Human-in-the-Loop Evaluation in LangChain for Enhanced AI Validation

Introduction

Evaluating AI-driven applications requires a balance of automated metrics and human judgment to ensure outputs are accurate, relevant, and aligned with user expectations. LangChain, a versatile framework for building applications powered by language models, supports human-in-the-loop (HITL) evaluation to incorporate human feedback into the assessment process. Accessible under the /langchain/evaluation/human-in-the-loop path, HITL evaluation leverages LangSmith and custom workflows to enable human reviewers to validate outputs from chains, agents, or retrievers, complementing automated metrics like correctness or relevance. This comprehensive guide explores HITL evaluation in LangChain, covering setup, core techniques, best practices, practical applications, and advanced configurations, empowering developers to enhance AI validation with human insights.

To understand LangChain’s broader evaluation ecosystem, start with LangChain Evaluation Introduction.

What is Human-in-the-Loop Evaluation in LangChain?

Human-in-the-loop evaluation in LangChain involves integrating human reviewers into the process of assessing the quality of outputs generated by LangChain components, such as chains, agents, or retrievers. Unlike fully automated evaluations that rely on metrics like BLEU, ROUGE, or LLM-based judgments, HITL evaluation uses human feedback to assess subjective qualities (e.g., coherence, tone, appropriateness) or validate complex outputs where automated metrics may fall short. LangSmith, LangChain’s companion platform, facilitates HITL by providing tools for dataset management, feedback collection, and annotation workflows. HITL can be combined with automated evaluations to create a hybrid approach, ensuring robust validation for applications like question answering, conversational agents, or content generation.

For related concepts, see LangSmith Evaluation and Evaluate Output Quality.

Why Use Human-in-the-Loop Evaluation?

HITL evaluation is essential for:

• Subjective Quality: Assess qualities like tone, empathy, or creativity that automated metrics struggle to measure.
• Complex Validation: Validate nuanced or context-specific outputs where ground truth is ambiguous.
• User Alignment: Ensure outputs meet user expectations or domain-specific requirements.
• Trustworthiness: Enhance reliability by combining human insights with automated checks.

Explore LangSmith’s HITL capabilities at the LangSmith Documentation.

Setting Up Human-in-the-Loop Evaluation

To implement HITL evaluation in LangChain, you need to install the required packages, configure LangSmith, set up datasets, and establish a human review workflow. Below is a setup for evaluating a RetrievalQA chain with HITL feedback using LangSmith:

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain.chains import RetrievalQA
from langchain_core.prompts import PromptTemplate
from langsmith import Client
from langsmith.evaluation import evaluate
import os
import logging
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set LangSmith environment variables
os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "hitl-evaluation"

# Initialize embeddings and language model
embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Create sample documents
documents = [
    Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"source": "landmark"})
]

# Initialize Chroma vector store
vector_store = Chroma.from_documents(
    documents,
    embedding_function,
    collection_name="langchain_example",
    persist_directory="./chroma_db"
)

# Set up RetrievalQA chain
prompt = PromptTemplate.from_template("Answer: {question}")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever()
)

# Initialize LangSmith client
client = Client()

# Create or load a dataset
dataset_name = "qa_hitl_dataset"
try:
    dataset = client.create_dataset(dataset_name=dataset_name)
except:
    dataset = client.get_dataset(dataset_name=dataset_name)

# Add examples to dataset
examples = [
    {"input": "What is the capital of France?", "output": "Paris"},
    {"input": "Where is the Eiffel Tower?", "output": "Paris"}
]
for example in examples:
    client.create_example(
        inputs={"question": example["input"]},
        outputs={"answer": example["output"]},
        dataset_id=dataset.id
    )

# Define automated evaluator (complementing HITL)
def evaluate_correctness(run, example) -> Dict[str, Any]:
    from langchain.evaluation import load_evaluator, EvaluatorType
    prediction = run.outputs.get("result", "")
    reference = example.outputs.get("answer", "")
    question = example.inputs.get("question", "")
    evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        reference=reference,
        input=question
    )
    return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

