feat: cf-core v0.19.0 — add PDF extraction, VectorStore, LLMRouter.embed()

circuitforge_core/documents/pdf.py

@@ -0,0 +1,133 @@
+# circuitforge_core/documents/pdf.py
+"""
+circuitforge_core.documents.pdf — PDF text extraction and page-level chunking.
+
+Primary path: pdfplumber (selectable text layers).
+Fallback: pytesseract OCR (scanned / image-only pages).
+
+Usage::
+
+    from circuitforge_core.documents.pdf import PDFExtractor
+
+    chunks = PDFExtractor().chunk_pages("/path/to/book.pdf")
+    for chunk in chunks:
+        print(f"[p.{chunk.page_number}] ({chunk.source}) {chunk.text[:80]}")
+"""
+
+from __future__ import annotations
+
+import io
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+try:
+    import pdfplumber
+except ImportError:  # pragma: no cover
+    pdfplumber = None  # type: ignore[assignment]
+
+try:
+    import pytesseract
+except ImportError:  # pragma: no cover
+    pytesseract = None  # type: ignore[assignment]
+
+try:
+    from PIL import Image
+except ImportError:  # pragma: no cover
+    Image = None  # type: ignore[assignment]
+
+
+@dataclass(frozen=True)
+class PageChunk:
+    """Text content extracted from a single PDF page."""
+
+    page_number: int  # 1-indexed
+    text: str
+    source: str  # "text_layer" | "ocr"
+    word_count: int
+
+
+class PDFExtractor:
+    """
+    Extract page-level text chunks from PDF files.
+
+    Args:
+        ocr_min_words: Pages with fewer words from the text layer trigger OCR.
+    """
+
+    def __init__(self, ocr_min_words: int = 10) -> None:
+        self.ocr_min_words = ocr_min_words
+
+    def chunk_pages(self, pdf_path: str | Path) -> list[PageChunk]:
+        """
+        Primary entry point. Returns one PageChunk per page.
+
+        Uses text-layer extraction per page; falls back to OCR when text is sparse.
+        Empty PDFs return an empty list.
+        """
+        if pdfplumber is None:
+            raise ImportError(
+                "pdfplumber is required for PDF extraction. "
+                "Install it with: pip install pdfplumber"
+            )
+
+        path = Path(pdf_path)
+        chunks: list[PageChunk] = []
+
+        with pdfplumber.open(path) as pdf:
+            for i, page in enumerate(pdf.pages, start=1):
+                text = page.extract_text() or ""
+                words = text.split()
+
+                if len(words) >= self.ocr_min_words:
+                    chunks.append(
+                        PageChunk(
+                            page_number=i,
+                            text=text.strip(),
+                            source="text_layer",
+                            word_count=len(words),
+                        )
+                    )
+                else:
+                    logger.debug(
+                        "pdf: page %d sparse (%d words), falling back to OCR",
+                        i,
+                        len(words),
+                    )
+                    chunks.append(self._ocr_page(page, i))
+
+        return chunks
+
+    def _ocr_page(self, page: object, page_number: int) -> PageChunk:
+        """Render page to image and extract text via tesseract."""
+        try:
+            rendered = page.to_image(resolution=200).original  # type: ignore[attr-defined]
+            rendered = _ensure_pil_image(rendered)
+            text = pytesseract.image_to_string(rendered)  # type: ignore[union-attr]
+            words = text.split()
+            return PageChunk(
+                page_number=page_number,
+                text=text.strip(),
+                source="ocr",
+                word_count=len(words),
+            )
+        except Exception as exc:
+            logger.warning("pdf: OCR failed for page %d: %s", page_number, exc)
+            return PageChunk(
+                page_number=page_number, text="", source="ocr", word_count=0
+            )
+
+
+def _ensure_pil_image(rendered: object) -> object:
+    """Return *rendered* as a PIL Image, converting from bytes if needed."""
+    if Image is None:
+        return rendered
+    try:
+        if not isinstance(rendered, Image.Image):
+            rendered = Image.open(io.BytesIO(rendered))  # type: ignore[arg-type]
+    except TypeError:
+        # Image may be patched (e.g. in tests); skip the conversion.
+        pass
+    return rendered

circuitforge_core/llm/router.py

@@ -43,6 +43,7 @@ When llm.yaml is absent, the router builds a minimal config from environment
 variables: ANTHROPIC_API_KEY, OPENAI_API_KEY / OPENAI_BASE_URL, OLLAMA_HOST.
 Ollama on localhost:11434 is always included as the lowest-cost local fallback.
 """
+
 import logging
 import os
 import yaml
@@ -70,7 +71,8 @@ class LLMRouter:
                 )
             logger.info(
                 "[LLMRouter] No llm.yaml found — using env-var auto-config "
-                "(backends: %s)", ", ".join(env_config["fallback_order"])
+                "(backends: %s)",
+                ", ".join(env_config["fallback_order"]),
             )
             self.config = env_config
 
