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release v0.4.1
Session-Id: 4877348c-5eef-45ed-9b1a-8f17056240e9 Session-Msg: 1265
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README.md

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flex compiles AI agent conversations and knowledge bases into SQLite. Each knowledge base is a self-describing cell — chunks, embeddings, graph intelligence, and extensible functionality.
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Instead of encoding retrieval intelligence into the pipeline — re-rankers, learned scoring, custom APIs — flex exposes the schema and lets the agent compose its own queries over a rich data surface.
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Instead of encoding retrieval intelligence into the pipeline flex exposes the schema and lets the agent compose its own queries over a rich data surface.
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```bash
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curl -sSL https://getflex.dev/install.sh | bash
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Flex keeps embeddings as numpy arrays you can modulate — diverse, unlike, trajectory, contrastive, temporal decay — operations on the embedding space itself.
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The retrieval engine is published independently as [flexvec](https://github.com/damian-delmas/flexvec) for use in any SQLite database.
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The retrieval engine is published independently as [flexvec](https://github.com/damiandelmas/flexvec) for use in any SQLite database.
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Every query runs three steps. The agent writes the query.
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flex/cli.py

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console = Console()
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_warnings: list[str] = [] # accumulate phase failures for exit code
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# Stop running worker/MCP before init to avoid DB lock contention
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_kill_pid_services()
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console.print()
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# 0. Pre-flight: check system deps before doing anything

flex/onnx/fetch.py

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# Bundled model lives alongside this file in flex/onnx/
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BUNDLED_DIR = Path(__file__).parent
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BASE_URL = "https://github.com/damian-delmas/flex/releases/download/v0.1.1"
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BASE_URL = "https://github.com/damiandelmas/flex/releases/download/v0.1.1"
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FILES = [
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("model.onnx", "30ff8ad63546f9efd85019f394445f566ea595c119b08aa6663058af9e18fa87"),

pyproject.toml

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[project]
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name = "getflex"
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version = "0.4.0"
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version = "0.4.1"
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description = "SQLite knowledge engine for AI Agents"
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license = { text = "MIT" }
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requires-python = ">=3.12"

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