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ORM integration

This guide demonstrates how to configure an AEA to interact with a database using python-sql objects.

Discussion

Object-relational-mapping (ORM) is the idea of being able to write SQL queries, using the object-oriented paradigm of your preferred programming language. The scope of the specific demo is to demonstrate how to create an easy configurable AEA that reads data from a database using ORMs.

  • We assume, that you followed the guide for the thermometer-skills.
  • We assume, that we have a database genericdb.db with table name data. This table contains the following columns timestamp and thermometer
  • We assume, that we have a hardware thermometer sensor that adds the readings in the genericdb database (although you can follow the guide without having access to a sensor).

Since the AEA framework enables us to use third-party libraries hosted on PyPI we can directly reference the external dependencies. The aea install command will install each dependency that the specific AEA needs and which is listed in the skill's YAML file.

Communication

This diagram shows the communication between the various entities as data is successfully sold by the seller AEA to the buyer.

sequenceDiagram participant Search participant Buyer_AEA participant Seller_AEA participant Blockchain activate Buyer_AEA activate Search activate Seller_AEA activate Blockchain Seller_AEA->>Search: register_service Buyer_AEA->>Search: search Search-->>Buyer_AEA: list_of_agents Buyer_AEA->>Seller_AEA: call_for_proposal Seller_AEA->>Buyer_AEA: propose Buyer_AEA->>Seller_AEA: accept Seller_AEA->>Buyer_AEA: match_accept Buyer_AEA->>Blockchain: transfer_funds Buyer_AEA->>Seller_AEA: send_transaction_hash Seller_AEA->>Blockchain: check_transaction_status Seller_AEA->>Buyer_AEA: send_data deactivate Buyer_AEA deactivate Search deactivate Seller_AEA deactivate Blockchain

Preparation instructions

Dependencies

Follow the Preliminaries and Installation sections from the AEA quick start.

Demo instructions

A demo to run a scenario with a true ledger transaction on Fetch.ai testnet network or Ethereum ropsten network. This demo assumes the buyer trusts the seller AEA to send the data upon successful payment.

Create the seller AEA

First, fetch the seller AEA, which will provide data: 

aea fetch fetchai/thermometer_aea:0.18.0 --alias my_thermometer_aea
cd my_thermometer_aea
aea install
aea build

Alternatively, create from scratch.

The following steps create the seller from scratch: 

aea create my_thermometer_aea
cd my_thermometer_aea
aea add connection fetchai/p2p_libp2p:0.14.0
aea add connection fetchai/soef:0.15.0
aea add connection fetchai/ledger:0.12.0
aea add skill fetchai/thermometer:0.18.0
aea install
aea build
aea config set agent.default_connection fetchai/p2p_libp2p:0.14.0
aea config set --type dict agent.default_routing \
'{
  "fetchai/ledger_api:0.9.0": "fetchai/ledger:0.12.0",
  "fetchai/oef_search:0.12.0": "fetchai/soef:0.15.0"
}'

Create the buyer client

In another terminal, fetch the AEA that will query the seller AEA. 

aea fetch fetchai/thermometer_client:0.19.0 --alias my_thermometer_client
cd my_thermometer_client
aea install
aea build

Alternatively, create from scratch.

The following steps create the car data client from scratch: 

aea create my_thermometer_client
cd my_thermometer_client
aea add connection fetchai/p2p_libp2p:0.14.0
aea add connection fetchai/soef:0.15.0
aea add connection fetchai/ledger:0.12.0
aea add skill fetchai/thermometer_client:0.18.0
aea install
aea build
aea config set agent.default_connection fetchai/p2p_libp2p:0.14.0
aea config set --type dict agent.default_routing \
'{
  "fetchai/ledger_api:0.9.0": "fetchai/ledger:0.12.0",
  "fetchai/oef_search:0.12.0": "fetchai/soef:0.15.0"
}'

Add keys for the thermometer AEA

First, create the private key for the thermometer AEA based on the network you want to transact. To generate and add a private-public key pair for Fetch.ai AgentLand use: 

aea generate-key fetchai
aea add-key fetchai fetchai_private_key.txt

Next, create a private key used to secure the AEA's communications: 

aea generate-key fetchai fetchai_connection_private_key.txt
aea add-key fetchai fetchai_connection_private_key.txt --connection

Finally, certify the key for use by the connections that request that: 

aea issue-certificates

Add keys and generate wealth for the thermometer client AEA

The thermometer client needs to have some wealth to purchase the thermometer information.

