Add Couchbase support [VectorDB]

cookbook/agent_concepts/knowledge/vector_dbs/couchbase_db.py

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+# install couchbase-sdk - `pip install couchbase`
+
+from agno.agent import Agent
+from agno.embedder.openai import OpenAIEmbedder
+from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
+from agno.vectordb.couchbase import CouchbaseSearch
+from couchbase.options import ClusterOptions, KnownConfigProfiles
+from couchbase.auth import PasswordAuthenticator
+from couchbase.management.search import SearchIndex
+import os
+import time
+# Couchbase connection settings
+username = os.getenv("COUCHBASE_USER")  # Replace with your username
+password = os.getenv("COUCHBASE_PASSWORD")     # Replace with your password
+connection_string = os.getenv("COUCHBASE_CONNECTION_STRING")
+
+# Create cluster options with authentication
+auth = PasswordAuthenticator(username, password)
+cluster_options = ClusterOptions(auth)
+cluster_options.apply_profile(KnownConfigProfiles.WanDevelopment)
+
+# Define the vector search index
+search_index = SearchIndex(
+    name="vector_search",
+    source_type="gocbcore",
+    idx_type="fulltext-index",
+    source_name="recipe_bucket",
+    plan_params={
+        "index_partitions": 1,
+        "num_replicas": 0
+    },
+    params={
+        "doc_config": {
+            "docid_prefix_delim": "",
+            "docid_regexp": "",
+            "mode": "scope.collection.type_field",
+            "type_field": "type"
+        },
+        "mapping": {
+            "default_analyzer": "standard",
+            "default_datetime_parser": "dateTimeOptional",
+            "index_dynamic": True,
+            "store_dynamic": True,
+            "default_mapping": {
+                "dynamic": True,
+                "enabled": False
+            },
+            "types": {
+                "recipe_scope.recipes": {
+                    "dynamic": False,
+                    "enabled": True,
+                    "properties": {
+                        "content": {
+                            "enabled": True,
+                            "fields": [{
+                                "docvalues": True,
+                                "include_in_all": False,
+                                "include_term_vectors": False,
+                                "index": True,
+                                "name": "content",
+                                "store": True,
+                                "type": "text"
+                            }]
+                        },
+                        "embedding": {
+                            "enabled": True,
+                            "dynamic": False,
+                            "fields": [{
+                                "vector_index_optimized_for": "recall",
+                                "docvalues": True,
+                                "dims": 3072,
+                                "include_in_all": False,
+                                "include_term_vectors": False,
+                                "index": True,
+                                "name": "embedding",
+                                "similarity": "dot_product",
+                                "store": True,
+                                "type": "vector"
+                            }]
+                        },
+                        "meta": {
+                            "dynamic": True,
+                            "enabled": True,
+                            "properties": {
+                                "name": {
+                                    "enabled": True,
+                                    "fields": [{
+                                        "docvalues": True,
+                                        "include_in_all": False,
+                                        "include_term_vectors": False,
+                                        "index": True,
+                                        "name": "name",
+                                        "store": True,
+                                        "analyzer": "keyword",
+                                        "type": "text"
+                                    }]
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+    }
+)
+
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=CouchbaseSearch(
+        bucket_name="recipe_bucket",
+        scope_name="recipe_scope", 
+        collection_name="recipes",
+        couchbase_connection_string=connection_string,
+        cluster_options=cluster_options,
+        search_index=search_index,
+        embedder=OpenAIEmbedder(id="text-embedding-3-large", dimensions=3072, api_key=os.getenv("OPENAI_API_KEY")),
+        wait_until_index_ready=60,
+        overwrite=True
+    ),
+)
+
+# Load the knowledge base
+knowledge_base.load(recreate=True)
+
+time.sleep(20) # wait for the vector index to be sync with kv
+# Create and use the agent
+agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
+agent.print_response("How to make Thai curry?", markdown=True)

libs/agno/agno/vectordb/couchbase/__init__.py

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+from agno.vectordb.couchbase.couchbase import CouchbaseSearch
+
+__all__ = ["CouchbaseSearch"]