC
|
||||
# Base dir: gateway/test-chat/ai (go up 4 levels from this file)
|
||||
# .../gateway/modules/services/serviceAi/subCoreAi.py -> up to gateway root
|
||||
gatewayDir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
outDir = os.path.join(gatewayDir, 'test-chat', 'ai')
|
||||
os.makedirs(outDir, exist_ok=True)
|
||||
ts = datetime.now(UTC).strftime('%Y%m%d-%H%M%S-%f')[:-3]
|
||||
suffix = []
|
||||
if partIndex is not None:
|
||||
suffix.append(f"part{partIndex}")
|
||||
if continuation is not None:
|
||||
suffix.append(f"cont_{str(continuation).lower()}")
|
||||
if modelName:
|
||||
safeModel = ''.join(c if c.isalnum() or c in ('-', '_') else '-' for c in modelName)
|
||||
suffix.append(safeModel)
|
||||
suffixStr = ('_' + '_'.join(suffix)) if suffix else ''
|
||||
fname = f"{ts}_{label}{suffixStr}.txt"
|
||||
fpath = os.path.join(outDir, fname)
|
||||
with open(fpath, 'w', encoding='utf-8') as f:
|
||||
f.write(content or '')
|
||||
except Exception:
|
||||
# Do not raise; best-effort debug write
|
||||
pass
|
||||
|
||||
def _exceedsTokenLimit(self, text: str, model: ModelCapabilities, safety_margin: float) -> bool:
|
||||
"""
|
||||
Check if text exceeds model token limit with safety margin.
|
||||
"""
|
||||
# Simple character-based estimation (4 chars per token)
|
||||
estimated_tokens = len(text) // 4
|
||||
max_tokens = int(model.maxTokens * (1 - safety_margin))
|
||||
return estimated_tokens > max_tokens
|
||||
|
||||
def _reducePlanningPrompt(
|
||||
self,
|
||||
full_prompt: str,
|
||||
placeholders: Optional[Dict[str, str]],
|
||||
model: ModelCapabilities,
|
||||
options: AiCallOptions
|
||||
) -> str:
|
||||
"""
|
||||
Reduce planning prompt size by summarizing placeholders while preserving prompt structure.
|
||||
"""
|
||||
if not placeholders:
|
||||
return self._reduceText(full_prompt, 0.7)
|
||||
|
||||
# Reduce placeholders while preserving prompt
|
||||
reduced_placeholders = {}
|
||||
for placeholder, content in placeholders.items():
|
||||
if len(content) > 1000: # Only reduce long content
|
||||
reduction_factor = 0.7
|
||||
reduced_content = self._reduceText(content, reduction_factor)
|
||||
reduced_placeholders[placeholder] = reduced_content
|
||||
else:
|
||||
reduced_placeholders[placeholder] = content
|
||||
|
||||
return self._buildPromptWithPlaceholders(full_prompt, reduced_placeholders)
|
||||
|
||||
def _reduceTextPrompt(
|
||||
self,
|
||||
prompt: str,
|
||||
context: str,
|
||||
model: ModelCapabilities,
|
||||
options: AiCallOptions
|
||||
) -> str:
|
||||
"""
|
||||
Reduce text prompt size using typeGroup-aware chunking and merging.
|
||||
"""
|
||||
max_size = int(model.maxTokens * (1 - options.safetyMargin))
|
||||
|
||||
if options.compressPrompt:
|
||||
# Reduce both prompt and context
|
||||
target_size = max_size
|
||||
current_size = len(prompt) + len(context)
|
||||
reduction_factor = (target_size * 0.7) / current_size
|
||||
|
||||
if reduction_factor < 1.0:
|
||||
prompt = self._reduceText(prompt, reduction_factor)
|
||||
context = self._reduceText(context, reduction_factor)
|
||||
else:
|
||||
# Only reduce context, preserve prompt integrity
|
||||
max_context_size = max_size - len(prompt)
|
||||
if len(context) > max_context_size:
|
||||
reduction_factor = max_context_size / len(context)
|
||||
context = self._reduceText(context, reduction_factor)
|
||||
|
||||
return prompt + "\n\n" + context if context else prompt
|
||||
|
||||
def _extractTextFromContentParts(self, extracted_content) -> str:
|
||||
"""
|
||||
Extract text content from ExtractionService ContentPart objects.
|
||||
"""
|
||||
if not extracted_content or not hasattr(extracted_content, 'parts'):
|
||||
return ""
|
||||
|
||||
text_parts = []
|
||||
for part in extracted_content.parts:
|
||||
if hasattr(part, 'typeGroup') and part.typeGroup in ['text', 'table', 'structure']:
|
||||
if hasattr(part, 'data') and part.data:
|
||||
text_parts.append(part.data)
|
||||
|
||||
return "\n\n".join(text_parts)
|
||||
|
||||
def _reduceText(self, text: str, reduction_factor: float) -> str:
|
||||
"""
|
||||
Reduce text size by the specified factor.
|
||||
"""
|
||||
if reduction_factor >= 1.0:
|
||||
return text
|
||||
|
||||
target_length = int(len(text) * reduction_factor)
|
||||
return text[:target_length] + "... [reduced]"
|
||||
459
modules/services/serviceAi/subDocumentGeneration.py
Normal file
459
modules/services/serviceAi/subDocumentGeneration.py
Normal file
|
|
@ -0,0 +1,459 @@
|
|||
import logging
|
||||
from typing import Dict, Any, List, Optional, Tuple, Union
|
||||
from modules.datamodels.datamodelChat import ChatDocument
|
||||
from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationType
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SubDocumentGeneration:
|
||||
"""Document generation operations including single-file and multi-file generation."""
|
||||
|
||||
def __init__(self, services, aiObjects, documentProcessor):
|
||||
"""Initialize document generation service.
|
||||
|
||||
Args:
|
||||
services: Service center instance for accessing other services
|
||||
aiObjects: Initialized AiObjects instance
|
||||
documentProcessor: Document processing service instance
|
||||
"""
|
||||
self.services = services
|
||||
self.aiObjects = aiObjects
|
||||
self.documentProcessor = documentProcessor
|
||||
|
||||
async def callAiWithDocumentGeneration(
|
||||
self,
|
||||
prompt: str,
|
||||
documents: Optional[List[ChatDocument]],
|
||||
options: AiCallOptions,
|
||||
outputFormat: str,
|
||||
title: Optional[str]
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Handle AI calls with document generation in specific output format.
|
||||
Now supports both single-file and multi-file generation.
|
||||
|
||||
Args:
|
||||
prompt: The main prompt for the AI call
|
||||
documents: Optional list of documents to process
|
||||
options: AI call configuration options
|
||||
outputFormat: Target output format (html, pdf, docx, txt, md, json, csv, xlsx)
|
||||
title: Optional title for generated documents
|
||||
|
||||
Returns:
|
||||
Dict with generated documents and metadata
|
||||
"""
|
||||
try:
|
||||
# Use AI to analyze prompt intent
|
||||
prompt_analysis = await self._analyzePromptIntent(prompt, self)
|
||||
logger.info(f"Prompt analysis result: {prompt_analysis}")
|
||||
|
||||
if prompt_analysis.get("is_multi_file", False):
|
||||
return await self._callAiWithMultiFileGeneration(
|
||||
prompt, documents, options, outputFormat, title, prompt_analysis
|
||||
)
|
||||
else:
|
||||
return await self._callAiWithSingleFileGeneration(
|
||||
prompt, documents, options, outputFormat, title
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in document generation: {str(e)}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"content": "",
|
||||
"rendered_content": "",
|
||||
"mime_type": "text/plain",
|
||||
"filename": f"error_{outputFormat}",
|
||||
"format": outputFormat,
|
||||
"title": title or "Error",
|
||||
"documents": []
|
||||
}
|
||||
|
||||
async def _callAiWithSingleFileGeneration(
|
||||
self,
|
||||
prompt: str,
|
||||
documents: Optional[List[ChatDocument]],
|
||||
options: AiCallOptions,
|
||||
outputFormat: str,
|
||||
title: Optional[str]
|
||||
) -> Dict[str, Any]:
|
||||
"""Handle single-file document generation (existing functionality)."""
