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Code

cookbook/knowledge/vector_db/couchbase_db/couchbase_db.py
import os
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.couchbase import CouchbaseSearch
from couchbase.auth import PasswordAuthenticator
from couchbase.management.search import SearchIndex
from couchbase.options import ClusterOptions, KnownConfigProfiles

# Couchbase connection settings
username = os.getenv("COUCHBASE_USER")
password = os.getenv("COUCHBASE_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": 1536,
                                    "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",
                                        }
                                    ],
                                }
                            },
                        },
                    },
                }
            },
        },
    },
)
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(
        dimensions=1536,
    ),
    wait_until_index_ready=60,
    overwrite=True,
)

knowledge = Knowledge(
    name="Couchbase Knowledge Base",
    description="This is a knowledge base that uses a Couchbase DB",
    vector_db=vector_db,
)

knowledge.add_content(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
)
agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
    read_chat_history=True,
)

agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)

vector_db.delete_by_name("Recipes")
# or
vector_db.delete_by_metadata({"doc_type": "recipe_book"})

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Start Couchbase

docker run -d --name couchbase-server \
  -p 8091-8096:8091-8096 \
  -p 11210:11210 \
  -e COUCHBASE_ADMINISTRATOR_USERNAME=Administrator \
  -e COUCHBASE_ADMINISTRATOR_PASSWORD=password \
  couchbase:latest
Then access http://localhost:8091 and create:
  • Bucket: recipe_bucket
  • Scope: recipe_scope
  • Collection: recipes
3

Install libraries

pip install -U couchbase pypdf openai agno
4

Set environment variables

export COUCHBASE_USER="Administrator"
export COUCHBASE_PASSWORD="password"
export COUCHBASE_CONNECTION_STRING="couchbase://localhost"
export OPENAI_API_KEY=xxx
5

Run Agent

python cookbook/knowledge/vector_db/couchbase_db/couchbase_db.py