import ZodStream from "zod-stream";
import { z } from "zod";
 
const client = new ZodStream({
  debug: true  // Enable debug logging
});
 
// Define your extraction schema
const schema = z.object({
  content: z.string(),
  metadata: z.object({
    confidence: z.number(),
    category: z.string()
  })
});
 
// Create streaming extraction
const stream = await client.create({
  completionPromise: async () => {
    const response = await fetch("/api/extract", {
      method: "POST",
      body: JSON.stringify({ prompt: "..." })
    });
    return response.body;
  },
  response_model: {
    schema,
    name: "ContentExtraction"
  }
});
 
// Process with validation metadata
for await (const chunk of stream) {
  console.log({
    data: chunk,              // Partial extraction result
    isValid: chunk._meta._isValid,    // Current validation state
    activePath: chunk._meta._activePath,    // Currently processing path
    completedPaths: chunk._meta._completedPaths  // Completed paths
  });
}

// Define schema for a complex analysis
const schema = z.object({
  user: z.object({
    id: z.string(),
    preferences: z.object({
      theme: z.string(),
      language: z.string()
    })
  }),
  content: z.object({
    title: z.string(),
    body: z.string(),
    metadata: z.object({
      keywords: z.array(z.string()),
      category: z.string()
    })
  }),
  recommendations: z.array(z.object({
    id: z.string(),
    score: z.number(),
    reason: z.string()
  }))
});
 
// Process data as it becomes available
for await (const chunk of stream) {
  // Start personalizing UI as soon as user preferences are ready
  if (isPathComplete(['user', 'preferences'], chunk)) {
    applyUserTheme(chunk.user.preferences.theme);
    setLanguage(chunk.user.preferences.language);
  }
 
  // Begin content indexing once we have title and keywords
  if (isPathComplete(['content', 'metadata', 'keywords'], chunk) && 
      isPathComplete(['content', 'title'], chunk)) {
    indexContent({
      title: chunk.content.title,
      keywords: chunk.content.metadata.keywords
    });
  }
 
  // Start fetching recommended content in parallel
  chunk._meta._completedPaths.forEach(path => {
    if (path[0] === 'recommendations' && path.length === 2) {
      const index = path[1] as number;
      const recommendation = chunk.recommendations[index];
      
      if (recommendation?.id) {
        prefetchContent(recommendation.id);
      }
    }
  });
}

type CompletionMeta = {
  _isValid: boolean;           // Schema validation status
  _activePath: (string | number)[];     // Current parsing path
  _completedPaths: (string | number)[][]; // All completed paths
}
 
// Example chunk
{
  content: "partial content...",
  metadata: {
    confidence: 0.95
  },
  _meta: {
    _isValid: false,  // Not valid yet
    _activePath: ["metadata", "category"],
    _completedPaths: [
      ["content"],
      ["metadata", "confidence"]
    ]
  }
}

const schema = z.object({
  users: z.array(z.object({
    name: z.string(),
    age: z.number()
  }))
});
 
const client = new ZodStream();
const stub = client.getSchemaStub({
  schema,
  defaultData: {
    users: [{ name: "loading...", age: 0 }]
  }
});

const client = new ZodStream({ debug: true });
 
// Logs will include:
// - Stream initialization
// - Validation results
// - Path completion
// - Errors with full context

import { withResponseModel } from "zod-stream";
import { z } from "zod";
 
const schema = z.object({
  sentiment: z.string(),
  keywords: z.array(z.string()),
  confidence: z.number()
});
 
// Configure for OpenAI tools mode
const params = withResponseModel({
  response_model: {
    schema,
    name: "Analysis",
    description: "Extract sentiment and keywords"
  },
  mode: "TOOLS",
  params: {
    messages: [{ role: "user", content: "Analyze this text..." }],
    model: "gpt-4"
  }
});
 
const completion = await oai.chat.completions.create({
  ...params,
  stream: true
});

import { MODE } from "zod-stream";
 
const modes = {
  FUNCTIONS: "FUNCTIONS",   // OpenAI function calling
  TOOLS: "TOOLS",          // OpenAI tools API
  JSON: "JSON",            // Direct JSON response
  MD_JSON: "MD_JSON",      // JSON in markdown blocks
  JSON_SCHEMA: "JSON_SCHEMA" // JSON with schema validation
} as const;