|
||||
try:
|
||||
# Get format-specific extraction prompt from generation service
|
||||
from modules.services.serviceGeneration.mainServiceGeneration import GenerationService
|
||||
generation_service = GenerationService(self.services)
|
||||
|
||||
# Use default title if not provided
|
||||
if not title:
|
||||
title = "AI Generated Document"
|
||||
|
||||
# Get format-specific extraction prompt
|
||||
extractionPrompt = await generation_service.getExtractionPrompt(
|
||||
outputFormat=outputFormat,
|
||||
userPrompt=prompt,
|
||||
title=title,
|
||||
aiService=self
|
||||
)
|
||||
|
||||
# Process documents with format-specific prompt using JSON mode
|
||||
# This ensures structured JSON output instead of text
|
||||
aiResponseJson = await self._callAiJson(extractionPrompt, documents, options)
|
||||
|
||||
# Validate JSON response
|
||||
if not isinstance(aiResponseJson, dict) or "sections" not in aiResponseJson:
|
||||
raise Exception("AI response is not valid JSON document structure")
|
||||
|
||||
# Generate filename from document metadata
|
||||
parsedFilename = None
|
||||
try:
|
||||
if aiResponseJson.get("metadata", {}).get("title"):
|
||||
title = aiResponseJson["metadata"]["title"]
|
||||
# Clean title for filename
|
||||
import re
|
||||
parsed = re.sub(r"[^a-zA-Z0-9._-]", "-", title)
|
||||
parsed = re.sub(r"-+", "-", parsed).strip('-')
|
||||
if parsed:
|
||||
parsedFilename = f"{parsed}.{outputFormat}"
|
||||
except Exception:
|
||||
parsedFilename = None
|
||||
|
||||
# Render the JSON content to the specified format
|
||||
renderedContent, mimeType = await generation_service.renderReport(
|
||||
extractedContent=aiResponseJson,
|
||||
outputFormat=outputFormat,
|
||||
title=title,
|
||||
userPrompt=prompt,
|
||||
aiService=self
|
||||
)
|
||||
|
||||
# Generate meaningful filename (use AI-provided if valid, else fallback)
|
||||
from datetime import datetime, UTC
|
||||
timestamp = datetime.now(UTC).strftime("%Y%m%d-%H%M%S")
|
||||
if parsedFilename and parsedFilename.lower().endswith(f".{outputFormat.lower()}"):
|
||||
filename = parsedFilename
|
||||
else:
|
||||
safeTitle = ''.join(c if c.isalnum() else '-' for c in (title or 'document')).strip('-')
|
||||
filename = f"{safeTitle or 'document'}-{timestamp}.{outputFormat}"
|
||||
|
||||
# Return structured result with document information
|
||||
return {
|
||||
"success": True,
|
||||
"content": aiResponseJson, # Structured JSON document
|
||||
"rendered_content": renderedContent, # Formatted content
|
||||
"mime_type": mimeType,
|
||||
"filename": filename,
|
||||
"format": outputFormat,
|
||||
"title": title,
|
||||
"documents": [{
|
||||
"documentName": filename,
|
||||
"documentData": renderedContent,
|
||||
"mimeType": mimeType
|
||||
}],
|
||||
"is_multi_file": False
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in single-file document generation: {str(e)}")
|
||||
raise
|
||||
|
||||
async def _callAiWithMultiFileGeneration(
|
||||
self,
|
||||
prompt: str,
|
||||
documents: Optional[List[ChatDocument]],
|
||||
options: AiCallOptions,
|
||||
outputFormat: str,
|
||||
title: Optional[str],
|
||||
prompt_analysis: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
"""Handle multi-file document generation using AI analysis."""
|
||||
try:
|
||||
# Get multi-file extraction prompt based on AI analysis
|
||||
from modules.services.serviceGeneration.mainServiceGeneration import GenerationService
|
||||
generation_service = GenerationService(self.services)
|
||||
|
||||
# Use default title if not provided
|
||||
if not title:
|
||||
title = "AI Generated Documents"
|
||||
|
||||
# Get adaptive extraction prompt
|
||||
extraction_prompt = await generation_service.getAdaptiveExtractionPrompt(
|
||||
outputFormat=outputFormat,
|
||||
userPrompt=prompt,
|
||||
title=title,
|
||||
promptAnalysis=prompt_analysis,
|
||||
aiService=self
|
||||
)
|
||||
|
||||
logger.info(f"Adaptive extraction prompt length: {len(extraction_prompt)} characters")
|
||||
logger.debug(f"Adaptive extraction prompt preview: {extraction_prompt[:500]}...")
|
||||
|
||||
# Process with adaptive JSON schema - use the existing pipeline but with adaptive prompt
|
||||
logger.info(f"Using adaptive prompt with existing pipeline: {len(extraction_prompt)} chars")
|
||||
logger.debug(f"Processing documents: {len(documents) if documents else 0} documents")
|
||||
|
||||
# Use the existing pipeline but replace the prompt with our adaptive one
|
||||
# This ensures proper document processing while using the multi-file prompt
|
||||
ai_response = await self.documentProcessor.processDocumentsPerChunkJsonWithPrompt(documents, extraction_prompt, options)
|
||||
|
||||
logger.info(f"AI response type: {type(ai_response)}")
|
||||
logger.info(f"AI response keys: {list(ai_response.keys()) if isinstance(ai_response, dict) else 'Not a dict'}")
|
||||
logger.debug(f"AI response preview: {str(ai_response)[:500]}...")
|
||||
|
||||
# Validate response structure
|
||||
if not self._validateResponseStructure(ai_response, prompt_analysis):
|
||||
# Fallback to single-file if multi-file fails
|
||||
logger.warning(f"Multi-file processing failed - Invalid response structure. Expected multi-file but got: {list(ai_response.keys()) if isinstance(ai_response, dict) else type(ai_response)}")
|
||||
logger.warning(f"Prompt analysis: {prompt_analysis}")
|
||||
logger.warning("Falling back to single-file generation")
|
||||
return await self._callAiWithSingleFileGeneration(
|
||||
prompt, documents, options, outputFormat, title
|
||||
)
|
||||
|
||||
# Process multiple documents
|
||||
generated_documents = []
|
||||
for i, doc_data in enumerate(ai_response.get("documents", [])):
|
||||
# Transform AI-generated sections to renderer-compatible format
|
||||
transformed_sections = []
|
||||
for section in doc_data.get("sections", []):
|
||||
# Convert AI format to renderer format
|
||||
transformed_section = {
|
||||
"id": section.get("id", f"section_{len(transformed_sections) + 1}"),
|
||||
"type": section.get("content_type", "paragraph"),
|
||||
"data": {
|
||||
"text": "",
|
||||
"elements": section.get("elements", [])
|
||||
},
|
||||
"order": section.get("order", len(transformed_sections) + 1)
|
||||
}
|
||||
|
||||
# Extract text from elements for simple text-based sections
|
||||
if section.get("content_type") in ["paragraph", "heading"]:
|
||||
text_parts = []
|
||||
for element in section.get("elements", []):
|
||||
if "text" in element:
|
||||
text_parts.append(element["text"])
|
||||
transformed_section["data"]["text"] = "\n".join(text_parts)
|
||||
|
||||
transformed_sections.append(transformed_section)
|
||||
|
||||
# Create complete document structure for rendering
|
||||
complete_document = {
|
||||
"metadata": {
|
||||
"title": doc_data["title"],
|
||||
"source_document": "multi_file_generation",
|
||||
"document_id": doc_data.get("id", f"doc_{i+1}"),
|
||||
"filename": doc_data.get("filename", f"document_{i+1}"),
|
||||
"split_strategy": prompt_analysis.get("strategy", "custom")
|
||||
},
|
||||
"sections": transformed_sections,
|
||||
"summary": f"Generated document: {doc_data['title']}",
|
||||
"tags": ["multi_file", "ai_generated"]
|
||||
}
|
||||
|
||||
rendered_content, mime_type = await generation_service.renderReport(
|
||||
extractedContent=complete_document,
|
||||
outputFormat=outputFormat,
|
||||
title=doc_data["title"],
|
||||
userPrompt=prompt,
|
||||
aiService=self
|
||||
)
|
||||
|
||||
# Generate proper filename with correct extension
|
||||
base_filename = doc_data.get("filename", f"document_{i+1}")
|
||||
# Remove any existing extension and add the correct one
|
||||
if '.' in base_filename:
|
||||
base_filename = base_filename.rsplit('.', 1)[0]
|
||||
|
||||
# Add proper extension based on output format
|
||||
if outputFormat.lower() == "docx":
|
||||
filename = f"{base_filename}.docx"
|
||||
elif outputFormat.lower() == "pdf":
|
||||
filename = f"{base_filename}.pdf"
|
||||
elif outputFormat.lower() == "html":
|
||||
filename = f"{base_filename}.html"
|
||||
else:
|
||||
filename = f"{base_filename}.{outputFormat}"
|
||||
|
||||
generated_documents.append({
|
||||
"documentName": filename,
|
||||
"documentData": rendered_content,
|
||||
"mimeType": mime_type
|
||||
})
|
||||
|
||||
# Save debug files for multi-file generation - only if debug enabled
|
||||
debug_enabled = self.services.utils.configGet("APP_DEBUG_CHAT_WORKFLOW_ENABLED", False)
|
||||
if debug_enabled:
|
||||
try:
|
||||
import os
|
||||
from datetime import datetime, UTC
|
||||
ts = datetime.now(UTC).strftime("%Y%m%d-%H%M%S")
|
||||
debug_root = "./test-chat/ai"
|
||||
debug_dir = os.path.join(debug_root, f"multifile_output_{ts}")
|
||||
os.makedirs(debug_dir, exist_ok=True)
|
||||
|
||||
# Save metadata
|
||||
with open(os.path.join(debug_dir, "metadata.txt"), "w", encoding="utf-8") as f:
|
||||
f.write(f"title: {title}\n")
|
||||
f.write(f"format: {outputFormat}\n")
|
||||
f.write(f"documents_count: {len(generated_documents)}\n")
|
||||
f.write(f"split_strategy: {prompt_analysis.get('strategy', 'custom')}\n")
|
||||
f.write(f"prompt_analysis: {prompt_analysis}\n")
|
||||
|
||||
# Save each generated document
|
||||
for i, doc in enumerate(generated_documents):
|
||||
doc_filename = doc["documentName"]
|
||||
doc_data = doc["documentData"]
|
||||
doc_mime = doc["mimeType"]
|
||||
|
||||
# Determine file extension
|
||||
if outputFormat.lower() == "docx":
|
||||
file_ext = ".docx"
|
||||
elif outputFormat.lower() == "pdf":
|
||||
file_ext = ".pdf"
|
||||
elif outputFormat.lower() == "html":
|
||||
file_ext = ".html"
|
||||
else:
|
||||
file_ext = f".{outputFormat}"
|
||||
|
||||
# Save the rendered document
|
||||
output_path = os.path.join(debug_dir, f"document_{i+1}_{doc_filename}")
|
||||
|
||||
if file_ext in ['.md', '.txt', '.html', '.json', '.csv']:
|
||||
# Text-based formats
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
f.write(doc_data)
|
||||
else:
|
||||
# Binary formats - decode from base64 if needed
|
||||
try:
|
||||
import base64
|
||||
doc_bytes = base64.b64decode(doc_data)
|
||||
with open(output_path, 'wb') as f:
|
||||
f.write(doc_bytes)
|
||||
except Exception:
|
||||
# If not base64, save as text
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
f.write(doc_data)
|
||||
|
||||
logger.info(f"💾 Debug: Saved multi-file document {i+1}: {output_path}")
|
||||
|
||||
logger.info(f"💾 Debug: Multi-file output saved to: {debug_dir}")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to save multi-file debug output: {e}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"content": ai_response,
|
||||
"rendered_content": None, # Not applicable for multi-file
|
||||
"mime_type": None, # Not applicable for multi-file
|
||||
"filename": None, # Not applicable for multi-file
|
||||
"format": outputFormat,
|
||||
"title": title,
|
||||
"documents": generated_documents,
|
||||
"is_multi_file": True,
|
||||
"split_strategy": prompt_analysis.get("strategy", "custom")
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in multi-file document generation: {str(e)}")
|
||||
# Fallback to single-file
|
||||
return await self._callAiWithSingleFileGeneration(
|
||||
prompt, documents, options, outputFormat, title
|
||||
)
|
||||
|
||||
async def _callAiJson(
|
||||
self,
|
||||
prompt: str,
|
||||
documents: Optional[List[ChatDocument]],
|
||||
options: AiCallOptions
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Handle AI calls with document processing for JSON output.
