Rag on AI Knowledge Base

Emerging Architectures for LLM Applications

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

Reference architecture for the LLM application stack, covering data pipelines, embedding models, vector databases, orchestration frameworks, and operational tooling.

LangChain for LLM Application Development

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

Harrison Chase (LangChain CEO) and Andrew Ng’s one-hour introduction to building LLM applications with LangChain. Covers chains, memory, prompts, and agents. The fastest way to understand what LangChain does and whether it’s the right framework for your use case. Pair with the RAG from Scratch learning path to go deeper into retrieval-augmented generation patterns.

Building Agentic RAG with LlamaIndex

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

Combines RAG with agentic patterns using LlamaIndex — covering router queries, tool retrieval, and multi-document agents. This goes beyond basic RAG (query → retrieve → generate) to show how agents can dynamically choose retrieval strategies and combine multiple data sources. Take this after understanding basic RAG concepts from the RAG from Scratch learning path.

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).

LLM Course

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

Hugging Face’s newer LLM-specific course covering the full stack: using LLMs, building LLM applications, fine-tuning, and deploying. More focused on the modern LLM workflow than the NLP course. Includes RAG, quantization, and model evaluation. Take this for an open-source-first perspective on the same topics covered by the provider-specific learning paths in this knowledge base.

Evaluation & Testing

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

Build a comprehensive evaluation practice for AI applications. This path spans 7 sources to cover the full evaluation landscape: foundational concepts, practical implementation, RAG-specific metrics, LLM-as-judge patterns, and agent evaluation challenges.

Evaluation is the most cross-cutting concern in AI development — every provider and framework has a different take. OpenAI provides hosted evals, RAGAS specializes in RAG metrics, DSPy uses metrics for optimization, LangSmith offers traceability, and W&B Weave treats evaluation as a core development primitive. This path helps you pick the right tools and combine them.

📚 Core Concepts

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🚀 Get Started

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

02 Prepare Dataset

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

Learn how to prepare datasets for fine-tuning models across various use cases, from tone to coding and RAG

Adapting Metrics to Target Language

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

Adding to your CI pipeline with Pytest

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

AG-UI

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

AG-UI Integration

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

Agent Evaluation Quickstart

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

Agentic Multi-Stage RAG with Cohere Tools API

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

This page describes how to build a powerful, multi-stage agent with the Cohere platform.

Agentic or Tool use

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

Agentic RAG for PDFs with mixed data

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

This page describes building a powerful, multi-step chatbot with Cohere’s models.

Align an LLM as a Judge

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

Aligning LLM Evaluators with Human Judgment

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

Amazon Bedrock

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

Pinecone as a Knowledge Base for Amazon Bedrock

Amazon Bedrock

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

An Overview of Tool Use with Cohere

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

Learn when to use leverage multi-step tool use in your workflows.

Analysis of Form 10-K/10-Q Using Cohere and RAG

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

This page describes how to use Cohere’s large language models to build an agent able to analyze financial forms like a 10-K or a 10-Q.

Answer correctness

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

Applications

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Arize

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

Aspect Critique

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

Assistants File Search

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

Use File Search as a built-in RAG tool for assistants.

Athina AI

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

Basic RAG

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

Learn how to build a basic RAG system by combining retrieval and generation for AI-powered knowledge-based responses

Basic RAG: Retrieval-Augmented Generation with Cohere

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

This page describes how to work with Cohere’s basic retrieval-augmented generation functionality.

Bedrock Invoke Agent Tool

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

Enables CrewAI agents to invoke Amazon Bedrock Agents and leverage their capabilities within your workflows

Bedrock Knowledge Base Retriever

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

Retrieve information from Amazon Bedrock Knowledge Bases using natural language queries

Box Integration

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

File storage and document management with Box integration for CrewAI.

Bring your own bucket (BYOB)

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

Build a custom RAG agent with LangGraph

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

Build a custom RAG agent with LangGraph

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

This guide shows how to build a RAG pipeline where the agent decides when and how to retrieve documents, rather than always retrieving on every query. Focus on the routing logic that lets the agent skip retrieval for questions it can answer directly — this is the key difference between naive RAG and agentic RAG. Compare this approach with Pinecone’s retrieval patterns and Cohere’s reranking strategies to see how different parts of the RAG stack complement each other. A common pitfall is over-retrieving: adding too many retrieval nodes or fetching too many documents degrades both latency and answer quality, so tune your retrieval thresholds carefully.

Build a multi-source knowledge base with routing

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

Build a multi-source knowledge base with routing

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

This guide demonstrates a router pattern where a top-level agent dispatches queries to specialized sub-agents based on the knowledge domain, which is one of the most practical multi-agent architectures for production systems. The routing mechanism avoids the cost and latency of sending every query to every knowledge source by using an LLM to classify intent first. Focus on how the router prompt is structured and how fallback behavior works when no sub-agent matches, as these are the failure modes you will encounter most in real deployments. Compare this approach with CrewAI’s delegation model and LangGraph’s conditional edges to understand the tradeoffs between explicit routing and emergent coordination.