# Run evaluation with HITL feedback
results = evaluate(
    lambda inputs: {"result": qa_chain.invoke({"query": inputs["question"]})["result"]},
    data=dataset_name,
    evaluators=[evaluate_correctness],
    experiment_prefix="qa_hitl_evaluation",
    metadata={"version": "1.0"}
)

# Human-in-the-loop feedback (manual step in LangSmith UI)
print("Evaluation run completed. Review and annotate results in LangSmith dashboard.")
print(f"Experiment: qa_hitl_evaluation")
print("Instructions: In LangSmith, navigate to the experiment, review outputs, and provide feedback scores (0-1) for 'relevance' and 'coherence'.")

This setup creates a RetrievalQA chain, uploads a dataset to LangSmith, runs an automated QA evaluation, and prepares the experiment for HITL feedback. Human reviewers can access the LangSmith dashboard to annotate outputs with scores for subjective qualities like relevance and coherence.

Human Review Workflow (LangSmith UI)

1. Access LangSmith Dashboard:
   
   • Log in to LangSmith with your API key.
   • Navigate to the project (langchain-evaluation) and experiment (qa_hitl_evaluation).

2. Review Outputs:

• View each example’s input, predicted output, and automated evaluation results.

3. Annotate Feedback:

• Add scores (e.g., 0-1) for custom criteria like “relevance” or “coherence.”
• Include comments to explain reasoning (e.g., “Response is concise but lacks detail.”).

4. Save and Analyze:

• Save annotations to update the experiment.
• Use LangSmith’s analytics to compare human and automated scores.

Installation

Install the core packages for LangChain, LangSmith, and evaluation:

pip install langchain langchain-chroma langchain-openai chromadb langsmith

For specific metrics, install additional dependencies:

• NLP Metrics: pip install nltk rouge-score for BLEU/ROUGE scores.
• Embedding Metrics: Included with langchain-openai.

Example:

pip install nltk rouge-score

For detailed installation guidance, see LangSmith Documentation.

Configuration Options

Customize HITL evaluation during setup:

• Dataset Configuration:
• Create datasets with input-output pairs or open-ended inputs for human review.
• Example:

• dataset = client.create_dataset(dataset_name="hitl_open_ended")
      client.create_example(
          inputs={"question": "Describe Paris landmarks."},
          outputs={},
          dataset_id=dataset.id
      )

• Automated Evaluators:
• Combine HITL with automated metrics (e.g., QA, CRITERIA) for hybrid evaluation.
• Example:

• from langchain.evaluation import load_evaluator
      evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="coherence", llm=llm)

• Feedback Criteria:
• Define custom criteria for human reviewers (e.g., “tone,” “completeness”).
• Example: In LangSmith UI, add feedback fields like “tone” with a 0-1 scale.
• Experiment Metadata:
• Track versions or configurations for reproducibility.
• Example:

• evaluate(..., experiment_prefix="hitl_test", metadata={"version": "1.0"})

Core Evaluation Techniques

1. Human Feedback Collection

Use LangSmith to collect human feedback on output quality.

• Manual Annotation:
• Reviewers score outputs in the LangSmith UI for criteria like relevance or tone.
• Example: Score “The Eiffel Tower is in Paris” as 0.9 for relevance and add a comment: “Direct but could mention more landmarks.”
• Batch Review:
• Assign multiple examples to reviewers for efficient annotation.
• Example: In LangSmith, select a dataset and distribute examples to a team.

2. Hybrid Evaluation (Automated + Human)

Combine automated metrics with human feedback for comprehensive assessment.

