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get_async_callback_manager_for_config | """Get an async callback manager for a config.
Args:
config (RunnableConfig): The config.
Returns:
AsyncCallbackManager: The async callback manager.
"""
from langchain_core.callbacks.manager import AsyncCallbackManager
return AsyncCallbackManager.configure(inheritable_callbacks=config.get(
'callbacks'), inheritable_tags=config.get('tags'), inheritable_metadata
=config.get('metadata')) | def get_async_callback_manager_for_config(config: RunnableConfig
) ->AsyncCallbackManager:
"""Get an async callback manager for a config.
Args:
config (RunnableConfig): The config.
Returns:
AsyncCallbackManager: The async callback manager.
"""
from langchain_core.callbacks.manager import AsyncCallbackManager
return AsyncCallbackManager.configure(inheritable_callbacks=config.get(
'callbacks'), inheritable_tags=config.get('tags'),
inheritable_metadata=config.get('metadata')) | Get an async callback manager for a config.
Args:
config (RunnableConfig): The config.
Returns:
AsyncCallbackManager: The async callback manager. |
index | """
Executes when the index is created.
Args:
dims_length: Numeric length of the embedding vectors,
or None if not using vector-based query.
vector_query_field: The field containing the vector
representations in the index.
similarity: The similarity strategy to use,
or None if not using one.
Returns:
Dict: The Elasticsearch settings and mappings for the strategy.
""" | @abstractmethod
def index(self, dims_length: Union[int, None], vector_query_field: str,
similarity: Union[DistanceStrategy, None]) ->Dict:
"""
Executes when the index is created.
Args:
dims_length: Numeric length of the embedding vectors,
or None if not using vector-based query.
vector_query_field: The field containing the vector
representations in the index.
similarity: The similarity strategy to use,
or None if not using one.
Returns:
Dict: The Elasticsearch settings and mappings for the strategy.
""" | Executes when the index is created.
Args:
dims_length: Numeric length of the embedding vectors,
or None if not using vector-based query.
vector_query_field: The field containing the vector
representations in the index.
similarity: The similarity strategy to use,
or None if not using one.
Returns:
Dict: The Elasticsearch settings and mappings for the strategy. |
_stream_completion_with_retry | responses = self.client.call(**_kwargs)
for resp in responses:
yield check_response(resp) | @retry_decorator
def _stream_completion_with_retry(**_kwargs: Any) ->Any:
responses = self.client.call(**_kwargs)
for resp in responses:
yield check_response(resp) | null |
_import_alpha_vantage | from langchain_community.utilities.alpha_vantage import AlphaVantageAPIWrapper
return AlphaVantageAPIWrapper | def _import_alpha_vantage() ->Any:
from langchain_community.utilities.alpha_vantage import AlphaVantageAPIWrapper
return AlphaVantageAPIWrapper | null |
batch_sync_run | with ThreadPoolExecutor() as executor:
results = list(executor.map(_completion_with_retry, prompt))
return results | def batch_sync_run() ->List:
with ThreadPoolExecutor() as executor:
results = list(executor.map(_completion_with_retry, prompt))
return results | null |
input_keys | """Will be whatever keys the prompt expects.
:meta private:
"""
return ['problem_description'] | @property
def input_keys(self) ->List[str]:
"""Will be whatever keys the prompt expects.
:meta private:
"""
return ['problem_description'] | Will be whatever keys the prompt expects.
:meta private: |
__post_init__ | """
Initialize the store.
"""
from hologres_vector import HologresVector
self.storage = HologresVector(self.connection_string, ndims=self.ndims,
table_name=self.table_name, table_schema={'document': 'text'},
pre_delete_table=self.pre_delete_table) | def __post_init__(self) ->None:
"""
Initialize the store.
"""
from hologres_vector import HologresVector
self.storage = HologresVector(self.connection_string, ndims=self.ndims,
table_name=self.table_name, table_schema={'document': 'text'},
pre_delete_table=self.pre_delete_table) | Initialize the store. |
test_loademptynotebook_emptylistreturned | documents = EverNoteLoader(self.example_notebook_path('empty_export.enex'),
False).load()
assert len(documents) == 0 | def test_loademptynotebook_emptylistreturned(self) ->None:
documents = EverNoteLoader(self.example_notebook_path(
'empty_export.enex'), False).load()
assert len(documents) == 0 | null |
__init__ | self.secrets_map = secrets_map or dict()
self.valid_namespaces = [*DEFAULT_NAMESPACES, *valid_namespaces
] if valid_namespaces else DEFAULT_NAMESPACES | def __init__(self, secrets_map: Optional[Dict[str, str]]=None,
valid_namespaces: Optional[List[str]]=None) ->None:
self.secrets_map = secrets_map or dict()
self.valid_namespaces = [*DEFAULT_NAMESPACES, *valid_namespaces
] if valid_namespaces else DEFAULT_NAMESPACES | null |
_get_table_indexes | indexes = self._inspector.get_indexes(table.name)
indexes_formatted = '\n'.join(map(_format_index, indexes))
return f"""Table Indexes:
{indexes_formatted}""" | def _get_table_indexes(self, table: Table) ->str:
indexes = self._inspector.get_indexes(table.name)
indexes_formatted = '\n'.join(map(_format_index, indexes))
return f'Table Indexes:\n{indexes_formatted}' | null |
parse_result | _result = super().parse_result(result)
if self.args_only:
pydantic_args = self.pydantic_schema.parse_raw(_result)
else:
fn_name = _result['name']
_args = _result['arguments']
pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args)
return pydantic_args | def parse_result(self, result: List[Generation], *, partial: bool=False) ->Any:
_result = super().parse_result(result)
if self.args_only:
pydantic_args = self.pydantic_schema.parse_raw(_result)
else:
fn_name = _result['name']
_args = _result['arguments']
pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args)
return pydantic_args | null |
tool | """Make tools out of functions, can be used with or without arguments.
Args:
*args: The arguments to the tool.
return_direct: Whether to return directly from the tool rather
than continuing the agent loop.
args_schema: optional argument schema for user to specify
infer_schema: Whether to infer the schema of the arguments from
the function's signature. This also makes the resultant tool
accept a dictionary input to its `run()` function.
Requires:
- Function must be of type (str) -> str
- Function must have a docstring
Examples:
.. code-block:: python
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return
"""
def _make_with_name(tool_name: str) ->Callable:
def _make_tool(dec_func: Union[Callable, Runnable]) ->BaseTool:
if isinstance(dec_func, Runnable):
runnable = dec_func
if runnable.input_schema.schema().get('type') != 'object':
raise ValueError('Runnable must have an object schema.')
async def ainvoke_wrapper(callbacks: Optional[Callbacks]=None,
**kwargs: Any) ->Any:
return await runnable.ainvoke(kwargs, {'callbacks': callbacks})
def invoke_wrapper(callbacks: Optional[Callbacks]=None, **
kwargs: Any) ->Any:
return runnable.invoke(kwargs, {'callbacks': callbacks})
coroutine = ainvoke_wrapper
func = invoke_wrapper
schema: Optional[Type[BaseModel]] = runnable.input_schema
description = repr(runnable)
elif inspect.iscoroutinefunction(dec_func):
coroutine = dec_func
func = None
schema = args_schema
description = None
else:
coroutine = None
func = dec_func
schema = args_schema
description = None
if infer_schema or args_schema is not None:
return StructuredTool.from_function(func, coroutine, name=
tool_name, description=description, return_direct=
return_direct, args_schema=schema, infer_schema=infer_schema)
if func.__doc__ is None:
raise ValueError(
'Function must have a docstring if description not provided and infer_schema is False.'
)
return Tool(name=tool_name, func=func, description=
f'{tool_name} tool', return_direct=return_direct, coroutine=
coroutine)
return _make_tool
if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable
):
return _make_with_name(args[0])(args[1])
elif len(args) == 1 and isinstance(args[0], str):
return _make_with_name(args[0])
elif len(args) == 1 and callable(args[0]):
return _make_with_name(args[0].__name__)(args[0])
elif len(args) == 0:
def _partial(func: Callable[[str], str]) ->BaseTool:
return _make_with_name(func.__name__)(func)
return _partial
else:
raise ValueError('Too many arguments for tool decorator') | def tool(*args: Union[str, Callable, Runnable], return_direct: bool=False,
args_schema: Optional[Type[BaseModel]]=None, infer_schema: bool=True
) ->Callable:
"""Make tools out of functions, can be used with or without arguments.
Args:
*args: The arguments to the tool.
return_direct: Whether to return directly from the tool rather
than continuing the agent loop.
args_schema: optional argument schema for user to specify
infer_schema: Whether to infer the schema of the arguments from
the function's signature. This also makes the resultant tool
accept a dictionary input to its `run()` function.