@@ -103,7 +105,9 @@ class LLMRouter:
             backends["openai"] = {
                 "type": "openai_compat",
                 "enabled": True,
-                "base_url": os.environ.get("OPENAI_BASE_URL", "https://api.openai.com/v1"),
+                "base_url": os.environ.get(
+                    "OPENAI_BASE_URL", "https://api.openai.com/v1"
+                ),
                 "model": os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
                 "api_key": os.environ.get("OPENAI_API_KEY"),
                 "supports_images": True,
@@ -156,6 +160,7 @@ class LLMRouter:
         Caller MUST call ctx.__exit__(None, None, None) in a finally block.
         """
         import os
+
         orch_cfg = backend.get("cf_orch")
         if not orch_cfg:
             return None
@@ -164,6 +169,7 @@ class LLMRouter:
             return None
         try:
             from circuitforge_orch.client import CFOrchClient
+
             client = CFOrchClient(orch_url)
             service = orch_cfg.get("service", "vllm")
             candidates = orch_cfg.get("model_candidates", [])
@@ -181,14 +187,21 @@ class LLMRouter:
             alloc = ctx.__enter__()
             return (ctx, alloc)
         except Exception as exc:
-            logger.warning("[LLMRouter] cf_orch allocation failed, using base_url directly: %s", exc)
+            logger.warning(
+                "[LLMRouter] cf_orch allocation failed, using base_url directly: %s",
+                exc,
+            )
             return None
 
-    def complete(self, prompt: str, system: str | None = None,
-                 model_override: str | None = None,
-                 fallback_order: list[str] | None = None,
-                 images: list[str] | None = None,
-                 max_tokens: int | None = None) -> str:
+    def complete(
+        self,
+        prompt: str,
+        system: str | None = None,
+        model_override: str | None = None,
+        fallback_order: list[str] | None = None,
+        images: list[str] | None = None,
+        max_tokens: int | None = None,
+    ) -> str:
         """
         Generate a completion. Tries each backend in fallback_order.
 
@@ -206,7 +219,11 @@ class LLMRouter:
                 "AI inference is disabled in the public demo. "
                 "Run your own instance to use AI features."
             )
-        order = fallback_order if fallback_order is not None else self.config["fallback_order"]
+        order = (
+            fallback_order
+            if fallback_order is not None
+            else self.config["fallback_order"]
+        )
         for name in order:
             backend = self.config["backends"][name]
 
@@ -283,10 +300,14 @@ class LLMRouter:
                     if images and supports_images:
                         content = [{"type": "text", "text": prompt}]
                         for img in images:
-                            content.append({
-                                "type": "image_url",
-                                "image_url": {"url": f"data:image/png;base64,{img}"},
-                            })
+                            content.append(
+                                {
+                                    "type": "image_url",
+                                    "image_url": {
+                                        "url": f"data:image/png;base64,{img}"
+                                    },
+                                }
+                            )
                         messages.append({"role": "user", "content": content})
                     else:
                         messages.append({"role": "user", "content": prompt})
@@ -311,18 +332,27 @@ class LLMRouter:
             elif backend["type"] == "anthropic":
                 api_key = os.environ.get(backend["api_key_env"], "")
                 if not api_key:
-                    print(f"[LLMRouter] {name}: {backend['api_key_env']} not set, skipping")
+                    print(
+                        f"[LLMRouter] {name}: {backend['api_key_env']} not set, skipping"
+                    )
                     continue
                 try:
                     import anthropic as _anthropic
+
                     client = _anthropic.Anthropic(api_key=api_key)
                     if images and supports_images:
                         content = []
                         for img in images:
-                            content.append({
-                                "type": "image",
-                                "source": {"type": "base64", "media_type": "image/png", "data": img},
-                            })
+                            content.append(
+                                {
+                                    "type": "image",
+                                    "source": {
+                                        "type": "base64",
+                                        "media_type": "image/png",
+                                        "data": img,
+                                    },
+                                }
+                            )
                         content.append({"type": "text", "text": prompt})
                     else:
                         content = prompt
@@ -342,6 +372,81 @@ class LLMRouter:
 
         raise RuntimeError("All LLM backends exhausted")
 
