repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/readers/website_reader.py | cookbook/08_knowledge/readers/website_reader.py | from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.website_reader import WebsiteReader
from agno.models.openai import OpenAIChat
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/readers/web_search_reader_async.py | cookbook/08_knowledge/readers/web_search_reader_async.py | import asyncio
from agno.agent import Agent
from agno.db.postgres.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.web_search_reader import WebSearchReader
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
db = Post... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/readers/arxiv_reader_async.py | cookbook/08_knowledge/readers/arxiv_reader_async.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.arxiv_reader import ArxivReader
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge = Knowledge(
# Table name: ai.arxiv_documents
vec... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/readers/arxiv_reader.py | cookbook/08_knowledge/readers/arxiv_reader.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.arxiv_reader import ArxivReader
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Create a knowledge base with the ArXiv documents
knowledge = Knowledge(
# Tab... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/search_type/vector_search.py | cookbook/08_knowledge/search_type/vector_search.py | from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Load knowledge base using vector search
vector_db = PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.vector)
knowledge = Knowledge(
n... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/search_type/hybrid_search.py | cookbook/08_knowledge/search_type/hybrid_search.py | from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Load knowledge base using hybrid search
hybrid_db = PgVector(table_name="recipes", db_url=db_url, search_type=SearchType.hybrid)
knowledge = Knowledge(
n... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/search_type/__init__.py | cookbook/08_knowledge/search_type/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/search_type/keyword_search.py | cookbook/08_knowledge/search_type/keyword_search.py | from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector, SearchType
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Load knowledge base using keyword search
keyword_db = PgVector(
table_name="recipes", db_url=db_url, search_type=SearchType.keyword
)
knowledge = Knowled... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/async_agentic_filtering.py | cookbook/08_knowledge/filters/async_agentic_filtering.py | import asyncio
import os
from agno.agent import Agent
from agno.db.sqlite import AsyncSqliteDb
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector i... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/filtering_with_conditions_on_team.py | cookbook/08_knowledge/filters/filtering_with_conditions_on_team.py | """
This example demonstrates how to use knowledge filter expressions with teams.
Knowledge filters allow you to restrict knowledge searches to specific documents
or metadata criteria, enabling personalized and contextual responses.
"""
from agno.agent import Agent
from agno.filters import AND, IN, NOT
from agno.know... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/filtering.py | cookbook/08_knowledge/filters/filtering.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.lancedb import LanceDb
# Download all sample sales documents and get their paths
downloaded_csv_paths = download_knowl... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/filtering_with_invalid_keys.py | cookbook/08_knowledge/filters/filtering_with_invalid_keys.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.lancedb import LanceDb
# Download all sample sales documents and get their paths
downloaded_csv_paths = download_knowl... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/filtering_on_load.py | cookbook/08_knowledge/filters/filtering_on_load.py | from agno.agent import Agent
from agno.db.postgres.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector import PgVector
# Download all sample sales files and get th... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/agentic_filtering_with_output_schema.py | cookbook/08_knowledge/filters/agentic_filtering_with_output_schema.py | from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector import PgVector
from pydanti... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/agentic_filtering.py | cookbook/08_knowledge/filters/agentic_filtering.py | from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector import PgVector
# Download ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/__init__.py | cookbook/08_knowledge/filters/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/async_filtering.py | cookbook/08_knowledge/filters/async_filtering.py | import asyncio
import os
from agno.agent import Agent
from agno.db.sqlite import AsyncSqliteDb
from agno.filters import IN
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector import PgVector... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/filtering_with_conditions_on_agent.py | cookbook/08_knowledge/filters/filtering_with_conditions_on_agent.py | """
This example demonstrates how to use knowledge filter expressions with agents.
Knowledge filters allow you to restrict knowledge searches to specific documents
or metadata criteria, enabling personalized and contextual responses.
