{"repo":"decisionfacts/semantic-ai","free":true,"listed":false,"github":"https://github.com/decisionfacts/semantic-ai","clone":"git clone https://github.com/decisionfacts/semantic-ai.git","description":"An open source framework for Retrieval-Augmented System (RAG) uses semantic search helps to retrieve the expected results and generate human readable conversational response with the help of LLM (Large Language Model).","language":"Python","stars":22,"topics":["approximate-nearest-neighbor-search","deep-neural-networks","document-parser","docx","fastapi","inference-api","llama2","llm","machine-learning","ocr"],"license":"Apache-2.0","category":"ai-agents","readme_excerpt":"Semantic AI Lib An open-source framework for Retrieval-Augmented System (RAG) uses semantic search to retrieve the expected results and generate human-readable conversational responses with the help of LLM (Large Language Model). Semantic AI Library Documentation Docs here Requirements Python 3.10+ asyncio Installation Set the environment variable Set the credentials in .env file. Only give the credential for an one connector, an one indexer and an one llm model config. other fields put as empty Method 1: To load the .env file. Env file should have the credentials Method 2: Un-Structure 1. Import the module 2. To download the files from a given source, extract the content from the downloaded files and index the extracted data in the given vector db. After completion of download, extract and index, we can generate the answer from indexed vector db. That code given below. 3. To generate the answer from indexed vector db using retrieval LLM model. Suppose the job is running for a long time, we can watch the number of files processed, the number of files failed, and that filename stored in the text file that is processed and failed in the 'EXTRACTED DIR PATH/meta' directory. Example To connect the source and get the connection object. We can see that in the examples folder. Example: SharePoint connector Structure 1. Import the module 2. The database connection Sqlite: Mysql: 3. To generate the answer from db using retrieval LLM model. Run in the server Open your browser at http:/","default_branch":null,"files":null,"tree":[],"storefront":"/r/decisionfacts","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/decisionfacts/semantic-ai/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}