+    def embed(
+        self,
+        texts: list[str],
+        model_override: str | None = None,
+        fallback_order: list[str] | None = None,
+    ) -> list[list[float]]:
+        """
+        Generate embeddings for a list of texts.
+
+        Only openai_compat backends are tried — Ollama and vLLM expose
+        /v1/embeddings; anthropic and vision_service do not.
+
+        Uses ``embedding_model`` from backend config when present;
+        falls back to ``model`` (the chat model) otherwise.
+
+        Args:
+            texts:          Texts to embed (batched in a single API call).
+            model_override: Override the embedding model for this call.
+            fallback_order: Override the backend fallback order for this call.
+
+        Returns:
+            List of float vectors, one per input text, in input order.
+
+        Raises:
+            RuntimeError: If all eligible backends are exhausted.
+        """
+        if os.environ.get("DEMO_MODE", "").lower() in ("1", "true", "yes"):
+            raise RuntimeError(
+                "AI inference is disabled in the public demo. "
+                "Run your own instance to use AI features."
+            )
+        order = (
+            fallback_order
+            if fallback_order is not None
+            else self.config["fallback_order"]
+        )
+        for name in order:
+            backend = self.config["backends"][name]
+            if not backend.get("enabled", True):
+                continue
+            if backend["type"] != "openai_compat":
+                continue
+
+            orch_ctx = orch_alloc = None
+            orch_result = self._try_cf_orch_alloc(backend)
+            if orch_result is not None:
+                orch_ctx, orch_alloc = orch_result
+                backend = {**backend, "base_url": orch_alloc.url + "/v1"}
+            elif not self._is_reachable(backend["base_url"]):
+                print(f"[LLMRouter] {name}: unreachable, skipping")
+                continue
+
+            try:
+                client = OpenAI(
+                    base_url=backend["base_url"],
+                    api_key=backend.get("api_key") or "any",
+                )
+                model = model_override or backend.get(
+                    "embedding_model", backend["model"]
+                )
+                resp = client.embeddings.create(model=model, input=texts)
+                print(f"[LLMRouter] embed: used backend {name} ({model})")
+                return [item.embedding for item in resp.data]
+            except Exception as e:
+                print(f"[LLMRouter] {name}: embed error — {e}, trying next")
+                continue
+            finally:
+                if orch_ctx is not None:
+                    try:
+                        orch_ctx.__exit__(None, None, None)
+                    except Exception:
+                        pass
+
+        raise RuntimeError("All LLM backends exhausted for embed()")
+
 
 # Module-level singleton for convenience
 _router: LLMRouter | None = None

circuitforge_core/vector/__init__.py

@@ -0,0 +1,4 @@
+from .base import VectorMatch, VectorStore
+from .sqlite_vec import LocalSQLiteVecStore
+
+__all__ = ["VectorMatch", "VectorStore", "LocalSQLiteVecStore"]

circuitforge_core/vector/base.py

@@ -0,0 +1,50 @@
+"""
+circuitforge_core.vector.base — VectorStore ABC and shared types.
+
+Concrete implementations: LocalSQLiteVecStore (local), QdrantStore (cloud Paid tier).
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass(frozen=True)
+class VectorMatch:
+    """A single result from a vector similarity search."""
+
+    entry_id: str
+    score: float  # lower is better (L2 / cosine distance)
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+class VectorStore(ABC):
+    """Abstract interface for vector storage backends."""
+
+    @abstractmethod
+    def upsert(
+        self, entry_id: str, vector: list[float], metadata: dict[str, Any]
+    ) -> None:
+        """Insert or replace a vector and its metadata."""
+
+    @abstractmethod
+    def query(
+        self,
+        vector: list[float],
+        top_k: int = 10,
+        filter_metadata: dict[str, Any] | None = None,
+    ) -> list[VectorMatch]:
+        """Return the top_k nearest vectors. Optional metadata filter applied post-search."""
+
+    @abstractmethod
+    def delete(self, entry_id: str) -> None:
+        """Remove a single vector by string ID. No-op if not found."""
+
+    @abstractmethod
+    def delete_where(self, filter_metadata: dict[str, Any]) -> int:
+        """Remove all vectors whose metadata matches all key-value pairs. Returns count removed.
+
+        Raises ValueError if filter_metadata is empty (would delete entire store).
+        """

