feat: add PageIndex SDK with local/cloud dual-mode support

.gitignore

@@ -4,3 +4,7 @@ __pycache__
 .env*
 .venv/
 logs/
+pageindex.egg-info/
+*.db
+venv/
+uv.lock

examples/cloud_demo.py

@@ -0,0 +1,62 @@
+"""
+Agentic Vectorless RAG with PageIndex SDK - Cloud Demo
+
+Uses CloudClient for fully-managed document indexing and QA.
+No LLM API key needed — the cloud service handles everything.
+
+Steps:
+  1 — Upload and index a PDF via PageIndex cloud
+  2 — Stream a question with tool call visibility
+
+Requirements:
+    pip install pageindex
+    export PAGEINDEX_API_KEY=your-api-key
+"""
+import asyncio
+import os
+from pathlib import Path
+import requests
+from pageindex import CloudClient
+
+_EXAMPLES_DIR = Path(__file__).parent
+PDF_URL = "https://arxiv.org/pdf/1706.03762.pdf"
+PDF_PATH = _EXAMPLES_DIR / "documents" / "attention.pdf"
+
+# Download PDF if needed
+if not PDF_PATH.exists():
+    print(f"Downloading {PDF_URL} ...")
+    PDF_PATH.parent.mkdir(parents=True, exist_ok=True)
+    with requests.get(PDF_URL, stream=True, timeout=30) as r:
+        r.raise_for_status()
+        with open(PDF_PATH, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+    print("Download complete.\n")
+
+client = CloudClient(api_key=os.environ["PAGEINDEX_API_KEY"])
+col = client.collection()
+
+doc_id = col.add(str(PDF_PATH))
+print(f"Indexed: {doc_id}\n")
+
+# Streaming query
+stream = col.query("What is the main contribution of this paper?", stream=True)
+
+async def main():
+    streamed_text = False
+    async for event in stream:
+        if event.type == "answer_delta":
+            print(event.data, end="", flush=True)
+            streamed_text = True
+        elif event.type == "tool_call":
+            if streamed_text:
+                print()
+                streamed_text = False
+            args = event.data.get("args", "")
+            print(f"[tool call] {event.data['name']}({args})")
+        elif event.type == "answer_done":
+            print()
+            streamed_text = False
+
+asyncio.run(main())

examples/local_demo.py

@@ -0,0 +1,69 @@
+"""
+Agentic Vectorless RAG with PageIndex SDK - Local Demo
+
+A simple example of using LocalClient for self-hosted document indexing
+and agent-based QA. The agent uses OpenAI Agents SDK to reason over
+the document's tree structure index.
+
+Steps:
+  1 — Download and index a PDF
+  2 — Stream a question with tool call visibility
+
+Requirements:
+    pip install pageindex
+    export OPENAI_API_KEY=your-api-key   # or any LiteLLM-supported provider
+"""
+import asyncio
+from pathlib import Path
+import requests
+from pageindex import LocalClient
+
+_EXAMPLES_DIR = Path(__file__).parent
+PDF_URL = "https://arxiv.org/pdf/1706.03762.pdf"
+PDF_PATH = _EXAMPLES_DIR / "documents" / "attention.pdf"
+WORKSPACE = _EXAMPLES_DIR / "workspace"
+MODEL = "gpt-4o-2024-11-20"  # any LiteLLM-supported model
+
+# Download PDF if needed
+if not PDF_PATH.exists():
+    print(f"Downloading {PDF_URL} ...")
+    PDF_PATH.parent.mkdir(parents=True, exist_ok=True)
+    with requests.get(PDF_URL, stream=True, timeout=30) as r:
+        r.raise_for_status()
+        with open(PDF_PATH, "wb") as f:
+            for chunk in r.iter_content(chunk_size=8192):
+                if chunk:
+                    f.write(chunk)
+    print("Download complete.\n")
+
+client = LocalClient(model=MODEL, storage_path=str(WORKSPACE))
+col = client.collection()
+
+doc_id = col.add(str(PDF_PATH))
+print(f"Indexed: {doc_id}\n")
+
+# Streaming query
+stream = col.query(
+    "What is the main architecture proposed in this paper and how does self-attention work?",
+    stream=True,
+)
+
+async def main():
+    streamed_text = False
+    async for event in stream:
+        if event.type == "answer_delta":
+            print(event.data, end="", flush=True)
+            streamed_text = True
+        elif event.type == "tool_call":
+            if streamed_text:
+                print()
+                streamed_text = False
+            print(f"[tool call] {event.data['name']}")
+        elif event.type == "tool_result":
+            preview = str(event.data)[:200] + "..." if len(str(event.data)) > 200 else event.data
+            print(f"[tool output] {preview}")
+        elif event.type == "answer_done":
+            print()
+            streamed_text = False
+
+asyncio.run(main())

