refactor:refine embedding and cli

python/pyproject.toml

@@ -7,6 +7,8 @@ dependencies = [
     "fastapi>=0.104.0",
     "uvicorn>=0.24.0",
     "pydantic>=2.0.0",
+    "openai>=1.52.0",
+    "lance>=0.17.0",
 ]
 description = "Python bindings for the lance-graph Cypher engine"
 authors = [{ name = "Lance Devs", email = "[email protected]" }]
@@ -43,8 +45,6 @@ build-backend = "maturin"
 [project.optional-dependencies]
 tests = ["pytest", "pyarrow>=14", "pandas", "ruff"]
 dev = ["ruff", "pyright"]
-llm = ["openai>=1.52.0"]
-lance-storage = ["lance>=0.17.0"]
 
 [project.scripts]
 knowledge_graph = "knowledge_graph.main:main"

python/python/knowledge_graph/cli/__init__.py

@@ -0,0 +1,9 @@
+from .ingest import extract_and_add, preview_extraction
+from .interactive import list_datasets, run_interactive
+
+__all__ = [
+    "run_interactive",
+    "list_datasets",
+    "preview_extraction",
+    "extract_and_add",
+]

python/python/knowledge_graph/cli/embedding_support.py

@@ -0,0 +1,19 @@
+"""Shared helpers for preparing rows and embeddings (internal)."""
+
+from __future__ import annotations
+
+from .ingest import (
+    _assign_embeddings,
+    _format_entity_embedding_input,
+    _format_relationship_embedding_input,
+    _prepare_entity_rows,
+    _prepare_relationship_rows,
+)
+
+__all__ = [
+    "_assign_embeddings",
+    "_format_entity_embedding_input",
+    "_format_relationship_embedding_input",
+    "_prepare_entity_rows",
+    "_prepare_relationship_rows",
+]

