Search Results for "build"

...By adapting LangChain concepts to the Rust programming language, the project emphasizes performance, safety, and efficient memory management. Developers can use the framework to build chatbots, autonomous agents, and knowledge-augmented AI systems that interact with external data sources. The library provides abstractions for model providers, prompt templates, conversation memory, and vector search integrations. It also enables the construction of multi-step pipelines where LLM outputs feed into subsequent actions or tool calls.

Korvus is an open-source retrieval-augmented generation (RAG) pipeline designed to run entirely inside PostgreSQL, allowing developers to build AI search and knowledge systems directly within a database environment. The project consolidates the typical steps of a RAG pipeline—including embedding generation, document retrieval, reranking, and text generation—into a single query executed within the Postgres ecosystem. By leveraging PostgresML and vector extensions such as pgvector, Korvus eliminates the need for external microservices typically used for AI search architectures, reducing both system complexity and latency. ...

...With llm-chain, you can focus on building powerful AI applications. Create reusable and easily customizable prompt templates for consistent and structured interactions with LLMs. Build powerful chains of prompts that allow you to execute more complex tasks, step by step, leveraging the full potential of LLMs. Provides seamless integration with LLaMa models, enabling natural language understanding and generation tasks with Facebook's research models. Incorporates support for Stanford's Alpaca models, expanding the range of available language models for advanced AI applications. ...

"Building Large Language Model Applications: Application Development and Architecture Design" is an open source e-book about the real-world application of LLM. It introduces the basics and applications of large language models, as well as how to build your own models. These include writing, developing, and managing prompts, exploring what the best large language models can bring, and pattern and architecture design for LLM application development.

...It works by decomposing larger objectives into smaller steps and gathering information from the internet and other outside sources as needed. The project is written in Rust and emphasizes modularity, allowing developers to compose different “Autos” depending on the workflow they want to build. Its architecture separates responsibility between a dynamic agent that reasons about what to do next and a static agent that plans and executes tool chains in a defined order. The repository describes this approach as a way to improve flexibility and consistency compared with simpler agent loops, while still acknowledging that the project is highly experimental and not focused on backward compatibility.