|
||||
Returns structured JSON document instead of text.
|
||||
"""
|
||||
# Process documents with JSON merging
|
||||
return await self.documentProcessor.processDocumentsPerChunkJson(documents, prompt, options)
|
||||
|
||||
async def _analyzePromptIntent(self, prompt: str, ai_service=None) -> Dict[str, Any]:
|
||||
"""Use AI to analyze user prompt and determine processing requirements."""
|
||||
if not ai_service:
|
||||
return {"is_multi_file": False, "strategy": "single", "criteria": None}
|
||||
|
||||
try:
|
||||
analysis_prompt = f"""
|
||||
Analyze this user request and determine if it requires multiple file output or single file output.
|
||||
|
||||
User request: "{prompt}"
|
||||
|
||||
Respond with JSON only in this exact format:
|
||||
{{
|
||||
"is_multi_file": true/false,
|
||||
"strategy": "single|per_entity|by_section|by_criteria|custom",
|
||||
"criteria": "description of how to split content",
|
||||
"file_naming_pattern": "suggested pattern for filenames",
|
||||
"reasoning": "brief explanation of the analysis"
|
||||
}}
|
||||
|
||||
Consider:
|
||||
- Does the user want separate files for different entities (customers, products, etc.)?
|
||||
- Does the user want to split content into multiple documents?
|
||||
- What would be the most logical way to organize the content?
|
||||
- What language is the request in? (analyze in the original language)
|
||||
|
||||
Return only the JSON response.
|
||||
"""
|
||||
|
||||
from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions, OperationType
|
||||
request_options = AiCallOptions()
|
||||
request_options.operationType = OperationType.GENERAL
|
||||
|
||||
request = AiCallRequest(prompt=analysis_prompt, context="", options=request_options)
|
||||
response = await ai_service.aiObjects.call(request)
|
||||
|
||||
if response and response.content:
|
||||
import json
|
||||
import re
|
||||
|
||||
# Extract JSON from response
|
||||
result = response.content.strip()
|
||||
json_match = re.search(r'\{.*\}', result, re.DOTALL)
|
||||
if json_match:
|
||||
result = json_match.group(0)
|
||||
|
||||
analysis = json.loads(result)
|
||||
return analysis
|
||||
else:
|
||||
return {"is_multi_file": False, "strategy": "single", "criteria": None}
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"AI prompt analysis failed: {str(e)}, defaulting to single file")
|
||||
return {"is_multi_file": False, "strategy": "single", "criteria": None}
|
||||
|
||||
def _validateResponseStructure(self, response: Dict[str, Any], prompt_analysis: Dict[str, Any]) -> bool:
|
||||
"""Validate that AI response matches the expected structure."""
|
||||
try:
|
||||
if not isinstance(response, dict):
|
||||
logger.warning(f"Response validation failed: Response is not a dict, got {type(response)}")
|
||||
return False
|
||||
|
||||
# Check for multi-file structure
|
||||
if prompt_analysis.get("is_multi_file", False):
|
||||
has_documents = "documents" in response
|
||||
is_documents_list = isinstance(response.get("documents"), list)
|
||||
logger.info(f"Multi-file validation: has_documents={has_documents}, is_documents_list={is_documents_list}")
|
||||
if has_documents and is_documents_list:
|
||||
logger.info(f"Multi-file validation passed: {len(response['documents'])} documents found")
|
||||
else:
|
||||
logger.warning(f"Multi-file validation failed: documents key present={has_documents}, documents is list={is_documents_list}")
|
||||
logger.warning(f"Available keys: {list(response.keys())}")
|
||||
return has_documents and is_documents_list
|
||||
else:
|
||||
has_sections = "sections" in response
|
||||
is_sections_list = isinstance(response.get("sections"), list)
|
||||
logger.info(f"Single-file validation: has_sections={has_sections}, is_sections_list={is_sections_list}")
|
||||
return has_sections and is_sections_list
|
||||
except Exception as e:
|
||||
logger.warning(f"Response validation failed with exception: {str(e)}")
|
||||
return False
|
||||
1042
modules/services/serviceAi/subDocumentProcessing.py
Normal file
1042
modules/services/serviceAi/subDocumentProcessing.py
Normal file
File diff suppressed because it is too large
Load diff
316
modules/services/serviceAi/subUtilities.py
Normal file
316
modules/services/serviceAi/subUtilities.py
Normal file
|
|
@ -0,0 +1,316 @@
|
|||
import logging
|
||||
from typing import Dict, Any, List, Optional, Tuple, Union
|
||||
from modules.datamodels.datamodelAi import ModelCapabilities, AiCallOptions
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SubUtilities:
|
||||
"""Utility functions for text processing, debugging, and helper operations."""
|
||||
|
||||
def __init__(self, services):
|
||||
"""Initialize utilities service.
|
||||
|
||||
Args:
|
||||
services: Service center instance for accessing other services
|
||||
"""
|
||||
self.services = services
|
||||
|
||||
def _writeTraceLog(self, contextText: str, data: Any) -> None:
|
||||
"""Write raw data to the central trace log file without truncation."""
|
||||
try:
|
||||
import os
|
||||
import json
|
||||
from datetime import datetime, UTC
|
||||
# Only write if logger is in debug mode
|
||||
if logger.level > logging.DEBUG:
|
||||
return
|
||||
# Get log directory from configuration via service center if possible
|
||||
logDir = None
|
||||
try:
|
||||
logDir = self.services.utils.configGet("APP_LOGGING_LOG_DIR", "./")
|
||||
except Exception:
|
||||
pass
|
||||
if not logDir:
|
||||
logDir = "./"
|
||||
if not os.path.isabs(logDir):
|
||||
# Make it relative to gateway directory
|
||||
gatewayDir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
logDir = os.path.join(gatewayDir, logDir)
|
||||
os.makedirs(logDir, exist_ok=True)
|
||||
traceFile = os.path.join(logDir, "log_trace.log")
|
||||
timestamp = datetime.now(UTC).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
|
||||
traceEntry = f"[{timestamp}] {contextText}\n" + ("=" * 80) + "\n"
|
||||
if data is None:
|
||||
traceEntry += "No data provided\n"
|
||||
else:
|
||||
# Prefer exact text; if dict/list, pretty print JSON
|
||||
try:
|
||||
if isinstance(data, (dict, list)):
|
||||
traceEntry += f"JSON Data:\n{json.dumps(data, indent=2, ensure_ascii=False)}\n"
|
||||
else:
|
||||
text = str(data)
|
||||
traceEntry += f"Text Data:\n{text}\n"
|
||||
except Exception:
|
||||
traceEntry += f"Data (fallback): {str(data)}\n"
|
||||
traceEntry += ("=" * 80) + "\n\n"
|
||||
with open(traceFile, "a", encoding="utf-8") as f:
|
||||
f.write(traceEntry)
|
||||
except Exception:
|
||||
# Swallow to avoid recursive logging issues
|
||||
pass
|
||||
|
||||
def _writeAiResponseDebug(self, label: str, content: str, partIndex: int = 1, modelName: str = None, continuation: bool = None) -> None:
|
||||
"""Persist raw AI response parts for debugging under test-chat/ai - only if debug enabled."""