"""
from agno.agent import Agent
from agno.filters import AND, EQ, IN, NOT
from agno... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_pgvector.py | cookbook/08_knowledge/filters/vector_dbs/filtering_pgvector.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pgvector import PgVector
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filter... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_mongo_db.py | cookbook/08_knowledge/filters/vector_dbs/filtering_mongo_db.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.mongodb import MongoVectorDb
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_fi... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_qdrant_db.py | cookbook/08_knowledge/filters/vector_dbs/filtering_qdrant_db.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.qdrant import Qdrant
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_sa... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/__init__.py | cookbook/08_knowledge/filters/vector_dbs/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_pinecone.py | cookbook/08_knowledge/filters/vector_dbs/filtering_pinecone.py | from os import getenv
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.pineconedb import PineconeDb
# Download all sample CVs and get their paths
downloaded_cv_paths ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_surrealdb.py | cookbook/08_knowledge/filters/vector_dbs/filtering_surrealdb.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.surrealdb import SurrealDb
from surrealdb import Surreal
# SurrealDB connection parameters
SURREALDB_URL = "ws://local... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_weaviate.py | cookbook/08_knowledge/filters/vector_dbs/filtering_weaviate.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate
# Download all sample CVs and get their paths
downloaded_cv_paths = do... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_chroma_db.py | cookbook/08_knowledge/filters/vector_dbs/filtering_chroma_db.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.chroma import ChromaDb
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_lance_db.py | cookbook/08_knowledge/filters/vector_dbs/filtering_lance_db.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.lancedb import LanceDb
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/filters/vector_dbs/filtering_milvus.py | cookbook/08_knowledge/filters/vector_dbs/filtering_milvus.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.milvus import Milvus
# Download all sample CVs and get their paths
downloaded_cv_paths = download_knowledge_filters_sa... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/custom_retriever/async_retriever.py | cookbook/08_knowledge/custom_retriever/async_retriever.py | import asyncio
from typing import Optional
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant
from qdrant_client import AsyncQdrantClient
# -------------------------------------------------------... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/custom_retriever/__init__.py | cookbook/08_knowledge/custom_retriever/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/custom_retriever/retriever.py | cookbook/08_knowledge/custom_retriever/retriever.py | from typing import Optional
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant
from qdrant_client import QdrantClient
# ---------------------------------------------------------
# This section lo... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/custom_retriever/retriever_with_dependencies.py | cookbook/08_knowledge/custom_retriever/retriever_with_dependencies.py | """
Example demonstrating custom knowledge retriever with runtime dependencies.
This cookbook shows how to access dependencies passed at runtime (e.g., via agent.run(dependencies={...}))
in a custom knowledge retriever function.
Key points:
1. Add 'run_context' parameter to your retriever function signature
2. Depend... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/__init__.py | cookbook/08_knowledge/vector_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/couchbase_db/async_couchbase_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/couchbase_db/async_couchbase_db_with_batch_embedder.py | """
Couchbase Vector DB Example
==========================
Setup Couchbase Cluster (Local via Docker):
-------------------------------------------
1. Run Couchbase locally:
docker run -d --name couchbase-server \
-p 8091-8096:8091-8096 \
-p 11210:11210 \
-e COUCHBASE_ADMINISTRATOR_USERNAME=Administr... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/couchbase_db/couchbase_db.py | cookbook/08_knowledge/vector_db/couchbase_db/couchbase_db.py | """
Couchbase Vector DB Example
==========================
Setup Couchbase Cluster (Local via Docker):
-------------------------------------------
1. Run Couchbase locally:
docker run -d --name couchbase-server \
-p 8091-8096:8091-8096 \
-p 11210:11210 \
-e COUCHBASE_ADMINISTRATOR_USERNAME=Administr... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/couchbase_db/__init__.py | cookbook/08_knowledge/vector_db/couchbase_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/couchbase_db/async_couchbase_db.py | cookbook/08_knowledge/vector_db/couchbase_db/async_couchbase_db.py | """
Couchbase Vector DB Example
==========================
Setup Couchbase Cluster (Local via Docker):
-------------------------------------------
1. Run Couchbase locally:
docker run -d --name couchbase-server \
-p 8091-8096:8091-8096 \
-p 11210:11210 \
-e COUCHBASE_ADMINISTRATOR_USERNAME=Administr... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pgvector/pgvector_hybrid_search.py | cookbook/08_knowledge/vector_db/pgvector/pgvector_hybrid_search.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.pgvector import PgVector, SearchType
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
knowledge = Knowledge(
name="My PG Vector Knowledge Base",
