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Tru

Bases: SingletonPerName

Tru is the main class that provides an entry points to trulens-eval. Tru lets you:

  • Log chain prompts and outputs
  • Log chain Metadata
  • Run and log feedback functions
  • Run streamlit dashboard to view experiment results

All data is logged to the current working directory to default.sqlite.

Source code in trulens_eval/trulens_eval/tru.py 
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class Tru(SingletonPerName):
    """
    Tru is the main class that provides an entry points to trulens-eval. Tru lets you:

    * Log chain prompts and outputs
    * Log chain Metadata
    * Run and log feedback functions
    * Run streamlit dashboard to view experiment results

    All data is logged to the current working directory to default.sqlite.
    """
    DEFAULT_DATABASE_FILE = "default.sqlite"

    # Process or Thread of the deferred feedback function evaluator.
    evaluator_proc = None

    # Process of the dashboard app.
    dashboard_proc = None

    def Chain(self, *args, **kwargs):
        """
        Create a TruChain with database managed by self.
        """

        from trulens_eval.tru_chain import TruChain

        return TruChain(tru=self, *args, **kwargs)

    def __init__(self):
        """
        TruLens instrumentation, logging, and feedback functions for chains.
        Creates a local database 'default.sqlite' in current working directory.
        """

        if hasattr(self, "db"):
            # Already initialized by SingletonByName mechanism.
            return

        self.db = LocalSQLite(Tru.DEFAULT_DATABASE_FILE)

    def reset_database(self):
        """
        Reset the database. Clears all tables.
        """

        self.db.reset_database()

    def add_record(
        self,
        prompt: str,
        response: str,
        record_json: JSON,
        tags: Optional[str] = "",
        ts: Optional[int] = None,
        total_tokens: Optional[int] = None,
        total_cost: Optional[float] = None,
    ):
        """
        Add a record to the database.

        Parameters:

            prompt (str): Chain input or "prompt".

            response (str): Chain output or "response".

            record_json (JSON): Record as produced by `TruChain.call_with_record`.

            tags (str, optional): Additional metadata to include with the record.

            ts (int, optional): Timestamp of record creation.

            total_tokens (int, optional): The number of tokens generated in
            producing the response.

            total_cost (float, optional): The cost of producing the response.

        Returns:
            str: Unique record identifier.

        """
        ts = ts or datetime.now()
        total_tokens = total_tokens or record_json['_cost']['total_tokens']
        total_cost = total_cost or record_json['_cost']['total_cost']

        chain_id = record_json['chain_id']

        record_id = self.db.insert_record(
            chain_id=chain_id,
            input=prompt,
            output=response,
            record_json=record_json,
            ts=ts,
            tags=tags,
            total_tokens=total_tokens,
            total_cost=total_cost
        )

        return record_id

    def run_feedback_functions(
        self,
        record_json: JSON,
        feedback_functions: Sequence['Feedback'],
        chain_json: Optional[JSON] = None,
    ) -> Sequence[JSON]:
        """
        Run a collection of feedback functions and report their result.

        Parameters:

            record_json (JSON): The record on which to evaluate the feedback
            functions.

            chain_json (JSON, optional): The chain that produced the given record.
            If not provided, it is looked up from the given database `db`.

            feedback_functions (Sequence[Feedback]): A collection of feedback
            functions to evaluate.

        Returns nothing.
        """

        chain_id = record_json['chain_id']

        if chain_json is None:
            chain_json = self.db.get_chain(chain_id=chain_id)
            if chain_json is None:
                raise RuntimeError(
                    "Chain {chain_id} not present in db. "
                    "Either add it with `tru.add_chain` or provide `chain_json` to `tru.run_feedback_functions`."
                )

        else:
            assert chain_id == chain_json[
                'chain_id'], "Record was produced by a different chain."