First, create the private key for the thermometer client AEA based on the network you want to transact. To generate and add a private-public key pair for Fetch.ai use: 

aea generate-key fetchai
aea add-key fetchai fetchai_private_key.txt

Then, create some wealth for your car data buyer based on the network you want to transact with. On the Fetch.ai AgentLand network: 

aea generate-wealth fetchai

Next, create a private key used to secure the AEA's communications: 

aea generate-key fetchai fetchai_connection_private_key.txt
aea add-key fetchai fetchai_connection_private_key.txt --connection

Then, create some wealth for your thermometer client based on the network you want to transact with. On the Fetch.ai testnet network: 

aea generate-key fetchai

Update the seller and buyer AEA skill configurations

In my_thermometer_aea/vendor/fetchai/skills/thermometer/skill.yaml, replace the data_for_sale with your data: 

models:
  ...
  strategy:
    args:
      currency_id: FET
      data_for_sale:
        temperature: 26
      has_data_source: false
      is_ledger_tx: true
      ledger_id: fetchai
      location:
        latitude: 51.5194
        longitude: 0.127
      service_data:
        key: seller_service
        value: thermometer_data
      service_id: thermometer_data
      unit_price: 10
    class_name: Strategy
dependencies:
  SQLAlchemy: {}
The data_model and the service_data are used to register the service in the SOEF search node and make your agent discoverable. The name of each attribute must be a key in the service_data dictionary.

In my_thermometer_client/vendor/fetchai/skills/thermometer_client/skill.yaml) ensure you have matching data.

models:
  ...
  strategy:
    args:
      currency_id: FET
      is_ledger_tx: true
      ledger_id: fetchai
      location:
        latitude: 51.5194
        longitude: 0.127
      max_negotiations: 1
      max_tx_fee: 1
      max_unit_price: 20
      search_query:
        constraint_type: ==
        search_key: seller_service
        search_value: thermometer_data
      search_radius: 5.0
      service_id: thermometer_data
    class_name: Strategy

After changing the skill configuration files you should run the following command for both agents to install each dependency: 

aea install

Modify the seller's strategy

Before being able to modify a package we need to eject it from vendor:

aea eject skill fetchai/thermometer:0.18.0

This will move the package to your skills directory and reset the version to 0.1.0 and the author to your author handle.

Open the strategy.py (in my_thermometer_aea/skills/thermometer/strategy.py) with your IDE and modify the following.

Import the newly installed library to your strategy. 

import sqlalchemy as db
Then modify your strategy's __init__ function to match the following code: 
class Strategy(GenericStrategy):
    """This class defines a strategy for the agent."""

    def __init__(self, **kwargs) -> None:
        """
        Initialize the strategy of the agent.

        :param register_as: determines whether the agent registers as seller, buyer or both
        :param search_for: determines whether the agent searches for sellers, buyers or both

        :return: None
        """
        self._db_engine = db.create_engine("sqlite:///genericdb.db")
        self._tbl = self.create_database_and_table()
        self.insert_data()
        super().__init__(**kwargs)

At the end of the file modify the collect_from_data_source function : 

    def collect_from_data_source(self) -> Dict[str, str]:
        """Implement the logic to collect data."""
        connection = self._db_engine.connect()
        query = db.select([self._tbl])
        result_proxy = connection.execute(query)
        data_points = result_proxy.fetchall()
        return {"data": json.dumps(list(map(tuple, data_points)))}
Also, create two new functions, one that will create a connection with the database, and another one will populate the database with some fake data:

    def create_database_and_table(self):
        """Creates a database and a table to store the data if not exists."""
        metadata = db.MetaData()

        tbl = db.Table(
            "data",
            metadata,
            db.Column("timestamp", db.Integer()),
            db.Column("temprature", db.String(255), nullable=False),
        )
        metadata.create_all(self._db_engine)
        return tbl

    def insert_data(self):
        """Insert data in the database."""
        connection = self._db_engine.connect()
        for _ in range(10):
            query = db.insert(self._tbl).values(  # nosec
                timestamp=time.time(), temprature=str(random.randrange(10, 25))
            )
            connection.execute(query)

After modifying the skill we need to fingerprint it:

aea fingerprint skill {YOUR_AUTHOR_HANDLE}/thermometer:0.1.0

Run both AEAs

Run both AEAs from their respective terminals.

First, run the thermometer AEA:

aea run

Once you see a message of the form To join its network use multiaddr 'SOME_ADDRESS' take note of the address. (Alternatively, use aea get-multiaddress fetchai -c -i fetchai/p2p_libp2p:0.14.0 -u public_uri to retrieve the address.) This is the entry peer address for the local agent communication network created by the thermometer AEA.

Then, in the thermometer client, run this command (replace SOME_ADDRESS with the correct value as described above): 

aea config set --type dict vendor.fetchai.connections.p2p_libp2p.config \
'{
  "delegate_uri": "127.0.0.1:11001",
  "entry_peers": ["SOME_ADDRESS"],
  "local_uri": "127.0.0.1:9001",
  "log_file": "libp2p_node.log",
  "public_uri": "127.0.0.1:9001"
}'
This allows the thermometer client to connect to the same local agent communication network as the thermometer.

Then run the thermometer client AEA: 

aea run

You will see that the AEAs negotiate and then transact using the configured testnet.

Delete the AEAs

When you're done, go up a level and delete the AEAs. 

cd ..
aea delete my_thermometer_aea
aea delete my_thermometer_client