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test_load_success | """Test that returns one document"""
loader = BibtexLoader(file_path=str(BIBTEX_EXAMPLE_FILE))
docs = loader.load()
assert len(docs) == 1
doc = docs[0]
assert doc.page_content
assert set(doc.metadata) == {'id', 'published_year', 'title', 'publication',
'authors', 'abstract'} | @pytest.mark.requires('fitz', 'bibtexparser')
def test_load_success() ->None:
"""Test that returns one document"""
loader = BibtexLoader(file_path=str(BIBTEX_EXAMPLE_FILE))
docs = loader.load()
assert len(docs) == 1
doc = docs[0]
assert doc.page_content
assert set(doc.metadata) == {'id', 'published_year', 'title',
'publication', 'authors', 'abstract'} | Test that returns one document |
parse | expected_keys = [rs.name for rs in self.response_schemas]
return parse_and_check_json_markdown(text, expected_keys) | def parse(self, text: str) ->Any:
expected_keys = [rs.name for rs in self.response_schemas]
return parse_and_check_json_markdown(text, expected_keys) | null |
_default_params | """Get the default parameters for calling text generation inference API."""
return {'max_new_tokens': self.max_new_tokens, 'top_k': self.top_k, 'top_p':
self.top_p, 'typical_p': self.typical_p, 'temperature': self.
temperature, 'repetition_penalty': self.repetition_penalty,
'return_full_text': self.return_full_text, 'truncate': self.truncate,
'stop_sequences': self.stop_sequences, 'seed': self.seed, 'do_sample':
self.do_sample, 'watermark': self.watermark, **self.model_kwargs} | @property
def _default_params(self) ->Dict[str, Any]:
"""Get the default parameters for calling text generation inference API."""
return {'max_new_tokens': self.max_new_tokens, 'top_k': self.top_k,
'top_p': self.top_p, 'typical_p': self.typical_p, 'temperature':
self.temperature, 'repetition_penalty': self.repetition_penalty,
'return_full_text': self.return_full_text, 'truncate': self.
truncate, 'stop_sequences': self.stop_sequences, 'seed': self.seed,
'do_sample': self.do_sample, 'watermark': self.watermark, **self.
model_kwargs} | Get the default parameters for calling text generation inference API. |
__init__ | """
Initialize the loader with a list of URL paths.
Args:
urls (List[str]): A list of URLs to scrape content from.
Raises:
ImportError: If the required 'playwright' package is not installed.
"""
self.urls = urls
try:
import playwright
except ImportError:
raise ImportError(
'playwright is required for AsyncChromiumLoader. Please install it with `pip install playwright`.'
) | def __init__(self, urls: List[str]):
"""
Initialize the loader with a list of URL paths.
Args:
urls (List[str]): A list of URLs to scrape content from.
Raises:
ImportError: If the required 'playwright' package is not installed.
"""
self.urls = urls
try:
import playwright
except ImportError:
raise ImportError(
'playwright is required for AsyncChromiumLoader. Please install it with `pip install playwright`.'
) | Initialize the loader with a list of URL paths.
Args:
urls (List[str]): A list of URLs to scrape content from.
Raises:
ImportError: If the required 'playwright' package is not installed. |
save_json | """Save dict to local file path.
Parameters:
data (dict): The dictionary to be saved.
file_path (str): Local file path.
"""
with open(file_path, 'w') as outfile:
json.dump(data, outfile) | def save_json(data: dict, file_path: str) ->None:
"""Save dict to local file path.
Parameters:
data (dict): The dictionary to be saved.
file_path (str): Local file path.
"""
with open(file_path, 'w') as outfile:
json.dump(data, outfile) | Save dict to local file path.
Parameters:
data (dict): The dictionary to be saved.
file_path (str): Local file path. |
lazy_load | """
Lazy load the chat sessions from the Slack dump file and yield them
in the required format.
:return: Iterator of chat sessions containing messages.
"""
with zipfile.ZipFile(str(self.zip_path), 'r') as zip_file:
for file_path in zip_file.namelist():
if file_path.endswith('.json'):
messages = self._read_json(zip_file, file_path)
yield self._load_single_chat_session(messages) | def lazy_load(self) ->Iterator[ChatSession]:
"""
Lazy load the chat sessions from the Slack dump file and yield them
in the required format.
:return: Iterator of chat sessions containing messages.
"""
with zipfile.ZipFile(str(self.zip_path), 'r') as zip_file:
for file_path in zip_file.namelist():
if file_path.endswith('.json'):
messages = self._read_json(zip_file, file_path)
yield self._load_single_chat_session(messages) | Lazy load the chat sessions from the Slack dump file and yield them
in the required format.
:return: Iterator of chat sessions containing messages. |
_create_retry_decorator | """Creates a retry decorator for Vertex / Palm LLMs."""
errors = [google.api_core.exceptions.ResourceExhausted, google.api_core.
exceptions.ServiceUnavailable, google.api_core.exceptions.Aborted,
google.api_core.exceptions.DeadlineExceeded, google.api_core.exceptions
.GoogleAPIError]
decorator = create_base_retry_decorator(error_types=errors, max_retries=
max_retries, run_manager=run_manager)
return decorator | def _create_retry_decorator(llm: BaseLLM, *, max_retries: int=1,
run_manager: Optional[Union[AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun]]=None) ->Callable[[Any], Any]:
"""Creates a retry decorator for Vertex / Palm LLMs."""
errors = [google.api_core.exceptions.ResourceExhausted, google.api_core
.exceptions.ServiceUnavailable, google.api_core.exceptions.Aborted,
google.api_core.exceptions.DeadlineExceeded, google.api_core.
exceptions.GoogleAPIError]
decorator = create_base_retry_decorator(error_types=errors, max_retries
=max_retries, run_manager=run_manager)
return decorator | Creates a retry decorator for Vertex / Palm LLMs. |
_import_forefrontai | from langchain_community.llms.forefrontai import ForefrontAI
return ForefrontAI | def _import_forefrontai() ->Any:
from langchain_community.llms.forefrontai import ForefrontAI
return ForefrontAI | null |
_import_openai_chat | from langchain_community.llms.openai import OpenAIChat
return OpenAIChat | def _import_openai_chat() ->Any:
from langchain_community.llms.openai import OpenAIChat
return OpenAIChat | null |
_get_invoke_url | """Helper method to get invoke URL from a model name, URL, or endpoint stub"""
if not invoke_url:
if not model_name:
raise ValueError('URL or model name must be specified to invoke')
if model_name in self.available_models:
invoke_url = self.available_models[model_name]
elif f'playground_{model_name}' in self.available_models:
invoke_url = self.available_models[f'playground_{model_name}']
else:
available_models_str = '\n'.join([f'{k} - {v}' for k, v in self.
available_models.items()])
raise ValueError(
f"""Unknown model name {model_name} specified.
Available models are:
{available_models_str}"""
)
if not invoke_url:
raise ValueError('URL or model name must be specified to invoke')
if 'http' not in invoke_url:
invoke_url = f'{self.call_invoke_base}/{invoke_url}'
return invoke_url | def _get_invoke_url(self, model_name: Optional[str]=None, invoke_url:
Optional[str]=None) ->str:
"""Helper method to get invoke URL from a model name, URL, or endpoint stub"""
if not invoke_url:
if not model_name:
raise ValueError('URL or model name must be specified to invoke')
if model_name in self.available_models:
invoke_url = self.available_models[model_name]
elif f'playground_{model_name}' in self.available_models:
invoke_url = self.available_models[f'playground_{model_name}']
else:
available_models_str = '\n'.join([f'{k} - {v}' for k, v in self
.available_models.items()])
raise ValueError(
f"""Unknown model name {model_name} specified.