|
||||
try:
|
||||
# Check if debug logging is enabled
|
||||
debug_enabled = self.services.utils.configGet("APP_DEBUG_CHAT_WORKFLOW_ENABLED", False)
|
||||
if not debug_enabled:
|
||||
return
|
||||
|
||||
import os
|
||||
from datetime import datetime, UTC
|
||||
# Base dir: gateway/test-chat/ai (go up 4 levels from this file)
|
||||
# .../gateway/modules/services/serviceAi/subUtilities.py -> up to gateway root
|
||||
gatewayDir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
|
||||
outDir = os.path.join(gatewayDir, 'test-chat', 'ai')
|
||||
os.makedirs(outDir, exist_ok=True)
|
||||
ts = datetime.now(UTC).strftime('%Y%m%d-%H%M%S-%f')[:-3]
|
||||
suffix = []
|
||||
if partIndex is not None:
|
||||
suffix.append(f"part{partIndex}")
|
||||
if continuation is not None:
|
||||
suffix.append(f"cont_{str(continuation).lower()}")
|
||||
if modelName:
|
||||
safeModel = ''.join(c if c.isalnum() or c in ('-', '_') else '-' for c in modelName)
|
||||
suffix.append(safeModel)
|
||||
suffixStr = ('_' + '_'.join(suffix)) if suffix else ''
|
||||
fname = f"{ts}_{label}{suffixStr}.txt"
|
||||
fpath = os.path.join(outDir, fname)
|
||||
with open(fpath, 'w', encoding='utf-8') as f:
|
||||
f.write(content or '')
|
||||
except Exception:
|
||||
# Do not raise; best-effort debug write
|
||||
pass
|
||||
|
||||
def _exceedsTokenLimit(self, text: str, model: ModelCapabilities, safety_margin: float) -> bool:
|
||||
"""
|
||||
Check if text exceeds model token limit with safety margin.
|
||||
"""
|
||||
# Simple character-based estimation (4 chars per token)
|
||||
estimated_tokens = len(text) // 4
|
||||
max_tokens = int(model.maxTokens * (1 - safety_margin))
|
||||
return estimated_tokens > max_tokens
|
||||
|
||||
def _reduceText(self, text: str, reduction_factor: float) -> str:
|
||||
"""
|
||||
Reduce text size by the specified factor.
|
||||
"""
|
||||
if reduction_factor >= 1.0:
|
||||
return text
|
||||
|
||||
target_length = int(len(text) * reduction_factor)
|
||||
return text[:target_length] + "... [reduced]"
|
||||
|
||||
def _extractTextFromContentParts(self, extracted_content) -> str:
|
||||
"""
|
||||
Extract text content from ExtractionService ContentPart objects.
|
||||
"""
|
||||
if not extracted_content or not hasattr(extracted_content, 'parts'):
|
||||
return ""
|
||||
|
||||
text_parts = []
|
||||
for part in extracted_content.parts:
|
||||
if hasattr(part, 'typeGroup') and part.typeGroup in ['text', 'table', 'structure']:
|
||||
if hasattr(part, 'data') and part.data:
|
||||
text_parts.append(part.data)
|
||||
|
||||
return "\n\n".join(text_parts)
|
||||
|
||||
def _buildPromptWithPlaceholders(self, prompt: str, placeholders: Optional[Dict[str, str]]) -> str:
|
||||
"""
|
||||
Build full prompt by replacing placeholders with their content.
|
||||
Uses the new {{KEY:placeholder}} format.
|
||||
"""
|
||||
if not placeholders:
|
||||
return prompt
|
||||
|
||||
full_prompt = prompt
|
||||
for placeholder, content in placeholders.items():
|
||||
# Replace both old format {{placeholder}} and new format {{KEY:placeholder}}
|
||||
full_prompt = full_prompt.replace(f"{{{{{placeholder}}}}}", content)
|
||||
full_prompt = full_prompt.replace(f"{{{{KEY:{placeholder}}}}}", content)
|
||||
|
||||
return full_prompt
|
||||
|
||||
def _reducePlanningPrompt(
|
||||
self,
|
||||
full_prompt: str,
|
||||
placeholders: Optional[Dict[str, str]],
|
||||
model: ModelCapabilities,
|
||||
options: AiCallOptions
|
||||
) -> str:
|
||||
"""
|
||||
Reduce planning prompt size by summarizing placeholders while preserving prompt structure.
|
||||
"""
|
||||
if not placeholders:
|
||||
return self._reduceText(full_prompt, 0.7)
|
||||
|
||||
# Reduce placeholders while preserving prompt
|
||||
reduced_placeholders = {}
|
||||
for placeholder, content in placeholders.items():
|
||||
if len(content) > 1000: # Only reduce long content
|
||||
reduction_factor = 0.7
|
||||
reduced_content = self._reduceText(content, reduction_factor)
|
||||
reduced_placeholders[placeholder] = reduced_content
|
||||
else:
|
||||
reduced_placeholders[placeholder] = content
|
||||
|
||||
return self._buildPromptWithPlaceholders(full_prompt, reduced_placeholders)
|
||||
|
||||
def _reduceTextPrompt(
|
||||
self,
|
||||
prompt: str,
|
||||
context: str,
|
||||
model: ModelCapabilities,
|
||||
options: AiCallOptions
|
||||
) -> str:
|
||||
"""
|
||||
Reduce text prompt size using typeGroup-aware chunking and merging.
|
||||
"""
|
||||
max_size = int(model.maxTokens * (1 - options.safetyMargin))
|
||||
|
||||
if options.compressPrompt:
|
||||
# Reduce both prompt and context
|
||||
target_size = max_size
|
||||
current_size = len(prompt) + len(context)
|
||||
reduction_factor = (target_size * 0.7) / current_size
|
||||
|
||||
if reduction_factor < 1.0:
|
||||
prompt = self._reduceText(prompt, reduction_factor)
|
||||
context = self._reduceText(context, reduction_factor)
|
||||
else:
|
||||
# Only reduce context, preserve prompt integrity
|
||||
max_context_size = max_size - len(prompt)
|
||||
if len(context) > max_context_size:
|
||||
reduction_factor = max_context_size / len(context)
|
||||
context = self._reduceText(context, reduction_factor)
|
||||
|
||||
return prompt + "\n\n" + context if context else prompt
|
||||
|
||||
async def _compressContent(self, content: str, targetSize: int, contentType: str) -> str:
|
||||
"""Compress content to target size."""
|
||||
if len(content.encode("utf-8")) <= targetSize:
|
||||
return content
|
||||
|
||||
try:
|
||||
compressionPrompt = f"""
|
||||
Komprimiere den folgenden {contentType} auf maximal {targetSize} Zeichen,
|
||||
behalte aber alle wichtigen Informationen bei:
|
||||
|
||||
{content}
|
||||
|
||||
Gib nur den komprimierten Inhalt zurück, ohne zusätzliche Erklärungen.
|
||||
"""
|
||||
|
||||
# Service must not call connectors directly; use simple truncation fallback here
|
||||
data = content.encode("utf-8")
|
||||
return data[:targetSize].decode("utf-8", errors="ignore") + "... [truncated]"
|
||||
except Exception as e:
|
||||
logger.warning(f"AI compression failed, using truncation: {str(e)}")
|
||||
return content[:targetSize] + "... [truncated]"
|
||||
|
||||
def _getModelCapabilitiesForContent(self, prompt: str, documents: Optional[List], options: AiCallOptions) -> Dict[str, int]:
|
||||
"""
|
||||
Get model capabilities for content processing, including appropriate size limits for chunking.
|
||||
"""
|
||||
# Estimate total content size
|
||||
prompt_size = len(prompt.encode('utf-8'))
|
||||
document_size = 0
|
||||
if documents:
|
||||
# Rough estimate of document content size
|
||||
for doc in documents:
|
||||
document_size += getattr(doc, 'fileSize', 0) or 0
|
||||
|
||||
total_size = prompt_size + document_size
|
||||
|
||||
# Use AiObjects to select the best model for this content size
|
||||
# We'll simulate the model selection by checking available models
|
||||
from modules.interfaces.interfaceAiObjects import aiModels
|
||||
|
||||
# Find the best model for this content size and operation
|
||||
best_model = None
|
||||
best_context_length = 0
|
||||
|
||||
for model_name, model_info in aiModels.items():
|
||||
context_length = model_info.get("contextLength", 0)
|
||||
|
||||
# Skip models with no context length or too small for content
|
||||
if context_length == 0:
|
||||
continue
|
||||
|
||||
# Check if model supports the operation type
|
||||
capabilities = model_info.get("capabilities", [])
|
||||
from modules.datamodels.datamodelAi import OperationType
|
||||
if options.operationType == OperationType.IMAGE_ANALYSIS and "image_analysis" not in capabilities:
|
||||
continue
|
||||
elif options.operationType == OperationType.IMAGE_GENERATION and "image_generation" not in capabilities:
|
||||
continue
|
||||
elif options.operationType == OperationType.WEB_RESEARCH and "web_search" not in capabilities:
|
||||
continue
|
||||
elif "text_generation" not in capabilities:
|
||||
continue
|
||||
|
||||
# Prefer models that can handle the content without chunking, but allow chunking if needed
|
||||
if context_length >= total_size * 0.8: # 80% of content size
|
||||
if context_length > best_context_length:
|
||||
best_model = model_info
|
||||
best_context_length = context_length
|
||||
elif best_model is None: # Fallback to largest available model
|
||||
if context_length > best_context_length:
|
||||
best_model = model_info
|
||||
best_context_length = context_length
|
||||
|
||||
# Fallback to a reasonable default if no model found
|
||||
if best_model is None:
|
||||
best_model = {
|
||||
"contextLength": 128000, # GPT-4o default
|
||||
"llmName": "gpt-4o"
|
||||
}
|
||||
|
||||
# Calculate appropriate sizes
|
||||
# Convert tokens to bytes (rough estimate: 1 token ≈ 4 characters)
|
||||
context_length_bytes = int(best_model["contextLength"] * 4)
|
||||
max_context_bytes = int(context_length_bytes * 0.9) # 90% of context length
|
||||
text_chunk_size = int(max_context_bytes * 0.7) # 70% of max context for text chunks
|
||||
image_chunk_size = int(max_context_bytes * 0.8) # 80% of max context for image chunks
|
||||
|
||||
logger.debug(f"Selected model: {best_model.get('llmName', 'unknown')} with context length: {best_model['contextLength']}")
|
||||
logger.debug(f"Content size: {total_size} bytes, Max context: {max_context_bytes} bytes")
|
||||
logger.debug(f"Text chunk size: {text_chunk_size} bytes, Image chunk size: {image_chunk_size} bytes")
|
||||
|
||||
return {
|
||||
"maxContextBytes": max_context_bytes,
|
||||
"textChunkSize": text_chunk_size,
|
||||
"imageChunkSize": image_chunk_size
|
||||
}
|
||||
|
||||
def _getModelsForOperation(self, operation_type: str, options: AiCallOptions) -> List[ModelCapabilities]:
|
||||
"""
|
||||
Get models capable of handling the specific operation with capability filtering.
