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on_agent_finish_common | self.agent_ends += 1
self.ends += 1 | def on_agent_finish_common(self) ->None:
self.agent_ends += 1
self.ends += 1 | null |
_import_requests_tool_RequestsPatchTool | from langchain_community.tools.requests.tool import RequestsPatchTool
return RequestsPatchTool | def _import_requests_tool_RequestsPatchTool() ->Any:
from langchain_community.tools.requests.tool import RequestsPatchTool
return RequestsPatchTool | null |
test_clone_different_repo | """
Test that trying to clone a different repository into a directory already
containing a clone raises a ValueError.
"""
clone_url = init_repo(tmpdir, 'remote_repo')
repo_path = tmpdir.mkdir('local_repo').strpath
loader = GitLoader(repo_path=repo_path, clone_url=clone_url)
documents = loader.load()
assert len(documents) == 1
other_clone_url = init_repo(tmpdir, 'other_remote_repo')
other_loader = GitLoader(repo_path=repo_path, clone_url=other_clone_url)
with pytest.raises(ValueError):
other_loader.load() | @pytest.mark.requires('git')
def test_clone_different_repo(tmpdir: py.path.local) ->None:
"""
Test that trying to clone a different repository into a directory already
containing a clone raises a ValueError.
"""
clone_url = init_repo(tmpdir, 'remote_repo')
repo_path = tmpdir.mkdir('local_repo').strpath
loader = GitLoader(repo_path=repo_path, clone_url=clone_url)
documents = loader.load()
assert len(documents) == 1
other_clone_url = init_repo(tmpdir, 'other_remote_repo')
other_loader = GitLoader(repo_path=repo_path, clone_url=other_clone_url)
with pytest.raises(ValueError):
other_loader.load() | Test that trying to clone a different repository into a directory already
containing a clone raises a ValueError. |
test_init_fail_no_text_column | index = mock_index(index_details)
with pytest.raises(ValueError) as ex:
DatabricksVectorSearch(index, embedding=DEFAULT_EMBEDDING_MODEL)
assert '`text_column` is required for this index.' in str(ex.value) | @pytest.mark.requires('databricks', 'databricks.vector_search')
@pytest.mark.parametrize('index_details', [
DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, DIRECT_ACCESS_INDEX])
def test_init_fail_no_text_column(index_details: dict) ->None:
index = mock_index(index_details)
with pytest.raises(ValueError) as ex:
DatabricksVectorSearch(index, embedding=DEFAULT_EMBEDDING_MODEL)
assert '`text_column` is required for this index.' in str(ex.value) | null |
save_context | """Save the context of this model run to memory."""
mem = outputs.get(self.add_memory_key)
now = outputs.get(self.now_key)
if mem:
self.add_memory(mem, now=now) | def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) ->None:
"""Save the context of this model run to memory."""
mem = outputs.get(self.add_memory_key)
now = outputs.get(self.now_key)
if mem:
self.add_memory(mem, now=now) | Save the context of this model run to memory. |
test_bedrock_batch | """Test batch tokens from BedrockChat."""
result = chat.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str) | @pytest.mark.scheduled
def test_bedrock_batch(chat: BedrockChat) ->None:
"""Test batch tokens from BedrockChat."""
result = chat.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str) | Test batch tokens from BedrockChat. |
on_retriever_start_common | self.starts += 1
self.retriever_starts += 1 | def on_retriever_start_common(self) ->None:
self.starts += 1
self.retriever_starts += 1 | null |
config_specs | with _enums_for_spec_lock:
if (which_enum := _enums_for_spec.get(self.which)):
pass
else:
which_enum = StrEnum(self.which.name or self.which.id, ((v, v) for
v in list(self.alternatives.keys()) + [self.default_key]))
_enums_for_spec[self.which] = cast(Type[StrEnum], which_enum)
return get_unique_config_specs([ConfigurableFieldSpec(id=self.which.id,
name=self.which.name, description=self.which.description, annotation=
which_enum, default=self.default_key, is_shared=self.which.is_shared)] +
([prefix_config_spec(s, f'{self.which.id}=={self.default_key}') for s in
self.default.config_specs] if self.prefix_keys else self.default.
config_specs) + [(prefix_config_spec(s, f'{self.which.id}=={alt_key}') if
self.prefix_keys else s) for alt_key, alt in self.alternatives.items() if
isinstance(alt, RunnableSerializable) for s in alt.config_specs]) | @property
def config_specs(self) ->List[ConfigurableFieldSpec]:
with _enums_for_spec_lock:
if (which_enum := _enums_for_spec.get(self.which)):
pass
else:
which_enum = StrEnum(self.which.name or self.which.id, ((v, v) for
v in list(self.alternatives.keys()) + [self.default_key]))
_enums_for_spec[self.which] = cast(Type[StrEnum], which_enum)
return get_unique_config_specs([ConfigurableFieldSpec(id=self.which.id,
name=self.which.name, description=self.which.description,
annotation=which_enum, default=self.default_key, is_shared=self.
which.is_shared)] + ([prefix_config_spec(s,
f'{self.which.id}=={self.default_key}') for s in self.default.
config_specs] if self.prefix_keys else self.default.config_specs) +
[(prefix_config_spec(s, f'{self.which.id}=={alt_key}') if self.
prefix_keys else s) for alt_key, alt in self.alternatives.items() if
isinstance(alt, RunnableSerializable) for s in alt.config_specs]) | null |
test_extra_kwargs | chat = ChatBaichuan(temperature=0.88, top_p=0.7)
assert chat.temperature == 0.88
assert chat.top_p == 0.7 | def test_extra_kwargs() ->None:
chat = ChatBaichuan(temperature=0.88, top_p=0.7)
assert chat.temperature == 0.88
assert chat.top_p == 0.7 | null |
_create_stream | if self.stop is not None and stop is not None:
raise ValueError('`stop` found in both the input and default params.')
elif self.stop is not None:
stop = self.stop
elif stop is None:
stop = []
params = self._default_params
if 'model' in kwargs:
params['model'] = kwargs['model']
if 'options' in kwargs:
params['options'] = kwargs['options']
else:
params['options'] = {**params['options'], 'stop': stop, **kwargs}
if payload.get('messages'):
request_payload = {'messages': payload.get('messages', []), **params}
else:
request_payload = {'prompt': payload.get('prompt'), 'images': payload.
get('images', []), **params}
response = requests.post(url=api_url, headers={'Content-Type':
'application/json'}, json=request_payload, stream=True, timeout=self.
timeout)
response.encoding = 'utf-8'
if response.status_code != 200:
if response.status_code == 404:
raise OllamaEndpointNotFoundError(
f'Ollama call failed with status code 404. Maybe your model is not found and you should pull the model with `ollama pull {self.model}`.'
)
else:
optional_detail = response.json().get('error')
raise ValueError(
f'Ollama call failed with status code {response.status_code}. Details: {optional_detail}'
)
return response.iter_lines(decode_unicode=True) | def _create_stream(self, api_url: str, payload: Any, stop: Optional[List[
str]]=None, **kwargs: Any) ->Iterator[str]:
if self.stop is not None and stop is not None:
raise ValueError('`stop` found in both the input and default params.')
elif self.stop is not None:
stop = self.stop
elif stop is None:
stop = []
params = self._default_params
if 'model' in kwargs:
params['model'] = kwargs['model']
if 'options' in kwargs:
params['options'] = kwargs['options']
else:
params['options'] = {**params['options'], 'stop': stop, **kwargs}
if payload.get('messages'):
request_payload = {'messages': payload.get('messages', []), **params}
else:
request_payload = {'prompt': payload.get('prompt'), 'images':
payload.get('images', []), **params}
response = requests.post(url=api_url, headers={'Content-Type':
'application/json'}, json=request_payload, stream=True, timeout=
self.timeout)
response.encoding = 'utf-8'
if response.status_code != 200:
if response.status_code == 404:
raise OllamaEndpointNotFoundError(
f'Ollama call failed with status code 404. Maybe your model is not found and you should pull the model with `ollama pull {self.model}`.'
)
else:
optional_detail = response.json().get('error')
raise ValueError(
f'Ollama call failed with status code {response.status_code}. Details: {optional_detail}'
)
return response.iter_lines(decode_unicode=True) | null |
test_memory_with_message_store | """Test the memory with a message store."""
message_history = UpstashRedisChatMessageHistory(url=URL, token=TOKEN, ttl=
10, session_id='my-test-session')
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() | @pytest.mark.requires('upstash_redis')
def test_memory_with_message_store() ->None:
"""Test the memory with a message store."""
message_history = UpstashRedisChatMessageHistory(url=URL, token=TOKEN,
ttl=10, session_id='my-test-session')
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() | Test the memory with a message store. |
test_all_imports | assert set(__all__) == set(EXPECTED_ALL) | def test_all_imports() ->None:
assert set(__all__) == set(EXPECTED_ALL) | null |
drop | """
Helper function: Drop data
"""
get_named_result(self.connection,
f'DROP TABLE IF EXISTS {self.config.database}.{self.config.table}') | def drop(self) ->None:
"""
Helper function: Drop data
"""
get_named_result(self.connection,
f'DROP TABLE IF EXISTS {self.config.database}.{self.config.table}') | Helper function: Drop data |
test_bilibili_loader | """Test Bilibili Loader."""
loader = BiliBiliLoader(['https://www.bilibili.com/video/BV1xt411o7Xu/',
'https://www.bilibili.com/video/av330407025/'])
docs = loader.load()
assert len(docs) == 2
assert len(docs[0].page_content) > 0
assert docs[1].metadata['owner']['mid'] == 398095160
assert docs[1].page_content == ''
assert docs[1].metadata['owner']['mid'] == 398095160 | def test_bilibili_loader() ->None:
"""Test Bilibili Loader."""
loader = BiliBiliLoader(['https://www.bilibili.com/video/BV1xt411o7Xu/',
'https://www.bilibili.com/video/av330407025/'])
docs = loader.load()
assert len(docs) == 2
assert len(docs[0].page_content) > 0
assert docs[1].metadata['owner']['mid'] == 398095160
assert docs[1].page_content == ''
assert docs[1].metadata['owner']['mid'] == 398095160 | Test Bilibili Loader. |
_call | try:
from predibase import PredibaseClient
pc = PredibaseClient(token=self.predibase_api_key.get_secret_value())
except ImportError as e:
raise ImportError(
'Could not import Predibase Python package. Please install it with `pip install predibase`.'
) from e
except ValueError as e:
raise ValueError('Your API key is not correct. Please try again') from e
results = pc.prompt(prompt, model_name=self.model)
return results[0].response | def _call(self, prompt: str, stop: Optional[List[str]]=None, run_manager:
Optional[CallbackManagerForLLMRun]=None, **kwargs: Any) ->str:
try:
from predibase import PredibaseClient
pc = PredibaseClient(token=self.predibase_api_key.get_secret_value())
except ImportError as e:
raise ImportError(
'Could not import Predibase Python package. Please install it with `pip install predibase`.'
) from e
except ValueError as e:
raise ValueError('Your API key is not correct. Please try again'
) from e
results = pc.prompt(prompt, model_name=self.model)
return results[0].response | null |
truncate_run_iterative | """Utility to truncate a list of runs dictionaries to only keep the specified
keys in each run.
:param runs: The list of runs to truncate.
:param keep_keys: The keys to keep in each run.
:return: The truncated list of runs.
"""
def truncate_single(run: Dict[str, Any]) ->Dict[str, Any]:
"""Utility to truncate a single run dictionary to only keep the specified
keys.
:param run: The run dictionary to truncate.