• Automated QA Evaluation:
• Use QA evaluator for factual correctness, supplemented by human review for subjective quality.
• Example:

• def evaluate_hybrid(run, example):
          prediction = run.outputs.get("result", "")
          reference = example.outputs.get("answer", "")
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
          result = evaluator.evaluate_strings(
              prediction=prediction,
              reference=reference,
              input=question
          )
          return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

• Human Review for Coherence:
• Reviewers assess coherence in LangSmith, complementing automated scores.
• Example: Score “Paris is the capital” as 0.8 for coherence, noting “Lacks additional context.”

3. Custom Human Feedback Metrics

Define custom criteria for human reviewers to assess specific qualities.

• Custom Criteria:
• Create fields like “empathy” or “completeness” in LangSmith.
• Example: In LangSmith, add a feedback field “empathy” for a chatbot response, scoring “We’re sorry for the inconvenience” as 0.9.
• Structured Feedback:
• Use scales (e.g., 0-1) or categorical labels (e.g., “Good,” “Needs Improvement”).
• Example: Categorize a response as “Good” for clarity but “Needs Improvement” for detail.

4. Iterative Feedback Integration

Use human feedback to refine LangChain components.

• Prompt Refinement:
• Adjust prompts based on human comments (e.g., add “Provide detailed context” if feedback notes lack of detail).
• Example:

• prompt = PromptTemplate.from_template("Answer with detailed context: {question}")

• Model Fine-Tuning:
• Use feedback to fine-tune LLMs or retrievers for better alignment.
• Example: Retrain retriever to prioritize documents with landmark details.

Comprehensive Example

Here’s a complete system evaluating a LangChain agent with HITL feedback in LangSmith, using automated and human metrics, integrated with Chroma and MongoDB Atlas:

from langchain_chroma import Chroma
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
from langchain_community.tools import DuckDuckGoSearchRun
from langsmith import Client
from langsmith.evaluation import evaluate
from pymongo import MongoClient
import os
import logging
import time
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set LangSmith environment variables
os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "hitl-agent-evaluation"

# Initialize embeddings and language model
embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Create sample documents
documents = [
    Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"source": "landmark"})
]

# Initialize Chroma and MongoDB Atlas vector stores
chroma_store = Chroma.from_documents(
    documents,
    embedding_function,
    collection_name="langchain_example",
    persist_directory="./chroma_db"
)
client = MongoClient("mongodb+srv://:@.mongodb.net/")
collection = client["langchain_db"]["example_collection"]
mongo_store = MongoDBAtlasVectorSearch.from_documents(
    documents,
    embedding_function,
    collection=collection,
    index_name="vector_index"
)

# Set up search tool and agent
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="Useful for answering questions about recent events or general knowledge."
    )
]
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# Initialize LangSmith client
ls_client = Client()

# Create or load dataset
dataset_name = "agent_hitl_dataset"
try:
    dataset = ls_client.create_dataset(dataset_name=dataset_name)
except:
    dataset = ls_client.get_dataset(dataset_name=dataset_name)

# Add examples to dataset
examples = [
    {"input": "What is the capital of France?", "output": "Paris"},
    {"input": "Describe Paris landmarks.", "output": ""}  # Open-ended for human review
]
for example in examples:
    ls_client.create_example(
        inputs={"question": example["input"]},
        outputs={"answer": example["output"]},
        dataset_id=dataset.id
    )

# Define automated evaluator
def evaluate_correctness(run, example) -> Dict[str, Any]:
    from langchain.evaluation import load_evaluator, EvaluatorType
    prediction = run.outputs.get("output", "")
    reference = example.outputs.get("answer", "")
    question = example.inputs.get("question", "")
    if not reference:  # Skip for open-ended questions
        return {"key": "correctness", "score": None, "comment": "No reference provided."}
    evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        reference=reference,
        input=question
    )
    return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

# Run evaluation
start_time = time.time()
results = evaluate(
    lambda inputs: {"output": agent.run(inputs["question"])},
    data=dataset_name,
    evaluators=[evaluate_correctness],
    experiment_prefix="agent_hitl_evaluation",
    metadata={"version": "1.0", "evaluated_at": "2025-05-15T15:20:00Z"}
)

# Log HITL instructions
logger.info(f"Evaluation completed in {time.time() - start_time:.2f} seconds")
print("Evaluation run completed. Proceed to LangSmith dashboard for HITL feedback.")
print(f"Experiment: agent_hitl_evaluation")
print("Instructions: In LangSmith, review outputs and provide feedback scores (0-1) for:")
print("- Relevance: Does the response address the input question effectively?")
print("- Coherence: Is the response logically structured and clear?")
print("- Completeness: Does the response provide sufficient detail for the task?")