|
||||
"""
|
||||
# Use the actual AI objects model selection instead of hardcoded default
|
||||
if hasattr(self, 'aiObjects') and self.aiObjects:
|
||||
# Let AiObjects handle the model selection
|
||||
return []
|
||||
else:
|
||||
# Fallback to default model if AiObjects not available
|
||||
default_model = ModelCapabilities(
|
||||
name="default",
|
||||
maxTokens=4000,
|
||||
capabilities=["text", "reasoning"] if operation_type == "planning" else ["text"],
|
||||
costPerToken=0.001,
|
||||
processingTime=1.0,
|
||||
isAvailable=True
|
||||
)
|
||||
return [default_model]
|
||||
384
modules/services/serviceAi/subWebResearch.py
Normal file
384
modules/services/serviceAi/subWebResearch.py
Normal file
|
|
@ -0,0 +1,384 @@
|
|||
import logging
|
||||
from typing import Dict, Any, List, Optional, Tuple, Union
|
||||
from modules.datamodels.datamodelWeb import (
|
||||
WebResearchRequest,
|
||||
WebResearchActionResult,
|
||||
WebResearchDocumentData,
|
||||
WebResearchActionDocument,
|
||||
WebSearchResultItem,
|
||||
)
|
||||
from modules.interfaces.interfaceAiObjects import AiObjects
|
||||
from modules.shared.configuration import APP_CONFIG
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SubWebResearch:
|
||||
"""Web research operations including search, crawling, and analysis."""
|
||||
|
||||
def __init__(self, services, aiObjects):
|
||||
"""Initialize web research service.
|
||||
|
||||
Args:
|
||||
services: Service center instance for accessing other services
|
||||
aiObjects: Initialized AiObjects instance
|
||||
"""
|
||||
self.services = services
|
||||
self.aiObjects = aiObjects
|
||||
|
||||
async def webResearch(self, request: WebResearchRequest) -> WebResearchActionResult:
|
||||
"""Perform web research using interface functions."""
|
||||
try:
|
||||
logger.info(f"WEB RESEARCH STARTED")
|
||||
logger.info(f"User Query: {request.user_prompt}")
|
||||
logger.info(f"Max Results: {request.max_results}, Max Pages: {request.options.max_pages}")
|
||||
|
||||
# Global URL index to track all processed URLs across the entire research session
|
||||
global_processed_urls = set()
|
||||
|
||||
# Step 1: Find relevant websites - either provided URLs or AI-determined main URLs
|
||||
logger.info(f"=== STEP 1: INITIAL MAIN URLS LIST ===")
|
||||
|
||||
if request.urls:
|
||||
# Use provided URLs as initial main URLs
|
||||
websites = request.urls
|
||||
logger.info(f"Using provided URLs ({len(websites)}):")
|
||||
for i, url in enumerate(websites, 1):
|
||||
logger.info(f" {i}. {url}")
|
||||
else:
|
||||
# Use AI to determine main URLs based on user's intention
|
||||
logger.info(f"AI analyzing user intent: '{request.user_prompt}'")
|
||||
|
||||
# Use AI to generate optimized Tavily search query and search parameters
|
||||
query_optimizer_prompt = f"""You are a search query optimizer.
|
||||
|
||||
USER QUERY: {request.user_prompt}
|
||||
|
||||
Your task: Create a search query and parameters for the USER QUERY given.
|
||||
|
||||
RULES:
|
||||
1. The search query MUST be related to the user query above
|
||||
2. Extract key terms from the user query
|
||||
3. Determine appropriate country/language based on the query context
|
||||
4. Keep search query short (2-6 words)
|
||||
|
||||
Return ONLY this JSON format:
|
||||
{{
|
||||
"user_prompt": "search query based on user query above",
|
||||
"country": "Full English country name (ISO-3166; map codes via pycountry/i18n-iso-countries)",
|
||||
"language": "language_code_or_null",
|
||||
"topic": "general|news|academic_or_null",
|
||||
"time_range": "d|w|m|y_or_null",
|
||||
"selection_strategy": "single|multiple|specific_page",
|
||||
"selection_criteria": "what URLs to prioritize",
|
||||
"expected_url_patterns": ["pattern1", "pattern2"],
|
||||
"estimated_result_count": number
|
||||
}}"""
|
||||
|
||||
# Get AI response for query optimization
|
||||
from modules.datamodels.datamodelAi import AiCallRequest, AiCallOptions
|
||||
ai_request = AiCallRequest(
|
||||
prompt=query_optimizer_prompt,
|
||||
options=AiCallOptions()
|
||||
)
|
||||
ai_response_obj = await self.aiObjects.call(ai_request)
|
||||
ai_response = ai_response_obj.content
|
||||
logger.debug(f"AI query optimizer response: {ai_response}")
|
||||
|
||||
# Parse AI response to extract search query
|
||||
import json
|
||||
try:
|
||||
# Clean the response by removing markdown code blocks
|
||||
cleaned_response = ai_response.strip()
|
||||
if cleaned_response.startswith('```json'):
|
||||
cleaned_response = cleaned_response[7:] # Remove ```json
|
||||
if cleaned_response.endswith('```'):
|
||||
cleaned_response = cleaned_response[:-3] # Remove ```
|
||||
cleaned_response = cleaned_response.strip()
|
||||
|
||||
query_data = json.loads(cleaned_response)
|
||||
search_query = query_data.get("user_prompt", request.user_prompt)
|
||||
ai_country = query_data.get("country")
|
||||
ai_language = query_data.get("language")
|
||||
ai_topic = query_data.get("topic")
|
||||
ai_time_range = query_data.get("time_range")
|
||||
selection_strategy = query_data.get("selection_strategy", "multiple")
|
||||
selection_criteria = query_data.get("selection_criteria", "relevant URLs")
|
||||
expected_patterns = query_data.get("expected_url_patterns", [])
|
||||
estimated_count = query_data.get("estimated_result_count", request.max_results)
|
||||
|
||||
logger.info(f"AI optimized search query: '{search_query}'")
|
||||
logger.info(f"Selection strategy: {selection_strategy}")
|
||||
logger.info(f"Selection criteria: {selection_criteria}")
|
||||
logger.info(f"Expected URL patterns: {expected_patterns}")
|
||||
logger.info(f"Estimated result count: {estimated_count}")
|
||||
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("Failed to parse AI response as JSON, using original query")
|
||||
search_query = request.user_prompt
|
||||
ai_country = None
|
||||
ai_language = None
|
||||
ai_topic = None
|
||||
ai_time_range = None
|
||||
selection_strategy = "multiple"
|
||||
|
||||
# Perform the web search with AI-determined parameters
|
||||
search_kwargs = {
|
||||
"query": search_query,
|
||||
"max_results": request.max_results,
|
||||
"search_depth": request.options.search_depth,
|
||||
"auto_parameters": False # Use explicit parameters
|
||||
}
|
||||
|
||||
# Add parameters only if they have valid values
|
||||
def _normalizeCountry(c: Optional[str]) -> Optional[str]:
|
||||
if not c:
|
||||
return None
|
||||
s = str(c).strip()
|
||||
if not s or s.lower() in ['null', 'none', 'undefined']:
|
||||
return None
|
||||
# Map common codes to full English names when easy to do without extra deps
|
||||
mapping = {
|
||||
'ch': 'Switzerland', 'che': 'Switzerland',
|
||||
'de': 'Germany', 'ger': 'Germany', 'deu': 'Germany',
|
||||
'at': 'Austria', 'aut': 'Austria',
|
||||
'us': 'United States', 'usa': 'United States', 'uni ted states': 'United States',
|
||||
'uk': 'United Kingdom', 'gb': 'United Kingdom', 'gbr': 'United Kingdom'
|
||||
}
|
||||
key = s.lower()
|
||||
if key in mapping:
|
||||
return mapping[key]
|
||||
# If looks like full name, capitalize first letter only (Tavily accepts English names)
|
||||
return s
|
||||
|
||||
norm_ai_country = _normalizeCountry(ai_country)
|
||||
norm_req_country = _normalizeCountry(request.options.country)
|
||||
if norm_ai_country:
|
||||
search_kwargs["country"] = norm_ai_country
|
||||
elif norm_req_country:
|
||||
search_kwargs["country"] = norm_req_country
|
||||
|
||||
if ai_language and ai_language not in ['null', '', 'none', 'undefined']:
|
||||
search_kwargs["language"] = ai_language
|
||||
elif request.options.language and request.options.language not in ['null', '', 'none', 'undefined']:
|
||||
search_kwargs["language"] = request.options.language
|
||||
|
||||
if ai_topic and ai_topic in ['general', 'news', 'academic']:
|
||||
search_kwargs["topic"] = ai_topic
|
||||
elif request.options.topic and request.options.topic in ['general', 'news', 'academic']:
|
||||
search_kwargs["topic"] = request.options.topic
|
||||
|
||||
if ai_time_range and ai_time_range in ['d', 'w', 'm', 'y']:
|
||||
search_kwargs["time_range"] = ai_time_range
|
||||
elif request.options.time_range and request.options.time_range in ['d', 'w', 'm', 'y']:
|
||||
search_kwargs["time_range"] = request.options.time_range
|
||||
|
||||
# Constrain by expected domains if provided by AI
|
||||
try:
|
||||
include_domains = []
|
||||
for p in expected_patterns or []:
|
||||
if not isinstance(p, str):
|
||||
continue
|
||||
# Extract bare domain from pattern or URL
|
||||
import re
|
||||
m = re.search(r"(?:https?://)?([^/\s]+)", p.strip())
|
||||
if m:
|
||||
domain = m.group(1).lower()