:return: The truncated run dictionary
"""
new_dict = {}
for key in run:
if key in keep_keys:
new_dict[key] = run.get(key)
return new_dict
return list(map(truncate_single, runs)) | def truncate_run_iterative(self, runs: List[Dict[str, Any]], keep_keys:
Tuple[str, ...]=()) ->List[Dict[str, Any]]:
"""Utility to truncate a list of runs dictionaries to only keep the specified
keys in each run.
:param runs: The list of runs to truncate.
:param keep_keys: The keys to keep in each run.
:return: The truncated list of runs.
"""
def truncate_single(run: Dict[str, Any]) ->Dict[str, Any]:
"""Utility to truncate a single run dictionary to only keep the specified
keys.
:param run: The run dictionary to truncate.
:return: The truncated run dictionary
"""
new_dict = {}
for key in run:
if key in keep_keys:
new_dict[key] = run.get(key)
return new_dict
return list(map(truncate_single, runs)) | Utility to truncate a list of runs dictionaries to only keep the specified
keys in each run.
:param runs: The list of runs to truncate.
:param keep_keys: The keys to keep in each run.
:return: The truncated list of runs. |
add_vectors | return self._add_vectors(self._client, self.table_name, vectors, documents,
ids, self.chunk_size) | def add_vectors(self, vectors: List[List[float]], documents: List[Document],
ids: List[str]) ->List[str]:
return self._add_vectors(self._client, self.table_name, vectors,
documents, ids, self.chunk_size) | null |
_raw_similarity_search_with_score | """Return raw opensearch documents (dict) including vectors,
scores most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of dict with its scores most similar to the query.
Optional Args:
same as `similarity_search`
"""
embedding = self.embedding_function.embed_query(query)
search_type = kwargs.get('search_type', 'approximate_search')
vector_field = kwargs.get('vector_field', 'vector_field')
index_name = kwargs.get('index_name', self.index_name)
filter = kwargs.get('filter', {})
if self.is_aoss and search_type != 'approximate_search' and search_type != SCRIPT_SCORING_SEARCH:
raise ValueError(
'Amazon OpenSearch Service Serverless only supports `approximate_search` and `script_scoring`'
)
if search_type == 'approximate_search':
boolean_filter = kwargs.get('boolean_filter', {})
subquery_clause = kwargs.get('subquery_clause', 'must')
efficient_filter = kwargs.get('efficient_filter', {})
lucene_filter = kwargs.get('lucene_filter', {})
if boolean_filter != {} and efficient_filter != {}:
raise ValueError(
'Both `boolean_filter` and `efficient_filter` are provided which is invalid'
)
if lucene_filter != {} and efficient_filter != {}:
raise ValueError(
'Both `lucene_filter` and `efficient_filter` are provided which is invalid. `lucene_filter` is deprecated'
)
if lucene_filter != {} and boolean_filter != {}:
raise ValueError(
'Both `lucene_filter` and `boolean_filter` are provided which is invalid. `lucene_filter` is deprecated'
)
if efficient_filter == {} and boolean_filter == {} and lucene_filter == {
} and filter != {}:
if self.engine in ['faiss', 'lucene']:
efficient_filter = filter
else:
boolean_filter = filter
if boolean_filter != {}:
search_query = _approximate_search_query_with_boolean_filter(embedding,
boolean_filter, k=k, vector_field=vector_field, subquery_clause
=subquery_clause)
elif efficient_filter != {}:
search_query = _approximate_search_query_with_efficient_filter(
embedding, efficient_filter, k=k, vector_field=vector_field)
elif lucene_filter != {}:
warnings.warn(
'`lucene_filter` is deprecated. Please use the keyword argument `efficient_filter`'
)
search_query = _approximate_search_query_with_efficient_filter(
embedding, lucene_filter, k=k, vector_field=vector_field)
else:
search_query = _default_approximate_search_query(embedding, k=k,
vector_field=vector_field)
elif search_type == SCRIPT_SCORING_SEARCH:
space_type = kwargs.get('space_type', 'l2')
pre_filter = kwargs.get('pre_filter', MATCH_ALL_QUERY)
search_query = _default_script_query(embedding, k, space_type,
pre_filter, vector_field)
elif search_type == PAINLESS_SCRIPTING_SEARCH:
space_type = kwargs.get('space_type', 'l2Squared')
pre_filter = kwargs.get('pre_filter', MATCH_ALL_QUERY)
search_query = _default_painless_scripting_query(embedding, k,
space_type, pre_filter, vector_field)
else:
raise ValueError('Invalid `search_type` provided as an argument')
response = self.client.search(index=index_name, body=search_query)
return [hit for hit in response['hits']['hits']] | def _raw_similarity_search_with_score(self, query: str, k: int=4, **kwargs: Any
) ->List[dict]:
"""Return raw opensearch documents (dict) including vectors,
scores most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of dict with its scores most similar to the query.
Optional Args:
same as `similarity_search`
"""
embedding = self.embedding_function.embed_query(query)
search_type = kwargs.get('search_type', 'approximate_search')
vector_field = kwargs.get('vector_field', 'vector_field')
index_name = kwargs.get('index_name', self.index_name)
filter = kwargs.get('filter', {})
if (self.is_aoss and search_type != 'approximate_search' and
search_type != SCRIPT_SCORING_SEARCH):
raise ValueError(
'Amazon OpenSearch Service Serverless only supports `approximate_search` and `script_scoring`'
)
if search_type == 'approximate_search':
boolean_filter = kwargs.get('boolean_filter', {})
subquery_clause = kwargs.get('subquery_clause', 'must')
efficient_filter = kwargs.get('efficient_filter', {})
lucene_filter = kwargs.get('lucene_filter', {})
if boolean_filter != {} and efficient_filter != {}:
raise ValueError(
'Both `boolean_filter` and `efficient_filter` are provided which is invalid'
)
if lucene_filter != {} and efficient_filter != {}:
raise ValueError(
'Both `lucene_filter` and `efficient_filter` are provided which is invalid. `lucene_filter` is deprecated'
)
if lucene_filter != {} and boolean_filter != {}:
raise ValueError(
'Both `lucene_filter` and `boolean_filter` are provided which is invalid. `lucene_filter` is deprecated'
)
if efficient_filter == {} and boolean_filter == {
} and lucene_filter == {} and filter != {}:
if self.engine in ['faiss', 'lucene']:
efficient_filter = filter
else:
boolean_filter = filter
if boolean_filter != {}:
search_query = _approximate_search_query_with_boolean_filter(
embedding, boolean_filter, k=k, vector_field=vector_field,
subquery_clause=subquery_clause)
elif efficient_filter != {}:
search_query = _approximate_search_query_with_efficient_filter(
embedding, efficient_filter, k=k, vector_field=vector_field)
elif lucene_filter != {}:
warnings.warn(
'`lucene_filter` is deprecated. Please use the keyword argument `efficient_filter`'
)
search_query = _approximate_search_query_with_efficient_filter(
embedding, lucene_filter, k=k, vector_field=vector_field)
else:
search_query = _default_approximate_search_query(embedding, k=k,
vector_field=vector_field)
elif search_type == SCRIPT_SCORING_SEARCH:
space_type = kwargs.get('space_type', 'l2')
pre_filter = kwargs.get('pre_filter', MATCH_ALL_QUERY)
search_query = _default_script_query(embedding, k, space_type,
pre_filter, vector_field)
elif search_type == PAINLESS_SCRIPTING_SEARCH:
space_type = kwargs.get('space_type', 'l2Squared')
pre_filter = kwargs.get('pre_filter', MATCH_ALL_QUERY)
search_query = _default_painless_scripting_query(embedding, k,
space_type, pre_filter, vector_field)
else:
raise ValueError('Invalid `search_type` provided as an argument')
response = self.client.search(index=index_name, body=search_query)
return [hit for hit in response['hits']['hits']] | Return raw opensearch documents (dict) including vectors,
scores most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of dict with its scores most similar to the query.
Optional Args:
same as `similarity_search` |
_import_elastic_vector_search | from langchain_community.vectorstores.elastic_vector_search import ElasticVectorSearch
return ElasticVectorSearch | def _import_elastic_vector_search() ->Any:
from langchain_community.vectorstores.elastic_vector_search import ElasticVectorSearch
return ElasticVectorSearch | null |
_import_oci_md_tgi | from langchain_community.llms.oci_data_science_model_deployment_endpoint import OCIModelDeploymentTGI
return OCIModelDeploymentTGI | def _import_oci_md_tgi() ->Any:
from langchain_community.llms.oci_data_science_model_deployment_endpoint import OCIModelDeploymentTGI
return OCIModelDeploymentTGI | null |
test_vald_search | """Test end to end construction and search."""
docsearch = _vald_from_texts()
time.sleep(WAIT_TIME)
output = docsearch.similarity_search('foo', k=3)
assert output == [Document(page_content='foo'), Document(page_content='bar'
), Document(page_content='baz')] | def test_vald_search() ->None:
"""Test end to end construction and search."""
docsearch = _vald_from_texts()
time.sleep(WAIT_TIME)
output = docsearch.similarity_search('foo', k=3)
assert output == [Document(page_content='foo'), Document(page_content=
'bar'), Document(page_content='baz')] | Test end to end construction and search. |
observation_prefix | """Prefix to append the observation with."""
return 'Observation: ' | @property
def observation_prefix(self) ->str:
"""Prefix to append the observation with."""
return 'Observation: ' | Prefix to append the observation with. |
test_load_jsonlines | file_path = '/workspaces/langchain/test.json'
expected_docs = [Document(page_content='value1', metadata={'source':
file_path, 'seq_num': 1}), Document(page_content='value2', metadata={
'source': file_path, 'seq_num': 2})]
mocker.patch('pathlib.Path.open', return_value=io.StringIO(
"""
{"text": "value1"}
{"text": "value2"}
"""
))
loader = JSONLoader(file_path=file_path, jq_schema='.', content_key='text',
json_lines=True)
result = loader.load()
assert result == expected_docs | def test_load_jsonlines(mocker: MockerFixture) ->None:
file_path = '/workspaces/langchain/test.json'
expected_docs = [Document(page_content='value1', metadata={'source':
file_path, 'seq_num': 1}), Document(page_content='value2', metadata
={'source': file_path, 'seq_num': 2})]
mocker.patch('pathlib.Path.open', return_value=io.StringIO(
"""
{"text": "value1"}
{"text": "value2"}
"""
))
loader = JSONLoader(file_path=file_path, jq_schema='.', content_key=
'text', json_lines=True)
result = loader.load()
assert result == expected_docs | null |
_Await | self.write('(')
self.write('await')
if t.value:
self.write(' ')
self.dispatch(t.value)
self.write(')') | def _Await(self, t):
self.write('(')
self.write('await')
if t.value:
self.write(' ')
self.dispatch(t.value)
self.write(')') | null |
pause_to_reflect | """Reflect on recent observations and generate 'insights'."""
if self.verbose:
logger.info('Character is reflecting')
new_insights = []
topics = self._get_topics_of_reflection()
for topic in topics:
insights = self._get_insights_on_topic(topic, now=now)
for insight in insights:
self.add_memory(insight, now=now)
new_insights.extend(insights)
return new_insights | def pause_to_reflect(self, now: Optional[datetime]=None) ->List[str]:
"""Reflect on recent observations and generate 'insights'."""
if self.verbose:
logger.info('Character is reflecting')
new_insights = []
topics = self._get_topics_of_reflection()
for topic in topics:
insights = self._get_insights_on_topic(topic, now=now)
for insight in insights:
self.add_memory(insight, now=now)
new_insights.extend(insights)
return new_insights | Reflect on recent observations and generate 'insights'. |
completion_with_retry | """Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(**kwargs: Any) ->Any:
ordered_generation_requests = get_ordered_generation_requests(
models_priority_list, **kwargs)
return llm.client.generate(ordered_generation_requests=
ordered_generation_requests, is_stream=kwargs.get('stream', False))
return _completion_with_retry(**kwargs) | def completion_with_retry(llm: GPTRouter, models_priority_list: List[
GPTRouterModel], run_manager: Optional[CallbackManagerForLLMRun]=None,
**kwargs: Any) ->Union[GenerationResponse, Generator[
ChunkedGenerationResponse]]:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(**kwargs: Any) ->Any:
ordered_generation_requests = get_ordered_generation_requests(
models_priority_list, **kwargs)
return llm.client.generate(ordered_generation_requests=
ordered_generation_requests, is_stream=kwargs.get('stream', False))
return _completion_with_retry(**kwargs) | Use tenacity to retry the completion call. |
add_message | """Store a message in the cache.