Requires:
- Function must be of type (str) -> str
- Function must have a docstring
Examples:
.. code-block:: python
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return
"""
def _make_with_name(tool_name: str) ->Callable:
def _make_tool(dec_func: Union[Callable, Runnable]) ->BaseTool:
if isinstance(dec_func, Runnable):
runnable = dec_func
if runnable.input_schema.schema().get('type') != 'object':
raise ValueError('Runnable must have an object schema.')
async def ainvoke_wrapper(callbacks: Optional[Callbacks]=
None, **kwargs: Any) ->Any:
return await runnable.ainvoke(kwargs, {'callbacks':
callbacks})
def invoke_wrapper(callbacks: Optional[Callbacks]=None, **
kwargs: Any) ->Any:
return runnable.invoke(kwargs, {'callbacks': callbacks})
coroutine = ainvoke_wrapper
func = invoke_wrapper
schema: Optional[Type[BaseModel]] = runnable.input_schema
description = repr(runnable)
elif inspect.iscoroutinefunction(dec_func):
coroutine = dec_func
func = None
schema = args_schema
description = None
else:
coroutine = None
func = dec_func
schema = args_schema
description = None
if infer_schema or args_schema is not None:
return StructuredTool.from_function(func, coroutine, name=
tool_name, description=description, return_direct=
return_direct, args_schema=schema, infer_schema=
infer_schema)
if func.__doc__ is None:
raise ValueError(
'Function must have a docstring if description not provided and infer_schema is False.'
)
return Tool(name=tool_name, func=func, description=
f'{tool_name} tool', return_direct=return_direct, coroutine
=coroutine)
return _make_tool
if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1],
Runnable):
return _make_with_name(args[0])(args[1])
elif len(args) == 1 and isinstance(args[0], str):
return _make_with_name(args[0])
elif len(args) == 1 and callable(args[0]):
return _make_with_name(args[0].__name__)(args[0])
elif len(args) == 0:
def _partial(func: Callable[[str], str]) ->BaseTool:
return _make_with_name(func.__name__)(func)
return _partial
else:
raise ValueError('Too many arguments for tool decorator') | Make tools out of functions, can be used with or without arguments.
Args:
*args: The arguments to the tool.
return_direct: Whether to return directly from the tool rather
than continuing the agent loop.
args_schema: optional argument schema for user to specify
infer_schema: Whether to infer the schema of the arguments from
the function's signature. This also makes the resultant tool
accept a dictionary input to its `run()` function.
Requires:
- Function must be of type (str) -> str
- Function must have a docstring
Examples:
.. code-block:: python
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return |
delete_collection | self.logger.debug('Trying to delete collection')
with Session(self._bind) as session:
collection = self.get_collection(session)
if not collection:
self.logger.warning('Collection not found')
return
session.delete(collection)
session.commit() | def delete_collection(self) ->None:
self.logger.debug('Trying to delete collection')
with Session(self._bind) as session:
collection = self.get_collection(session)
if not collection:
self.logger.warning('Collection not found')
return
session.delete(collection)
session.commit() | null |
test_write_file_with_root_dir | """Test the WriteFile tool when a root dir is specified."""
with TemporaryDirectory() as temp_dir:
tool = WriteFileTool(root_dir=temp_dir)
tool.run({'file_path': 'file.txt', 'text': 'Hello, world!'})
assert (Path(temp_dir) / 'file.txt').exists()
assert (Path(temp_dir) / 'file.txt').read_text() == 'Hello, world!' | def test_write_file_with_root_dir() ->None:
"""Test the WriteFile tool when a root dir is specified."""
with TemporaryDirectory() as temp_dir:
tool = WriteFileTool(root_dir=temp_dir)
tool.run({'file_path': 'file.txt', 'text': 'Hello, world!'})
assert (Path(temp_dir) / 'file.txt').exists()
assert (Path(temp_dir) / 'file.txt').read_text() == 'Hello, world!' | Test the WriteFile tool when a root dir is specified. |
create_pbi_agent | """Construct a Power BI agent from an LLM and tools."""
from langchain.agents import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.chains.llm import LLMChain
if toolkit is None:
if powerbi is None:
raise ValueError('Must provide either a toolkit or powerbi dataset')
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
prompt_params = {'format_instructions': format_instructions
} if format_instructions is not None else {}
agent = ZeroShotAgent(llm_chain=LLMChain(llm=llm, prompt=ZeroShotAgent.
create_prompt(tools, prefix=prefix.format(top_k=top_k).format(tables=
tables), suffix=suffix, input_variables=input_variables, **
prompt_params), callback_manager=callback_manager, verbose=verbose),
allowed_tools=[tool.name for tool in tools], **kwargs)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools,
callback_manager=callback_manager, verbose=verbose, **
agent_executor_kwargs or {}) | def create_pbi_agent(llm: BaseLanguageModel, toolkit: Optional[
PowerBIToolkit]=None, powerbi: Optional[PowerBIDataset]=None,
callback_manager: Optional[BaseCallbackManager]=None, prefix: str=
POWERBI_PREFIX, suffix: str=POWERBI_SUFFIX, format_instructions:
Optional[str]=None, examples: Optional[str]=None, input_variables:
Optional[List[str]]=None, top_k: int=10, verbose: bool=False,
agent_executor_kwargs: Optional[Dict[str, Any]]=None, **kwargs: Any
) ->AgentExecutor:
"""Construct a Power BI agent from an LLM and tools."""
from langchain.agents import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.chains.llm import LLMChain
if toolkit is None:
if powerbi is None:
raise ValueError('Must provide either a toolkit or powerbi dataset'
)
toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
tools = toolkit.get_tools()
tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
prompt_params = {'format_instructions': format_instructions
} if format_instructions is not None else {}
agent = ZeroShotAgent(llm_chain=LLMChain(llm=llm, prompt=ZeroShotAgent.
create_prompt(tools, prefix=prefix.format(top_k=top_k).format(
tables=tables), suffix=suffix, input_variables=input_variables, **
prompt_params), callback_manager=callback_manager, verbose=verbose),
allowed_tools=[tool.name for tool in tools], **kwargs)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools,
callback_manager=callback_manager, verbose=verbose, **
agent_executor_kwargs or {}) | Construct a Power BI agent from an LLM and tools. |
test_pandas_output_parser_row_no_column | try:
parser.parse_folder('row:1[num_legs]')
assert False, 'Should have raised OutputParserException'
except OutputParserException:
assert True | def test_pandas_output_parser_row_no_column() ->None:
try:
parser.parse_folder('row:1[num_legs]')
assert False, 'Should have raised OutputParserException'
except OutputParserException:
assert True | null |
operations_from_names | """Initializes Long-Running Operations from their names."""
try:
from google.longrunning.operations_pb2 import GetOperationRequest
except ImportError as exc:
raise ImportError(
'long running operations package not found, please install it with `pip install gapic-google-longrunning`'
) from exc
return [self._client.get_operation(request=GetOperationRequest(name=name)) for
name in operation_names] | def operations_from_names(self, operation_names: List[str]) ->List['Operation'
]:
"""Initializes Long-Running Operations from their names."""
try:
from google.longrunning.operations_pb2 import GetOperationRequest
except ImportError as exc:
raise ImportError(
'long running operations package not found, please install it with `pip install gapic-google-longrunning`'
) from exc
return [self._client.get_operation(request=GetOperationRequest(name=
name)) for name in operation_names] | Initializes Long-Running Operations from their names. |
_import_sagemaker_endpoint | from langchain_community.llms.sagemaker_endpoint import SagemakerEndpoint
return SagemakerEndpoint | def _import_sagemaker_endpoint() ->Any:
from langchain_community.llms.sagemaker_endpoint import SagemakerEndpoint
return SagemakerEndpoint | null |
_import_searchapi_tool_SearchAPIResults | from langchain_community.tools.searchapi.tool import SearchAPIResults
return SearchAPIResults | def _import_searchapi_tool_SearchAPIResults() ->Any:
from langchain_community.tools.searchapi.tool import SearchAPIResults
return SearchAPIResults | null |
from_llm | """Initialize the LabeledPairwiseStringEvalChain from an LLM.
Args:
llm (BaseLanguageModel): The LLM to use.
prompt (PromptTemplate, optional): The prompt to use.
criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
**kwargs (Any): Additional keyword arguments.
Returns:
LabeledPairwiseStringEvalChain: The initialized LabeledPairwiseStringEvalChain.
Raises:
ValueError: If the input variables are not as expected.
"""
expected_input_vars = {'prediction', 'prediction_b', 'input', 'reference',
'criteria'}
prompt_ = prompt or COMPARISON_TEMPLATE_WITH_REFERENCE
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f'Input variables should be {expected_input_vars}, but got {prompt_.input_variables}'
)
criteria_ = resolve_pairwise_criteria(criteria)
criteria_str = '\n'.join(f'{k}: {v}' for k, v in criteria_.items())
criteria_str = CRITERIA_INSTRUCTIONS + criteria_str if criteria_str else ''
return cls(llm=llm, prompt=prompt_.partial(criteria=criteria_str), **kwargs) | @classmethod
def from_llm(cls, llm: BaseLanguageModel, *, prompt: Optional[
PromptTemplate]=None, criteria: Optional[Union[CRITERIA_TYPE, str]]=
None, **kwargs: Any) ->PairwiseStringEvalChain:
"""Initialize the LabeledPairwiseStringEvalChain from an LLM.
