Update the quickstart section (#12)

Author: habedi

.github/ISSUE_TEMPLATE/config.yml

@@ -1 +1,5 @@
-blank_issues_enabled: true
+blank_issues_enabled: false
+contact_links:
+  - name: Discussions
+    url: https://github.com/CogitatorTech/infera/discussions
+    about: Please ask and answer general questions here

.github/workflows/dist_pipeline.yml

@@ -1,6 +1,9 @@
 name: Build Extension Binaries
 on:
   workflow_dispatch:
+  pull_request:
+    branches:
+      - main
   push:
     tags:
       - 'v*'

Makefile

@@ -61,12 +61,17 @@ rust-coverage: ## Generate code coverage report for Infera crate
 .PHONY: rust-lint
 rust-lint: rust-format ## Run linter checks on Rust files
 	@echo "Linting Rust files..."
-	@cargo clippy --manifest-path infera/Cargo.toml --features "tract" -- -D warnings
+	@cargo clippy --manifest-path infera/Cargo.toml --features "tract" -- -D warnings -D clippy::unwrap_used -D clippy::expect_used
 
 .PHONY: rust-fix-lint
 rust-fix-lint: ## Fix Rust linter warnings
 	@echo "Fixing linter warnings..."
-	@cargo clippy --fix --allow-dirty --allow-staged --manifest-path infera/Cargo.toml --features "tract" -- -D warnings
+	@cargo clippy --fix --allow-dirty --allow-staged --manifest-path infera/Cargo.toml --features "tract" -- -D warnings -D clippy::unwrap_used -D clippy::expect_used
+
+.PHONY: rust-careful
+careful: ## Run security checks on Rust code
+	@echo "Running security checks..."
+	@cargo careful
 
 .PHONY: rust-clean
 rust-clean: ## Clean Rust build artifacts
@@ -110,7 +115,7 @@ install-deps: ## Set up development environment (for Debian-based systems)
 	@echo "Setting up development environment..."
 	@sudo apt-get install -y cmake clang-format snap python3-pip
 	@sudo snap install rustup --classic
-	@cargo install cargo-tarpaulin cbindgen cargo-edit cargo-audit cargo-outdated
+	@cargo install cargo-tarpaulin cbindgen cargo-edit cargo-audit cargo-outdated cargo-careful
 	@cd infera && cargo check --features "tract"
 	@git submodule update --init --recursive
 	@pip install --user --upgrade pip uv

README.md

@@ -55,6 +55,21 @@ See the [ROADMAP.md](ROADMAP.md) for the list of implemented and planned feature
 
 ### Quickstart
 
+#### Install from Community Extensions Repository
+
+You can install and load Infera from
+the [DuckDB community extensions](https://duckdb.org/community_extensions/extensions/infera) repository by running the
+following SQL commands in the DuckDB shell:
+
+```sql
+install infera from community;
+load infera;
+```
+
+#### Build from Source
+
+Alternatively, you can build Infera from source and use it by following these steps:
+
 1. Clone the repository and build the Infera extension from source:
 
 ```bash
@@ -71,13 +86,21 @@ make release
 ./build/release/duckdb
 ```
 
