fix: resolve CI/CD issues with proper package structure and imports

src/local_api_docs_search/search/vectorstore.py

@@ -0,0 +1,305 @@
+"""Vector storage operations using ChromaDB."""
+
+import logging
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import chromadb
+from chromadb.config import Settings
+
+from local_api_docs_search.models.document import Document, IndexStats, SourceType
+
+logger = logging.getLogger(__name__)
+
+
+class VectorStore:
+    """ChromaDB-based vector storage for document embeddings."""
+
+    COLLECTION_NAME = "api_docs"
+
+    def __init__(
+        self,
+        persist_dir: Path,
+        collection_name: Optional[str] = None,
+    ):
+        """Initialize the vector store.
+
+        Args:
+            persist_dir: Directory for persistence
+            collection_name: Name of the collection (default: api_docs)
+        """
+        self._persist_dir = Path(persist_dir)
+        self._persist_dir.mkdir(parents=True, exist_ok=True)
+        self._collection_name = collection_name or self.COLLECTION_NAME
+        self._client: Optional[chromadb.Client] = None
+        self._collection: Optional[chromadb.Collection] = None
+
+    def _get_client(self) -> chromadb.Client:
+        """Get or create the ChromaDB client."""
+        if self._client is None:
+            self._client = chromadb.Client(
+                Settings(
+                    persist_directory=str(self._persist_dir),
+                    anonymized_telemetry=False,
+                )
+            )
+        return self._client
+
+    def _get_collection(self) -> chromadb.Collection:
+        """Get or create the collection."""
+        if self._collection is None:
+            client = self._get_client()
+            try:
+                self._collection = client.get_collection(self._collection_name)
+            except ValueError:
+                self._collection = client.create_collection(self._collection_name)
+                logger.info(f"Created new collection: {self._collection_name}")
+        return self._collection
+
+    def add_documents(
+        self,
+        documents: List[Document],
+        embeddings: List[List[float]],
+        batch_size: int = 100,
+    ) -> int:
+        """Add documents and their embeddings to the store.
+
+        Args:
+            documents: List of Document objects
+            embeddings: List of embedding vectors
+            batch_size: Documents per batch
+
+        Returns:
+            Number of documents added
+        """
+        if not documents:
+            return 0
+
+        collection = self._get_collection()
+
+        total_added = 0
+        for i in range(0, len(documents), batch_size):
+            batch_docs = documents[i : i + batch_size]
+            batch_embeddings = embeddings[i : i + batch_size]
+
+            ids = [doc.id for doc in batch_docs]
+            contents = [doc.content for doc in batch_docs]
+            metadatas = [
+                {
+                    "source_type": doc.source_type.value,
+                    "title": doc.title,
+                    "file_path": doc.file_path,
+                    **doc.metadata,
+                }
+                for doc in batch_docs
+            ]
+
+            try:
+                collection.add(
+                    ids=ids,
+                    documents=contents,
+                    embeddings=batch_embeddings,
+                    metadatas=metadatas,
+                )
+                total_added += len(batch_docs)
+                logger.debug(f"Added batch of {len(batch_docs)} documents")
+            except Exception as e:
+                logger.error(f"Failed to add batch: {e}")
+
+        logger.info(f"Added {total_added} documents to collection")
+        return total_added
+
+    def search(
+        self,
+        query_embedding: List[float],
+        n_results: int = 10,
+        source_type: Optional[SourceType] = None,
+    ) -> List[Dict[str, Any]]:
+        """Search for similar documents.
+
+        Args:
+            query_embedding: Query embedding vector
+            n_results: Number of results to return
+            source_type: Optional filter by source type
+
+        Returns:
+            List of search results with documents and scores
+        """
+        collection = self._get_collection()
+
+        where_filter = None
+        if source_type:
+            where_filter = {"source_type": source_type.value}
+
+        try:
+            results = collection.query(
+                query_embeddings=[query_embedding],
+                n_results=n_results,
+                where=where_filter,
+                include=["documents", "metadatas", "distances"],
+            )
+        except Exception as e:
+            logger.error(f"Search failed: {e}")
+            return []
+
+        search_results = []
+        if results["ids"] and results["ids"][0]:
+            for i in range(len(results["ids"][0])):
+                result = {
+                    "id": results["ids"][0][i],
+                    "content": results["documents"][0][i],
+                    "metadata": results["metadatas"][0][i],
+                    "distance": results["distances"][0][i],
+                    "score": 1.0 - results["distances"][0][i],
+                }
+                search_results.append(result)
+
+        return search_results
+
+    def delete_index(self) -> bool:
+        """Delete the entire index.