Args:
llm (BaseLanguageModel): The LLM to use.
prompt (PromptTemplate, optional): The prompt to use.
criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
**kwargs (Any): Additional keyword arguments.
Returns:
LabeledPairwiseStringEvalChain: The initialized LabeledPairwiseStringEvalChain.
Raises:
ValueError: If the input variables are not as expected.
"""
expected_input_vars = {'prediction', 'prediction_b', 'input',
'reference', 'criteria'}
prompt_ = prompt or COMPARISON_TEMPLATE_WITH_REFERENCE
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f'Input variables should be {expected_input_vars}, but got {prompt_.input_variables}'
)
criteria_ = resolve_pairwise_criteria(criteria)
criteria_str = '\n'.join(f'{k}: {v}' for k, v in criteria_.items())
criteria_str = CRITERIA_INSTRUCTIONS + criteria_str if criteria_str else ''
return cls(llm=llm, prompt=prompt_.partial(criteria=criteria_str), **kwargs
) | Initialize the LabeledPairwiseStringEvalChain from an LLM.
Args:
llm (BaseLanguageModel): The LLM to use.
prompt (PromptTemplate, optional): The prompt to use.
criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
**kwargs (Any): Additional keyword arguments.
Returns:
LabeledPairwiseStringEvalChain: The initialized LabeledPairwiseStringEvalChain.
Raises:
ValueError: If the input variables are not as expected. |
zep_retriever | mock_zep_client: ZepClient = mocker.patch('zep_python.ZepClient', autospec=True
)
mock_zep_client.memory = mocker.patch('zep_python.memory.client.MemoryClient',
autospec=True)
mock_zep_client.memory.search_memory.return_value = copy.deepcopy(
search_results)
mock_zep_client.memory.asearch_memory.return_value = copy.deepcopy(
search_results)
zep = ZepRetriever(session_id='123', url='http://localhost:8000')
zep.zep_client = mock_zep_client
return zep | @pytest.fixture
@pytest.mark.requires('zep_python')
def zep_retriever(mocker: MockerFixture, search_results: List[
MemorySearchResult]) ->ZepRetriever:
mock_zep_client: ZepClient = mocker.patch('zep_python.ZepClient',
autospec=True)
mock_zep_client.memory = mocker.patch(
'zep_python.memory.client.MemoryClient', autospec=True)
mock_zep_client.memory.search_memory.return_value = copy.deepcopy(
search_results)
mock_zep_client.memory.asearch_memory.return_value = copy.deepcopy(
search_results)
zep = ZepRetriever(session_id='123', url='http://localhost:8000')
zep.zep_client = mock_zep_client
return zep | null |
from_loaders | """Create a vectorstore index from loaders."""
docs = []
for loader in loaders:
docs.extend(loader.load())
return self.from_documents(docs) | def from_loaders(self, loaders: List[BaseLoader]) ->VectorStoreIndexWrapper:
"""Create a vectorstore index from loaders."""
docs = []
for loader in loaders:
docs.extend(loader.load())
return self.from_documents(docs) | Create a vectorstore index from loaders. |
delete | """Evict from cache if there's an entry."""
doc_id = self._make_id(prompt, llm_string)
return self.collection.delete_one(doc_id) | def delete(self, prompt: str, llm_string: str) ->None:
"""Evict from cache if there's an entry."""
doc_id = self._make_id(prompt, llm_string)
return self.collection.delete_one(doc_id) | Evict from cache if there's an entry. |
test_rust_code_splitter | splitter = RecursiveCharacterTextSplitter.from_language(Language.RUST,
chunk_size=CHUNK_SIZE, chunk_overlap=0)
code = """
fn main() {
println!("Hello, World!");
}
"""
chunks = splitter.split_text(code)
assert chunks == ['fn main() {', 'println!("Hello', ',', 'World!");', '}'] | def test_rust_code_splitter() ->None:
splitter = RecursiveCharacterTextSplitter.from_language(Language.RUST,
chunk_size=CHUNK_SIZE, chunk_overlap=0)
code = '\nfn main() {\n println!("Hello, World!");\n}\n '
chunks = splitter.split_text(code)
assert chunks == ['fn main() {', 'println!("Hello', ',', 'World!");', '}'] | null |
is_valid | try:
ast.parse(self.code)
return True
except SyntaxError:
return False | def is_valid(self) ->bool:
try:
ast.parse(self.code)
return True
except SyntaxError:
return False | null |
get_format_instructions | return 'Your response should be a list of comma separated values, eg: `foo, bar, baz`' | def get_format_instructions(self) ->str:
return (
'Your response should be a list of comma separated values, eg: `foo, bar, baz`'
) | null |
run | """Run the directive.
Called any time :example_links:`ClassName` is used
in the template *.rst files."""
class_or_func_name = self.arguments[0]
links = imported_classes.get(class_or_func_name, {})
list_node = nodes.bullet_list()
for doc_name, link in links.items():
item_node = nodes.list_item()
para_node = nodes.paragraph()
link_node = nodes.reference()
link_node['refuri'] = link
link_node.append(nodes.Text(doc_name))
para_node.append(link_node)
item_node.append(para_node)
list_node.append(item_node)
if list_node.children:
title_node = nodes.title()
title_node.append(nodes.Text(f'Examples using {class_or_func_name}'))
return [title_node, list_node]
return [list_node] | def run(self):
"""Run the directive.
Called any time :example_links:`ClassName` is used
in the template *.rst files."""
class_or_func_name = self.arguments[0]
links = imported_classes.get(class_or_func_name, {})
list_node = nodes.bullet_list()
for doc_name, link in links.items():
item_node = nodes.list_item()
para_node = nodes.paragraph()
link_node = nodes.reference()
link_node['refuri'] = link
link_node.append(nodes.Text(doc_name))
para_node.append(link_node)
item_node.append(para_node)
list_node.append(item_node)
if list_node.children:
title_node = nodes.title()
title_node.append(nodes.Text(f'Examples using {class_or_func_name}'))
return [title_node, list_node]
return [list_node] | Run the directive.
Called any time :example_links:`ClassName` is used
in the template *.rst files. |
test_prompt | messages = [SystemMessage(content='sys-msg'), HumanMessage(content=
'usr-msg-1'), AIMessage(content='ai-msg-1'), HumanMessage(content=
'usr-msg-2')]
actual = model.predict_messages(messages).content
expected = 'sys-msg USER: usr-msg-1 ASSISTANT: ai-msg-1 </s>USER: usr-msg-2 '
assert actual == expected | def test_prompt(model: Vicuna) ->None:
messages = [SystemMessage(content='sys-msg'), HumanMessage(content=
'usr-msg-1'), AIMessage(content='ai-msg-1'), HumanMessage(content=
'usr-msg-2')]
actual = model.predict_messages(messages).content
expected = (
'sys-msg USER: usr-msg-1 ASSISTANT: ai-msg-1 </s>USER: usr-msg-2 ')
assert actual == expected | null |
return_stopped_response | """Return response when agent has been stopped due to max iterations."""
if early_stopping_method == 'force':
return AgentFinish({'output':
'Agent stopped due to iteration limit or time limit.'}, '')
elif early_stopping_method == 'generate':
agent_decision = self.plan(intermediate_steps, with_functions=False, **
kwargs)
if isinstance(agent_decision, AgentFinish):
return agent_decision
else:
raise ValueError(
f'got AgentAction with no functions provided: {agent_decision}')
else:
raise ValueError(
f'early_stopping_method should be one of `force` or `generate`, got {early_stopping_method}'
) | def return_stopped_response(self, early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) ->AgentFinish:
"""Return response when agent has been stopped due to max iterations."""
if early_stopping_method == 'force':
return AgentFinish({'output':
'Agent stopped due to iteration limit or time limit.'}, '')
elif early_stopping_method == 'generate':
agent_decision = self.plan(intermediate_steps, with_functions=False,
**kwargs)
if isinstance(agent_decision, AgentFinish):
return agent_decision
else:
raise ValueError(
f'got AgentAction with no functions provided: {agent_decision}'
)
else:
raise ValueError(
f'early_stopping_method should be one of `force` or `generate`, got {early_stopping_method}'
) | Return response when agent has been stopped due to max iterations. |
embed_documents | """Call out to Cohere's embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
response = requests.post('https://api.llmrails.com/v1/embeddings', headers=
{'X-API-KEY': self.api_key.get_secret_value()}, json={'input': texts,
'model': self.model}, timeout=60)
return [item['embedding'] for item in response.json()['data']] | def embed_documents(self, texts: List[str]) ->List[List[float]]:
"""Call out to Cohere's embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
response = requests.post('https://api.llmrails.com/v1/embeddings',
headers={'X-API-KEY': self.api_key.get_secret_value()}, json={
'input': texts, 'model': self.model}, timeout=60)
return [item['embedding'] for item in response.json()['data']] | Call out to Cohere's embedding endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text. |
test_chroma | """Test end to end construction and search."""
texts = ['foo', 'bar', 'baz']
docsearch = Chroma.from_texts(collection_name='test_collection', texts=
texts, embedding=FakeEmbeddings())
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')] | def test_chroma() ->None:
"""Test end to end construction and search."""
texts = ['foo', 'bar', 'baz']
docsearch = Chroma.from_texts(collection_name='test_collection', texts=
texts, embedding=FakeEmbeddings())
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')] | Test end to end construction and search. |
input_keys | """
Returns a list of input keys expected by the prompt.