-3. Run the following SQL commands in the shell to try Infera out:
+> [!NOTE]
+> After building from source, the Infera binary will be `build/release/extension/infera/infera.duckdb_extension`.
+> You can load it using the `load 'build/release/extension/infera/infera.duckdb_extension';` in the DuckDB shell.
+> Note that the extension binary will only work with the DuckDB version that it was built against.
+> You can download the pre-built binaries from the [releases page](https://github.com/CogitatorTech/infera/releases) for
+> your platform.
+
+
+#### Trying Infera
 
 ```sql
--- Normally, we need to load the extension first,
--- but the `duckdb` binary that we built in the previous step
--- already has Infera statically linked to it.
--- So, we don't need to load the extension explicitly.
+-- 0. Install and load Infera 
+-- Skip this step if you built from source and ran `./build/release/duckdb`
+install infera from community;
+load infera;
 
 -- 1. Load a simple linear model from a remote URL
 select infera_load_model('linear_model',
@@ -93,21 +116,10 @@ select infera_unload_model('linear_model');
 
 -- 4. Check the Infera version
 select infera_get_version();
-````
+```
 
 [![Simple Demo 1](https://asciinema.org/a/745806.svg)](https://asciinema.org/a/745806)
 
-> [!NOTE]
-> After building from source, the Infera binary will be `build/release/extension/infera/infera.duckdb_extension`.
-> You can load it using the `load 'build/release/extension/infera/infera.duckdb_extension';` in the DuckDB shell.
-> Note that the extension binary will only work with the DuckDB version that it was built against.
-> At the moment, Infera is not available as
-> a [DuckDB community extension](https://duckdb.org/community_extensions/list_of_extensions).
-> Nevertheless, you can still use Infera by building it from source yourself, or downloading pre-built binaries from
-> the [releases page](https://github.com/CogitatorTech/infera/releases) for your platform.
-> Please check the [this page](https://duckdb.org/docs/stable/extensions/installing_extensions.html) for more details on
-> how to use extensions in DuckDB.
-
 ---
 
 ### Documentation

docs/README.md

@@ -2,21 +2,21 @@
 
 The table below includes the information about all SQL functions exposed by Infera.
 
-| #  | Function                                                     | Return Type      | Description                                                                                                                                 |
-|----|:-------------------------------------------------------------|:-----------------|:--------------------------------------------------------------------------------------------------------------------------------------------|
-| 1  | `infera_load_model(name VARCHAR, path_or_url VARCHAR)`       | `BOOLEAN`        | Loads an ONNX model from a local file path or a remote URL and assigns it a unique name. Returns `true` on success.                         |
-| 2  | `infera_unload_model(name VARCHAR)`                          | `BOOLEAN`        | Unloads a model, freeing its associated resources. Returns `true` on success.                                                               |
-| 3  | `infera_set_autoload_dir(path VARCHAR)`                      | `VARCHAR (JSON)` | Scans a directory for `.onnx` files, loads them automatically, and returns a JSON report of loaded models and any errors.                   |
-| 4  | `infera_get_loaded_models()`                                 | `VARCHAR (JSON)` | Returns a JSON array containing the names of all currently loaded models.                                                                   |
+| #  | Function                                                     | Return Type      | Description                                                                                                                                               |
+|----|:-------------------------------------------------------------|:-----------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1  | `infera_load_model(name VARCHAR, path_or_url VARCHAR)`       | `BOOLEAN`        | Loads an ONNX model from a local file path or a remote URL and assigns it a unique name. Returns `true` on success.                                       |
+| 2  | `infera_unload_model(name VARCHAR)`                          | `BOOLEAN`        | Unloads a model, freeing its associated resources. Returns `true` on success.                                                                             |
+| 3  | `infera_set_autoload_dir(path VARCHAR)`                      | `VARCHAR (JSON)` | Scans a directory for `.onnx` files, loads them automatically, and returns a JSON report of loaded models and any errors.                                 |
+| 4  | `infera_get_loaded_models()`                                 | `VARCHAR (JSON)` | Returns a JSON array containing the names of all currently loaded models.                                                                                 |
 | 5  | `infera_get_model_info(name VARCHAR)`                        | `VARCHAR (JSON)` | Returns a JSON object with metadata about a specific loaded model (name, input/output shapes). If the model is not loaded, this function raises an error. |
-| 6  | `infera_predict(name VARCHAR, features... FLOAT)`            | `FLOAT`          | Performs inference on a batch of data, returning a single float value for each input row.                                                   |