Available models are:
{available_models_str}"""
)
if not invoke_url:
raise ValueError('URL or model name must be specified to invoke')
if 'http' not in invoke_url:
invoke_url = f'{self.call_invoke_base}/{invoke_url}'
return invoke_url | Helper method to get invoke URL from a model name, URL, or endpoint stub |
get_type_to_cls_dict | return {'ai21': _import_ai21, 'aleph_alpha': _import_aleph_alpha,
'amazon_api_gateway': _import_amazon_api_gateway, 'amazon_bedrock':
_import_bedrock, 'anthropic': _import_anthropic, 'anyscale':
_import_anyscale, 'arcee': _import_arcee, 'aviary': _import_aviary,
'azure': _import_azure_openai, 'azureml_endpoint':
_import_azureml_endpoint, 'bananadev': _import_bananadev, 'baseten':
_import_baseten, 'beam': _import_beam, 'cerebriumai':
_import_cerebriumai, 'chat_glm': _import_chatglm, 'clarifai':
_import_clarifai, 'cohere': _import_cohere, 'ctransformers':
_import_ctransformers, 'ctranslate2': _import_ctranslate2, 'databricks':
_import_databricks, 'databricks-chat': _import_databricks_chat,
'deepinfra': _import_deepinfra, 'deepsparse': _import_deepsparse,
'edenai': _import_edenai, 'fake-list': _import_fake, 'forefrontai':
_import_forefrontai, 'giga-chat-model': _import_gigachat, 'google_palm':
_import_google_palm, 'gooseai': _import_gooseai, 'gradient':
_import_gradient_ai, 'gpt4all': _import_gpt4all, 'huggingface_endpoint':
_import_huggingface_endpoint, 'huggingface_hub':
_import_huggingface_hub, 'huggingface_pipeline':
_import_huggingface_pipeline, 'huggingface_textgen_inference':
_import_huggingface_text_gen_inference, 'human-input': _import_human,
'koboldai': _import_koboldai, 'llamacpp': _import_llamacpp, 'textgen':
_import_textgen, 'minimax': _import_minimax, 'mlflow': _import_mlflow,
'mlflow-chat': _import_mlflow_chat, 'mlflow-ai-gateway':
_import_mlflow_ai_gateway, 'modal': _import_modal, 'mosaic':
_import_mosaicml, 'nebula': _import_symblai_nebula, 'nibittensor':
_import_bittensor, 'nlpcloud': _import_nlpcloud, 'ollama':
_import_ollama, 'openai': _import_openai, 'openlm': _import_openlm,
'pai_eas_endpoint': _import_pai_eas_endpoint, 'petals': _import_petals,
'pipelineai': _import_pipelineai, 'predibase': _import_predibase,
'opaqueprompts': _import_opaqueprompts, 'replicate': _import_replicate,
'rwkv': _import_rwkv, 'sagemaker_endpoint': _import_sagemaker_endpoint,
'self_hosted': _import_self_hosted, 'self_hosted_hugging_face':
_import_self_hosted_hugging_face, 'stochasticai': _import_stochasticai,
'together': _import_together, 'tongyi': _import_tongyi, 'titan_takeoff':
_import_titan_takeoff, 'titan_takeoff_pro': _import_titan_takeoff_pro,
'vertexai': _import_vertex, 'vertexai_model_garden':
_import_vertex_model_garden, 'openllm': _import_openllm,
'openllm_client': _import_openllm, 'vllm': _import_vllm, 'vllm_openai':
_import_vllm_openai, 'watsonxllm': _import_watsonxllm, 'writer':
_import_writer, 'xinference': _import_xinference, 'javelin-ai-gateway':
_import_javelin_ai_gateway, 'qianfan_endpoint':
_import_baidu_qianfan_endpoint, 'yandex_gpt': _import_yandex_gpt,
'VolcEngineMaasLLM': _import_volcengine_maas} | def get_type_to_cls_dict() ->Dict[str, Callable[[], Type[BaseLLM]]]:
return {'ai21': _import_ai21, 'aleph_alpha': _import_aleph_alpha,
'amazon_api_gateway': _import_amazon_api_gateway, 'amazon_bedrock':
_import_bedrock, 'anthropic': _import_anthropic, 'anyscale':
_import_anyscale, 'arcee': _import_arcee, 'aviary': _import_aviary,
'azure': _import_azure_openai, 'azureml_endpoint':
_import_azureml_endpoint, 'bananadev': _import_bananadev, 'baseten':
_import_baseten, 'beam': _import_beam, 'cerebriumai':
_import_cerebriumai, 'chat_glm': _import_chatglm, 'clarifai':
_import_clarifai, 'cohere': _import_cohere, 'ctransformers':
_import_ctransformers, 'ctranslate2': _import_ctranslate2,
'databricks': _import_databricks, 'databricks-chat':
_import_databricks_chat, 'deepinfra': _import_deepinfra,
'deepsparse': _import_deepsparse, 'edenai': _import_edenai,
'fake-list': _import_fake, 'forefrontai': _import_forefrontai,
'giga-chat-model': _import_gigachat, 'google_palm':
_import_google_palm, 'gooseai': _import_gooseai, 'gradient':
_import_gradient_ai, 'gpt4all': _import_gpt4all,
'huggingface_endpoint': _import_huggingface_endpoint,
'huggingface_hub': _import_huggingface_hub, 'huggingface_pipeline':
_import_huggingface_pipeline, 'huggingface_textgen_inference':
_import_huggingface_text_gen_inference, 'human-input':
_import_human, 'koboldai': _import_koboldai, 'llamacpp':
_import_llamacpp, 'textgen': _import_textgen, 'minimax':
_import_minimax, 'mlflow': _import_mlflow, 'mlflow-chat':
_import_mlflow_chat, 'mlflow-ai-gateway': _import_mlflow_ai_gateway,
'modal': _import_modal, 'mosaic': _import_mosaicml, 'nebula':
_import_symblai_nebula, 'nibittensor': _import_bittensor,
'nlpcloud': _import_nlpcloud, 'ollama': _import_ollama, 'openai':
_import_openai, 'openlm': _import_openlm, 'pai_eas_endpoint':
_import_pai_eas_endpoint, 'petals': _import_petals, 'pipelineai':
_import_pipelineai, 'predibase': _import_predibase, 'opaqueprompts':
_import_opaqueprompts, 'replicate': _import_replicate, 'rwkv':
_import_rwkv, 'sagemaker_endpoint': _import_sagemaker_endpoint,
'self_hosted': _import_self_hosted, 'self_hosted_hugging_face':
_import_self_hosted_hugging_face, 'stochasticai':
_import_stochasticai, 'together': _import_together, 'tongyi':
_import_tongyi, 'titan_takeoff': _import_titan_takeoff,
'titan_takeoff_pro': _import_titan_takeoff_pro, 'vertexai':
_import_vertex, 'vertexai_model_garden':
_import_vertex_model_garden, 'openllm': _import_openllm,
'openllm_client': _import_openllm, 'vllm': _import_vllm,
'vllm_openai': _import_vllm_openai, 'watsonxllm':
_import_watsonxllm, 'writer': _import_writer, 'xinference':
_import_xinference, 'javelin-ai-gateway':
_import_javelin_ai_gateway, 'qianfan_endpoint':
_import_baidu_qianfan_endpoint, 'yandex_gpt': _import_yandex_gpt,
'VolcEngineMaasLLM': _import_volcengine_maas} | null |
create | ... | @overload
@staticmethod
def create(messages: Sequence[Dict[str, Any]], *, provider: str=
'ChatOpenAI', stream: Literal[False]=False, **kwargs: Any) ->dict:
... | null |
_default_params | """Get the default parameters for calling Writer API."""
return {'minTokens': self.min_tokens, 'maxTokens': self.max_tokens,
'temperature': self.temperature, 'topP': self.top_p, 'stop': self.stop,
'presencePenalty': self.presence_penalty, 'repetitionPenalty': self.
repetition_penalty, 'bestOf': self.best_of, 'logprobs': self.logprobs,
'n': self.n} | @property
def _default_params(self) ->Mapping[str, Any]:
"""Get the default parameters for calling Writer API."""
return {'minTokens': self.min_tokens, 'maxTokens': self.max_tokens,
'temperature': self.temperature, 'topP': self.top_p, 'stop': self.
stop, 'presencePenalty': self.presence_penalty, 'repetitionPenalty':
self.repetition_penalty, 'bestOf': self.best_of, 'logprobs': self.
logprobs, 'n': self.n} | Get the default parameters for calling Writer API. |
_scrape | from bs4 import BeautifulSoup
if parser is None:
if url.endswith('.xml'):
parser = 'xml'
else:
parser = self.default_parser
self._check_parser(parser)
html_doc = self._fetch_valid_connection_docs(url)
if not getattr(html_doc, 'ok', False):
return None
if self.raise_for_status:
html_doc.raise_for_status()
if self.encoding is not None:
html_doc.encoding = self.encoding
elif self.autoset_encoding:
html_doc.encoding = html_doc.apparent_encoding
return BeautifulSoup(html_doc.text, parser, **bs_kwargs or {}) | def _scrape(self, url: str, parser: Union[str, None]=None, bs_kwargs:
Optional[dict]=None) ->Any:
from bs4 import BeautifulSoup
if parser is None:
if url.endswith('.xml'):
parser = 'xml'
else:
parser = self.default_parser
self._check_parser(parser)
html_doc = self._fetch_valid_connection_docs(url)
if not getattr(html_doc, 'ok', False):
return None
if self.raise_for_status:
html_doc.raise_for_status()
if self.encoding is not None:
html_doc.encoding = self.encoding
elif self.autoset_encoding:
html_doc.encoding = html_doc.apparent_encoding
return BeautifulSoup(html_doc.text, parser, **bs_kwargs or {}) | null |
lazy_load | for doc in self.load():
yield doc | def lazy_load(self) ->Iterator[Document]:
for doc in self.load():
yield doc | null |
test__validate_example_inputs_for_chain_input_mapper | mock_ = mock.MagicMock()
mock_.inputs = {'foo': 'bar', 'baz': 'qux'}
chain = mock.MagicMock()
chain.input_keys = ['not foo', 'not baz', 'not qux']
def wrong_output_format(inputs: dict) ->str:
assert 'foo' in inputs
assert 'baz' in inputs
return 'hehe'
with pytest.raises(InputFormatError, match='must be a dictionary'):
_validate_example_inputs_for_chain(mock_, chain, wrong_output_format)
def wrong_output_keys(inputs: dict) ->dict:
assert 'foo' in inputs
assert 'baz' in inputs
return {'not foo': 'foo', 'not baz': 'baz'}
with pytest.raises(InputFormatError, match='Missing keys after loading example'
):
_validate_example_inputs_for_chain(mock_, chain, wrong_output_keys)
def input_mapper(inputs: dict) ->dict:
assert 'foo' in inputs
assert 'baz' in inputs
return {'not foo': inputs['foo'], 'not baz': inputs['baz'], 'not qux':
'qux'}
_validate_example_inputs_for_chain(mock_, chain, input_mapper) | def test__validate_example_inputs_for_chain_input_mapper() ->None:
mock_ = mock.MagicMock()
mock_.inputs = {'foo': 'bar', 'baz': 'qux'}
chain = mock.MagicMock()
chain.input_keys = ['not foo', 'not baz', 'not qux']
def wrong_output_format(inputs: dict) ->str:
assert 'foo' in inputs
assert 'baz' in inputs
return 'hehe'
with pytest.raises(InputFormatError, match='must be a dictionary'):
_validate_example_inputs_for_chain(mock_, chain, wrong_output_format)
def wrong_output_keys(inputs: dict) ->dict:
assert 'foo' in inputs
assert 'baz' in inputs
return {'not foo': 'foo', 'not baz': 'baz'}
with pytest.raises(InputFormatError, match=
'Missing keys after loading example'):
_validate_example_inputs_for_chain(mock_, chain, wrong_output_keys)
def input_mapper(inputs: dict) ->dict:
assert 'foo' in inputs
assert 'baz' in inputs
return {'not foo': inputs['foo'], 'not baz': inputs['baz'],
'not qux': 'qux'}
_validate_example_inputs_for_chain(mock_, chain, input_mapper) | null |
yield_keys | """Get an iterator over keys that match the given prefix.
Args:
prefix (str): The prefix to match.
Returns:
Iterator[K | str]: An iterator over keys that match the given prefix.
This method is allowed to return an iterator over either K or str
depending on what makes more sense for the given store.
""" | @abstractmethod
def yield_keys(self, *, prefix: Optional[str]=None) ->Union[Iterator[K],
Iterator[str]]:
"""Get an iterator over keys that match the given prefix.
Args:
prefix (str): The prefix to match.
Returns:
Iterator[K | str]: An iterator over keys that match the given prefix.
This method is allowed to return an iterator over either K or str
depending on what makes more sense for the given store.
""" | Get an iterator over keys that match the given prefix.
Args:
prefix (str): The prefix to match.
Returns:
Iterator[K | str]: An iterator over keys that match the given prefix.
This method is allowed to return an iterator over either K or str
depending on what makes more sense for the given store. |
check_only_one_provider_selected | """
This tool has no feature to combine providers results.
Therefore we only allow one provider
"""
if len(v) > 1:
raise ValueError(
'Please select only one provider. The feature to combine providers results is not available for this tool.'
)
return v | @validator('providers')
def check_only_one_provider_selected(cls, v: List[str]) ->List[str]:
"""
This tool has no feature to combine providers results.
Therefore we only allow one provider
"""
if len(v) > 1:
raise ValueError(
'Please select only one provider. The feature to combine providers results is not available for this tool.'
)
return v | This tool has no feature to combine providers results.