+
+        Returns:
+            True if successful
+        """
+        try:
+            client = self._get_client()
+            client.delete_collection(self._collection_name)
+            self._collection = None
+            logger.info(f"Deleted collection: {self._collection_name}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to delete collection: {e}")
+            return False
+
+    def get_stats(self) -> IndexStats:
+        """Get statistics about the index.
+
+        Returns:
+            IndexStats object
+        """
+        collection = self._get_collection()
+
+        total = collection.count()
+
+        source_counts = {type.value: 0 for type in SourceType}
+
+        try:
+            all_metadata = collection.get(include=["metadatas"])
+            for metadata in all_metadata.get("metadatas", []):
+                source_type = metadata.get("source_type")
+                if source_type in source_counts:
+                    source_counts[source_type] += 1
+        except Exception as e:
+            logger.warning(f"Failed to get source counts: {e}")
+
+        return IndexStats(
+            total_documents=total,
+            openapi_count=source_counts[SourceType.OPENAPI.value],
+            readme_count=source_counts[SourceType.README.value],
+            code_count=source_counts[SourceType.CODE.value],
+        )
+
+    def get_all_documents(
+        self, limit: int = 1000, offset: int = 0
+    ) -> List[Document]:
+        """Get all documents from the store.
+
+        Args:
+            limit: Maximum number of documents
+            offset: Offset for pagination
+
+        Returns:
+            List of Document objects
+        """
+        collection = self._get_collection()
+
+        try:
+            results = collection.get(limit=limit, offset=offset, include=["documents", "metadatas"])
+        except Exception as e:
+            logger.error(f"Failed to get documents: {e}")
+            return []
+
+        documents = []
+        for i in range(len(results["ids"])):
+            metadata = results["metadatas"][i]
+            doc = Document(
+                id=results["ids"][i],
+                content=results["documents"][i],
+                source_type=SourceType(metadata["source_type"]),
+                title=metadata["title"],
+                file_path=metadata["file_path"],
+                metadata={k: v for k, v in metadata.items() if k not in ["source_type", "title", "file_path"]},
+            )
+            documents.append(doc)
+
+        return documents
+
+    def delete_by_ids(self, ids: List[str]) -> int:
+        """Delete documents by IDs.
+
+        Args:
+            ids: List of document IDs to delete
+
+        Returns:
+            Number of documents deleted
+        """
+        if not ids:
+            return 0
+
+        collection = self._get_collection()
+
+        try:
+            collection.delete(ids=ids)
+            logger.info(f"Deleted {len(ids)} documents")
+            return len(ids)
+        except Exception as e:
+            logger.error(f"Failed to delete documents: {e}")
+            return 0
+
+    def delete_by_source_type(self, source_type: SourceType) -> int:
+        """Delete all documents of a given source type.
+
+        Args:
+            source_type: Source type to delete
+
+        Returns:
+            Number of documents deleted
+        """
+        collection = self._get_collection()
+
+        try:
+            results = collection.get(where={"source_type": source_type.value})
+            if results["ids"]:
+                return self.delete_by_ids(results["ids"])
+        except Exception as e:
+            logger.error(f"Failed to delete by source type: {e}")
+
+        return 0
+
+    def exists(self) -> bool:
+        """Check if the collection exists.
+
+        Returns:
+            True if collection exists
+        """
+        try:
+            client = self._get_client()
+            client.get_collection(self._collection_name)
+            return True
+        except ValueError:
+            return False
+
+    def count(self) -> int:
+        """Get the document count.
+
+        Returns:
+            Number of documents in the store
+        """
+        collection = self._get_collection()
+        return collection.count()
+
+    def close(self) -> None:
+        """Close the client connection."""
+        self._client = None
+        self._collection = None