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Getting started with instinct.cpp library

Install from package manager

WIP #1

Build from sources

Build requirements:

  • CMake 3.26+
  • Compiler that supports C++ 20: GCC 12+ or Clang 15+
  • Conan 2+ (Optional)

Simple run the install command:

conan install conanfile.py --build=missing --output-folder=build

As I am still working on publishing this package to conan center, you have to install to local conan cache:

conan create .

Quick start

In your project's conanfile.py, add instinct-cpp as requirement. A working example is hosted here.

from conan import ConanFile
from conan.tools.build import check_min_cppstd

class YourRecipe(ConanFile):
    settings = "os", "compiler", "build_type", "arch"
    generators = "CMakeToolchain", "CMakeDeps"
    
    def validate(self):
        check_min_cppstd(self, 20)

    def requirements(self):
        # toggle off all switches
        # you can turn on for further experiments
        self.requires("instinct-cpp/0.1.5")

And prepare dependencies:

conan install conanfile.py --build=missing

Once installed, let's build a simple text completion task using Ollama API.

#include <instinct/chain/message_chain.hpp>
#include <instinct/input_parser/PromptValueVariantInputParser.hpp>
#include <instinct/chat_model/ollama_chat.hpp>
#include <instinct/output_parser/string_output_parser.hpp>
#include <instinct/prompt/plain_prompt_template.hpp>

int main() {
    using namespace INSTINCT_CORE_NS;
    using namespace INSTINCT_LLM_NS;

    const auto input_parser = CreatePromptVariantInputParser();
    const auto string_prompt = CreatePlainPromptTemplate("Answer following question in one sentence: {question}");
    const auto output_parser = CreateStringOutputParser();
    const auto chat_model = CreateOllamaChatModel();
    const auto xn = input_parser | string_prompt |  chat_model->AsModelFunction() | output_parser;
    const auto result = xn->Invoke("Why sky is blue?");
    std::cout << result <<std::endl;
}

What's next

You can learn more about frameworks by following links below: