strat-chatbot/app/vector_store.py

"""ChromaDB vector store for rule embeddings."""

from pathlib import Path
from typing import Optional
import chromadb
from chromadb.config import Settings as ChromaSettings
from sentence_transformers import SentenceTransformer
import numpy as np
from .config import settings
from .models import RuleDocument, RuleSearchResult


class VectorStore:
    """Wrapper around ChromaDB for rule retrieval."""

    def __init__(self, persist_dir: Path, embedding_model: str):
        """Initialize vector store with embedding model."""
        self.persist_dir = Path(persist_dir)
        self.persist_dir.mkdir(parents=True, exist_ok=True)

        chroma_settings = ChromaSettings(
            anonymized_telemetry=False, is_persist_directory_actually_writable=True
        )

        self.client = chromadb.PersistentClient(
            path=str(self.persist_dir), settings=chroma_settings
        )

        self.embedding_model = SentenceTransformer(embedding_model)

    def get_collection(self):
        """Get or create the rules collection."""
        return self.client.get_or_create_collection(
            name="rules", metadata={"hnsw:space": "cosine"}
        )

    def add_document(self, doc: RuleDocument) -> None:
        """Add a single rule document to the vector store."""
        embedding = self.embedding_model.encode(doc.content).tolist()

        collection = self.get_collection()
        collection.add(
            ids=[doc.metadata.rule_id],
            embeddings=[embedding],
            documents=[doc.content],
            metadatas=[doc.to_chroma_metadata()],
        )

    def add_documents(self, docs: list[RuleDocument]) -> None:
        """Add multiple documents in batch."""
        if not docs:
            return

        ids = [doc.metadata.rule_id for doc in docs]
        contents = [doc.content for doc in docs]
        embeddings = self.embedding_model.encode(contents).tolist()
        metadatas = [doc.to_chroma_metadata() for doc in docs]

        collection = self.get_collection()
        collection.add(
            ids=ids, embeddings=embeddings, documents=contents, metadatas=metadatas
        )

    def search(
        self, query: str, top_k: int = 10, section_filter: Optional[str] = None
    ) -> list[RuleSearchResult]:
        """Search for relevant rules using semantic similarity."""
        query_embedding = self.embedding_model.encode(query).tolist()

        collection = self.get_collection()

        where = None
        if section_filter:
            where = {"section": section_filter}

        results = collection.query(
            query_embeddings=[query_embedding],
            n_results=top_k,
            where=where,
            include=["documents", "metadatas", "distances"],
        )

        search_results = []
        if results and results["documents"] and results["documents"][0]:
            for i in range(len(results["documents"][0])):
                metadata = results["metadatas"][0][i]
                distance = results["distances"][0][i]
                similarity = max(
                    0.0, min(1.0, 1 - distance)
                )  # Clamp to [0, 1]: cosine distance ranges 0–2

                search_results.append(
                    RuleSearchResult(
                        rule_id=metadata["rule_id"],
                        title=metadata["title"],
                        content=results["documents"][0][i],
                        section=metadata["section"],
                        similarity=similarity,
                    )
                )

        return search_results

    def delete_rule(self, rule_id: str) -> None:
        """Remove a rule by its ID."""
        collection = self.get_collection()
        collection.delete(ids=[rule_id])

    def clear_all(self) -> None:
        """Delete all rules from the collection."""
        self.client.delete_collection("rules")
        self.get_collection()  # Recreate empty collection

    def get_rule(self, rule_id: str) -> Optional[RuleSearchResult]:
        """Retrieve a specific rule by ID."""
        collection = self.get_collection()
        result = collection.get(ids=[rule_id], include=["documents", "metadatas"])

        if result and result["documents"] and result["documents"][0]:
            metadata = result["metadatas"][0][0]
            return RuleSearchResult(
                rule_id=metadata["rule_id"],
                title=metadata["title"],
                content=result["documents"][0][0],
                section=metadata["section"],
                similarity=1.0,
            )
        return None

    def list_all_rules(self) -> list[RuleSearchResult]:
        """Return all rules in the store."""
        collection = self.get_collection()
        result = collection.get(include=["documents", "metadatas"])

        all_rules = []
        if result and result["documents"]:
            for i in range(len(result["documents"])):
                metadata = result["metadatas"][i]
                all_rules.append(
                    RuleSearchResult(
                        rule_id=metadata["rule_id"],
                        title=metadata["title"],
                        content=result["documents"][i],
                        section=metadata["section"],
                        similarity=1.0,
                    )
                )

        return all_rules

    def count(self) -> int:
        """Return the number of rules in the store."""
        collection = self.get_collection()
        return collection.count()

    def get_stats(self) -> dict:
        """Get statistics about the vector store."""
        collection = self.get_collection()
        all_rules = self.list_all_rules()
        sections = {}
        for rule in all_rules:
            sections[rule.section] = sections.get(rule.section, 0) + 1

        return {
            "total_rules": len(all_rules),
            "sections": sections,
            "persist_directory": str(self.persist_dir),
        }