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Level: Beginner Time to complete: 15 minutes Goal: After completing this tutorial, you’ll have learned how to build an indexing pipeline that will preprocess files based on their file type, using the FileTypeRouter.
Level: Advanced Time to complete: 20 minutes Components Used: Agent, AgentTool, DuckduckgoApiWebSearch, OpenAIChatGenerator, DocumentWriter Prerequisites: You need Haystack 3.1 or newer, an OpenAI API Key, and a Notion Integration set up beforehand Goal: After completing this tutorial, you’ll have…
⭐️ Highlights 🪝CompactionHook and Built-in Compactors for Context Length Management Experimental context compaction for Agent. Runs before LLM calls and shortens the conversation once it crosses a configured fraction of the context window.
This notebook demonstrates how to setup breakpoints in a Haystack pipeline. In this case, we will set up break points in a hybrid retrieval-augmented generation (RAG) pipeline.
Level: Advanced Time to complete: 20 minutes Components Used: Agent, ChatPromptBuilder, InMemoryBM25Retriever OpenAIChatGenerator Archived Components Used: InMemoryChatMessageStore, ChatMessageRetriever, ChatMessageWriter Prerequisites: You need an OpenAI API Key Goal: After completing this…
Evaluation measures performance using metrics like precision, recall, and relevancy, providing a clear picture of your pipeline’s strengths and weaknesses using LLMs or ground-truth labels.
by Tuana Celik ( LI, Twitter/X) This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval cookbook & full article 2️⃣ Query Expansion & the full article 3️⃣ Query Decomposition cookbook & full article 4️⃣ Automated Metadata Enrichment In this…
In this cookbook we will show how to calculate a hallucination risk based on the research paper LLMs are Bayesian, in Expectation, not in Realization and this GitHub repo, https://github.com/leochlon/hallbayes. In this notebook, we’ll use the OpenAIChatGenerator from haystack-experimental.
Notebook by Kacper Łukawski 🚀 Part of Haystack 3.0 Launch Week: five days of new drops (July 20–24). In this notebook, we build a fully local, fully async agent that uses a skill to change how it reports back, and a custom bash tool to actually use the machine it runs on.
Notebook by Bilge Yücel 🚀 Part of Haystack 3.0 Launch Week: five days of new drops (July 20–24). Every call to Agent.run() returns metadata alongside the agent’s reply. In this cookbook you’ll use that metadata, and Agent hooks, to enforce soft and hard budget policies.
Today, we’re happy to announce the release of a new major version: Haystack 3.0 🎉 Haystack is an open-source AI orchestration framework for building production-grade agents with full control and flexibility, and 3.0 is the release where agents move to the center of the framework.
⭐️ Highlights Haystack 3.0 is a major release for building production-grade agents with full control and flexibility. It ships a wave of new capabilities: a more capable Agent with hooks and first-class skills, built-in run introspection, first-class async for serving, a leaner core, and safer…
Level: Advanced Time to complete: 20 minutes Components Used: Agent, OpenAIChatGenerator Prerequisites: You need an OpenAI API Key Goal: After completing this tutorial, you’ll have learned how to implement human-in-the-loop workflows in Haystack agents using confirmation strategies, create custom…
⭐️ Highlights 📦 Slimming down Haystack core ahead of 3.0 This release begins the migration of many components out of haystack core and into dedicated integration packages, in preparation for Haystack 3.0.
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