Spelling variant issue with with_structured_output() and Cohere

[jackpeplinski]

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Example Code

Using code from Langchain doc's How to use reference examples when doing extraction.

!pip install langchain langchain-cohere langchain-mistralai

from typing import List, Optional

from pydantic import BaseModel, Field
import uuid
from typing import Dict, List, TypedDict
import pprint

from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    ToolMessage,
)
from pydantic import BaseModel, Field
from langchain_mistralai import ChatMistralAI
from langchain_cohere import ChatCohere

class Example(TypedDict):
    """A representation of an example consisting of text input and expected tool calls.

    For extraction, the tool calls are represented as instances of pydantic model.
    """

    input: str  # This is the example text
    tool_calls: List[BaseModel]  # Instances of pydantic model that should be extracted

class Person(BaseModel):
    """Information about a person."""

    # ^ Doc-string for the entity Person.
    # This doc-string is sent to the LLM as the description of the schema Person,
    # and it can help to improve extraction results.

    # Note that:
    # 1. Each field is an `optional` -- this allows the model to decline to extract it!
    # 2. Each field has a `description` -- this description is used by the LLM.
    # Having a good description can help improve extraction results.
    name: Optional[str] = Field(..., description="The name of the person")
    hair_color: Optional[str] = Field(
        ..., description="The color of the person's hair if known"
    )
    height_in_meters: Optional[str] = Field(..., description="Height in METERs")

class Data(BaseModel):
    """Extracted data about people."""

    # Creates a model so that we can extract multiple entities.
    people: List[Person]

def tool_example_to_messages(example: Example) -> List[BaseMessage]:
    """Convert an example into a list of messages that can be fed into an LLM.

    This code is an adapter that converts our example to a list of messages
    that can be fed into a chat model.

    The list of messages per example corresponds to:

    1) HumanMessage: contains the content from which content should be extracted.
    2) AIMessage: contains the extracted information from the model
    3) ToolMessage: contains confirmation to the model that the model requested a tool correctly.

    The ToolMessage is required because some of the chat models are hyper-optimized for agents
    rather than for an extraction use case.
    """
    messages: List[BaseMessage] = [HumanMessage(content=example["input"])]
    tool_calls = []
    for tool_call in example["tool_calls"]:
        tool_calls.append(
            {
                "id": str(uuid.uuid4()),
                "args": tool_call.dict(),
                # The name of the function right now corresponds
                # to the name of the pydantic model
                # This is implicit in the API right now,
                # and will be improved over time.
                "name": tool_call.__class__.__name__,
            },
        )
    messages.append(AIMessage(content="", tool_calls=tool_calls))
    tool_outputs = example.get("tool_outputs") or [
        "You have correctly called this tool."
    ] * len(tool_calls)
    for output, tool_call in zip(tool_outputs, tool_calls):
        messages.append(ToolMessage(content=output, tool_call_id=tool_call["id"]))
    return messages

examples = [
    (
        "The ocean is vast and blue. It's more than 20,000 feet deep. There are many fish in it.",
        Data(people=[]),
    ),
    (
        "Fiona traveled far from France to Spain.",
        Data(people=[Person(name="Fiona", height_in_meters=None, hair_color=None)]),
    ),
]

# Define a custom prompt to provide instructions and any additional context.
# 1) You can add examples into the prompt template to improve extraction quality
# 2) Introduce additional parameters to take context into account (e.g., include metadata
#    about the document from which the text was extracted.)
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are an expert extraction algorithm. "
            "Only extract relevant information from the text. "
            "If you do not know the value of an attribute asked "
            "to extract, return null for the attribute's value.",
        ),
        # # ↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓
        # MessagesPlaceholder("examples"),  # <-- EXAMPLES!
        # # ↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑
        ("human", "{text}"),
    ]
)

messages = []

for text, tool_call in examples:
    messages.extend(
        tool_example_to_messages({"input": text, "tool_calls": [tool_call]})
    )

# Use MistralAI OR Cohere to see the difference INSERT YOUR KEY BELOW
cohere_api_key = "INSERT-KEY-HERE" 
llm = ChatCohere(cohere_api_key=cohere_api_key)

mistral_api_key = "INSERT-KEY-HERE"
llm = ChatMistralAI(mistral_api_key=mistral_api_key, model="mistral-large-latest")

runnable = prompt | llm.with_structured_output(
    schema=Data
)

runnable.invoke(
    {
        "text": "My name is Harrison. My hair colour is black.",
        "examples": messages,
    }
)

Description

Using MistralAI, the code returns:

Data(people=[Person(name='Harrison', hair_color='black', height_in_meters=None)])

Using Cohere, the code throws an error (note the u in colour):

ValidationError: 2 validation errors for Data
people.0.hair_color
  Field required [type=missing, input_value={'hair_colour': 'black', 'name': 'Harrison'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/missing
people.0.height_in_meters
  Field required [type=missing, input_value={'hair_colour': 'black', 'name': 'Harrison'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/missing

Why is Cohere adding a u to color when Data is a list of persons and Person has the attribute hair_color not hair_colour? Cohere was founded in Canada where color is spelled with a u, but shouldn't their API return the response in the structure I specify?

