Haystack

One Name. One Product Family. One Look 💙 We’re unifying the Haystack ecosystem at @deepset_ai under one name and a new logo, reflecting its role as a framework, a community, and the foundation of our enterprise platform. 👉 Read the announcement: haystack.deepset.ai/blog/announcin…

Full release notes 👇 haystack.deepset.ai/release-notes/…

🚀 Haystack 2.26 is out! Here are a few highlights: 🤖 More flexible Agents with dynamic system prompts You can now use Jinja2 templating in `system_prompt` to inject runtime variables and dynamically adapt behavior, such as changing language or tone, or adding conditional logic, without redefining prompts. 🔍 LLMRanker for smarter reranking LLMRanker uses an LLM to reason about relevance rather than rely on similarity scores (like cross-encoders). This leads to better results for complex queries and helps provide higher-quality context for RAG and agent workflows while keeping context windows lean. 🧠 Discover supported models programmatically Chat generators now expose SUPPORTED_MODELS variable, so you can easily see which models are supported. 💙 Big thanks to our contributors to this release!

Just published a notebook on @GeminiApp Cookbook w/ @bilgeycl 📓 Cross-modal Retrieval with Gemini Embedding 2 and @Haystack_AI: github.com/google-gemini/… 🔎 Several examples of search: text→text text→any (PDF included) image→text search Thx @patloeber! @googleaidevs

📚 It had been a while since we published a technical blog post, and Gemini Embedding 2 from @GoogleDeepMind team felt like a good reason to write one together with @theanakin87 💙 It's Google's first fully multimodal embedding model, meaning you can embed text, images, audio, video, and PDFs into a single shared vector space. What’s exciting about multimodal embedding models is that they enable cross-modal retrieval. For example, you can search a product catalog with a text query like “grey running shoes with white soles” 👟, or upload a photo of a monument to retrieve relevant articles describing it 🗽 In the post, we show how to use Gemini Embedding 2 with @Haystack_AI for both text and multimodal data, supported from day 0 🎉 Blog: haystack.deepset.ai/blog/multimoda…

Start building with Gemini Embedding 2, our most capable and first fully multimodal embedding model built on the Gemini architecture. Now available in preview via the Gemini API and in Vertex AI.

🚀 @GoogleDeepMind just launched Gemini Embedding 2 today and Haystack supports it from Day 0. Embed text, images, video, audio, and PDFs into one vector space and build multimodal search or cross-modal retrieval systems. Learn how in our latest blog post 👇 haystack.deepset.ai/blog/multimoda…

When @AIatMeta (Facebook AI back then) released it in 2017, most people hadn't even considered that you could search across millions of vectors in milliseconds. FAISS made it real, and the wave of vector databases that followed owes a lot to what it proved was possible. Years later, it is still standing. When your project doesn't need a full-blown vector database - think local development, lightweight deployments, or small to medium datasets - FAISS is often the smartest choice. Fast, dependency-free, and battle-hardened by nearly a decade of production use. We've now integrated it into Haystack as a Document Store. Drop in FAISSDocumentStore for in-memory or persistent vector search, and pair it with FAISSEmbeddingRetriever to plug directly into your pipelines and agents. GPU support included. 🐍 pip install faiss-haystack 🔗 Documentation: haystack.deepset.ai/integrations/f…

Duplicates and near-duplicates are common in Retrieval Augmented Generation. Even if you try not to repeat yourself, your documents will often contain paraphrases of the same information, which adds no value for the LLM and only clutters your context. This increases token usage costs and can push out useful results that did not rank as highly. Diversification reranks results to keep the most unique ones while preserving relevance. We didn’t want to reinvent the wheel, so we integrated directly with pyversity by @thomas_v_dongen, a small yet comprehensive Python library that provides many diversification algorithms out of the box, including MMR and DPP. It is now integrated into Haystack as a ranker component that fits into your existing pipelines and agents! 🐍 pip install pyversity-haystack 🔗 Documentation: haystack.deepset.ai/integrations/p…

Didn't have to go far to find a great solution - @thomas_v_dongen's pyversity covers an impressive range of algorithms out of the box. Huge props for building something that thorough! Now fully integrated with @Haystack_AI haystack.deepset.ai/integrations/p…

@YaoWu91252427 @deepset_ai Thank you for your contributions to Haystack! Keep them coming 🙌

5 PRs merged into @deepset_ai last month — from Anthropic thinking content support to hyperlink extraction in PPTX/XLSX converters. Love this community.

