vigyan 0.1.0

High accuracy agentic search on scientific documents with citations

Vigyan — SDK for agentic search on scientific documents with citations

Overview

Vigyan provides a small, clean Python SDK to parse scientific PDFs, embed the content, index it in a vector database, and answer research questions with citation-aware metadata (paper, page range, paragraph ids, etc.).

Design Principles

• Clear interfaces: VectorStore and DocumentParser decouple concerns.
• Storage-agnostic domain models from vigyan.models: Document, Chunk, and QueryHit.
• Adapter implementations: LanceDB vector store with built-in embedding, GROBID parser.
• Domain-named Corpus modules: CorpusIngestor, CorpusRetriever, and run_research_query orchestrate ingestion, retrieval, and cited answers.

Install

Requires Python 3.12+.

Dependencies include lancedb, httpx, lxml, and pydantic (declared in pyproject.toml).

Quick Start

from vigyan.corpus import CorpusIngestor, CorpusRetriever
from vigyan.parsers import GrobidParser
from vigyan.vectordb import LanceDBVectorStore
from vigyan.agent import run_research_query

# Configure adapters
store = LanceDBVectorStore(embedding_model="text-embedding-3-small")
parser = GrobidParser(server_url="http://localhost:8070")  # GROBID must be running

# Ingest a PDF with automatic metadata via GROBID
pdf_bytes = open("paper.pdf", "rb").read()
ingestor = CorpusIngestor(parser=parser, store=store)
ingestor.ingest_pdf(pdf_bytes, meta=None)

# Retrieve relevant passages directly
retriever = CorpusRetriever(store=store)
hits = retriever.retrieve("protein folding with attention", top_k=5)
for h in hits:
    print(h.citation, "-", h.title)
    print(h.text)

# Or run the research agent for a cited answer
answer = run_research_query(
    "What does this corpus say about protein folding with attention?",
    db_uri="./vigyan_db",
    embed_model="text-embedding-3-small",
)
print(answer.answer)
for citation in answer.citations:
    print(f"[{citation.index}] {citation.citation}")

CLai Web Agent

clai web cannot pass Pydantic AI deps directly, so Vigyan's importable agent resolves vector-store deps from environment variables when explicit SDK deps are not provided:

export VIGYAN_DB_URI=./vigyan_db
export VIGYAN_EMBED_MODEL=text-embedding-3-small
# Optional:
# export VIGYAN_TOP_K=8
# export VIGYAN_FILTERS="year >= 2020"

uv run clai web --agent src.vigyan.agent.research_agent:agent

The normal SDK path still uses explicit deps via run_research_query(...).

Notes

• OpenAI-compatible key must be available in the environment for embedding.
• GROBID must be running for parsing and metadata extraction. You can swap in a different DocumentParser implementation if preferred.
• The LanceDB store uses auto-embedding via the LanceDB registry, supporting OpenAI and other providers.