Therefore we only allow one provider |
validate | redact = config.get('redact')
return self._detect_pii(prompt_value=prompt_value, config=config
) if redact else self._contains_pii(prompt_value=prompt_value, config=
config) | def validate(self, prompt_value: str, config: Any=None) ->str:
redact = config.get('redact')
return self._detect_pii(prompt_value=prompt_value, config=config
) if redact else self._contains_pii(prompt_value=prompt_value,
config=config) | null |
test_tracer_llm_run_on_error_callback | """Test tracer on an LLM run with an error and a callback."""
exception = Exception('test')
uuid = uuid4()
compare_run = Run(id=str(uuid), start_time=datetime.now(timezone.utc),
end_time=datetime.now(timezone.utc), events=[{'name': 'start', 'time':
datetime.now(timezone.utc)}, {'name': 'error', 'time': datetime.now(
timezone.utc)}], extra={}, execution_order=1, child_execution_order=1,
serialized=SERIALIZED, inputs=dict(prompts=[]), outputs=None, error=
repr(exception), run_type='llm', trace_id=uuid, dotted_order=
f'20230101T000000000000Z{uuid}')
class FakeTracerWithLlmErrorCallback(FakeTracer):
error_run = None
def _on_llm_error(self, run: Run) ->None:
self.error_run = run
tracer = FakeTracerWithLlmErrorCallback()
tracer.on_llm_start(serialized=SERIALIZED, prompts=[], run_id=uuid)
tracer.on_llm_error(exception, run_id=uuid)
assert tracer.error_run is not None
_compare_run_with_error(tracer.error_run, compare_run) | @freeze_time('2023-01-01')
def test_tracer_llm_run_on_error_callback() ->None:
"""Test tracer on an LLM run with an error and a callback."""
exception = Exception('test')
uuid = uuid4()
compare_run = Run(id=str(uuid), start_time=datetime.now(timezone.utc),
end_time=datetime.now(timezone.utc), events=[{'name': 'start',
'time': datetime.now(timezone.utc)}, {'name': 'error', 'time':
datetime.now(timezone.utc)}], extra={}, execution_order=1,
child_execution_order=1, serialized=SERIALIZED, inputs=dict(prompts
=[]), outputs=None, error=repr(exception), run_type='llm', trace_id
=uuid, dotted_order=f'20230101T000000000000Z{uuid}')
class FakeTracerWithLlmErrorCallback(FakeTracer):
error_run = None
def _on_llm_error(self, run: Run) ->None:
self.error_run = run
tracer = FakeTracerWithLlmErrorCallback()
tracer.on_llm_start(serialized=SERIALIZED, prompts=[], run_id=uuid)
tracer.on_llm_error(exception, run_id=uuid)
assert tracer.error_run is not None
_compare_run_with_error(tracer.error_run, compare_run) | Test tracer on an LLM run with an error and a callback. |
max_marginal_relevance_search_by_vector | """Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: 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.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.__query_collection(query_embeddings=embedding, n_results=
fetch_k, where=filter, where_document=where_document, include=[
'metadatas', 'documents', 'distances', 'embeddings'], **kwargs)
mmr_selected = maximal_marginal_relevance(np.array(embedding, dtype=np.
float32), results['embeddings'][0], k=k, lambda_mult=lambda_mult)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results | def max_marginal_relevance_search_by_vector(self, embedding: List[float], k:
int=DEFAULT_K, fetch_k: int=20, lambda_mult: float=0.5, filter:
Optional[Dict[str, str]]=None, where_document: 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:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: 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.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.__query_collection(query_embeddings=embedding, n_results
=fetch_k, where=filter, where_document=where_document, include=[
'metadatas', 'documents', 'distances', 'embeddings'], **kwargs)
mmr_selected = maximal_marginal_relevance(np.array(embedding, dtype=np.
float32), results['embeddings'][0], k=k, lambda_mult=lambda_mult)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in
mmr_selected]
return selected_results | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: 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.
Defaults to 0.5.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List of Documents selected by maximal marginal relevance. |
on_chain_end | pass | def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) ->None:
pass | null |
get_tools | """Get the tools in the toolkit."""
tools: List[BaseTool] = [AzureCogsFormRecognizerTool(),
AzureCogsSpeech2TextTool(), AzureCogsText2SpeechTool(),
AzureCogsTextAnalyticsHealthTool()]
if sys.platform.startswith('linux') or sys.platform.startswith('win'):
tools.append(AzureCogsImageAnalysisTool())
return tools | def get_tools(self) ->List[BaseTool]:
"""Get the tools in the toolkit."""
tools: List[BaseTool] = [AzureCogsFormRecognizerTool(),
AzureCogsSpeech2TextTool(), AzureCogsText2SpeechTool(),
AzureCogsTextAnalyticsHealthTool()]
if sys.platform.startswith('linux') or sys.platform.startswith('win'):
tools.append(AzureCogsImageAnalysisTool())
return tools | Get the tools in the toolkit. |
_default_params | """Get the default parameters for calling OpenAI API."""
return {'model': self.model, 'tokens_to_generate': self.max_tokens,
'temperature': self.temperature, 'top_p': self.top_p, **self.model_kwargs} | @property
def _default_params(self) ->Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return {'model': self.model, 'tokens_to_generate': self.max_tokens,
'temperature': self.temperature, 'top_p': self.top_p, **self.
model_kwargs} | Get the default parameters for calling OpenAI API. |
prompt_safety_callback | return self.on_after_prompt_safety.__func__ is not BaseModerationCallbackHandler.on_after_prompt_safety | @property
def prompt_safety_callback(self) ->bool:
return (self.on_after_prompt_safety.__func__ is not
BaseModerationCallbackHandler.on_after_prompt_safety) | null |
load | """Load documents from the Notion database.
Returns:
List[Document]: List of documents.
"""
page_summaries = self._retrieve_page_summaries()
return list(self.load_page(page_summary) for page_summary in page_summaries) | def load(self) ->List[Document]:
"""Load documents from the Notion database.
Returns:
List[Document]: List of documents.
"""
page_summaries = self._retrieve_page_summaries()
return list(self.load_page(page_summary) for page_summary in page_summaries
) | Load documents from the Notion database.
Returns:
List[Document]: List of documents. |
get_abbr | words = s.split(' ')
first_letters = [word[0] for word in words]
return ''.join(first_letters) | def get_abbr(s: str) ->str:
words = s.split(' ')
first_letters = [word[0] for word in words]
return ''.join(first_letters) | null |
_call | return super()._call(run_manager=run_manager, inputs=inputs) | def _call(self, inputs: Dict[str, Any], run_manager: Optional[
CallbackManagerForChainRun]=None) ->Dict[str, Any]:
return super()._call(run_manager=run_manager, inputs=inputs) | null |
_import_ainetwork_app | from langchain_community.tools.ainetwork.app import AINAppOps
return AINAppOps | def _import_ainetwork_app() ->Any:
from langchain_community.tools.ainetwork.app import AINAppOps
return AINAppOps | null |
test_load_valid_dict_content | file_path = '/workspaces/langchain/test.json'
expected_docs = [Document(page_content='{"text": "value1"}', metadata={
'source': file_path, 'seq_num': 1}), Document(page_content=
'{"text": "value2"}', metadata={'source': file_path, 'seq_num': 2})]
mocker.patch('builtins.open', mocker.mock_open())
mocker.patch('pathlib.Path.read_text', return_value=
"""
[{"text": "value1"}, {"text": "value2"}]
""")
loader = JSONLoader(file_path=file_path, jq_schema='.[]', text_content=False)
result = loader.load()
assert result == expected_docs | def test_load_valid_dict_content(mocker: MockerFixture) ->None:
file_path = '/workspaces/langchain/test.json'
expected_docs = [Document(page_content='{"text": "value1"}', metadata={
'source': file_path, 'seq_num': 1}), Document(page_content=
'{"text": "value2"}', metadata={'source': file_path, 'seq_num': 2})]
mocker.patch('builtins.open', mocker.mock_open())
mocker.patch('pathlib.Path.read_text', return_value=
"""
[{"text": "value1"}, {"text": "value2"}]
""")
loader = JSONLoader(file_path=file_path, jq_schema='.[]', text_content=
False)
result = loader.load()
assert result == expected_docs | null |
test_tracing_session_env_var | from langchain.agents import AgentType, initialize_agent, load_tools
os.environ['LANGCHAIN_WANDB_TRACING'] = 'true'
llm = OpenAI(temperature=0)
tools = load_tools(['llm-math', 'serpapi'], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.
ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run(questions[0]) | def test_tracing_session_env_var() ->None:
from langchain.agents import AgentType, initialize_agent, load_tools
os.environ['LANGCHAIN_WANDB_TRACING'] = 'true'
llm = OpenAI(temperature=0)
tools = load_tools(['llm-math', 'serpapi'], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.
ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run(questions[0]) | null |
_default_params | return {'max_tokens': self.max_tokens, 'n_predict': self.n_predict, 'top_k':
self.top_k, 'top_p': self.top_p, 'temp': self.temp, 'n_batch': self.
n_batch, 'repeat_penalty': self.repeat_penalty, 'repeat_last_n': self.
repeat_last_n} | def _default_params(self) ->Dict[str, Any]:
return {'max_tokens': self.max_tokens, 'n_predict': self.n_predict,
'top_k': self.top_k, 'top_p': self.top_p, 'temp': self.temp,
'n_batch': self.n_batch, 'repeat_penalty': self.repeat_penalty,
'repeat_last_n': self.repeat_last_n} | null |
test_runnable_branch_stream_with_callbacks | """Verify that stream works for RunnableBranch when using callbacks."""
tracer = FakeTracer()
def raise_value_error(x: str) ->Any:
"""Raise a value error."""
raise ValueError(f'x is {x}')
llm_res = "i'm a textbot"
llm = FakeStreamingListLLM(responses=[llm_res], sleep=0.01)
branch = RunnableBranch[str, Any]((lambda x: x == 'error',
raise_value_error), (lambda x: x == 'hello', llm), lambda x: x)
config: RunnableConfig = {'callbacks': [tracer]}
assert list(branch.stream('hello', config=config)) == list(llm_res)
assert len(tracer.runs) == 1
assert tracer.runs[0].error is None
assert tracer.runs[0].outputs == {'output': llm_res}
with pytest.raises(ValueError):
for _ in branch.stream('error', config=config):
pass
assert len(tracer.runs) == 2
assert "ValueError('x is error')" in str(tracer.runs[1].error)
assert tracer.runs[1].outputs is None
assert list(branch.stream('bye', config=config)) == ['bye']
assert len(tracer.runs) == 3
assert tracer.runs[2].error is None
assert tracer.runs[2].outputs == {'output': 'bye'} | def test_runnable_branch_stream_with_callbacks() ->None:
"""Verify that stream works for RunnableBranch when using callbacks."""
tracer = FakeTracer()
def raise_value_error(x: str) ->Any:
"""Raise a value error."""
raise ValueError(f'x is {x}')
llm_res = "i'm a textbot"
llm = FakeStreamingListLLM(responses=[llm_res], sleep=0.01)
branch = RunnableBranch[str, Any]((lambda x: x == 'error',
raise_value_error), (lambda x: x == 'hello', llm), lambda x: x)
config: RunnableConfig = {'callbacks': [tracer]}
assert list(branch.stream('hello', config=config)) == list(llm_res)
assert len(tracer.runs) == 1
assert tracer.runs[0].error is None
assert tracer.runs[0].outputs == {'output': llm_res}
with pytest.raises(ValueError):
for _ in branch.stream('error', config=config):
pass
assert len(tracer.runs) == 2
assert "ValueError('x is error')" in str(tracer.runs[1].error)
assert tracer.runs[1].outputs is None
assert list(branch.stream('bye', config=config)) == ['bye']
assert len(tracer.runs) == 3
assert tracer.runs[2].error is None
assert tracer.runs[2].outputs == {'output': 'bye'} | Verify that stream works for RunnableBranch when using callbacks. |
_buffer_to_array | return np.frombuffer(buffer, dtype=dtype).tolist() | def _buffer_to_array(buffer: bytes, dtype: Any=np.float32) ->List[float]:
return np.frombuffer(buffer, dtype=dtype).tolist() | null |
delete | """Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
if not ids or len(ids) == 0:
return True
from google.cloud import bigquery
job_config = bigquery.QueryJobConfig(query_parameters=[bigquery.