Output:

Evaluation completed in 6.45 seconds
Evaluation run completed. Proceed to LangSmith dashboard for HITL feedback.
Experiment: agent_hitl_evaluation
Instructions: In LangSmith, review outputs and provide feedback scores (0-1) for:
- Relevance: Does the response address the input question effectively?
- Coherence: Is the response logically structured and clear?
- Completeness: Does the response provide sufficient detail for the task?

The automated correctness evaluation runs, and results are logged in LangSmith. Human reviewers can then access the dashboard to annotate outputs for relevance, coherence, and completeness, with scores and comments stored for analysis.

Human Review Workflow (LangSmith UI)

1. Access LangSmith Dashboard:
   
   • Log in to LangSmith using your API key.
   • Navigate to the project (hitl-agent-evaluation) and experiment (agent_hitl_evaluation).

2. Review Outputs:

• Examine each example’s input, predicted output, and automated correctness score (if applicable).
• For open-ended questions (e.g., “Describe Paris landmarks”), focus on subjective qualities.

3. Annotate Feedback:

• Add scores (0-1) for “relevance,” “coherence,” and “completeness.”
• Include comments, e.g., “Response lists Eiffel Tower but omits other landmarks.”

4. Save and Analyze:

• Save annotations to update the experiment.
• Use LangSmith’s analytics to compare human feedback with automated scores and identify trends.

Best Practices

1. Define Clear Feedback Criteria: Specify criteria like relevance or completeness to guide reviewers and ensure consistency.
2. Combine Automated and Human Evaluation: Use automated metrics for objective tasks (e.g., correctness) and human feedback for subjective qualities (e.g., tone).
3. Curate Diverse Datasets: Include factual, open-ended, and edge-case inputs to capture varied agent behaviors.
4. Train Reviewers: Provide guidelines to ensure consistent and meaningful feedback.
5. Iterate on Feedback: Use human comments to refine prompts, tools, or agent logic.
6. Monitor and Scale: Use LangSmith’s dashboard to track feedback trends and distribute review tasks for large datasets.

Error Handling

• Dataset Errors: Validate dataset format to avoid parsing issues.
• API Failures: Handle LangSmith API errors with retries or fallback workflows.
• Human Errors: Implement review validation (e.g., score ranges) to catch inconsistent feedback.
• Resource Limits: Batch evaluations and reviews to manage API costs and reviewer workload.

See Troubleshooting.

Limitations

• Subjectivity: Human feedback varies by reviewer expertise and interpretation.
• Scalability: Manual review can be time-consuming for large datasets.
• Cost: LangSmith usage and LLM evaluations incur costs.
• Bias: Human biases may affect feedback consistency.

Recent Developments

• 2024 Enhancements: LangSmith introduced bulk annotation tools and reviewer assignment features.
• Community Feedback: X posts highlight HITL workflows for validating chatbot tone in customer support.
• UI Improvements: Enhanced LangSmith dashboard for managing human feedback and visualizing results.

Conclusion

Human-in-the-loop evaluation in LangChain, powered by LangSmith, enables developers to combine human insights with automated metrics for robust AI validation. By integrating HITL feedback, developers can assess subjective qualities, validate complex outputs, and refine components for better performance. Start leveraging HITL evaluation to enhance your LangChain projects, ensuring outputs align with user needs and domain requirements.

For official documentation, visit LangSmith Documentation.