542 lines
22 KiB
Python
542 lines
22 KiB
Python
import asyncio
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import json
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import logging
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from importlib.metadata import version as pkg_version
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from pathlib import Path
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from rich.console import Console
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from rich.markdown import Markdown
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from rich.progress import Progress
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from haiku.rag.client import HaikuRAG
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from haiku.rag.config import AppConfig, Config
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from haiku.rag.graph.agui import AGUIConsoleRenderer, stream_graph
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from haiku.rag.graph.research.dependencies import ResearchContext
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from haiku.rag.graph.research.graph import build_research_graph
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from haiku.rag.graph.research.state import ResearchDeps, ResearchState
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from haiku.rag.mcp import create_mcp_server
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from haiku.rag.monitor import FileWatcher
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from haiku.rag.store.models.chunk import Chunk
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from haiku.rag.store.models.document import Document
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logger = logging.getLogger(__name__)
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class HaikuRAGApp:
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def __init__(self, db_path: Path, config: AppConfig = Config):
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self.db_path = db_path
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self.config = config
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self.console = Console()
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async def info(self):
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"""Display read-only information about the database without modifying it."""
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import lancedb
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# Basic: show path
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self.console.print("[bold]haiku.rag database info[/bold]")
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self.console.print(
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f" [repr.attrib_name]path[/repr.attrib_name]: {self.db_path}"
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)
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if not self.db_path.exists():
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self.console.print("[red]Database path does not exist.[/red]")
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return
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# Connect without going through Store to avoid upgrades/validation writes
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try:
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db = lancedb.connect(self.db_path)
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table_names = set(db.table_names())
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except Exception as e:
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self.console.print(f"[red]Failed to open database: {e}[/red]")
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return
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try:
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ldb_version = pkg_version("lancedb")
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except Exception:
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ldb_version = "unknown"
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try:
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hr_version = pkg_version("haiku.rag-slim")
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except Exception:
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hr_version = "unknown"
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try:
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docling_version = pkg_version("docling")
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except Exception:
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docling_version = "unknown"
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# Read settings (if present) to find stored haiku.rag version and embedding config
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stored_version = "unknown"
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embed_provider: str | None = None
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embed_model: str | None = None
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vector_dim: int | None = None
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if "settings" in table_names:
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settings_tbl = db.open_table("settings")
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arrow = settings_tbl.search().where("id = 'settings'").limit(1).to_arrow()
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rows = arrow.to_pylist() if arrow is not None else []
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if rows:
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raw = rows[0].get("settings") or "{}"
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data = json.loads(raw) if isinstance(raw, str) else (raw or {})
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stored_version = str(data.get("version", stored_version))
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embeddings = data.get("embeddings", {})
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embed_provider = embeddings.get("provider")
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embed_model = embeddings.get("model")
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vector_dim = embeddings.get("vector_dim")
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num_docs = 0
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if "documents" in table_names:
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docs_tbl = db.open_table("documents")
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num_docs = int(docs_tbl.count_rows()) # type: ignore[attr-defined]
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# Table versions per table (direct API)
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doc_versions = (
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len(list(db.open_table("documents").list_versions()))
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if "documents" in table_names
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else 0
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)
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chunk_versions = (
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len(list(db.open_table("chunks").list_versions()))
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if "chunks" in table_names
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else 0
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)
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self.console.print(
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f" [repr.attrib_name]haiku.rag version (db)[/repr.attrib_name]: {stored_version}"
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)
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if embed_provider or embed_model or vector_dim:
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provider_part = embed_provider or "unknown"
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model_part = embed_model or "unknown"
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dim_part = f"{vector_dim}" if vector_dim is not None else "unknown"
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self.console.print(
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" [repr.attrib_name]embeddings[/repr.attrib_name]: "
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f"{provider_part}/{model_part} (dim: {dim_part})"
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)
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else:
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self.console.print(
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" [repr.attrib_name]embeddings[/repr.attrib_name]: unknown"
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)
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self.console.print(
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f" [repr.attrib_name]documents[/repr.attrib_name]: {num_docs}"
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)
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self.console.print(
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f" [repr.attrib_name]versions (documents)[/repr.attrib_name]: {doc_versions}"
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)
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self.console.print(
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f" [repr.attrib_name]versions (chunks)[/repr.attrib_name]: {chunk_versions}"
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)
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self.console.rule()
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self.console.print("[bold]Versions[/bold]")
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self.console.print(
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f" [repr.attrib_name]haiku.rag[/repr.attrib_name]: {hr_version}"
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)
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self.console.print(