ArrayQueryParameter('ids', 'STRING', ids)])
self.bq_client.query(
f"""
DELETE FROM `{self.full_table_id}` WHERE {self.doc_id_field}
IN UNNEST(@ids)
"""
, job_config=job_config).result()
return True | def delete(self, ids: Optional[List[str]]=None, **kwargs: Any) ->Optional[bool
]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
if not ids or len(ids) == 0:
return True
from google.cloud import bigquery
job_config = bigquery.QueryJobConfig(query_parameters=[bigquery.
ArrayQueryParameter('ids', 'STRING', ids)])
self.bq_client.query(
f"""
DELETE FROM `{self.full_table_id}` WHERE {self.doc_id_field}
IN UNNEST(@ids)
"""
, job_config=job_config).result()
return True | Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented. |
configurable_fields | return self.default.configurable_fields(**{**self.fields, **kwargs}) | def configurable_fields(self, **kwargs: AnyConfigurableField
) ->RunnableSerializable[Input, Output]:
return self.default.configurable_fields(**{**self.fields, **kwargs}) | null |
get_lc_namespace | """Get the namespace of the langchain object."""
return ['langchain', 'schema', 'messages'] | @classmethod
def get_lc_namespace(cls) ->List[str]:
"""Get the namespace of the langchain object."""
return ['langchain', 'schema', 'messages'] | Get the namespace of the langchain object. |
_build_payload | payload: Dict[str, Any] = {'messages': [{'role': 'user', 'content': m} for
m in messages], 'profanity_check': self.profanity}
if self.temperature is not None:
payload['temperature'] = self.temperature
if self.max_tokens is not None:
payload['max_tokens'] = self.max_tokens
if self.model:
payload['model'] = self.model
if self.verbose:
logger.info('Giga request: %s', payload)
return payload | def _build_payload(self, messages: List[str]) ->Dict[str, Any]:
payload: Dict[str, Any] = {'messages': [{'role': 'user', 'content': m} for
m in messages], 'profanity_check': self.profanity}
if self.temperature is not None:
payload['temperature'] = self.temperature
if self.max_tokens is not None:
payload['max_tokens'] = self.max_tokens
if self.model:
payload['model'] = self.model
if self.verbose:
logger.info('Giga request: %s', payload)
return payload | null |
test_visit_comparison | comp = Comparison(comparator=Comparator.LT, attribute='foo', value=['1', '2'])
expected = {'foo': {'$lt': ['1', '2']}}
actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
assert expected == actual | def test_visit_comparison() ->None:
comp = Comparison(comparator=Comparator.LT, attribute='foo', value=['1',
'2'])
expected = {'foo': {'$lt': ['1', '2']}}
actual = DEFAULT_TRANSLATOR.visit_comparison(comp)
assert expected == actual | null |
get_sub_prompts | """Get the sub prompts for llm call."""
if stop is not None:
if 'stop' in params:
raise ValueError('`stop` found in both the input and default params.')
params['stop'] = stop
if params['max_tokens'] == -1:
if len(prompts) != 1:
raise ValueError(
'max_tokens set to -1 not supported for multiple inputs.')
params['max_tokens'] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [prompts[i:i + self.batch_size] for i in range(0, len(prompts
), self.batch_size)]
return sub_prompts | def get_sub_prompts(self, params: Dict[str, Any], prompts: List[str], stop:
Optional[List[str]]=None) ->List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
if 'stop' in params:
raise ValueError(
'`stop` found in both the input and default params.')
params['stop'] = stop
if params['max_tokens'] == -1:
if len(prompts) != 1:
raise ValueError(
'max_tokens set to -1 not supported for multiple inputs.')
params['max_tokens'] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [prompts[i:i + self.batch_size] for i in range(0, len(
prompts), self.batch_size)]
return sub_prompts | Get the sub prompts for llm call. |
_determine_prediction_key | prediction_key = None
if config.prediction_key:
prediction_key = config.prediction_key
if run_outputs and prediction_key not in run_outputs:
logger.warning(
f"Prediction key {prediction_key} not in chain's specified output keys {run_outputs}. Evaluation behavior may be undefined."
)
elif run_outputs and len(run_outputs) == 1:
prediction_key = run_outputs[0]
elif run_outputs is not None and len(run_outputs) > 1:
logger.warning(
f'Chain expects multiple output keys: {run_outputs}, Evaluation behavior may be undefined. Specify a prediction_key in the RunEvalConfig to avoid this warning.'
)
return prediction_key | def _determine_prediction_key(config: smith_eval.RunEvalConfig, run_outputs:
Optional[List[str]]) ->Optional[str]:
prediction_key = None
if config.prediction_key:
prediction_key = config.prediction_key
if run_outputs and prediction_key not in run_outputs:
logger.warning(
f"Prediction key {prediction_key} not in chain's specified output keys {run_outputs}. Evaluation behavior may be undefined."
)
elif run_outputs and len(run_outputs) == 1:
prediction_key = run_outputs[0]
elif run_outputs is not None and len(run_outputs) > 1:
logger.warning(
f'Chain expects multiple output keys: {run_outputs}, Evaluation behavior may be undefined. Specify a prediction_key in the RunEvalConfig to avoid this warning.'
)
return prediction_key | null |
_reset | for k, v in self.metrics.items():
self.metrics[k] = 0 | def _reset(self) ->None:
for k, v in self.metrics.items():
self.metrics[k] = 0 | null |
as_retriever | tags = kwargs.pop('tags', None) or []
tags.extend(self._get_retriever_tags())
return RedisVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags) | def as_retriever(self, **kwargs: Any) ->RedisVectorStoreRetriever:
tags = kwargs.pop('tags', None) or []
tags.extend(self._get_retriever_tags())
return RedisVectorStoreRetriever(vectorstore=self, **kwargs, tags=tags) | null |
test_invocation_params_stop_sequences | llm = HuggingFaceTextGenInference()
assert llm._default_params['stop_sequences'] == []
runtime_stop = None
assert llm._invocation_params(runtime_stop)['stop_sequences'] == []
assert llm._default_params['stop_sequences'] == []
runtime_stop = ['stop']
assert llm._invocation_params(runtime_stop)['stop_sequences'] == ['stop']
assert llm._default_params['stop_sequences'] == []
llm = HuggingFaceTextGenInference(stop_sequences=['.'])
runtime_stop = ['stop']
assert llm._invocation_params(runtime_stop)['stop_sequences'] == ['.', 'stop']
assert llm._default_params['stop_sequences'] == ['.'] | def test_invocation_params_stop_sequences() ->None:
llm = HuggingFaceTextGenInference()
assert llm._default_params['stop_sequences'] == []
runtime_stop = None
assert llm._invocation_params(runtime_stop)['stop_sequences'] == []
assert llm._default_params['stop_sequences'] == []
runtime_stop = ['stop']
assert llm._invocation_params(runtime_stop)['stop_sequences'] == ['stop']
assert llm._default_params['stop_sequences'] == []
llm = HuggingFaceTextGenInference(stop_sequences=['.'])
runtime_stop = ['stop']
assert llm._invocation_params(runtime_stop)['stop_sequences'] == ['.',
'stop']
assert llm._default_params['stop_sequences'] == ['.'] | null |
test_public_api | """Test for regressions or changes in the public API."""
assert set(public_api) == set(_EXPECTED) | def test_public_api() ->None:
"""Test for regressions or changes in the public API."""
assert set(public_api) == set(_EXPECTED) | Test for regressions or changes in the public API. |
_get_prompt_input_key | """Get the input key for the prompt."""
if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key | def _get_prompt_input_key(self, inputs: Dict[str, Any]) ->str:
"""Get the input key for the prompt."""
if self.input_key is None:
return get_prompt_input_key(inputs, self.memory_variables)
return self.input_key | Get the input key for the prompt. |
on_decision | self.denom += 1 | def on_decision(self) ->None:
self.denom += 1 | null |
test_parse_int_value | _test_parse_value(x) | @pytest.mark.parametrize('x', (-1, 0, 1000000))
def test_parse_int_value(x: int) ->None:
_test_parse_value(x) | null |
_convert_run_to_wb_span | """Base utility to create a span from a run.
:param run: The run to convert.
:return: The converted Span.
"""
attributes = {**run.extra} if run.extra else {}
attributes['execution_order'] = run.execution_order
return self.trace_tree.Span(span_id=str(run.id) if run.id is not None else
None, name=run.name, start_time_ms=int(run.start_time.timestamp() *
1000), end_time_ms=int(run.end_time.timestamp() * 1000) if run.end_time
is not None else None, status_code=self.trace_tree.StatusCode.SUCCESS if
run.error is None else self.trace_tree.StatusCode.ERROR, status_message
=run.error, attributes=attributes) | def _convert_run_to_wb_span(self, run: Run) ->'Span':
"""Base utility to create a span from a run.
:param run: The run to convert.
:return: The converted Span.
"""
attributes = {**run.extra} if run.extra else {}
attributes['execution_order'] = run.execution_order
return self.trace_tree.Span(span_id=str(run.id) if run.id is not None else
None, name=run.name, start_time_ms=int(run.start_time.timestamp() *
1000), end_time_ms=int(run.end_time.timestamp() * 1000) if run.
end_time is not None else None, status_code=self.trace_tree.
StatusCode.SUCCESS if run.error is None else self.trace_tree.
StatusCode.ERROR, status_message=run.error, attributes=attributes) | Base utility to create a span from a run.
:param run: The run to convert.