This method defines the input keys that the prompt expects in order to perform
its processing. It ensures that the specified keys are available for providing
input to the prompt.
Returns:
List[str]: A list of input keys.
Note:
This method is considered private and may not be intended for direct
external use.
"""
return [self.input_key] | @property
def input_keys(self) ->List[str]:
"""
Returns a list of input keys expected by the prompt.
This method defines the input keys that the prompt expects in order to perform
its processing. It ensures that the specified keys are available for providing
input to the prompt.
Returns:
List[str]: A list of input keys.
Note:
This method is considered private and may not be intended for direct
external use.
"""
return [self.input_key] | Returns a list of input keys expected by the prompt.
This method defines the input keys that the prompt expects in order to perform
its processing. It ensures that the specified keys are available for providing
input to the prompt.
Returns:
List[str]: A list of input keys.
Note:
This method is considered private and may not be intended for direct
external use. |
on_chain_end | if self.__has_valid_config is False:
return
try:
output = _parse_output(outputs)
self.__track_event('chain', 'end', run_id=str(run_id), parent_run_id=
str(parent_run_id) if parent_run_id else None, output=output,
app_id=self.__app_id)
except Exception as e:
logger.error(f'[LLMonitor] An error occurred in on_chain_end: {e}') | def on_chain_end(self, outputs: Dict[str, Any], *, run_id: UUID,
parent_run_id: Union[UUID, None]=None, **kwargs: Any) ->Any:
if self.__has_valid_config is False:
return
try:
output = _parse_output(outputs)
self.__track_event('chain', 'end', run_id=str(run_id),
parent_run_id=str(parent_run_id) if parent_run_id else None,
output=output, app_id=self.__app_id)
except Exception as e:
logger.error(f'[LLMonitor] An error occurred in on_chain_end: {e}') | null |
weaviate_url | """Return the weaviate url."""
from weaviate import Client
url = 'http://localhost:8080'
yield url
client = Client(url)
client.schema.delete_all() | @pytest.fixture(scope='class', autouse=True)
def weaviate_url(self) ->Union[str, Generator[str, None, None]]:
"""Return the weaviate url."""
from weaviate import Client
url = 'http://localhost:8080'
yield url
client = Client(url)
client.schema.delete_all() | Return the weaviate url. |
llm_prefix | """Prefix to append the LLM call with."""
return '' | @property
def llm_prefix(self) ->str:
"""Prefix to append the LLM call with."""
return '' | Prefix to append the LLM call with. |
test_jobs_call | """Test that call gives correct answer for jobs search."""
search = SearchApiAPIWrapper(engine='google_jobs')
output = search.run('AI')
assert 'years of experience' in output | def test_jobs_call() ->None:
"""Test that call gives correct answer for jobs search."""
search = SearchApiAPIWrapper(engine='google_jobs')
output = search.run('AI')
assert 'years of experience' in output | Test that call gives correct answer for jobs search. |
delete | raise NotImplementedError | def delete(self, ids: Optional[List[str]]=None, **kwargs: Any) ->Optional[bool
]:
raise NotImplementedError | null |
from_embeddings | """Construct ScaNN wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the ScaNN database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import ScaNN
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
scann = ScaNN.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(texts, embeddings, embedding, metadatas=metadatas, ids=
ids, **kwargs) | @classmethod
def from_embeddings(cls, text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings, metadatas: Optional[List[dict]]=None, ids:
Optional[List[str]]=None, **kwargs: Any) ->ScaNN:
"""Construct ScaNN wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the ScaNN database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import ScaNN
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
scann = ScaNN.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(texts, embeddings, embedding, metadatas=metadatas,
ids=ids, **kwargs) | Construct ScaNN wrapper from raw documents.
This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the ScaNN database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import ScaNN
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
scann = ScaNN.from_embeddings(text_embedding_pairs, embeddings) |
_get_paths | """Get all relative paths in the navbar."""
relative_paths = []
course_menu = soup.find('ul', {'class': 'courseMenu'})
if course_menu is None:
raise ValueError('No course menu found.')
for link in course_menu.find_all('a'):
href = link.get('href')
if href is not None and href.startswith('/'):
relative_paths.append(href)
return relative_paths | def _get_paths(self, soup: Any) ->List[str]:
"""Get all relative paths in the navbar."""
relative_paths = []
course_menu = soup.find('ul', {'class': 'courseMenu'})
if course_menu is None:
raise ValueError('No course menu found.')
for link in course_menu.find_all('a'):
href = link.get('href')
if href is not None and href.startswith('/'):
relative_paths.append(href)
return relative_paths | Get all relative paths in the navbar. |
_selector_effective | if not self.visible_only:
return selector
return f'{selector} >> visible=1' | def _selector_effective(self, selector: str) ->str:
if not self.visible_only:
return selector
return f'{selector} >> visible=1' | null |
visit_structured_query | if structured_query.filter is None:
kwargs = {}
else:
kwargs = {'where_filter': structured_query.filter.accept(self)}
return structured_query.query, kwargs | def visit_structured_query(self, structured_query: StructuredQuery) ->Tuple[
str, dict]:
if structured_query.filter is None:
kwargs = {}
else:
kwargs = {'where_filter': structured_query.filter.accept(self)}
return structured_query.query, kwargs | null |
validate_environment | """Validate that the python package exists in environment."""
try:
import arxiv
values['arxiv_search'] = arxiv.Search
values['arxiv_exceptions'
] = arxiv.ArxivError, arxiv.UnexpectedEmptyPageError, arxiv.HTTPError
values['arxiv_result'] = arxiv.Result
except ImportError:
raise ImportError(
'Could not import arxiv python package. Please install it with `pip install arxiv`.'
)
return values | @root_validator()
def validate_environment(cls, values: Dict) ->Dict:
"""Validate that the python package exists in environment."""
try:
import arxiv
values['arxiv_search'] = arxiv.Search
values['arxiv_exceptions'
] = arxiv.ArxivError, arxiv.UnexpectedEmptyPageError, arxiv.HTTPError
values['arxiv_result'] = arxiv.Result
except ImportError:
raise ImportError(
'Could not import arxiv python package. Please install it with `pip install arxiv`.'
)
return values | Validate that the python package exists in environment. |
test_load_load_extra_metadata | """Test that returns extra metadata fields."""
loader = BibtexLoader(file_path=str(BIBTEX_EXAMPLE_FILE),
load_extra_metadata=True)
doc = loader.load()[0]
assert set(doc.metadata) == {'id', 'published_year', 'title', 'publication',
'authors', 'abstract', 'booktitle', 'editor', 'organization'} | @pytest.mark.requires('fitz', 'bibtexparser')
def test_load_load_extra_metadata() ->None:
"""Test that returns extra metadata fields."""
loader = BibtexLoader(file_path=str(BIBTEX_EXAMPLE_FILE),
load_extra_metadata=True)
doc = loader.load()[0]
assert set(doc.metadata) == {'id', 'published_year', 'title',
'publication', 'authors', 'abstract', 'booktitle', 'editor',
'organization'} | Test that returns extra metadata fields. |
input_keys | """Input keys for the chain."""
return ['user_input', 'context', 'response'] | @property
def input_keys(self) ->List[str]:
"""Input keys for the chain."""
return ['user_input', 'context', 'response'] | Input keys for the chain. |
_create_api_planner_tool | from langchain.chains.llm import LLMChain
endpoint_descriptions = [f'{name} {description}' for name, description, _ in
api_spec.endpoints]
prompt = PromptTemplate(template=API_PLANNER_PROMPT, input_variables=[
'query'], partial_variables={'endpoints': '- ' + '- '.join(
endpoint_descriptions)})
chain = LLMChain(llm=llm, prompt=prompt)
tool = Tool(name=API_PLANNER_TOOL_NAME, description=
API_PLANNER_TOOL_DESCRIPTION, func=chain.run)
return tool | def _create_api_planner_tool(api_spec: ReducedOpenAPISpec, llm:
BaseLanguageModel) ->Tool:
from langchain.chains.llm import LLMChain
endpoint_descriptions = [f'{name} {description}' for name, description,
_ in api_spec.endpoints]
prompt = PromptTemplate(template=API_PLANNER_PROMPT, input_variables=[
'query'], partial_variables={'endpoints': '- ' + '- '.join(
endpoint_descriptions)})
chain = LLMChain(llm=llm, prompt=prompt)
tool = Tool(name=API_PLANNER_TOOL_NAME, description=
API_PLANNER_TOOL_DESCRIPTION, func=chain.run)
return tool | null |
_log_evaluation_feedback | results = self._select_eval_results(evaluator_response)
for res in results:
source_info_: Dict[str, Any] = {}
if res.evaluator_info:
source_info_ = {**res.evaluator_info, **source_info_}
run_id_ = getattr(res, 'target_run_id') if hasattr(res, 'target_run_id'
) and res.target_run_id is not None else run.id
self.client.create_feedback(run_id_, res.key, score=res.score, value=
res.value, comment=res.comment, correction=res.correction,
source_info=source_info_, source_run_id=res.source_run_id or
source_run_id, feedback_source_type=langsmith.schemas.
