docs(frontend): add documentation for concrete-rust

docs/SUMMARY.md

@@ -38,6 +38,7 @@
 * [Debugging and artifact](execution-analysis/debug.md)
 * [Performance](optimization/summary.md)
 * [GPU acceleration](execution-analysis/gpu_acceleration.md)
+* [Rust integration](execution-analysis/rust_integration.md)
 * Other
   * [Statistics](compilation/statistics.md)
   * [Progressbar](execution-analysis/progressbar.md)

docs/execution-analysis/rust_integration.md

@@ -0,0 +1,206 @@
+# Rust Integration
+
+This document explains how to use Fully Homomorphic Encryption (FHE) modules developed with **concrete-python** directly in Rust programs using the **Concrete** toolchain.
+
+This workflow enables rapid prototyping in Python and seamless deployment in Rust, combining the flexibility of Python with the safety and performance of Rust.
+
+## Overview
+
+- Write and compile FHE modules in Python using `concrete-python`.
+- Import the compiled module artifact into Rust using the `concrete-macro` crate.
+- Use the generated Rust APIs for encryption, evaluation, and decryption.
+
+## Prerequisites
+
+- Python 3.8+
+- Rust 1.70+
+- `concrete-python` (>=2.10)
+- `concrete` and `concrete-macro` Rust crates (>=2.10.1-rc1)
+
+## Regular example
+
+### Step 1: Define and Compile a Module in Python
+
+Write your FHE logic in Python and compile it to an artifact compatible with the Rust toolchain. Here is an example of a small and simple module:
+
+```python
+from concrete import fhe
+
+@fhe.module()
+class MyModule:
+    @fhe.function({"x": "encrypted"})
+    def inc(x):
+        return (x + 1) % 256
+
+    @fhe.function({"x": "encrypted"})
+    def dec(x):
+        return (x - 1) % 256
+
+inputset = fhe.inputset(fhe.uint8)
+module = MyModule.compile({"inc": inputset, "dec": inputset})
+
+module.server.save(path="MyModule.zip", via_mlir=True)
+```
+
+This produces a `MyModule.zip` artifact containing the compiled FHE module.
+
+### Step 2: Set Up the Rust Project
+
+Initialize a new Rust project and add the required dependencies.
+
+```shell
+cargo init
+cargo add concrete@=2.10.1-rc1 concrete-macro@=2.10.1-rc1
+```
+
+Place the `MyModule.zip` artifact in your project directory.
+
+### Step 3: Import the Python-Compiled Module in Rust
+
+Use the `concrete_macro::from_concrete_python_export_zip!` macro to import the module at build time.
+
+```rust
+mod my_module {
+    use concrete_macro::from_concrete_python_export_zip;
+    from_concrete_python_export_zip!("MyModule.zip");
+}
+```
+
+This macro unpacks the artifact, triggers recompilation, reads metadata, and generates Rust APIs for the module's functions.
+
+### Step 4: Use the Module in Rust
+
+You can now use the FHE functions in Rust. The following example demonstrates a full FHE workflow:
+
+```rust
+use concrete::common::Tensor;
+
+fn main() {
+    // Prepare input and expected output tensors
+    let input = Tensor::new(vec![5], vec![]);
+    let expected_output = Tensor::new(vec![6], vec![]);
+
+    // Key generation
+    let mut secret_csprng = concrete::common::SecretCsprng::new(0u128);
+    let mut encryption_csprng = concrete::common::EncryptionCsprng::new(0u128);
+    let keyset = my_module::new_keyset(secret_csprng.pin_mut(), encryption_csprng.pin_mut());
+    let client_keyset = keyset.get_client();
+
+    // Create client stub for the 'inc' function
+    let mut inc_client = my_module::client::inc::ClientFunction::new(&client_keyset, encryption_csprng);
+
+    // Encrypt input and obtain evaluation keys
+    let encrypted_input = inc_client.prepare_inputs(input);
+    let evaluation_keys = keyset.get_server();
+
+    // Create server stub for the 'inc' function
+    let mut inc_server = my_module::server::inc::ServerFunction::new();
+
+    // Evaluate the function on encrypted data
+    let encrypted_output = inc_server.invoke(&evaluation_keys, encrypted_input);
+
+    // Decrypt the output
+    let decrypted_output = inc_client.process_outputs(encrypted_output);
+
+    // Check correctness
+    assert_eq!(decrypted_output.values(), expected_output.values());
+}
+```
+
+## TFHE-rs Ciphertext Interoperability
+
+Starting from Concrete v2.11, you can define and use modules that operate directly on TFHE-rs ciphertexts, enabling seamless interoperability between Concrete and TFHE-rs in Rust.
+
+### Step 1: Define and Compile a Module with TFHE-rs Types in Python
+
+You can define a module in Python that uses TFHE-rs integer types as arguments and outputs. For example:
+
+```python
+from concrete import fhe
+from concrete.fhe import tfhers
+
+TFHERS_UINT_8_3_2_4096 = tfhers.TFHERSIntegerType(
+    False,
+    bit_width=8,
+    carry_width=3,
+    msg_width=2,
+    params=tfhers.CryptoParams(
+        lwe_dimension=909,
+        glwe_dimension=1,
+        polynomial_size=4096,
+        pbs_base_log=15,
+        pbs_level=2,
+        lwe_noise_distribution=0,
+        glwe_noise_distribution=2.168404344971009e-19,
+        encryption_key_choice=tfhers.EncryptionKeyChoice.BIG,
+    ),
+)
+
+@fhe.module()
+class MyModule:
+
+    @fhe.function({"x": "encrypted", "y": "encrypted"})
+    def my_func(x, y):
+        x = tfhers.to_native(x)
+        y = tfhers.to_native(y)
+        return tfhers.from_native(x + y, TFHERS_UINT_8_3_2_4096)
+
+def t(v):
+    return tfhers.TFHERSInteger(TFHERS_UINT_8_3_2_4096, v)
+
+inputset = [(t(0), t(0)), (t(2**6), t(2**6))]
+my_module = MyModule.compile({"my_func": inputset})
+my_module.server.save("test_tfhers.zip", via_mlir=True)
+```
+
+This produces a `test_tfhers.zip` artifact compatible with Rust and TFHE-rs.
+
+### Step 2: Use the Module with TFHE-rs Ciphertexts in Rust
+
+You can import and use the module in Rust, passing and receiving native TFHE-rs ciphertexts:
+
+```rust
+mod precompile {
+    use concrete_macro::from_concrete_python_export_zip;
+    from_concrete_python_export_zip!("src/test_tfhers.zip");
+}
+
+use tfhe::prelude::{FheDecrypt, FheEncrypt};
+use tfhe::shortint::parameters::v0_10::classic::gaussian::p_fail_2_minus_64::ks_pbs::V0_10_PARAM_MESSAGE_2_CARRY_3_KS_PBS_GAUSSIAN_2M64;
+use tfhe::{generate_keys, FheUint8};
+
+fn main() {
+    // Key generation for TFHE-rs
+    let config = tfhe::ConfigBuilder::with_custom_parameters(V0_10_PARAM_MESSAGE_2_CARRY_3_KS_PBS_GAUSSIAN_2M64);
+    let (client_key, _) = generate_keys(config);
+
+    // Build Concrete keyset with TFHE-rs client key
+    let mut secret_csprng = concrete::common::SecretCsprng::new(0u128);
+    let mut encryption_csprng = concrete::common::EncryptionCsprng::new(0u128);
+    let keyset = precompile::KeysetBuilder::new()
+        .with_key_for_my_func_0_arg(&client_key)
+        .generate(secret_csprng.pin_mut(), encryption_csprng.pin_mut());
+    let server_keyset = keyset.get_server();
+
+    // Encrypt inputs using TFHE-rs
+    let arg_0 = FheUint8::encrypt(6u8, &client_key);
+    let arg_1 = FheUint8::encrypt(4u8, &client_key);
+
+    // Evaluate the Concrete circuit on TFHE-rs ciphertexts
+    let mut server = precompile::server::my_func::ServerFunction::new();
+    let output = server.invoke(&server_keyset, arg_0, arg_1);
+
+    // Decrypt the result using TFHE-rs
+    let decrypted: u8 = output.decrypt(&client_key);
+    assert_eq!(decrypted, 10);
+}
+```
+
+This workflow allows you to combine the high-level graph optimizations of Concrete with the operator-level flexibility of TFHE-rs, all within Rust.
+
+## Notes
+
+- The module must be compiled with `via_mlir=True` to be loaded in the Rust program.
+- The Rust API is currently in beta and may evolve in future releases.
+- The Python and Rust environments must use compatible versions of the Concrete toolchain.
+- When using TFHE-rs ciphertext interoperability, ensure that the TFHE-rs client key used for encryption matches the one registered in the Concrete keyset for the corresponding argument.