description="This is ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pgvector/async_pg_vector_with_batch_embedder.py | cookbook/08_knowledge/vector_db/pgvector/async_pg_vector_with_batch_embedder.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
vector_db = PgVector(
table_name="recipes", db_url=db_url, embed... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pgvector/pgvector_db.py | cookbook/08_knowledge/vector_db/pgvector/pgvector_db.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
vector_db = PgVector(table_name="vectors", db_url=db_url)
knowledge = Knowledge(
name="My PG Vector Knowledge Base",
description="Thi... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pgvector/__init__.py | cookbook/08_knowledge/vector_db/pgvector/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pgvector/async_pg_vector.py | cookbook/08_knowledge/vector_db/pgvector/async_pg_vector.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
vector_db = PgVector(table_name="recipes", db_url=db_url)
knowledge_base = Knowledge(
vector_db=vector_db,
)
agent = Age... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/cassandra_db/async_cassandra_db.py | cookbook/08_knowledge/vector_db/cassandra_db/async_cassandra_db.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.mistral import MistralEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.mistral import MistralChat
from agno.vectordb.cassandra import Cassandra
try:
from cassandra.cluster import Cluster # type: ignore
except (Import... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/cassandra_db/__init__.py | cookbook/08_knowledge/vector_db/cassandra_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/cassandra_db/async_cassandra_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/cassandra_db/async_cassandra_db_with_batch_embedder.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.mistral import MistralEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.mistral import MistralChat
from agno.vectordb.cassandra import Cassandra
try:
from cassandra.cluster import Cluster # type: ignore
except (Import... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/cassandra_db/cassandra_db.py | cookbook/08_knowledge/vector_db/cassandra_db/cassandra_db.py | from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.cassandra import Cassandra
try:
from cassandra.cluster import Cluster # type: ignore
except (ImportError, ModuleNotFoundError):
raise ImportError(
"C... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pinecone_db/pinecone_db.py | cookbook/08_knowledge/vector_db/pinecone_db/pinecone_db.py | from os import getenv
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pineconedb import PineconeDb
api_key = getenv("PINECONE_API_KEY")
index_name = "thai-recipe-index"
vector_db = PineconeDb(
name=index_name,
dimension=1536,
metric="cosine",
spec={"serv... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pinecone_db/__init__.py | cookbook/08_knowledge/vector_db/pinecone_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/pinecone_db/async_pinecone_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/pinecone_db/async_pinecone_db_with_batch_embedder.py | import asyncio
from os import getenv
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.pineconedb import PineconeDb
agent = Agent(
model=OpenAIChat(
id="gpt-4o-m... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/llamaindex_db/async_llamaindex_db.py | cookbook/08_knowledge/vector_db/llamaindex_db/async_llamaindex_db.py | import asyncio
from pathlib import Path
from shutil import rmtree
import httpx
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.llamaindex.llamaindexdb import LlamaIndexVectorDb
from llama_index.core import (
SimpleDirectoryRea... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/llamaindex_db/__init__.py | cookbook/08_knowledge/vector_db/llamaindex_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/llamaindex_db/llamaindex_db.py | cookbook/08_knowledge/vector_db/llamaindex_db/llamaindex_db.py | """
pip install llama-index-core llama-index-readers-file llama-index-embeddings-openai agno
"""
from pathlib import Path
from shutil import rmtree
import httpx
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.llamaindex.llamainde... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/chroma_db/async_chroma_db.py | cookbook/08_knowledge/vector_db/chroma_db/async_chroma_db.py | # install chromadb - `pip install chromadb`
import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.chroma import ChromaDb
# Initialize ChromaDB
vector_db = ChromaDb(collection="recipes", path="tmp/chromadb", persistent_client=True)
# Create knowledge base
knowl... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/chroma_db/chroma_db.py | cookbook/08_knowledge/vector_db/chroma_db/chroma_db.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.chroma import ChromaDb
# Create Knowledge Instance with ChromaDB
knowledge = Knowledge(
name="Basic SDK Knowledge Base",
description="Agno 2.0 Knowledge Implementation with ChromaDB",
vector_db=Ch... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/chroma_db/async_chroma_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/chroma_db/async_chroma_db_with_batch_embedder.py | # install chromadb - `pip install chromadb`
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.chroma import ChromaDb
# Initialize ChromaDB
vector_db = ChromaDb(
collection="recipes",
path="tm... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/chroma_db/__init__.py | cookbook/08_knowledge/vector_db/chroma_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/chroma_db/chroma_db_hybrid_search.py | cookbook/08_knowledge/vector_db/chroma_db/chroma_db_hybrid_search.py | """
ChromaDB with Hybrid Search using Reciprocal Rank Fusion (RRF)
This example demonstrates how to use ChromaDB with hybrid search,
which combines dense vector similarity search (semantic) with
full-text search (keyword/lexical) using RRF fusion.