circuitforge_core/vector/sqlite_vec.py

@@ -0,0 +1,185 @@
+# circuitforge_core/vector/sqlite_vec.py
+"""
+circuitforge_core.vector.sqlite_vec -- sqlite-vec backed VectorStore.
+
+Suitable for single-user local deployments. Cloud Paid tier replaces
+this with QdrantStore via the same VectorStore ABC.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+import sqlite3
+import struct
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Any, Generator
+
+import sqlite_vec
+
+from .base import VectorMatch, VectorStore
+
+logger = logging.getLogger(__name__)
+
+_SAFE_IDENTIFIER = re.compile(r"^[a-zA-Z_][a-zA-Z0-9_]*$")
+
+
+def _serialize(vector: list[float]) -> bytes:
+    return struct.pack(f"<{len(vector)}f", *vector)
+
+
+class LocalSQLiteVecStore(VectorStore):
+    """
+    VectorStore backed by sqlite-vec virtual tables.
+
+    Uses two tables per logical store:
+    - ``<table>_vecs``:  vec0 virtual table (rowid-indexed float vectors)
+    - ``<table>_meta``:  companion table mapping rowid to string ID + JSON metadata
+
+    Args:
+        db_path:    Path to SQLite database file.
+        table:      Logical name prefix (default ``"vecs"``).
+        dimensions: Vector length; must match the embedding model (default 768).
+    """
+
+    def __init__(
+        self,
+        db_path: str | Path,
+        table: str = "vecs",
+        dimensions: int = 768,
+    ) -> None:
+        if not _SAFE_IDENTIFIER.match(table):
+            raise ValueError(
+                f"table must be a valid SQL identifier (letters, digits, underscores): {table!r}"
+            )
+        self.db_path = str(db_path)
+        self.table = table
+        self.dimensions = dimensions
+        self._init_tables()
+
+    @contextmanager
+    def _conn(self) -> Generator[sqlite3.Connection, None, None]:
+        conn = sqlite3.connect(self.db_path)
+        conn.enable_load_extension(True)
+        sqlite_vec.load(conn)
+        conn.enable_load_extension(False)
+        conn.row_factory = sqlite3.Row
+        try:
+            yield conn
+            conn.commit()
+        except Exception:
+            conn.rollback()
+            raise
+        finally:
+            conn.close()
+
+    def _init_tables(self) -> None:
+        with self._conn() as conn:
+            conn.execute(f"""
+                CREATE VIRTUAL TABLE IF NOT EXISTS {self.table}_vecs
+                USING vec0(embedding float[{self.dimensions}])
+            """)
+            conn.execute(f"""
+                CREATE TABLE IF NOT EXISTS {self.table}_meta (
+                    rowid    INTEGER PRIMARY KEY,
+                    entry_id TEXT NOT NULL UNIQUE,
+                    metadata TEXT NOT NULL DEFAULT '{{}}'
+                )
+            """)
+
+    def upsert(
+        self, entry_id: str, vector: list[float], metadata: dict[str, Any]
+    ) -> None:
+        with self._conn() as conn:
+            row = conn.execute(
+                f"SELECT rowid FROM {self.table}_meta WHERE entry_id = ?", [entry_id]
+            ).fetchone()
+
+            if row:
+                rowid = row["rowid"]
+                conn.execute(
+                    f"UPDATE {self.table}_vecs SET embedding = ? WHERE rowid = ?",
+                    [_serialize(vector), rowid],
+                )
+                conn.execute(
+                    f"UPDATE {self.table}_meta SET metadata = ? WHERE rowid = ?",
+                    [json.dumps(metadata), rowid],
+                )
+            else:
+                cursor = conn.execute(
+                    f"INSERT INTO {self.table}_meta(entry_id, metadata) VALUES (?, ?)",
+                    [entry_id, json.dumps(metadata)],
+                )
+                rowid = cursor.lastrowid
+                conn.execute(
+                    f"INSERT INTO {self.table}_vecs(rowid, embedding) VALUES (?, ?)",
+                    [rowid, _serialize(vector)],
+                )
+
+    def query(
+        self,
+        vector: list[float],
+        top_k: int = 10,
+        filter_metadata: dict[str, Any] | None = None,
+    ) -> list[VectorMatch]:
+        with self._conn() as conn:
+            rows = conn.execute(
+                f"""
+                SELECT m.entry_id, v.distance, m.metadata
+                FROM {self.table}_vecs v
+                JOIN {self.table}_meta m ON m.rowid = v.rowid
+                WHERE v.embedding MATCH ? AND k = ?
+                ORDER BY v.distance
+                """,
+                [_serialize(vector), top_k],
+            ).fetchall()
+            results = [
+                VectorMatch(
+                    entry_id=r["entry_id"],
+                    score=r["distance"],
+                    metadata=json.loads(r["metadata"]),
+                )
+                for r in rows
+            ]
+
+        if filter_metadata:
+            results = [
+                r
+                for r in results
+                if all(r.metadata.get(k) == v for k, v in filter_metadata.items())
+            ]
+        return results
+
+    def delete(self, entry_id: str) -> None:
+        with self._conn() as conn:
+            row = conn.execute(
+                f"SELECT rowid FROM {self.table}_meta WHERE entry_id = ?", [entry_id]
+            ).fetchone()
+            if row:
+                rowid = row["rowid"]
+                conn.execute(f"DELETE FROM {self.table}_vecs WHERE rowid = ?", [rowid])
+                conn.execute(f"DELETE FROM {self.table}_meta WHERE rowid = ?", [rowid])
+
+    def delete_where(self, filter_metadata: dict[str, Any]) -> int:
+        if not filter_metadata:
+            raise ValueError(
+                "delete_where requires a non-empty filter; refusing to delete entire store"
+            )
+        with self._conn() as conn:
+            rows = conn.execute(
+                f"SELECT rowid, metadata FROM {self.table}_meta"
+            ).fetchall()
+            to_delete = [
+                r["rowid"]
+                for r in rows
+                if all(
+                    json.loads(r["metadata"]).get(k) == v
+                    for k, v in filter_metadata.items()
+                )
+            ]
+            for rowid in to_delete:
+                conn.execute(f"DELETE FROM {self.table}_vecs WHERE rowid = ?", [rowid])
+                conn.execute(f"DELETE FROM {self.table}_meta WHERE rowid = ?", [rowid])
+            return len(to_delete)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "circuitforge-core"
-version = "0.18.0"
+version = "0.19.0"
 description = "Shared scaffold for CircuitForge products (MIT)"
 requires-python = ">=3.11"
 dependencies = [
@@ -107,6 +107,14 @@ gestures-mediapipe = [
     "opencv-python>=4.8",
     "numpy>=1.24",
 ]
+pdf = [
+    "pdfplumber>=0.11",
+    "pytesseract>=0.3",
+    "Pillow>=10.0",
+]
+vector = [
+    "sqlite-vec>=0.1",
+]
 dev = [
     "circuitforge-core[manage]",
     "pytest>=8.0",