-| 7  | `infera_predict_multi(name VARCHAR, features... FLOAT)`      | `VARCHAR (JSON)` | Performs inference and returns all outputs as a JSON-encoded array. This is useful for models that produce multiple predictions per sample. |
-| 8  | `infera_predict_multi_list(name VARCHAR, features... FLOAT)` | `LIST[FLOAT]`    | Performs inference and returns all outputs as a typed list of floats. Useful for multi-output models without JSON parsing.                  |
-| 9  | `infera_predict_from_blob(name VARCHAR, data BLOB)`          | `LIST[FLOAT]`    | Performs inference on raw `BLOB` data (for example, used for an image tensor), returning the result as a list of floats.                    |
-| 10 | `infera_is_model_loaded(name VARCHAR)`                       | `BOOLEAN`        | Returns `true` if the given model is currently loaded, otherwise `false`.                                                                   |
-| 11 | `infera_get_version()`                                       | `VARCHAR (JSON)` | Returns a JSON object with version and build information for the Infera extension.                                                          |
-| 12 | `infera_clear_cache()`                                       | `BOOLEAN`        | Clears the entire model cache directory, freeing up disk space. Returns `true` on success.                                                  |
-| 13 | `infera_get_cache_info()`                                    | `VARCHAR (JSON)` | Returns cache statistics including directory path, total size in bytes, file count, and configured size limit.                              |
+| 6  | `infera_predict(name VARCHAR, features... FLOAT)`            | `FLOAT`          | Performs inference on a batch of data, returning a single float value for each input row.                                                                 |
+| 7  | `infera_predict_multi(name VARCHAR, features... FLOAT)`      | `VARCHAR (JSON)` | Performs inference and returns all outputs as a JSON-encoded array. This is useful for models that produce multiple predictions per sample.               |
+| 8  | `infera_predict_multi_list(name VARCHAR, features... FLOAT)` | `LIST[FLOAT]`    | Performs inference and returns all outputs as a typed list of floats. Useful for multi-output models without JSON parsing.                                |
+| 9  | `infera_predict_from_blob(name VARCHAR, data BLOB)`          | `LIST[FLOAT]`    | Performs inference on raw `BLOB` data (for example, used for an image tensor), returning the result as a list of floats.                                  |
+| 10 | `infera_is_model_loaded(name VARCHAR)`                       | `BOOLEAN`        | Returns `true` if the given model is currently loaded, otherwise `false`.                                                                                 |
+| 11 | `infera_get_version()`                                       | `VARCHAR (JSON)` | Returns a JSON object with version and build information for the Infera extension.                                                                        |
+| 12 | `infera_clear_cache()`                                       | `BOOLEAN`        | Clears the entire model cache directory, freeing up disk space. Returns `true` on success.                                                                |
+| 13 | `infera_get_cache_info()`                                    | `VARCHAR (JSON)` | Returns cache statistics including directory path, total size in bytes, file count, and configured size limit.                                            |
 
 > [!NOTE]
 > The `features...` arguments accept `FLOAT` as well as values from `DOUBLE`, `INTEGER`, `BIGINT`, and `DECIMAL`

infera/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "infera"
-version = "0.2.0"
+version = "0.3.0"
 edition = "2021"
 publish = false
 

infera/bindings/infera_extension.cpp

@@ -527,7 +527,7 @@ static void LoadInternal(ExtensionLoader &loader) {
 
 void InferaExtension::Load(ExtensionLoader &loader) { LoadInternal(loader); }
 std::string InferaExtension::Name() { return "infera"; }
-std::string InferaExtension::Version() const { return "v0.2.0"; }
+std::string InferaExtension::Version() const { return "v0.3.0"; }
 
 } // namespace duckdb
 

infera/src/config.rs

@@ -1,4 +1,3 @@
-// src/config.rs
 // Centralized configuration management for Infera
 
 use once_cell::sync::Lazy;

infera/src/engine.rs

@@ -1,4 +1,3 @@
-// src/engine.rs
 // Contains the core ONNX inference logic using the Tract library.
 
 use crate::error::InferaError;
@@ -213,7 +212,11 @@ pub(crate) fn run_inference_blob_impl(
     let blob_bytes = unsafe { std::slice::from_raw_parts(blob_data, blob_len) };
     let float_vec: Vec<f32> = blob_bytes
         .chunks_exact(4)
-        .map(|chunk| f32::from_ne_bytes(chunk.try_into().unwrap()))
+        .map(|chunk| {
+            // SAFETY: chunks_exact(4) guarantees exactly 4 bytes, so this conversion cannot fail
+            let array: [u8; 4] = chunk.try_into().unwrap_or_default();
+            f32::from_ne_bytes(array)
+        })
         .collect();
     let expected_elements: usize = model
         .input_shape

infera/src/error.rs

@@ -1,4 +1,3 @@
-// src/error.rs
 // Contains the InferaError enum and thread-local error handling logic.
 
 use std::cell::RefCell;