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f" [repr.attrib_name]lancedb[/repr.attrib_name]: {ldb_version}"
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)
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self.console.print(
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f" [repr.attrib_name]docling[/repr.attrib_name]: {docling_version}"
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)
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async def list_documents(self, filter: str | None = None):
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, allow_create=False
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) as self.client:
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documents = await self.client.list_documents(filter=filter)
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for doc in documents:
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self._rich_print_document(doc, truncate=True)
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async def add_document_from_text(self, text: str, metadata: dict | None = None):
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async with HaikuRAG(db_path=self.db_path, config=self.config) as self.client:
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doc = await self.client.create_document(text, metadata=metadata)
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self._rich_print_document(doc, truncate=True)
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self.console.print(
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f"[bold green]Document {doc.id} added successfully.[/bold green]"
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)
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async def add_document_from_source(
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self, source: str, title: str | None = None, metadata: dict | None = None
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):
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async with HaikuRAG(db_path=self.db_path, config=self.config) as self.client:
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result = await self.client.create_document_from_source(
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source, title=title, metadata=metadata
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)
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if isinstance(result, list):
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for doc in result:
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self._rich_print_document(doc, truncate=True)
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self.console.print(
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f"[bold green]{len(result)} documents added successfully.[/bold green]"
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)
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else:
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self._rich_print_document(result, truncate=True)
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self.console.print(
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f"[bold green]Document {result.id} added successfully.[/bold green]"
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)
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async def get_document(self, doc_id: str):
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, allow_create=False
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) as self.client:
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doc = await self.client.get_document_by_id(doc_id)
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if doc is None:
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self.console.print(f"[red]Document with id {doc_id} not found.[/red]")
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return
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self._rich_print_document(doc, truncate=False)
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async def delete_document(self, doc_id: str):
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async with HaikuRAG(db_path=self.db_path, config=self.config) as self.client:
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deleted = await self.client.delete_document(doc_id)
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if deleted:
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self.console.print(
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f"[bold green]Document {doc_id} deleted successfully.[/bold green]"
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)
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else:
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self.console.print(
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f"[yellow]Document with id {doc_id} not found.[/yellow]"
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)
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async def search(self, query: str, limit: int = 5, filter: str | None = None):
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, allow_create=False
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) as self.client:
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results = await self.client.search(query, limit=limit, filter=filter)
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if not results:
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self.console.print("[yellow]No results found.[/yellow]")
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return
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for chunk, score in results:
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self._rich_print_search_result(chunk, score)
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async def ask(
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self,
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question: str,
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cite: bool = False,
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deep: bool = False,
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verbose: bool = False,
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):
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"""Ask a question using the RAG system.
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Args:
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question: The question to ask
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cite: Include citations in the answer
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deep: Use deep QA mode (multi-step reasoning)
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verbose: Show verbose output
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"""
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, allow_create=False
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) as self.client:
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try:
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if deep:
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from haiku.rag.graph.deep_qa.dependencies import DeepQAContext
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from haiku.rag.graph.deep_qa.graph import build_deep_qa_graph
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from haiku.rag.graph.deep_qa.state import DeepQADeps, DeepQAState
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graph = build_deep_qa_graph(config=self.config)
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context = DeepQAContext(
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original_question=question, use_citations=cite
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)
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state = DeepQAState.from_config(context=context, config=self.config)
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deps = DeepQADeps(client=self.client)
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if verbose:
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# Use AG-UI renderer to process and display events
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from haiku.rag.graph.agui import AGUIConsoleRenderer
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renderer = AGUIConsoleRenderer(self.console)
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result_dict = await renderer.render(
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stream_graph(graph, state, deps)
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)
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# Result should be a dict with 'answer' key
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answer = result_dict.get("answer", "") if result_dict else ""
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else:
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# Run without rendering events, just get the result
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result = await graph.run(state=state, deps=deps)
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answer = result.answer
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else:
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answer = await self.client.ask(question, cite=cite)
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self.console.print(f"[bold blue]Question:[/bold blue] {question}")
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self.console.print()
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self.console.print("[bold green]Answer:[/bold green]")
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self.console.print(Markdown(answer))
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except Exception as e:
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self.console.print(f"[red]Error: {e}[/red]")
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async def research(self, question: str, verbose: bool = False):
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"""Run research via the pydantic-graph pipeline.