Build a RAG agent with LangChain

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

Build a RAG agent with LangChain

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

This guide assembles LangChain’s retrieval primitives into a complete RAG agent capable of querying external knowledge and synthesizing answers with citations. The key architectural decision covered here is when to use a simple retrieval chain versus a full agentic RAG loop where the model decides whether and how to retrieve. Pay attention to the prompt engineering patterns for grounding LLM responses in retrieved context, as poorly constructed prompts are the primary cause of hallucination in RAG systems. Read the retrieval guide first for the underlying abstractions, then use this guide to see how they combine in a production-oriented pattern.

Build Chatbots with MongoDB and Cohere

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

This page describes how to build a chatbot that provides actionable insights on technology company market reports.

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!

Building Agentic RAG with Cohere

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

Hands-on tutorials on building agentic RAG applications with Cohere

Building RAG as Agent

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

Building RAG models with Cohere

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

This page walks through building a retrieval-augmented generation model with Cohere.

Caching

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Cancelling Tasks

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

Citations and References

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

Citations and references enable models to ground responses with sources, ideal for RAG and agentic applications

Cloudera AI

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

Vector embedding, RAG, and semantic search at scale

Cluster Storage

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

Code Docs RAG Search

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

The ‘CodeDocsSearchTool’ is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within code documentation.

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 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 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 Command R+ Model

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

Command R+ is Cohere’s optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use.

Cohere's Command R7B Model

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

Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents.

Comet Opik

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

Command A

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

Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length.

Compare Embeddings for retriever

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

Compare LLMs using Ragas Evaluations

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

Compare models provided by VertexAI on RAG-based Q&A task using Ragas metrics

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

Components Guide

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

Configure checkpointer backend

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

Configure Agent Server to use PostgreSQL, MongoDB, or a custom implementation for checkpoint storage.

Context Entities Recall

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

Context Precision

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Context Recall

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

Context snippets overview

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

Retrieve context snippets from your assistant’s knowledge base.

Cost Analysis

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

Create an assistant

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

Create and deploy a Pinecone Assistant for your knowledge base.

CrewAI Run Automation Tool

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

Enables CrewAI agents to invoke CrewAI Platform automations and leverage external crew services within your workflows.

CSV RAG Search

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

The ‘CSVSearchTool’ is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within a CSV file’s content.

Custom Multi-hop Query

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

Custom Single-hop Query

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

Customise models

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Customizations

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Customizing Test Data Generation

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

Data modeling

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

Learn how to structure records for efficient data retrieval and management in Pinecone.

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.

Data storage and privacy

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

Datasets

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

Deep Agents overview

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

Build agents that can plan, use subagents, and leverage file systems for complex tasks

Deep Agents overview

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

Build agents that can plan, use subagents, and leverage file systems for complex tasks

Deep Dive Into Evaluating RAG Outputs

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

This page contains information on evaluating the output of RAG systems.

Development

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

Directory RAG Search

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

The ‘DirectorySearchTool’ is a powerful RAG (Retrieval-Augmented Generation) tool designed for semantic searches within a directory’s content.

Distributed Architecture

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

How Chroma scales out with independent services, object storage, SSD caches, and a shared system database.

Document Library

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

Document Library enhances agents with uploaded documents via Mistral Cloud’s built-in RAG tool

DOCX RAG Search

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

The ‘DOCXSearchTool’ is a RAG tool designed for semantic searching within DOCX documents.

DSPy Optimizer

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

Effective Chunking Strategies for RAG

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

This page describes various chunking strategies you can use to get better RAG performance.

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.

Enable blob storage

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

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).

End-to-end RAG using Elasticsearch and Cohere

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

This page contains a basic tutorial on how to get Cohere and ElasticSearch to work well together.

Evals In Prod

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

Evaluate a New LLM

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

Evaluate a prompt

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

Evaluate a RAG application

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

Evaluate a simple LLM application

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

This is the canonical first hands-on with RAGAS, and it matters because it shows the metric-driven evaluation loop on a simple LLM app before you add retrieval complexity. Focus on how RAGAS frames a sample, a metric, and a score — the same abstractions scale up to full RAG evaluation. A subtle gotcha is that many RAGAS metrics call an LLM under the hood, so scores carry cost and run-to-run variance you must account for. Read this before the RAG tutorial, which layers retrieval metrics on top.

