How 𝐋𝐚𝐧𝐠𝐒𝐦𝐢𝐭𝐡, 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡, 𝐎𝐥𝐥𝐚𝐦𝐚 & 𝐅𝐀𝐈𝐒𝐒 Gave Me End-to-End Observability in a Local AI Chatbot

I just built a self-contained AI chatbot—no cloud dependencies, no API keys, just pure local power with 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡, 𝐎𝐥𝐥𝐚𝐦𝐚, 𝐅𝐀𝐈𝐒𝐒, 𝐚𝐧𝐝 𝐋𝐚𝐧𝐠𝐒𝐦𝐢𝐭𝐡.

Tech Stack:

🤖 Build a fully local AI chatbot that answers questions from uploaded documents, with zero cloud dependencies.
🔧 LangChain: Orchestrate the chatbot logic using modular, state-based workflows (e.g., retrieve → generate → feedback).
📚 Ollama + FAISS + TF-IDF: Run a local llama3 model for response generation, and use TF-IDF + FAISS for fast, document-based context retrieval.
🖥 Streamlit: Provide an interactive web interface where users can upload files and chat with the bot in real time.
📊 LangSmith: Enable full observability — trace queries, inspect prompts, monitor latency, and analyze errors or retrieval issues end-to-end.

At first, it answered questions well enough.
But the real game-changer?
I could trace every step of its reasoning.

𝐋𝐚𝐧𝐠𝐒𝐦𝐢𝐭𝐡 gave me the transparency I never knew I needed, revealing the exact document chunks retrieved, the prompts fed to the model, execution times, and even where things went off track.

🚧 The Problem: “It Works” Isn’t Enough

At first, my chatbot seemed to be doing well — it returned reasonable answers to most questions. But then… weird things started to happen..
● Was the wrong chunk retrieved?
● Was the prompt malformed?
● Did the model hallucinate?

Without insight into what was happening step by step, debugging was pure guesswork.

𝐇𝐨𝐰 𝐋𝐚𝐧𝐠𝐒𝐦𝐢𝐭𝐡 𝐇𝐞𝐥𝐩𝐞𝐝 𝐌𝐞 𝐃𝐞𝐛𝐮𝐠 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞

⇒ Tracing
● View each query, chunk retrieval, and LLM response in real-time
● Confirm the right part of the document was being used
● Inspect the exact prompt given to llama3 via Ollama

⇒ Error Analysis
● Trace misfires back to irrelevant or empty document chunks
● Compare expected vs. actual outputs
● Catch malformed inputs or slow model responses

⇒ Performance Metrics
● Track latency for each step (retriever, LLM)
● Identify slowdowns during Ollama inference
● Start tagging “slow” or “retrieval_miss” runs for dashboards

📊 Scaling Visibility with LangSmith Dashboards

LangSmith doesn’t just log traces — it helps you monitor trends over time.
Using their dashboard tools, I now track:
🧠 Number of LLM calls
🕒 Average latency per query
📉 Retrieval failures
💸 Token usage (if using APIs like OpenAI or Anthropic)
❌ Error Rates: Identify failed runs, exceptions, or empty prompts

I’ve published the full working project on GitHub — complete with TF-IDF + FAISS retrieval, Ollama model integration, LangSmith observability, and a Streamlit interface.
https://lnkd.in/etpCMPiS