deepset-ai--haystack/docs-website/versioned_docs/version-2.21/pipeline-components/validators/jsonschemavalidator.mdx

---
title: "JsonSchemaValidator"
id: jsonschemavalidator
slug: "/jsonschemavalidator"
description: "Use this component to ensure that an LLM-generated chat message JSON adheres to a specific schema."
---

# JsonSchemaValidator

Use this component to ensure that an LLM-generated chat message JSON adheres to a specific schema.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [Generator](../generators.mdx) |
| **Mandatory run variables** | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  instances to be validated – the last message in this list is the one that is validated |
| **Output variables** | `validated`: A list of messages if the last message is valid  <br /> <br />`validation_error`: A list of messages if the last message is invalid |
| **API reference** | [Validators](/reference/validators-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/validators/json_schema.py |

</div>

## Overview

`JsonSchemaValidator` checks the JSON content of a `ChatMessage` against a given [JSON Schema](https://json-schema.org/). If a message's JSON content follows the provided schema, it's moved to the `validated` output. If not, it's moved to the `validation_error`output. When there's an error, the component uses either the provided custom `error_template` or a default template to create the error message. These error `ChatMessages` can be used in Haystack recovery loops.

## Usage

### In a pipeline

In this simple pipeline, the `MessageProducer` sends a list of chat messages to a Generator through `BranchJoiner`. The resulting messages from the Generator are sent to `JsonSchemaValidator`, and the error `ChatMessages` are sent back to `BranchJoiner` for a recovery loop.

```python
from typing import List

from haystack import Pipeline
from haystack import component
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.joiners import BranchJoiner
from haystack.components.validators import JsonSchemaValidator
from haystack.dataclasses import ChatMessage

@component
class MessageProducer:

    @component.output_types(messages=List[ChatMessage])
    def run(self, messages: List[ChatMessage]) -> dict:
        return {"messages": messages}

p = Pipeline()
p.add_component("llm", OpenAIChatGenerator(model="gpt-4-1106-preview",
                                           generation_kwargs={"response_format": {"type": "json_object"}}))
p.add_component("schema_validator", JsonSchemaValidator())
p.add_component("branch_joiner", BranchJoiner(List[ChatMessage]))
p.add_component("message_producer", MessageProducer())

p.connect("message_producer.messages", "branch_joiner")
p.connect("branch_joiner", "llm")
p.connect("llm.replies", "schema_validator.messages")
p.connect("schema_validator.validation_error", "branch_joiner")

result = p.run(
    data={"message_producer": {
        "messages": [ChatMessage.from_user("Generate JSON for person with name 'John' and age 30")]},
          "schema_validator": {"json_schema": {"type": "object",
                                               "properties": {"name": {"type": "string"},
                                                              "age": {"type": "integer"}}}}})
print(result)

>> {'schema_validator': {'validated': [ChatMessage(_role=<ChatRole.ASSISTANT:
>> 'assistant'>, _content=[TextContent(text='\n{\n  "name": "John",\n  "age": 30\n}')],
>> _name=None, _meta={'model': 'gpt-4-1106-preview', 'index': 0, 'finish_reason': 'stop',
>> 'usage': {'completion_tokens': 17, 'prompt_tokens': 20, 'total_tokens': 37,
>> 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0,
>> 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details':
>> {'audio_tokens': 0, 'cached_tokens': 0}}})]}}
```