Embeddings on AI Knowledge Base

RAG from Scratch

Mon, 01 Jan 0001 00:00:00 +0000

Build a complete Retrieval-Augmented Generation pipeline from the ground up. Learn embeddings, vector search, reranking, and how to wire retrieval into LLM generation with citations.

This path draws on Cohere (strong RAG docs), Pinecone (vector DB), and LangChain (orchestration).

Actions in ChatKit

Mon, 01 Jan 0001 00:00:00 +0000

Embed a widget to build your own chat experiences.

Amazon Bedrock

Mon, 01 Jan 0001 00:00:00 +0000

An Overview of The Cohere Platform

Mon, 01 Jan 0001 00:00:00 +0000

Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications.

An Overview of the Developer Playground

Mon, 01 Jan 0001 00:00:00 +0000

The Cohere Playground is a powerful visual interface for testing Cohere’s generation and embedding language models without coding.

Architecture

Mon, 01 Jan 0001 00:00:00 +0000

Learn how Pinecone’s architecture enables fast, relevant vector search at any scale.

Article Recommender via Embedding & Classification

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to build a generative-AI tool to recommend articles with Cohere.

Baseten

Mon, 01 Jan 0001 00:00:00 +0000

Batch Embedding Jobs with the Embed API

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use the Embed Jobs API to handle large text data efficiently with a focus on creating datasets and running embed jobs.

Building a RAG Workflow

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to build a RAG workflow with Together AI embedding and chat endpoints!

ChatKit

Mon, 01 Jan 0001 00:00:00 +0000

Embed a widget to build your own chat experiences.

Check data freshness

Mon, 01 Jan 0001 00:00:00 +0000

Monitor data freshness in Pinecone using log sequence numbers and vector counts.

Chroma BM25

Mon, 01 Jan 0001 00:00:00 +0000

Chroma Cloud Qwen

Mon, 01 Jan 0001 00:00:00 +0000

Chroma Cloud Splade

Mon, 01 Jan 0001 00:00:00 +0000

Cloudera AI

Mon, 01 Jan 0001 00:00:00 +0000

Vector embedding, RAG, and semantic search at scale

Cloudflare Workers AI

Mon, 01 Jan 0001 00:00:00 +0000

Code Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Code embeddings enable retrieval, clustering, and analytics for code databases and coding assistants using Mistral AI’s API

Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Using Cohere and Pinecone to generate and index high-quality vector embeddings

Cohere and LangChain (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

Integrate Cohere with LangChain for advanced chat features, RAG, embeddings, and reranking; this guide includes code examples for each feature.

Cohere Embed on LangChain (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to work with Cohere’s embeddings models and LangChain.

Cohere Tools on LangChain (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

Explore code examples for multi-step and single-step tool usage in chatbots, harnessing internet search and vector storage.

Cohere's Embed Models (Details and Application)

Mon, 01 Jan 0001 00:00:00 +0000

Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints.

Compare Embeddings for retriever

Mon, 01 Jan 0001 00:00:00 +0000

Configure Collections

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to configure Chroma collection index settings and embedding functions.

Create and manage vectors with metadata

Mon, 01 Jan 0001 00:00:00 +0000

Data retrieval with GPT Actions

Mon, 01 Jan 0001 00:00:00 +0000

Learn about performing data retrieval using APIs, relational databases, and vector databases with GPT Actions.

Databricks

Mon, 01 Jan 0001 00:00:00 +0000

Using Databricks and Pinecone to create and index vector embeddings at scale

Embed a report

Mon, 01 Jan 0001 00:00:00 +0000

Embed W&B reports directly into Notion or with an HTML IFrame element.

Embed objects

Mon, 01 Jan 0001 00:00:00 +0000

W&B’s Embedding Projector allows users to plot multi-dimensional embeddings on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE.

Embedding Functions

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to use embedding functions in Chroma to create vector representations of your data.

