Automated evaluation pipelines in LangChain - Step-by-Step Execution

Concept Flow - Automated evaluation pipelines

Define evaluation criteria

↓

Prepare input data

↓

Run model with input

↓

Collect model output

↓

Apply evaluation metrics

↓

Aggregate results

↓

Report or store evaluation

The pipeline starts by setting criteria, then runs the model on inputs, collects outputs, evaluates them, and finally reports results.

Execution Sample

LangChain

from langchain.evaluation import EvaluationChain

# Create evaluation chain
eval_chain = EvaluationChain.from_llm(llm)

# Run evaluation
results = eval_chain.evaluate(inputs, references)

This code sets up an evaluation chain with a language model and runs it on inputs compared to references.

Execution Table

Step	Action	Input	Output	Notes
1	Define evaluation criteria	Metric: accuracy	Criteria set	Sets how outputs will be judged
2	Prepare input data	Inputs: ['Hello']	Prepared inputs	Data ready for model
3	Run model with input	Input: 'Hello'	Model output: 'Hi'	Model generates response
4	Collect model output	Model output: 'Hi'	Collected output	Output stored for eval
5	Apply evaluation metrics	Output vs Reference: 'Hi' vs 'Hello'	Score: 0.0	Calculates similarity score
6	Aggregate results	Scores: [0.0]	Aggregate score: 0.0	Combines scores if multiple
7	Report or store evaluation	Aggregate score: 0.0	Report generated	Final results ready
8	Exit	All inputs processed	Evaluation complete	Pipeline ends

💡 All inputs processed and evaluation results reported

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
inputs	['Hello']	['Hello']	['Hello']	['Hello']	['Hello']
model_output	None	None	'Hi'	'Hi'	'Hi'
evaluation_score	None	None	None	0.0	0.0
aggregate_score	None	None	None	None	0.0

Key Moments - 3 Insights

Why do we need to prepare input data before running the model?

Preparing inputs ensures the model receives data in the correct format, as shown in Step 2 of the execution_table.

How is the evaluation score calculated?

The score compares model output to the reference using the chosen metric, demonstrated in Step 5 where output 'Hi' is compared to 'Hello'.

What happens after all inputs are processed?

The pipeline aggregates scores and reports results, ending the process as shown in Steps 6 and 7.

Visual Quiz - 3 Questions

Look at the execution_table, what is the model output at Step 3?

A 'Hi'

B 'Hello'

C 'Hey'

D None

💡 Hint

Check the 'Output' column in Step 3 of the execution_table.

At which step is the evaluation score first calculated?

A Step 4

B Step 5

C Step 2

D Step 6

💡 Hint

Look for when 'Score' appears in the 'Output' column in the execution_table.

If the input changes, which step will be affected first?

A Step 1

B Step 5

C Step 2

D Step 7

💡 Hint

Input preparation happens at Step 2 according to the execution_table.

Concept Snapshot

Automated evaluation pipelines run models on inputs,
compare outputs to references using metrics,
aggregate scores, and report results.
Steps: define criteria, prepare data, run model,
evaluate output, aggregate, then report.
This automates checking model quality.

Full Transcript

An automated evaluation pipeline in Langchain starts by defining how to judge model outputs. Then it prepares the input data so the model can understand it. Next, the model runs on these inputs and produces outputs. These outputs are collected and compared to reference answers using evaluation metrics like accuracy. The scores from these comparisons are combined into an aggregate score. Finally, the pipeline reports or stores the evaluation results. This process repeats for all inputs until complete. This helps developers check how well their models perform automatically.

Practice

1. What is the main purpose of an automated evaluation pipeline in Langchain?

easy

A. To quickly test language model outputs against expected answers

B. To train new language models from scratch

C. To manually review each model output for quality

D. To deploy language models to production servers

Solution

1. Step 1: Understand the role of evaluation pipelines

   Evaluation pipelines automatically compare model outputs to expected answers to check correctness.

2. Step 2: Identify the main benefit

   This automation speeds up testing and helps catch errors early without manual review.