|
||||
# strip leading www.
|
||||
if domain.startswith('www.'):
|
||||
domain = domain[4:]
|
||||
include_domains.append(domain)
|
||||
# Deduplicate
|
||||
if include_domains:
|
||||
seen = set()
|
||||
uniq = []
|
||||
for d in include_domains:
|
||||
if d not in seen:
|
||||
seen.add(d)
|
||||
uniq.append(d)
|
||||
search_kwargs["include_domains"] = uniq
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Log the parameters being used
|
||||
logger.info(f"Search parameters: country={search_kwargs.get('country', 'not_set')}, language={search_kwargs.get('language', 'not_set')}, topic={search_kwargs.get('topic', 'not_set')}, time_range={search_kwargs.get('time_range', 'not_set')}, include_domains={search_kwargs.get('include_domains', [])}")
|
||||
|
||||
search_results = await self.aiObjects.search_websites(**search_kwargs)
|
||||
|
||||
logger.debug(f"Web search returned {len(search_results)} results:")
|
||||
for i, result in enumerate(search_results, 1):
|
||||
logger.debug(f" {i}. {result.url} - {result.title}")
|
||||
|
||||
# Deduplicate while preserving order
|
||||
seen = set()
|
||||
search_urls = []
|
||||
for r in search_results:
|
||||
u = str(r.url)
|
||||
if u not in seen:
|
||||
seen.add(u)
|
||||
search_urls.append(u)
|
||||
|
||||
logger.info(f"After initial deduplication: {len(search_urls)} unique URLs from {len(search_results)} search results")
|
||||
|
||||
if not search_urls:
|
||||
logger.error("No relevant websites found")
|
||||
return WebResearchActionResult(success=False, error="No relevant websites found")
|
||||
|
||||
# Now use AI to determine the main URLs based on user's intention
|
||||
logger.info(f"AI selecting main URLs from {len(search_urls)} search results based on user intent")
|
||||
|
||||
# Create a prompt for AI to identify main URLs based on user's intention
|
||||
ai_prompt = f"""
|
||||
Select the most relevant URLs from these search results:
|
||||
|
||||
{chr(10).join([f"{i+1}. {url}" for i, url in enumerate(search_urls)])}
|
||||
|
||||
Return only the URLs that are most relevant for the user's query.
|
||||
One URL per line.
|
||||
"""
|
||||
# Create AI call request
|
||||
ai_request = AiCallRequest(
|
||||
prompt=ai_prompt,
|
||||
options=AiCallOptions()
|
||||
)
|
||||
ai_response_obj = await self.aiObjects.call(ai_request)
|
||||
ai_response = ai_response_obj.content
|
||||
logger.debug(f"AI response for main URL selection: {ai_response}")
|
||||
|
||||
# Parse AI response to extract URLs
|
||||
websites = []
|
||||
for line in ai_response.strip().split('\n'):
|
||||
line = line.strip()
|
||||
if line and ('http://' in line or 'https://' in line):
|
||||
# Extract URL from the line
|
||||
for word in line.split():
|
||||
if word.startswith('http://') or word.startswith('https://'):
|
||||
websites.append(word.rstrip('.,;'))
|
||||
break
|
||||
|
||||
if not websites:
|
||||
logger.warning("AI did not identify any main URLs, using first few search results")
|
||||
websites = search_urls[:3] # Fallback to first 3 search results
|
||||
|
||||
# Deduplicate while preserving order
|
||||
seen = set()
|
||||
unique_websites = []
|
||||
for url in websites:
|
||||
if url not in seen:
|
||||
seen.add(url)
|
||||
unique_websites.append(url)
|
||||
|
||||
websites = unique_websites
|
||||
logger.info(f"After AI selection deduplication: {len(websites)} unique URLs from {len(websites)} AI-selected URLs")
|
||||
|
||||
logger.info(f"AI selected {len(websites)} main URLs (after deduplication):")
|
||||
for i, url in enumerate(websites, 1):
|
||||
logger.info(f" {i}. {url}")
|
||||
|
||||
# Step 2: Smart website selection using AI interface
|
||||
logger.info(f"=== STEP 2: FILTERED URL LIST BY USER PROMPT'S INTENTION ===")
|
||||
logger.info(f"AI analyzing {len(websites)} URLs for relevance to: '{request.user_prompt}'")
|
||||
|
||||
selectedWebsites, aiResponse = await self.aiObjects.selectRelevantWebsites(websites, request.user_prompt)
|
||||
|
||||
logger.debug(f"AI Response: {aiResponse}")
|
||||
logger.debug(f"AI selected {len(selectedWebsites)} most relevant URLs:")
|
||||
for i, url in enumerate(selectedWebsites, 1):
|
||||
logger.debug(f" {i}. {url}")
|
||||
|
||||
# Show which were filtered out
|
||||
filtered_out = [url for url in websites if url not in selectedWebsites]
|
||||
if filtered_out:
|
||||
logger.debug(f"Filtered out {len(filtered_out)} less relevant URLs:")
|
||||
for i, url in enumerate(filtered_out, 1):
|
||||
logger.debug(f" {i}. {url}")
|
||||
|
||||
# Step 3+4+5: Recursive crawling with configurable depth
|
||||
# Get configuration parameters
|
||||
max_depth = int(APP_CONFIG.get("Web_Research_MAX_DEPTH", "2"))
|
||||
max_links_per_domain = int(APP_CONFIG.get("Web_Research_MAX_LINKS_PER_DOMAIN", "4"))
|
||||
crawl_timeout_minutes = int(APP_CONFIG.get("Web_Research_CRAWL_TIMEOUT_MINUTES", "10"))
|
||||
crawl_timeout_seconds = crawl_timeout_minutes * 60
|
||||
|
||||
# Use the configured max_depth or the request's pages_search_depth, whichever is smaller
|
||||
effective_depth = min(max_depth, request.options.pages_search_depth)
|
||||
|
||||
logger.info(f"=== STEP 3+4+5: RECURSIVE CRAWLING (DEPTH {effective_depth}) ===")
|
||||
logger.info(f"Starting recursive crawl of {len(selectedWebsites)} main websites...")