Embedding Functions

Mon, 01 Jan 0001 00:00:00 +0000

Embedding Functions

Mon, 01 Jan 0001 00:00:00 +0000

Embedding values changed when upserted

Mon, 01 Jan 0001 00:00:00 +0000

Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to get an embedding vector for a given text input.

Embeddings & Reranking

Mon, 01 Jan 0001 00:00:00 +0000

Generate embeddings and rerank results for semantic search

Embeddings Overview

Mon, 01 Jan 0001 00:00:00 +0000

Mistral’s Embeddings API for text and code vector representations — supporting retrieval, clustering, and classification with open-weight models.

End-to-end example of RAG with Chat, Embed, and Rerank

Mon, 01 Jan 0001 00:00:00 +0000

Guide on using Cohere’s Retrieval Augmented Generation (RAG) capabilities covering the Chat, Embed, and Rerank endpoints (API v2).

Generate dense embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Generate dense vector embeddings for the given texts using the specified model. Provide either ‘instructions’ or both ’task’ and ’target’ alongside ’texts’.

Generate sparse embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Generate sparse vector embeddings for the given texts using the specified model. Provide either ‘instructions’ or both ’task’ and ’target’ alongside ’texts’. Set ‘fetch_labels’ to true to include token labels in the response.

Glossary

Mon, 01 Jan 0001 00:00:00 +0000

Glossary of key AI and LLM terms, including LLMs, text generation, tokens, MoE, RAG, fine-tuning, function calling, embeddings, and temperature

Google Gemini

Mon, 01 Jan 0001 00:00:00 +0000

Guides Using Custom Embedding Models

Mon, 01 Jan 0001 00:00:00 +0000

Haystack and Cohere (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

Build custom LLM applications with Haystack, now integrated with Cohere for embedding, generation, chat, and retrieval.

Hugging Face

Mon, 01 Jan 0001 00:00:00 +0000

Hugging Face Inference Endpoints

Mon, 01 Jan 0001 00:00:00 +0000

Using Hugging Face Inference Endpoints and Pinecone to generate and index high-quality vector embeddings

Hugging Face Server

Mon, 01 Jan 0001 00:00:00 +0000

Hybrid Search with RRF

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to combine multiple ranking strategies using Reciprocal Rank Fusion (RRF). RRF is ideal for hybrid search scenarios where you want to merge results from different ranking methods (e.g., dense and sparse embeddings).

Instructor

Mon, 01 Jan 0001 00:00:00 +0000

Integrating Embedding Models with Other Tools

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to integrate Cohere embeddings with open-source vector search engines for enhanced applications.

Introduction to Embeddings at Cohere

Mon, 01 Jan 0001 00:00:00 +0000

Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression.

Jina AI

Mon, 01 Jan 0001 00:00:00 +0000

Milvus and Cohere (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes integrating Cohere with the Milvus vector database.

Mistral

Mon, 01 Jan 0001 00:00:00 +0000

MongoDB and Cohere (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

Build semantic search and RAG systems using Cohere and MongoDB Atlas Vector Search.

MongoDB Vector Search Tool

Mon, 01 Jan 0001 00:00:00 +0000

The ‘MongoDBVectorSearchTool’ performs vector search on MongoDB Atlas with optional indexing helpers.

Morph

Mon, 01 Jan 0001 00:00:00 +0000

Multimodal Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to work with multimodal data in Chroma collections.

Nomic

Mon, 01 Jan 0001 00:00:00 +0000

Ollama

Mon, 01 Jan 0001 00:00:00 +0000

OpenAI

Mon, 01 Jan 0001 00:00:00 +0000

OpenCLIP

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Mon, 01 Jan 0001 00:00:00 +0000

Connect to databases, vector stores, and data warehouses for comprehensive data access

Perplexity

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone and Cohere (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to integrate Cohere with the Pinecone vector database.

Pinecone documentation

Mon, 01 Jan 0001 00:00:00 +0000

Pinecone is the leading vector database for building accurate and performant AI applications at scale in production.

