@ -237,18 +237,18 @@ async def _gather_with_concurrency(
return results
async def _tracer_initializer ( session _name: Optional [ str ] ) - > Optional [ LangChainTracer ] :
async def _tracer_initializer ( project _name: Optional [ str ] ) - > Optional [ LangChainTracer ] :
"""
Initialize a tracer to share across tasks .
Args :
session_name: The session name for the tracer .
project_name: The project name for the tracer .
Returns :
A LangChainTracer instance with an active session .
A LangChainTracer instance with an active project .
"""
if session _name:
tracer = LangChainTracer ( session_name= session _name)
if project _name:
tracer = LangChainTracer ( project_name= project _name)
return tracer
else :
return None
@ -260,12 +260,12 @@ async def arun_on_examples(
* ,
concurrency_level : int = 5 ,
num_repetitions : int = 1 ,
session _name: Optional [ str ] = None ,
project _name: Optional [ str ] = None ,
verbose : bool = False ,
tags : Optional [ List [ str ] ] = None ,
) - > Dict [ str , Any ] :
"""
Run the chain on examples and store traces to the specified session name .
Run the chain on examples and store traces to the specified project name .
Args :
examples : Examples to run the model or chain over
@ -276,7 +276,7 @@ async def arun_on_examples(
num_repetitions : Number of times to run the model on each example .
This is useful when testing success rates or generating confidence
intervals .
session_name: Session name to use when tracing runs .
project_name: Project name to use when tracing runs .
verbose : Whether to print progress .
tags : Tags to add to the traces .
@ -307,7 +307,7 @@ async def arun_on_examples(
await _gather_with_concurrency (
concurrency_level ,
functools . partial ( _tracer_initializer , session _name) ,
functools . partial ( _tracer_initializer , project _name) ,
* ( functools . partial ( process_example , e ) for e in examples ) ,
)
return results
@ -386,11 +386,11 @@ def run_on_examples(
llm_or_chain_factory : MODEL_OR_CHAIN_FACTORY ,
* ,
num_repetitions : int = 1 ,
session _name: Optional [ str ] = None ,
project _name: Optional [ str ] = None ,
verbose : bool = False ,
tags : Optional [ List [ str ] ] = None ,
) - > Dict [ str , Any ] :
""" Run the chain on examples and store traces to the specified session name.
""" Run the chain on examples and store traces to the specified project name.
Args :
examples : Examples to run model or chain over .
@ -401,14 +401,14 @@ def run_on_examples(
num_repetitions : Number of times to run the model on each example .
This is useful when testing success rates or generating confidence
intervals .
session_name: Session name to use when tracing runs .
project_name: Project name to use when tracing runs .
verbose : Whether to print progress .
tags : Tags to add to the run traces .
Returns :
A dictionary mapping example ids to the model outputs .
"""
results : Dict [ str , Any ] = { }
tracer = LangChainTracer ( session_name= session_name ) if session _name else None
tracer = LangChainTracer ( project_name= project_name ) if project _name else None
for i , example in enumerate ( examples ) :
result = run_llm_or_chain (
example ,
@ -425,13 +425,13 @@ def run_on_examples(
return results
def _get_ session _name(
session _name: Optional [ str ] ,
def _get_ project _name(
project _name: Optional [ str ] ,
llm_or_chain_factory : MODEL_OR_CHAIN_FACTORY ,
dataset_name : str ,
) - > str :
if session _name is not None :
return session _name
if project _name is not None :
return project _name
current_time = datetime . now ( ) . strftime ( " % Y- % m- %d - % H- % M- % S " )
if isinstance ( llm_or_chain_factory , BaseLanguageModel ) :
model_name = llm_or_chain_factory . __class__ . __name__
@ -446,13 +446,13 @@ async def arun_on_dataset(
* ,
concurrency_level : int = 5 ,
num_repetitions : int = 1 ,
session _name: Optional [ str ] = None ,
project _name: Optional [ str ] = None ,
verbose : bool = False ,
client : Optional [ LangChainPlusClient ] = None ,
tags : Optional [ List [ str ] ] = None ,
) - > Dict [ str , Any ] :
"""
Run the chain on a dataset and store traces to the specified session name .
