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Vector Store API

Vector Store API is a Python project that uses FastAPI, LangChain, and Chroma to accept text or files, embed the input, and store vectors with metadata. The README describes upload and storage as implemented, while its similarity-search endpoint remains unfinished.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Vector Store API — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Consider it when you need a starting point for API-based embedding ingestion into Chroma and can complete or supply any unfinished retrieval functionality.

Who it is for

  • Developers building a RAG application who need a content-ingestion and vector-storage component.
  • Python developers adapting a FastAPI and LangChain project to store embeddings in Chroma.

Documented uses

  • Accept text or file input through an API endpoint.
  • Convert submitted content into vectors using an embedding library or service.
  • Store vectors and associated metadata in Chroma.
Before you choose

Requirements & limits

  • The README marks the upload-and-store endpoint complete but leaves the similarity-search endpoint unfinished.
  • Embedding depends on an external library or service; the README gives Hugging Face Transformers for text and TensorFlow for images as examples.
  • The documented setup names Python virtual environments, FastAPI, Uvicorn, and a Chroma client.
  • The repository states that the project is MIT-licensed.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare a local development environment

Set up the listed project dependencies for local development and testing.

Before you start

  • A local copy of the project.
  • Python virtual-environment support.
  • pip.
  • A choice of Chroma or another vector database client library.

Steps

  1. Create a virtual environment to manage project dependencies.

  2. Install FastAPI and Uvicorn using pip.

  3. Install Chroma or another vector database client library for the project.

Expected outcome

The documented environment prerequisites for local API development are prepared.

Guide limitations

  • These setup steps do not document how to launch the API or submit requests.
  • The README marks search-endpoint implementation and testing as incomplete.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
ehsanghaffar/vector-store-api · Official homepage title · retained in its source language
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