Chat

import maitai

maitai_client = maitai.MaitaiAsync()

messages = [
    {"role": "system", "content": "You are a helpful ordering assistant..."},
    {"role": "user", "content": "Generate a response to the customer..."},
]

response = await maitai_client.chat.completions.create(
    messages=messages,
    model="llama3-70b-8192", ## Remove this line to set model in Portal
    session_id="YOUR_SESSION_ID",
    intent="CONVERSATION",
    application="demo_app",
    metadata={} ## Store custom tags here for future reference
}

{
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "model": "llama3-70b-8192",
    "system_fingerprint": "fp_44709d6fcb",
    "choices": [{
        "index": 0,
        "message": {
            "role": "assistant",
            "content": "\n\nHello there, how may I assist you today?",
        },
        "logprobs": null,
        "finish_reason": "stop"
    }],
    "usage": {
        "prompt_tokens": 9,
        "completion_tokens": 12,
        "total_tokens": 21
    }
}

import maitai

maitai_client = maitai.MaitaiAsync()

messages = [
    {"role": "system", "content": "You are a helpful ordering assistant..."},
    {"role": "user", "content": "Generate a response to the customer..."},
]

response = await maitai_client.chat.completions.create(
    messages=messages,
    model="llama3-70b-8192", ## Remove this line to set model in Portal
    session_id="YOUR_SESSION_ID",
    intent="CONVERSATION",
    application="demo_app",
    metadata={} ## Store custom tags here for future reference
}

{
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "model": "llama3-70b-8192",
    "system_fingerprint": "fp_44709d6fcb",
    "choices": [{
        "index": 0,
        "message": {
            "role": "assistant",
            "content": "\n\nHello there, how may I assist you today?",
        },
        "logprobs": null,
        "finish_reason": "stop"
    }],
    "usage": {
        "prompt_tokens": 9,
        "completion_tokens": 12,
        "total_tokens": 21
    }
}

Create chat completion

Creates a model response for the given chat conversation. Passing parameters in through the SDK overrides the Portal configuration.

Maitai Specific Parameters

session_id

string

required

A unique identifier you set for the session.

intent

string

required

Specifies the type the intention of the request.

Examples: CONVERSATION

application

string

required

The reference to the application the request is created from.

metadata

map<string, string>

Optional metadata used to reference the request/session later.

reference_id

string

Optional. A reference identifier for identifying the request in your session.

callback

function(EvaluationResponse)

Optional. A callback function that may be used to handle the resulting Maitai evaluation asynchronously.

evaluation_enabled

boolean

default:true

Indicates whether or not to evaluate model output.

apply_corrections

boolean

default:false

Indicates whether or not to apply corrections if a fault is found during evaluation. This is also referred to as “autocorrect”.

safe_mode

boolean

default:false

Safe Mode ensures that corrections all LLM outputs are evaluated and corrected, with no consideration for latency.

assistant

boolean

default:false

Whether to run as an Assistant. See Assistants for more info

user_id

string

default:""

Optional. Unique identifier for a user, used for long term memory.

context_retrieval_enabled

boolean

default:false

Indicates whether or not to retrieve and inject applicable context parts.

context_query

string

default:""

Optional. A query used for retrieving applicable context. If not provided while context_retrieval_enabled is true, a query will be inferred.

fallback_config

object

Optional. Configuration for fallback model.

server_side_inference

boolean

default:false

Indicates whether the inference should be performed server-side. Only OpenAI models can be run client-side.

user_id

string

default:""

The unique identifier for the end-user. If this is supplied, we’ll create, store, and search core memories to provide inter-session context.

Model Provider Parameters

messages

array

required

A list of messages comprising the conversation so far.

System message

object

User message

object

Assistant message

object

Tool message

object

Function message

object

deprecated

model

string

ID of the model to use. Providing in parameters overwrites portal config.

frequency_penalty

number or null

default:0

Defaults to 0. Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.

logit_bias

map

default:"null"

Defaults to null. Modify the likelihood of specified tokens appearing in the completion.

Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

logprobs

boolean or null

default:false

Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

top_logprobs

integer or null

An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.

max_tokens

integer or null

The maximum number of tokens that can be generated in the chat completion.

The total length of input tokens and generated tokens is limited by the model’s context length.

max_completion_tokens

integer or null

The maximum number of tokens that can be generated in the chat completion.

The total length of input tokens and generated tokens is limited by the model’s context length.

integer or null

default:1

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.

presence_penalty

number or null

default:0

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics.

response_format

object

An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.