tests/test_documents/test_pdf.py

@@ -0,0 +1,107 @@
+# tests/test_documents/test_pdf.py
+from __future__ import annotations
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from circuitforge_core.documents.pdf import PDFExtractor, PageChunk
+
+
+def _mock_page(text: str) -> MagicMock:
+    page = MagicMock()
+    page.extract_text.return_value = text
+    return page
+
+
+def _mock_pdf(pages: list[MagicMock]) -> MagicMock:
+    pdf = MagicMock()
+    pdf.__enter__ = MagicMock(return_value=pdf)
+    pdf.__exit__ = MagicMock(return_value=False)
+    pdf.pages = pages
+    return pdf
+
+
+def test_chunk_pages_single_text_layer_page():
+    page = _mock_page(
+        "Fireball deals 8d6 fire damage on a failed Dexterity saving throw."
+    )
+    with patch("circuitforge_core.documents.pdf.pdfplumber") as mock_pl:
+        mock_pl.open.return_value = _mock_pdf([page])
+        chunks = PDFExtractor().chunk_pages("/fake/book.pdf")
+    assert len(chunks) == 1
+    assert chunks[0].page_number == 1
+    assert chunks[0].source == "text_layer"
+    assert "Fireball" in chunks[0].text
+    assert chunks[0].word_count >= 10
+
+
+def test_chunk_pages_numbers_from_one():
+    pages = [_mock_page(f"Rule text for page {i} " * 10) for i in range(1, 4)]
+    with patch("circuitforge_core.documents.pdf.pdfplumber") as mock_pl:
+        mock_pl.open.return_value = _mock_pdf(pages)
+        chunks = PDFExtractor().chunk_pages("/fake/book.pdf")
+    assert [c.page_number for c in chunks] == [1, 2, 3]
+
+
+def test_page_chunk_is_frozen():
+    chunk = PageChunk(page_number=1, text="hello", source="text_layer", word_count=1)
+    with pytest.raises(Exception):
+        chunk.text = "modified"  # type: ignore[misc]
+
+
+def test_pdfplumber_not_installed():
+    """pdfplumber=None guard raises ImportError with install hint."""
+    import circuitforge_core.documents.pdf as pdf_mod
+
+    with patch.object(pdf_mod, "pdfplumber", None):
+        with pytest.raises(ImportError, match="pdfplumber"):
+            PDFExtractor().chunk_pages("/fake/book.pdf")
+
+
+def test_chunk_pages_triggers_ocr_for_sparse_page():
+    """Page with fewer words than ocr_min_words falls back to OCR."""
+    sparse_page = _mock_page("few words only")  # 3 words < default 10
+    mock_image = MagicMock()
+    rendered = MagicMock()
+    rendered.original = mock_image
+
+    sparse_page.to_image.return_value = rendered
+
+    with (
+        patch("circuitforge_core.documents.pdf.pdfplumber") as mock_pl,
+        patch("circuitforge_core.documents.pdf.pytesseract") as mock_tess,
+        patch("circuitforge_core.documents.pdf.Image") as mock_pil,
+    ):
+        mock_pl.open.return_value = _mock_pdf([sparse_page])
+        mock_pil.open.return_value = mock_image
+        mock_tess.image_to_string.return_value = (
+            "Full OCR extracted rulebook text about saving throws."
+        )
+
+        chunks = PDFExtractor(ocr_min_words=10).chunk_pages("/fake/scan.pdf")
+
+    assert chunks[0].source == "ocr"
+    assert "OCR extracted" in chunks[0].text
+
+
+def test_chunk_pages_ocr_failure_returns_empty_chunk():
+    """OCR render failure results in empty chunk, not an exception."""
+    sparse_page = _mock_page("")
+    sparse_page.to_image.side_effect = RuntimeError("render failed")
+
+    with patch("circuitforge_core.documents.pdf.pdfplumber") as mock_pl:
+        mock_pl.open.return_value = _mock_pdf([sparse_page])
+        chunks = PDFExtractor().chunk_pages("/fake/broken.pdf")
+
+    assert len(chunks) == 1
+    assert chunks[0].text == ""
+    assert chunks[0].source == "ocr"
+    assert chunks[0].word_count == 0
+
+
+def test_chunk_pages_empty_pdf_returns_empty_list():
+    with patch("circuitforge_core.documents.pdf.pdfplumber") as mock_pl:
+        mock_pl.open.return_value = _mock_pdf([])
+        chunks = PDFExtractor().chunk_pages("/fake/empty.pdf")
+    assert chunks == []

