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Documentation Index

Fetch the complete documentation index at: https://docs.dottxt.ai/llms.txt

Use this file to discover all available pages before exploring further.

If you’re migrating from OpenAI, Anthropic, Gemini, or another provider, use one stable path to dottxt.

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Shared migration pattern

  1. Point your client at dottxt (baseURL + dottxt API key).
  2. Send raw JSON Schema in response_format (avoid helper methods like parse that rewrite schemas).
This keeps the request shape explicit and avoids provider SDK transformations that can silently change optionality and constraints.

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OpenAI-compatible example: change two lines

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Extract: John Smith <john@acme.com>, VP Engineering"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "contact",
            "strict": True,
            "schema": {
                "type": "object",
                "properties": {
                    "name": {"type": "string"},
                    "email": {"type": "string"},
                    "role": {"type": "string"}
                },
                "required": ["name", "email", "role"],
                "additionalProperties": False
            }
        }
    }
)

import json
contact = json.loads(response.choices[0].message.content)
Two changes:
  1. Set base_url and api_key to point at dottxt
  2. Swap the model name
Everything else, including response_format, messages, and response parsing, stays the same.

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What changes when you switch

With OpenAI Structured Outputs, strict: true comes with a restricted JSON Schema subset. All object fields must be listed in required, objects must set additionalProperties: false, and some schema shapes are rejected outright. With dottxt, your schema is used as-is:
What you writeOpenAI behaviordottxt behavior
A field not in requiredNot allowed in strict modeField is genuinely optional
minLength: 3 on a stringSupportedEnforced during generation
pattern: "^[A-Z]{2}$"SupportedEnforced during generation
minimum: 0 on a numberSupportedEnforced during generation
anyOf at root levelRejectedSupported
if / then / elseRejectedSupported
Unsupported featureExplicit errorExplicit error
See the full provider comparison for details.

​
Code you can delete

When your provider does not enforce the schema you actually want, you compensate with application code. Here’s what that often looks like in practice: not the API call itself, but everything around it. 
# Without full schema enforcement: the schema says minLength, pattern, optional fields...
# but your application still has to clean up and validate the result itself

import json
from datetime import datetime

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": text}],
    response_format={"type": "json_schema", "json_schema": { ... }}
)
result = json.loads(response.choices[0].message.content)

# Validate what the schema was supposed to enforce
if not result.get("vendor") or len(result["vendor"]) > 120:
    raise ValueError("vendor missing or too long")

# Normalize date to match the contract your application expects
raw_date = result.get("date", "")
for fmt in ("%Y-%m-%d", "%B %d, %Y", "%m/%d/%Y", "%b %d %Y"):
    try:
        result["date"] = datetime.strptime(raw_date, fmt).date().isoformat()
        break
    except ValueError:
        continue

# Normalize currency to the format your downstream code expects
currency = result.get("currency", "").upper().strip()
if currency in ("US DOLLARS", "USD$", "DOLLARS"):
    currency = "USD"
result["currency"] = currency

# Handle optional fields yourself when the response contract is not enforced directly
if result.get("notes") in ("", "N/A", "None", "n/a"):
    result["notes"] = None

# Retry if the output still doesn't conform
if not validate(result):
    # try again, hope for better luck
    response = client.chat.completions.create(...)
With dottxt, the same task: 
import json
import os
from openai import OpenAI
from pydantic import BaseModel, Field

class Invoice(BaseModel):
    vendor: str = Field(min_length=1, max_length=120)
    date: str = Field(json_schema_extra={"format": "date"})
    currency: str = Field(pattern=r"^[A-Z]{3}$")
    line_items: list[dict] = Field(min_length=1, max_length=50)
    total: float = Field(ge=0)
    notes: str | None = Field(default=None, max_length=300)

client = OpenAI(
    base_url="https://api.dottxt.ai/v1",
    api_key=os.environ["DOTTXT_API_KEY"],
)

response = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[{"role": "user", "content": text}],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "invoice",
            "schema": Invoice.model_json_schema(),
        },
    },
)
invoice = Invoice.model_validate_json(response.choices[0].message.content)
# invoice.date is already "2026-02-12"
# invoice.currency is already "USD"
# invoice.notes is None when the source text doesn't contain any
# no validation, no normalization, no retry
The validation code, the date normalization, the currency cleanup, the empty-string-to-None conversion, and the retry loop all exist because the schema wasn’t enforced. Delete the workarounds, keep the schema.

​
Richer schemas that now work

Once you’re on dottxt, you can use JSON Schema patterns that OpenAI’s strict mode rejects: 
response = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[
        {"role": "user", "content": "Extract: John Smith <john@acme.com>, VP Engineering"}
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "contact",
            "schema": {
                "type": "object",
                "properties": {
                    "name": {
                        "type": "string",
                        "minLength": 1
                    },
                    "email": {
                        "type": "string",
                        "pattern": "^[^@]+@[^@]+$"
                    },
                    "role": {
                        "type": "string"
                    },
                    "tags": {
                        "type": "array",
                        "items": {"type": "string"},
                        "minItems": 1,
                        "maxItems": 5
                    }
                },
                "required": ["name", "email"],
                "additionalProperties": false
            }
        }
    }
)
name is always non-empty. email matches the pattern. tags has 1–5 items. role is optional; it may or may not appear in the output. None of this works with OpenAI’s strict mode.

​
TypeScript

The same approach works with any OpenAI-compatible TypeScript client: 
import OpenAI from "openai";

const client = new OpenAI();

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "user", content: "Extract: John Smith <john@acme.com>, VP Engineering" }
  ],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "contact",
      strict: true,
      schema: {
        type: "object",
        properties: {
          name: { type: "string" },
          email: { type: "string" },
          role: { type: "string" }
        },
        required: ["name", "email", "role"],
        additionalProperties: false
      }
    }
  }
});

const contact = JSON.parse(response.choices[0].message.content!);