            if self.db.get_chain(chain_id=chain_json['chain_id']) is None:
                logging.warn(
                    "Chain {chain_id} was not present in database. Adding it."
                )
                self.add_chain(chain_json=chain_json)

        evals = []

        for func in feedback_functions:
            evals.append(
                TP().promise(
                    lambda f: f.run_on_record(
                        chain_json=chain_json, record_json=record_json
                    ), func
                )
            )

        evals = map(lambda p: p.get(), evals)

        return list(evals)

    def add_chain(
        self, chain_json: JSON, chain_id: Optional[str] = None
    ) -> None:
        """
        Add a chain to the database.        
        """

        self.db.insert_chain(chain_id=chain_id, chain_json=chain_json)

    def add_feedback(self, result_json: JSON) -> None:
        """
        Add a single feedback result to the database.
        """

        if 'record_id' not in result_json or result_json['record_id'] is None:
            raise RuntimeError(
                "Result does not include record_id. "
                "To log feedback, log the record first using `tru.add_record`."
            )

        self.db.insert_feedback(result_json=result_json, status=2)

    def add_feedbacks(self, result_jsons: Iterable[JSON]) -> None:
        """
        Add multiple feedback results to the database.
        """

        for result_json in result_jsons:
            self.add_feedback(result_json=result_json)

    def get_chain(self, chain_id: str) -> JSON:
        """
        Look up a chain from the database.
        """

        return self.db.get_chain(chain_id)

    def get_records_and_feedback(self, chain_ids: List[str]):
        """
        Get records, their feeback results, and feedback names from the database.
        """

        df, feedback_columns = self.db.get_records_and_feedback(chain_ids)

        return df, feedback_columns

    def start_evaluator(self, fork=False) -> Union[Process, Thread]:
        """
        Start a deferred feedback function evaluation thread.
        """

        assert not fork, "Fork mode not yet implemented."

        if self.evaluator_proc is not None:
            raise RuntimeError("Evaluator is already running in this process.")

        from trulens_eval.tru_feedback import Feedback

        if not fork:
            self.evaluator_stop = threading.Event()

        def runloop():
            while fork or not self.evaluator_stop.is_set():
                print("Looking for things to do. Stop me with `tru.stop_evaluator()`.", end='')
                Feedback.evaluate_deferred(tru=self)
                TP().finish(timeout=10)
                if fork:
                    sleep(10)
                else:
                    self.evaluator_stop.wait(10)

            print("Evaluator stopped.")

        if fork:
            proc = Process(target=runloop)
        else:
            proc = Thread(target=runloop)

        # Start a persistent thread or process that evaluates feedback functions.

        self.evaluator_proc = proc
        proc.start()

        return proc

    def stop_evaluator(self):
        """
        Stop the deferred feedback evaluation thread.
        """

        if self.evaluator_proc is None:
            raise RuntimeError("Evaluator not running this process.")

        if isinstance(self.evaluator_proc, Process):
            self.evaluator_proc.terminate()

        elif isinstance(self.evaluator_proc, Thread):
            self.evaluator_stop.set()
            self.evaluator_proc.join()
            self.evaluator_stop = None

        self.evaluator_proc = None

    def stop_dashboard(self) -> None:
        """Stop existing dashboard if running.

        Raises:
            ValueError: Dashboard is already running.
        """
        if Tru.dashboard_proc is None:
            raise ValueError("Dashboard not running.")

        Tru.dashboard_proc.kill()
        Tru.dashboard_proc = None

    def run_dashboard(self, _dev: bool = False) -> Process:
        """ Runs a streamlit dashboard to view logged results and chains

        Raises:
            ValueError: Dashboard is already running.

        Returns:
            Process: Process containing streamlit dashboard.
        """

        if Tru.dashboard_proc is not None:
            raise ValueError("Dashboard already running. Run tru.stop_dashboard() to stop existing dashboard.")