Evaluate a simple RAG system

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

Evaluate a simple RAG system

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

This is the practical RAG evaluation walkthrough and the page most teams should run first when they need to measure a retrieval pipeline rather than guess at it. Pay attention to the distinction between retrieval metrics like context precision and recall and generation metrics like faithfulness and answer relevancy, because a RAG system can fail at either stage and the fix differs entirely. A common mistake is optimizing answer quality while ignoring context recall, leaving the model fluent but ungrounded. Start with the simple-evals page first if you are new to RAGAS.

Evaluate a Text-to-SQL Agent

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

Evaluate an AI Agent

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

Evaluate an AI Workflow

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

Evaluate and Improve a RAG App

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

Evaluate RAG applications

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

Build and evaluate RAG applications using Weave with LLM judges

Evaluating Multi-turn Conversations

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

Evaluation

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

Evaluation Dataset

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

Evaluation Sample

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

Evaluations with Vertex AI models

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

Experimentation

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

Factual Correctness

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

Faithfulness

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

File search

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

Built-in file search tool that lets models search uploaded files for relevant information before generating responses, providing managed RAG without external infrastructure.

Fine-tuning

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

Fine-tuning models incurs a $2 monthly storage fee per model; see pricing for details

General Purpose Metrics

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

Generate Parallel Queries for Better RAG Retrieval

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

Build an agentic RAG system that can expand a user query into a more optimized set of queries for retrieval.

Getting Started Rag

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

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 Drive Integration

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

File storage and management with Google Drive integration for CrewAI.

Google Gemini

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

Griptape

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

Guides Rag Evaluation

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

Guides Rag Triad

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

Guides Tracing Rag

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

Haystack

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.

Haystack Integration

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

Helicone

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

How To Implement Contextual RAG From Anthropic

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

An open source line-by-line implementation and explanation of Contextual RAG from Anthropic!

How to Start with the Cohere Toolkit

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

Build and deploy RAG applications quickly with the Cohere Toolkit, which offers pre-built front-end and back-end components.

Human Input on Execution

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

Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent’s attributes and tools.

Improve RAG

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

Improvement

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

Installation

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

Integrate with Azure Blob Storage

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

Set up Azure Blob Storage integration for data import.

Integrate with Google Cloud Storage

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

Integrate Google Cloud Storage for bulk data import

Integrations

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

Intro to Retrieval

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

Ground LLMs in your own data using retrieval-augmented generation.

Introduction

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

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.

Iterate and Improve Prompts

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

JSON RAG Search

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

The ‘JSONSearchTool’ is designed to search JSON files and return the most relevant results.

Judge Alignment Quickstart

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

Langchain

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

LangChain

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

Langfuse

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

LangGraph

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

Langsmith

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

LangSmith

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

List of available metrics

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

LlamaIndex

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

Using LlamaIndex and Pinecone to build semantic search and RAG applications

LlamaIndex

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

LlamaIndex

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

Automatically trace and debug LlamaIndex applications with Weave, capturing all LLM calls, RAG pipelines, agent steps, and evaluations for comprehensive observability of your data-connected AI workflows.

LlamaIndex Agent Evaluation Quickstart

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

LlamaIndex Agents

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

LlamaStack

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

LLM Benchmarking Quickstart

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

Manage artifact storage and memory allocation

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

Manage storage, memory allocation of W&B Artifacts.

Manage bucket storage and costs

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

Understand how W&B uses object storage, how deletion maps to bucket bytes, and how to reduce usage on self-managed, Dedicated Cloud, and bring-your-own-bucket deployments.

Manage storage

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

Ways to manage W&B data storage.

Manage storage integrations

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

Update and manage cloud storage integrations.

Manage user settings

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

Manage your profile information, account defaults, alerts, participation in beta products, GitHub integration, storage usage, account activation, and create teams in your user settings.

Mastering Flow State Management

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

A comprehensive guide to managing, persisting, and leveraging state in CrewAI Flows for building robust AI applications.

MDX RAG Search

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

The ‘MDXSearchTool’ is designed to search MDX files and return the most relevant results.

Memory

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

Leveraging the unified memory system in CrewAI to enhance agent capabilities.

Metrics

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

Metrics Ragas

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

Migrating Monolithic Prompts to Command A with RAG

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

This page contains a discussion of how to automatically migrating monolothic prompts.

Modify Prompts

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.

Multi modal faithfulness

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

Multi modal relevance

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

Multi-Hop RAG

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

MySQL RAG Search

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

The ‘MySQLSearchTool’ is designed to search MySQL databases and return the most relevant results.

Noise Sensitivity

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

Non-English Testset Generation

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

Nvidia Metrics

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

OCI Gen AI

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

Openlayer

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

Optimizing LLM Accuracy

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

Learn strategies to enhance the accuracy of large language models using techniques like prompt engineering, retrieval-augmented generation, and fine-tuning.

Overview

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

Leverage AI services, generate images, process vision, and build intelligent systems

Overview

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

Interact with cloud services, storage systems, and cloud-based AI platforms

Overview

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

PDF RAG Search

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

The ‘PDFSearchTool’ is designed to search PDF files and return the most relevant results.

