{"id":17229,"date":"2025-06-25T09:52:11","date_gmt":"2025-06-25T16:52:11","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=17229"},"modified":"2025-07-11T10:01:50","modified_gmt":"2025-07-11T17:01:50","slug":"couchbase-vector-store-agno","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/couchbase-vector-store-agno\/","title":{"rendered":"Introducing Couchbase as a Vector Store in Agno"},"content":{"rendered":"<p>We\u2019re excited to announce that Couchbase is now supported as a vector store in Agno. This integration brings together the best of Agno\u2019s agent orchestration capabilities and Couchbase\u2019s high-performance, scalable vector store. It allows developers to build intelligent, multi-agent systems powered by fast and efficient vector search.<\/p>\n<p>Agno is an open-source, full-stack framework for building multi-agent systems. It offers a clean, composable, and Pythonic approach to building AI agents with the tools, memory, and reasoning capabilities. It&#8217;s easy to use, extremely fast, and supports multi-modal inputs and outputs.<\/p>\n<p>Let\u2019s explore this integration further!<\/p>\n<h2>Setting up Agno with Couchbase<\/h2>\n<p>To get started with Agno and Couchbase, you\u2019ll need to follow a few simple steps.<\/p>\n<h3>Installing Agno<\/h3>\n<p>You can install Agno and other required dependencies by running the following command:<\/p>\n<pre class=\"nums:false lang:sh decode:true\">pip install -U agno couchbase openai<\/pre>\n<p>You can now begin using Agno\u2019s CLI to configure agents and vector stores.<\/p>\n<h3>Connecting to Couchbase and performing vector search<\/h3>\n<p>Now that Agno and Couchbase dependencies are installed, you can connect Couchbase as a vector store and perform vector searches. Here\u2019s how:<\/p>\n<p>Import the packages and initialize the Couchbase database instance.<\/p>\n<pre class=\"nums:false lang:python decode:true\">from agno.agent import Agent\r\nfrom agno.embedder.openai import OpenAIEmbedder\r\nfrom agno.knowledge.pdf_url import PDFUrlKnowledgeBase\r\nfrom agno.vectordb.couchbase import CouchbaseSearch\r\nfrom couchbase.options import ClusterOptions, KnownConfigProfiles\r\nfrom couchbase.auth import PasswordAuthenticator\r\nfrom couchbase.management.search import SearchIndex\r\n\r\n# Couchbase connection settings\r\nusername = os.getenv(\"COUCHBASE_USER\")\r\npassword = os.getenv(\"COUCHBASE_PASSWORD\")\r\nconnection_string = os.getenv(\"COUCHBASE_CONNECTION_STRING\")\r\n\r\n# Create cluster options with authentication\r\nauth = PasswordAuthenticator(username, password)\r\ncluster_options = ClusterOptions(auth)\r\ncluster_options.apply_profile(KnownConfigProfiles.WanDevelopment)<\/pre>\n<p>Specify the username, password and connection string to your Couchbase Cluster.<\/p>\n<h2><b>Initialize Vector Store\u00a0<\/b><\/h2>\n<p>Now we initialize the Couchbase Vector Store:<\/p>\n<pre class=\"nums:false lang:python decode:true\"> vector_db=CouchbaseSearch(\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0bucket_name=\"recipe_bucket\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0scope_name=\"recipe_scope\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0collection_name=\"recipes\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0couchbase_connection_string=connection_string,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0cluster_options=cluster_options,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0search_index=\"vector_search_fts_index\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0embedder=OpenAIEmbedder(\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0id=\"text-embedding-3-large\",\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0dimensions=3072,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0api_key=os.getenv(\"OPENAI_API_KEY\")\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0),\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0wait_until_index_ready=60,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0overwrite=True\r\n\u00a0\u00a0\u00a0),<\/pre>\n<p>Specify the bucket, scope and collection for your Couchbase cluster. Also, define what Embedding model you will use to generate the embeddings.<\/p>\n<h3>Load data<\/h3>\n<p>Create a PDF url knowledge base instance and load the data into the instance. We use a public recipe pdf data as an example.<\/p>\n<pre class=\"nums:false lang:python decode:true\"># Create knowledge base\r\nknowledge_base = PDFUrlKnowledgeBase(\r\n\u00a0\u00a0\u00a0\u00a0urls=[\"https:\/\/phi-public.s3.amazonaws.com\/recipes\/ThaiRecipes.pdf\"],\r\n\u00a0\u00a0\u00a0\u00a0vector_db=vector_db,\r\n)\r\n\r\nknowledge_base.load(recreate=False)\u00a0 # Comment out after first run\r\n\r\n# Wait for the vector index to sync with KV\r\ntime.sleep(20)<\/pre>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-17233\" style=\"border: 1px solid Gainsboro;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-1024x127.png\" alt=\"\" width=\"900\" height=\"112\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-1024x127.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-300x37.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-768x95.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-1536x190.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-18x2.png 18w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2-1320x163.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image2-2.png 1999w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/p>\n<h2>Use Agno agent to perform vector search<\/h2>\n<p>Once you\u2019ve configured your Couchbase vector store, and inserted the documents, integrate the knowledge base into an agent, then we can ask the agent a question and get a response.<\/p>\n<pre class=\"nums:false lang:python decode:true\"># Create and use the agent\r\nagent = Agent(knowledge=knowledge_base, show_tool_calls=True)\r\nagent.print_response(\"How to make Thai curry?\", markdown=True)\r\n<\/pre>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-17234\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2025\/06\/image1.gif\" alt=\"\" width=\"800\" height=\"429\" \/><\/p>\n<h3>Conclusion<\/h3>\n<p>With the integration of Agno&#8217;s robust agentic framework with Couchbase&#8217;s high-performance vector search capabilities, developers can create scalable, AI-driven applications that efficiently handle complex data retrieval and reasoning tasks. This enables agents to perform semantic searches, enhance contextual understanding, and deliver more accurate responses. Whether you\u2019re working on semantic search, RAG applications, or other AI-driven use cases, this setup ensures efficiency and accuracy.<\/p>\n<h3>Next steps<\/h3>\n<p>More information is available in the <a href=\"https:\/\/docs.agno.com\/\" target=\"_blank\" rel=\"noopener\">Agno documentation<\/a>, including an integration guide to <a href=\"https:\/\/docs.agno.com\/vectordb\/couchbase\" target=\"_blank\" rel=\"noopener\">Couchbase<\/a>.<\/p>\n<p>Happy coding!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We\u2019re excited to announce that Couchbase is now supported as a vector store in Agno. This integration brings together the best of Agno\u2019s agent orchestration capabilities and Couchbase\u2019s high-performance, scalable vector store. It allows developers to build intelligent, multi-agent systems [&hellip;]<\/p>\n","protected":false},"author":85559,"featured_media":17232,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,2242,9139,9936,9937],"tags":[],"ppma_author":[10069],"class_list":["post-17229","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-connectors","category-python","category-search","category-vector-search"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.8 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Introducing Couchbase as a Vector Store in Agno - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"This integration brings together the best of Agno\u2019s agent orchestration capabilities and 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