forktex-intelligence 1.5.0

Unified SDK for the ForkTex Intelligence API — one common interface for chat, vision, embeddings, extract, audio, video and any future modality

forktex-intelligence

Standalone Python SDK for the ForkTex Intelligence API.

forktex-intelligence is the Python SDK for the ForkTex Intelligence platform: a single common interface — intel.invoke(model, inputs) — for every stateless AI op (chat with vision, embeddings, reranking, file extraction, audio, video) plus the stateful Knowledge pillar (Space / KnowledgeEntry entity wrappers). Each call returns a typed Outputs carrying text / vectors / chunks / cost / usage; the SDK validates inputs against the model descriptor before any HTTP round-trip.

Install

pip install forktex-intelligence

Requires Python 3.12+. Tested on 3.12, 3.13, 3.14.

Quick Start

Unified invoke — one call for every modality

from forktex_intelligence import Intelligence, Inputs

async with Intelligence(api_key="ftx-...") as intel:
    # 1) Pick a model from the catalog (rich descriptor: inputs / outputs /
    #    capabilities / cost / context / architecture / runs_locally).
    model = await intel.find_model(destination="chat", capability="vision")

    # 2) Same interface for every modality.
    out = await intel.invoke(
        model,
        Inputs.user("Describe this image", images=["https://example.com/cat.jpg"]),
    )
    print(out.text)
    print(out.usage, out.cost, out.currency)

Embed / extract / audio / video — same shape

async with Intelligence(api_key="ftx-...") as intel:
    embed_model = await intel.find_model(destination="embed")
    out = await intel.invoke(embed_model, Inputs(text=["hello", "world"]))
    print(out.vectors)              # list[list[float]]

    extract_model = await intel.find_model(destination="extract")
    with open("paper.pdf", "rb") as f:
        out = await intel.invoke(
            extract_model,
            Inputs(file_bytes=f.read(), filename="paper.pdf",
                   content_type="application/pdf"),
        )
    print(out.text, len(out.chunks or []))

Streaming

async with Intelligence(api_key="ftx-...") as intel:
    model = await intel.find_model(destination="chat", capability="stream")
    async for delta in intel.stream(model, Inputs.user("Long answer")):
        if delta.kind == "text":
            print(delta.text, end="", flush=True)
        elif delta.kind == "done":
            break

Streaming a non-streamable model raises ValueError client-side — no HTTP round-trip wasted. Sending images to a non-vision model raises before HTTP too.

Cost estimation (no HTTP)

from forktex_intelligence import estimate_cost

est = estimate_cost(model, Inputs.user("hello"), expected_output_tokens=200)
print(est)   # Decimal('0.000345') or None for free / local models

See examples/ for register → key → invoke walkthroughs.

Configuration

Pass endpoint and API key explicitly:

Intelligence(endpoint="https://intelligence.forktex.com/api", api_key="sk-...")

Or via an IntelligenceSettings object:

from forktex_intelligence import IntelligenceSettings, Intelligence

settings = IntelligenceSettings(endpoint="...", api_key="...")
async with Intelligence(settings=settings) as ai:
    ...

When used via the forktex CLI, settings are loaded from environment variables and .forktex/ config files automatically.

Variable	Description	Default
FORKTEX_INTELLIGENCE_ENDPOINT	Intelligence API endpoint	https://intelligence.forktex.com/api
FORKTEX_INTELLIGENCE_API_KEY	Intelligence API key	(required)

Local dev (point at your make local stack)

export FORKTEX_INTELLIGENCE_ENDPOINT=http://localhost:8001/api
export FORKTEX_INTELLIGENCE_API_KEY=dev-key

Or programmatically:

from forktex_intelligence import Intelligence
intel = Intelligence(endpoint="http://localhost:8001/api", api_key="dev-key")

What's in the package

Module	Purpose
forktex_intelligence.api	High-level Intelligence facade (root AI methods, intel.chat, intel.knowledge, intel.config, …)
forktex_intelligence.ai	Per-modality sub-handles (ChatHandle, EmbedHandle, ExtractHandle, AudioHandle, VideoHandle)
forktex_intelligence.resources	Entity wrappers — Space, KnowledgeEntry
forktex_intelligence.tool_loop	run_with_tools(...) streaming-with-tools async iterator
forktex_intelligence.inputs	Inputs, Outputs, OutputDelta — the unified shape any model accepts and returns
forktex_intelligence.validation	Client-side capability gate — refuses incompatible inputs before HTTP
forktex_intelligence.cost	estimate(model, inputs) — best-effort cost from descriptor pricing
forktex_intelligence.client	Low-level ForktexIntelligenceClient (raw HTTP; advanced)
forktex_intelligence.client.generated	Wire-level Pydantic models + _GeneratedOperations from the OpenAPI spec
forktex_intelligence.streams	SSE event types and parser
forktex_intelligence.config	IntelligenceSettings — endpoint, API key

All response models come from the OpenAPI codegen pipeline — one source of truth shared between the server and every consumer.

Repository

This SDK lives inside the forktex/intelligence monorepo alongside the API server (api/). The SDK package is independently versioned and published to PyPI.

Development

The Makefile is generated by forktex fsd makefile sync from forktex.json — do not hand-edit.

make help              # list every available target
make deps              # editable install with the dev group
make format            # ruff format
make lint              # ruff check
make test              # pytest tests/
make codegen-check     # verify the generated client imports cleanly
make build             # python3 -m build → dist/
make ci                # format-check + lint + license-check + audit + test + build
make clean             # remove caches and dist/

make ci is the single command that gates a publish: format-check, lint, dual-license header check, dependency CVE audit, full test suite, and python -m build + twine check.

License headers

Every source file carries the AGPL-3.0 + Commercial dual-license SPDX header, applied idempotently via:

make license-check    # CI gate — fails if any source file is missing the header
make license-fix      # add or refresh headers across src/, tests/, scripts/
make license-strip    # remove headers (used before license-model changes)

License

Dual-licensed — AGPL-3.0-or-later for open-source use, commercial for everything else (proprietary products, SaaS without source release, redistribution in closed-source form). See LICENSE and NOTICE for the full terms.

Commercial licensing inquiries: [e-mail].

The 1.0.0 release on PyPI remains under MIT; from 0.2.3 onwards the package ships AGPL-3.0+Commercial.