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RAG & Knowledge Systems

Semantic AI Lib

Semantic AI Lib is an open-source Python framework for building retrieval-augmented generation systems. It downloads and extracts source files, indexes their content in a configured vector database, and uses semantic retrieval with an LLM to generate conversational responses.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Semantic AI Lib — 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

Use it when you are assembling a Python RAG application and can configure the document source, indexer and LLM that the pipeline needs.

Who it is for

  • Python developers building document-question-answering or knowledge-base chat applications
  • Teams connecting organizational content sources to vector search and LLM responses
  • Developers who want to expose a configured RAG workflow through a server

Documented uses

  • Download documents from a configured connector and extract their content.
  • Index extracted material in a vector database for semantic retrieval.
  • Submit a query against indexed content and generate a conversational answer with an LLM.
  • Start the framework's documented server and consult its interactive API documentation.
Before you choose

Requirements & limits

The project is described as open source under the Apache-2.0 license. The README requires Python 3.10 or later with asyncio and documents pip installation.

  • Configuration requires credentials for a selected connector, indexer and LLM; the README recommends configuring one of each at a time.
  • Named connector examples include SharePoint, S3, Google Cloud Storage, Google Drive and Confluence.
  • Named indexer options include Elasticsearch, Qdrant and OpenSearch.
  • The README includes SQLite and MySQL database-search examples in addition to its vector-indexing workflow.
  • The documented application workflow separates downloading, extraction, indexing and query generation into asynchronous stages.

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

Set up a RAG pipeline configuration

Install the framework and configure one document connector, one indexer and one LLM.

Before you start

  • Python 3.10 or later with asyncio
  • Credentials for one selected connector, indexer and LLM; use the matching settings shown in the README

Steps

  1. Install with the command below.

    Documented command
    python -m pip install semantic-ai

  2. Create a .env file with the settings and credentials for one connector, one indexer and one LLM; leave unrelated configuration fields empty.

  3. Load the settings from the environment file using the documented dotenv method, or initialize Settings from semantic_ai.config.

Expected outcome

The framework is installed and its settings are initialized for the selected source, indexer and model.

Guide limitations

  • This setup does not run the download, extraction, indexing or answer-generation stages.
  • The selected external services and their credentials must be configured separately.
Documentation 1 ↗
Source-based practical guide

Run the documented file-to-answer workflow

Download configured source files, extract and index their contents, then generate an answer from the indexed vector database.

Before you start

  • Python 3.10 or later with asyncio
  • An initialized configuration for one connector, one indexer and one LLM
  • Access credentials and settings for the selected external services

Steps

  1. Import asyncio and semantic_ai in Python.

  2. Call await semantic_ai.download() to download files from the configured source.

  3. Call await semantic_ai.extract() and then await semantic_ai.index().

  4. Create a search object with await semantic_ai.search() and generate a response with await search_obj.generate(query).

Expected outcome

The documented sequence indexes extracted source content and passes a query to the retrieval-and-generation workflow.

Guide limitations

  • The result depends on a valid connector, indexer and LLM configuration.
  • The README describes the workflow but does not specify a particular document format or guarantee the quality of generated answers.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Semantic AI LibDownload documents from a configured connector and extract their content.ServerThe project is described as open source under the Apache-2.0 license. The README requires Python 3.10 or later with asyncio and documents pip installation.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
decisionfacts/semantic-ai · Official homepage title · retained in its source language
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