Args:
message (BaseMessage): The message object to store.
Raises:
SdkException: Momento service or network error.
Exception: Unexpected response.
"""
from momento.responses import CacheListPushBack
item = json.dumps(message_to_dict(message))
push_response = self.cache_client.list_push_back(self.cache_name, self.key,
item, ttl=self.ttl)
if isinstance(push_response, CacheListPushBack.Success):
return None
elif isinstance(push_response, CacheListPushBack.Error):
raise push_response.inner_exception
else:
raise Exception(f'Unexpected response: {push_response}') | def add_message(self, message: BaseMessage) ->None:
"""Store a message in the cache.
Args:
message (BaseMessage): The message object to store.
Raises:
SdkException: Momento service or network error.
Exception: Unexpected response.
"""
from momento.responses import CacheListPushBack
item = json.dumps(message_to_dict(message))
push_response = self.cache_client.list_push_back(self.cache_name, self.
key, item, ttl=self.ttl)
if isinstance(push_response, CacheListPushBack.Success):
return None
elif isinstance(push_response, CacheListPushBack.Error):
raise push_response.inner_exception
else:
raise Exception(f'Unexpected response: {push_response}') | Store a message in the cache.
Args:
message (BaseMessage): The message object to store.
Raises:
SdkException: Momento service or network error.
Exception: Unexpected response. |
_run | schedule = self.account.schedule()
calendar = schedule.get_default_calendar()
event = calendar.new_event()
event.body = body
event.subject = subject
event.start = dt.strptime(start_datetime, UTC_FORMAT)
event.end = dt.strptime(end_datetime, UTC_FORMAT)
for attendee in attendees:
event.attendees.add(attendee)
event.save()
output = 'Event sent: ' + str(event)
return output | def _run(self, body: str, attendees: List[str], subject: str,
start_datetime: str, end_datetime: str, run_manager: Optional[
CallbackManagerForToolRun]=None) ->str:
schedule = self.account.schedule()
calendar = schedule.get_default_calendar()
event = calendar.new_event()
event.body = body
event.subject = subject
event.start = dt.strptime(start_datetime, UTC_FORMAT)
event.end = dt.strptime(end_datetime, UTC_FORMAT)
for attendee in attendees:
event.attendees.add(attendee)
event.save()
output = 'Event sent: ' + str(event)
return output | null |
test_loadnotebook_eachnotehasexpectedmetadata | documents = EverNoteLoader(self.example_notebook_path(
'sample_notebook.enex'), False).load()
metadata_note1 = documents[0].metadata
assert 'title' in metadata_note1.keys()
assert 'created' in metadata_note1.keys()
assert 'updated' in metadata_note1.keys()
assert 'note-attributes.author' in metadata_note1.keys()
assert 'content' not in metadata_note1.keys()
assert 'content-raw' not in metadata_note1.keys()
assert 'resource' not in metadata_note1.keys()
assert metadata_note1['title'] == 'Test'
assert metadata_note1['note-attributes.author'] == 'Michael McGarry'
assert isinstance(metadata_note1['created'], time.struct_time)
assert isinstance(metadata_note1['updated'], time.struct_time)
assert metadata_note1['created'].tm_year == 2023
assert metadata_note1['created'].tm_mon == 5
assert metadata_note1['created'].tm_mday == 11
assert metadata_note1['updated'].tm_year == 2024
assert metadata_note1['updated'].tm_mon == 7
assert metadata_note1['updated'].tm_mday == 14
metadata_note2 = documents[1].metadata
assert 'title' in metadata_note2.keys()
assert 'created' in metadata_note2.keys()
assert 'updated' not in metadata_note2.keys()
assert 'note-attributes.author' in metadata_note2.keys()
assert 'note-attributes.source' in metadata_note2.keys()
assert 'content' not in metadata_note2.keys()
assert 'content-raw' not in metadata_note2.keys()
assert 'resource' not in metadata_note2.keys()
assert metadata_note2['title'] == 'Summer Training Program'
assert metadata_note2['note-attributes.author'] == 'Mike McGarry'
assert metadata_note2['note-attributes.source'] == 'mobile.iphone'
assert isinstance(metadata_note2['created'], time.struct_time)
assert metadata_note2['created'].tm_year == 2022
assert metadata_note2['created'].tm_mon == 12
assert metadata_note2['created'].tm_mday == 27 | def test_loadnotebook_eachnotehasexpectedmetadata(self) ->None:
documents = EverNoteLoader(self.example_notebook_path(
'sample_notebook.enex'), False).load()
metadata_note1 = documents[0].metadata
assert 'title' in metadata_note1.keys()
assert 'created' in metadata_note1.keys()
assert 'updated' in metadata_note1.keys()
assert 'note-attributes.author' in metadata_note1.keys()
assert 'content' not in metadata_note1.keys()
assert 'content-raw' not in metadata_note1.keys()
assert 'resource' not in metadata_note1.keys()
assert metadata_note1['title'] == 'Test'
assert metadata_note1['note-attributes.author'] == 'Michael McGarry'
assert isinstance(metadata_note1['created'], time.struct_time)
assert isinstance(metadata_note1['updated'], time.struct_time)
assert metadata_note1['created'].tm_year == 2023
assert metadata_note1['created'].tm_mon == 5
assert metadata_note1['created'].tm_mday == 11
assert metadata_note1['updated'].tm_year == 2024
assert metadata_note1['updated'].tm_mon == 7
assert metadata_note1['updated'].tm_mday == 14
metadata_note2 = documents[1].metadata
assert 'title' in metadata_note2.keys()
assert 'created' in metadata_note2.keys()
assert 'updated' not in metadata_note2.keys()
assert 'note-attributes.author' in metadata_note2.keys()
assert 'note-attributes.source' in metadata_note2.keys()
assert 'content' not in metadata_note2.keys()
assert 'content-raw' not in metadata_note2.keys()
assert 'resource' not in metadata_note2.keys()
assert metadata_note2['title'] == 'Summer Training Program'
assert metadata_note2['note-attributes.author'] == 'Mike McGarry'
assert metadata_note2['note-attributes.source'] == 'mobile.iphone'
assert isinstance(metadata_note2['created'], time.struct_time)
assert metadata_note2['created'].tm_year == 2022
assert metadata_note2['created'].tm_mon == 12
assert metadata_note2['created'].tm_mday == 27 | null |
load | p = Path(self.path)
docs = []
items = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
for i in items:
if i.is_file():
if self._is_visible(i.relative_to(p)) or self.load_hidden:
try:
loader = PyPDFLoader(str(i), extract_images=self.extract_images
)
sub_docs = loader.load()
for doc in sub_docs:
doc.metadata['source'] = str(i)
docs.extend(sub_docs)
except Exception as e:
if self.silent_errors:
logger.warning(e)
else:
raise e
return docs | def load(self) ->List[Document]:
p = Path(self.path)
docs = []
items = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
for i in items:
if i.is_file():
if self._is_visible(i.relative_to(p)) or self.load_hidden:
try:
loader = PyPDFLoader(str(i), extract_images=self.
extract_images)
sub_docs = loader.load()
for doc in sub_docs:
doc.metadata['source'] = str(i)
docs.extend(sub_docs)
except Exception as e:
if self.silent_errors:
logger.warning(e)
else:
raise e
return docs | null |
test_as_import_path | """Test that the path is converted to a LangChain import path."""
assert path.PACKAGE_DIR == ROOT / 'langchain_core'
assert path.as_import_path(HERE, relative_to=ROOT) == 'tests.unit_tests._api'
assert path.as_import_path(__file__, relative_to=ROOT
) == 'tests.unit_tests._api.test_path'
assert path.as_import_path(__file__, suffix='create_agent', relative_to=ROOT
) == 'tests.unit_tests._api.test_path.create_agent' | def test_as_import_path() ->None:
"""Test that the path is converted to a LangChain import path."""
assert path.PACKAGE_DIR == ROOT / 'langchain_core'
assert path.as_import_path(HERE, relative_to=ROOT
) == 'tests.unit_tests._api'
assert path.as_import_path(__file__, relative_to=ROOT
) == 'tests.unit_tests._api.test_path'
assert path.as_import_path(__file__, suffix='create_agent', relative_to
=ROOT) == 'tests.unit_tests._api.test_path.create_agent' | Test that the path is converted to a LangChain import path. |
chat | return ChatFireworks(model_kwargs={'temperature': 0, 'max_tokens': 512}) | @pytest.fixture
def chat() ->ChatFireworks:
return ChatFireworks(model_kwargs={'temperature': 0, 'max_tokens': 512}) | null |
from_texts | """
Create a BM25Retriever from a list of texts.
Args:
texts: A list of texts to vectorize.
metadatas: A list of metadata dicts to associate with each text.
bm25_params: Parameters to pass to the BM25 vectorizer.
preprocess_func: A function to preprocess each text before vectorization.
**kwargs: Any other arguments to pass to the retriever.
Returns:
A BM25Retriever instance.
"""
try:
from rank_bm25 import BM25Okapi
except ImportError:
raise ImportError(
'Could not import rank_bm25, please install with `pip install rank_bm25`.'
)
texts_processed = [preprocess_func(t) for t in texts]
bm25_params = bm25_params or {}
vectorizer = BM25Okapi(texts_processed, **bm25_params)
metadatas = metadatas or ({} for _ in texts)
docs = [Document(page_content=t, metadata=m) for t, m in zip(texts, metadatas)]
return cls(vectorizer=vectorizer, docs=docs, preprocess_func=
preprocess_func, **kwargs) | @classmethod
def from_texts(cls, texts: Iterable[str], metadatas: Optional[Iterable[dict
]]=None, bm25_params: Optional[Dict[str, Any]]=None, preprocess_func:
Callable[[str], List[str]]=default_preprocessing_func, **kwargs: Any
) ->BM25Retriever:
"""
Create a BM25Retriever from a list of texts.
Args:
texts: A list of texts to vectorize.
metadatas: A list of metadata dicts to associate with each text.
bm25_params: Parameters to pass to the BM25 vectorizer.
preprocess_func: A function to preprocess each text before vectorization.
**kwargs: Any other arguments to pass to the retriever.
Returns:
A BM25Retriever instance.
"""
try:
from rank_bm25 import BM25Okapi
except ImportError:
raise ImportError(
'Could not import rank_bm25, please install with `pip install rank_bm25`.'