|
||||
logger.info(f"Search depth: {effective_depth} levels (max configured: {max_depth})")
|
||||
logger.info(f"Max links per domain: {max_links_per_domain}")
|
||||
logger.info(f"Crawl timeout: {crawl_timeout_minutes} minutes")
|
||||
|
||||
# Use recursive crawling with URL index to avoid duplicates
|
||||
import asyncio
|
||||
try:
|
||||
allContent = await asyncio.wait_for(
|
||||
self.aiObjects.crawlRecursively(
|
||||
urls=selectedWebsites,
|
||||
max_depth=effective_depth,
|
||||
extract_depth=request.options.extract_depth,
|
||||
max_per_domain=max_links_per_domain,
|
||||
global_processed_urls=global_processed_urls
|
||||
),
|
||||
timeout=crawl_timeout_seconds
|
||||
)
|
||||
logger.info(f"Crawling completed within timeout: {len(allContent)} pages crawled")
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning(f"Crawling timed out after {crawl_timeout_minutes} minutes, using partial results")
|
||||
# crawlRecursively now handles timeouts gracefully and returns partial results
|
||||
# Try to get the partial results that were collected
|
||||
allContent = {}
|
||||
|
||||
if not allContent:
|
||||
logger.error("Could not extract content from any websites")
|
||||
return WebResearchActionResult(success=False, error="Could not extract content from any websites")
|
||||
|
||||
logger.info(f"=== WEB RESEARCH COMPLETED ===")
|
||||
logger.info(f"Successfully crawled {len(allContent)} URLs total")
|
||||
logger.info(f"Crawl depth: {effective_depth} levels")
|
||||
|
||||
# Create simple result with raw content
|
||||
sources = [WebSearchResultItem(title=url, url=url) for url in selectedWebsites]
|
||||
|
||||
# Get all additional links (all URLs except main ones)
|
||||
additional_links = [url for url in allContent.keys() if url not in selectedWebsites]
|
||||
|
||||
# Combine all content into a single result
|
||||
combinedContent = ""
|
||||
for url, content in allContent.items():
|
||||
combinedContent += f"\n\n=== {url} ===\n{content}\n"
|
||||
|
||||
documentData = WebResearchDocumentData(
|
||||
user_prompt=request.user_prompt,
|
||||
websites_analyzed=len(allContent),
|
||||
additional_links_found=len(additional_links),
|
||||
analysis_result=combinedContent, # Raw content, no analysis
|
||||
sources=sources,
|
||||
additional_links=additional_links,
|
||||
individual_content=allContent, # Individual URL -> content mapping
|
||||
debug_info={
|
||||
"crawl_depth": effective_depth,
|
||||
"max_configured_depth": max_depth,
|
||||
"max_links_per_domain": max_links_per_domain,
|
||||
"crawl_timeout_minutes": crawl_timeout_minutes,
|
||||
"total_urls_crawled": len(allContent),
|
||||
"main_urls": len(selectedWebsites),
|
||||
"additional_urls": len(additional_links)
|
||||
}
|
||||
)
|
||||
|
||||
document = WebResearchActionDocument(
|
||||
documentName=f"web_research_{request.user_prompt[:50]}.json",
|
||||
documentData=documentData,
|
||||
mimeType="application/json"
|
||||
)
|
||||
|
||||
return WebResearchActionResult(
|
||||
success=True,
|
||||
documents=[document],
|
||||
resultLabel="web_research_results"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in web research: {str(e)}")
|
||||
return WebResearchActionResult(success=False, error=str(e))
|
||||
|
|
@ -1,117 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to demonstrate enhanced extractor format support.
|
||||
Shows all supported file extensions and MIME types for each extractor.
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# Add the gateway module to the path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'modules'))
|
||||
|
||||
from modules.services.serviceExtraction.subRegistry import ExtractorRegistry
|
||||
|
||||
def test_extractor_formats():
|
||||
"""Test and display all supported formats from extractors."""
|
||||
print("🔍 Testing Plug-and-Play Extractor System")
|
||||
print("=" * 60)
|
||||
|
||||
# Create registry
|
||||
registry = ExtractorRegistry()
|
||||
|
||||
# Get all supported formats
|
||||
formats = registry.getAllSupportedFormats()
|
||||
|
||||
print("\n📋 Supported File Extensions by Extractor:")
|
||||
print("-" * 50)
|
||||
for extractor_name, extensions in formats["extensions"].items():
|
||||
if extensions:
|
||||
print(f" {extractor_name:20} → {', '.join(extensions)}")
|
||||
else:
|
||||
print(f" {extractor_name:20} → (all extensions - fallback)")
|
||||
|
||||
print("\n📋 Supported MIME Types by Extractor:")
|
||||
print("-" * 50)
|
||||
for extractor_name, mime_types in formats["mime_types"].items():
|
||||
if mime_types:
|
||||
print(f" {extractor_name:20} → {', '.join(mime_types)}")
|
||||
else:
|
||||
print(f" {extractor_name:20} → (all MIME types - fallback)")
|
||||
|
||||
# Test individual extractors
|
||||
print("\n🧪 Testing Individual Extractors:")
|
||||
print("-" * 50)
|
||||
|
||||
# Get all registered extractors
|
||||
for key, extractor in registry._map.items():
|
||||
if hasattr(extractor, 'getSupportedExtensions') and hasattr(extractor, 'getSupportedMimeTypes'):
|
||||
extensions = extractor.getSupportedExtensions()
|
||||
mime_types = extractor.getSupportedMimeTypes()
|
||||
print(f"\n {extractor.__class__.__name__}:")
|
||||
print(f" Extensions: {extensions}")
|
||||
print(f" MIME Types: {mime_types}")
|
||||
|
||||
# Test detection with various file types
|
||||
print("\n🔬 Testing File Detection:")
|
||||
print("-" * 50)
|
||||
|
||||
test_files = [
|
||||
# Document formats
|
||||
("document.pdf", "application/pdf"),
|
||||
("spreadsheet.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"),
|
||||
("presentation.pptx", "application/vnd.openxmlformats-officedocument.presentationml.presentation"),
|
||||
("document.docx", "application/vnd.openxmlformats-officedocument.wordprocessingml.document"),
|
||||
|
||||
# Text and code files
|
||||
("readme.txt", "text/plain"),
|
||||
("readme.md", "text/markdown"),
|
||||
("app.log", "text/plain"),
|
||||
("Main.java", "text/x-java-source"),
|
||||
("script.js", "text/javascript"),
|
||||
("component.tsx", "text/typescript"),
|
||||
("main.py", "text/x-python"),
|
||||
("config.yaml", "text/x-yaml"),
|
||||
("package.json", "application/json"),
|
||||
("data.csv", "text/csv"),
|
||||
("config.xml", "application/xml"),
|
||||
("webpage.html", "text/html"),
|
||||
("styles.css", "text/css"),
|
||||
("script.sh", "text/x-sh"),
|
||||
("Dockerfile", "text/plain"),
|
||||
(".gitignore", "text/plain"),
|
||||
("app.config", "text/plain"),
|
||||
("database.sql", "text/x-sql"),
|
||||
("schema.ddl", "application/sql"),
|
||||
|
||||
# Images
|
||||
("image.png", "image/png"),
|
||||
("photo.jpg", "image/jpeg"),
|
||||
|
||||
# Unknown
|
||||
("unknown.xyz", "application/octet-stream")
|
||||
]
|
||||
|
||||
for filename, mime_type in test_files:
|
||||
extractor = registry.resolve(mime_type, filename)
|
||||
if extractor:
|
||||
print(f" {filename:25} ({mime_type:50}) → {extractor.__class__.__name__}")
|
||||
else:
|
||||
print(f" {filename:25} ({mime_type:50}) → No extractor found")
|
||||
|
||||
print("\n✅ Plug-and-Play extractor system test completed!")
|
||||
print("\nKey improvements:")
|
||||
print(" • 🔌 TRUE PLUG-AND-PLAY: Just add extractor file, it auto-registers!")
|
||||
print(" • 📋 No more manual registration of file types")
|
||||
print(" • 🔍 Auto-discovery scans extractors directory")
|
||||
print(" • 📝 Each extractor declares its own supported formats")
|
||||
print(" • 🚀 Easy to add new file types - just create new extractor")
|
||||
print(" • 🧹 Clean, maintainable code with no redundancy")
|
||||
print("\nTo add a new file type:")
|
||||
print(" 1. Create extractorXyz.py in extractors/ directory")
|
||||
print(" 2. Implement Extractor interface with getSupportedExtensions()")
|
||||
print(" 3. That's it! No registry changes needed!")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_extractor_formats()
|
||||
|
|
@ -1,83 +0,0 @@
|
|||
"""
|
||||
Simple test to verify image processing works correctly.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import sys
|
||||
import os
|
||||
import base64
|
||||
import logging
|
||||
|
||||
# Add the gateway module to the path
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'modules'))
|
||||
|
||||
from modules.datamodels.datamodelAi import AiCallOptions, OperationType
|
||||
from modules.services.serviceAi.mainServiceAi import AiService
|
||||
|
||||
# Set up logging
|
||||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
async def test_image_processing():
|
||||
"""Test image processing with a simple base64 image."""
|
||||
print("🧪 Testing image processing...")
|
||||
logger.info("🧪 Testing image processing...")
|
||||
|
||||
try:
|
||||
print("🔧 Initializing AI service...")
|
||||
logger.info("🔧 Initializing AI service...")
|
||||
|
||||
# Initialize AI service
|
||||
ai_service = await AiService.create()
|
||||
print("✅ AI service initialized successfully")
|
||||
logger.info("✅ AI service initialized successfully")
|
||||
|
||||
# Create a simple test image (1x1 pixel PNG in base64)
|
||||
test_image_base64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
|
||||
print(f"📸 Test image base64 length: {len(test_image_base64)}")
|
||||
logger.info(f"📸 Test image base64 length: {len(test_image_base64)}")
|
||||
|
||||
# Test the readImage method directly
|
||||
print("📸 Testing readImage method...")
|
||||
logger.info("📸 Testing readImage method...")
|
||||
|
||||
result = await ai_service.readImage(
|
||||
prompt="What do you see in this image?",
|
||||
imageData=test_image_base64,
|
||||
mimeType="image/png"
|
||||
)
|
||||
|
||||
print(f"✅ Image processing result: {result}")
|
||||
logger.info(f"✅ Image processing result: {result}")
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Image processing test failed: {str(e)}")
|
||||
logger.error(f"❌ Image processing test failed: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
return False
|
||||
|
||||
async def main():
|
||||
"""Main function to run the image processing test."""
|
||||
print("🎯 Starting Image Processing Test")
|
||||
print("=" * 60)
|
||||
logger.info("🎯 Starting Image Processing Test")
|
||||
logger.info("=" * 60)
|
||||
|
||||
success = await test_image_processing()
|
||||
|
||||
if success:
|
||||
print("🎉 Image processing test completed successfully!")
|
||||
logger.info("🎉 Image processing test completed successfully!")
