haiku.rag/src/haiku/rag/app.py
2025-10-23 13:30:21 +03:00

540 lines
21 KiB
Python

import asyncio
import json
import logging
from importlib.metadata import version as pkg_version
from pathlib import Path
from rich.console import Console
from rich.markdown import Markdown
from rich.progress import Progress
from haiku.rag.client import HaikuRAG
from haiku.rag.config import Config
from haiku.rag.mcp import create_mcp_server
from haiku.rag.monitor import FileWatcher
from haiku.rag.research.dependencies import ResearchContext
from haiku.rag.research.graph import (
PlanNode,
ResearchDeps,
ResearchState,
build_research_graph,
)
from haiku.rag.research.stream import stream_research_graph
from haiku.rag.store.models.chunk import Chunk
from haiku.rag.store.models.document import Document
logger = logging.getLogger(__name__)
class HaikuRAGApp:
def __init__(self, db_path: Path):
self.db_path = db_path
self.console = Console()
async def info(self):
"""Display read-only information about the database without modifying it."""
import lancedb
# Basic: show path
self.console.print("[bold]haiku.rag database info[/bold]")
self.console.print(
f" [repr.attrib_name]path[/repr.attrib_name]: {self.db_path}"
)
if not self.db_path.exists():
self.console.print("[red]Database path does not exist.[/red]")
return
# Connect without going through Store to avoid upgrades/validation writes
try:
db = lancedb.connect(self.db_path)
table_names = set(db.table_names())
except Exception as e:
self.console.print(f"[red]Failed to open database: {e}[/red]")
return
try:
ldb_version = pkg_version("lancedb")
except Exception:
ldb_version = "unknown"
try:
hr_version = pkg_version("haiku.rag")
except Exception:
hr_version = "unknown"
try:
docling_version = pkg_version("docling")
except Exception:
docling_version = "unknown"
# Read settings (if present) to find stored haiku.rag version and embedding config
stored_version = "unknown"
embed_provider: str | None = None
embed_model: str | None = None
vector_dim: int | None = None
if "settings" in table_names:
settings_tbl = db.open_table("settings")
arrow = settings_tbl.search().where("id = 'settings'").limit(1).to_arrow()
rows = arrow.to_pylist() if arrow is not None else []
if rows:
raw = rows[0].get("settings") or "{}"
data = json.loads(raw) if isinstance(raw, str) else (raw or {})
stored_version = str(data.get("version", stored_version))
embed_provider = data.get("EMBEDDINGS_PROVIDER")
embed_model = data.get("EMBEDDINGS_MODEL")
vector_dim = (
int(data.get("EMBEDDINGS_VECTOR_DIM")) # pyright: ignore[reportArgumentType]
if data.get("EMBEDDINGS_VECTOR_DIM") is not None
else None
)
num_docs = 0
if "documents" in table_names:
docs_tbl = db.open_table("documents")
num_docs = int(docs_tbl.count_rows()) # type: ignore[attr-defined]
# Table versions per table (direct API)
doc_versions = (
len(list(db.open_table("documents").list_versions()))
if "documents" in table_names
else 0
)
chunk_versions = (
len(list(db.open_table("chunks").list_versions()))
if "chunks" in table_names
else 0
)
self.console.print(
f" [repr.attrib_name]haiku.rag version (db)[/repr.attrib_name]: {stored_version}"
)
if embed_provider or embed_model or vector_dim:
provider_part = embed_provider or "unknown"
model_part = embed_model or "unknown"
dim_part = f"{vector_dim}" if vector_dim is not None else "unknown"
self.console.print(
" [repr.attrib_name]embeddings[/repr.attrib_name]: "
f"{provider_part}/{model_part} (dim: {dim_part})"
)
else:
self.console.print(
" [repr.attrib_name]embeddings[/repr.attrib_name]: unknown"
)
self.console.print(
f" [repr.attrib_name]documents[/repr.attrib_name]: {num_docs}"
)
self.console.print(
f" [repr.attrib_name]versions (documents)[/repr.attrib_name]: {doc_versions}"
)
self.console.print(
f" [repr.attrib_name]versions (chunks)[/repr.attrib_name]: {chunk_versions}"
