Parse Partial JSON Stream - Turn your slow AI app into an engaging real-time app

• Convert a stream of token into a parsable JSON object before the stream ends.
• Implement Streaming UI in LLM-based AI application.
• Leverage OpenAI Function Calling for early stream processing.
• Parse JSON streams into distinct entities.
• Engage your users with a real-time experience.

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Install

To install dependencies:

npm install --save openai-partial-stream

Usage with simple stream

Turn a stream of token into a parsable JSON object as soon as possible.

import OpenAi from "openai";
import { OpenAiHandler, StreamMode } from "openai-partial-stream";

// Set your OpenAI API key as an environment variable: OPENAI_API_KEY
const openai = new OpenAi({ apiKey: process.env.OPENAI_API_KEY });

const stream = await openai.chat.completions.create({
  messages: [{ role: "system", content: "Say hello to the world." }],
  model: "gpt-3.5-turbo", // OR "gpt-4"
  stream: true, // ENABLE STREAMING
  temperature: 1,
  functions: [
    {
      name: "say_hello",
      description: "say hello",
      parameters: {
        type: "object",
        properties: {
          sentence: {
            type: "string",
            description: "The sentence generated",
          },
        },
      },
    },
  ],
  function_call: { name: "say_hello" },
});

const openAiHandler = new OpenAiHandler(StreamMode.StreamObjectKeyValueTokens);
const entityStream = openAiHandler.process(stream);

for await (const item of entityStream) {
  console.log(item);
}

Output:

{ index: 0, status: 'PARTIAL', data: {} }
{ index: 0, status: 'PARTIAL', data: { sentence: '' } }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hello' } }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hello,' } }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hello, world' } }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hello, world!' } }
{ index: 0, status: 'COMPLETED', data: { sentence: 'Hello, world!' } }

Usage with stream and entity parsing

Validate the data against a schema and only return the data when it is valid.

import { z } from "zod";
import OpenAi from "openai";
import { OpenAiHandler, StreamMode, Entity } from "openai-partial-stream";

// Set your OpenAI API key as an environment variable: OPENAI_API_KEY
const openai = new OpenAi({ apiKey: process.env.OPENAI_API_KEY });

const stream = await openai.chat.completions.create({
  messages: [{ role: "system", content: "Say hello to the world." }],
  model: "gpt-3.5-turbo", // OR "gpt-4"
  stream: true, // ENABLE STREAMING
  temperature: 1,
  functions: [
    {
      name: "say_hello",
      description: "say hello",
      parameters: {
        type: "object",
        properties: {
          sentence: {
            type: "string",
            description: "The sentence generated",
          },
        },
      },
    },
  ],
  function_call: { name: "say_hello" },
});

const openAiHandler = new OpenAiHandler(StreamMode.StreamObjectKeyValueTokens);
const entityStream = openAiHandler.process(stream);

// Entity Parsing to validate the data
const HelloSchema = z.object({
  sentence: z.string().optional(),
});

const entityHello = new Entity("sentence", HelloSchema);
const helloEntityStream = entityHello.genParse(entityStream);

for await (const item of helloEntityStream) {
  console.log(item);
}

Output:

{ index: 0, status: 'PARTIAL', data: {}, entity: 'sentence' }
{ index: 0, status: 'PARTIAL', data: { sentence: '' }, entity: 'sentence' }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hi' }, entity: 'sentence' }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hi,' }, entity: 'sentence' }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hi, world' }, entity: 'sentence' }
{ index: 0, status: 'PARTIAL', data: { sentence: 'Hi, world!' }, entity: 'sentence' }
{ index: 0, status: 'COMPLETED', data: { sentence: 'Hi, world!' }, entity: 'sentence'}

Usage with stream and entity parsing with multiple entities

import { z } from "zod";
import OpenAi from "openai";
import { OpenAiHandler, StreamMode, Entity } from "openai-partial-stream";

// Intanciate OpenAI client with your API key
const openai = new OpenAi({
  apiKey: process.env.OPENAI_API_KEY,
});

const PostcodeSchema = z.object({
  name: z.string().optional(),
  postcode: z.string().optional(),
  population: z.number().optional(),
});

