The AI agent used to handle queries.
ai_agent_ask
The 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_turbo
The model used for the AI Agent for basic text.
8400
1
The 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}`
10000
The 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 travel
System 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_turbo
The model used for the AI Agent for basic text.
8400
1
The 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}`
10000
The 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 travel
System 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_002
The model used for the AI Agent for calculating embeddings.
basic
The strategy used for the AI Agent for calculating embeddings.
64
1
The number of tokens per chunk.
The parameters for the LLM endpoint specific to OpenAI / Google models.
openai__gpt_3_5_turbo
The model used for the AI Agent for basic text.
8400
1
The 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}`
10000
The 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 travel
System 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_002
The model used for the AI Agent for calculating embeddings.
basic
The strategy used for the AI Agent for calculating embeddings.
64
1
The number of tokens per chunk.
The parameters for the LLM endpoint specific to OpenAI / Google models.
openai__gpt_3_5_turbo
The model used for the AI Agent for basic text.
8400
1
The 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}`
10000
The 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 travel
System messages try to help the LLM "understand" its role and what it is supposed to do.
{
"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"
}
}