)
self.console.rule()
self.console.print("[bold]Versions[/bold]")
self.console.print(
f" [repr.attrib_name]haiku.rag[/repr.attrib_name]: {hr_version}"
)
self.console.print(
f" [repr.attrib_name]lancedb[/repr.attrib_name]: {ldb_version}"
)
self.console.print(
f" [repr.attrib_name]docling[/repr.attrib_name]: {docling_version}"
)
async def list_documents(self):
async with HaikuRAG(db_path=self.db_path) as self.client:
documents = await self.client.list_documents()
for doc in documents:
self._rich_print_document(doc, truncate=True)
async def add_document_from_text(self, text: str, metadata: dict | None = None):
async with HaikuRAG(db_path=self.db_path) as self.client:
doc = await self.client.create_document(text, metadata=metadata)
self._rich_print_document(doc, truncate=True)
self.console.print(
f"[bold green]Document {doc.id} added successfully.[/bold green]"
)
async def add_document_from_source(
self, source: str, title: str | None = None, metadata: dict | None = None
):
async with HaikuRAG(db_path=self.db_path) as self.client:
result = await self.client.create_document_from_source(
source, title=title, metadata=metadata
)
if isinstance(result, list):
for doc in result:
self._rich_print_document(doc, truncate=True)
self.console.print(
f"[bold green]{len(result)} documents added successfully.[/bold green]"
)
else:
self._rich_print_document(result, truncate=True)
self.console.print(
f"[bold green]Document {result.id} added successfully.[/bold green]"
)
async def get_document(self, doc_id: str):
async with HaikuRAG(db_path=self.db_path) as self.client:
doc = await self.client.get_document_by_id(doc_id)
if doc is None:
self.console.print(f"[red]Document with id {doc_id} not found.[/red]")
return
self._rich_print_document(doc, truncate=False)
async def delete_document(self, doc_id: str):
async with HaikuRAG(db_path=self.db_path) as self.client:
deleted = await self.client.delete_document(doc_id)
if deleted:
self.console.print(
f"[bold green]Document {doc_id} deleted successfully.[/bold green]"
)
else:
self.console.print(
f"[yellow]Document with id {doc_id} not found.[/yellow]"
)
async def search(self, query: str, limit: int = 5):
async with HaikuRAG(db_path=self.db_path) as self.client:
results = await self.client.search(query, limit=limit)
if not results:
self.console.print("[yellow]No results found.[/yellow]")
return
for chunk, score in results:
self._rich_print_search_result(chunk, score)
async def ask(
self,
question: str,
cite: bool = False,
deep: bool = False,
verbose: bool = False,
):
async with HaikuRAG(db_path=self.db_path) as self.client:
try:
if deep:
from rich.console import Console
from haiku.rag.qa.deep.dependencies import DeepQAContext
from haiku.rag.qa.deep.graph import build_deep_qa_graph
from haiku.rag.qa.deep.nodes import DeepQAPlanNode
from haiku.rag.qa.deep.state import DeepQADeps, DeepQAState
graph = build_deep_qa_graph()
context = DeepQAContext(
original_question=question, use_citations=cite
)
state = DeepQAState(context=context)
deps = DeepQADeps(
client=self.client, console=Console() if verbose else None
)
start_node = DeepQAPlanNode(
provider=Config.qa.provider,
model=Config.qa.model,
)
result = await graph.run(
start_node=start_node, state=state, deps=deps
)
answer = result.output.answer
else:
answer = await self.client.ask(question, cite=cite)
self.console.print(f"[bold blue]Question:[/bold blue] {question}")
self.console.print()
self.console.print("[bold green]Answer:[/bold green]")
self.console.print(Markdown(answer))
except Exception as e:
self.console.print(f"[red]Error: {e}[/red]")
async def research(
self,
question: str,
max_iterations: int = 3,
confidence_threshold: float = 0.8,
max_concurrency: int = 1,
verbose: bool = False,
):
"""Run research via the pydantic-graph pipeline (default)."""