)
texts_processed = [preprocess_func(t) for t in texts]
bm25_params = bm25_params or {}
vectorizer = BM25Okapi(texts_processed, **bm25_params)
metadatas = metadatas or ({} for _ in texts)
docs = [Document(page_content=t, metadata=m) for t, m in zip(texts,
metadatas)]
return cls(vectorizer=vectorizer, docs=docs, preprocess_func=
preprocess_func, **kwargs) | Create a BM25Retriever from a list of texts.
Args:
texts: A list of texts to vectorize.
metadatas: A list of metadata dicts to associate with each text.
bm25_params: Parameters to pass to the BM25 vectorizer.
preprocess_func: A function to preprocess each text before vectorization.
**kwargs: Any other arguments to pass to the retriever.
Returns:
A BM25Retriever instance. |
merge_content | """Merge two message contents.
Args:
first_content: The first content.
second_content: The second content.
Returns:
The merged content.
"""
if isinstance(first_content, str):
if isinstance(second_content, str):
return first_content + second_content
else:
return_list: List[Union[str, Dict]] = [first_content]
return return_list + second_content
elif isinstance(second_content, List):
return first_content + second_content
elif isinstance(first_content[-1], str):
return first_content[:-1] + [first_content[-1] + second_content]
else:
return first_content + [second_content] | def merge_content(first_content: Union[str, List[Union[str, Dict]]],
second_content: Union[str, List[Union[str, Dict]]]) ->Union[str, List[
Union[str, Dict]]]:
"""Merge two message contents.
Args:
first_content: The first content.
second_content: The second content.
Returns:
The merged content.
"""
if isinstance(first_content, str):
if isinstance(second_content, str):
return first_content + second_content
else:
return_list: List[Union[str, Dict]] = [first_content]
return return_list + second_content
elif isinstance(second_content, List):
return first_content + second_content
elif isinstance(first_content[-1], str):
return first_content[:-1] + [first_content[-1] + second_content]
else:
return first_content + [second_content] | Merge two message contents.
Args:
first_content: The first content.
second_content: The second content.
Returns:
The merged content. |
_get_schema_type | if schema is None:
return None
schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema)
if schema_type == 'array':
schema_type = APIProperty._get_schema_type_for_array(schema)
elif schema_type == 'object':
raise NotImplementedError('Objects not yet supported')
elif schema_type in PRIMITIVE_TYPES:
if schema.enum:
schema_type = APIProperty._get_schema_type_for_enum(parameter, schema)
else:
pass
else:
raise NotImplementedError(f'Unsupported type: {schema_type}')
return schema_type | @staticmethod
def _get_schema_type(parameter: Parameter, schema: Optional[Schema]
) ->SCHEMA_TYPE:
if schema is None:
return None
schema_type: SCHEMA_TYPE = APIProperty._cast_schema_list_type(schema)
if schema_type == 'array':
schema_type = APIProperty._get_schema_type_for_array(schema)
elif schema_type == 'object':
raise NotImplementedError('Objects not yet supported')
elif schema_type in PRIMITIVE_TYPES:
if schema.enum:
schema_type = APIProperty._get_schema_type_for_enum(parameter,
schema)
else:
pass
else:
raise NotImplementedError(f'Unsupported type: {schema_type}')
return schema_type | null |
from_model_id | """Construct the pipeline object from model_id and task."""
try:
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline as hf_pipeline
except ImportError:
raise ValueError(
'Could not import transformers python package. Please install it with `pip install transformers`.'
)
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == 'text-generation':
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
elif task in ('text2text-generation', 'summarization'):
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs
)
else:
raise ValueError(
f'Got invalid task {task}, currently only {VALID_TASKS} are supported'
)
except ImportError as e:
raise ValueError(
f'Could not load the {task} model due to missing dependencies.') from e
if tokenizer.pad_token is None:
tokenizer.pad_token_id = model.config.eos_token_id
if (getattr(model, 'is_loaded_in_4bit', False) or getattr(model,
'is_loaded_in_8bit', False)) and device is not None:
logger.warning(
f'Setting the `device` argument to None from {device} to avoid the error caused by attempting to move the model that was already loaded on the GPU using the Accelerate module to the same or another device.'
)
device = None
if device is not None and importlib.util.find_spec('torch') is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or device >= cuda_device_count:
raise ValueError(
f'Got device=={device}, device is required to be within [-1, {cuda_device_count})'
)
if device_map is not None and device < 0:
device = None
if device is not None and device < 0 and cuda_device_count > 0:
logger.warning(
'Device has %d GPUs available. Provide device={deviceId} to `from_model_id` to use availableGPUs for execution. deviceId is -1 (default) for CPU and can be a positive integer associated with CUDA device id.'
, cuda_device_count)
if 'trust_remote_code' in _model_kwargs:
_model_kwargs = {k: v for k, v in _model_kwargs.items() if k !=
'trust_remote_code'}
_pipeline_kwargs = pipeline_kwargs or {}
pipeline = hf_pipeline(task=task, model=model, tokenizer=tokenizer, device=
device, device_map=device_map, batch_size=batch_size, model_kwargs=
_model_kwargs, **_pipeline_kwargs)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f'Got invalid task {pipeline.task}, currently only {VALID_TASKS} are supported'
)
return cls(pipeline=pipeline, model_id=model_id, model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs, batch_size=batch_size, **kwargs) | @classmethod
def from_model_id(cls, model_id: str, task: str, device: Optional[int]=-1,
device_map: Optional[str]=None, model_kwargs: Optional[dict]=None,
pipeline_kwargs: Optional[dict]=None, batch_size: int=
DEFAULT_BATCH_SIZE, **kwargs: Any) ->HuggingFacePipeline:
"""Construct the pipeline object from model_id and task."""
try:
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline as hf_pipeline
except ImportError:
raise ValueError(
'Could not import transformers python package. Please install it with `pip install transformers`.'
)
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == 'text-generation':
model = AutoModelForCausalLM.from_pretrained(model_id, **
_model_kwargs)
elif task in ('text2text-generation', 'summarization'):
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **
_model_kwargs)
else:
raise ValueError(
f'Got invalid task {task}, currently only {VALID_TASKS} are supported'
)
except ImportError as e:
raise ValueError(
f'Could not load the {task} model due to missing dependencies.'
) from e
if tokenizer.pad_token is None:
tokenizer.pad_token_id = model.config.eos_token_id
if (getattr(model, 'is_loaded_in_4bit', False) or getattr(model,
'is_loaded_in_8bit', False)) and device is not None:
logger.warning(
f'Setting the `device` argument to None from {device} to avoid the error caused by attempting to move the model that was already loaded on the GPU using the Accelerate module to the same or another device.'
)
device = None
if device is not None and importlib.util.find_spec('torch') is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or device >= cuda_device_count:
raise ValueError(
f'Got device=={device}, device is required to be within [-1, {cuda_device_count})'
)
if device_map is not None and device < 0:
device = None
if device is not None and device < 0 and cuda_device_count > 0:
logger.warning(
'Device has %d GPUs available. Provide device={deviceId} to `from_model_id` to use availableGPUs for execution. deviceId is -1 (default) for CPU and can be a positive integer associated with CUDA device id.'
, cuda_device_count)
if 'trust_remote_code' in _model_kwargs:
_model_kwargs = {k: v for k, v in _model_kwargs.items() if k !=
'trust_remote_code'}
_pipeline_kwargs = pipeline_kwargs or {}
pipeline = hf_pipeline(task=task, model=model, tokenizer=tokenizer,
device=device, device_map=device_map, batch_size=batch_size,
model_kwargs=_model_kwargs, **_pipeline_kwargs)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f'Got invalid task {pipeline.task}, currently only {VALID_TASKS} are supported'
)
return cls(pipeline=pipeline, model_id=model_id, model_kwargs=
_model_kwargs, pipeline_kwargs=_pipeline_kwargs, batch_size=
batch_size, **kwargs) | Construct the pipeline object from model_id and task. |
_handle_missing_document | replacement_result = self.collection.find_one_and_replace(filter={'_id':
missing_document['_id']}, replacement=missing_document)
return replacement_result['data']['document']['_id'] | def _handle_missing_document(missing_document: DocDict) ->str:
replacement_result = self.collection.find_one_and_replace(filter={'_id':
missing_document['_id']}, replacement=missing_document)
return replacement_result['data']['document']['_id'] | null |
create_anonymizer_mapping | """Creates or updates the mapping used to anonymize and/or deanonymize text.
This method exploits the results returned by the
analysis and anonymization processes.
If is_reversed is True, it constructs a mapping from each original
entity to its anonymized value.
If is_reversed is False, it constructs a mapping from each
anonymized entity back to its original text value.
If there are multiple entities of the same type, the mapping will
include a count to differentiate them. For example, if there are
two names in the input text, the mapping will include NAME_1 and NAME_2.
Example of mapping:
{
"PERSON": {
"<original>": "<anonymized>",
"John Doe": "Slim Shady"
},
"PHONE_NUMBER": {
"111-111-1111": "555-555-5555"
}
...
}
"""
analyzer_results.sort(key=lambda d: d.start)
anonymizer_results.items.sort(key=lambda d: d.start)
mapping: MappingDataType = defaultdict(dict)
count: dict = defaultdict(int)
for analyzed, anonymized in zip(analyzer_results, anonymizer_results.items):
original_value = original_text[analyzed.start:analyzed.end]
entity_type = anonymized.entity_type
if is_reversed:
cond = original_value in mapping[entity_type].values()
else:
cond = original_value in mapping[entity_type]
if cond:
continue
if anonymized.text in mapping[entity_type].values(
) or anonymized.text in mapping[entity_type]:
anonymized_value = format_duplicated_operator(anonymized.text,
count[entity_type] + 2)
count[entity_type] += 1
else:
anonymized_value = anonymized.text
mapping_key, mapping_value = (anonymized_value, original_value
) if is_reversed else (original_value, anonymized_value)
mapping[entity_type][mapping_key] = mapping_value
return mapping | def create_anonymizer_mapping(original_text: str, analyzer_results: List[
'RecognizerResult'], anonymizer_results: 'EngineResult', is_reversed:
bool=False) ->MappingDataType:
"""Creates or updates the mapping used to anonymize and/or deanonymize text.
This method exploits the results returned by the
analysis and anonymization processes.
If is_reversed is True, it constructs a mapping from each original
entity to its anonymized value.
If is_reversed is False, it constructs a mapping from each
anonymized entity back to its original text value.
If there are multiple entities of the same type, the mapping will
include a count to differentiate them. For example, if there are
two names in the input text, the mapping will include NAME_1 and NAME_2.
Example of mapping:
{
"PERSON": {
"<original>": "<anonymized>",
"John Doe": "Slim Shady"
},
"PHONE_NUMBER": {
"111-111-1111": "555-555-5555"
}
...
}
"""
analyzer_results.sort(key=lambda d: d.start)
anonymizer_results.items.sort(key=lambda d: d.start)
mapping: MappingDataType = defaultdict(dict)
count: dict = defaultdict(int)
for analyzed, anonymized in zip(analyzer_results, anonymizer_results.items
):
original_value = original_text[analyzed.start:analyzed.end]
entity_type = anonymized.entity_type
if is_reversed:
cond = original_value in mapping[entity_type].values()
else:
cond = original_value in mapping[entity_type]
if cond:
continue
if anonymized.text in mapping[entity_type].values(
) or anonymized.text in mapping[entity_type]:
anonymized_value = format_duplicated_operator(anonymized.text,
count[entity_type] + 2)
count[entity_type] += 1
else:
anonymized_value = anonymized.text
mapping_key, mapping_value = (anonymized_value, original_value
) if is_reversed else (original_value, anonymized_value)
mapping[entity_type][mapping_key] = mapping_value
return mapping | Creates or updates the mapping used to anonymize and/or deanonymize text.