FeedbackSourceType.MODEL)
return results | def _log_evaluation_feedback(self, evaluator_response: Union[
EvaluationResult, EvaluationResults], run: Run, source_run_id: Optional
[UUID]=None) ->List[EvaluationResult]:
results = self._select_eval_results(evaluator_response)
for res in results:
source_info_: Dict[str, Any] = {}
if res.evaluator_info:
source_info_ = {**res.evaluator_info, **source_info_}
run_id_ = getattr(res, 'target_run_id') if hasattr(res, 'target_run_id'
) and res.target_run_id is not None else run.id
self.client.create_feedback(run_id_, res.key, score=res.score,
value=res.value, comment=res.comment, correction=res.correction,
source_info=source_info_, source_run_id=res.source_run_id or
source_run_id, feedback_source_type=langsmith.schemas.
FeedbackSourceType.MODEL)
return results | null |
template_is_valid | """Check that prefix, suffix, and input variables are consistent."""
if values['validate_template']:
input_variables = values['input_variables']
expected_input_variables = set(values['suffix'].input_variables)
expected_input_variables |= set(values['partial_variables'])
if values['prefix'] is not None:
expected_input_variables |= set(values['prefix'].input_variables)
missing_vars = expected_input_variables.difference(input_variables)
if missing_vars:
raise ValueError(
f'Got input_variables={input_variables}, but based on prefix/suffix expected {expected_input_variables}'
)
else:
values['input_variables'] = sorted(set(values['suffix'].input_variables
) | set(values['prefix'].input_variables if values['prefix'] else [
]) - set(values['partial_variables']))
return values | @root_validator()
def template_is_valid(cls, values: Dict) ->Dict:
"""Check that prefix, suffix, and input variables are consistent."""
if values['validate_template']:
input_variables = values['input_variables']
expected_input_variables = set(values['suffix'].input_variables)
expected_input_variables |= set(values['partial_variables'])
if values['prefix'] is not None:
expected_input_variables |= set(values['prefix'].input_variables)
missing_vars = expected_input_variables.difference(input_variables)
if missing_vars:
raise ValueError(
f'Got input_variables={input_variables}, but based on prefix/suffix expected {expected_input_variables}'
)
else:
values['input_variables'] = sorted(set(values['suffix'].
input_variables) | set(values['prefix'].input_variables if
values['prefix'] else []) - set(values['partial_variables']))
return values | Check that prefix, suffix, and input variables are consistent. |
_dont_flip_the_cos_score | """Keep similarity from client unchanged ad it's in [0:1] already."""
return similarity0to1 | @staticmethod
def _dont_flip_the_cos_score(similarity0to1: float) ->float:
"""Keep similarity from client unchanged ad it's in [0:1] already."""
return similarity0to1 | Keep similarity from client unchanged ad it's in [0:1] already. |
input_keys | return [self.input_key] | @property
def input_keys(self) ->List[str]:
return [self.input_key] | null |
build_extra | """Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get('model_kwargs', {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f'Found {field_name} supplied twice.')
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f'Parameters {invalid_model_kwargs} should be specified explicitly. Instead they were passed in as part of `model_kwargs` parameter.'
)
values['model_kwargs'] = extra
return values | @root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) ->Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get('model_kwargs', {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f'Found {field_name} supplied twice.')
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f'Parameters {invalid_model_kwargs} should be specified explicitly. Instead they were passed in as part of `model_kwargs` parameter.'
)
values['model_kwargs'] = extra
return values | Build extra kwargs from additional params that were passed in. |
_import_mlflow | from langchain_community.llms.mlflow import Mlflow
return Mlflow | def _import_mlflow() ->Any:
from langchain_community.llms.mlflow import Mlflow
return Mlflow | null |
similarity_search | """Perform similarity retrieval based on text.
Args:
query: Vectorize text for retrieval.，should not be empty.
k: top n.
search_filter: Additional filtering conditions.
Returns:
document_list: List of documents.
"""
embedding = self.embedding.embed_query(query)
return self.create_results(self.inner_embedding_query(embedding=embedding,
search_filter=search_filter, k=k)) | def similarity_search(self, query: str, k: int=4, search_filter: Optional[
Dict[str, Any]]=None, **kwargs: Any) ->List[Document]:
"""Perform similarity retrieval based on text.
Args:
query: Vectorize text for retrieval.，should not be empty.
k: top n.
search_filter: Additional filtering conditions.
Returns:
document_list: List of documents.
"""
embedding = self.embedding.embed_query(query)
return self.create_results(self.inner_embedding_query(embedding=
embedding, search_filter=search_filter, k=k)) | Perform similarity retrieval based on text.
Args:
query: Vectorize text for retrieval.，should not be empty.
k: top n.
search_filter: Additional filtering conditions.
Returns:
document_list: List of documents. |
search | """Try to search for wiki page.
If page exists, return the page summary, and a PageWithLookups object.
If page does not exist, return similar entries.
Args:
search: search string.
Returns: a Document object or error message.
"""
import wikipedia
try:
page_content = wikipedia.page(search).content
url = wikipedia.page(search).url
result: Union[str, Document] = Document(page_content=page_content,
metadata={'page': url})
except wikipedia.PageError:
result = f'Could not find [{search}]. Similar: {wikipedia.search(search)}'
except wikipedia.DisambiguationError:
result = f'Could not find [{search}]. Similar: {wikipedia.search(search)}'
return result | def search(self, search: str) ->Union[str, Document]:
"""Try to search for wiki page.
If page exists, return the page summary, and a PageWithLookups object.
If page does not exist, return similar entries.
Args:
search: search string.
Returns: a Document object or error message.
"""
import wikipedia
try:
page_content = wikipedia.page(search).content
url = wikipedia.page(search).url
result: Union[str, Document] = Document(page_content=page_content,
metadata={'page': url})
except wikipedia.PageError:
result = (
f'Could not find [{search}]. Similar: {wikipedia.search(search)}')
except wikipedia.DisambiguationError:
result = (
f'Could not find [{search}]. Similar: {wikipedia.search(search)}')
return result | Try to search for wiki page.
If page exists, return the page summary, and a PageWithLookups object.
If page does not exist, return similar entries.
Args:
search: search string.
Returns: a Document object or error message. |
test_blob_from_str_data | """Test reading blob from a file path."""
content = b'Hello, World!'
blob = Blob.from_data(content)
assert blob.encoding == 'utf-8'
assert blob.path is None
assert blob.mimetype is None
assert blob.source is None
assert blob.data == b'Hello, World!'
assert blob.as_bytes() == content
assert blob.as_string() == 'Hello, World!'
with blob.as_bytes_io() as bytes_io:
assert bytes_io.read() == content | def test_blob_from_str_data() ->None:
"""Test reading blob from a file path."""
content = b'Hello, World!'
blob = Blob.from_data(content)
assert blob.encoding == 'utf-8'
assert blob.path is None
assert blob.mimetype is None
assert blob.source is None
assert blob.data == b'Hello, World!'
assert blob.as_bytes() == content
assert blob.as_string() == 'Hello, World!'
with blob.as_bytes_io() as bytes_io:
assert bytes_io.read() == content | Test reading blob from a file path. |
run_query | return db.run(query) | def run_query(query):
return db.run(query) | null |
_import_meilisearch | from langchain_community.vectorstores.meilisearch import Meilisearch
return Meilisearch | def _import_meilisearch() ->Any:
from langchain_community.vectorstores.meilisearch import Meilisearch
return Meilisearch | null |
load_and_split | """Load Documents and split into chunks. Chunks are returned as Documents.
Args:
text_splitter: TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns:
List of Documents.
"""
from langchain.text_splitter import RecursiveCharacterTextSplitter
if text_splitter is None:
_text_splitter: TextSplitter = RecursiveCharacterTextSplitter()
else:
_text_splitter = text_splitter
docs = self.load()
return _text_splitter.split_documents(docs) | def load_and_split(self, text_splitter: Optional[TextSplitter]=None) ->List[
Document]:
"""Load Documents and split into chunks. Chunks are returned as Documents.
Args:
text_splitter: TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns:
List of Documents.
"""
from langchain.text_splitter import RecursiveCharacterTextSplitter
if text_splitter is None:
_text_splitter: TextSplitter = RecursiveCharacterTextSplitter()
else:
_text_splitter = text_splitter
docs = self.load()
return _text_splitter.split_documents(docs) | Load Documents and split into chunks. Chunks are returned as Documents.
Args:
text_splitter: TextSplitter instance to use for splitting documents.
Defaults to RecursiveCharacterTextSplitter.