:return: The converted Span. |
output_keys | return ['feedback'] | @property
def output_keys(self) ->List[str]:
return ['feedback'] | null |
_default_params | """Get the default parameters for calling GPTRouter API."""
return {'max_tokens': self.max_tokens, 'stream': self.streaming, 'n': self.
n, 'temperature': self.temperature, **self.model_kwargs} | @property
def _default_params(self) ->Dict[str, Any]:
"""Get the default parameters for calling GPTRouter API."""
return {'max_tokens': self.max_tokens, 'stream': self.streaming, 'n':
self.n, 'temperature': self.temperature, **self.model_kwargs} | Get the default parameters for calling GPTRouter API. |
_check_for_cluster | import redis
try:
cluster_info = redis_client.info('cluster')
return cluster_info['cluster_enabled'] == 1
except redis.exceptions.RedisError:
return False | def _check_for_cluster(redis_client: RedisType) ->bool:
import redis
try:
cluster_info = redis_client.info('cluster')
return cluster_info['cluster_enabled'] == 1
except redis.exceptions.RedisError:
return False | null |
test_visit_structured_query | query = 'What is the capital of France?'
structured_query = StructuredQuery(query=query, filter=None)
expected: Tuple[str, Dict] = (query, {})
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
comp = Comparison(comparator=Comparator.EQ, attribute='foo', value='1')
structured_query = StructuredQuery(query=query, filter=comp)
expected = query, {'where_filter': {'path': ['foo'], 'operator': 'Equal',
'valueText': '1'}}
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
op = Operation(operator=Operator.AND, arguments=[Comparison(comparator=
Comparator.EQ, attribute='foo', value=2), Comparison(comparator=
Comparator.EQ, attribute='bar', value='baz')])
structured_query = StructuredQuery(query=query, filter=op)
expected = query, {'where_filter': {'operator': 'And', 'operands': [{'path':
['foo'], 'operator': 'Equal', 'valueInt': 2}, {'path': ['bar'],
'operator': 'Equal', 'valueText': 'baz'}]}}
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual | def test_visit_structured_query() ->None:
query = 'What is the capital of France?'
structured_query = StructuredQuery(query=query, filter=None)
expected: Tuple[str, Dict] = (query, {})
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
comp = Comparison(comparator=Comparator.EQ, attribute='foo', value='1')
structured_query = StructuredQuery(query=query, filter=comp)
expected = query, {'where_filter': {'path': ['foo'], 'operator':
'Equal', 'valueText': '1'}}
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual
op = Operation(operator=Operator.AND, arguments=[Comparison(comparator=
Comparator.EQ, attribute='foo', value=2), Comparison(comparator=
Comparator.EQ, attribute='bar', value='baz')])
structured_query = StructuredQuery(query=query, filter=op)
expected = query, {'where_filter': {'operator': 'And', 'operands': [{
'path': ['foo'], 'operator': 'Equal', 'valueInt': 2}, {'path': [
'bar'], 'operator': 'Equal', 'valueText': 'baz'}]}}
actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query)
assert expected == actual | null |
test_semantic_hybrid_search | """Test end to end construction and search."""
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model=model, chunk_size=1)
vector_store: AzureSearch = AzureSearch(azure_search_endpoint=
vector_store_address, azure_search_key=vector_store_password,
index_name=index_name, embedding_function=embeddings.embed_query,
semantic_configuration_name='default')
vector_store.add_texts(['Test 1', 'Test 2', 'Test 3'], [{'title': 'Title 1',
'any_metadata': 'Metadata 1'}, {'title': 'Title 2', 'any_metadata':
'Metadata 2'}, {'title': 'Title 3', 'any_metadata': 'Metadata 3'}])
time.sleep(1)
res = vector_store.semantic_hybrid_search(query="What's Azure Search?", k=3)
assert len(res) == 3 | def test_semantic_hybrid_search() ->None:
"""Test end to end construction and search."""
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model=model, chunk_size=1)
vector_store: AzureSearch = AzureSearch(azure_search_endpoint=
vector_store_address, azure_search_key=vector_store_password,
index_name=index_name, embedding_function=embeddings.embed_query,
semantic_configuration_name='default')
vector_store.add_texts(['Test 1', 'Test 2', 'Test 3'], [{'title':
'Title 1', 'any_metadata': 'Metadata 1'}, {'title': 'Title 2',
'any_metadata': 'Metadata 2'}, {'title': 'Title 3', 'any_metadata':
'Metadata 3'}])
time.sleep(1)
res = vector_store.semantic_hybrid_search(query="What's Azure Search?", k=3
)
assert len(res) == 3 | Test end to end construction and search. |
_check_response | if 'data' not in response:
raise RuntimeError(f'Voyage API Error. Message: {json.dumps(response)}')
return response | def _check_response(response: dict) ->dict:
if 'data' not in response:
raise RuntimeError(f'Voyage API Error. Message: {json.dumps(response)}'
)
return response | null |
test_edenai_call_with_old_params | """
Test simple call to edenai with using `params`
to pass optional parameters to api
"""
llm = EdenAI(provider='openai', params={'temperature': 0.2, 'max_tokens': 250})
output = llm('Say foo:')
assert llm._llm_type == 'edenai'
assert llm.feature == 'text'
assert llm.subfeature == 'generation'
assert isinstance(output, str) | def test_edenai_call_with_old_params() ->None:
"""
Test simple call to edenai with using `params`
to pass optional parameters to api
"""
llm = EdenAI(provider='openai', params={'temperature': 0.2,
'max_tokens': 250})
output = llm('Say foo:')
assert llm._llm_type == 'edenai'
assert llm.feature == 'text'
assert llm.subfeature == 'generation'
assert isinstance(output, str) | Test simple call to edenai with using `params`
to pass optional parameters to api |
test_json_distance_evaluator_evaluate_strings_simple_diff | prediction = '{"a": 1}'
reference = '{"a": 2}'
result = json_distance_evaluator._evaluate_strings(prediction=prediction,
reference=reference)
pytest.approx(1 / 7, result['score']) | @pytest.mark.requires('rapidfuzz')
def test_json_distance_evaluator_evaluate_strings_simple_diff(
json_distance_evaluator: JsonEditDistanceEvaluator) ->None:
prediction = '{"a": 1}'
reference = '{"a": 2}'
result = json_distance_evaluator._evaluate_strings(prediction=
prediction, reference=reference)
pytest.approx(1 / 7, result['score']) | null |
test_deep_stream | prompt = SystemMessagePromptTemplate.from_template('You are a nice assistant.'
) + '{question}'
llm = FakeStreamingListLLM(responses=['foo-lish'])
chain = prompt | llm | StrOutputParser()
stream = chain.stream({'question': 'What up'})
chunks = []
for chunk in stream:
chunks.append(chunk)
assert len(chunks) == len('foo-lish')
assert ''.join(chunks) == 'foo-lish'
chunks = []
for chunk in (chain | RunnablePassthrough()).stream({'question': 'What up'}):
chunks.append(chunk)
assert len(chunks) == len('foo-lish')
assert ''.join(chunks) == 'foo-lish' | def test_deep_stream() ->None:
prompt = SystemMessagePromptTemplate.from_template(
'You are a nice assistant.') + '{question}'
llm = FakeStreamingListLLM(responses=['foo-lish'])
chain = prompt | llm | StrOutputParser()
stream = chain.stream({'question': 'What up'})
chunks = []
for chunk in stream:
chunks.append(chunk)
assert len(chunks) == len('foo-lish')
assert ''.join(chunks) == 'foo-lish'
chunks = []
for chunk in (chain | RunnablePassthrough()).stream({'question': 'What up'}
):
chunks.append(chunk)
assert len(chunks) == len('foo-lish')
assert ''.join(chunks) == 'foo-lish' | null |
load | """
Load and return all Documents from the provided URLs.
Returns:
List[Document]: A list of Document objects
containing the scraped content from each URL.
"""
return list(self.lazy_load()) | def load(self) ->List[Document]:
"""
Load and return all Documents from the provided URLs.
Returns:
List[Document]: A list of Document objects
containing the scraped content from each URL.
"""
return list(self.lazy_load()) | Load and return all Documents from the provided URLs.
Returns:
List[Document]: A list of Document objects
containing the scraped content from each URL. |
_import_google_finance | from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
return GoogleFinanceAPIWrapper | def _import_google_finance() ->Any:
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
return GoogleFinanceAPIWrapper | null |
test_redis_vector_field_validation | """Test validation for RedisVectorField's datatype."""
from langchain_core.pydantic_v1 import ValidationError
with pytest.raises(ValidationError):
RedisVectorField(name='vector', dims=128, algorithm='INVALID_ALGO',
datatype='INVALID_TYPE')
vector_field = RedisVectorField(name='vector', dims=128, algorithm=
'SOME_ALGO', datatype='FLOAT32')
assert vector_field.datatype == 'FLOAT32' | def test_redis_vector_field_validation() ->None:
"""Test validation for RedisVectorField's datatype."""
from langchain_core.pydantic_v1 import ValidationError
with pytest.raises(ValidationError):
RedisVectorField(name='vector', dims=128, algorithm='INVALID_ALGO',
datatype='INVALID_TYPE')
vector_field = RedisVectorField(name='vector', dims=128, algorithm=
'SOME_ALGO', datatype='FLOAT32')
assert vector_field.datatype == 'FLOAT32' | Test validation for RedisVectorField's datatype. |
save_context | """Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self.buffer = self.predict_new_summary(self.chat_memory.messages[-2:], self
.buffer) | def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) ->None:
"""Save context from this conversation to buffer."""
super().save_context(inputs, outputs)
self.buffer = self.predict_new_summary(self.chat_memory.messages[-2:],
self.buffer) | Save context from this conversation to buffer. |
test_from_texts_with_metadatas_benchmark | """Test end to end construction and search."""
texts = [document.page_content for document in documents] * data_multiplier
uuids = [uuid.uuid4().hex for _ in range(len(texts))]
metadatas = [{'page': i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(texts, embedding_openai, ids=uuids,
metadatas=metadatas, index_name=index_name, namespace=namespace_name,
pool_threads=pool_threads, batch_size=batch_size, embeddings_chunk_size
=embeddings_chunk_size)
query = 'What did the president say about Ketanji Brown Jackson'
_ = docsearch.similarity_search(query, k=1, namespace=namespace_name) | @pytest.mark.skipif(reason='slow to run for benchmark')
@pytest.mark.parametrize(
'pool_threads,batch_size,embeddings_chunk_size,data_multiplier', [(1,
32, 32, 1000), (1, 32, 1000, 1000), (4, 32, 1000, 1000), (20, 64, 5000,
1000)])
def test_from_texts_with_metadatas_benchmark(self, pool_threads: int,
batch_size: int, embeddings_chunk_size: int, data_multiplier: int,
documents: List[Document], embedding_openai: OpenAIEmbeddings) ->None:
"""Test end to end construction and search."""
