Tech With Tim·1yHow to Build a Local AI Agent With Python (Ollama, LangChain & RAG)
Learn how to build a local AI agent using Python, LangChain, Ollama, and ChromaDB. The project demonstrates setting up an AI to query and interpret data from a CSV file, such as restaurant reviews, using retrieval augmented generation. All processes run locally without requiring external accounts or cloud services, making it a highly accessible project.
AI·1yWant AI to Actually Understand Your Code? This Tool Says It Can Help
CocoIndex is a tool designed to index and query your codebase, facilitating the construction of a data pipeline. It uses Tree-sitter to intelligently chunk code based on syntax and provides built-in Rust integration. The process involves reading code files, extracting extensions, chunking code, generating embeddings, and storing them in a vector database. CocoIndex supports various languages and allows users to embed code using models from Hugging Face. It leverages Postgres for managing the data index, with plans to support other databases.
Couchbase·1yA Guide to LLM Embeddings
LLM embeddings are numerical representations generated by large language models (LLMs) like GPT and BERT, capturing semantic meaning to enable efficient text processing, similarity search, and retrieval. They are used in various applications such as search engines, recommendation systems, and AI agents. Embeddings can be fine-tuned for specific domains and integrated into solutions using tools like Couchbase Capella. The key components of LLMs include tokenization, embedding layers, attention mechanisms, and feedforward layers. Different types of embeddings serve various tasks and can be tailored for optimal performance based on the use case.
Baeldung·1yImplementing Semantic Search Using Spring AI and PGVector
Learn to implement a semantic search application using Spring AI, PGVector, and Ollama. This tutorial covers the basics of semantic search, including key concepts like word embeddings, semantic similarity, and cosine similarity. You'll set up a Spring Boot project, configure it for PGVector, and enhance it with an Ollama LLM for more context-aware responses. Example code for ingesting book data and performing searches highlights practical implementation steps.