🚀 Haystack 2.25 is here! This release focuses on improving the user experience of the Agent component and making tool selection more scalable and efficient. 🛠️ Dynamic Tool Discovery with SearchableToolset With SearchableToolset, agents dynamically discover relevant tools using BM25 keyword search instead of exposing hundreds of tools to the LLM upfront. This approach: – Reduces LLM context usage (and token costs) – Improves tool selection accuracy – Works especially well when connecting to MCP servers Agents load only the tools they actually need, making large tool ecosystems practical without overwhelming the model. 📝 Reusable Jinja2 Prompt Templates for Agents Agents now natively support templated` user_prompt` and `required_variables`, simplifying how you structure and invoke agents. With this update, you can define prompts once and pass variables at runtime. This means: – Less boilerplate – Cleaner agent invocation – More reusable and maintainable agent workflows 💙 Big thanks to our contributors to this release! 👇 Full release notes in the next post Explore the release and let us know what you build with it 💬

🧪 Haystack & @mem0ai integration We've added @mem0ai support to haystack-experimental, bringing persistent memory capabilities to your pipelines. Mem0 allows your Haystack applications to maintain memory across sessions - storing user preferences, and extracted facts that persist beyond individual pipeline runs. This enables truly stateful AI systems that learn and adapt over time. 🔍 Key capabilities: - Cross-session memory persistence for agents and chatbots - User and session-level context management - Seamless integration with Haystack's agents This integration is experimental - we're looking for early adopters to test it and share feedback! 📖 Cookbook: haystack.deepset.ai/cookbook/memor…

Haystack 2.24 is live 🚀 Here are the highlights: 🔌 Simpler Pipelines New improvements in the Pipeline logic simplify the component connections, eliminating the need for OutputAdapter and joiners in many common setups, such as query rewriting and hybrid retrieval. This results in cleaner and shorter pipelines even for complex use cases. 🏁 Rankers handles duplicate documents Rankers now deduplicate documents on top of ranking, enabling cleaner hybrid retrieval pipelines with less components. 📃 File Support in Chat Generators You can now attach files (e.g., PDFs) to ChatMessage using the new FileContent dataclass when working with OpenAI and Azure chat generators (Responses API). This enables smoother document-based prompting in deterministic flows and agentic settings. 💙 Big thanks to our contributors to this releaseH 👇 Full release notes in the comments

📃@PaddlePaddle released a new VLM: PaddleOCR-VL-1.5 (0.9B) and you can already use it in your document-heavy Haystack pipelines. PaddleOCR-VL-1.5 goes beyond classic OCR: it understands document layout and structure, extracting tables, formulas, charts, and key elements from messy real-world PDFs and images. This makes it a powerful building block for reliable RAG pipelines and document-centric AI applications. For teams building AI systems for reasoning over complex documents, this enables more accurate retrieval, grounding, and reasoning across document structure. 🔍 Why it’s exciting: - 94.5% accuracy on OmniDocBench v1.5 - Irregular-shaped localization for real-world documents (skew, warp, photos) - Strong improvements in table, formula, and text spotting - Multilingual support, including rare scripts and complex layouts 🔗 Model: huggingface.co/PaddlePaddle/P… 🔗 Docs: haystack.deepset.ai/integrations/p…

🚀 Haystack 2.23 is out! This release focuses on making agents safer, more visual, and components easier to customize with fewer custom hooks and less boilerplate. Here are the highlights: 🔄 Human-in-the-Loop for Agents Agents can now pause for human confirmation before executing tools. You can define confirmation behavior per tool (always ask, ask once, or never ask), ideal for workflows with sensitive operations. 🖼️ Image Support for Tool Results Tools can now return images alongside text. Agents can retrieve and reason over images, unlocking multimodal workflows like visual search, image inspection, and richer tool outputs. 🧩 Simpler Serialization for Custom Components Custom components now serialize and deserialize automatically in most cases, even when they include complex objects as parameters. This means less boilerplate, easier pipeline snapshots for recovery, and simpler custom component API. 💙 Big thanks to our contributors for the release 👉 Full release notes: haystack.deepset.ai/release-notes/…

@bilgeycl @qdrant_engine 🚀🚀

Oh wow, this is literally what I built with @Haystack_AI and @qdrant_engine 😄 A movie recommendation agent that understands intent and creates the right search queries + metadata

🚀 Haystack v2.22 is out! This release brings smarter doc chunking, improved component behavior, and easier tool-to-LLM integration with fewer manual steps. Highlights ✂️ New `EmbeddingBasedDocumentSplitter` for smarter document chunking 🛠️ `outputs_to_string` lets tools return multiple strings with ease 🔥 Components now auto-trigger `warm_up()` on first run 🐍 Python 3.10+ is now required Big thanks to our contributors 💙 Full release notes👇 🔗 haystack.deepset.ai/release-notes/…