Run the chain on a dataset and store traces to the specified project name .
Args :
client : Client to use to read the dataset .
@ -464,7 +464,7 @@ async def arun_on_dataset(
num_repetitions : Number of times to run the model on each example .
This is useful when testing success rates or generating confidence
intervals .
session_name: Name of the session to store the traces in .
project_name: Name of the project to store the traces in .
Defaults to { dataset_name } - { chain class name } - { datetime } .
verbose : Whether to print progress .
client : Client to use to read the dataset . If not provided , a new
@ -472,11 +472,10 @@ async def arun_on_dataset(
tags : Tags to add to each run in the sesssion .
Returns :
A dictionary containing the run ' s session name and the resulting model outputs.
A dictionary containing the run ' s project name and the resulting model outputs.
"""
client_ = client or LangChainPlusClient ( )
session_name = _get_session_name ( session_name , llm_or_chain_factory , dataset_name )
client_ . create_session ( session_name , mode = " eval " )
project_name = _get_project_name ( project_name , llm_or_chain_factory , dataset_name )
dataset = client_ . read_dataset ( dataset_name = dataset_name )
examples = client_ . list_examples ( dataset_id = str ( dataset . id ) )
@ -485,12 +484,12 @@ async def arun_on_dataset(
llm_or_chain_factory ,
concurrency_level = concurrency_level ,
num_repetitions = num_repetitions ,
session_name= session _name,
project_name= project _name,
verbose = verbose ,
tags = tags ,
)
return {
" session_name" : session _name,
" project_name" : project _name,
" results " : results ,
}
@ -500,12 +499,12 @@ def run_on_dataset(
llm_or_chain_factory : MODEL_OR_CHAIN_FACTORY ,
* ,
num_repetitions : int = 1 ,
session _name: Optional [ str ] = None ,
project _name: Optional [ str ] = None ,
verbose : bool = False ,
client : Optional [ LangChainPlusClient ] = None ,
tags : Optional [ List [ str ] ] = None ,
) - > Dict [ str , Any ] :
""" Run the chain on a dataset and store traces to the specified session name.
""" Run the chain on a dataset and store traces to the specified project name.
Args :
dataset_name : Name of the dataset to run the chain on .
@ -516,7 +515,7 @@ def run_on_dataset(
num_repetitions : Number of times to run the model on each example .
This is useful when testing success rates or generating confidence
intervals .
session_name: Name of the session to store the traces in .
project_name: Name of the project to store the traces in .
Defaults to { dataset_name } - { chain class name } - { datetime } .
verbose : Whether to print progress .
client : Client to use to access the dataset . If None , a new client
@ -524,22 +523,21 @@ def run_on_dataset(
tags : Tags to add to each run in the sesssion .
Returns :
A dictionary containing the run ' s session name and the resulting model outputs.
A dictionary containing the run ' s project name and the resulting model outputs.
"""
client_ = client or LangChainPlusClient ( )
session_name = _get_session_name ( session_name , llm_or_chain_factory , dataset_name )
client_ . create_session ( session_name , mode = " eval " )
project_name = _get_project_name ( project_name , llm_or_chain_factory , dataset_name )
dataset = client_ . read_dataset ( dataset_name = dataset_name )
examples = client_ . list_examples ( dataset_id = str ( dataset . id ) )
results = run_on_examples (
examples ,
llm_or_chain_factory ,
num_repetitions = num_repetitions ,
session_name= session _name,
project_name= project _name,
verbose = verbose ,
tags = tags ,
)
return {
" session_name" : session _name,
" project_name" : project _name,
" results " : results ,
}
Zander Chase
1 year ago
committed by
GitHub