tests/test_llm_router.py

@@ -11,69 +11,81 @@ def _make_router(config: dict) -> LLMRouter:
 
 
 def test_complete_uses_first_reachable_backend():
-    router = _make_router({
-        "fallback_order": ["local"],
-        "backends": {
-            "local": {
-                "type": "openai_compat",
-                "base_url": "http://localhost:11434/v1",
-                "model": "llama3",
-                "supports_images": False,
-            }
+    router = _make_router(
+        {
+            "fallback_order": ["local"],
+            "backends": {
+                "local": {
+                    "type": "openai_compat",
+                    "base_url": "http://localhost:11434/v1",
+                    "model": "llama3",
+                    "supports_images": False,
+                }
+            },
         }
-    })
+    )
     mock_client = MagicMock()
     mock_client.chat.completions.create.return_value = MagicMock(
         choices=[MagicMock(message=MagicMock(content="hello"))]
     )
-    with patch.object(router, "_is_reachable", return_value=True), \
-         patch("circuitforge_core.llm.router.OpenAI", return_value=mock_client):
+    with (
+        patch.object(router, "_is_reachable", return_value=True),
+        patch("circuitforge_core.llm.router.OpenAI", return_value=mock_client),
+    ):
         result = router.complete("say hello")
     assert result == "hello"
 
 
 def test_complete_falls_back_on_unreachable_backend():
-    router = _make_router({
-        "fallback_order": ["unreachable", "working"],
-        "backends": {
-            "unreachable": {
-                "type": "openai_compat",
-                "base_url": "http://nowhere:1/v1",
-                "model": "x",
-                "supports_images": False,
+    router = _make_router(
+        {
+            "fallback_order": ["unreachable", "working"],
+            "backends": {
+                "unreachable": {
+                    "type": "openai_compat",
+                    "base_url": "http://nowhere:1/v1",
+                    "model": "x",
+                    "supports_images": False,
+                },
+                "working": {
+                    "type": "openai_compat",
+                    "base_url": "http://localhost:11434/v1",
+                    "model": "llama3",
+                    "supports_images": False,
+                },
             },
-            "working": {
-                "type": "openai_compat",
-                "base_url": "http://localhost:11434/v1",
-                "model": "llama3",
-                "supports_images": False,
-            }
         }
-    })
+    )
     mock_client = MagicMock()
     mock_client.chat.completions.create.return_value = MagicMock(
         choices=[MagicMock(message=MagicMock(content="fallback"))]
     )
+
     def reachable(url):
         return "nowhere" not in url
-    with patch.object(router, "_is_reachable", side_effect=reachable), \
-         patch("circuitforge_core.llm.router.OpenAI", return_value=mock_client):
+
+    with (
+        patch.object(router, "_is_reachable", side_effect=reachable),
+        patch("circuitforge_core.llm.router.OpenAI", return_value=mock_client),
+    ):
         result = router.complete("test")
     assert result == "fallback"
 