texts = [document.page_content for document in documents] * data_multiplier
uuids = [uuid.uuid4().hex for _ in range(len(texts))]
metadatas = [{'page': i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(texts, embedding_openai, ids=uuids,
metadatas=metadatas, index_name=index_name, namespace=
namespace_name, pool_threads=pool_threads, batch_size=batch_size,
embeddings_chunk_size=embeddings_chunk_size)
query = 'What did the president say about Ketanji Brown Jackson'
_ = docsearch.similarity_search(query, k=1, namespace=namespace_name) | Test end to end construction and search. |
_identifying_params | """Get the identifying parameters."""
return {**{'model_url': self.model_url, 'user_id': self.user_id, 'app_id':
self.app_id, 'model_id': self.model_id}} | @property
def _identifying_params(self) ->Dict[str, Any]:
"""Get the identifying parameters."""
return {**{'model_url': self.model_url, 'user_id': self.user_id,
'app_id': self.app_id, 'model_id': self.model_id}} | Get the identifying parameters. |
test_deeplake_overwrite_flag | """Test overwrite behavior"""
import deeplake
dataset_path = './tests/persist_dir'
if deeplake.exists(dataset_path):
deeplake.delete(dataset_path)
texts = ['foo', 'bar', 'baz']
docsearch = DeepLake.from_texts(dataset_path=dataset_path, texts=texts,
embedding=FakeEmbeddings())
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')]
docsearch = DeepLake(dataset_path=dataset_path, embedding_function=
FakeEmbeddings())
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')]
docsearch = DeepLake(dataset_path=dataset_path, embedding_function=
FakeEmbeddings(), overwrite=False)
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')]
docsearch = DeepLake(dataset_path=dataset_path, embedding_function=
FakeEmbeddings(), overwrite=True)
with pytest.raises(ValueError):
output = docsearch.similarity_search('foo', k=1) | def test_deeplake_overwrite_flag() ->None:
"""Test overwrite behavior"""
import deeplake
dataset_path = './tests/persist_dir'
if deeplake.exists(dataset_path):
deeplake.delete(dataset_path)
texts = ['foo', 'bar', 'baz']
docsearch = DeepLake.from_texts(dataset_path=dataset_path, texts=texts,
embedding=FakeEmbeddings())
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')]
docsearch = DeepLake(dataset_path=dataset_path, embedding_function=
FakeEmbeddings())
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')]
docsearch = DeepLake(dataset_path=dataset_path, embedding_function=
FakeEmbeddings(), overwrite=False)
output = docsearch.similarity_search('foo', k=1)
assert output == [Document(page_content='foo')]
docsearch = DeepLake(dataset_path=dataset_path, embedding_function=
FakeEmbeddings(), overwrite=True)
with pytest.raises(ValueError):
output = docsearch.similarity_search('foo', k=1) | Test overwrite behavior |
test_api_key_is_secret_string | llm = AI21(ai21_api_key='secret-api-key')
assert isinstance(llm.ai21_api_key, SecretStr) | def test_api_key_is_secret_string() ->None:
llm = AI21(ai21_api_key='secret-api-key')
assert isinstance(llm.ai21_api_key, SecretStr) | null |
validate_tools | selected_tools = values.get('selected_tools') or []
for tool_name in selected_tools:
if tool_name not in _FILE_TOOLS:
raise ValueError(
f'File Tool of name {tool_name} not supported. Permitted tools: {list(_FILE_TOOLS)}'
)
return values | @root_validator
def validate_tools(cls, values: dict) ->dict:
selected_tools = values.get('selected_tools') or []
for tool_name in selected_tools:
if tool_name not in _FILE_TOOLS:
raise ValueError(
f'File Tool of name {tool_name} not supported. Permitted tools: {list(_FILE_TOOLS)}'
)
return values | null |
_create_retry_decorator | import openai
return retry(reraise=True, stop=stop_after_attempt(embeddings.max_retries),
wait=wait_exponential(multiplier=1, min=embeddings.retry_min_seconds,
max=embeddings.retry_max_seconds), retry=retry_if_exception_type(openai
.error.Timeout) | retry_if_exception_type(openai.error.APIError) |
retry_if_exception_type(openai.error.APIConnectionError) |
retry_if_exception_type(openai.error.RateLimitError) |
retry_if_exception_type(openai.error.ServiceUnavailableError),
before_sleep=before_sleep_log(logger, logging.WARNING)) | def _create_retry_decorator(embeddings: OpenAIEmbeddings) ->Callable[[Any], Any
]:
import openai
return retry(reraise=True, stop=stop_after_attempt(embeddings.
max_retries), wait=wait_exponential(multiplier=1, min=embeddings.
retry_min_seconds, max=embeddings.retry_max_seconds), retry=
retry_if_exception_type(openai.error.Timeout) |
retry_if_exception_type(openai.error.APIError) |
retry_if_exception_type(openai.error.APIConnectionError) |
retry_if_exception_type(openai.error.RateLimitError) |
retry_if_exception_type(openai.error.ServiceUnavailableError),
before_sleep=before_sleep_log(logger, logging.WARNING)) | null |
_dict2document | summary = doc.pop('Summary')
return Document(page_content=summary, metadata=doc) | def _dict2document(self, doc: dict) ->Document:
summary = doc.pop('Summary')
return Document(page_content=summary, metadata=doc) | null |
test_run_returns_no_result | """Test that gives no result."""
output = api_client.run('1605.08386WWW')
assert 'No good PubMed Result was found' == output | def test_run_returns_no_result(api_client: PubMedAPIWrapper) ->None:
"""Test that gives no result."""
output = api_client.run('1605.08386WWW')
assert 'No good PubMed Result was found' == output | Test that gives no result. |
test_load_fail_no_func | """Test that fails to load"""
with pytest.raises(ValidationError) as exc_info:
TensorflowDatasetLoader(dataset_name='mlqa/en', split_name='test',
load_max_docs=MAX_DOCS)
assert 'Please provide a function' in str(exc_info.value) | def test_load_fail_no_func() ->None:
"""Test that fails to load"""
with pytest.raises(ValidationError) as exc_info:
TensorflowDatasetLoader(dataset_name='mlqa/en', split_name='test',
load_max_docs=MAX_DOCS)
assert 'Please provide a function' in str(exc_info.value) | Test that fails to load |
trim_first_node | """Remove the first node if it exists and has a single outgoing edge,
ie. if removing it would not leave the graph without a "first" node."""
first_node = self.first_node()
if first_node:
if len(self.nodes) == 1 or len([edge for edge in self.edges if edge.
source == first_node.id]) == 1:
self.remove_node(first_node) | def trim_first_node(self) ->None:
"""Remove the first node if it exists and has a single outgoing edge,
ie. if removing it would not leave the graph without a "first" node."""
first_node = self.first_node()
if first_node:
if len(self.nodes) == 1 or len([edge for edge in self.edges if edge
.source == first_node.id]) == 1:
self.remove_node(first_node) | Remove the first node if it exists and has a single outgoing edge,
ie. if removing it would not leave the graph without a "first" node. |
create_file | """
Creates a new file on the Github repo
Parameters:
file_query(str): a string which contains the file path
and the file contents. The file path is the first line
in the string, and the contents are the rest of the string.
For example, "hello_world.md
# Hello World!"
Returns:
str: A success or failure message
"""
if self.active_branch == self.github_base_branch:
return (
f"You're attempting to commit to the directly to the{self.github_base_branch} branch, which is protected. Please create a new branch and try again."
)
file_path = file_query.split('\n')[0]
file_contents = file_query[len(file_path) + 2:]
try:
try:
file = self.github_repo_instance.get_contents(file_path, ref=self.
active_branch)
if file:
return (
f'File already exists at `{file_path}` on branch `{self.active_branch}`. You must use `update_file` to modify it.'
)
except Exception:
pass
self.github_repo_instance.create_file(path=file_path, message='Create ' +
file_path, content=file_contents, branch=self.active_branch)
return 'Created file ' + file_path
except Exception as e:
return 'Unable to make file due to error:\n' + str(e) | def create_file(self, file_query: str) ->str:
"""
Creates a new file on the Github repo
Parameters:
file_query(str): a string which contains the file path
and the file contents. The file path is the first line
in the string, and the contents are the rest of the string.
For example, "hello_world.md
# Hello World!"
Returns:
str: A success or failure message
"""
if self.active_branch == self.github_base_branch:
return (
f"You're attempting to commit to the directly to the{self.github_base_branch} branch, which is protected. Please create a new branch and try again."
)
file_path = file_query.split('\n')[0]
file_contents = file_query[len(file_path) + 2:]
try:
try:
file = self.github_repo_instance.get_contents(file_path, ref=
self.active_branch)
if file:
return (
f'File already exists at `{file_path}` on branch `{self.active_branch}`. You must use `update_file` to modify it.'
)
except Exception:
pass
self.github_repo_instance.create_file(path=file_path, message=
'Create ' + file_path, content=file_contents, branch=self.
active_branch)
return 'Created file ' + file_path
except Exception as e:
return 'Unable to make file due to error:\n' + str(e) | Creates a new file on the Github repo
Parameters:
file_query(str): a string which contains the file path
and the file contents. The file path is the first line
in the string, and the contents are the rest of the string.
For example, "hello_world.md
# Hello World!"
Returns:
str: A success or failure message
|
check_blob_is_valid | """Verify that either data or path is provided."""
if 'data' not in values and 'path' not in values:
raise ValueError('Either data or path must be provided')
return values | @root_validator(pre=True)
def check_blob_is_valid(cls, values: Mapping[str, Any]) ->Mapping[str, Any]:
"""Verify that either data or path is provided."""
if 'data' not in values and 'path' not in values:
raise ValueError('Either data or path must be provided')
return values | Verify that either data or path is provided. |
ingest_documents | """
Ingest PDF to Redis from the data/ directory that
contains Edgar 10k filings data for Nike.
"""
company_name = 'Nike'
data_path = 'data/'
doc = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
print('Parsing 10k filing doc for NIKE', doc)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500,
chunk_overlap=100, add_start_index=True)
loader = UnstructuredFileLoader(doc, mode='single', strategy='fast')
chunks = loader.load_and_split(text_splitter)
print('Done preprocessing. Created', len(chunks), 'chunks of the original pdf')
embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
_ = Redis.from_texts(texts=[(f'Company: {company_name}. ' + chunk.
page_content) for chunk in chunks], metadatas=[chunk.metadata for chunk in
chunks], embedding=embedder, index_name=INDEX_NAME, index_schema=
INDEX_SCHEMA, redis_url=REDIS_URL) | def ingest_documents():
"""
Ingest PDF to Redis from the data/ directory that
contains Edgar 10k filings data for Nike.