pageindex/__init__.py

@@ -1,4 +1,40 @@
+# pageindex/__init__.py
+# Upstream exports (backward compatibility)
 from .page_index import *
 from .page_index_md import md_to_tree
 from .retrieve import get_document, get_document_structure, get_page_content
-from .client import PageIndexClient
+
+# SDK exports
+from .client import PageIndexClient, LocalClient, CloudClient
+from .config import IndexConfig
+from .collection import Collection
+from .parser.protocol import ContentNode, ParsedDocument, DocumentParser
+from .storage.protocol import StorageEngine
+from .events import QueryEvent
+from .errors import (
+    PageIndexError,
+    CollectionNotFoundError,
+    DocumentNotFoundError,
+    IndexingError,
+    CloudAPIError,
+    FileTypeError,
+)
+
+__all__ = [
+    "PageIndexClient",
+    "LocalClient",
+    "CloudClient",
+    "IndexConfig",
+    "Collection",
+    "ContentNode",
+    "ParsedDocument",
+    "DocumentParser",
+    "StorageEngine",
+    "QueryEvent",
+    "PageIndexError",
+    "CollectionNotFoundError",
+    "DocumentNotFoundError",
+    "IndexingError",
+    "CloudAPIError",
+    "FileTypeError",
+]

pageindex/agent.py

@@ -0,0 +1,93 @@
+# pageindex/agent.py
+from __future__ import annotations
+from typing import AsyncIterator
+from .events import QueryEvent
+from .backend.protocol import AgentTools
+
+
+SYSTEM_PROMPT = """
+You are PageIndex, a document QA assistant.
+TOOL USE:
+- Call list_documents() to see available documents.
+- Call get_document(doc_id) to confirm status and page/line count.
+- Call get_document_structure(doc_id) to identify relevant page ranges.
+- Call get_page_content(doc_id, pages="5-7") with tight ranges; never fetch the whole document.
+- Before each tool call, output one short sentence explaining the reason.
+IMAGES:
+- Page content may contain image references like ![image](path). Always preserve these in your answer so the downstream UI can render them.
+- Place images near the relevant context in your answer.
+Answer based only on tool output. Be concise.
+"""
+
+
+class QueryStream:
+    """Streaming query result, similar to OpenAI's RunResultStreaming.
+
+    Usage:
+        stream = col.query("question", stream=True)
+        async for event in stream:
+            if event.type == "answer_delta":
+                print(event.data, end="", flush=True)
+    """
+
+    def __init__(self, tools: AgentTools, question: str, model: str = None):
+        from agents import Agent
+        from agents.model_settings import ModelSettings
+        self._agent = Agent(
+            name="PageIndex",
+            instructions=SYSTEM_PROMPT,
+            tools=tools.function_tools,
+            mcp_servers=tools.mcp_servers,
+            model=model,
+            model_settings=ModelSettings(parallel_tool_calls=False),
+        )
+        self._question = question
+
+    async def stream_events(self) -> AsyncIterator[QueryEvent]:
+        """Async generator yielding QueryEvent as they arrive."""
+        from agents import Runner, ItemHelpers
+        from agents.stream_events import RawResponsesStreamEvent, RunItemStreamEvent
+        from openai.types.responses import ResponseTextDeltaEvent
+
+        streamed_run = Runner.run_streamed(self._agent, self._question)
+        async for event in streamed_run.stream_events():
+            if isinstance(event, RawResponsesStreamEvent):
+                if isinstance(event.data, ResponseTextDeltaEvent):
+                    yield QueryEvent(type="answer_delta", data=event.data.delta)
+            elif isinstance(event, RunItemStreamEvent):
+                item = event.item
+                if item.type == "tool_call_item":
+                    raw = item.raw_item
+                    yield QueryEvent(type="tool_call", data={
+                        "name": raw.name, "args": getattr(raw, "arguments", "{}"),
+                    })
+                elif item.type == "tool_call_output_item":
+                    yield QueryEvent(type="tool_result", data=str(item.output))
+                elif item.type == "message_output_item":
+                    text = ItemHelpers.text_message_output(item)
+                    if text:
+                        yield QueryEvent(type="answer_done", data=text)
+
+    def __aiter__(self):
+        return self.stream_events()
+
+
+class AgentRunner:
+    def __init__(self, tools: AgentTools, model: str = None):
+        self._tools = tools
+        self._model = model
+
+    def run(self, question: str) -> str:
+        """Sync non-streaming query. Returns answer string."""
+        from agents import Agent, Runner
+        from agents.model_settings import ModelSettings
+        agent = Agent(
+            name="PageIndex",
+            instructions=SYSTEM_PROMPT,
+            tools=self._tools.function_tools,
+            mcp_servers=self._tools.mcp_servers,
+            model=self._model,
+            model_settings=ModelSettings(parallel_tool_calls=False),
+        )
+        result = Runner.run_sync(agent, question)
+        return result.final_output