Hybrid search is useful when you want to:
- Combine semantic understan... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/milvus_db.py | cookbook/08_knowledge/vector_db/milvus_db/milvus_db.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.milvus import Milvus
# Initialize Milvus
# Set the uri and token for your Milvus server.
# - If you only need a local vector database for small scale data or prototyping, setting the uri as a local file, e.g... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/async_milvus_db_hybrid_search.py | cookbook/08_knowledge/vector_db/milvus_db/async_milvus_db_hybrid_search.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.milvus import Milvus, SearchType
# Initialize Milvus
# Set the uri and token for your Milvus server.
# - If you only need a local vector database for small scale data or prototyping, setting the uri as a loc... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/milvus_db_hybrid_search.py | cookbook/08_knowledge/vector_db/milvus_db/milvus_db_hybrid_search.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.milvus import Milvus, SearchType
# Initialize Milvus
# Set the uri and token for your Milvus server.
# - If you only need a local vector database for small scale data or prototyping, setting the uri as a local file, e.g.`./... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/milvus_db_range_search.py | cookbook/08_knowledge/vector_db/milvus_db/milvus_db_range_search.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.milvus import Milvus
# Initialize Milvus with a local file database
vector_db = Milvus(
collection="recipes_range_search",
uri="/tmp/milvus_range.db",
)
# Create knowledge base
knowledge = Knowledge(... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/async_milvus_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/milvus_db/async_milvus_db_with_batch_embedder.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.milvus import Milvus
agent = Agent(
model=OpenAIChat(
id="gpt-4o-mini",
),
knowledge=Kno... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/__init__.py | cookbook/08_knowledge/vector_db/milvus_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/milvus_db/async_milvus_db.py | cookbook/08_knowledge/vector_db/milvus_db/async_milvus_db.py | # install pymilvus - `pip install pymilvus`
import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.milvus import Milvus
# Initialize Milvus
# Set the uri and token for your Milvus server.
# - If you only need a local vector database for small scale data or prot... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lightrag/lightrag.py | cookbook/08_knowledge/vector_db/lightrag/lightrag.py | import asyncio
from os import getenv
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.wikipedia_reader import WikipediaReader
from agno.vectordb.lightrag import LightRag
vector_db = LightRag(
api_key=getenv("LIGHTRAG_API_KEY"),
)
knowledge = Knowledge(
na... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lightrag/__init__.py | cookbook/08_knowledge/vector_db/lightrag/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/langchain/langchain_db.py | cookbook/08_knowledge/vector_db/langchain/langchain_db.py | """
pip install langchain langchain-community langchain-openai langchain-chroma agno
"""
import pathlib
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.langchaindb import LangChainVectorDb
from langchain.text_splitter import Char... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/langchain/__init__.py | cookbook/08_knowledge/vector_db/langchain/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/langchain/async_langchain_db.py | cookbook/08_knowledge/vector_db/langchain/async_langchain_db.py | """
pip install langchain langchain-community langchain-openai langchain-chroma agno
"""
import asyncio
import pathlib
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.langchaindb import LangChainVectorDb
from langchain.text_split... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/upstash_db/__init__.py | cookbook/08_knowledge/vector_db/upstash_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/upstash_db/async_upstash_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/upstash_db/async_upstash_db_with_batch_embedder.py | import asyncio
from os import getenv
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.upstashdb import UpstashVectorDb
agent = Agent(
model=OpenAIChat(
id="gpt-... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/upstash_db/upstash_db.py | cookbook/08_knowledge/vector_db/upstash_db/upstash_db.py | # install upstash-vector - `uv pip install upstash-vector`
# Add OPENAI_API_KEY to your environment variables for the agent response
import os
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.upstashdb import UpstashVectorDb