Returns:
List of Documents. |
model_cfg_sys_msg | return Vicuna(llm=FakeLLM(), system_message=SystemMessage(content='sys-msg')) | @pytest.fixture
def model_cfg_sys_msg() ->Vicuna:
return Vicuna(llm=FakeLLM(), system_message=SystemMessage(content=
'sys-msg')) | null |
validate_client | values['ydc_api_key'] = get_from_dict_or_env(values, 'ydc_api_key',
'YDC_API_KEY')
return values | @root_validator(pre=True)
def validate_client(cls, values: Dict[str, Any]) ->Dict[str, Any]:
values['ydc_api_key'] = get_from_dict_or_env(values, 'ydc_api_key',
'YDC_API_KEY')
return values | null |
test_partial_text_and_metadata | """
Test loading a board cards removing some text and metadata.
"""
trello_loader = TrelloLoader.from_credentials('QA', api_key='API_KEY',
token='API_TOKEN', extra_metadata='list', include_card_name=False,
include_checklist=False, include_comments=False)
documents = trello_loader.load()
texts = ['Closed Card Title', 'Checklist 1', 'Item 1:pending',
'This is a comment on a Closed Card.']
for text in texts:
self.assertFalse(text in documents[0].page_content)
self.assertEqual(documents[0].metadata, {'title': 'Closed Card Title', 'id':
'12350aca6952888df7975903', 'url':
'https://trello.com/card/12350aca6952888df7975903', 'list': 'Done'},
'Metadata of Closed Card Matches.') | def test_partial_text_and_metadata(self) ->None:
"""
Test loading a board cards removing some text and metadata.
"""
trello_loader = TrelloLoader.from_credentials('QA', api_key='API_KEY',
token='API_TOKEN', extra_metadata='list', include_card_name=False,
include_checklist=False, include_comments=False)
documents = trello_loader.load()
texts = ['Closed Card Title', 'Checklist 1', 'Item 1:pending',
'This is a comment on a Closed Card.']
for text in texts:
self.assertFalse(text in documents[0].page_content)
self.assertEqual(documents[0].metadata, {'title': 'Closed Card Title',
'id': '12350aca6952888df7975903', 'url':
'https://trello.com/card/12350aca6952888df7975903', 'list': 'Done'},
'Metadata of Closed Card Matches.') | Test loading a board cards removing some text and metadata. |
to_sql_model | return self.model_class(session_id=session_id, message=json.dumps(
message_to_dict(message))) | def to_sql_model(self, message: BaseMessage, session_id: str) ->Any:
return self.model_class(session_id=session_id, message=json.dumps(
message_to_dict(message))) | null |
test_namespace | """Test that a namespace is prepended to all keys properly."""
store = RedisStore(client=redis_client, ttl=None, namespace='meow')
key_value_pairs = [('key1', b'value1'), ('key2', b'value2')]
store.mset(key_value_pairs)
assert sorted(redis_client.scan_iter('*')) == [b'meow/key1', b'meow/key2']
store.mdelete(['key1'])
assert sorted(redis_client.scan_iter('*')) == [b'meow/key2']
assert list(store.yield_keys()) == ['key2']
assert list(store.yield_keys(prefix='key*')) == ['key2']
assert list(store.yield_keys(prefix='key1')) == [] | def test_namespace(redis_client: Redis) ->None:
"""Test that a namespace is prepended to all keys properly."""
store = RedisStore(client=redis_client, ttl=None, namespace='meow')
key_value_pairs = [('key1', b'value1'), ('key2', b'value2')]
store.mset(key_value_pairs)
assert sorted(redis_client.scan_iter('*')) == [b'meow/key1', b'meow/key2']
store.mdelete(['key1'])
assert sorted(redis_client.scan_iter('*')) == [b'meow/key2']
assert list(store.yield_keys()) == ['key2']
assert list(store.yield_keys(prefix='key*')) == ['key2']
assert list(store.yield_keys(prefix='key1')) == [] | Test that a namespace is prepended to all keys properly. |
_persist_run | """
Persist a run by adding it to the traced_runs list.
Parameters
----------
run : Run
The run to be persisted.
"""
run_ = run.copy()
run_.reference_example_id = self.example_id
self.traced_runs.append(run_) | def _persist_run(self, run: Run) ->None:
"""
Persist a run by adding it to the traced_runs list.
Parameters
----------
run : Run
The run to be persisted.
"""
run_ = run.copy()
run_.reference_example_id = self.example_id
self.traced_runs.append(run_) | Persist a run by adding it to the traced_runs list.
Parameters
----------
run : Run
The run to be persisted. |
test_memory_with_message_store | memory = ConversationBufferMemory(memory_key='baz', chat_memory=
message_history, return_messages=True)
memory.chat_memory.add_ai_message('This is me, the AI')
memory.chat_memory.add_user_message('This is me, the human')
messages = memory.chat_memory.messages
messages_json = json.dumps([message_to_dict(msg) for msg in messages])
assert 'This is me, the AI' in messages_json
assert 'This is me, the human' in messages_json
memory.chat_memory.clear()
assert memory.chat_memory.messages == [] | def test_memory_with_message_store(message_history: MomentoChatMessageHistory
) ->None:
memory = ConversationBufferMemory(memory_key='baz', chat_memory=
message_history, return_messages=True)
memory.chat_memory.add_ai_message('This is me, the AI')
memory.chat_memory.add_user_message('This is me, the human')
messages = memory.chat_memory.messages
messages_json = json.dumps([message_to_dict(msg) for msg in messages])
assert 'This is me, the AI' in messages_json
assert 'This is me, the human' in messages_json
memory.chat_memory.clear()
assert memory.chat_memory.messages == [] | null |
embed | """Embed a list of strings.
Args:
texts: List[str] The list of strings to embed.
batch_size: [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
embeddings_task_type: [str] optional embeddings task type,
one of the following
RETRIEVAL_QUERY - Text is a query
in a search/retrieval setting.
RETRIEVAL_DOCUMENT - Text is a document
in a search/retrieval setting.
SEMANTIC_SIMILARITY - Embeddings will be used
for Semantic Textual Similarity (STS).
CLASSIFICATION - Embeddings will be used for classification.
CLUSTERING - Embeddings will be used for clustering.
Returns:
List of embeddings, one for each text.
"""
if len(texts) == 0:
return []
embeddings: List[List[float]] = []
first_batch_result: List[List[float]] = []
if batch_size > 0:
batches = VertexAIEmbeddings._prepare_batches(texts, batch_size)
else:
first_batch_result, batches = self._prepare_and_validate_batches(texts,
embeddings_task_type)
embeddings.extend(first_batch_result)
tasks = []
for batch in batches:
tasks.append(self.instance['task_executor'].submit(self.
_get_embeddings_with_retry, texts=batch, embeddings_type=
embeddings_task_type))
if len(tasks) > 0:
wait(tasks)
for t in tasks:
embeddings.extend(t.result())
return embeddings | def embed(self, texts: List[str], batch_size: int=0, embeddings_task_type:
Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT',
'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING']]=None) ->List[List
[float]]:
"""Embed a list of strings.
Args:
texts: List[str] The list of strings to embed.
batch_size: [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
embeddings_task_type: [str] optional embeddings task type,
one of the following
RETRIEVAL_QUERY - Text is a query
in a search/retrieval setting.
RETRIEVAL_DOCUMENT - Text is a document
in a search/retrieval setting.
SEMANTIC_SIMILARITY - Embeddings will be used
for Semantic Textual Similarity (STS).
CLASSIFICATION - Embeddings will be used for classification.
CLUSTERING - Embeddings will be used for clustering.
Returns:
List of embeddings, one for each text.
"""
if len(texts) == 0:
return []
embeddings: List[List[float]] = []
first_batch_result: List[List[float]] = []
if batch_size > 0:
batches = VertexAIEmbeddings._prepare_batches(texts, batch_size)
else:
first_batch_result, batches = self._prepare_and_validate_batches(texts,
embeddings_task_type)
embeddings.extend(first_batch_result)
tasks = []
for batch in batches:
tasks.append(self.instance['task_executor'].submit(self.
_get_embeddings_with_retry, texts=batch, embeddings_type=
embeddings_task_type))
if len(tasks) > 0:
wait(tasks)
for t in tasks:
embeddings.extend(t.result())
return embeddings | Embed a list of strings.
Args:
texts: List[str] The list of strings to embed.
batch_size: [int] The batch size of embeddings to send to the model.
If zero, then the largest batch size will be detected dynamically
at the first request, starting from 250, down to 5.
embeddings_task_type: [str] optional embeddings task type,
one of the following
RETRIEVAL_QUERY - Text is a query
in a search/retrieval setting.
RETRIEVAL_DOCUMENT - Text is a document
in a search/retrieval setting.
SEMANTIC_SIMILARITY - Embeddings will be used
for Semantic Textual Similarity (STS).
CLASSIFICATION - Embeddings will be used for classification.
CLUSTERING - Embeddings will be used for clustering.
Returns:
List of embeddings, one for each text. |
input_keys | """Will be whatever keys the router chain prompt expects.
:meta private:
"""
return self.router_chain.input_keys | @property
def input_keys(self) ->List[str]:
"""Will be whatever keys the router chain prompt expects.
:meta private:
"""
return self.router_chain.input_keys | Will be whatever keys the router chain prompt expects.