This method exploits the results returned by the
analysis and anonymization processes.
If is_reversed is True, it constructs a mapping from each original
entity to its anonymized value.
If is_reversed is False, it constructs a mapping from each
anonymized entity back to its original text value.
If there are multiple entities of the same type, the mapping will
include a count to differentiate them. For example, if there are
two names in the input text, the mapping will include NAME_1 and NAME_2.
Example of mapping:
{
"PERSON": {
"<original>": "<anonymized>",
"John Doe": "Slim Shady"
},
"PHONE_NUMBER": {
"111-111-1111": "555-555-5555"
}
...
} |
__init__ | """Initialize the NLTK splitter."""
super().__init__(**kwargs)
try:
from nltk.tokenize import sent_tokenize
self._tokenizer = sent_tokenize
except ImportError:
raise ImportError(
'NLTK is not installed, please install it with `pip install nltk`.')
self._separator = separator
self._language = language | def __init__(self, separator: str='\n\n', language: str='english', **kwargs:
Any) ->None:
"""Initialize the NLTK splitter."""
super().__init__(**kwargs)
try:
from nltk.tokenize import sent_tokenize
self._tokenizer = sent_tokenize
except ImportError:
raise ImportError(
'NLTK is not installed, please install it with `pip install nltk`.'
)
self._separator = separator
self._language = language | Initialize the NLTK splitter. |
requires_reference | """Whether the evaluation requires a reference text."""
return False | @property
def requires_reference(self) ->bool:
"""Whether the evaluation requires a reference text."""
return False | Whether the evaluation requires a reference text. |
from_llm | """Load QA Generate Chain from LLM."""
return cls(llm=llm, prompt=PROMPT, **kwargs) | @classmethod
def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) ->QAGenerateChain:
"""Load QA Generate Chain from LLM."""
return cls(llm=llm, prompt=PROMPT, **kwargs) | Load QA Generate Chain from LLM. |
_default_params | """Get the default parameters for calling Tongyi Qwen API."""
normal_params = {'model': self.model_name, 'top_p': self.top_p, 'api_key':
self.dashscope_api_key}
return {**normal_params, **self.model_kwargs} | @property
def _default_params(self) ->Dict[str, Any]:
"""Get the default parameters for calling Tongyi Qwen API."""
normal_params = {'model': self.model_name, 'top_p': self.top_p,
'api_key': self.dashscope_api_key}
return {**normal_params, **self.model_kwargs} | Get the default parameters for calling Tongyi Qwen API. |
_run | """Use the tool."""
res = self.api_wrapper.results(query, self.max_results, source=self.backend)
res_strs = [', '.join([f'{k}: {v}' for k, v in d.items()]) for d in res]
return ', '.join([f'[{rs}]' for rs in res_strs]) | def _run(self, query: str, run_manager: Optional[CallbackManagerForToolRun]
=None) ->str:
"""Use the tool."""
res = self.api_wrapper.results(query, self.max_results, source=self.backend
)
res_strs = [', '.join([f'{k}: {v}' for k, v in d.items()]) for d in res]
return ', '.join([f'[{rs}]' for rs in res_strs]) | Use the tool. |
_import_myscale_settings | from langchain_community.vectorstores.myscale import MyScaleSettings
return MyScaleSettings | def _import_myscale_settings() ->Any:
from langchain_community.vectorstores.myscale import MyScaleSettings
return MyScaleSettings | null |
_create_chat_result | generations = []
for m in response['data']['messages']:
message = _convert_dict_to_message(m)
gen = ChatGeneration(message=message)
generations.append(gen)
token_usage = response['usage']
llm_output = {'token_usage': token_usage, 'model': self.model}
return ChatResult(generations=generations, llm_output=llm_output) | def _create_chat_result(self, response: Mapping[str, Any]) ->ChatResult:
generations = []
for m in response['data']['messages']:
message = _convert_dict_to_message(m)
gen = ChatGeneration(message=message)
generations.append(gen)
token_usage = response['usage']
llm_output = {'token_usage': token_usage, 'model': self.model}
return ChatResult(generations=generations, llm_output=llm_output) | null |
extract_cypher | """Extract Cypher code from a text.
Args:
text: Text to extract Cypher code from.
Returns:
Cypher code extracted from the text.
"""
pattern = '```(.*?)```'
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text | def extract_cypher(text: str) ->str:
"""Extract Cypher code from a text.
Args:
text: Text to extract Cypher code from.
Returns:
Cypher code extracted from the text.
"""
pattern = '```(.*?)```'
matches = re.findall(pattern, text, re.DOTALL)
return matches[0] if matches else text | Extract Cypher code from a text.
Args:
text: Text to extract Cypher code from.
Returns:
Cypher code extracted from the text. |
_get_span | import regex
minor = quote
major = context
errs_ = 0
s = regex.search(f'({minor}){{e<={errs_}}}', major)
while s is None and errs_ <= errs:
errs_ += 1
s = regex.search(f'({minor}){{e<={errs_}}}', major)
if s is not None:
yield from s.spans() | def _get_span(self, quote: str, context: str, errs: int=100) ->Iterator[str]:
import regex
minor = quote
major = context
errs_ = 0
s = regex.search(f'({minor}){{e<={errs_}}}', major)
while s is None and errs_ <= errs:
errs_ += 1
s = regex.search(f'({minor}){{e<={errs_}}}', major)
if s is not None:
yield from s.spans() | null |
_FUNCTION_COMPOSER | """
Composer for functions.
Args:
op_name: Name of the function.
Returns:
Callable that takes a list of arguments and returns a string.
"""
def f(*args: Any) ->str:
args_: map[str] = map(str, args)
return f"{op_name}({','.join(args_)})"
return f | def _FUNCTION_COMPOSER(op_name: str) ->Callable:
"""
Composer for functions.
Args:
op_name: Name of the function.
Returns:
Callable that takes a list of arguments and returns a string.
"""
def f(*args: Any) ->str:
args_: map[str] = map(str, args)
return f"{op_name}({','.join(args_)})"
return f | Composer for functions.
Args:
op_name: Name of the function.
Returns:
Callable that takes a list of arguments and returns a string. |
_get_embedding_dimension | if self._embedding_dimension is None:
self._embedding_dimension = len(self.embedding.embed_query(
'This is a sample sentence.'))
return self._embedding_dimension | def _get_embedding_dimension(self) ->int:
if self._embedding_dimension is None:
self._embedding_dimension = len(self.embedding.embed_query(
'This is a sample sentence.'))
return self._embedding_dimension | null |
_select_eval_results | if isinstance(results, EvaluationResult):
results_ = [results]
elif isinstance(results, dict) and 'results' in results:
results_ = cast(List[EvaluationResult], results['results'])
else:
raise TypeError(
f'Invalid evaluation result type {type(results)}. Expected EvaluationResult or EvaluationResults.'
)
return results_ | def _select_eval_results(self, results: Union[EvaluationResult,
EvaluationResults]) ->List[EvaluationResult]:
if isinstance(results, EvaluationResult):
results_ = [results]
elif isinstance(results, dict) and 'results' in results:
results_ = cast(List[EvaluationResult], results['results'])
else:
raise TypeError(
f'Invalid evaluation result type {type(results)}. Expected EvaluationResult or EvaluationResults.'
)
return results_ | null |
test_parse_with_language | llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```json
{
"action": "foo",
"action_input": "bar"
}
```
"""
action, action_input = get_action_and_input(llm_output)
assert action == 'foo'
assert action_input == 'bar' | def test_parse_with_language() ->None:
llm_output = """I can use the `foo` tool to achieve the goal.
Action:
```json
{
"action": "foo",
"action_input": "bar"
}
```
"""
action, action_input = get_action_and_input(llm_output)
assert action == 'foo'
assert action_input == 'bar' | null |
test_loc_call | search = DataForSeoAPIWrapper(params={'location_name': 'Spain',
'language_code': 'es'})
output = search.results('iphone')
assert '/es/' in output[0]['url'] | def test_loc_call() ->None:
search = DataForSeoAPIWrapper(params={'location_name': 'Spain',
'language_code': 'es'})
output = search.results('iphone')
assert '/es/' in output[0]['url'] | null |
llm_prefix | """Prefix to append the LLM call with."""
return 'Thought:' | @property
def llm_prefix(self) ->str:
"""Prefix to append the LLM call with."""
return 'Thought:' | Prefix to append the LLM call with. |
assert_docs | for doc in docs:
assert doc.page_content
assert doc.metadata
main_meta = {'Published', 'Title', 'Authors', 'Summary'}
assert set(doc.metadata).issuperset(main_meta)
if all_meta:
assert len(set(doc.metadata)) > len(main_meta)
else:
assert len(set(doc.metadata)) == len(main_meta) | def assert_docs(docs: List[Document], all_meta: bool=False) ->None:
for doc in docs:
assert doc.page_content
assert doc.metadata
main_meta = {'Published', 'Title', 'Authors', 'Summary'}
assert set(doc.metadata).issuperset(main_meta)
if all_meta:
assert len(set(doc.metadata)) > len(main_meta)
else:
assert len(set(doc.metadata)) == len(main_meta) | null |
test_math_question_2 | """Test simple question."""
question = """Michael had 58 golf balls. On tuesday, he lost 23 golf balls.
On wednesday, he lost 2 more. How many golf balls did he have
at the end of wednesday?"""
prompt = MATH_PROMPT.format(question=question)
queries = {prompt: _MATH_SOLUTION_2}
fake_llm = FakeLLM(queries=queries)
fake_pal_chain = PALChain.from_math_prompt(fake_llm, timeout=None)
output = fake_pal_chain.run(question)
assert output == '33' | def test_math_question_2() ->None:
"""Test simple question."""
question = """Michael had 58 golf balls. On tuesday, he lost 23 golf balls.
On wednesday, he lost 2 more. How many golf balls did he have
at the end of wednesday?"""
prompt = MATH_PROMPT.format(question=question)
queries = {prompt: _MATH_SOLUTION_2}
fake_llm = FakeLLM(queries=queries)
fake_pal_chain = PALChain.from_math_prompt(fake_llm, timeout=None)
output = fake_pal_chain.run(question)
assert output == '33' | Test simple question. |
_setter | values[done] = value
done.set()
return value | def _setter(done: threading.Event, values: Values, value: T) ->T:
values[done] = value
done.set()
return value | null |
get_label | cost = None
if event.selected:
chosen_action = event.selected.index
cost = (-1.0 * event.selected.score if event.selected.score is not None
else None)
prob = event.selected.probability
return chosen_action, cost, prob
else:
return None, None, None | def get_label(self, event: PickBestEvent) ->tuple:
cost = None
if event.selected:
chosen_action = event.selected.index
cost = (-1.0 * event.selected.score if event.selected.score is not
None else None)
prob = event.selected.probability
return chosen_action, cost, prob
else:
return None, None, None | null |
test_run_with_callback | """Test run method works when callback manager is passed."""
handler = FakeCallbackHandler()
chain = FakeChain(callbacks=[handler])
output = chain.run('bar')
assert output == 'baz'
assert handler.starts == 1
assert handler.ends == 1
assert handler.errors == 0 | def test_run_with_callback() ->None:
"""Test run method works when callback manager is passed."""
handler = FakeCallbackHandler()
chain = FakeChain(callbacks=[handler])
output = chain.run('bar')
assert output == 'baz'
assert handler.starts == 1
assert handler.ends == 1
assert handler.errors == 0 | Test run method works when callback manager is passed. |
max_marginal_relevance_search | """Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int = 4): Number of Documents to return.
fetch_k (int = 20): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float = 0.5): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Optional.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding_vector = self.embedding.embed_query(query)
return self.max_marginal_relevance_search_by_vector(embedding_vector, k,
fetch_k, lambda_mult=lambda_mult, filter=filter) | def max_marginal_relevance_search(self, query: str, k: int=4, fetch_k: int=
20, lambda_mult: float=0.5, filter: Optional[Dict[str, str]]=None, **
kwargs: Any) ->List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int = 4): Number of Documents to return.
fetch_k (int = 20): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float = 0.5): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Optional.