# How to connect to an Upstash Vector index
# ... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/surrealdb/async_surreal_db.py | cookbook/08_knowledge/vector_db/surrealdb/async_surreal_db.py | # Run SurrealDB in a container before running this script
# docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start --user root --pass root
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/surrealdb/surreal_db.py | cookbook/08_knowledge/vector_db/surrealdb/surreal_db.py | # Run SurrealDB in a container before running this script
# docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start --user root --pass root
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.surrea... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/surrealdb/__init__.py | cookbook/08_knowledge/vector_db/surrealdb/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/mongo_db/mongo_db_hybrid_search.py | cookbook/08_knowledge/vector_db/mongo_db/mongo_db_hybrid_search.py | import typer
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.mongodb import MongoVectorDb
from agno.vectordb.search import SearchType
from rich.prompt import Prompt
# MongoDB Atlas connection string
"""
Example connection strings:
"mongodb+srv://<username>:<password>@clus... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/mongo_db/async_mongo_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/mongo_db/async_mongo_db_with_batch_embedder.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.mongodb import MongoVectorDb
mdb_connection_string = (
"mongodb+srv://mongoadmin:secret@cluster0.mongodb... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/mongo_db/async_mongo_db.py | cookbook/08_knowledge/vector_db/mongo_db/async_mongo_db.py | """
1. Create a MongoDB Atlas Account:
- Go to https://www.mongodb.com/cloud/atlas/register
- Sign up for a free account
2. Create a New Cluster:
- Click "Build a Database"
- Choose the FREE tier (M0)
- Select your preferred cloud provider and region
- Click "Create Cluster"
3. Set Up Database Acces... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/mongo_db/mongo_db.py | cookbook/08_knowledge/vector_db/mongo_db/mongo_db.py | """
1. Create a MongoDB Atlas Account:
- Go to https://www.mongodb.com/cloud/atlas/register
- Sign up for a free account
2. Create a New Cluster:
- Click "Build a Database"
- Choose the FREE tier (M0)
- Select your preferred cloud provider and region
- Click "Create Cluster"
3. Set Up Database Acces... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/mongo_db/cosmos_mongodb_vcore.py | cookbook/08_knowledge/vector_db/mongo_db/cosmos_mongodb_vcore.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.mongodb import MongoVectorDb
"""
Example connection strings:
"mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"
"... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/mongo_db/__init__.py | cookbook/08_knowledge/vector_db/mongo_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/qdrant_db/qdrant_db_hybrid_search.py | cookbook/08_knowledge/vector_db/qdrant_db/qdrant_db_hybrid_search.py | import typer
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType
from rich.prompt import Prompt
COLLECTION_NAME = "thai-recipes"
vector_db = Qdrant(
collection=COLLECTION_NAME,
url="http://localhost:633... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/qdrant_db/async_qdrant_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/qdrant_db/async_qdrant_db_with_batch_embedder.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.qdrant import Qdrant
agent = Agent(
model=OpenAIChat(
id="gpt-4o-mini",
),
knowledge=Kno... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/qdrant_db/qdrant_db.py | cookbook/08_knowledge/vector_db/qdrant_db/qdrant_db.py | from agno.agent import Agent
from agno.db.postgres.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant
COLLECTION_NAME = "thai-recipes"
vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")
contents_db = PostgresDb(
db_url="postg... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/qdrant_db/async_qdrant_db.py | cookbook/08_knowledge/vector_db/qdrant_db/async_qdrant_db.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.qdrant import Qdrant
COLLECTION_NAME = "thai-recipes"
vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")
# Create knowledge base
knowledge = Knowledge(
vector_db=vector_db,
)
a... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/qdrant_db/__init__.py | cookbook/08_knowledge/vector_db/qdrant_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/singlestore_db/__init__.py | cookbook/08_knowledge/vector_db/singlestore_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/singlestore_db/singlestore_db.py | cookbook/08_knowledge/vector_db/singlestore_db/singlestore_db.py | """
# Run the setup script
```shell
./cookbook/scripts/run_singlestore.sh
```
# Create the database
- Visit http://localhost:8080 and login with `root` and `admin`