|
||||
else:
|
||||
print("❌ Image processing test failed!")
|
||||
logger.error("❌ Image processing test failed!")
|
||||
|
||||
print("=" * 60)
|
||||
logger.info("=" * 60)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,263 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script for multi-file processing implementation.
|
||||
This script tests the new multi-file functionality without breaking existing single-file processing.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
from typing import Dict, Any, List
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
async def test_multi_file_detection():
|
||||
"""Test AI-powered multi-file detection."""
|
||||
print("=== Testing Multi-File Detection ===")
|
||||
|
||||
# Mock AI service for testing
|
||||
class MockAiService:
|
||||
async def call(self, request):
|
||||
class MockResponse:
|
||||
def __init__(self, content):
|
||||
self.content = content
|
||||
return MockResponse('{"is_multi_file": true, "strategy": "per_entity", "criteria": "customer_id", "file_naming_pattern": "{customer_name}_data.docx", "reasoning": "User wants separate files for each customer"}')
|
||||
|
||||
class MockAiObjects:
|
||||
def __init__(self):
|
||||
self.call = MockAiService().call
|
||||
|
||||
# Import the AI service
|
||||
try:
|
||||
from modules.services.serviceAi.mainServiceAi import AiService
|
||||
|
||||
# Create mock service center
|
||||
class MockServiceCenter:
|
||||
def __init__(self):
|
||||
self.utils = MockUtils()
|
||||
|
||||
class MockUtils:
|
||||
def debugLogToFile(self, message, category):
|
||||
print(f"[{category}] {message}")
|
||||
|
||||
# Create AI service instance
|
||||
ai_service = AiService(MockServiceCenter())
|
||||
ai_service.aiObjects = MockAiObjects()
|
||||
|
||||
# Test prompts
|
||||
test_prompts = [
|
||||
"Create one file for each customer in the document",
|
||||
"Split the data into separate files by category",
|
||||
"Generate individual files for each product",
|
||||
"Create a single report with all data",
|
||||
"Erstelle eine Datei für jeden Kunden", # German
|
||||
"Créer un fichier par section" # French
|
||||
]
|
||||
|
||||
for prompt in test_prompts:
|
||||
print(f"\nTesting prompt: '{prompt}'")
|
||||
try:
|
||||
analysis = await ai_service._analyzePromptIntent(prompt, ai_service)
|
||||
print(f" Analysis: {analysis}")
|
||||
|
||||
if analysis.get("is_multi_file"):
|
||||
print(f" ✓ Detected as multi-file with strategy: {analysis.get('strategy')}")
|
||||
else:
|
||||
print(f" ✓ Detected as single-file")
|
||||
|
||||
except Exception as e:
|
||||
print(f" ✗ Error: {str(e)}")
|
||||
|
||||
print("\n=== Multi-File Detection Test Complete ===")
|
||||
return True
|
||||
|
||||
except ImportError as e:
|
||||
print(f"Import error: {e}")
|
||||
print("Make sure you're running from the gateway directory")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"Error during testing: {e}")
|
||||
return False
|
||||
|
||||
async def test_json_schema_validation():
|
||||
"""Test JSON schema validation for both single and multi-file."""
|
||||
print("\n=== Testing JSON Schema Validation ===")
|
||||
|
||||
try:
|
||||
from modules.services.serviceGeneration.subJsonSchema import (
|
||||
get_document_subJsonSchema,
|
||||
get_multi_document_subJsonSchema,
|
||||
get_adaptive_json_schema,
|
||||
validate_json_document
|
||||
)
|
||||
|
||||
# Test single document schema
|
||||
single_doc_schema = get_document_subJsonSchema()
|
||||
print(f"✓ Single document schema loaded: {len(single_doc_schema)} properties")
|
||||
|
||||
# Test multi-document schema
|
||||
multi_doc_schema = get_multi_document_subJsonSchema()
|
||||
print(f"✓ Multi-document schema loaded: {len(multi_doc_schema)} properties")
|
||||
|
||||
# Test adaptive schema selection
|
||||
single_analysis = {"is_multi_file": False}
|
||||
multi_analysis = {"is_multi_file": True}
|
||||
|
||||
single_schema = get_adaptive_json_schema(single_analysis)
|
||||
multi_schema = get_adaptive_json_schema(multi_analysis)
|
||||
|
||||
print(f"✓ Adaptive schema selection working")
|
||||
print(f" Single-file schema type: {single_schema.get('type', 'unknown')}")
|
||||
print(f" Multi-file schema type: {multi_schema.get('type', 'unknown')}")
|
||||
|
||||
# Test validation with sample data
|
||||
single_doc_data = {
|
||||
"metadata": {"title": "Test Document"},
|
||||
"sections": [
|
||||
{
|
||||
"id": "section_1",
|
||||
"content_type": "paragraph",
|
||||
"elements": [{"text": "Test content"}],
|
||||
"order": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
multi_doc_data = {
|
||||
"metadata": {
|
||||
"title": "Test Documents",
|
||||
"splitStrategy": "per_entity"
|
||||
},
|
||||
"documents": [
|
||||
{
|
||||
"id": "doc_1",
|
||||
"title": "Document 1",
|
||||
"filename": "doc1.docx",
|
||||
"sections": [
|
||||
{
|
||||
"id": "section_1",
|
||||
"content_type": "paragraph",
|
||||
"elements": [{"text": "Content 1"}],
|
||||
"order": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
single_valid = validate_json_document(single_doc_data)
|
||||
multi_valid = validate_json_document(multi_doc_data)
|
||||
|
||||
print(f"✓ Single document validation: {'PASS' if single_valid else 'FAIL'}")
|
||||
print(f"✓ Multi-document validation: {'PASS' if multi_valid else 'FAIL'}")
|
||||
|
||||
print("\n=== JSON Schema Validation Test Complete ===")
|
||||
return True
|
||||
|
||||
except ImportError as e:
|
||||
print(f"Import error: {e}")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"Error during schema testing: {e}")
|
||||
return False
|
||||
|
||||
async def test_prompt_builder():
|
||||
"""Test adaptive prompt building."""
|
||||
print("\n=== Testing Prompt Builder ===")
|
||||
|
||||
try:
|
||||
from modules.services.serviceGeneration.subPromptBuilder import (
|
||||
buildAdaptiveExtractionPrompt,
|
||||
buildGenericExtractionPrompt
|
||||
)
|
||||
|
||||
# Mock services
|
||||
class MockServices:
|
||||
def __init__(self):
|
||||
self.utils = MockUtils()
|
||||
|
||||
class MockUtils:
|
||||
def debugLogToFile(self, message, category):
|
||||
print(f"[{category}] {message}")
|
||||
|
||||
services = MockServices()
|
||||
|
||||
# Test adaptive prompt building
|
||||
prompt_analysis = {
|
||||
"is_multi_file": True,
|
||||
"strategy": "per_entity",
|
||||
"criteria": "customer_id",
|
||||
"file_naming_pattern": "{customer_name}_data.docx"
|
||||
}
|
||||
|
||||
adaptive_prompt = await buildAdaptiveExtractionPrompt(
|
||||
outputFormat="docx",
|
||||
userPrompt="Create one file for each customer",
|
||||
title="Customer Data",
|
||||
promptAnalysis=prompt_analysis,
|
||||
aiService=None,
|
||||
services=services
|
||||
)
|
||||
|
||||
print(f"✓ Adaptive prompt generated: {len(adaptive_prompt)} characters")
|
||||
print(f" Contains multi-file instructions: {'documents' in adaptive_prompt}")
|
||||
|
||||
# Test generic prompt building
|
||||
generic_prompt = await buildGenericExtractionPrompt(
|
||||
outputFormat="docx",
|
||||
userPrompt="Create a single report",
|
||||
title="Report",
|
||||
aiService=None,
|
||||
services=services
|
||||
)
|
||||
|
||||
print(f"✓ Generic prompt generated: {len(generic_prompt)} characters")
|
||||
print(f" Contains single-file instructions: {'sections' in generic_prompt}")
|
||||
|
||||
print("\n=== Prompt Builder Test Complete ===")
|
||||
return True
|
||||
|
||||
except ImportError as e:
|
||||
print(f"Import error: {e}")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"Error during prompt builder testing: {e}")
|
||||
return False
|
||||
|
||||
async def main():
|
||||
"""Run all tests."""
|
||||
print("Starting Multi-File Processing Tests...")
|
||||
print("=" * 50)
|
||||
|
||||
tests = [
|
||||
test_multi_file_detection,
|
||||
test_json_schema_validation,
|
||||
test_prompt_builder
|
||||
]
|
||||
|
||||
results = []
|
||||
for test in tests:
|
||||
try:
|
||||
result = await test()
|
||||
results.append(result)
|
||||
except Exception as e:
|
||||
print(f"Test failed with exception: {e}")
|
||||
results.append(False)
|
||||
|
||||
print("\n" + "=" * 50)
|
||||
print("Test Results Summary:")
|
||||
print(f" Tests run: {len(tests)}")
|
||||
print(f" Passed: {sum(results)}")
|
||||
print(f" Failed: {len(tests) - sum(results)}")
|
||||
|
||||
if all(results):
|
||||
print("\n🎉 All tests passed! Multi-file processing is ready.")
|
||||
else:
|
||||
print("\n⚠️ Some tests failed. Check the implementation.")
|
||||
|
||||
return all(results)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Loading…
Reference in a new issue