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Args:
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question: The research question
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verbose: Show AG-UI event stream during execution
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"""
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, allow_create=False
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) as client:
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try:
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self.console.print("[bold cyan]Starting research[/bold cyan]")
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self.console.print(f"[bold blue]Question:[/bold blue] {question}")
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self.console.print()
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graph = build_research_graph(config=self.config)
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context = ResearchContext(original_question=question)
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state = ResearchState.from_config(context=context, config=self.config)
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deps = ResearchDeps(client=client)
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if verbose:
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# Use AG-UI renderer to process and display events
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renderer = AGUIConsoleRenderer(self.console)
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report_dict = await renderer.render(
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stream_graph(graph, state, deps)
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)
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else:
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# Run without rendering events, just get the result
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report = await graph.run(state=state, deps=deps)
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report_dict = (
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report.model_dump() if hasattr(report, "model_dump") else report
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)
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if report_dict is None:
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self.console.print("[red]Research did not produce a report.[/red]")
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return
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# Convert dict to ResearchReport model
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from haiku.rag.graph.research.models import ResearchReport
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report = ResearchReport.model_validate(report_dict)
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# Display the report
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self.console.print("[bold green]Research Report[/bold green]")
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self.console.rule()
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# Title and Executive Summary
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self.console.print(f"[bold]{report.title}[/bold]")
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self.console.print()
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self.console.print("[bold cyan]Executive Summary:[/bold cyan]")
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self.console.print(report.executive_summary)
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self.console.print()
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# Confidence (from last evaluation)
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if state.last_eval:
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conf = state.last_eval.confidence_score # type: ignore[attr-defined]
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self.console.print(f"[bold cyan]Confidence:[/bold cyan] {conf:.1%}")
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self.console.print()
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# Main Findings
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if report.main_findings:
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self.console.print("[bold cyan]Main Findings:[/bold cyan]")
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for finding in report.main_findings:
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self.console.print(f"• {finding}")
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self.console.print()
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# (Themes section removed)
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# Conclusions
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if report.conclusions:
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self.console.print("[bold cyan]Conclusions:[/bold cyan]")
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for conclusion in report.conclusions:
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self.console.print(f"• {conclusion}")
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self.console.print()
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# Recommendations
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if report.recommendations:
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self.console.print("[bold cyan]Recommendations:[/bold cyan]")
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for rec in report.recommendations:
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self.console.print(f"• {rec}")
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self.console.print()
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# Limitations
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if report.limitations:
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self.console.print("[bold yellow]Limitations:[/bold yellow]")
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for limitation in report.limitations:
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self.console.print(f"• {limitation}")
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self.console.print()
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# Sources Summary
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if report.sources_summary:
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self.console.print("[bold cyan]Sources:[/bold cyan]")
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self.console.print(report.sources_summary)
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except Exception as e:
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self.console.print(f"[red]Error during research: {e}[/red]")
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async def rebuild(self):
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, skip_validation=True
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) as client:
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try:
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documents = await client.list_documents()
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total_docs = len(documents)
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if total_docs == 0:
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self.console.print(
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"[yellow]No documents found in database.[/yellow]"
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)
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return
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self.console.print(
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f"[bold cyan]Rebuilding database with {total_docs} documents...[/bold cyan]"
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)
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with Progress() as progress:
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task = progress.add_task("Rebuilding...", total=total_docs)
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async for _ in client.rebuild_database():
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progress.update(task, advance=1)
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self.console.print(
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"[bold green]Database rebuild completed successfully.[/bold green]"
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)
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except Exception as e:
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self.console.print(f"[red]Error rebuilding database: {e}[/red]")
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async def vacuum(self):
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"""Run database maintenance: optimize and cleanup table history."""
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try:
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async with HaikuRAG(
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db_path=self.db_path, config=self.config, skip_validation=True
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) as client:
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await client.vacuum()
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self.console.print(
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"[bold green]Vacuum completed successfully.[/bold green]"
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)
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except Exception as e:
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self.console.print(f"[red]Error during vacuum: {e}[/red]")
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def show_settings(self):
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"""Display current configuration settings."""
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self.console.print("[bold]haiku.rag configuration[/bold]")
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self.console.print()
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# Get all config fields dynamically
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for field_name, field_value in self.config.model_dump().items():
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# Format the display value
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if isinstance(field_value, str) and (
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"key" in field_name.lower()
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or "password" in field_name.lower()
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or "token" in field_name.lower()
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):
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# Hide sensitive values but show if they're set
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display_value = "✓ Set" if field_value else "✗ Not set"
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else:
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display_value = field_value
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self.console.print(
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f" [repr.attrib_name]{field_name}[/repr.attrib_name]: {display_value}"
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)
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def _rich_print_document(self, doc: Document, truncate: bool = False):
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"""Format a document for display."""
