AI agent for question requests
The AI agent used to handle queries.
ai_agent_askThe type of AI agent used to handle queries.
Value is always ai_agent_ask
AI agent tool used to handle basic text.
The parameters for the LLM endpoint specific to OpenAI / Google models.
openai__gpt_3_5_turboThe model used for the AI Agent for basic text.
84001The number of tokens for completion.
(\{user_question\}[\s\S]*?\{content\}|\{content\}[\s\S]*?\{user_question\})It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`10000The prompt template contains contextual information of the request and the user prompt.
When passing prompt_template parameters, you must include inputs for {user_question} and {content}.
Input for {current_date} is optional, depending on the use.
You are a helpful travel assistant specialized in budget travelSystem messages try to help the LLM "understand" its role and what it is supposed to do.
AI agent tool used to handle basic text.
The parameters for the LLM endpoint specific to OpenAI / Google models.
openai__gpt_3_5_turboThe model used for the AI Agent for basic text.
84001The number of tokens for completion.
(\{user_question\}[\s\S]*?\{content\}|\{content\}[\s\S]*?\{user_question\})It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`10000The prompt template contains contextual information of the request and the user prompt.
When passing prompt_template parameters, you must include inputs for {user_question} and {content}.
Input for {current_date} is optional, depending on the use.
You are a helpful travel assistant specialized in budget travelSystem messages try to help the LLM "understand" its role and what it is supposed to do.
AI agent tool used to to handle longer text.
openai__text_embedding_ada_002The model used for the AI Agent for calculating embeddings.
basicThe strategy used for the AI Agent for calculating embeddings.
641The number of tokens per chunk.
The parameters for the LLM endpoint specific to OpenAI / Google models.
openai__gpt_3_5_turboThe model used for the AI Agent for basic text.
84001The number of tokens for completion.
(\{user_question\}[\s\S]*?\{content\}|\{content\}[\s\S]*?\{user_question\})It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`10000The prompt template contains contextual information of the request and the user prompt.
When passing prompt_template parameters, you must include inputs for {user_question} and {content}.
Input for {current_date} is optional, depending on the use.
You are a helpful travel assistant specialized in budget travelSystem messages try to help the LLM "understand" its role and what it is supposed to do.
AI agent tool used to to handle longer text.
openai__text_embedding_ada_002The model used for the AI Agent for calculating embeddings.
basicThe strategy used for the AI Agent for calculating embeddings.
641The number of tokens per chunk.
The parameters for the LLM endpoint specific to OpenAI / Google models.
openai__gpt_3_5_turboThe model used for the AI Agent for basic text.
84001The number of tokens for completion.
(\{user_question\}[\s\S]*?\{content\}|\{content\}[\s\S]*?\{user_question\})It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`10000The prompt template contains contextual information of the request and the user prompt.
When passing prompt_template parameters, you must include inputs for {user_question} and {content}.
Input for {current_date} is optional, depending on the use.
You are a helpful travel assistant specialized in budget travelSystem messages try to help the LLM "understand" its role and what it is supposed to do.
Response Example
{
"type": "ai_agent_ask",
"basic_text": {
"llm_endpoint_params": {
"type": "openai_params",
"frequency_penalty": 1.5,
"presence_penalty": 1.5,
"stop": "<|im_end|>",
"temperature": 0,
"top_p": 1
},
"model": "openai__gpt_3_5_turbo",
"num_tokens_for_completion": 8400,
"prompt_template": "It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`",
"system_message": "You are a helpful travel assistant specialized in budget travel"
},
"basic_text_multi": {
"llm_endpoint_params": {
"type": "openai_params",
"frequency_penalty": 1.5,
"presence_penalty": 1.5,
"stop": "<|im_end|>",
"temperature": 0,
"top_p": 1
},
"model": "openai__gpt_3_5_turbo",
"num_tokens_for_completion": 8400,
"prompt_template": "It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`",
"system_message": "You are a helpful travel assistant specialized in budget travel"
},
"long_text": {
"embeddings": {
"model": "openai__text_embedding_ada_002",
"strategy": {
"id": "basic",
"num_tokens_per_chunk": 64
}
},
"llm_endpoint_params": {
"type": "openai_params",
"frequency_penalty": 1.5,
"presence_penalty": 1.5,
"stop": "<|im_end|>",
"temperature": 0,
"top_p": 1
},
"model": "openai__gpt_3_5_turbo",
"num_tokens_for_completion": 8400,
"prompt_template": "It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`",
"system_message": "You are a helpful travel assistant specialized in budget travel"
},
"long_text_multi": {
"embeddings": {
"model": "openai__text_embedding_ada_002",
"strategy": {
"id": "basic",
"num_tokens_per_chunk": 64
}
},
"llm_endpoint_params": {
"type": "openai_params",
"frequency_penalty": 1.5,
"presence_penalty": 1.5,
"stop": "<|im_end|>",
"temperature": 0,
"top_p": 1
},
"model": "openai__gpt_3_5_turbo",
"num_tokens_for_completion": 8400,
"prompt_template": "It is `{current_date}`, consider these travel options `{content}` and answer `{user_question}`",
"system_message": "You are a helpful travel assistant specialized in budget travel"
}
}