Returns:
List of Documents selected by maximal marginal relevance.
"""
embedding_vector = self.embedding.embed_query(query)
return self.max_marginal_relevance_search_by_vector(embedding_vector, k,
fetch_k, lambda_mult=lambda_mult, filter=filter) | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query (str): Text to look up documents similar to.
k (int = 4): Number of Documents to return.
fetch_k (int = 20): Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float = 0.5): Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Optional.
Returns:
List of Documents selected by maximal marginal relevance. |
load_memory_variables | """
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
"""
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
buffer_string = get_buffer_string(self.buffer[-self.k * 2:], human_prefix=
self.human_prefix, ai_prefix=self.ai_prefix)
output = chain.predict(history=buffer_string, input=inputs[prompt_input_key])
if output.strip() == 'NONE':
entities = []
else:
entities = [w.strip() for w in output.split(',')]
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = self.entity_store.get(entity, '')
self.entity_cache = entities
if self.return_messages:
buffer: Any = self.buffer[-self.k * 2:]
else:
buffer = buffer_string
return {self.chat_history_key: buffer, 'entities': entity_summaries} | def load_memory_variables(self, inputs: Dict[str, Any]) ->Dict[str, Any]:
"""
Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet.
"""
chain = LLMChain(llm=self.llm, prompt=self.entity_extraction_prompt)
if self.input_key is None:
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
else:
prompt_input_key = self.input_key
buffer_string = get_buffer_string(self.buffer[-self.k * 2:],
human_prefix=self.human_prefix, ai_prefix=self.ai_prefix)
output = chain.predict(history=buffer_string, input=inputs[
prompt_input_key])
if output.strip() == 'NONE':
entities = []
else:
entities = [w.strip() for w in output.split(',')]
entity_summaries = {}
for entity in entities:
entity_summaries[entity] = self.entity_store.get(entity, '')
self.entity_cache = entities
if self.return_messages:
buffer: Any = self.buffer[-self.k * 2:]
else:
buffer = buffer_string
return {self.chat_history_key: buffer, 'entities': entity_summaries} | Returns chat history and all generated entities with summaries if available,
and updates or clears the recent entity cache.
New entity name can be found when calling this method, before the entity
summaries are generated, so the entity cache values may be empty if no entity
descriptions are generated yet. |
__add__ | ... | def __add__(self, __x: _T_contra) ->_T_co:
... | null |
load | return list(self.lazy_load()) | def load(self) ->List[Document]:
return list(self.lazy_load()) | null |
__str__ | result = ''
for task in self.tasks:
result += f'{task}\n'
result += f'status: {task.status}\n'
if task.failed():
result += f'message: {task.message}\n'
if task.completed():
result += f'result: {task.result}\n'
return result | def __str__(self) ->str:
result = ''
for task in self.tasks:
result += f'{task}\n'
result += f'status: {task.status}\n'
if task.failed():
result += f'message: {task.message}\n'
if task.completed():
result += f'result: {task.result}\n'
return result | null |
_load_credentials | """Load credentials."""
try:
from google.auth.transport.requests import Request
from google.oauth2 import service_account
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from youtube_transcript_api import YouTubeTranscriptApi
except ImportError:
raise ImportError(
'You must run`pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib youtube-transcript-api` to use the Google Drive loader'
)
creds = None
if self.service_account_path.exists():
return service_account.Credentials.from_service_account_file(str(self.
service_account_path))
if self.token_path.exists():
creds = Credentials.from_authorized_user_file(str(self.token_path), SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(str(self.
credentials_path), SCOPES)
creds = flow.run_local_server(port=0)
with open(self.token_path, 'w') as token:
token.write(creds.to_json())
return creds | def _load_credentials(self) ->Any:
"""Load credentials."""
try:
from google.auth.transport.requests import Request
from google.oauth2 import service_account
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from youtube_transcript_api import YouTubeTranscriptApi
except ImportError:
raise ImportError(
'You must run`pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib youtube-transcript-api` to use the Google Drive loader'
)
creds = None
if self.service_account_path.exists():
return service_account.Credentials.from_service_account_file(str(
self.service_account_path))
if self.token_path.exists():
creds = Credentials.from_authorized_user_file(str(self.token_path),
SCOPES)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(str(self.
credentials_path), SCOPES)
creds = flow.run_local_server(port=0)
with open(self.token_path, 'w') as token:
token.write(creds.to_json())
return creds | Load credentials. |
_default_params | """Get the default parameters for calling the Aleph Alpha API."""
return {'maximum_tokens': self.maximum_tokens, 'temperature': self.
temperature, 'top_k': self.top_k, 'top_p': self.top_p,
'presence_penalty': self.presence_penalty, 'frequency_penalty': self.
frequency_penalty, 'n': self.n, 'repetition_penalties_include_prompt':
self.repetition_penalties_include_prompt,
'use_multiplicative_presence_penalty': self.
use_multiplicative_presence_penalty, 'penalty_bias': self.penalty_bias,
'penalty_exceptions': self.penalty_exceptions,
'penalty_exceptions_include_stop_sequences': self.
penalty_exceptions_include_stop_sequences, 'best_of': self.best_of,
'logit_bias': self.logit_bias, 'log_probs': self.log_probs, 'tokens':
self.tokens, 'disable_optimizations': self.disable_optimizations,
'minimum_tokens': self.minimum_tokens, 'echo': self.echo,
'use_multiplicative_frequency_penalty': self.
use_multiplicative_frequency_penalty, 'sequence_penalty': self.
sequence_penalty, 'sequence_penalty_min_length': self.
sequence_penalty_min_length, 'use_multiplicative_sequence_penalty':
self.use_multiplicative_sequence_penalty, 'completion_bias_inclusion':
self.completion_bias_inclusion,
'completion_bias_inclusion_first_token_only': self.
completion_bias_inclusion_first_token_only, 'completion_bias_exclusion':
self.completion_bias_exclusion,
'completion_bias_exclusion_first_token_only': self.
completion_bias_exclusion_first_token_only,
'contextual_control_threshold': self.contextual_control_threshold,
'control_log_additive': self.control_log_additive,
'repetition_penalties_include_completion': self.
repetition_penalties_include_completion, 'raw_completion': self.
raw_completion} | @property
def _default_params(self) ->Dict[str, Any]:
"""Get the default parameters for calling the Aleph Alpha API."""
return {'maximum_tokens': self.maximum_tokens, 'temperature': self.
temperature, 'top_k': self.top_k, 'top_p': self.top_p,
'presence_penalty': self.presence_penalty, 'frequency_penalty':
self.frequency_penalty, 'n': self.n,
'repetition_penalties_include_prompt': self.
repetition_penalties_include_prompt,
'use_multiplicative_presence_penalty': self.
use_multiplicative_presence_penalty, 'penalty_bias': self.
penalty_bias, 'penalty_exceptions': self.penalty_exceptions,
'penalty_exceptions_include_stop_sequences': self.
penalty_exceptions_include_stop_sequences, 'best_of': self.best_of,
'logit_bias': self.logit_bias, 'log_probs': self.log_probs,
'tokens': self.tokens, 'disable_optimizations': self.
disable_optimizations, 'minimum_tokens': self.minimum_tokens,
'echo': self.echo, 'use_multiplicative_frequency_penalty': self.
use_multiplicative_frequency_penalty, 'sequence_penalty': self.
sequence_penalty, 'sequence_penalty_min_length': self.
sequence_penalty_min_length, 'use_multiplicative_sequence_penalty':
self.use_multiplicative_sequence_penalty,
'completion_bias_inclusion': self.completion_bias_inclusion,
'completion_bias_inclusion_first_token_only': self.
completion_bias_inclusion_first_token_only,
'completion_bias_exclusion': self.completion_bias_exclusion,
'completion_bias_exclusion_first_token_only': self.
completion_bias_exclusion_first_token_only,
'contextual_control_threshold': self.contextual_control_threshold,
'control_log_additive': self.control_log_additive,
'repetition_penalties_include_completion': self.
repetition_penalties_include_completion, 'raw_completion': self.
raw_completion} | Get the default parameters for calling the Aleph Alpha API. |
__getitem__ | ... | @overload
def __getitem__(self, item: slice) ->Tuple[AsyncIterator[T], ...]:
... | null |
__init__ | """
Args:
file_path: The path to the file to load.
mode: The mode to use when loading the file. Can be one of "single",
"multi", or "all". Default is "single".
**unstructured_kwargs: Any kwargs to pass to the unstructured.
"""
validate_unstructured_version(min_unstructured_version='0.6.3')
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs) | def __init__(self, file_path: str, mode: str='single', **
unstructured_kwargs: Any):
"""
Args:
file_path: The path to the file to load.
mode: The mode to use when loading the file. Can be one of "single",
"multi", or "all". Default is "single".
**unstructured_kwargs: Any kwargs to pass to the unstructured.
"""
validate_unstructured_version(min_unstructured_version='0.6.3')
super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs) | Args:
file_path: The path to the file to load.
mode: The mode to use when loading the file. Can be one of "single",
"multi", or "all". Default is "single".
**unstructured_kwargs: Any kwargs to pass to the unstructured. |
install_python_packages | """Install python packages in the sandbox."""
self.session.install_python_packages(package_names) | def install_python_packages(self, package_names: Union[str, List[str]]) ->None:
"""Install python packages in the sandbox."""
self.session.install_python_packages(package_names) | Install python packages in the sandbox. |
_llm_type | """Return type of llm."""
return 'titan_takeoff' | @property
def _llm_type(self) ->str:
"""Return type of llm."""
return 'titan_takeoff' | Return type of llm. |
format_response_payload | """Formats the response body according to the output
schema of the model. Returns the data type that is
received from the response.
""" | @abstractmethod
def format_response_payload(self, output: bytes) ->str:
"""Formats the response body according to the output
schema of the model. Returns the data type that is
received from the response.
""" | Formats the response body according to the output
schema of the model. Returns the data type that is
received from the response. |
on_tool_error | """Run when tool errors."""
self.metrics['step'] += 1
self.metrics['errors'] += 1 | def on_tool_error(self, error: BaseException, **kwargs: Any) ->None:
"""Run when tool errors."""
self.metrics['step'] += 1
self.metrics['errors'] += 1 | Run when tool errors. |
_stop | return ['Observation:'] | @property
def _stop(self) ->List[str]:
return ['Observation:'] | null |
_search | """
Return docs similar to query.
Args:
query (str, optional): Text to look up similar docs.
embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
k (int): Number of Documents to return.
distance_metric (Optional[str], optional): `L2` for Euclidean, `L1` for
Nuclear, `max` for L-infinity distance, `cos` for cosine similarity,
'dot' for dot product.
filter (Union[Dict, Callable], optional): Additional filter prior
to the embedding search.