Performing Tasks Sequentially with Cohere's RAG

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

Build an agentic RAG system that can handle user queries that require tasks to be performed in a sequence.

Persona Generation

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

PG RAG Search

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

The ‘PGSearchTool’ is designed to search PostgreSQL databases and return the most relevant results.

Prompt

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

Prompt Evaluation Quickstart

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

Querying Structured Data (SQL)

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

Build an agentic RAG system that can query structured data (SQL).

Querying Structured Data (Tables)

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

Build an agentic RAG system that can query structured data (tables).

Quick Start

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

Quickstart: Retrieval Augmented Generation (RAG)

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

How to build a RAG workflow in under 5 mins!

R2R

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

RAG

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

RAG Citations

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

Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for RAG. It covers both non-streaming and streaming modes (API v2).

RAG Evaluation

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

RAG Integrations

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

RAG Streaming

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

Guide on implementing streaming for RAG with Cohere and details on the events stream (API v2).

RAG Tool

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

The ‘RagTool’ is a dynamic knowledge base tool for answering questions using Retrieval-Augmented Generation.

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.

Ragas CLI

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

Rerank

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

Learn how to improve the relevance of your search and RAG systems with reranking.

Response Relevancy

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

Retrieval

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

Retrieval

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

LangChain’s retrieval guide covers the foundational abstractions for document loading, splitting, embedding, and querying that underpin every RAG application built on the framework. Understanding the Retriever interface is critical because it is the common contract that vector stores, BM25 indexes, and custom retrieval strategies all implement. Focus on how retrievers compose with chains and agents, since the retrieval step is often the performance bottleneck in production RAG pipelines. Read this before the RAG-specific guide to ensure you understand the building blocks before seeing them assembled into a full application.

Retrieval

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

Learn how to search your data using semantic similarity with the OpenAI API.

Retrieval Augmented Generation (RAG)

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

Guide on using Cohere’s Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations.

Retrieval augmented generation (RAG) - Cohere on Azure AI Foundry

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

A guide for performing retrieval augmented generation (RAG) with Cohere’s Command models on Azure AI Foundry (API v2).

Retrieval augmented generation (RAG) - quickstart

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

A quickstart guide for performing retrieval augmented generation (RAG) with Cohere’s Command models (v2 API).

Retrieval evaluation using LLM-as-a-judge via Pydantic AI

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

This page contains a tutorial on how to evaluate retrieval systems using LLMs as judges via Pydantic AI.

Retrieval-Augmented Generation (RAG)

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

Routing Queries to Data Sources

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

Build an agentic RAG system that routes queries to the most relevant tools based on the query’s nature.

Rubric-Based Evaluation

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

Run Config

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

Run your first experiment

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

S3 Reader Tool

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

The ‘S3ReaderTool’ enables CrewAI agents to read files from Amazon S3 buckets.

S3 Writer Tool

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

The ‘S3WriterTool’ enables CrewAI agents to write content to files in Amazon S3 buckets.

Scrapegraph Scrape Tool

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

The ‘ScrapegraphScrapeTool’ leverages Scrapegraph AI’s SmartScraper API to intelligently extract content from websites.

Scrapfly Scrape Website Tool

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

The ‘ScrapflyScrapeWebsiteTool’ leverages Scrapfly’s web scraping API to extract content from websites in various formats.

Semantic Search with Cohere Models

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

This is a tutorial describing how to leverage Cohere’s models for semantic search.

Semantic Similarity

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

Single-hop Query Testset

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

SQL

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Summarization

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

Summarizing Text with the Chat Endpoint

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

Learn how to perform text summarization using Cohere’s Chat endpoint with features like length control and RAG.

Swarm

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Systematic Prompt Optimization

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

Testset Generation

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

Testset Generation for Agents or Tool use cases

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

Testset Generation for RAG

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Testset Generation for RAG

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

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

Text-to-SQL Evaluation Quickstart

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

Tonic Validate

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

Tools

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

Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.

Tracing and logging evaluations with Observability tools

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

Traditional NLP Metrics

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

Tutorials

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

TXT RAG Search

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

The ‘TXTSearchTool’ is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file.

Understand Cost and Usage of Operations

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Using Pre-chunked Data

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Utilizing User Feedback

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

Website RAG Search

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

The ‘WebsiteSearchTool’ is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a website.

Workflow Evaluation Quickstart

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

XML RAG Search

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

The ‘XMLSearchTool’ is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a XML file.

YouTube Channel RAG Search

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

The ‘YoutubeChannelSearchTool’ is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a Youtube channel.

YouTube Video RAG Search

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

The ‘YoutubeVideoSearchTool’ is designed to perform a RAG (Retrieval-Augmented Generation) search within the content of a Youtube video.

Zeno

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