Generate Text pipe.generateText()

You can use a pipe to get any LLM to generate text based on a user prompt. For example, you can ask a pipe to generate a text completion based on a user prompt like "Who is an AI Engineer?" or give it a an entire doc and ask it to summarize it.

The Langbase AI SDK provides a generateText() function to generate text using pipes with any LLM.

Deprecation Notice

This SDK method has been deprecated. Please use the new run SDK method with stream false.

API reference

Deprecated

`generateText(options)`

Generate a text completion using generateText() function.

Function Signature

   generateText(options)

   // With types.
   generateText(options: GenerateOptions)

options

Name
options
Type
GenerateOptions
Description
GenerateOptions Object
interface GenerateOptions { messages?: Message[]; variables?: Variable[]; chat?: boolean; threadId?: string; }
Following are the properties of the options object.

messages

Name
messages
Type
Array<Message>
Description
A messages array including the following properties. Optional if variables are provided.
Message Object
interface Message { role: 'user' | 'assistant' | 'system'| 'tool'; content: string; name?: string; tool_call_id?: string; }
- Name
  role
  Type
  'user' | 'assistant' | 'system'| 'tool'
  Description
  The role of the author of this message.
- Name
  content
  Type
  string
  Description
  The contents of the chunk message.
- Name
  name
  Type
  string
  Description
  The name of the tool called by LLM
- Name
  tool_call_id
  Type
  string
  Description
  The id of the tool called by LLM

variables

Name
variables
Type
Array<Variable>
Description
A variables array including the name and value params. Optional if messages are provided.
Variable Object
interface Variable { name: string; value: string; }
- Name
  name
  Type
  string
  Description
  The name of the variable.
- Name
  value
  Type
  string
  Description
  The value of the variable.

chat

Name
chat
Type
boolean
Description
For a chat pipe, set chat to true.
This is useful when you want to use a chat pipe to generate text as it returns a threadId. Defaults to false.

threadId

Name
threadId
Type
string | null
Description
The ID of the thread. Useful for a chat pipe to continue the conversation in the same thread. Optional.
- If threadId is not provided, a new thread will be created. E.g. first message of a new chat will not have a threadId.
- After the first message, a new threadId will be returned.
- Use this threadId to continue the conversation in the same thread from the second message onwards.

Usage example

Install the SDK

npm i langbase
pnpm i langbase
yarn add langbase

.env file

# Add your Pipe API key here.
LANGBASE_MY_PIPE_API_KEY="pipe_12345"
# … add more keys if you have more pipes.

Generate Pipe: Use generateText()

import {Pipe} from 'langbase';

// 1. Initiate the Pipe.
const myPipe = new Pipe({
  apiKey: process.env.LANGBASE_PIPE_API_KEY!,
});

// 2. Generate the text by asking a question.
const {completion} = await myPipe.generateText({
	messages: [{role: 'user', content: 'Who is an AI Engineer?'}],
});
console.log(completion);

Variables with generateText()

// 1. Initiate the Pipe.
// … same as above
// 2. Stream text by asking a question.
const {completion} = await myPipe.streamText({
  messages: [{role: 'user', content: 'Who is {{question}}?'}],
  variables: [{name: 'question', value: 'AI Engineer'}],
});

Chat Pipe: Use generateText()

import {Pipe} from 'langbase';

// Initiate the Pipe.
const myPipe = new Pipe({
  apiKey: process.env.LANGBASE_PIPE_API_KEY!,
});

// Message 1: Tell something to the LLM.
const {completion, threadId} = await myPipe.generateText({
  messages: [{role: 'user', content: 'My company is called Langbase'}],
  chat: true,
});
console.log(completion);

// Message 2: Ask something about the first message.
// Continue the conversation in the same thread by sending
// `threadId` from the second message onwards.
const {completion: completion2} = await myPipe.generateText({
  messages: [{role: 'user', content: 'Tell me the name of my company?'}],
  chat: true,
  threadId,
});

console.log(completion2);
// You'll see any LLM will know the company is `Langbase`
// since it's the same chat thread. This is how you can
// continue a conversation in the same thread.

Response

Response of generateText() is a Promise<GenerateResponse>.

GenerateResponse Object

interface GenerateResponse {
  completion: string;
  threadId?: string;
  id: string;
  object: string;
  created: number;
  model: string;
  system_fingerprint: string | null;
  choices: ChoiceGenerate[];
  usage: Usage;
}

Name
completion
Type
string
Description
The generated text completion.
Name
threadId
Type
string
Description
The ID of the thread. Useful for a chat pipe to continue the conversation in the same thread. Optional.
Name
id
Type
string
Description
The ID of the raw response.
Name
object
Type
string
Description
The object type name of the response.
Name
created
Type
number
Description
The timestamp of the response creation.
Name
model
Type
string
Description
The model used to generate the response.
Name
system_fingerprint
Type
string
Description
This fingerprint represents the backend configuration that the model runs with.
Name
choices
Type
ChoiceGenerate[]
Description
A list of chat completion choices. Can contain more than one elements if n is greater than 1.
Choice Object for generateText()
interface ChoiceGenerate { index: number; message: Message; logprobs: boolean | null; finish_reason: string; }

Name
usage
Type
Usage
Description
The usage object including the following properties.
Usage Object
interface Usage { prompt_tokens: number; completion_tokens: number; total_tokens: number; }

Response of generateText()

{
  "completion": "AI Engineer is a person who designs, builds, and maintains AI systems.",
  "raw": {
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1720131129,
    "model": "gpt-4o-mini",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "AI Engineer is a person who designs, builds, and maintains AI systems.",
        },
        "logprobs": null,
        "finish_reason": "stop"
      }
    ],
    usage: { prompt_tokens: 28, completion_tokens: 36, total_tokens: 64 },
    "system_fingerprint": "fp_123"
  }
}

Response with function call

// Completion is null when an LLM responds
// with a tool function call.
{
  "completion": null,
  "raw": {
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1720131129,
    "model": "gpt-3.5-turbo-0125",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": null,
          "tool_calls": [
            {
              "id": "call_abc123",
              "type": "function",
              "function": {
                "name": "get_current_weather",
                "arguments": "{\n\"location\": \"Boston, MA\"\n}"
              }
            }
          ]
        },
        "logprobs": null,
        "finish_reason": "stop"
      }
    ],
    usage: { prompt_tokens: 28, completion_tokens: 36, total_tokens: 64 },
    "system_fingerprint": null
  }
}

Deprecated methods

Generate Text pipe.generateText()

API reference

generateText(options)

Function Signature

options

GenerateOptions Object

messages

Message Object

variables

Variable Object

chat

threadId

Usage example

Install the SDK

.env file

Generate Pipe: Use generateText()

Variables with generateText()

Chat Pipe: Use generateText()

Response

GenerateResponse Object

Choice Object for generateText()

index

message

logprobs

finish_reason

Usage Object

prompt_tokens

completion_tokens

total_tokens

Response of generateText()

Response with function call

`generateText(options)`