Qdrant and Cohere (Integration Guide)

Mon, 01 Jan 0001 00:00:00 +0000

This page describes how to integrate Cohere with the Qdrant vector database.

Qdrant Vector Search Tool

Mon, 01 Jan 0001 00:00:00 +0000

Semantic search capabilities for CrewAI agents using Qdrant vector database

Quickstart

Mon, 01 Jan 0001 00:00:00 +0000

Quickstart guide for setting up a Mistral AI account, configuring billing, and using the API for models and embeddings

RAG Integrations

Mon, 01 Jan 0001 00:00:00 +0000

RAG With Chat Embed and Rerank via Pinecone

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic tutorial on how to build a RAG-powered chatbot.

Return all vectors in an index

Mon, 01 Jan 0001 00:00:00 +0000

Roboflow

Mon, 01 Jan 0001 00:00:00 +0000

Semantic search

Mon, 01 Jan 0001 00:00:00 +0000

Find semantically similar records using dense vectors.

Semantic search - Cohere on Azure AI Foundry

Mon, 01 Jan 0001 00:00:00 +0000

A guide for performing text semantic search with Cohere’s Embed models on Azure AI Foundry (API v2).

Semantic search - quickstart

Mon, 01 Jan 0001 00:00:00 +0000

A quickstart guide for performing text semantic search with Cohere’s Embed models (v2 API).

Semantic Search with Cohere Embed Jobs

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic tutorial on how to use Cohere’s Embed Jobs functionality.

Semantic Search with Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Examples on how to use the Embed endpoint to perform semantic search (API v2).

Sentence Transformer

Mon, 01 Jan 0001 00:00:00 +0000

Serverless Pricing

Mon, 01 Jan 0001 00:00:00 +0000

Per-token serverless pricing for text, vision, and embedding models, including Priority and Fast serving paths

Serverless Semantic Search with Cohere and Pinecone

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a basic tutorial on how to get Cohere and the Pinecone vector database to work well together.

Sparse Vector Search Setup

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to configure and use sparse vectors for keyword-based search, and combine them with dense embeddings for powerful hybrid search capabilities.

Text Classification Using Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

This page discusses the creation of a text classification model using word vector embeddings.

Text Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Generate and use text embeddings with Mistral AI’s API for NLP tasks like similarity, classification, and retrieval

Text2Vec

Mon, 01 Jan 0001 00:00:00 +0000

Together AI

Mon, 01 Jan 0001 00:00:00 +0000

Unlocking the Power of Multimodal Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Multimodal embeddings convert text and images into embeddings for search and classification (API v2).

Update records

Mon, 01 Jan 0001 00:00:00 +0000

Update vectors and metadata for existing records

Vector embeddings

Mon, 01 Jan 0001 00:00:00 +0000

Learn how to turn text into numbers, unlocking use cases like search, clustering, and more with OpenAI API embeddings.

Visualization

Mon, 01 Jan 0001 00:00:00 +0000

Embed tracing dashboards and visualize agent runs directly in notebooks and web apps.

Voyage AI

Mon, 01 Jan 0001 00:00:00 +0000

Using Voyage AI and Pinecone to generate and index high-quality vector embeddings

VoyageAI

Mon, 01 Jan 0001 00:00:00 +0000

Weaviate Vector Search

Mon, 01 Jan 0001 00:00:00 +0000

The ‘WeaviateVectorSearchTool’ is designed to search a Weaviate vector database for semantically similar documents using hybrid search.

Web QA with embeddings

Mon, 01 Jan 0001 00:00:00 +0000

How to build an AI that can answer questions about your website.

Wikipedia Semantic Search with Cohere + Weaviate

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a description of building a Wikipedia-focused search engine with Cohere’s LLM platform and the Weaviate vector database.

Wikipedia Semantic Search with Cohere Embedding Archives

Mon, 01 Jan 0001 00:00:00 +0000

This page contains a description of building a Wikipedia-focused semantic search engine with Cohere’s LLM platform and the Weaviate vector database.