// Call the API with stream enabled and a function
const stream = await openai.chat.completions.create({
  messages: [
    {
      role: "system",
      content: "Give me 3 cities and their postcodes in California.",
    },
  ],
  model: "gpt-3.5-turbo", // OR "gpt-4"
  stream: true, // ENABLE STREAMING
  temperature: 1.1,
  functions: [
    {
      name: "set_postcode",
      description: "Set a postcode and a city",
      parameters: {
        type: "object",
        properties: {
          // The name of the entity
          postcodes: {
            type: "array",
            items: {
              type: "object",
              properties: {
                name: {
                  type: "string",
                  description: "Name of the city",
                },
                postcode: {
                  type: "string",
                  description: "The postcode of the city",
                },
                population: {
                  type: "number",
                  description: "The population of the city",
                },
              },
            },
          },
        },
      },
    },
  ],
  function_call: { name: "set_postcode" },
});

// Select the mode of the stream parser
// - StreamObjectKeyValueTokens: (REALTIME)     Stream of JSON objects, key value pairs and tokens
// - StreamObjectKeyValue:       (PROGRESSIVE)  Stream of JSON objects and key value pairs
// - StreamObject:               (ONE-BY-ONE)   Stream of JSON objects
// - NoStream:                   (ALL-TOGETHER) All the data is returned at the end of the process
const mode = StreamMode.StreamObject;

// Create an instance of the handler
const openAiHandler = new OpenAiHandler(mode);
// Process the stream
const entityStream = openAiHandler.process(stream);
// Create an entity with the schema to validate the data
const entityPostcode = new Entity("postcodes", PostcodeSchema);
// Parse the stream to an entity, using the schema to validate the data
const postcodeEntityStream = entityPostcode.genParseArray(entityStream);

// Iterate over the stream of entities
for await (const item of postcodeEntityStream) {
  if (item) {
    // Display the entity
    console.log(item);
  }
}

Output:

{ index: 0, status: 'COMPLETED', data: { name: 'Los Angeles', postcode: '90001', population: 3971883 }, entity: 'postcodes' }
{ index: 1, status: 'COMPLETED', data: { name: 'San Francisco', postcode: '94102', population: 883305 }, entity: 'postcodes' }
{ index: 2, status: 'COMPLETED', data: { name: 'San Diego', postcode: '92101', population: 1425976 }, entity: 'postcodes'}

Modes

Select a mode from the list below that best suits your requirements:

1. NoStream
2. StreamObject
3. StreamObjectKeyValue
4. StreamObjectKeyValueTokens

---

NoStream

Results are returned only after the entire query completes.

NoStream Details
✅ Single query retrieves all data
✅ Reduces network traffic
⚠️ User experience may be compromised due to extended wait times

---

StreamObject

An event is generated for each item in the list. Items appear as they become ready.

StreamObject Details
✅ Each message corresponds to a fully-formed item
✅ Fewer messages
✅ All essential fields are received at once
⚠️ Some delay: users need to wait until an item is fully ready to update the UI

---

StreamObjectKeyValue

Objects are received in fragments: both a key and its corresponding value are sent together.

StreamObjectKeyValue Details
✅ Users can engage with portions of the UI
✅ Supports more regular UI updates
⚠️ Higher network traffic
⚠️ Challenges in enforcing keys due to incomplete objects

---

StreamObjectKeyValueTokens

Keys are received in full, while values are delivered piecemeal until they're complete. This method offers token-by-token UI updating.

StreamObjectKeyValueToken Details
✅ Offers a dynamic user experience
✅ Enables step-by-step content consumption
✅ Decreases user waiting times
⚠️ Possible UI inconsistencies due to values arriving incrementally
⚠️ Augmented network traffic

Demo

Stream of JSON object progressively by key value pairs:

Color_Streaming_Mode_3_colors.mov

Stream of JSON objects in realtime:

json_stream_sf.mp4

References

npm pakcage