async with HaikuRAG(db_path=self.db_path) as client:
try:
if verbose:
self.console.print("[bold cyan]Starting research[/bold cyan]")
self.console.print(f"[bold blue]Question:[/bold blue] {question}")
self.console.print()
graph = build_research_graph()
context = ResearchContext(original_question=question)
state = ResearchState(
context=context,
max_iterations=max_iterations,
confidence_threshold=confidence_threshold,
max_concurrency=max_concurrency,
)
deps = ResearchDeps(
client=client, console=self.console if verbose else None
)
start = PlanNode(
provider=Config.research.provider or Config.qa.provider,
model=Config.research.model or Config.qa.model,
)
report = None
async for event in stream_research_graph(graph, start, state, deps):
if event.type == "report":
report = event.report
break
if event.type == "error":
self.console.print(
f"[red]Error during research: {event.message}[/red]"
)
return
if report is None:
self.console.print("[red]Research did not produce a report.[/red]")
return
# Display the report
self.console.print("[bold green]Research Report[/bold green]")
self.console.rule()
# Title and Executive Summary
self.console.print(f"[bold]{report.title}[/bold]")
self.console.print()
self.console.print("[bold cyan]Executive Summary:[/bold cyan]")
self.console.print(report.executive_summary)
self.console.print()
# Confidence (from last evaluation)
if state.last_eval:
conf = state.last_eval.confidence_score # type: ignore[attr-defined]
self.console.print(f"[bold cyan]Confidence:[/bold cyan] {conf:.1%}")
self.console.print()
# Main Findings
if report.main_findings:
self.console.print("[bold cyan]Main Findings:[/bold cyan]")
for finding in report.main_findings:
self.console.print(f"{finding}")
self.console.print()
# (Themes section removed)
# Conclusions
if report.conclusions:
self.console.print("[bold cyan]Conclusions:[/bold cyan]")
for conclusion in report.conclusions:
self.console.print(f"{conclusion}")
self.console.print()
# Recommendations
if report.recommendations:
self.console.print("[bold cyan]Recommendations:[/bold cyan]")
for rec in report.recommendations:
self.console.print(f"{rec}")
self.console.print()
# Limitations
if report.limitations:
self.console.print("[bold yellow]Limitations:[/bold yellow]")
for limitation in report.limitations:
self.console.print(f"{limitation}")
self.console.print()
# Sources Summary
if report.sources_summary:
self.console.print("[bold cyan]Sources:[/bold cyan]")
self.console.print(report.sources_summary)
except Exception as e:
self.console.print(f"[red]Error during research: {e}[/red]")
async def rebuild(self):
async with HaikuRAG(db_path=self.db_path, skip_validation=True) as client:
try:
documents = await client.list_documents()
total_docs = len(documents)
if total_docs == 0:
self.console.print(
"[yellow]No documents found in database.[/yellow]"
)
return
self.console.print(
f"[bold cyan]Rebuilding database with {total_docs} documents...[/bold cyan]"
)
with Progress() as progress:
task = progress.add_task("Rebuilding...", total=total_docs)
async for _ in client.rebuild_database():
progress.update(task, advance=1)
self.console.print(
"[bold green]Database rebuild completed successfully.[/bold green]"
)
except Exception as e:
self.console.print(f"[red]Error rebuilding database: {e}[/red]")
async def vacuum(self):
"""Run database maintenance: optimize and cleanup table history."""