- ``Dict`` - Key-value search on tensors of htype json, on an
AND basis (a sample must satisfy all key-value filters to be True)
Dict = {"tensor_name_1": {"key": value},
"tensor_name_2": {"key": value}}
- ``Function`` - Any function compatible with `deeplake.filter`.
use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
fetch_k (int): Number of Documents for MMR algorithm.
return_score (bool): Return the score.
exec_option (str, optional): Supports 3 ways to perform searching.
Could be "python", "compute_engine" or "tensor_db".
- ``python`` - Pure-python implementation for the client.
WARNING: not recommended for big datasets.
- ``compute_engine`` - C++ implementation of Deep Lake Compute
Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified in
the Vector Store initialization. If True, the distance metric is set
to "deepmemory_distance", which represents the metric with which the
model was trained. The search is performed using the Deep Memory model.
If False, the distance metric is set to "COS" or whatever distance
metric user specifies.
**kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a tuple of (Document, score)
Raises:
ValueError: if both `embedding` and `embedding_function` are not specified.
"""
if kwargs.get('tql'):
return self._search_tql(tql=kwargs['tql'], exec_option=exec_option,
return_score=return_score, embedding=embedding, embedding_function=
embedding_function, distance_metric=distance_metric,
use_maximal_marginal_relevance=use_maximal_marginal_relevance,
filter=filter)
if embedding_function:
if isinstance(embedding_function, Embeddings):
_embedding_function = embedding_function.embed_query
else:
_embedding_function = embedding_function
elif self._embedding_function:
_embedding_function = self._embedding_function.embed_query
else:
_embedding_function = None
if embedding is None:
if _embedding_function is None:
raise ValueError(
'Either `embedding` or `embedding_function` needs to be specified.'
)
embedding = _embedding_function(query) if query else None
if isinstance(embedding, list):
embedding = np.array(embedding, dtype=np.float32)
if len(embedding.shape) > 1:
embedding = embedding[0]
result = self.vectorstore.search(embedding=embedding, k=fetch_k if
use_maximal_marginal_relevance else k, distance_metric=distance_metric,
filter=filter, exec_option=exec_option, return_tensors=['embedding',
'metadata', 'text', self._id_tensor_name], deep_memory=deep_memory)
scores = result['score']
embeddings = result['embedding']
metadatas = result['metadata']
texts = result['text']
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get('lambda_mult', 0.5)
indices = maximal_marginal_relevance(embedding, embeddings, k=min(k,
len(texts)), lambda_mult=lambda_mult)
scores = [scores[i] for i in indices]
texts = [texts[i] for i in indices]
metadatas = [metadatas[i] for i in indices]
docs = [Document(page_content=text, metadata=metadata) for text, metadata in
zip(texts, metadatas)]
if return_score:
return [(doc, score) for doc, score in zip(docs, scores)]
return docs | def _search(self, query: Optional[str]=None, embedding: Optional[Union[List
[float], np.ndarray]]=None, embedding_function: Optional[Callable]=None,
k: int=4, distance_metric: Optional[str]=None,
use_maximal_marginal_relevance: bool=False, fetch_k: Optional[int]=20,
filter: Optional[Union[Dict, Callable]]=None, return_score: bool=False,
exec_option: Optional[str]=None, deep_memory: bool=False, **kwargs: Any
) ->Any[List[Document], List[Tuple[Document, float]]]:
"""
Return docs similar to query.
Args:
query (str, optional): Text to look up similar docs.
embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
k (int): Number of Documents to return.
distance_metric (Optional[str], optional): `L2` for Euclidean, `L1` for
Nuclear, `max` for L-infinity distance, `cos` for cosine similarity,
'dot' for dot product.
filter (Union[Dict, Callable], optional): Additional filter prior
to the embedding search.
- ``Dict`` - Key-value search on tensors of htype json, on an
AND basis (a sample must satisfy all key-value filters to be True)
Dict = {"tensor_name_1": {"key": value},
"tensor_name_2": {"key": value}}
- ``Function`` - Any function compatible with `deeplake.filter`.
use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
fetch_k (int): Number of Documents for MMR algorithm.
return_score (bool): Return the score.
exec_option (str, optional): Supports 3 ways to perform searching.
Could be "python", "compute_engine" or "tensor_db".
- ``python`` - Pure-python implementation for the client.
WARNING: not recommended for big datasets.
- ``compute_engine`` - C++ implementation of Deep Lake Compute
Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified in
the Vector Store initialization. If True, the distance metric is set
to "deepmemory_distance", which represents the metric with which the
model was trained. The search is performed using the Deep Memory model.
If False, the distance metric is set to "COS" or whatever distance
metric user specifies.
**kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a tuple of (Document, score)
Raises:
ValueError: if both `embedding` and `embedding_function` are not specified.
"""
if kwargs.get('tql'):
return self._search_tql(tql=kwargs['tql'], exec_option=exec_option,
return_score=return_score, embedding=embedding,
embedding_function=embedding_function, distance_metric=
distance_metric, use_maximal_marginal_relevance=
use_maximal_marginal_relevance, filter=filter)
if embedding_function:
if isinstance(embedding_function, Embeddings):
_embedding_function = embedding_function.embed_query
else:
_embedding_function = embedding_function
elif self._embedding_function:
_embedding_function = self._embedding_function.embed_query
else:
_embedding_function = None
if embedding is None:
if _embedding_function is None:
raise ValueError(
'Either `embedding` or `embedding_function` needs to be specified.'
)
embedding = _embedding_function(query) if query else None
if isinstance(embedding, list):
embedding = np.array(embedding, dtype=np.float32)
if len(embedding.shape) > 1:
embedding = embedding[0]
result = self.vectorstore.search(embedding=embedding, k=fetch_k if
use_maximal_marginal_relevance else k, distance_metric=
distance_metric, filter=filter, exec_option=exec_option,
return_tensors=['embedding', 'metadata', 'text', self.
_id_tensor_name], deep_memory=deep_memory)
scores = result['score']
embeddings = result['embedding']
metadatas = result['metadata']
texts = result['text']
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get('lambda_mult', 0.5)
indices = maximal_marginal_relevance(embedding, embeddings, k=min(k,
len(texts)), lambda_mult=lambda_mult)
scores = [scores[i] for i in indices]
texts = [texts[i] for i in indices]
metadatas = [metadatas[i] for i in indices]
docs = [Document(page_content=text, metadata=metadata) for text,
metadata in zip(texts, metadatas)]
if return_score:
return [(doc, score) for doc, score in zip(docs, scores)]
return docs | Return docs similar to query.
Args:
query (str, optional): Text to look up similar docs.
embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
k (int): Number of Documents to return.
distance_metric (Optional[str], optional): `L2` for Euclidean, `L1` for
Nuclear, `max` for L-infinity distance, `cos` for cosine similarity,
'dot' for dot product.
filter (Union[Dict, Callable], optional): Additional filter prior
to the embedding search.
- ``Dict`` - Key-value search on tensors of htype json, on an
AND basis (a sample must satisfy all key-value filters to be True)
Dict = {"tensor_name_1": {"key": value},
"tensor_name_2": {"key": value}}
- ``Function`` - Any function compatible with `deeplake.filter`.
use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
fetch_k (int): Number of Documents for MMR algorithm.
return_score (bool): Return the score.
exec_option (str, optional): Supports 3 ways to perform searching.
Could be "python", "compute_engine" or "tensor_db".
- ``python`` - Pure-python implementation for the client.
WARNING: not recommended for big datasets.
- ``compute_engine`` - C++ implementation of Deep Lake Compute
Engine for the client. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database for storage
and query execution. Only for data in Deep Lake Managed Database.
Use runtime = {"db_engine": True} during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improving
search results. Defaults to False if deep_memory is not specified in
the Vector Store initialization. If True, the distance metric is set
to "deepmemory_distance", which represents the metric with which the
model was trained. The search is performed using the Deep Memory model.
If False, the distance metric is set to "COS" or whatever distance
metric user specifies.
**kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a tuple of (Document, score)
Raises:
ValueError: if both `embedding` and `embedding_function` are not specified. |
get_initial_label | """Return the markdown label for a new LLMThought that doesn't have
an associated tool yet.
"""
return f'{THINKING_EMOJI} **Thinking...**' | def get_initial_label(self) ->str:
"""Return the markdown label for a new LLMThought that doesn't have
an associated tool yet.
"""
return f'{THINKING_EMOJI} **Thinking...**' | Return the markdown label for a new LLMThought that doesn't have
an associated tool yet. |
embed_documents | """Call out to Aleph Alpha's asymmetric Document endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
try:
from aleph_alpha_client import Prompt, SemanticEmbeddingRequest, SemanticRepresentation
except ImportError:
raise ValueError(
'Could not import aleph_alpha_client python package. Please install it with `pip install aleph_alpha_client`.'
)
document_embeddings = []
for text in texts:
document_params = {'prompt': Prompt.from_text(text), 'representation':
SemanticRepresentation.Document, 'compress_to_size': self.
compress_to_size, 'normalize': self.normalize,
'contextual_control_threshold': self.contextual_control_threshold,
'control_log_additive': self.control_log_additive}
document_request = SemanticEmbeddingRequest(**document_params)
document_response = self.client.semantic_embed(request=document_request,
model=self.model)
document_embeddings.append(document_response.embedding)
return document_embeddings | def embed_documents(self, texts: List[str]) ->List[List[float]]:
"""Call out to Aleph Alpha's asymmetric Document endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
try:
from aleph_alpha_client import Prompt, SemanticEmbeddingRequest, SemanticRepresentation
except ImportError:
raise ValueError(
'Could not import aleph_alpha_client python package. Please install it with `pip install aleph_alpha_client`.'
)
document_embeddings = []
for text in texts:
document_params = {'prompt': Prompt.from_text(text),
'representation': SemanticRepresentation.Document,
'compress_to_size': self.compress_to_size, 'normalize': self.
normalize, 'contextual_control_threshold': self.
contextual_control_threshold, 'control_log_additive': self.
control_log_additive}
document_request = SemanticEmbeddingRequest(**document_params)
document_response = self.client.semantic_embed(request=
document_request, model=self.model)
document_embeddings.append(document_response.embedding)
return document_embeddings | Call out to Aleph Alpha's asymmetric Document endpoint.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text. |
test_all_imports | """Simple test to make sure all things can be imported."""
for cls in llms.__all__:
assert issubclass(getattr(llms, cls), BaseLLM)
assert set(llms.__all__) == set(EXPECT_ALL) | def test_all_imports() ->None:
"""Simple test to make sure all things can be imported."""
for cls in llms.__all__:
assert issubclass(getattr(llms, cls), BaseLLM)
assert set(llms.__all__) == set(EXPECT_ALL) | Simple test to make sure all things can be imported. |
test_regex_dict_result | """Test regex dict result."""
regex_dict_parser = RegexDictParser(output_key_to_format=
DEF_OUTPUT_KEY_TO_FORMAT, no_update_value='N/A')
result_dict = regex_dict_parser.parse_folder(DEF_README)
print('parse_result:', result_dict)
assert DEF_EXPECTED_RESULT == result_dict | def test_regex_dict_result() ->None:
"""Test regex dict result."""