:meta private: |
__str__ | """Text representation of searx result."""
return self._data | def __str__(self) ->str:
"""Text representation of searx result."""
return self._data | Text representation of searx result. |
to_typescript | """Get typescript string representation of the operation."""
operation_name = self.operation_id
params = []
if self.request_body:
formatted_request_body_props = self._format_nested_properties(self.
request_body.properties)
params.append(formatted_request_body_props)
for prop in self.properties:
prop_name = prop.name
prop_type = self.ts_type_from_python(prop.type)
prop_required = '' if prop.required else '?'
prop_desc = f'/* {prop.description} */' if prop.description else ''
params.append(f'{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},')
formatted_params = '\n'.join(params).strip()
description_str = f'/* {self.description} */' if self.description else ''
typescript_definition = f"""
{description_str}
type {operation_name} = (_: {{
{formatted_params}
}}) => any;
"""
return typescript_definition.strip() | def to_typescript(self) ->str:
"""Get typescript string representation of the operation."""
operation_name = self.operation_id
params = []
if self.request_body:
formatted_request_body_props = self._format_nested_properties(self.
request_body.properties)
params.append(formatted_request_body_props)
for prop in self.properties:
prop_name = prop.name
prop_type = self.ts_type_from_python(prop.type)
prop_required = '' if prop.required else '?'
prop_desc = f'/* {prop.description} */' if prop.description else ''
params.append(
f'{prop_desc}\n\t\t{prop_name}{prop_required}: {prop_type},')
formatted_params = '\n'.join(params).strip()
description_str = f'/* {self.description} */' if self.description else ''
typescript_definition = f"""
{description_str}
type {operation_name} = (_: {{
{formatted_params}
}}) => any;
"""
return typescript_definition.strip() | Get typescript string representation of the operation. |
_Starred | self.write('*')
self.dispatch(t.value) | def _Starred(self, t):
self.write('*')
self.dispatch(t.value) | null |
_get_package_version | with open(package_dir.parent / 'pyproject.toml', 'r') as f:
pyproject = toml.load(f)
return pyproject['tool']['poetry']['version'] | def _get_package_version(package_dir: Path) ->str:
with open(package_dir.parent / 'pyproject.toml', 'r') as f:
pyproject = toml.load(f)
return pyproject['tool']['poetry']['version'] | null |
test_mock | assert True | def test_mock() ->None:
assert True | null |
_run_chain | """Run a chain on inputs."""
inputs_ = inputs if input_mapper is None else input_mapper(inputs)
if isinstance(chain, Chain) and isinstance(inputs_, dict) and len(inputs_
) == 1 and chain.input_keys:
val = next(iter(inputs_.values()))
output = chain(val, callbacks=callbacks, tags=tags)
else:
runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
output = chain.invoke(inputs_, config=runnable_config)
return output | def _run_chain(chain: Union[Chain, Runnable], inputs: Dict[str, Any],
callbacks: Callbacks, *, tags: Optional[List[str]]=None, input_mapper:
Optional[Callable[[Dict], Any]]=None) ->Union[Dict, str]:
"""Run a chain on inputs."""
inputs_ = inputs if input_mapper is None else input_mapper(inputs)
if isinstance(chain, Chain) and isinstance(inputs_, dict) and len(inputs_
) == 1 and chain.input_keys:
val = next(iter(inputs_.values()))
output = chain(val, callbacks=callbacks, tags=tags)
else:
runnable_config = RunnableConfig(tags=tags or [], callbacks=callbacks)
output = chain.invoke(inputs_, config=runnable_config)
return output | Run a chain on inputs. |
test_api_key_is_string | llm = Petals(huggingface_api_key='secret-api-key')
assert isinstance(llm.huggingface_api_key, SecretStr) | def test_api_key_is_string() ->None:
llm = Petals(huggingface_api_key='secret-api-key')
assert isinstance(llm.huggingface_api_key, SecretStr) | null |
_import_promptlayer | from langchain_community.llms.promptlayer_openai import PromptLayerOpenAI
return PromptLayerOpenAI | def _import_promptlayer() ->Any:
from langchain_community.llms.promptlayer_openai import PromptLayerOpenAI
return PromptLayerOpenAI | null |
lc_secrets | return {'fireworks_api_key': 'FIREWORKS_API_KEY'} | @property
def lc_secrets(self) ->Dict[str, str]:
return {'fireworks_api_key': 'FIREWORKS_API_KEY'} | null |
finalize | """Finalize the deprecation of a class."""
try:
obj.__doc__ = new_doc
except AttributeError:
pass
obj.__init__ = functools.wraps(obj.__init__)(wrapper)
return obj | def finalize(wrapper: Callable[..., Any], new_doc: str) ->T:
"""Finalize the deprecation of a class."""
try:
obj.__doc__ = new_doc
except AttributeError:
pass
obj.__init__ = functools.wraps(obj.__init__)(wrapper)
return obj | Finalize the deprecation of a class. |
load | """Load text from the url(s) in web_path."""
return list(self.lazy_load()) | def load(self) ->List[Document]:
"""Load text from the url(s) in web_path."""
return list(self.lazy_load()) | Load text from the url(s) in web_path. |
__init__ | """Initialize with a resource and an access token.
Args:
resource: The resource.
access_token: The access token.
"""
self.resource = resource
access_token = access_token or get_from_env('access_token',
'STRIPE_ACCESS_TOKEN')
self.headers = {'Authorization': f'Bearer {access_token}'} | def __init__(self, resource: str, access_token: Optional[str]=None) ->None:
"""Initialize with a resource and an access token.
Args:
resource: The resource.
access_token: The access token.
"""
self.resource = resource
access_token = access_token or get_from_env('access_token',
'STRIPE_ACCESS_TOKEN')
self.headers = {'Authorization': f'Bearer {access_token}'} | Initialize with a resource and an access token.
Args:
resource: The resource.
access_token: The access token. |
from_embeddings | """Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
Example:
.. code-block:: python
from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(texts, embeddings, embedding, metadatas=metadatas, ids=
ids, collection_name=collection_name, distance_strategy=
distance_strategy, pre_delete_collection=pre_delete_collection, **kwargs) | @classmethod
def from_embeddings(cls, text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings, metadatas: Optional[List[dict]]=None,
collection_name: str=_LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy=DEFAULT_DISTANCE_STRATEGY, ids:
Optional[List[str]]=None, pre_delete_collection: bool=False, **kwargs: Any
) ->TimescaleVector:
"""Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
Example:
.. code-block:: python
from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings)
"""
texts = [t[0] for t in text_embeddings]
embeddings = [t[1] for t in text_embeddings]
return cls.__from(texts, embeddings, embedding, metadatas=metadatas,
ids=ids, collection_name=collection_name, distance_strategy=
distance_strategy, pre_delete_collection=pre_delete_collection, **
kwargs) | Construct TimescaleVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
"Either pass it as a parameter
or set the TIMESCALE_SERVICE_URL environment variable.
Example:
.. code-block:: python
from langchain_community.vectorstores import TimescaleVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
tvs = TimescaleVector.from_embeddings(text_embedding_pairs, embeddings) |
get_content | return page['body'][self.name.lower()]['value'] | def get_content(self, page: dict) ->str:
return page['body'][self.name.lower()]['value'] | null |
agent_executor | return self._agent_executor | @property
def agent_executor(self) ->AgentExecutor:
return self._agent_executor | null |
test_ai_endpoints_streaming | """Test streaming tokens from ai endpoints."""
llm = ChatNVIDIA(model='llama2_13b', max_tokens=36)
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token.content, str) | def test_ai_endpoints_streaming() ->None:
"""Test streaming tokens from ai endpoints."""
llm = ChatNVIDIA(model='llama2_13b', max_tokens=36)
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token.content, str) | Test streaming tokens from ai endpoints. |
on_retry | self.on_retry_common() | def on_retry(self, *args: Any, **kwargs: Any) ->Any:
self.on_retry_common() | null |
_batch_block | it = iter(iterable)
while (item := list(itertools.islice(it, size))):
yield item | def _batch_block(iterable: Iterable, size: int) ->Generator[List[dict],
None, None]:
it = iter(iterable)
while (item := list(itertools.islice(it, size))):
yield item | null |
test_gradient_wrong_setup2 | with pytest.raises(Exception):
GradientEmbeddings(gradient_api_url=_GRADIENT_BASE_URL,
gradient_access_token=_GRADIENT_SECRET, gradient_workspace_id='',
model=_MODEL_ID) | def test_gradient_wrong_setup2() ->None:
with pytest.raises(Exception):
GradientEmbeddings(gradient_api_url=_GRADIENT_BASE_URL,
gradient_access_token=_GRADIENT_SECRET, gradient_workspace_id=
'', model=_MODEL_ID) | null |
_call | text = inputs[self.input_key]
results = self.client.create(text)
output = self._moderate(text, results['results'][0])
return {self.output_key: output} | def _call(self, inputs: Dict[str, str], run_manager: Optional[
CallbackManagerForChainRun]=None) ->Dict[str, str]:
text = inputs[self.input_key]
results = self.client.create(text)
output = self._moderate(text, results['results'][0])
return {self.output_key: output} | null |
get_operation | """Get the operation object for a given path and HTTP method."""
