LANGSMITH.md

📊 LANGSMITH.md

Overview

LangSmith (by LangChain) and LangFuse are powerful observability and analytics tools for LLM workflows. You can use them to track, analyze, and improve model performance — and even synthesize better training data using real user interactions.

This guide covers:

• How to connect LangSmith or LangFuse
• How to log successful Q&A pairs
• How to use logged data for fine-tuning or RAG
• Sample integration script
• Dashboard template ideas
• End-to-end feedback loop

---

🔌 Connecting to LangSmith / LangFuse

LangSmith Setup

1. Install LangChain and LangSmith SDK:

   pip install langchain langsmith

2. Set environment variables:

   export LANGCHAIN_TRACING_V2=true
   export LANGCHAIN_API_KEY="your_langsmith_api_key"
   export LANGCHAIN_PROJECT="your_project_name"

3. Wrap your chains:

   from langsmith import traceable
   
   @traceable(name="user_question")
   def answer_question(prompt):
       return llm(prompt)

LangFuse Setup

1. Install LangFuse SDK:

   npm install langfuse

2. Use their Node/TS SDK in your backend logic:

   const langfuse = new Langfuse({ publicKey: '...', secretKey: '...' });
   langfuse.log({ trace: { name: "query" }, input: ..., output: ... });

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✅ Tracking Successful Question/Answer Pairs

You can tag data based on feedback:

With LangSmith:

• Use metadata:

  trace_metadata = {
    "user_feedback": "👍",
    "task_type": "qa",
    "user_id": "123"
  }

• Mark high-quality interactions with tags like success=true

With LangFuse:

• Use feedback tagging or custom properties:

  langfuse.log({
    trace: { name: "answer_check" },
    input: question,
    output: answer,
    metadata: { user_score: 5, was_helpful: true }
  });

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🧪 Synthesizing Fine-Tuning Data

Once you've tracked a set of high-quality examples:

1. Export from LangSmith (UI or API):

   • Filter by tag: success=true
   • Extract prompt/response pairs

2. Format for fine-tuning:

   { "prompt": "How do I reset my password?", "completion": "Go to settings and click 'Reset Password'." }

3. Use for supervised fine-tuning or to enrich your RAG index.

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💻 Sample LangSmith Integration Script

from langchain.chat_models import ChatOpenAI
from langsmith.run_trees import RunTree
from langsmith import traceable

chat = ChatOpenAI()

@traceable(name="chat_response")
def respond_to_user(message: str):
    return chat.predict(message)

# Example usage
response = respond_to_user("What are the store hours?")
print(response)

---

📊 Sample Dashboard Template (LangSmith or LangFuse)

A good dashboard might include:

Success Rate Panel

• Total requests
• Percentage of successful/approved answers

Latency Panel

• Average response time
• P95 response time

Feedback Panel

• Most common 👍 / 👎 reasons
• Histogram of user ratings

Top Queries Table

• Query text
• User score or outcome
• Timestamp + tags

Drop-off Funnel

• Steps from initial query to completion or re-ask

---

🔁 End-to-End Feedback Loop

To close the loop from user interaction to model improvement:

1. Capture Live Interactions using LangSmith or LangFuse.
2. Tag & Rate Responses via:
   • Thumbs up/down buttons
   • 1–5 star rating
   • Free-text user comments
3. Log & Store Data into:
   • LangSmith's project dashboard
   • LangFuse logs with metadata
4. Aggregate Insights through analytics dashboards.
5. Export High-Quality Pairs and curate a fine-tuning dataset.
6. Retrain or Augment your model or RAG index.
7. Monitor Improvements over time via version comparison.

Optional additions:

• Slack or Discord bots to request feedback inline
• Airtable or Notion databases for tagging, editing, and review
• Auto-notify team when drops in accuracy occur

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📈 Bonus: Analytics to Improve UX

• Track latency, response quality, fallback usage.
• Identify high dropout paths or frequent re-asks.
• Use heatmaps or feedback tags to guide your iteration roadmap.

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Summary

LangSmith and LangFuse allow you to go beyond black-box LLM usage. With traceable observability and feedback tagging, you can:

• Identify what works
• Create datasets from real usage
• Improve LLM accuracy through continuous learning

Use observability as a data refinery to evolve smarter models.