        # Create .streamlit directory if it doesn't exist
        streamlit_dir = os.path.join(os.getcwd(), '.streamlit')
        os.makedirs(streamlit_dir, exist_ok=True)

        # Create config.toml file
        config_path = os.path.join(streamlit_dir, 'config.toml')
        with open(config_path, 'w') as f:
            f.write('[theme]\n')
            f.write('primaryColor="#0A2C37"\n')
            f.write('backgroundColor="#FFFFFF"\n')
            f.write('secondaryBackgroundColor="F5F5F5"\n')
            f.write('textColor="#0A2C37"\n')
            f.write('font="sans serif"\n')

        cred_path = os.path.join(streamlit_dir, 'credentials.toml')
        with open(cred_path, 'w') as f:
            f.write('[general]\n')
            f.write('email=""\n')

        #run leaderboard with subprocess
        leaderboard_path = pkg_resources.resource_filename(
            'trulens_eval', 'Leaderboard.py'
        )

        env_opts = {}
        if _dev:
            env_opts['env'] = os.environ
            env_opts['env']['PYTHONPATH'] = str(Path.cwd())

        proc = subprocess.Popen(
            ["streamlit", "run", "--server.headless=True", leaderboard_path], **env_opts
        )

        Tru.dashboard_proc = proc

        return proc

    start_dashboard = run_dashboard

Chain(*args, **kwargs)

Create a TruChain with database managed by self.

Source code in trulens_eval/trulens_eval/tru.py 
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def Chain(self, *args, **kwargs):
    """
    Create a TruChain with database managed by self.
    """

    from trulens_eval.tru_chain import TruChain

    return TruChain(tru=self, *args, **kwargs)

__init__()

TruLens instrumentation, logging, and feedback functions for chains. Creates a local database 'default.sqlite' in current working directory.

Source code in trulens_eval/trulens_eval/tru.py 
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def __init__(self):
    """
    TruLens instrumentation, logging, and feedback functions for chains.
    Creates a local database 'default.sqlite' in current working directory.
    """

    if hasattr(self, "db"):
        # Already initialized by SingletonByName mechanism.
        return

    self.db = LocalSQLite(Tru.DEFAULT_DATABASE_FILE)

add_chain(chain_json, chain_id=None)

Add a chain to the database.

Source code in trulens_eval/trulens_eval/tru.py 
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def add_chain(
    self, chain_json: JSON, chain_id: Optional[str] = None
) -> None:
    """
    Add a chain to the database.        
    """

    self.db.insert_chain(chain_id=chain_id, chain_json=chain_json)

add_feedback(result_json)

Add a single feedback result to the database.

Source code in trulens_eval/trulens_eval/tru.py 
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def add_feedback(self, result_json: JSON) -> None:
    """
    Add a single feedback result to the database.
    """

    if 'record_id' not in result_json or result_json['record_id'] is None:
        raise RuntimeError(
            "Result does not include record_id. "
            "To log feedback, log the record first using `tru.add_record`."
        )

    self.db.insert_feedback(result_json=result_json, status=2)

add_feedbacks(result_jsons)

Add multiple feedback results to the database.

Source code in trulens_eval/trulens_eval/tru.py 
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def add_feedbacks(self, result_jsons: Iterable[JSON]) -> None:
    """
    Add multiple feedback results to the database.
    """

    for result_json in result_jsons:
        self.add_feedback(result_json=result_json)

add_record(prompt, response, record_json, tags='', ts=None, total_tokens=None, total_cost=None)

Add a record to the database.

Parameters:

Name Type Description Default
prompt str

Chain input or "prompt".

 required
response str

Chain output or "response".

 required
record_json JSON

Record as produced by TruChain.call_with_record.

 required
tags str

Additional metadata to include with the record.

 ''
ts int

Timestamp of record creation.

 None
total_tokens int

The number of tokens generated in

 None
total_cost float

The cost of producing the response.

 None

Returns:

Name Type Description
str

Unique record identifier.
Source code in trulens_eval/trulens_eval/tru.py 
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def add_record(
    self,
    prompt: str,
    response: str,
    record_json: JSON,
    tags: Optional[str] = "",
    ts: Optional[int] = None,
    total_tokens: Optional[int] = None,
    total_cost: Optional[float] = None,
):
    """
    Add a record to the database.