docs/get-started/quick_start.md

@@ -107,7 +107,7 @@ inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1), (3, 2), (6, 1), (1, 7), (4,
 Here, our inputset consists of 10 integer pairs, ranging from a minimum of `(0, 0)` to a maximum of `(7, 7)`.
 
 {% hint style="warning" %}
-Choosing a representative inputset is critical to allow the compiler to find accurate bounds of all the intermediate values (see more details [here](https://docs.zama.ai/concrete/explanations/compilation#bounds-measurement)). Evaluating the circuit with input values under or over the bounds may result in undefined behavior.
+Choosing a representative inputset is critical to allow the compiler to find accurate bounds of all the intermediate values (see more details [here](https://docs.zama.ai/concrete/explanations/compiler_workflow#bounds-measurement)). Evaluating the circuit with input values under or over the bounds may result in undefined behavior.
 {% endhint %}
 
 {% hint style="warning" %}

frontends/concrete-python/examples/sha1/README.md

@@ -22,7 +22,7 @@ implementation.
 In our implementation, only the compression function is implemented in FHE, corresponding to the
 `_process_encrypted_chunk_server_side function`. The rest is done client-side in the clear,
 including the message expansion. While more of the process could be done in FHE, this tutorial
-focuses on demonstrating the use of [Modules](https://docs.zama.ai/concrete/compilation/modules).
+focuses on demonstrating the use of [Modules](https://docs.zama.ai/concrete/compilation/combining/composing_functions_with_modules).
 
 Our Module contains 7 functions which can be combined together:
 - `xor3` XORs three values together