"""
company_name = 'Nike'
data_path = 'data/'
doc = [os.path.join(data_path, file) for file in os.listdir(data_path)][0]
print('Parsing 10k filing doc for NIKE', doc)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500,
chunk_overlap=100, add_start_index=True)
loader = UnstructuredFileLoader(doc, mode='single', strategy='fast')
chunks = loader.load_and_split(text_splitter)
print('Done preprocessing. Created', len(chunks),
'chunks of the original pdf')
embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
_ = Redis.from_texts(texts=[(f'Company: {company_name}. ' + chunk.
page_content) for chunk in chunks], metadatas=[chunk.metadata for
chunk in chunks], embedding=embedder, index_name=INDEX_NAME,
index_schema=INDEX_SCHEMA, redis_url=REDIS_URL) | Ingest PDF to Redis from the data/ directory that
contains Edgar 10k filings data for Nike. |
_stream | params = self._convert_prompt_msg_params(messages, **kwargs)
for res in self.client.do(**params):
if res:
msg = _convert_dict_to_message(res)
chunk = ChatGenerationChunk(text=res['result'], message=
AIMessageChunk(content=msg.content, role='assistant',
additional_kwargs=msg.additional_kwargs))
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk) | def _stream(self, messages: List[BaseMessage], stop: Optional[List[str]]=
None, run_manager: Optional[CallbackManagerForLLMRun]=None, **kwargs: Any
) ->Iterator[ChatGenerationChunk]:
params = self._convert_prompt_msg_params(messages, **kwargs)
for res in self.client.do(**params):
if res:
msg = _convert_dict_to_message(res)
chunk = ChatGenerationChunk(text=res['result'], message=
AIMessageChunk(content=msg.content, role='assistant',
additional_kwargs=msg.additional_kwargs))
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk) | null |
run | """
Run any query statement in jaguardb
Args:
query (str): query statement to jaguardb
Returns:
None for invalid token, or
json result string
"""
if self._token == '':
logger.error(f'E0005 error run({query})')
return {}
resp = self._jag.post(query, self._token, withFile)
txt = resp.text
try:
js = json.loads(txt)
return js
except Exception:
return {} | def run(self, query: str, withFile: bool=False) ->dict:
"""
Run any query statement in jaguardb
Args:
query (str): query statement to jaguardb
Returns:
None for invalid token, or
json result string
"""
if self._token == '':
logger.error(f'E0005 error run({query})')
return {}
resp = self._jag.post(query, self._token, withFile)
txt = resp.text
try:
js = json.loads(txt)
return js
except Exception:
return {} | Run any query statement in jaguardb
Args:
query (str): query statement to jaguardb
Returns:
None for invalid token, or
json result string |
_combine_llm_outputs | overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
continue
token_usage = output['token_usage']
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {'token_usage': overall_token_usage} | def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) ->dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
continue
token_usage = output['token_usage']
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
return {'token_usage': overall_token_usage} | null |
test_iterative_text_splitter_discard_separator | chunk_size = 5
output = __test_iterative_text_splitter(chunk_size=chunk_size,
keep_separator=False)
assert output == ['....5', '..3', '...4', '....5', '...'] | def test_iterative_text_splitter_discard_separator() ->None:
chunk_size = 5
output = __test_iterative_text_splitter(chunk_size=chunk_size,
keep_separator=False)
assert output == ['....5', '..3', '...4', '....5', '...'] | null |
test_json_equality_evaluator_evaluate_strings_custom_operator_not_equal | def operator(x: dict, y: dict) ->bool:
return x['a'] == y['a']
evaluator = JsonEqualityEvaluator(operator=operator)
prediction = '{"a": 1}'
reference = '{"a": 2}'
result = evaluator.evaluate_strings(prediction=prediction, reference=reference)
assert result == {'score': False} | def test_json_equality_evaluator_evaluate_strings_custom_operator_not_equal(
) ->None:
def operator(x: dict, y: dict) ->bool:
return x['a'] == y['a']
evaluator = JsonEqualityEvaluator(operator=operator)
prediction = '{"a": 1}'
reference = '{"a": 2}'
result = evaluator.evaluate_strings(prediction=prediction, reference=
reference)
assert result == {'score': False} | null |
on_llm_end | """Run when LLM ends running.""" | def on_llm_end(self, response: LLMResult, **kwargs: Any) ->None:
"""Run when LLM ends running.""" | Run when LLM ends running. |
test_include_types2 | """Test include types from schema."""
url = os.environ.get('NEO4J_URI')
username = os.environ.get('NEO4J_USERNAME')
password = os.environ.get('NEO4J_PASSWORD')
assert url is not None
assert username is not None
assert password is not None
graph = Neo4jGraph(url=url, username=username, password=password)
graph.query('MATCH (n) DETACH DELETE n')
graph.query(
"CREATE (a:Actor {name:'Bruce Willis'})-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})<-[:DIRECTED]-(p:Person {name:'John'})"
)
graph.refresh_schema()
chain = GraphCypherQAChain.from_llm(OpenAI(temperature=0), graph=graph,
include_types=['Movie', 'ACTED_IN'])
expected_schema = """Node properties are the following:
Movie {title: STRING}
Relationship properties are the following:
The relationships are the following:
"""
assert chain.graph_schema == expected_schema | def test_include_types2() ->None:
"""Test include types from schema."""
url = os.environ.get('NEO4J_URI')
username = os.environ.get('NEO4J_USERNAME')
password = os.environ.get('NEO4J_PASSWORD')
assert url is not None
assert username is not None
assert password is not None
graph = Neo4jGraph(url=url, username=username, password=password)
graph.query('MATCH (n) DETACH DELETE n')
graph.query(
"CREATE (a:Actor {name:'Bruce Willis'})-[:ACTED_IN]->(:Movie {title: 'Pulp Fiction'})<-[:DIRECTED]-(p:Person {name:'John'})"
)
graph.refresh_schema()
chain = GraphCypherQAChain.from_llm(OpenAI(temperature=0), graph=graph,
include_types=['Movie', 'ACTED_IN'])
expected_schema = """Node properties are the following:
Movie {title: STRING}
Relationship properties are the following:
The relationships are the following:
"""
assert chain.graph_schema == expected_schema | Test include types from schema. |
__eq__ | """Create a Numeric equality filter expression.
Args:
other (Union[int, float]): The value to filter on.
Example:
>>> from langchain_community.vectorstores.redis import RedisNum
>>> filter = RedisNum("zipcode") == 90210
"""
self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.EQ)
return RedisFilterExpression(str(self)) | @check_operator_misuse
def __eq__(self, other: Union[int, float]) ->'RedisFilterExpression':
"""Create a Numeric equality filter expression.
Args:
other (Union[int, float]): The value to filter on.
Example:
>>> from langchain_community.vectorstores.redis import RedisNum
>>> filter = RedisNum("zipcode") == 90210
"""
self._set_value(other, self.SUPPORTED_VAL_TYPES, RedisFilterOperator.EQ)
return RedisFilterExpression(str(self)) | Create a Numeric equality filter expression.
Args:
other (Union[int, float]): The value to filter on.
Example:
>>> from langchain_community.vectorstores.redis import RedisNum
>>> filter = RedisNum("zipcode") == 90210 |
format | """Format the prompt template.
Args:
**kwargs: Keyword arguments to use for formatting.
Returns:
Formatted message.
""" | @abstractmethod
def format(self, **kwargs: Any) ->BaseMessage:
"""Format the prompt template.
Args:
**kwargs: Keyword arguments to use for formatting.
Returns:
Formatted message.
""" | Format the prompt template.
Args:
**kwargs: Keyword arguments to use for formatting.
Returns:
Formatted message. |
_embed | payload = {'inputs': input}
headers = {'Authorization': f'{self.mosaicml_api_token}', 'Content-Type':
'application/json'}
try:
response = requests.post(self.endpoint_url, headers=headers, json=payload)
except requests.exceptions.RequestException as e:
raise ValueError(f'Error raised by inference endpoint: {e}')
try:
if response.status_code == 429:
if not is_retry:
import time
time.sleep(self.retry_sleep)
return self._embed(input, is_retry=True)
raise ValueError(
f"""Error raised by inference API: rate limit exceeded.
Response: {response.text}"""
)
parsed_response = response.json()
if isinstance(parsed_response, dict):
output_keys = ['data', 'output', 'outputs']
for key in output_keys:
if key in parsed_response:
output_item = parsed_response[key]
break
else:
raise ValueError(
f'No key data or output in response: {parsed_response}')
if isinstance(output_item, list) and isinstance(output_item[0], list):
embeddings = output_item
else:
embeddings = [output_item]
else:
raise ValueError(f'Unexpected response type: {parsed_response}')
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f'Error raised by inference API: {e}.\nResponse: {response.text}')
return embeddings | def _embed(self, input: List[Tuple[str, str]], is_retry: bool=False) ->List[
List[float]]:
payload = {'inputs': input}
headers = {'Authorization': f'{self.mosaicml_api_token}',
'Content-Type': 'application/json'}
try:
response = requests.post(self.endpoint_url, headers=headers, json=
payload)
except requests.exceptions.RequestException as e:
raise ValueError(f'Error raised by inference endpoint: {e}')
try:
if response.status_code == 429:
if not is_retry:
import time
time.sleep(self.retry_sleep)
return self._embed(input, is_retry=True)
raise ValueError(
f"""Error raised by inference API: rate limit exceeded.
Response: {response.text}"""
)
parsed_response = response.json()
if isinstance(parsed_response, dict):
output_keys = ['data', 'output', 'outputs']
for key in output_keys:
if key in parsed_response:
output_item = parsed_response[key]
break
else:
raise ValueError(
f'No key data or output in response: {parsed_response}')
if isinstance(output_item, list) and isinstance(output_item[0],
list):
embeddings = output_item
else:
embeddings = [output_item]
else:
raise ValueError(f'Unexpected response type: {parsed_response}')
except requests.exceptions.JSONDecodeError as e:
raise ValueError(
f'Error raised by inference API: {e}.\nResponse: {response.text}')
return embeddings | null |
get_num_tokens | tokenized_text = self.client.tokenize(text.encode('utf-8'))
return len(tokenized_text) | def get_num_tokens(self, text: str) ->int:
tokenized_text = self.client.tokenize(text.encode('utf-8'))
return len(tokenized_text) | null |
on_agent_finish | """Do nothing"""
pass | def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) ->None:
"""Do nothing"""
pass | Do nothing |
test_similarity_search_by_vector | index = mock_index(index_details)
index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE
vectorsearch = default_databricks_vector_search(index)
query_embedding = DEFAULT_EMBEDDING_MODEL.embed_query('foo')
filters = {'some filter': True}
limit = 7
search_result = vectorsearch.similarity_search_by_vector(query_embedding, k
=limit, filters=filters)
index.similarity_search.assert_called_once_with(columns=[
DEFAULT_PRIMARY_KEY, DEFAULT_TEXT_COLUMN], query_vector=query_embedding,
filters=filters, num_results=limit)
assert len(search_result) == len(fake_texts)
assert sorted([d.page_content for d in search_result]) == sorted(fake_texts)
assert all([(DEFAULT_PRIMARY_KEY in d.metadata) for d in search_result]) | @pytest.mark.requires('databricks', 'databricks.vector_search')
@pytest.mark.parametrize('index_details', [
DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, DIRECT_ACCESS_INDEX])
def test_similarity_search_by_vector(index_details: dict) ->None:
index = mock_index(index_details)
index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE
vectorsearch = default_databricks_vector_search(index)
query_embedding = DEFAULT_EMBEDDING_MODEL.embed_query('foo')
filters = {'some filter': True}
limit = 7
search_result = vectorsearch.similarity_search_by_vector(query_embedding,
k=limit, filters=filters)
index.similarity_search.assert_called_once_with(columns=[
DEFAULT_PRIMARY_KEY, DEFAULT_TEXT_COLUMN], query_vector=
query_embedding, filters=filters, num_results=limit)
assert len(search_result) == len(fake_texts)
assert sorted([d.page_content for d in search_result]) == sorted(fake_texts
)
assert all([(DEFAULT_PRIMARY_KEY in d.metadata) for d in search_result]) | null |
_convert_message_to_document | """
Convert a message to a Document object.