    Parameters:

        prompt (str): Chain input or "prompt".

        response (str): Chain output or "response".

        record_json (JSON): Record as produced by `TruChain.call_with_record`.

        tags (str, optional): Additional metadata to include with the record.

        ts (int, optional): Timestamp of record creation.

        total_tokens (int, optional): The number of tokens generated in
        producing the response.

        total_cost (float, optional): The cost of producing the response.

    Returns:
        str: Unique record identifier.

    """
    ts = ts or datetime.now()
    total_tokens = total_tokens or record_json['_cost']['total_tokens']
    total_cost = total_cost or record_json['_cost']['total_cost']

    chain_id = record_json['chain_id']

    record_id = self.db.insert_record(
        chain_id=chain_id,
        input=prompt,
        output=response,
        record_json=record_json,
        ts=ts,
        tags=tags,
        total_tokens=total_tokens,
        total_cost=total_cost
    )

    return record_id

get_chain(chain_id)

Look up a chain from the database.

Source code in trulens_eval/trulens_eval/tru.py 
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def get_chain(self, chain_id: str) -> JSON:
    """
    Look up a chain from the database.
    """

    return self.db.get_chain(chain_id)

get_records_and_feedback(chain_ids)

Get records, their feeback results, and feedback names from the database.

Source code in trulens_eval/trulens_eval/tru.py 
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def get_records_and_feedback(self, chain_ids: List[str]):
    """
    Get records, their feeback results, and feedback names from the database.
    """

    df, feedback_columns = self.db.get_records_and_feedback(chain_ids)

    return df, feedback_columns

reset_database()

Reset the database. Clears all tables.

Source code in trulens_eval/trulens_eval/tru.py 
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def reset_database(self):
    """
    Reset the database. Clears all tables.
    """

    self.db.reset_database()

run_dashboard(_dev=False)

Runs a streamlit dashboard to view logged results and chains

Raises:

Type Description
ValueError

Dashboard is already running.

Returns:

Name Type Description
Process Process

Process containing streamlit dashboard.
Source code in trulens_eval/trulens_eval/tru.py 
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def run_dashboard(self, _dev: bool = False) -> Process:
    """ Runs a streamlit dashboard to view logged results and chains

    Raises:
        ValueError: Dashboard is already running.

    Returns:
        Process: Process containing streamlit dashboard.
    """

    if Tru.dashboard_proc is not None:
        raise ValueError("Dashboard already running. Run tru.stop_dashboard() to stop existing dashboard.")

    # Create .streamlit directory if it doesn't exist
    streamlit_dir = os.path.join(os.getcwd(), '.streamlit')
    os.makedirs(streamlit_dir, exist_ok=True)

    # Create config.toml file
    config_path = os.path.join(streamlit_dir, 'config.toml')
    with open(config_path, 'w') as f:
        f.write('[theme]\n')
        f.write('primaryColor="#0A2C37"\n')
        f.write('backgroundColor="#FFFFFF"\n')
        f.write('secondaryBackgroundColor="F5F5F5"\n')
        f.write('textColor="#0A2C37"\n')
        f.write('font="sans serif"\n')

    cred_path = os.path.join(streamlit_dir, 'credentials.toml')
    with open(cred_path, 'w') as f:
        f.write('[general]\n')
        f.write('email=""\n')

    #run leaderboard with subprocess
    leaderboard_path = pkg_resources.resource_filename(
        'trulens_eval', 'Leaderboard.py'
    )

    env_opts = {}
    if _dev:
        env_opts['env'] = os.environ
        env_opts['env']['PYTHONPATH'] = str(Path.cwd())

    proc = subprocess.Popen(
        ["streamlit", "run", "--server.headless=True", leaderboard_path], **env_opts
    )

    Tru.dashboard_proc = proc

    return proc

run_feedback_functions(record_json, feedback_functions, chain_json=None)

Run a collection of feedback functions and report their result.