Setting to {"type": "json_object"} enables JSON mode, which guarantees the message the model generates is valid JSON.

Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly “stuck” request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.

seed

integer or null

This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

stop

string / array / null

default:"null"

Up to 4 sequences where the API will stop generating further tokens.

stream

boolean or null

default:false

If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Example Python code.

stream_options

object or null

default:{"include_usage":true}

Optional. Defaults to null. Options for streaming response. Only set this when you set stream: true.

temperature

number or null

default:1

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

top_p

number or null

default:1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

tools

array

A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.

tool_choice

string

none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.

tool_choice

object

Specifies a tool the model should use. Use to force the model to call a specific function.

user

string

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.

function_call

string

deprecated

none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message or calling a function.

function_call

object

deprecated

Specifying a particular function via {"name": "my_function"} forces the model to call that function.

functions

array

deprecated

Deprecated in favor of tools.

A list of functions the model may generate JSON inputs for.

Returns

Returns a chat completion object, or a streamed sequence of chat completion chunk objects if the request is streamed.

The chat completion object

Represents a chat completion response returned by model, based on the provided input.

string

A unique identifier for the chat completion.

choice

array

A list of chat completion choices. Can be more than one if n is greater than 1.

is_correction

boolean

Boolean value representing whether or not the Choice is a result of a Maitai correction (autocorrect).

finish_reason

string

The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, `length` if the maximum number of tokens specified in the request was reached, `content_filter` if content was omitted due to a flag from our content filters, `tool_calls` if the model called a tool, or `function_call` (deprecated) if the model called a function.

index

integer

The index of the choice in the list of choices.

message

object

A chat completion message generated by the model.

logprobs

object or null

Log probability information for the choice.

created

integer

The Unix timestamp (in seconds) of when the chat completion was created.

model

string

The model used for the chat completion.

system_fingerprint

string

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

If cache this means that the result came out of the semantic cache.

object

string

The object type, which is always chat.completion.

usage

object

Usage statistics for the completion request.

The chat completion chunk object

Represents a streamed chunk of a chat completion response returned by model, based on the provided input.

string

A unique identifier for the chat completion. Each chunk has the same ID.

choices

array

A list of chat completion choices. Can be more than one if n is greater than 1.

delta

object

A chat completion delta generated by streamed model responses.

logprobs

object or null

Log probability information for the choice.

finish_reason

string

The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from our content filters, tool_calls if the model called a tool, or function_call (deprecated) if the model called a function.

index

integer

The index of the choice in the list of choices.

created

integer

The Unix timestamp (in seconds) of when the chat completion was created. Each chunk has the same timestamp.

model

string

The model used to generate the completion.

system_fingerprint

string

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

If cache this means that the result came out of the semantic cache.

object

string

The object type, which is always chat.completion.chunk.

usage

object

An optional field that will only be present when you set stream_options: {'"include_usage": true} in your request. When present, it contains a null value except for the last chunk which contains the token usage statistics for the entire request.

Helicone Support Evaluate

import maitai

maitai_client = maitai.MaitaiAsync()

messages = [
    {"role": "system", "content": "You are a helpful ordering assistant..."},
    {"role": "user", "content": "Generate a response to the customer..."},
]

response = await maitai_client.chat.completions.create(
    messages=messages,
    model="llama3-70b-8192", ## Remove this line to set model in Portal
    session_id="YOUR_SESSION_ID",
    intent="CONVERSATION",
    application="demo_app",
    metadata={} ## Store custom tags here for future reference
}

{
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "model": "llama3-70b-8192",
    "system_fingerprint": "fp_44709d6fcb",
    "choices": [{
        "index": 0,
        "message": {
            "role": "assistant",
            "content": "\n\nHello there, how may I assist you today?",
        },
        "logprobs": null,
        "finish_reason": "stop"
    }],
    "usage": {
        "prompt_tokens": 9,
        "completion_tokens": 12,
        "total_tokens": 21
    }
}

Get Started

Examples

Features

SDK Reference

Create chat completion

Maitai Specific Parameters

Model Provider Parameters

Returns

The chat completion object

The chat completion chunk object

Get Started

Examples

Features

SDK Reference

​Create chat completion

​Maitai Specific Parameters

​Model Provider Parameters

​Returns

​The chat completion object

​The chat completion chunk object

Create chat completion

Maitai Specific Parameters

Model Provider Parameters

Returns

The chat completion object

The chat completion chunk object