from openapi_pydantic import Operation
path_item = self._get_path_strict(path)
operation_obj = getattr(path_item, method, None)
if not isinstance(operation_obj, Operation):
raise ValueError(f'No {method} method found for {path}')
return operation_obj | def get_operation(self, path: str, method: str) ->Operation:
"""Get the operation object for a given path and HTTP method."""
from openapi_pydantic import Operation
path_item = self._get_path_strict(path)
operation_obj = getattr(path_item, method, None)
if not isinstance(operation_obj, Operation):
raise ValueError(f'No {method} method found for {path}')
return operation_obj | Get the operation object for a given path and HTTP method. |
buffer_as_messages | """Exposes the buffer as a list of messages in case return_messages is False."""
return self.chat_memory.messages[-self.k * 2:] if self.k > 0 else [] | @property
def buffer_as_messages(self) ->List[BaseMessage]:
"""Exposes the buffer as a list of messages in case return_messages is False."""
return self.chat_memory.messages[-self.k * 2:] if self.k > 0 else [] | Exposes the buffer as a list of messages in case return_messages is False. |
test_parse_bool_value | parsed = cast(Comparison, DEFAULT_PARSER.parse_folder(f'eq("x", {x})'))
actual = parsed.value
expected = x.lower() == 'true'
assert actual == expected | @pytest.mark.parametrize('x', ('true', 'True', 'TRUE', 'false', 'False',
'FALSE'))
def test_parse_bool_value(x: str) ->None:
parsed = cast(Comparison, DEFAULT_PARSER.parse_folder(f'eq("x", {x})'))
actual = parsed.value
expected = x.lower() == 'true'
assert actual == expected | null |
test_chat_openai_llm_output_contains_model_name | """Test llm_output contains model_name."""
chat = ChatOpenAI(max_tokens=10)
message = HumanMessage(content='Hello')
llm_result = chat.generate([[message]])
assert llm_result.llm_output is not None
assert llm_result.llm_output['model_name'] == chat.model_name | def test_chat_openai_llm_output_contains_model_name() ->None:
"""Test llm_output contains model_name."""
chat = ChatOpenAI(max_tokens=10)
message = HumanMessage(content='Hello')
llm_result = chat.generate([[message]])
assert llm_result.llm_output is not None
assert llm_result.llm_output['model_name'] == chat.model_name | Test llm_output contains model_name. |
test_metadata_not_shallow | """Test that metadatas are not shallow."""
texts = ['foo bar']
splitter = CharacterTextSplitter(separator=' ', chunk_size=3, chunk_overlap=0)
docs = splitter.create_documents(texts, [{'source': '1'}])
expected_docs = [Document(page_content='foo', metadata={'source': '1'}),
Document(page_content='bar', metadata={'source': '1'})]
assert docs == expected_docs
docs[0].metadata['foo'] = 1
assert docs[0].metadata == {'source': '1', 'foo': 1}
assert docs[1].metadata == {'source': '1'} | def test_metadata_not_shallow() ->None:
"""Test that metadatas are not shallow."""
texts = ['foo bar']
splitter = CharacterTextSplitter(separator=' ', chunk_size=3,
chunk_overlap=0)
docs = splitter.create_documents(texts, [{'source': '1'}])
expected_docs = [Document(page_content='foo', metadata={'source': '1'}),
Document(page_content='bar', metadata={'source': '1'})]
assert docs == expected_docs
docs[0].metadata['foo'] = 1
assert docs[0].metadata == {'source': '1', 'foo': 1}
assert docs[1].metadata == {'source': '1'} | Test that metadatas are not shallow. |
_run | return self.spec.value(tool_input) | def _run(self, tool_input: str, run_manager: Optional[
CallbackManagerForToolRun]=None) ->str:
return self.spec.value(tool_input) | null |
test_konko_streaming_info_test | """Ensure generation details are retained during streaming."""
class TestCallback(FakeCallbackHandler):
data_store: dict = {}
def on_llm_end(self, *args: Any, **kwargs: Any) ->Any:
self.data_store['generation'] = args[0]
callback_instance = TestCallback()
callback_mgr = CallbackManager([callback_instance])
chat_instance = ChatKonko(max_tokens=2, temperature=0, callback_manager=
callback_mgr)
list(chat_instance.stream('hey'))
gen_data = callback_instance.data_store['generation']
assert gen_data.generations[0][0].text == ' Hey' | def test_konko_streaming_info_test() ->None:
"""Ensure generation details are retained during streaming."""
class TestCallback(FakeCallbackHandler):
data_store: dict = {}
def on_llm_end(self, *args: Any, **kwargs: Any) ->Any:
self.data_store['generation'] = args[0]
callback_instance = TestCallback()
callback_mgr = CallbackManager([callback_instance])
chat_instance = ChatKonko(max_tokens=2, temperature=0, callback_manager
=callback_mgr)
list(chat_instance.stream('hey'))
gen_data = callback_instance.data_store['generation']
assert gen_data.generations[0][0].text == ' Hey' | Ensure generation details are retained during streaming. |
lc_serializable | return True | @property
def lc_serializable(self) ->bool:
return True | null |
test_generate_synthetic | synthetic_results = synthetic_data_generator.generate(subject=
'medical_billing', extra=
"""the name must be chosen at random. Make it something you wouldn't
normally choose."""
, runs=10)
assert len(synthetic_results) == 10
for row in synthetic_results:
assert isinstance(row, MedicalBilling) | @pytest.mark.requires('openai')
def test_generate_synthetic(synthetic_data_generator: SyntheticDataGenerator
) ->None:
synthetic_results = synthetic_data_generator.generate(subject=
'medical_billing', extra=
"""the name must be chosen at random. Make it something you wouldn't
normally choose."""
, runs=10)
assert len(synthetic_results) == 10
for row in synthetic_results:
assert isinstance(row, MedicalBilling) | null |
from_llm | llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs) | @classmethod
def from_llm(cls, llm: BaseLanguageModel, prompt: BasePromptTemplate=PROMPT,
**kwargs: Any) ->LLMBashChain:
llm_chain = LLMChain(llm=llm, prompt=prompt)
return cls(llm_chain=llm_chain, **kwargs) | null |
_call | return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop, **
kwargs) | def _call(self, prompt: str, stop: Optional[List[str]]=None, run_manager:
Optional[CallbackManagerForLLMRun]=None, **kwargs: Any) ->str:
return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop,
**kwargs) | null |
test_singlestoredb_filter_metadata_2 | """Test filtering by metadata field that is similar for each document"""
table_name = 'test_singlestoredb_filter_metadata_2'
drop(table_name)
docs = [Document(page_content=t, metadata={'index': i, 'category': 'budget'
}) for i, t in enumerate(texts)]
docsearch = SingleStoreDB.from_documents(docs, FakeEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE, table_name=
table_name, host=TEST_SINGLESTOREDB_URL)
output = docsearch.similarity_search('foo', k=1, filter={'category': 'budget'})
assert output == [Document(page_content='foo', metadata={'index': 0,
'category': 'budget'})]
drop(table_name) | @pytest.mark.skipif(not singlestoredb_installed, reason=
'singlestoredb not installed')
def test_singlestoredb_filter_metadata_2(texts: List[str]) ->None:
"""Test filtering by metadata field that is similar for each document"""
table_name = 'test_singlestoredb_filter_metadata_2'
drop(table_name)
docs = [Document(page_content=t, metadata={'index': i, 'category':
'budget'}) for i, t in enumerate(texts)]
docsearch = SingleStoreDB.from_documents(docs, FakeEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE, table_name=
table_name, host=TEST_SINGLESTOREDB_URL)
output = docsearch.similarity_search('foo', k=1, filter={'category':
'budget'})
assert output == [Document(page_content='foo', metadata={'index': 0,
'category': 'budget'})]
drop(table_name) | Test filtering by metadata field that is similar for each document |
route_question | if input.get('chat_history'):
return standalone_question
else:
return RunnablePassthrough() | def route_question(input):
if input.get('chat_history'):
return standalone_question
else:
return RunnablePassthrough() | null |
_split_generators | """
This function defines how the dataset's splits will be generated.
Args:
dl_manager (`DownloadManager`):
Helper for downloading datasets from files and online sources.
This is not being used for this test file.
"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
'split': 'train', 'name': self.config.name}), datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={'split': 'test', 'name': self.
config.name})] | def _split_generators(self, dl_manager: datasets.DownloadManager) ->List[
datasets.SplitGenerator]:
"""
This function defines how the dataset's splits will be generated.
Args:
dl_manager (`DownloadManager`):
Helper for downloading datasets from files and online sources.
This is not being used for this test file.
"""
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
'split': 'train', 'name': self.config.name}), datasets.
SplitGenerator(name=datasets.Split.TEST, gen_kwargs={'split':
'test', 'name': self.config.name})] | This function defines how the dataset's splits will be generated.
Args:
dl_manager (`DownloadManager`):
Helper for downloading datasets from files and online sources.
This is not being used for this test file. |
Subsets and Splits