Parameters:

Name Type Description Default
record_json JSON

The record on which to evaluate the feedback

 required
chain_json JSON

The chain that produced the given record.

 None
feedback_functions Sequence[Feedback]

A collection of feedback

 required

Returns nothing.

Source code in trulens_eval/trulens_eval/tru.py 
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def run_feedback_functions(
    self,
    record_json: JSON,
    feedback_functions: Sequence['Feedback'],
    chain_json: Optional[JSON] = None,
) -> Sequence[JSON]:
    """
    Run a collection of feedback functions and report their result.

    Parameters:

        record_json (JSON): The record on which to evaluate the feedback
        functions.

        chain_json (JSON, optional): The chain that produced the given record.
        If not provided, it is looked up from the given database `db`.

        feedback_functions (Sequence[Feedback]): A collection of feedback
        functions to evaluate.

    Returns nothing.
    """

    chain_id = record_json['chain_id']

    if chain_json is None:
        chain_json = self.db.get_chain(chain_id=chain_id)
        if chain_json is None:
            raise RuntimeError(
                "Chain {chain_id} not present in db. "
                "Either add it with `tru.add_chain` or provide `chain_json` to `tru.run_feedback_functions`."
            )

    else:
        assert chain_id == chain_json[
            'chain_id'], "Record was produced by a different chain."

        if self.db.get_chain(chain_id=chain_json['chain_id']) is None:
            logging.warn(
                "Chain {chain_id} was not present in database. Adding it."
            )
            self.add_chain(chain_json=chain_json)

    evals = []

    for func in feedback_functions:
        evals.append(
            TP().promise(
                lambda f: f.run_on_record(
                    chain_json=chain_json, record_json=record_json
                ), func
            )
        )

    evals = map(lambda p: p.get(), evals)

    return list(evals)

start_evaluator(fork=False)

Start a deferred feedback function evaluation thread.

Source code in trulens_eval/trulens_eval/tru.py 
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def start_evaluator(self, fork=False) -> Union[Process, Thread]:
    """
    Start a deferred feedback function evaluation thread.
    """

    assert not fork, "Fork mode not yet implemented."

    if self.evaluator_proc is not None:
        raise RuntimeError("Evaluator is already running in this process.")

    from trulens_eval.tru_feedback import Feedback

    if not fork:
        self.evaluator_stop = threading.Event()

    def runloop():
        while fork or not self.evaluator_stop.is_set():
            print("Looking for things to do. Stop me with `tru.stop_evaluator()`.", end='')
            Feedback.evaluate_deferred(tru=self)
            TP().finish(timeout=10)
            if fork:
                sleep(10)
            else:
                self.evaluator_stop.wait(10)

        print("Evaluator stopped.")

    if fork:
        proc = Process(target=runloop)
    else:
        proc = Thread(target=runloop)

    # Start a persistent thread or process that evaluates feedback functions.

    self.evaluator_proc = proc
    proc.start()

    return proc

stop_dashboard()

Stop existing dashboard if running.

Raises:

Type Description
ValueError

Dashboard is already running.
Source code in trulens_eval/trulens_eval/tru.py 
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def stop_dashboard(self) -> None:
    """Stop existing dashboard if running.

    Raises:
        ValueError: Dashboard is already running.
    """
    if Tru.dashboard_proc is None:
        raise ValueError("Dashboard not running.")

    Tru.dashboard_proc.kill()
    Tru.dashboard_proc = None

stop_evaluator()

Stop the deferred feedback evaluation thread.

Source code in trulens_eval/trulens_eval/tru.py 
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def stop_evaluator(self):
    """
    Stop the deferred feedback evaluation thread.
    """

    if self.evaluator_proc is None:
        raise RuntimeError("Evaluator not running this process.")

    if isinstance(self.evaluator_proc, Process):
        self.evaluator_proc.terminate()

    elif isinstance(self.evaluator_proc, Thread):
        self.evaluator_stop.set()
        self.evaluator_proc.join()
        self.evaluator_stop = None

    self.evaluator_proc = None