Personalizing
LLM Responses
Embedchain streamlines deploying personalized LLM apps in production, with support for both conventional and configurable approaches.
pip install embedchain
Backed by leaders from
Conventional but Configurable
Embedchain simplifies personalized LLM application development by efficiently processing unstructured data. It segments data, creates relevant embeddings, and stores them in a vector database for quick retrieval.
Rapidly deploy personalized LLM apps to production
Simple but Customizable APIs
Quickly launch your first personalized LLM application using Embedchain's user-friendly and customizable APIs.
Popular Large Language Models
Effortlessly incorporate your favorite LLMs from various providers such as OpenAI, Anthropic, Claude, HuggingFace, and Mistral etc.
Vector Stores
Effortlessly integrate your existing vector databases like Pinecone, Elasticsearch, OpenSearch, ChromaDB, Qdrant, and others.
Load data from anywhere
Seamlessly integrate with diverse data sources like PDF, CSV, Postgres, Notion, Slack, Discord, and GitHub & many more.
Deployment support
Rapidly deploy personalized LLM applications on platforms like AWS, Azure, GCP, Fly.io, and Render.com etc.
Built-in Observability
Embedchain's built-in observability streamlines LLM app debugging and accelerates development.
