vectorstore.py

# ============================================================================
# vectorstore.py — ChromaDB-backed vector store for the training dataset
# ============================================================================
#
# PURPOSE
# -------
# Semantic vector storage and retrieval using ChromaDB as the backend.
# Unlike training.py (which only holds vectors in RAM during a single
# classifier fit or clustering run), this module PERSISTS vectors to disk
# so students can index once and then run many semantic searches against
# the stored collection.
#
# Uses the same sentence-transformers model as training.py so vectors are
# comparable across all parts of the demo.
#
# WHAT GETS STORED
# ----------------
# For each of the 100 training_data.py sentences we store:
#   - sentence text (the document)
#   - 384-dim embedding vector (from all-MiniLM-L6-v2)
#   - metadata: {label, index}
#
# Persistence: ChromaDB writes to ./chroma_db/ under the app directory.
# On HuggingFace Spaces this persists for the life of the container but
# is wiped on Space restart (Spaces are ephemeral). That is fine for a
# teaching demo — students re-index at the start of each session.
#
# CONTRACT (what app.py imports from here)
# ----------------------------------------
#   get_collection()            -> chroma collection (creates on first call)
#   index_training_data()       -> {indexed, sentence_count, vector_dim}
#   search(query, n_results=5)  -> list of dicts with sentence, label, score
#   clear_collection()          -> drops all vectors
#   collection_stats()          -> {count, embedding_model, persist_dir}
#   preview_vectors(n=10)       -> list of {sentence, label, vector_head} dicts
#                                  used by the Vectorize sub-tab for inspection
# ============================================================================


import os
import providers
from training_data import TRAINING_EXAMPLES


# ----------------------------------------------------------------
# Configuration
# ----------------------------------------------------------------
PERSIST_DIR = os.environ.get("CHROMA_PERSIST_DIR", "./chroma_db")
COLLECTION_NAME = "training_sentences"
DEFAULT_EMBEDDING_PROVIDER = "MiniLM (local)"


# ----------------------------------------------------------------
# Lazy client for chromadb
# ----------------------------------------------------------------
_CLIENT = None
_COLLECTION = None


def _get_client():
    global _CLIENT
    if _CLIENT is None:
        import chromadb
        os.makedirs(PERSIST_DIR, exist_ok=True)
        _CLIENT = chromadb.PersistentClient(path=PERSIST_DIR)
    return _CLIENT


def get_collection():
    """Get or create the Chroma collection. Safe to call many times."""
    global _COLLECTION
    if _COLLECTION is None:
        client = _get_client()
        _COLLECTION = client.get_or_create_collection(
            name=COLLECTION_NAME,
            metadata={"hnsw:space": "cosine"},
        )
    return _COLLECTION


# ----------------------------------------------------------------
# Indexing — embed all 100 training sentences and persist to disk
# ----------------------------------------------------------------
def index_training_data(embedding_provider=DEFAULT_EMBEDDING_PROVIDER,

                        embedding_api_key=""):
    """Embed every sentence in TRAINING_EXAMPLES and write to the collection.



    Returns a dict with summary fields for UI display. If the collection

    already has rows they are cleared first so re-indexing is idempotent.

    """
    collection = get_collection()

    # Reset so re-indexing is predictable
    existing_count = collection.count()
    if existing_count > 0:
        existing_ids = collection.get().get("ids", [])
        if existing_ids:
            collection.delete(ids=existing_ids)

    sentences = [e["sentence"] for e in TRAINING_EXAMPLES]
    labels = [e["label"] for e in TRAINING_EXAMPLES]

    vectors = providers.embed_texts(
        sentences, embedding_provider, embedding_api_key,
    )

    ids = [f"sent_{i:03d}" for i in range(len(sentences))]
    metadatas = [
        {"label": lab, "index": i}
        for i, lab in enumerate(labels)
    ]

    collection.add(
        ids=ids,
        documents=sentences,
        embeddings=vectors.tolist(),
        metadatas=metadatas,
    )

    return {
        "indexed": len(sentences),
        "sentence_count": len(sentences),
        "vector_dim": int(vectors.shape[1]),
        "embedding_provider": embedding_provider,
        "embedding_model": providers.EMBEDDING_PROVIDERS[embedding_provider]["default_model"],
        "persist_dir": PERSIST_DIR,
        "collection_name": COLLECTION_NAME,
    }


# ----------------------------------------------------------------
# Search — embed a query and retrieve nearest neighbors
# ----------------------------------------------------------------
def search(query, n_results=5,

           embedding_provider=DEFAULT_EMBEDDING_PROVIDER,

           embedding_api_key=""):
    """Embed query and return top-N nearest training sentences."""
    collection = get_collection()
    if collection.count() == 0:
        return []

    q_vecs = providers.embed_texts(
        [query], embedding_provider, embedding_api_key,
    )
    q_vec = q_vecs[0]

    res = collection.query(
        query_embeddings=[q_vec.tolist()],
        n_results=int(n_results),
    )

    hits = []
    docs = (res.get("documents") or [[]])[0]
    metas = (res.get("metadatas") or [[]])[0]
    dists = (res.get("distances") or [[]])[0]
    for doc, meta, dist in zip(docs, metas, dists):
        similarity = float(1.0 - dist)
        hits.append({
            "sentence": doc,
            "label": (meta or {}).get("label"),
            "index": (meta or {}).get("index"),
            "distance": float(dist),
            "similarity": similarity,
        })
    return hits


# ----------------------------------------------------------------
# Utilities — clear, stats, preview
# ----------------------------------------------------------------
def clear_collection():
    collection = get_collection()
    ids = collection.get().get("ids", [])
    if ids:
        collection.delete(ids=ids)
    return {"cleared": len(ids)}


def collection_stats():
    collection = get_collection()
    return {
        "count": collection.count(),
        "persist_dir": PERSIST_DIR,
        "collection_name": COLLECTION_NAME,
    }


def preview_vectors(n=10,

                    embedding_provider=DEFAULT_EMBEDDING_PROVIDER,

                    embedding_api_key=""):
    """Return the first N sentences with the head of their embedding vectors."""
    rows = []
    sample = TRAINING_EXAMPLES[:int(n)]
    sentences = [e["sentence"] for e in sample]
    vectors = providers.embed_texts(
        sentences, embedding_provider, embedding_api_key,
    )

    for i, (ex, vec) in enumerate(zip(sample, vectors)):
        head = [round(float(x), 4) for x in vec[:8]]
        rows.append({
            "index": i,
            "sentence": ex["sentence"],
            "label": ex["label"],
            "vector_head": str(head),
            "vector_dim": int(vec.shape[0]),
        })
    return rows