mirror of
https://github.com/VectifyAI/PageIndex.git
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a0fb863957
- demos: use the post-cutoff attention-residuals paper (2603.15031) again so answers provably come from retrieval, and run the QA agent on retrieve_model - client: re-expose model / retrieve_model as public attributes (0.2.x surface) - pipeline: markdown summaries go back through generate_summaries_for_structure_md with the 200-token threshold (small nodes skip the LLM call) - README: restore (yes/no, default: X) flag docs with correct defaults; note that retrieve_model drives agent QA
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
"""
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Agentic Vectorless RAG with PageIndex SDK - Local Demo
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A simple example of using LocalClient for self-hosted document indexing
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and agent-based QA. The agent uses OpenAI Agents SDK to reason over
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the document's tree structure index.
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Steps:
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1 — Download and index a PDF
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2 — Stream a question with tool call visibility
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Requirements:
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pip install pageindex
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export OPENAI_API_KEY=your-api-key # or any LiteLLM-supported provider
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"""
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import asyncio
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from pathlib import Path
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import requests
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from pageindex import LocalClient
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_EXAMPLES_DIR = Path(__file__).parent
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PDF_URL = "https://arxiv.org/pdf/2603.15031"
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PDF_PATH = _EXAMPLES_DIR / "documents" / "attention-residuals.pdf"
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WORKSPACE = _EXAMPLES_DIR / "workspace"
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MODEL = "gpt-4o-2024-11-20" # any LiteLLM-supported model
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# Download PDF if needed
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if not PDF_PATH.exists():
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print(f"Downloading {PDF_URL} ...")
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PDF_PATH.parent.mkdir(parents=True, exist_ok=True)
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with requests.get(PDF_URL, stream=True, timeout=30) as r:
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r.raise_for_status()
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with open(PDF_PATH, "wb") as f:
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for chunk in r.iter_content(chunk_size=8192):
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if chunk:
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f.write(chunk)
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print("Download complete.\n")
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client = LocalClient(model=MODEL, storage_path=str(WORKSPACE))
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col = client.collection()
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doc_id = col.add(str(PDF_PATH))
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print(f"Indexed: {doc_id}\n")
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# Streaming query
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stream = col.query(
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"Explain Attention Residuals in simple language.",
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stream=True,
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)
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async def main():
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streamed_text = False
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async for event in stream:
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if event.type == "text_delta":
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print(event.data, end="", flush=True)
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streamed_text = True
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elif event.type == "tool_call":
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if streamed_text:
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print()
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streamed_text = False
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print(f"[tool call] {event.data['name']}")
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elif event.type == "tool_result":
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preview = str(event.data)[:200] + "..." if len(str(event.data)) > 200 else event.data
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print(f"[tool output] {preview}")
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elif event.type == "text_done":
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print()
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streamed_text = False
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asyncio.run(main())
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