regex_dict_parser = RegexDictParser(output_key_to_format=
DEF_OUTPUT_KEY_TO_FORMAT, no_update_value='N/A')
result_dict = regex_dict_parser.parse_folder(DEF_README)
print('parse_result:', result_dict)
assert DEF_EXPECTED_RESULT == result_dict | Test regex dict result. |
_import_arxiv | from langchain_community.utilities.arxiv import ArxivAPIWrapper
return ArxivAPIWrapper | def _import_arxiv() ->Any:
from langchain_community.utilities.arxiv import ArxivAPIWrapper
return ArxivAPIWrapper | null |
test_model | documents = ['hi', 'qianfan']
embedding = QianfanEmbeddingsEndpoint(model='Embedding-V1')
output = embedding.embed_documents(documents)
assert len(output) == 2 | def test_model() ->None:
documents = ['hi', 'qianfan']
embedding = QianfanEmbeddingsEndpoint(model='Embedding-V1')
output = embedding.embed_documents(documents)
assert len(output) == 2 | null |
_import_databricks | from langchain_community.llms.databricks import Databricks
return Databricks | def _import_databricks() ->Any:
from langchain_community.llms.databricks import Databricks
return Databricks | null |
_llm_type | """Return type of llm."""
return 'volc-engine-maas-llm' | @property
def _llm_type(self) ->str:
"""Return type of llm."""
return 'volc-engine-maas-llm' | Return type of llm. |
test_call | """Test that call gives the correct answer."""
chain = SerpAPIWrapper()
output = chain.run("What was Obama's first name?")
assert output == 'Barack Hussein Obama II' | def test_call() ->None:
"""Test that call gives the correct answer."""
chain = SerpAPIWrapper()
output = chain.run("What was Obama's first name?")
assert output == 'Barack Hussein Obama II' | Test that call gives the correct answer. |
_import_together | from langchain_community.llms.together import Together
return Together | def _import_together() ->Any:
from langchain_community.llms.together import Together
return Together | null |
run | """Run query through OpenAI and parse result."""
if is_openai_v1():
response = self.client.generate(prompt=query, n=self.n, size=self.size,
model=self.model_name, quality=self.quality)
image_urls = self.separator.join([item.url for item in response.data])
else:
response = self.client.create(prompt=query, n=self.n, size=self.size,
model=self.model_name)
image_urls = self.separator.join([item['url'] for item in response['data']]
)
return image_urls if image_urls else 'No image was generated' | def run(self, query: str) ->str:
"""Run query through OpenAI and parse result."""
if is_openai_v1():
response = self.client.generate(prompt=query, n=self.n, size=self.
size, model=self.model_name, quality=self.quality)
image_urls = self.separator.join([item.url for item in response.data])
else:
response = self.client.create(prompt=query, n=self.n, size=self.
size, model=self.model_name)
image_urls = self.separator.join([item['url'] for item in response[
'data']])
return image_urls if image_urls else 'No image was generated' | Run query through OpenAI and parse result. |
dict | """Return dictionary representation of agent."""
_dict = super().dict()
try:
_type = self._agent_type
except NotImplementedError:
_type = None
if isinstance(_type, AgentType):
_dict['_type'] = str(_type.value)
elif _type is not None:
_dict['_type'] = _type
return _dict | def dict(self, **kwargs: Any) ->Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
try:
_type = self._agent_type
except NotImplementedError:
_type = None
if isinstance(_type, AgentType):
_dict['_type'] = str(_type.value)
elif _type is not None:
_dict['_type'] = _type
return _dict | Return dictionary representation of agent. |
_Slice | if t.lower:
self.dispatch(t.lower)
self.write(':')
if t.upper:
self.dispatch(t.upper)
if t.step:
self.write(':')
self.dispatch(t.step) | def _Slice(self, t):
if t.lower:
self.dispatch(t.lower)
self.write(':')
if t.upper:
self.dispatch(t.upper)
if t.step:
self.write(':')
self.dispatch(t.step) | null |
__init__ | super().__init__()
self.ignore_case = ignore_case
self.ignore_punctuation = ignore_punctuation
self.ignore_numbers = ignore_numbers | def __init__(self, *, ignore_case: bool=False, ignore_punctuation: bool=
False, ignore_numbers: bool=False, **kwargs: Any):
super().__init__()
self.ignore_case = ignore_case
self.ignore_punctuation = ignore_punctuation
self.ignore_numbers = ignore_numbers | null |
test_embaas_embed_query | """Test embaas embeddings with multiple texts."""
text = 'foo'
embeddings = EmbaasEmbeddings()
output = embeddings.embed_query(text)
assert len(output) == 1024 | def test_embaas_embed_query() ->None:
"""Test embaas embeddings with multiple texts."""
text = 'foo'
embeddings = EmbaasEmbeddings()
output = embeddings.embed_query(text)
assert len(output) == 1024 | Test embaas embeddings with multiple texts. |
pretty_print_dict | return title + '\n' + json.dumps(d, indent=4) | def pretty_print_dict(title: str, d: dict) ->str:
return title + '\n' + json.dumps(d, indent=4) | null |
test_llm_chain_extractor_empty | texts = ['I love chocolate chip cookies—my mother makes great cookies.',
"Don't you just love Caesar salad?", "Let's go to Olive Garden!"]
doc = Document(page_content=' '.join(texts))
compressor = LLMChainExtractor.from_llm(ChatOpenAI())
actual = compressor.compress_documents([doc], 'Tell me about the Roman Empire')
assert len(actual) == 0 | def test_llm_chain_extractor_empty() ->None:
texts = ['I love chocolate chip cookies—my mother makes great cookies.',
"Don't you just love Caesar salad?", "Let's go to Olive Garden!"]
doc = Document(page_content=' '.join(texts))
compressor = LLMChainExtractor.from_llm(ChatOpenAI())
actual = compressor.compress_documents([doc],
'Tell me about the Roman Empire')
assert len(actual) == 0 | null |
get_lc_namespace | """Get the namespace of the langchain object."""
return ['langchain', 'llms', 'fireworks'] | @classmethod
def get_lc_namespace(cls) ->List[str]:
"""Get the namespace of the langchain object."""
return ['langchain', 'llms', 'fireworks'] | Get the namespace of the langchain object. |
_combine_llm_outputs | if llm_outputs[0] is None:
return {}
return llm_outputs[0] | def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) ->dict:
if llm_outputs[0] is None:
return {}
return llm_outputs[0] | null |
from_zapier_nla_wrapper | """Create a toolkit from a ZapierNLAWrapper."""
actions = zapier_nla_wrapper.list()
tools = [ZapierNLARunAction(action_id=action['id'], zapier_description=
action['description'], params_schema=action['params'], api_wrapper=
zapier_nla_wrapper) for action in actions]
return cls(tools=tools) | @classmethod
def from_zapier_nla_wrapper(cls, zapier_nla_wrapper: ZapierNLAWrapper
) ->'ZapierToolkit':
"""Create a toolkit from a ZapierNLAWrapper."""
actions = zapier_nla_wrapper.list()
tools = [ZapierNLARunAction(action_id=action['id'], zapier_description=
action['description'], params_schema=action['params'], api_wrapper=
zapier_nla_wrapper) for action in actions]
return cls(tools=tools) | Create a toolkit from a ZapierNLAWrapper. |
test_load_max_content_chars | """Test that cuts off document contents at max_content_chars."""
loader = BibtexLoader(file_path=str(BIBTEX_EXAMPLE_FILE), max_content_chars=10)
doc = loader.load()[0]
assert len(doc.page_content) == 10 | @pytest.mark.requires('fitz', 'bibtexparser')
def test_load_max_content_chars() ->None:
"""Test that cuts off document contents at max_content_chars."""
loader = BibtexLoader(file_path=str(BIBTEX_EXAMPLE_FILE),
max_content_chars=10)
doc = loader.load()[0]
assert len(doc.page_content) == 10 | Test that cuts off document contents at max_content_chars. |
copy_repo | """
Copies a repo, ignoring git folders.
Raises FileNotFound error if it can't find source
"""
def ignore_func(_, files):
return [f for f in files if f == '.git']
shutil.copytree(source, destination, ignore=ignore_func) | def copy_repo(source: Path, destination: Path) ->None:
"""
Copies a repo, ignoring git folders.
Raises FileNotFound error if it can't find source
"""
def ignore_func(_, files):
return [f for f in files if f == '.git']
shutil.copytree(source, destination, ignore=ignore_func) | Copies a repo, ignoring git folders.
Raises FileNotFound error if it can't find source |
_import_outline | from langchain_community.utilities.outline import OutlineAPIWrapper
return OutlineAPIWrapper | def _import_outline() ->Any:
from langchain_community.utilities.outline import OutlineAPIWrapper
return OutlineAPIWrapper | null |
test_clear | zep_chat.clear()
zep_chat.zep_client.memory.delete_memory.assert_called_once_with('test_session'
) | @pytest.mark.requires('zep_python')
def test_clear(mocker: MockerFixture, zep_chat: ZepChatMessageHistory) ->None:
zep_chat.clear()
zep_chat.zep_client.memory.delete_memory.assert_called_once_with(
'test_session') | null |
_call_before_predict | ... | @abstractmethod
def _call_before_predict(self, inputs: Dict[str, Any]) ->TEvent:
... | null |
test_pickbest_textembedder_w_full_label_w_emb | feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False,
model=MockEncoder())
str1 = '0'
str2 = '1'
str3 = '2'
encoded_str1 = rl_chain.stringify_embedding(list(encoded_keyword + str1))
encoded_str2 = rl_chain.stringify_embedding(list(encoded_keyword + str2))
encoded_str3 = rl_chain.stringify_embedding(list(encoded_keyword + str3))
ctx_str_1 = 'context1'
encoded_ctx_str_1 = rl_chain.stringify_embedding(list(encoded_keyword +
ctx_str_1))
named_actions = {'action1': rl_chain.Embed([str1, str2, str3])}
context = {'context': rl_chain.Embed(ctx_str_1)}
expected = f"""shared |context {encoded_ctx_str_1}
0:-0.0:1.0 |action1 {encoded_str1}
|action1 {encoded_str2}
|action1 {encoded_str3} """
selected = pick_best_chain.PickBestSelected(index=0, probability=1.0, score=0.0
)
event = pick_best_chain.PickBestEvent(inputs={}, to_select_from=
named_actions, based_on=context, selected=selected)
vw_ex_str = feature_embedder.format(event)
assert vw_ex_str == expected | @pytest.mark.requires('vowpal_wabbit_next')
def test_pickbest_textembedder_w_full_label_w_emb() ->None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=
False, model=MockEncoder())
str1 = '0'
str2 = '1'
str3 = '2'
encoded_str1 = rl_chain.stringify_embedding(list(encoded_keyword + str1))
encoded_str2 = rl_chain.stringify_embedding(list(encoded_keyword + str2))
encoded_str3 = rl_chain.stringify_embedding(list(encoded_keyword + str3))
ctx_str_1 = 'context1'
encoded_ctx_str_1 = rl_chain.stringify_embedding(list(encoded_keyword +
ctx_str_1))
named_actions = {'action1': rl_chain.Embed([str1, str2, str3])}
context = {'context': rl_chain.Embed(ctx_str_1)}
expected = f"""shared |context {encoded_ctx_str_1}
0:-0.0:1.0 |action1 {encoded_str1}
|action1 {encoded_str2}
|action1 {encoded_str3} """
selected = pick_best_chain.PickBestSelected(index=0, probability=1.0,
score=0.0)
event = pick_best_chain.PickBestEvent(inputs={}, to_select_from=
named_actions, based_on=context, selected=selected)
vw_ex_str = feature_embedder.format(event)
assert vw_ex_str == expected | null |
assert_docs | for doc in docs:
assert doc.page_content
assert doc.metadata
assert set(doc.metadata) == {'Published', 'Title', 'Authors', 'Summary'} | def assert_docs(docs: List[Document]) ->None:
for doc in docs:
assert doc.page_content
assert doc.metadata
assert set(doc.metadata) == {'Published', 'Title', 'Authors', 'Summary'
} | null |
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