Rust embedding

What this skill does

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title: GraphEmbed CLI Tool Specification\
version: 1.0.0\
description: \"A Rust-based CLI tool for generating text embeddings and
managing knowledge graphs\"

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# GraphEmbed CLI Tool

GraphEmbed CLI is a command-line application written in Rust that
integrates text embedding generation with knowledge graph management. It
allows users to create or import knowledge graphs, generate or ingest
vector embeddings for text data, and manipulate the graph's entities and
relationships. The tool supports standard graph data formats (JSON-LD,
RDF/Turtle, CSV) and offers querying and basic visualization
capabilities. This document provides an overview of the tool's features,
usage instructions, workflow, examples, and references to relevant
resources.

## Instructions

**Installation & Setup:** To install GraphEmbed, ensure you have Rust
installed (for source builds) or use a prebuilt binary if provided. You
can compile from source via Cargo:

    $ cargo install graphembed-cli

This will download and build the CLI. After installation, the command
`graphembed` should be available. For help on any command, run
`graphembed help` or `graphembed <command> --help`.

**Command Structure:** GraphEmbed uses subcommands for different
functionalities. General usage follows:

    graphembed <command> [OPTIONS] [ARGS...]

Key commands include:

- `embed` -- Generate embeddings from input text using a chosen model.
- `import` -- Load a knowledge graph from a file (JSON-LD, Turtle, RDF,
  or CSV).
- `export` -- Save the current knowledge graph to a file in a specified
  format.
- `add-entity` -- Create a new entity (node) in the graph.
- `add-rel` -- Create a relationship (edge) between two entities.
- `update` -- Modify an existing entity or relationship.
- `delete` -- Remove an entity or relationship from the graph.
- `query` -- Query the graph for specific patterns or run a SPARQL query
  (if supported).
- `visualize` -- Generate a simple visual representation of the graph.

**Embedding Generation:** The `embed` command produces a numerical
vector (embedding) from a given text input. By default, GraphEmbed
leverages Rust-compatible NLP models (Transformer-based) for embeddings.
You may specify an embedding model with `-m/--model`. For example,
`all-MiniLM-L6-v2` (a popular SentenceTransformer model) can be used if
available. Under the hood, the tool uses **Hugging Face Transformers**
via Rust libraries to compute embeddings. This means you can use
pre-trained models like BERT, MiniLM, etc., without needing Python. The
first time you request a particular model, the tool will download the
model weights if not already present. Ensure you have an internet
connection for model downloads or pre-download the model files. If no
model is specified, a default small embedding model is used.

- *Model backends:* GraphEmbed supports multiple backend frameworks for
  embeddings. It can use **Rust-BERT** (which wraps PyTorch models with
  the `tch` crate) for many Hugging Face models, or an **ONNX Runtime**
  backend for sentence transformers if compiled with the `onnx` feature
  (using the `ort` crate). You can choose the backend via features or
  CLI flags (e.g., `--backend torch` vs `--backend onnx`). The tool
  ensures that generating an embedding requires only the text input --
  the output is a vector of floats printed to stdout or saved to a file
  if specified.

**Ingesting Precomputed Vectors:** In addition to generating embeddings,
you can ingest precomputed embedding vectors into the system. The
`import` command will automatically detect if a given file is an
embedding file based on format (for example, a CSV of vectors or a JSON
array). Alternatively, a dedicated subcommand `ingest-vec` may be
provided (check `graphembed help` for the exact name if available).
Typically, you would prepare a CSV where each line contains an entity
identifier and a list of numerical components of the embedding.
GraphEmbed will read this and attach each vector to the corresponding
entity in the knowledge graph (creating the entity if it doesn't exist).
For example, a CSV with header `entity_id,dim1,dim2,...` can be
ingested. Ensure that the entity identifiers match those used in the
graph (case-sensitive). After ingestion, the embedding becomes a
property of the entity in the graph (accessible for querying or
similarity operations in future versions).

**Knowledge Graph Import/Export:** The `import` and `export` commands
handle reading from or writing to various knowledge graph formats: -
**JSON-LD (.jsonld)** -- A JSON-based linked data format. The tool can
parse JSON-LD files to create the internal graph. It will interpret
`@context`, `@id`, and other JSON-LD keywords properly, so imported data
retains semantic meaning. When exporting to JSON-LD, GraphEmbed will
produce a context and list of triples in JSON-LD structure. -
**RDF/Turtle (.ttl or .rdf)** -- The Turtle syntax (and generic RDF/XML
if `.rdf` is provided) is supported. Import will parse triples and build
the graph accordingly. Export will write out triples with prefixes and
IRIs as needed in Turtle format. - **CSV (.csv)** -- For simplicity,
GraphEmbed expects CSV files to represent triples or edges. Each row
should contain at least three columns: subject, predicate, object
(optionally a fourth for a literal type or language tag if needed). A
header row can be present with names like `subject,predicate,object`. If
no header is present, the tool assumes each line is a triple in order.
CSV import is useful for quickly loading edge lists or simple knowledge
graphs from spreadsheets. Exporting to CSV will produce a triple list in
a similar fashion (one triple per line). - The import command tries to
auto-detect format from file extension. You can override by specifying
`--format jsonld|turtle|csv|rdf` if needed. The tool uses robust parsers
under the hood (e.g., an RDF library for Turtle/JSON-LD) and will report
any parse errors with line numbers for easier debugging of file format
issues.

**Entities and Relationships (CRUD):** GraphEmbed maintains an internal
graph data structure where **entities** are nodes identified by unique
IDs or IRIs, and **relationships** are edges (typically labeled with a
predicate/property name). Using CLI commands, you can **create, update,
and delete** these: - **Creating Entities:** Use `add-entity` with a
unique identifier or label. For RDF-based graphs, this might create a
new URI (you can specify a CURIE or a full URI with `--id`, or let the
tool generate a blank node or namespaced URI). You can also attach
initial data like a type or properties via options. For example,
`graphembed add-entity "Alice" --type Person` might create an entity
with label "Alice" of type Person. - **Creating Relationships:** Use
`add-rel` (or `add-relationship`) specifying a subject, predicate, and
object. For instance, `graphembed add-rel "Alice" "knows" "Bob"` would
add a relationship stating Alice knows Bob. Under the hood, if "Alice"
and "Bob" are label identifiers for entities, the tool will map them to
their internal IDs (or create them if they didn't exist). Predicates can
be given as simple labels or as full URIs; the tool may map common
relation names to a default vocabulary or allow a `--uri` option to
specify an exact property URI. - **Updating:** The `update` command
allows changing an entity's attributes or a relationship's
predicate/target. For example, you might update an entity's name, or
attach a new attribute (like adding an `age` property to a Person). In
an RDF graph context, updating might just mean adding or replacing
certain triples. The CLI might provide flags like
`--set-property name="Alice A."` or similar to modify data.
Relationships could be updated by referencing them (e.g., by an ID or by
the subject-predicate-object triple pattern). - **Deleting:**