Argilla SDK Chatbot with distilabel – End‑to‑End Tutorial

TL;DR

We built a domain‑specific RAG chatbot for Argilla 2.0 by using distilabel to generate synthetic Q&A triples, fine‑tuning a BGE‑base embedding model with Matryoshka loss, storing the embeddings in a lightweight lancedb vector store, and deploying the chat interface on Hugging Face Spaces via Gradio.

Generating Synthetic Training Data with distilabel

Key outcome: A triplet dataset (anchor, positive, negative) that captures realistic queries and hard negatives for Argilla documentation.

  • The pipeline starts by loading the raw documentation chunks from the Hub dataset plaguss/argilla_sdk_docs_raw_unstructured and renaming the column chunks to anchor.
  • GenerateSentencePair (triplet mode) uses the LLM meta-llama/Meta-Llama-3-70B-Instruct to create a positive query for each chunk and a negative query that is unrelated but lexically similar.
  • A custom MultipleQueries task expands each positive query into three additional variations, quadrupling the dataset size.
  • MergeColumns and ExpandColumns collapse the original and generated queries into a single positive column, yielding one row per query‑anchor‑negative triple.

The full pipeline is defined in pipeline_docs_queries.py and pushes the final dataset to plaguss/argilla_sdk_docs_queries.

Exploring and Curating Datasets in Argilla

Key outcome: Three Argilla datasets for (1) raw documentation chunks, (2) embedding‑fine‑tuning triples, and (3) chatbot interaction logs.

  • Documentation chunks – fields filename and chunk with a binary label good_chunk for human validation.
  • Embedding triples – fields anchor, positive, negative with binary relevance labels is_positive_relevant and is_negative_irrelevant.
  • Chatbot logs – fields instruction and response plus metadata conv_id and turn; labels assess correctness and guard‑rail violations, and a free‑form feedback field captures user comments.

All datasets are created via the Argilla Python client (rg.Argilla) and can be inspected directly in the Argilla UI.

Fine‑Tuning the Embedding Model

Key outcome: A custom model plaguss/bge-base-argilla-sdk-matryoshka that outperforms the baseline BGE‑base on Argilla‑specific retrieval.

  1. Dataset preparation – Load the triplet dataset, keep columns anchor, positive, negative, add a unique id, and split 90 %/10 % for train/test.
  2. Baseline model – Start from BAAI/bge-base-en-v1.5 and set model‑card metadata.
  3. Loss function – Combine TripletLoss with MatryoshkaLoss (dimensions [768, 512, 256, 128, 64]).
  4. Training arguments – Adjust batch sizes for an Apple M2 Pro, use cosine scheduler, and select eval_dim_512_cosine_ndcg@10 as the metric.
  5. Training – Run SentenceTransformerTrainer; the best checkpoint is automatically pushed to the Hub.

The resulting model can be loaded with SentenceTransformer or via the sentence-transformers registry.

Building the Vector Database with lancedb

Key outcome: A portable, server‑less vector store that links each synthetic query to its documentation chunk.

  • lancedb.connect("./lancedb") creates a local SQLite‑like database.
  • A Docs schema (query, text, vector) is defined using LanceModel.
  • For each batch of the query dataset, embeddings are generated with the fine‑tuned model and inserted into the table.
  • Retrieval example – a cosine‑similarity search for "How can I get the current user?" returns the most relevant documentation chunks.
  • The entire database directory is archived (lancedb.tar.gz) and uploaded to the Hub alongside the dataset, enabling reproducible downloads.

Gradio Chat Interface and Deployment

Key outcome: An interactive web UI (https://huggingface.co/spaces/plaguss/argilla-sdk-chatbot-space) that answers Argilla SDK questions using RAG.

  • Database class – Handles lazy download of the lancedb archive, opens the table, and provides retrieve_doc_chunks which returns up to four deduplicated chunks for a given query.
  • Prompt engineering – A system prompt forces the LLM to answer only from the provided context. The user prompt template (ARGILLA_BOT_TEMPLATE) inserts the retrieved chunks.
  • LLM inference – Calls a Hugging Face inference endpoint (default: meta-llama/Meta-Llama-3-70B-Instruct) via InferenceClient. The response stream is yielded back to Gradio.
  • Conversation logging – After each turn, the interaction is logged to the Argilla chatbot‑log dataset, enabling continuous evaluation and future fine‑tuning.
  • Deployment – Adding requirements.txt and the Hugging  Face API token as a secret allows the app to be built automatically on Spaces.

Implications and Next Steps

Takeaway: The end‑to‑end workflow demonstrates how to turn any code‑centric documentation repository into a high‑quality, domain‑specific RAG chatbot with minimal manual labeling.

  • Scalability – The same pipeline can be applied to other libraries or internal SDKs by swapping the GitHub repo path.
  • Data quality – Improving chunk size, adding deduplication, and enriching the synthetic query generation (e.g., using structured prompts) can boost retrieval relevance.
  • Explainability – Adding source URLs or line numbers to the returned chunks would give users traceability.
  • Feedback loop – The Argilla interaction dataset provides a ready‑to‑use feedback loop for iterative model improvement.

By combining distilabel’s synthetic data generation, Matryoshka‑enhanced embedding fine‑tuning, and lightweight vector stores, developers can rapidly prototype reliable support bots for any technical product.

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