 
 def test_complete_raises_when_all_backends_exhausted():
-    router = _make_router({
-        "fallback_order": ["dead"],
-        "backends": {
-            "dead": {
-                "type": "openai_compat",
-                "base_url": "http://nowhere:1/v1",
-                "model": "x",
-                "supports_images": False,
-            }
+    router = _make_router(
+        {
+            "fallback_order": ["dead"],
+            "backends": {
+                "dead": {
+                    "type": "openai_compat",
+                    "base_url": "http://nowhere:1/v1",
+                    "model": "x",
+                    "supports_images": False,
+                }
+            },
         }
-    })
+    )
     with patch.object(router, "_is_reachable", return_value=False):
         with pytest.raises(RuntimeError, match="exhausted"):
             router.complete("test")
@@ -83,6 +95,126 @@ def test_try_cf_orch_alloc_import_path():
     """Verify lazy import points to circuitforge_orch, not circuitforge_core.resources."""
     import inspect
     from circuitforge_core.llm import router as router_module
+
     src = inspect.getsource(router_module.LLMRouter._try_cf_orch_alloc)
     assert "circuitforge_orch.client" in src
     assert "circuitforge_core.resources.client" not in src
+
+
+def test_embed_returns_vectors_from_openai_compat_backend():
+    router = _make_router(
+        {
+            "fallback_order": ["ollama"],
+            "backends": {
+                "ollama": {
+                    "type": "openai_compat",
+                    "base_url": "http://localhost:11434/v1",
+                    "model": "mistral:7b",
+                    "embedding_model": "nomic-embed-text",
+                    "supports_images": False,
+                }
+            },
+        }
+    )
+    mock_client = MagicMock()
+    mock_client.embeddings.create.return_value = MagicMock(
+        data=[
+            MagicMock(embedding=[0.1, 0.2, 0.3]),
+            MagicMock(embedding=[0.4, 0.5, 0.6]),
+        ]
+    )
+    with (
+        patch.object(router, "_is_reachable", return_value=True),
+        patch("circuitforge_core.llm.router.OpenAI", return_value=mock_client),
+    ):
+        result = router.embed(["hello world", "fireball rules"])
+
+    assert result == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+    mock_client.embeddings.create.assert_called_once_with(
+        model="nomic-embed-text",
+        input=["hello world", "fireball rules"],
+    )
+
+
+def test_embed_uses_chat_model_when_no_embedding_model_configured():
+    router = _make_router(
+        {
+            "fallback_order": ["ollama"],
+            "backends": {
+                "ollama": {
+                    "type": "openai_compat",
+                    "base_url": "http://localhost:11434/v1",
+                    "model": "llama3",
+                    "supports_images": False,
+                }
+            },
+        }
+    )
+    mock_client = MagicMock()
+    mock_client.embeddings.create.return_value = MagicMock(
+        data=[MagicMock(embedding=[0.9, 0.8])]
+    )
+    with (
+        patch.object(router, "_is_reachable", return_value=True),
+        patch("circuitforge_core.llm.router.OpenAI", return_value=mock_client),
+    ):
+        router.embed(["test"])
+
+    call_kwargs = mock_client.embeddings.create.call_args
+    assert call_kwargs.kwargs["model"] == "llama3"
+
+
+def test_embed_skips_non_openai_compat_backends():
+    router = _make_router(
+        {
+            "fallback_order": ["anthropic", "ollama"],
+            "backends": {
+                "anthropic": {
+                    "type": "anthropic",
+                    "enabled": True,
+                    "model": "claude-haiku-4-5-20251001",
+                    "api_key_env": "ANTHROPIC_API_KEY",
+                    "supports_images": True,
+                },
+                "ollama": {
+                    "type": "openai_compat",
+                    "base_url": "http://localhost:11434/v1",
+                    "model": "nomic-embed-text",
+                    "supports_images": False,
+                },
+            },
+        }
+    )
+    mock_client = MagicMock()
+    mock_client.embeddings.create.return_value = MagicMock(
+        data=[MagicMock(embedding=[0.1])]
+    )
+    mock_openai = MagicMock(return_value=mock_client)
+    with (
+        patch.object(router, "_is_reachable", return_value=True),
+        patch("circuitforge_core.llm.router.OpenAI", mock_openai),
+    ):
+        result = router.embed(["hello"])
+
+    assert result == [[0.1]]
+    # Only ollama reached the OpenAI constructor; anthropic was skipped by type check
+    mock_openai.assert_called_once()
+
+
+def test_embed_raises_when_all_backends_exhausted():
+    router = _make_router(
+        {
+            "fallback_order": ["dead"],
+            "backends": {
+                "dead": {
+                    "type": "openai_compat",
+                    "base_url": "http://nowhere:1/v1",
+                    "model": "x",
+                    "supports_images": False,
+                }
+            },
+        }
+    )
+    with patch.object(router, "_is_reachable", return_value=False):
+        with pytest.raises(RuntimeError, match="exhausted"):
+            router.embed(["test"])

tests/test_vector/test_base.py

@@ -0,0 +1,102 @@
+"""Tests for VectorStore ABC and VectorMatch."""
+
+from __future__ import annotations
+
+from dataclasses import FrozenInstanceError
+
+import pytest
+
+from circuitforge_core.vector.base import VectorMatch, VectorStore
+
+
+class _ConcreteStore(VectorStore):
+    """Minimal in-memory implementation for testing the ABC contract."""
+
+    def __init__(self) -> None:
+        self._data: dict[str, tuple[list[float], dict]] = {}
+
+    def upsert(self, entry_id: str, vector: list[float], metadata: dict) -> None:
+        self._data[entry_id] = (vector, metadata)
+
+    def query(
+        self,
+        vector: list[float],
+        top_k: int = 10,
+        filter_metadata: dict | None = None,
+    ) -> list[VectorMatch]:
+        results = [
+            VectorMatch(entry_id=k, score=0.0, metadata=v[1])
+            for k, v in self._data.items()
+        ]
+        if filter_metadata:
+            results = [
+                r
+                for r in results
+                if all(r.metadata.get(k) == val for k, val in filter_metadata.items())
+            ]
+        return results[:top_k]
+
+    def delete(self, entry_id: str) -> None:
+        self._data.pop(entry_id, None)
+
+    def delete_where(self, filter_metadata: dict) -> int:
+        to_remove = [
+            k
+            for k, (_, meta) in self._data.items()
+            if all(meta.get(fk) == fv for fk, fv in filter_metadata.items())
+        ]
+        for k in to_remove:
+            del self._data[k]
+        return len(to_remove)
+
+
+def test_vector_match_is_frozen():
+    match = VectorMatch(entry_id="a", score=0.1, metadata={})
+    with pytest.raises(FrozenInstanceError):
+        match.score = 0.5  # type: ignore[misc]
+
+
+def test_vector_match_metadata_is_dict():
+    match = VectorMatch(entry_id="a", score=0.1, metadata={"k": "v"})
+    assert isinstance(match.metadata, dict)
+    assert match.metadata["k"] == "v"
+
+
+def test_upsert_and_query():
+    store = _ConcreteStore()
+    store.upsert("chunk-1", [0.1, 0.2], {"doc_id": "book-a", "page": 1})
+    results = store.query([0.1, 0.2])
+    assert len(results) == 1
+    assert results[0].entry_id == "chunk-1"
+    assert results[0].metadata["page"] == 1
+
+
+def test_query_filter_metadata():
+    store = _ConcreteStore()
+    store.upsert("c1", [0.1], {"doc_id": "book-a"})
+    store.upsert("c2", [0.2], {"doc_id": "book-b"})
+    results = store.query([0.1], filter_metadata={"doc_id": "book-a"})
+    assert len(results) == 1
+    assert results[0].entry_id == "c1"
+
+
+def test_delete():
+    store = _ConcreteStore()
+    store.upsert("x", [0.1], {})
+    store.delete("x")
+    assert store.query([0.1]) == []
+
+
+def test_delete_where():
+    store = _ConcreteStore()
+    store.upsert("c1", [0.1], {"doc_id": "book-a"})
+    store.upsert("c2", [0.2], {"doc_id": "book-a"})
+    store.upsert("c3", [0.3], {"doc_id": "book-b"})
+    count = store.delete_where({"doc_id": "book-a"})
+    assert count == 2
+    assert len(store.query([0.1])) == 1
+
+
+def test_cannot_instantiate_abc_directly():
+    with pytest.raises(TypeError):
+        VectorStore()  # type: ignore[abstract]