python/python/knowledge_graph/cli/ingest.py

@@ -0,0 +1,220 @@
+"""Extraction preview and ingest helpers for the knowledge graph CLI."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import logging
+from dataclasses import asdict, is_dataclass
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Callable, Mapping
+
+from .. import extraction as kg_extraction
+
+if TYPE_CHECKING:
+    from ..embeddings import EmbeddingGenerator
+    from ..service import LanceKnowledgeGraph
+
+LOGGER = logging.getLogger(__name__)
+
+
+def preview_extraction(source: str, extractor: kg_extraction.BaseExtractor) -> None:
+    """Preview extracted knowledge from a text source or inline text."""
+    text = _resolve_text_input(source)
+    result = kg_extraction.preview_extraction(text, extractor=extractor)
+    print(json.dumps(_result_to_dict(result), indent=2))
+
+
+def extract_and_add(
+    source: str,
+    service: LanceKnowledgeGraph,
+    extractor: kg_extraction.BaseExtractor,
+    *,
+    embedding_generator: EmbeddingGenerator | None = None,
+) -> None:
+    """Extract knowledge and append it to the backing graph."""
+    import pyarrow as pa
+
+    text = _resolve_text_input(source)
+    result = kg_extraction.preview_extraction(text, extractor=extractor)
+    entity_rows, name_to_id = _prepare_entity_rows(
+        result.entities, embedding_generator=embedding_generator
+    )
+    relationships = result.relationships
+
+    if not entity_rows and not relationships:
+        print("No candidate entities or relationships detected.")
+        return
+
+    if entity_rows:
+        entity_table = pa.Table.from_pylist(entity_rows)
+        service.upsert_table("Entity", entity_table, merge=True)
+        message = f"Upserted {entity_table.num_rows} entity rows into dataset 'Entity'."
+        print(message)
+
+    relationship_rows = _prepare_relationship_rows(
+        relationships,
+        name_to_id,
+        embedding_generator=embedding_generator,
+    )
+    if relationship_rows:
+        rel_table = pa.Table.from_pylist(relationship_rows)
+        service.upsert_table("RELATIONSHIP", rel_table, merge=True)
+        message = (
+            "Upserted "
+            f"{rel_table.num_rows} relationship rows into dataset "
+            "'RELATIONSHIP'."
+        )
+        print(message)
+
+
+def _resolve_text_input(raw: str) -> str:
+    """Load text from a file if it exists, otherwise treat the string as content."""
+    candidate = Path(raw)
+    if candidate.exists():
+        if candidate.is_dir():
+            raise IsADirectoryError(f"Expected text file, got directory: {candidate}")
+        return candidate.read_text(encoding="utf-8")
+    return raw
+
+
+def _ensure_dict(item: object) -> dict:
+    if is_dataclass(item):
+        return asdict(item)  # type: ignore[arg-type]
+    if isinstance(item, dict):
+        return item
+    raise TypeError(f"Unsupported extraction item type: {type(item)!r}")
+
+
+def _result_to_dict(result: "kg_extraction.ExtractionResult") -> dict[str, list[dict]]:
+    return {
+        "entities": [asdict(entity) for entity in result.entities],
+        "relationships": [asdict(rel) for rel in result.relationships],
+    }
+
+
+def _prepare_entity_rows(
+    entities: list[Any],
+    *,
+    embedding_generator: EmbeddingGenerator | None = None,
+) -> tuple[list[dict[str, Any]], dict[str, str]]:
+    rows: list[dict[str, Any]] = []
+    name_to_id: dict[str, str] = {}
+    for entity in entities:
+        payload = _ensure_dict(entity)
+        name = str(payload.get("name", "")).strip()
+        entity_type = str(
+            payload.get("entity_type") or payload.get("type") or ""
+        ).strip()
+        if not name:
+            continue
+        base = f"{name}|{entity_type}".encode("utf-8")
+        entity_id = hashlib.md5(base).hexdigest()
+        payload["entity_id"] = entity_id
+        payload["entity_type"] = entity_type or "UNKNOWN"
+        payload["name_lower"] = name.lower()
+        rows.append(payload)
+        name_to_id.setdefault(name.lower(), entity_id)
+    if embedding_generator and rows:
+        _assign_embeddings(
+            rows,
+            embedding_generator,
+            _format_entity_embedding_input,
+        )
+    return rows, name_to_id
+
+
+def _prepare_relationship_rows(
+    relationships: list[Any],
+    name_to_id: dict[str, str],
+    *,
+    embedding_generator: EmbeddingGenerator | None = None,
+) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for relation in relationships:
+        payload = _ensure_dict(relation)
+        source_name = str(
+            payload.get("source_entity_name") or payload.get("source") or ""
+        ).strip()
+        target_name = str(
+            payload.get("target_entity_name") or payload.get("target") or ""
+        ).strip()
+        source_id = name_to_id.get(source_name.lower())
+        target_id = name_to_id.get(target_name.lower())
+        if not (source_id and target_id):
+            continue
+        payload["source_entity_id"] = source_id
+        payload["target_entity_id"] = target_id
+        payload["relationship_type"] = (
+            payload.get("relationship_type") or payload.get("type") or "RELATED_TO"
+        )
+        payload.setdefault("source_entity_name", source_name)
+        payload.setdefault("target_entity_name", target_name)
+        rows.append(payload)
+    if embedding_generator and rows:
+        _assign_embeddings(
+            rows,
+            embedding_generator,
+            _format_relationship_embedding_input,
+        )
+    return rows
+
+
+def _assign_embeddings(
+    rows: list[dict[str, Any]],
+    embedding_generator: EmbeddingGenerator,
+    formatter: Callable[[Mapping[str, Any]], str],
+) -> None:
+    texts: list[str] = []
+    indices: list[int] = []
+    for idx, row in enumerate(rows):
+        text = formatter(row)
+        if text:
+            texts.append(text)
+            indices.append(idx)
+    if not texts:
+        return
+    try:
+        vectors = embedding_generator.embed(texts)
+    except Exception as exc:  # pragma: no cover - defensive logging path
+        LOGGER.warning("Failed to generate embeddings: %s", exc)
+        return
+    if len(vectors) != len(indices):
+        LOGGER.warning(
+            "Mismatch between embedding count and row count: expected %s, got %s",
+            len(indices),
+            len(vectors),
+        )
+        return
+    for idx, vector in zip(indices, vectors):
+        rows[idx]["embedding"] = vector
+
+
+def _format_entity_embedding_input(row: Mapping[str, Any]) -> str:
+    name = str(row.get("name", "")).strip()
+    entity_type = str(row.get("entity_type", "")).strip()
+    context = str(row.get("context", "")).strip()
+    pieces = []
+    if name:
+        pieces.append(name)
+    if entity_type:
+        pieces.append(f"Type: {entity_type}")
+    if context:
+        pieces.append(f"Context: {context}")
+    return " | ".join(pieces)
+
+
+def _format_relationship_embedding_input(row: Mapping[str, Any]) -> str:
+    source = str(row.get("source_entity_name") or row.get("source") or "").strip()
+    target = str(row.get("target_entity_name") or row.get("target") or "").strip()
+    relationship_type = str(row.get("relationship_type", "")).strip()
+    description = str(row.get("description", "")).strip()
+    core: list[str] = []
+    if source or target:
+        if relationship_type:
+            core.append(f"{source} -[{relationship_type}]-> {target}".strip())
+        else:
+            core.append(f"{source} -> {target}".strip())
+    if description:
+        core.append(f"Description: {description}")
+    return " | ".join(part for part in core if part)