try:
async with HaikuRAG(db_path=self.db_path, skip_validation=True) as client:
await client.vacuum()
self.console.print(
"[bold green]Vacuum completed successfully.[/bold green]"
)
except Exception as e:
self.console.print(f"[red]Error during vacuum: {e}[/red]")
def show_settings(self):
"""Display current configuration settings."""
self.console.print("[bold]haiku.rag configuration[/bold]")
self.console.print()
# Get all config fields dynamically
for field_name, field_value in Config.model_dump().items():
# Format the display value
if isinstance(field_value, str) and (
"key" in field_name.lower()
or "password" in field_name.lower()
or "token" in field_name.lower()
):
# Hide sensitive values but show if they're set
display_value = "✓ Set" if field_value else "✗ Not set"
else:
display_value = field_value
self.console.print(
f" [repr.attrib_name]{field_name}[/repr.attrib_name]: {display_value}"
)
def _rich_print_document(self, doc: Document, truncate: bool = False):
"""Format a document for display."""
if truncate:
content = doc.content.splitlines()
if len(content) > 3:
content = content[:3] + ["\n"]
content = "\n".join(content)
content = Markdown(content)
else:
content = Markdown(doc.content)
title_part = (
f" [repr.attrib_name]title[/repr.attrib_name]: {doc.title}"
if doc.title
else ""
)
self.console.print(
f"[repr.attrib_name]id[/repr.attrib_name]: {doc.id} "
f"[repr.attrib_name]uri[/repr.attrib_name]: {doc.uri}"
+ title_part
+ f" [repr.attrib_name]meta[/repr.attrib_name]: {doc.metadata}"
)
self.console.print(
f"[repr.attrib_name]created at[/repr.attrib_name]: {doc.created_at} [repr.attrib_name]updated at[/repr.attrib_name]: {doc.updated_at}"
)
self.console.print("[repr.attrib_name]content[/repr.attrib_name]:")
self.console.print(content)
self.console.rule()
def _rich_print_search_result(self, chunk: Chunk, score: float):
"""Format a search result chunk for display."""
content = Markdown(chunk.content)
self.console.print(
f"[repr.attrib_name]document_id[/repr.attrib_name]: {chunk.document_id} "
f"[repr.attrib_name]score[/repr.attrib_name]: {score:.4f}"
)
if chunk.document_uri:
self.console.print("[repr.attrib_name]document uri[/repr.attrib_name]:")
self.console.print(chunk.document_uri)
if chunk.document_title:
self.console.print("[repr.attrib_name]document title[/repr.attrib_name]:")
self.console.print(chunk.document_title)
if chunk.document_meta:
self.console.print("[repr.attrib_name]document meta[/repr.attrib_name]:")
self.console.print(chunk.document_meta)
self.console.print("[repr.attrib_name]content[/repr.attrib_name]:")
self.console.print(content)
self.console.rule()
async def serve(
self,
enable_monitor: bool = True,
enable_mcp: bool = True,
mcp_transport: str | None = None,
mcp_port: int = 8001,
enable_a2a: bool = False,
a2a_host: str = "127.0.0.1",
a2a_port: int = 8000,
):
"""Start the server with selected services."""
async with HaikuRAG(self.db_path) as client:
tasks = []
# Start file monitor if enabled
if enable_monitor:
monitor = FileWatcher(
paths=Config.storage.monitor_directories, client=client
)
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)
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 A2A server if enabled
if enable_a2a:
try:
from haiku.rag.a2a import create_a2a_app
except ImportError as e:
logger.error(f"Failed to import A2A: {e}")
return
import uvicorn
logger.info(f"Starting A2A server on {a2a_host}:{a2a_port}")
async def run_a2a():
app = create_a2a_app(db_path=self.db_path)
config = uvicorn.Config(
app,
host=a2a_host,
port=a2a_port,
log_level="warning",
access_log=False,
)
server = uvicorn.Server(config)
await server.serve()
a2a_task = asyncio.create_task(run_a2a())
tasks.append(a2a_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)