tests/test_vector/test_sqlite_vec.py

@@ -0,0 +1,82 @@
+# tests/test_vector/test_sqlite_vec.py
+"""Integration tests for LocalSQLiteVecStore (uses a real in-memory sqlite-vec DB)."""
+
+from __future__ import annotations
+
+import pytest
+
+from circuitforge_core.vector.sqlite_vec import LocalSQLiteVecStore
+
+DIMS = 4  # small dimension for tests
+
+
+@pytest.fixture
+def store(tmp_path) -> LocalSQLiteVecStore:
+    return LocalSQLiteVecStore(db_path=tmp_path / "vecs.db", dimensions=DIMS)
+
+
+def _vec(val: float) -> list[float]:
+    return [val] * DIMS
+
+
+def test_upsert_and_query_returns_match(store):
+    store.upsert("doc-1::p1", _vec(0.1), {"doc_id": "doc-1", "page": 1})
+    results = store.query(_vec(0.1), top_k=5)
+    assert len(results) == 1
+    assert results[0].entry_id == "doc-1::p1"
+    assert results[0].metadata["page"] == 1
+
+
+def test_upsert_replaces_existing(store):
+    store.upsert("chunk-1", _vec(0.1), {"page": 1})
+    store.upsert("chunk-1", _vec(0.9), {"page": 99})
+    # Metadata check
+    results = store.query(_vec(0.9), top_k=5)
+    assert results[0].metadata["page"] == 99
+    # Vector check: querying with new vector should score better than querying with old
+    old_results = store.query(_vec(0.1), top_k=5)
+    new_results = store.query(_vec(0.9), top_k=5)
+    assert new_results[0].score < old_results[0].score
+
+
+def test_query_respects_top_k(store):
+    for i in range(5):
+        store.upsert(f"chunk-{i}", _vec(float(i) * 0.1), {"i": i})
+    results = store.query(_vec(0.0), top_k=2)
+    assert len(results) == 2
+
+
+def test_filter_metadata(store):
+    store.upsert("c1", _vec(0.1), {"doc_id": "book-a"})
+    store.upsert("c2", _vec(0.2), {"doc_id": "book-b"})
+    results = store.query(_vec(0.1), filter_metadata={"doc_id": "book-a"})
+    assert all(r.metadata["doc_id"] == "book-a" for r in results)
+
+
+def test_delete(store):
+    store.upsert("x", _vec(0.5), {})
+    store.delete("x")
+    assert store.query(_vec(0.5)) == []
+
+
+def test_delete_where(store):
+    store.upsert("c1", _vec(0.1), {"doc_id": "book-a"})
+    store.upsert("c2", _vec(0.2), {"doc_id": "book-a"})
+    store.upsert("c3", _vec(0.3), {"doc_id": "book-b"})
+    count = store.delete_where({"doc_id": "book-a"})
+    assert count == 2
+    assert len(store.query(_vec(0.1))) == 1
+
+
+def test_delete_nonexistent_is_noop(store):
+    store.delete("does-not-exist")  # should not raise
+
+
+def test_empty_query_returns_empty(store):
+    assert store.query(_vec(0.1)) == []
+
+
+def test_delete_where_raises_on_empty_filter(store):
+    store.upsert("c1", _vec(0.1), {"doc_id": "book-a"})
+    with pytest.raises(ValueError, match="empty"):
+        store.delete_where({})