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if truncate:
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content = doc.content.splitlines()
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if len(content) > 3:
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content = content[:3] + ["\n…"]
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content = "\n".join(content)
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content = Markdown(content)
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else:
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content = Markdown(doc.content)
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title_part = (
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f" [repr.attrib_name]title[/repr.attrib_name]: {doc.title}"
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if doc.title
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else ""
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)
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self.console.print(
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f"[repr.attrib_name]id[/repr.attrib_name]: {doc.id} "
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f"[repr.attrib_name]uri[/repr.attrib_name]: {doc.uri}"
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+ title_part
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+ f" [repr.attrib_name]meta[/repr.attrib_name]: {doc.metadata}"
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)
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self.console.print(
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f"[repr.attrib_name]created at[/repr.attrib_name]: {doc.created_at} [repr.attrib_name]updated at[/repr.attrib_name]: {doc.updated_at}"
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)
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self.console.print("[repr.attrib_name]content[/repr.attrib_name]:")
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self.console.print(content)
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self.console.rule()
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def _rich_print_search_result(self, chunk: Chunk, score: float):
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"""Format a search result chunk for display."""
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content = Markdown(chunk.content)
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self.console.print(
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f"[repr.attrib_name]document_id[/repr.attrib_name]: {chunk.document_id} "
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f"[repr.attrib_name]score[/repr.attrib_name]: {score:.4f}"
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)
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if chunk.document_uri:
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self.console.print("[repr.attrib_name]document uri[/repr.attrib_name]:")
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self.console.print(chunk.document_uri)
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if chunk.document_title:
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self.console.print("[repr.attrib_name]document title[/repr.attrib_name]:")
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self.console.print(chunk.document_title)
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if chunk.document_meta:
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self.console.print("[repr.attrib_name]document meta[/repr.attrib_name]:")
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self.console.print(chunk.document_meta)
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self.console.print("[repr.attrib_name]content[/repr.attrib_name]:")
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self.console.print(content)
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self.console.rule()
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async def serve(
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self,
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enable_monitor: bool = True,
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enable_mcp: bool = True,
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mcp_transport: str | None = None,
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mcp_port: int = 8001,
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enable_agui: bool = False,
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):
|
|
"""Start the server with selected services."""
|
|
async with HaikuRAG(self.db_path, config=self.config) as client:
|
|
tasks = []
|
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|
|
# Start file monitor if enabled
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|
if enable_monitor:
|
|
monitor = FileWatcher(client=client, config=self.config)
|
|
monitor_task = asyncio.create_task(monitor.observe())
|
|
tasks.append(monitor_task)
|
|
|
|
# Start MCP server if enabled
|
|
if enable_mcp:
|
|
server = create_mcp_server(self.db_path, config=self.config)
|
|
|
|
async def run_mcp():
|
|
if mcp_transport == "stdio":
|
|
await server.run_stdio_async()
|
|
else:
|
|
logger.info(f"Starting MCP server on port {mcp_port}")
|
|
await server.run_http_async(
|
|
transport="streamable-http", port=mcp_port
|
|
)
|
|
|
|
mcp_task = asyncio.create_task(run_mcp())
|
|
tasks.append(mcp_task)
|
|
|
|
# Start AG-UI server if enabled
|
|
if enable_agui:
|
|
|
|
async def run_agui():
|
|
import uvicorn
|
|
|
|
from haiku.rag.graph.agui import create_agui_server
|
|
|
|
logger.info(
|
|
f"Starting AG-UI server on {self.config.agui.host}:{self.config.agui.port}"
|
|
)
|
|
app = create_agui_server(self.config, db_path=self.db_path)
|
|
config = uvicorn.Config(
|
|
app=app,
|
|
host=self.config.agui.host,
|
|
port=self.config.agui.port,
|
|
log_level="info",
|
|
)
|
|
server = uvicorn.Server(config)
|
|
await server.serve()
|
|
|
|
agui_task = asyncio.create_task(run_agui())
|
|
tasks.append(agui_task)
|
|
|
|
if not tasks:
|
|
logger.warning("No services enabled")
|
|
return
|
|
|
|
try:
|
|
# Wait for any task to complete (or KeyboardInterrupt)
|
|
await asyncio.gather(*tasks)
|
|
except KeyboardInterrupt:
|
|
pass
|
|
finally:
|
|
# Cancel all tasks
|
|
for task in tasks:
|
|
task.cancel()
|
|
# Wait for cancellation
|
|
await asyncio.gather(*tasks, return_exceptions=True)
|