docs/examples/load-query-and-chat-marqo.md

from swarms import utils
from swarms.drivers import MarqoVectorStoreDriver
from swarms.engines import VectorQueryEngine
from swarms.loaders import WebLoader
from swarms.structures import Agent
from swarms.tools import KnowledgeBaseClient
import openai
from marqo import Client

# Set the OpenAI API key
openai.api_key_path = "../openai_api_key.txt"

# Define the namespace
namespace = "kyegomez"

# Initialize the vector store driver
vector_store = MarqoVectorStoreDriver(
    api_key=openai.api_key_path,
    url="http://localhost:8882",
    index="chat2",
    mq=Client(api_key="foobar", url="http://localhost:8882")
)

# Get a list of all indexes
#indexes = vector_store.get_indexes()
#print(indexes)

# Initialize the query engine
query_engine = VectorQueryEngine(vector_store_driver=vector_store)

# Initialize the knowledge base tool
kb_tool = KnowledgeBaseClient(
    description="Contains information about the Swarms Framework from www.swarms.ai",
    query_engine=query_engine,
    namespace=namespace
)

# Load artifacts from the web
artifacts = WebLoader(max_tokens=200).load("https://www.swarms.ai")

# Upsert the artifacts into the vector store
vector_store.upsert_text_artifacts({namespace: artifacts,})

# Initialize the agent
agent = Agent(tools=[kb_tool])

# Start the chat
utils.Chat(agent).start()