18 stories
Simple RAG example on the Oscars using Llama 3.1 open models and the Haystack LLM framework. Installation ! pip install transformers-haystack sentence-transformers-haystack bitsandbytes Authorization you need an Hugging Face account you need to accept Meta conditions here:…
In this notebook, we will build a Haystack Retrieval Augmented Generation (RAG) Pipeline using self-hosted AI models with NVIDIA Inference Microservices or NIMs. The notebook is associated with a technical blog demonstrating the steps to deploy NVIDIA NIMs with Haystack into production.
Evaluating the responses of Language Models and LLM-based applications often involves using model-based metrics that do not require ground truth labels.
Notebook by Sara Zanzottera and Bilge Yucel Amazon Sagemaker is a comprehensive, fully managed machine learning service that allows data scientists and developers to build, train, and deploy ML models efficiently.
Notebook by Bilge Yucel Amazon Bedrock is a fully managed service that provides high-performing foundation models from leading AI startups and Amazon through a single API. You can choose from various foundation models to find the one best suited for your use case.
by Tuana Celik ( LI, Twitter) This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval cookbook & full article 2️⃣ Query Expansion cookbook & full article 3️⃣ Query Decomposition cookbook & the full article 4️⃣ Automated Metadata Enrichment In this…
by Tuana Celik ( LI, Twitter) This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval cookbook & full article 2️⃣ Query Expansion cookbook & full article 3️⃣ Query Decomposition & the full article 4️⃣ Automated Metadata Enrichment Query…
Notebook by Bilge Yucel Multilingual Generative QA Using Cohere and Haystack In this notebook, we’ll delve into the details of multilingual retrieval and multilingual generation, and demonstrate how to build a Retrieval Augmented Generation (RAG) pipeline to generate answers from multilingual hotel…
When building applications with LLMs, writing effective prompts is a long process of trial and error. Often, if you switch models, you also have to change the prompt. What if you could automate this process?
Level: Advanced Time to complete: 25 minutes Components/Packages Used: agent-pack-haystack ( create_advanced_rag_agent, create_deep_research_agent), Agent, InMemoryDocumentStore Prerequisites: Haystack 3.1 or later, an OpenAI API key, and a Tavily API key (free tier available) Goal: After…
Level: Advanced Time to complete: 20 minutes Components Used: InMemoryDocumentStore, SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder, InMemoryEmbeddingRetriever, ChatPromptBuilder, OpenAIChatGenerator, Agent Prerequisites: You must have an OpenAI API Key and be familiar with…
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…
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.
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