context-forge-py 0.1.0

Context engineering toolkit for ranking, packing, and risk-scanning RAG context. Python port of @mukundakatta/context-forge.

context-forge-py

Context engineering toolkit for RAG and agent prompts. Chunk documents, score relevance (BM25), apply diversity-aware packing (MMR), flag prompt-injection risk, and emit citation-ready context blocks that fit a token budget. Zero runtime dependencies.

Python port of @mukundakatta/context-forge.

Install

pip install context-forge-py

Quick start

from context_forge import forge

result = forge(
    chunks=[
        {"id": "doc1#0", "text": "Pluto was reclassified as a dwarf planet in 2006.", "source": "wiki/pluto"},
        {"id": "doc2#0", "text": "Mars is the fourth planet from the Sun.", "source": "wiki/mars"},
    ],
    query="What happened to Pluto?",
    budget=200,
)

result.blocks      # list of kept chunks (ranked, diversified, packed)
result.used_tokens # int -- tokens used by kept blocks
result.dropped     # list of {id, reason}
result.risks       # injection findings (e.g. ignore_instructions, exfil_curl)
result.citations   # {block_id: {source, span: [start, end]}}

You can also use the lower-level pipeline and the document-level entry:

from context_forge import (
    pack_context,    # full pipeline: chunk -> score -> diversify -> risk-scan -> pack
    chunk_document,  # split a document into token-bounded chunks
    score_chunks,    # BM25 score against a query
    diversify,       # MMR re-rank for diversity
    pack_to_budget,  # greedy budget packer
    scan_injection,  # prompt-injection risk scan
    estimate_tokens, # ceil(len/4) heuristic
)

API

forge(chunks, query, budget=1200, *, lambda_=0.7, per_chunk_min=20)

Compose ranking + diversification + injection-scanning + budget-packing on a list of pre-chunked context. Returns a ForgedContext dataclass with blocks, used_tokens, dropped, risks, and citations.

pack_context(query, documents, budget_tokens=1200, max_tokens=200, overlap_tokens=20, lambda_=0.7, per_chunk_min=20)

Full pipeline starting from raw documents ({"id": ..., "text": ..., "source": ...}). Chunks each document, then runs the same score/diversify/scan/pack steps as forge.

Risk classes

scan_injection(text) returns findings with kind, severity, snippet, and index. Detected kinds include ignore_instructions, system_prefix, you_are_now, role_tag, exfil_curl, exfil_wget, exfil_base64, zero_width_char, and suspicious_url.

License

MIT