python/python/knowledge_graph/cli/interactive.py

@@ -0,0 +1,117 @@
+"""Interactive shell and CLI display helpers for the knowledge graph."""
+
+from __future__ import annotations
+
+import sys
+from typing import TYPE_CHECKING
+
+from ..store import LanceGraphStore
+
+if TYPE_CHECKING:
+    from ..config import KnowledgeGraphConfig
+    from ..service import LanceKnowledgeGraph
+
+if TYPE_CHECKING:
+    import pyarrow as pa
+
+
+def list_datasets(config: "KnowledgeGraphConfig") -> None:
+    """List the Lance datasets available under the configured root."""
+    store = LanceGraphStore(config)
+    store.ensure_layout()
+    datasets = store.list_datasets()
+    if not datasets:
+        print("No Lance datasets found. Load data or run extraction first.")
+        return
+    print("Available Lance datasets:")
+    for name, path in sorted(datasets.items()):
+        print(f"  - {name}: {path}")
+
+
+def run_interactive(service: "LanceKnowledgeGraph") -> None:
+    """Enter an interactive shell for issuing Cypher queries."""
+    print("Lance Knowledge Graph interactive shell")
+    print("Type ':help' for commands, or 'quit' to exit.")
+
+    while True:
+        try:
+            text = input("kg> ").strip()
+        except EOFError:
+            print()
+            break
+
+        if not text:
+            continue
+        lowered = text.lower()
+        if lowered in {"quit", "exit", "q"}:
+            break
+        if text.startswith(":"):
+            _handle_command(text, service)
+            continue
+
+        _execute_query(service, text)
+
+
+def _handle_command(command: str, service: "LanceKnowledgeGraph") -> None:
+    """Handle meta-commands in the interactive shell."""
+    cmd = command.strip()
+    if cmd in {":help", ":h"}:
+        print("Commands:")
+        print("  :help           Show this message")
+        print("  :datasets       List persisted Lance datasets")
+        print("  :config         Show the configured node/relationship mappings")
+        print("  quit/exit/q     Leave the shell")
+        return
+    if cmd in {":datasets", ":ls"}:
+        list_datasets(service.store.config)
+        return
+    if cmd in {":config", ":schema"}:
+        _print_config_summary(service)
+        return
+    print(f"Unknown command: {command}")
+
+
+def _print_config_summary(service: "LanceKnowledgeGraph") -> None:
+    """Print a brief summary of the graph configuration."""
+    config = service.config
+    # GraphConfig does not currently expose direct iterators; rely on repr.
+    print("Graph configuration:")
+    print(f"  {config!r}")
+
+
+def _execute_query(service: "LanceKnowledgeGraph", statement: str) -> None:
+    """Execute a single Cypher statement and print results."""
+    try:
+        result = service.run(statement)
+    except Exception as exc:  # pragma: no cover - CLI feedback path
+        print(f"Query failed: {exc}", file=sys.stderr)
+        return
+
+    _print_table(result)
+
+
+def _print_table(table: "pa.Table") -> None:
+    """Render a PyArrow table in a simple textual format."""
+    if table.num_rows == 0:
+        print("(no rows)")
+        return
+
+    column_names = table.column_names
+    columns = [table.column(i).to_pylist() for i in range(len(column_names))]
+    widths = []
+    for name, values in zip(column_names, columns):
+        str_values = ["" if value is None else str(value) for value in values]
+        if str_values:
+            width = max(len(name), *(len(value) for value in str_values))
+        else:
+            width = len(name)
+        widths.append(width)
+
+    header = " | ".join(name.ljust(width) for name, width in zip(column_names, widths))
+    separator = "-+-".join("-" * width for width in widths)
+    print(header)
+    print(separator)
+    for row_values in zip(*columns):
+        str_row = ["" if value is None else str(value) for value in row_values]
+        line = " | ".join(value.ljust(width) for value, width in zip(str_row, widths))
+        print(line)