Search Results for "fastapi"

...The context window extends up to 32K (FlashAttention), and Multi-Query Attention improves speed and memory use. The repo includes Python APIs, CLI & web demos, OpenAI-style/FASTAPI servers, and quantized checkpoints for lightweight local deployment on GPUs or CPU/MPS.

...The project implements a lightweight web service that allows developers to define extraction schemas and apply them to various sources such as plain text, HTML, or PDF documents. Built using FastAPI and the LangChain framework, the application exposes a REST API that can process documents and return structured outputs that match user-defined JSON schemas. Developers can create reusable “extractors” that define what type of information should be pulled from a document, along with example prompts that improve extraction quality through in-context learning.

Easy-to-use LLM fine-tuning framework (LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, ChatGLM2)

gemini-fullstack-langgraph-quickstart is a fullstack reference application from Google DeepMind’s Gemini team that demonstrates how to build a research-augmented conversational AI system using LangGraph and Google Gemini models. The project features a React (Vite) frontend and a LangGraph/FastAPI backend designed to work together seamlessly for real-time research and reasoning tasks. The backend agent dynamically generates search queries based on user input, retrieves information via the Google Search API, and performs reflective reasoning to identify knowledge gaps. It then iteratively refines its search until it produces a comprehensive, well-cited answer synthesized by the Gemini model. ...

Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers.

...The project focuses on simplifying the usual stack of model selection, document ingestion, vector storage, querying, and API deployment into a more unified developer experience. Its core idea is that a developer can create a query engine from a document set in just a few lines and then turn that same engine into a FastAPI application almost instantly. AutoLLM supports a broad range of language models and vector databases, which makes it useful for teams that want flexibility without rewriting their application architecture every time they switch providers. The framework also includes built-in readers for multiple content sources such as PDFs, DOCX files, notebooks, websites, and other document types, which helps shorten the time between raw data and a working knowledge application.

LangCorn is an API server that enables you to serve LangChain models and pipelines with ease, leveraging the power of FastAPI for a robust and efficient experience.

...And if you prefer, you can also deploy your LangChain apps on your own infrastructure to ensure data privacy. With long chain-serve, you can craft REST/WebSocket APIs, spin up LLM-powered conversational Slack bots, or wrap your LangChain apps into FastAPI packages on the cloud or on-premises.