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Run an evaluation with multimodal content

LangSmith lets you create dataset examples with file attachments—like images, audio files, or documents—so you can reference them when evaluating an application that uses multimodal inputs or outputs.

While you can include multimodal data in your examples by base64 encoding it, this approach is inefficient - the encoded data takes up more space than the original binary files, resulting in slower transfers to and from LangSmith. Using attachments instead provides two key benefits:

  1. Faster upload and download speeds due to more efficient binary file transfers
  2. Enhanced visualization of different file types in the LangSmith UI

1. Create examples with attachments​

To upload examples with attachments using the SDK, use the create_examples / update_examples Python methods or the uploadExamplesMultipart / updateExamplesMultipart TypeScript methods.

Requires langsmith>=0.3.13

import requests
import uuid
from pathlib import Path
from langsmith import Client

# Publicly available test files
pdf_url = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
wav_url = "https://openaiassets.blob.core.windows.net/$web/API/docs/audio/alloy.wav"
img_url = "https://www.w3.org/Graphics/PNG/nurbcup2si.png"

# Fetch the files as bytes
pdf_bytes = requests.get(pdf_url).content
wav_bytes = requests.get(wav_url).content
img_bytes = requests.get(img_url).content

# Create the dataset
ls_client = Client()
dataset_name = "attachment-test-dataset"
dataset = ls_client.create_dataset(
  dataset_name=dataset_name,
  description="Test dataset for evals with publicly available attachments",
)

inputs = {
  "audio_question": "What is in this audio clip?",
  "image_question": "What is in this image?",
}

outputs = {
  "audio_answer": "The sun rises in the east and sets in the west. This simple fact has been observed by humans for thousands of years.",
  "image_answer": "A mug with a blanket over it.",
}

# Define an example with attachments
example_id = uuid.uuid4()
example = {
  "id": example_id,
  "inputs": inputs,
  "outputs": outputs,
  "attachments": {
      "my_pdf": {"mime_type": "application/pdf", "data": pdf_bytes},
      "my_wav": {"mime_type": "audio/wav", "data": wav_bytes},
      "my_img": {"mime_type": "image/png", "data": img_bytes},
      # Example of an attachment specified via a local file path:
      # "my_local_img": {"mime_type": "image/png", "data": Path(__file__).parent / "my_local_img.png"},
  },
}

# Create the example
ls_client.create_examples(
  dataset_id=dataset.id, 
  examples=[example], 
  # Uncomment this flag if you'd like to upload attachments from local files:
  # dangerously_allow_filesystem=True
)
Uploading from filesystem

Along with being passed in as bytes, attachments can be specified as paths to local files. To do so pass in a path for the attachment data value and specify arg dangerously_allow_filesystem=True:

client.create_examples(..., dangerously_allow_filesystem=True)

2. Run evaluations​

Define a target function​

Now that we have a dataset that includes examples with attachments, we can define a target function to run over these examples. The following example simply uses OpenAI's GPT-4o model to answer questions about an image and an audio clip.

The target function you are evaluating must have two positional arguments in order to consume the attachments associated with the example, the first must be called inputs and the second must be called attachments.

  • The inputs argument is a dictionary that contains the input data for the example, excluding the attachments.
  • The attachments argument is a dictionary that maps the attachment name to a dictionary containing a presigned url, mime_type, and a reader of the bytes content of the file. You can use either the presigned url or the reader to get the file contents. Each value in the attachments dictionary is a dictionary with the following structure:
{
    "presigned_url": str,
    "mime_type": str,
    "reader": BinaryIO
}

from langsmith.wrappers import wrap_openai

import base64
from openai import OpenAI

client = wrap_openai(OpenAI())

# Define target function that uses attachments
def file_qa(inputs, attachments): # Read the audio bytes from the reader and encode them in base64
  audio_reader = attachments["my_wav"]["reader"]
  audio_b64 = base64.b64encode(audio_reader.read()).decode('utf-8')
  audio_completion = client.chat.completions.create(
      model="gpt-4o-audio-preview",
      messages=[
          {
              "role": "user",
              "content": [
                  {
                      "type": "text",
                      "text": inputs["audio_question"]
                  },
                  {
                      "type": "input_audio",
                      "input_audio": {
                          "data": audio_b64,
                          "format": "wav"
                      }
                  }
              ]
          }
      ]
  )

  # Most models support taking in an image URL directly in addition to base64 encoded images
  # You can pipe the image pre-signed URL directly to the model
  image_url = attachments["my_img"]["presigned_url"]
  image_completion = client.chat.completions.create(
      model="gpt-4o-mini",
      messages=[
        {
          "role": "user",
          "content": [
            {"type": "text", "text": inputs["image_question"]},
            {
              "type": "image_url",
              "image_url": {
                "url": image_url,
              },
            },
          ],
        }
      ],
  )

  return {
      "audio_answer": audio_completion.choices[0].message.content,
      "image_answer": image_completion.choices[0].message.content,
  }

Define custom evaluators​

The exact same rules apply as above to determine whether the evaluator should receive attachments.

The evaluator below uses an LLM to judge if the reasoning and the answer are consistent. To learn more about how to define llm-based evaluators, please see this guide.

# Assumes you've installed pydantic
from pydantic import BaseModel

def valid_image_description(outputs: dict, attachments: dict) -> bool:
  """Use an LLM to judge if the image description and images are consistent."""

  instructions = """
  Does the description of the following image make sense?
  Please carefully review the image and the description to determine if the description is valid."""

  class Response(BaseModel):
      description_is_valid: bool

  image_url = attachments["my_img"]["presigned_url"]
  response = client.beta.chat.completions.parse(
      model="gpt-4o",
      messages=[
          {
              "role": "system",
              "content": instructions
          },
          {
              "role": "user",
              "content": [
                  {"type": "image_url", "image_url": {"url": image_url}},
                  {"type": "text", "text": outputs["image_answer"]}
              ]
          }
      ],
      response_format=Response
  )

  return response.choices[0].message.parsed.description_is_valid

ls_client.evaluate(
  file_qa,
  data=dataset_name,
  evaluators=[valid_image_description],
)

Update examples with attachments​

In the code above, we showed how to add examples with attachments to a dataset. It is also possible to update these same examples using the SDK.

As with existing examples, datasets are versioned when you update them with attachments. Therefore, you can navigate to the dataset version history to see the changes made to each example. To learn more, please see this guide.

When updating an example with attachments, you can update attachments in a few different ways:

  • Pass in new attachments
  • Rename existing attachments
  • Delete existing attachments

Note that:

  • Any existing attachments that are not explicitly renamed or retained will be deleted.
  • An error will be raised if you pass in a non-existent attachment name to retain or rename.
  • New attachments take precedence over existing attachments in case the same attachment name appears in the attachments and attachment_operations fields.
example_update = {
  "id": example_id,
  "attachments": {
      # These are net new attachments
      "my_new_file": ("text/plain", b"foo bar"),
  },
  "inputs": inputs,
  "outputs": outputs,
  # Any attachments not in rename/retain will be deleted.
  # In this case, that would be "my_img" if we uploaded it.
  "attachments_operations": {
      # Retained attachments will stay exactly the same
      "retain": ["my_pdf"],
      # Renaming attachments preserves the original data
      "rename": {
          "my_wav": "my_new_wav",
      }
  },
}

ls_client.update_examples(dataset_id=dataset.id, updates=[example_update])

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