System Info

langchain==0.3.0
langchain-anthropic==0.2.1
langchain-cohere==0.3.0
langchain-community==0.3.0
langchain-core==0.3.5
langchain-experimental==0.3.1
langchain-mistralai==0.2.0
langchain-text-splitters==0.3.0
Linux-6.1.85+-x86_64-with-glibc2.35
3.10.12

[dosubot[bot]]

Hey @jackpeplinski! 👋 I'm here to help you with bugs, answer questions, and even guide you on becoming a contributor. While you're waiting for a human maintainer, I'm at your service!

The issue you're encountering with the ChatCohere class and the with_structured_output() method seems to be related to regional spelling variations, specifically the difference between "color" and "colour". This discrepancy is likely due to the language model's internal handling of text, which may be influenced by regional spelling conventions.

The with_structured_output() method in LangChain is designed to format outputs to match a given schema, but it does not inherently handle regional spelling variations. When using a Pydantic class for schema validation, the model-generated fields must exactly match the schema's field names. If the model returns "hair_colour" instead of "hair_color", the validation will fail because the field names do not match.

To address this issue, you can implement a custom post-processing step to normalize the field names before validation. Here's an example of how you can achieve this:

1. Define a normalization function:

   def normalize_fields(data: dict) -> dict:
       """Normalize field names to match the schema."""
       normalized_data = {}
       for key, value in data.items():
           # Normalize "colour" to "color"
           normalized_key = key.replace("colour", "color")
           if isinstance(value, dict):
               normalized_data[normalized_key] = normalize_fields(value)
           elif isinstance(value, list):
               normalized_data[normalized_key] = [normalize_fields(item) if isinstance(item, dict) else item for item in value]
           else:
               normalized_data[normalized_key] = value
       return normalized_data

2. Apply the normalization function before validation:

   raw_response = runnable.invoke(
       {
           "text": "My name is Harrison. My hair colour is black.",
           "examples": messages,
       }
   )
   
   normalized_response = normalize_fields(raw_response)
   validated_response = Data(**normalized_response)

By normalizing the field names before passing the data to the Pydantic model for validation, you can ensure that the schema validation succeeds regardless of regional spelling variations.

This approach should help you handle the spelling variant issue with the ChatCohere class and the with_structured_output() method.

To continue talking to Dosu, mention @dosu.

[jackpeplinski]

Isn't the point of using with_structured_output() that I shouldn't need to normalize the fields to match the schema I provide?

[dosubot[bot]]

Yes, the point of using with_structured_output() is that you shouldn't need to normalize the fields to match the schema you provide. The method takes care of importing a suitable output parser and formatting the schema in the right format for the model.

Supporting evidence:

For convenience, some LangChain chat models support a [`.with_structured_output()`](/docs/how_to/structured_output/#the-with_structured_output-method)
method. This method only requires a schema as input, and returns a dict or Pydantic object.
Generally, this method is only present on models that support one of the more advanced methods described below,
and will use one of them under the hood. It takes care of importing a suitable output parser and
formatting the schema in the right format for the model.

Additionally, the LangChain documentation provides examples of using with_structured_output() with Pydantic classes, TypedDict, and JSON Schema, all of which ensure that the model-generated output adheres to the specified schema without requiring manual normalization of fields ^[1][2].

Moreover, tests for the OllamaFunctions in LangChain's experimental library further demonstrate the effectiveness of with_structured_output(). For instance, the test_ollama_structured_output and test_ollama_structured_output_with_json tests show that the method correctly formats and validates the output according to the provided schema, whether using a Pydantic model or a JSON schema ^[3].

These tests confirm that with_structured_output() ensures the output adheres to the specified schema without requiring manual normalization.

To continue talking to Dosu, mention @dosu.

[eyurtsev]

@jackpeplinski with_structured_output is a standard interface to interact with different underlying chat model APIs. We can standardize the interface, but we can't fix the behavior of the underlying chat model. Try to providing some reference examples perhaps or improving the prompt or swapping to a different model provider.