3. Final Answer:

   To quickly test language model outputs against expected answers -> Option A

4. Quick Check:

   Automated testing = Quick evaluation [OK]

Hint: Evaluation pipelines compare outputs to expected answers fast [OK]

Common Mistakes:

• Confusing evaluation with training
• Thinking evaluation is manual
• Assuming deployment is part of evaluation

2. Which of the following is the correct way to create an evaluation pipeline in Langchain?

easy

A. pipeline = EvaluationPipeline(inputs, model, expected_outputs)

B. pipeline = EvaluationPipeline(model, inputs, expected_outputs)

C. pipeline = EvaluationPipeline(expected_outputs, inputs, model)

D. pipeline = EvaluationPipeline(inputs, expected_outputs, model)

Solution

1. Step 1: Recall the order of parameters

   The EvaluationPipeline constructor expects inputs first, then the model, then expected outputs.

2. Step 2: Match the correct parameter order

   pipeline = EvaluationPipeline(inputs, model, expected_outputs) matches this order exactly, others mix the sequence causing errors.

3. Final Answer:

   pipeline = EvaluationPipeline(inputs, model, expected_outputs) -> Option A

4. Quick Check:

   Inputs, model, expected outputs order [OK]

Hint: Remember: inputs first, then model, then expected outputs [OK]

Common Mistakes:

• Swapping model and inputs order
• Putting expected outputs before inputs
• Using wrong parameter sequence causing errors

3. Given this code snippet, what will be the output of results?

inputs = ["Hello", "World"]
model = lambda x: x.lower()
expected = ["hello", "world"]
pipeline = EvaluationPipeline(inputs, model, expected)
results = pipeline.run()

medium

A. [True, False]

B. [False, False]

C. [True, True]

D. RuntimeError

Solution

1. Step 1: Understand the model function

   The model converts each input string to lowercase, so "Hello" -> "hello" and "World" -> "world".

2. Step 2: Compare model outputs to expected

   Both outputs match the expected list exactly, so evaluation returns True for both.

3. Final Answer:

   [True, True] -> Option C

4. Quick Check:

   Lowercase matches expected = True [OK]

Hint: Check if model output matches expected exactly [OK]

Common Mistakes:

• Assuming case does not matter
• Expecting runtime error from lambda
• Mixing up True and False results

4. You wrote this evaluation pipeline but it raises an error:

inputs = ["Test"]
model = "not a function"
expected = ["test"]
pipeline = EvaluationPipeline(inputs, model, expected)
pipeline.run()

What is the likely cause?

medium

A. Inputs list cannot have only one item

B. Model must be a callable function, not a string

C. Expected outputs must be integers

D. EvaluationPipeline requires three arguments, but only two were given

Solution

1. Step 1: Check the model parameter type

   The model should be a function that processes inputs, but here it is a string, which is not callable.

2. Step 2: Understand the error cause

   Calling pipeline.run() tries to call the model on inputs, causing a TypeError because strings can't be called like functions.

3. Final Answer:

   Model must be a callable function, not a string -> Option B

4. Quick Check:

   Model callable required, string given [OK]

Hint: Model must be a function, not a string [OK]

Common Mistakes:

• Thinking inputs size causes error
• Expecting output type to be integer
• Miscounting constructor arguments

5. You want to evaluate a language model that sometimes returns empty strings for some inputs. How should you modify your automated evaluation pipeline to handle this edge case correctly?

hard

A. Replace empty string outputs with None before evaluation

B. Treat empty string outputs as incorrect regardless of expected answer

C. Ignore inputs that produce empty strings in the evaluation

D. Filter out empty string outputs before comparing to expected answers

Solution

1. Step 1: Identify the problem with empty strings

   Empty string outputs can cause false negatives if compared directly to expected answers.

2. Step 2: Implement filtering before comparison

   Filtering out empty strings ensures only meaningful outputs are evaluated, avoiding misleading failures.

3. Step 3: Avoid ignoring inputs or forcing None

   Ignoring inputs or replacing outputs can hide real issues or cause errors in evaluation.

4. Final Answer:

   Filter out empty string outputs before comparing to expected answers -> Option D

5. Quick Check:

   Filter empty outputs to avoid false errors [OK]

Hint: Filter empty outputs before evaluation to avoid false failures [OK]

Common Mistakes:

• Ignoring inputs with empty outputs
• Replacing empty strings with None causing errors
• Counting empty strings as always wrong