indx 0.0.1

Make directories AI-ready, not just files — turn a directory into a portable knowledge space.

indx

Make directories AI-ready, not just files. Point indx at a folder and get back a knowledge space: structure, folder lineage, file-to-file relationships, and semantic metadata that AI agents and RAG systems can reason over. Open-source · Python · CLI + SDK · Apache-2.0.

See it: indx demo (build → inspect → query, fully offline)

One command builds, inspects, and queries a bundled sample corpus — no user data, no installs, no API keys. Real captured output:

$ indx demo
indx demo — building a sample 'team handbook' knowledge space…

stage: walk
stage: parse
stage: chunk
stage: relate
stage: enrich
stage: embed-pack
✓ 7 docs · 7 chunks · 19 relations → /tmp/indx-demo-XXXX/demo (0.01s)
  components: parser=plaintext llm=none embedder=hash store=jsonl format=.indx

/tmp/indx-demo-XXXX/demo  schema=1 indx=0.0.1
  documents=7 chunks=7 relations=19 embeddings=7 embedding=hash/256
       Types                    Relations
  type       count        type         count
  markdown       6        references      14
  text           1        sibling          5

sample query (keyword/lexical, offline): how do I onboard?
  score  source                      text
  0.121  engineering/code-review.md  # Code Review  Code review keeps our codebase…
  0.098  people/remote-work.md       # Remote Work Policy  Acme Robotics is remote-…
  0.095  handbook/welcome.md         # Welcome to Acme Robotics  This is the Acme …

✓ that's the whole flow — built offline with keyword/lexical retrieval, no API key.
  run it on your own folder: indx ./your-docs --out ./ai-ready.indx --offline

The recording above is a trimmed, ANSI-stripped transcript of an actual indx demo run.

pip install indx
indx demo                                    # instant: build → inspect → query a bundled sample, fully offline, no data needed
indx ./docs --out ./ai-ready.indx --offline  # index your own folder, fully offline (zero extra deps)
indx inspect ./ai-ready.indx
indx query   ./ai-ready.indx "how do I onboard?"
indx app                                     # visual, config-driven tester: build → inspect → query in the browser (pip install indx[app])

The default stack targets cloud backends (docling parser, OpenAI LLM + embeddings, qdrant store) — install it with pip install indx[cloud] and set the matching API keys. --offline selects the zero-dependency core stack (plaintext parser → hash embedder → jsonl no-DB store → .indx archive), so every command above runs as-is on a bare pip install indx with no extras and nothing to configure. For a fully managed single-vendor build, three cloud profile extras wire every slot to that cloud's services with one install and one flag: pip install "indx[aws]" → indx ./docs --out ./out --aws (Textract → Bedrock → Titan → S3 Vectors), pip install "indx[azure]" → indx ./docs --out ./out --azure (Document Intelligence → Azure OpenAI → AI Search), pip install "indx[gcp]" → indx ./docs --out ./out --gcp (Document AI → Gemini → gemini-embedding → BigQuery).

Note what the offline core does and doesn't do. The hash embedder is a deterministic hashing trick, so offline query is keyword/lexical retrieval, not semantic vector search — true semantic search needs a real embedder extra (e.g. bge or openai) selected explicitly. Likewise, the offline enrich step derives metadata (type, topics, tags, summary) locally and without an LLM call; LLM/VLM enrichment is opt-in via the cloud/local extras.

indx composes file parsers (Docling, Unstructured, …) rather than replacing them, then layers on what they discard — the arrangement of files. Every major component (parser, LLM, embedder, vector store, output) is a swappable, typed slot, so you can run the cloud default stack or the fully offline core from the same CLI.

Plug a knowledge space into an AI agent

A .indx archive is a portable knowledge space — carry it like a USB drive and plug it into any agent framework in one line:

from indx.agent import connect

kb = connect("ai-ready/handbook.indx")   # load the "USB drive"
tools = kb.openai()                       # OpenAI Agents SDK …or .langchain() / .pydantic_ai() / .claude()

Or serve it to any MCP client — Claude Desktop, Cursor, the TypeScript Mastra framework — with no Python glue on the client side:

pip install "indx[agent]"            # all framework adapters + the MCP server
indx mcp ai-ready/handbook.indx      # serve indx_search / indx_overview / indx_get_document

Every connector exposes the same three read-only tools — search, overview, get-document — built on the same retrieval path as the CLI. See the AI agents guide.

Status

Alpha (0.0.1). The zero-dependency core path (plaintext parser → hash embedder → jsonl no-DB store → .indx archive) runs end to end and is fully air-gapped — reach it with indx demo or by adding --offline to any build. The optional cloud/local backends (docling, openai, ollama, bge-m3, qdrant, plus the managed AWS/Azure/GCP profiles, …) are implemented and selected through the registry: install the matching extra (e.g. pip install "indx[cloud]") and provide credentials to switch a slot onto it. The .indx archive format is at schema_version "1"; public APIs may still shift before 1.0 — see the CHANGELOG and the documentation.

Documentation

Full documentation — quickstart, guides, the pipeline & stages, and the API/CLI reference — lives at docs.indx.jp.

Development

python -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"
nox -s tests          # fast offline suite: unit + corpus
nox -l                # list every session (integration / docker / airgap / live / record-fixtures)

License

Apache-2.0.