- Create the database with your choice of name. Default setup script requires AGNO as database name. `CREATE DATABASE your_database_name;`
"""
import asy... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/singlestore_db/async_singlestore_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/singlestore_db/async_singlestore_db_with_batch_embedder.py | """
# Run the setup script
```shell
./cookbook/scripts/run_singlestore.sh
```
# Create the database
- Visit http://localhost:8080 and login with `root` and `admin`
- Create the database with your choice of name. Default setup script requires AGNO as database name. `CREATE DATABASE your_database_name;`
"""
import asy... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lance_db/lance_db_with_mistral_embedder.py | cookbook/08_knowledge/vector_db/lance_db/lance_db_with_mistral_embedder.py | import asyncio
from agno.knowledge.embedder.mistral import MistralEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.lancedb import LanceDb, SearchType
embedder_mi = MistralEmbedder()
vector_db = LanceDb(
uri="tmp/lancedb",
table_... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lance_db/lance_db_hybrid_search.py | cookbook/08_knowledge/vector_db/lance_db/lance_db_hybrid_search.py | from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.lancedb import LanceDb, SearchType
knowledge = Knowledge(
name="My PG Vector Knowledge Base",
description="This is a knowledge base that uses a PG Vector DB",
vector_db=L... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lance_db/async_lance_db_with_batch_embedder.py | cookbook/08_knowledge/vector_db/lance_db/async_lance_db_with_batch_embedder.py | import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIChat
from agno.vectordb.lancedb import LanceDb
agent = Agent(
model=OpenAIChat(
id="gpt-4o-mini",
),
knowledge=K... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lance_db/__init__.py | cookbook/08_knowledge/vector_db/lance_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false | |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/lance_db/lance_db.py | cookbook/08_knowledge/vector_db/lance_db/lance_db.py | import asyncio
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb
vector_db = LanceDb(
table_name="vectors",
uri="tmp/lancedb",
)
# Create Knowledge Instance with LanceDB
knowledge = Knowledge(
name="Basic SDK Knowledge Base",
descrip... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/redis_db/redis_db.py | cookbook/08_knowledge/vector_db/redis_db/redis_db.py | """
This example shows how to use Redis as a vector database with Agno.
To get started, either set the REDIS_URL environment variable to your Redis connection string,
or start the local Redis docker container using the following command:
./cookbook/scripts/run_redis.sh
"""
import os
from agno.agent import Agent
fro... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/redis_db/async_redis_db.py | cookbook/08_knowledge/vector_db/redis_db/async_redis_db.py | """
This example shows how to use Redis as a vector database with Agno.
To get started, either set the REDIS_URL environment variable to your Redis connection string,
or start the local Redis docker container using the following command:
./cookbook/scripts/run_redis.sh
"""
import asyncio
import os
from agno.agent i... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/redis_db/redis_db_with_cohere_reranker.py | cookbook/08_knowledge/vector_db/redis_db/redis_db_with_cohere_reranker.py | from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reranker.cohere import CohereReranker
from agno.models.openai import OpenAIChat
from agno.vectordb.redis import RedisDB
knowledge = Knowledge(
vector_db=RedisDB(... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/weaviate_db/async_weaviate_db.py | cookbook/08_knowledge/vector_db/weaviate_db/async_weaviate_db.py | """
This example demonstrates using Weaviate as a vector database for semantic search.
Installation:
pip install weaviate-client
You can use either Weaviate Cloud or a local instance.
Weaviate Cloud Setup:
1. Create account at https://console.weaviate.cloud/
2. Create a cluster and copy the "REST endpoint" and "... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/weaviate_db/weaviate_db_hybrid_search.py | cookbook/08_knowledge/vector_db/weaviate_db/weaviate_db_hybrid_search.py | import typer
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate
from rich.prompt import Prompt
vector_db = Weaviate(
collection="recipes",
search_type=SearchType.hybrid,
v... | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
agno-agi/agno | https://github.com/agno-agi/agno/blob/26b9daa0fb760c3ad8384e1ba9218b1bce854535/cookbook/08_knowledge/vector_db/weaviate_db/__init__.py | cookbook/08_knowledge/vector_db/weaviate_db/__init__.py | python | Apache-2.0 | 26b9daa0fb760c3ad8384e1ba9218b1bce854535 | 2026-01-04T14:41:49.609528Z | false |
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