// Results in OpenAI tool configuration
{
  tool_choice: {
    type: "function",
    function: { name: "Analysis" }
  },
  tools: [{
    type: "function",
    function: {
      name: "Analysis",
      description: "Extract sentiment and keywords",
      parameters: {/* Generated from schema */}
    }
  }],
  // ... other existing params are preserved
}

// Results in OpenAI function configuration
{
  function_call: { name: "Analysis" },
  functions: [{
    name: "Analysis",
    description: "Extract sentiment and keywords",
    parameters: {/* Generated from schema */}
  }],
  // ... other existing params are preserved
}

// Results in direct JSON response configuration
{
  response_format: { type: "json_object" },
  messages: [
    {
      role: "system",
      content: `
        Given a user prompt, you will return fully valid JSON based on the following description and schema.
        You will return no other prose. You will take into account any descriptions or required parameters within the schema
        and return a valid and fully escaped JSON object that matches the schema and those instructions.
 
        description: ${definition.description}
        json schema: ${JSON.stringify(definition)}
      `
    },
    // ... user messages are preserved
  ]
}

// Results in JSON schema-based configuration
{
  response_format: {
    type: "json_object",
    schema: {/* Schema without name and description */}
  },
  messages: [
    {
      role: "system",
      content: `
        Given a user prompt, you will return fully valid JSON based on the following description.
        You will return no other prose. You will take into account any descriptions or required parameters within the schema
        and return a valid and fully escaped JSON object that matches the schema and those instructions.
 
        description: ${definition.description}
      `
    },
    // ... user messages are preserved
  ]
}

// Similar to JSON mode but without response_format
{
  messages: [
    {
      role: "system",
      content: `
        Given a user prompt, you will return fully valid JSON based on the following description and schema.
        You will return no other prose. You will take into account any descriptions or required parameters within the schema
        and return a valid and fully escaped JSON object that matches the schema and those instructions.
 
        description: ${definition.description}
        json schema: ${JSON.stringify(definition)}
      `
    },
    // ... user messages are preserved
  ]
}

import { 
  OAIResponseParser,
  OAIResponseToolArgsParser,
  OAIResponseFnArgsParser,
  OAIResponseJSONParser
} from "zod-stream";
 
// Automatic format detection
const result = OAIResponseParser(response);
 
// Format-specific parsing
const toolArgs = OAIResponseToolArgsParser(response);
const fnArgs = OAIResponseFnArgsParser(response);
const jsonContent = OAIResponseJSONParser(response);

import { OAIStream, readableStreamToAsyncGenerator } from "zod-stream";
 
// Create streaming response
app.post("/api/stream", async (req, res) => {
  const completion = await oai.chat.completions.create({
    ...params,
    stream: true
  });
 
  return new Response(
    OAIStream({ res: completion })
  );
});
 
// Convert stream to async generator
const generator = readableStreamToAsyncGenerator(stream);
for await (const chunk of generator) {
  console.log(chunk);
}

import { isPathComplete } from "zod-stream";
 
const activePath = ["analysis", "sentiment"];
const isComplete = isPathComplete(activePath, {
  _meta: {
    _completedPaths: [["analysis", "sentiment"]],
    _activePath: ["analysis", "keywords"],
    _isValid: false
  }
});

const stream = await client.create({
  completionPromise: async () => response.body,
  response_model: { schema }
});
 
let finalResult
 
// Path tracking for progressive updates
for await (const chunk of stream) {
  finalResult = chunk
  // Check which paths are complete
  console.log("Completed paths:", chunk._meta._completedPaths);
  console.log("Current path:", chunk._meta._activePath);
}
 
// Final validation happens after stream completes
const isValid = finalResult._meta._isValid