Args:
message (dict): A message in the form of a dictionary.
channel_name (str): The name of the channel the message belongs to.
Returns:
Document: A Document object representing the message.
"""
text = message.get('text', '')
metadata = self._get_message_metadata(message, channel_name)
return Document(page_content=text, metadata=metadata) | def _convert_message_to_document(self, message: dict, channel_name: str
) ->Document:
"""
Convert a message to a Document object.
Args:
message (dict): A message in the form of a dictionary.
channel_name (str): The name of the channel the message belongs to.
Returns:
Document: A Document object representing the message.
"""
text = message.get('text', '')
metadata = self._get_message_metadata(message, channel_name)
return Document(page_content=text, metadata=metadata) | Convert a message to a Document object.
Args:
message (dict): A message in the form of a dictionary.
channel_name (str): The name of the channel the message belongs to.
Returns:
Document: A Document object representing the message. |
test_fauna_loader | """Test Fauna loader."""
loader = FaunaLoader(query=self.valid_fql_query, page_content_field=self.
valid_page_content_field, secret=self.fauna_secret, metadata_fields=
self.valid_metadata_fields)
docs = loader.load()
assert len(docs) > 0
for doc in docs:
assert doc.page_content != ''
assert 'id' in doc.metadata and doc.metadata['id'] != ''
assert 'ts' in doc.metadata and doc.metadata['ts'] != '' | def test_fauna_loader(self) ->None:
"""Test Fauna loader."""
loader = FaunaLoader(query=self.valid_fql_query, page_content_field=
self.valid_page_content_field, secret=self.fauna_secret,
metadata_fields=self.valid_metadata_fields)
docs = loader.load()
assert len(docs) > 0
for doc in docs:
assert doc.page_content != ''
assert 'id' in doc.metadata and doc.metadata['id'] != ''
assert 'ts' in doc.metadata and doc.metadata['ts'] != '' | Test Fauna loader. |
validate_environment | """Validates that the python package exists in environment."""
google_api_key = get_from_dict_or_env(values, 'google_api_key',
'GOOGLE_API_KEY')
if isinstance(google_api_key, SecretStr):
google_api_key = google_api_key.get_secret_value()
genai.configure(api_key=google_api_key)
return values | @root_validator()
def validate_environment(cls, values: Dict) ->Dict:
"""Validates that the python package exists in environment."""
google_api_key = get_from_dict_or_env(values, 'google_api_key',
'GOOGLE_API_KEY')
if isinstance(google_api_key, SecretStr):
google_api_key = google_api_key.get_secret_value()
genai.configure(api_key=google_api_key)
return values | Validates that the python package exists in environment. |
fuzzy_matching_strategy | """
Fuzzy matching strategy for deanonymization.
It uses fuzzy matching to find the position of the anonymized entity in the text.
It replaces all the anonymized entities with the original ones.
Args:
text: text to deanonymize
deanonymizer_mapping: mapping between anonymized entities and original ones
max_l_dist: maximum Levenshtein distance between the anonymized entity and the
text segment to consider it a match
Examples of matching:
Kaenu Reves -> Keanu Reeves
John F. Kennedy -> John Kennedy
"""
try:
from fuzzysearch import find_near_matches
except ImportError as e:
raise ImportError(
'Could not import fuzzysearch, please install with `pip install fuzzysearch`.'
) from e
for entity_type in deanonymizer_mapping:
for anonymized, original in deanonymizer_mapping[entity_type].items():
matches = find_near_matches(anonymized, text, max_l_dist=max_l_dist)
new_text = ''
last_end = 0
for m in matches:
new_text += text[last_end:m.start]
new_text += original
last_end = m.end
new_text += text[last_end:]
text = new_text
return text | def fuzzy_matching_strategy(text: str, deanonymizer_mapping:
MappingDataType, max_l_dist: int=3) ->str:
"""
Fuzzy matching strategy for deanonymization.
It uses fuzzy matching to find the position of the anonymized entity in the text.
It replaces all the anonymized entities with the original ones.
Args:
text: text to deanonymize
deanonymizer_mapping: mapping between anonymized entities and original ones
max_l_dist: maximum Levenshtein distance between the anonymized entity and the
text segment to consider it a match
Examples of matching:
Kaenu Reves -> Keanu Reeves
John F. Kennedy -> John Kennedy
"""
try:
from fuzzysearch import find_near_matches
except ImportError as e:
raise ImportError(
'Could not import fuzzysearch, please install with `pip install fuzzysearch`.'
) from e
for entity_type in deanonymizer_mapping:
for anonymized, original in deanonymizer_mapping[entity_type].items():
matches = find_near_matches(anonymized, text, max_l_dist=max_l_dist
)
new_text = ''
last_end = 0
for m in matches:
new_text += text[last_end:m.start]
new_text += original
last_end = m.end
new_text += text[last_end:]
text = new_text
return text | Fuzzy matching strategy for deanonymization.
It uses fuzzy matching to find the position of the anonymized entity in the text.
It replaces all the anonymized entities with the original ones.
Args:
text: text to deanonymize
deanonymizer_mapping: mapping between anonymized entities and original ones
max_l_dist: maximum Levenshtein distance between the anonymized entity and the
text segment to consider it a match
Examples of matching:
Kaenu Reves -> Keanu Reeves
John F. Kennedy -> John Kennedy |
from_documents | """Create an AtlasDB vectorstore from a list of documents.
Args:
name (str): Name of the collection to create.
api_key (str): Your nomic API key,
documents (List[Document]): List of documents to add to the vectorstore.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description for your project.
is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if
it already exists. Default False.
Generally useful during development and testing.
index_kwargs (Optional[dict]): Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns:
AtlasDB: Nomic's neural database and finest rhizomatic instrument
"""
if name is None or api_key is None:
raise ValueError('`name` and `api_key` cannot be None.')
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(name=name, api_key=api_key, texts=texts, embedding=
embedding, metadatas=metadatas, ids=ids, description=description,
is_public=is_public, reset_project_if_exists=reset_project_if_exists,
index_kwargs=index_kwargs) | @classmethod
def from_documents(cls: Type[AtlasDB], documents: List[Document], embedding:
Optional[Embeddings]=None, ids: Optional[List[str]]=None, name:
Optional[str]=None, api_key: Optional[str]=None, persist_directory:
Optional[str]=None, description: str='A description for your project',
is_public: bool=True, reset_project_if_exists: bool=False, index_kwargs:
Optional[dict]=None, **kwargs: Any) ->AtlasDB:
"""Create an AtlasDB vectorstore from a list of documents.
Args:
name (str): Name of the collection to create.
api_key (str): Your nomic API key,
documents (List[Document]): List of documents to add to the vectorstore.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description for your project.
is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if
it already exists. Default False.
Generally useful during development and testing.
index_kwargs (Optional[dict]): Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns:
AtlasDB: Nomic's neural database and finest rhizomatic instrument
"""
if name is None or api_key is None:
raise ValueError('`name` and `api_key` cannot be None.')
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return cls.from_texts(name=name, api_key=api_key, texts=texts,
embedding=embedding, metadatas=metadatas, ids=ids, description=
description, is_public=is_public, reset_project_if_exists=
reset_project_if_exists, index_kwargs=index_kwargs) | Create an AtlasDB vectorstore from a list of documents.
Args:
name (str): Name of the collection to create.
api_key (str): Your nomic API key,
documents (List[Document]): List of documents to add to the vectorstore.
embedding (Optional[Embeddings]): Embedding function. Defaults to None.
ids (Optional[List[str]]): Optional list of document IDs. If None,
ids will be auto created
description (str): A description for your project.
is_public (bool): Whether your project is publicly accessible.
True by default.
reset_project_if_exists (bool): Whether to reset this project if
it already exists. Default False.
Generally useful during development and testing.
index_kwargs (Optional[dict]): Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns:
AtlasDB: Nomic's neural database and finest rhizomatic instrument |
warn_once | """Warn once about the dangers of PythonREPL."""
logger.warning('Python REPL can execute arbitrary code. Use with caution.') | @functools.lru_cache(maxsize=None)
def warn_once() ->None:
"""Warn once about the dangers of PythonREPL."""
logger.warning('Python REPL can execute arbitrary code. Use with caution.') | Warn once about the dangers of PythonREPL. |
test_parsing_error | """Test that LLM Output without a bash block raises an exce"""
question = "Please echo 'hello world' to the terminal."
prompt = _PROMPT_TEMPLATE.format(question=question)
queries = {prompt: """
```text
echo 'hello world'
```
"""}
fake_llm = FakeLLM(queries=queries)
fake_llm_bash_chain = LLMBashChain.from_llm(fake_llm, input_key='q',
output_key='a')
with pytest.raises(OutputParserException):
fake_llm_bash_chain.run(question) | def test_parsing_error() ->None:
"""Test that LLM Output without a bash block raises an exce"""
question = "Please echo 'hello world' to the terminal."
prompt = _PROMPT_TEMPLATE.format(question=question)
queries = {prompt: "\n```text\necho 'hello world'\n```\n"}
fake_llm = FakeLLM(queries=queries)
fake_llm_bash_chain = LLMBashChain.from_llm(fake_llm, input_key='q',
output_key='a')
with pytest.raises(OutputParserException):
fake_llm_bash_chain.run(question) | Test that LLM Output without a bash block raises an exce |
on_llm_new_token | """Run on new LLM token. Only available when streaming is enabled."""
sys.stdout.write(token)
sys.stdout.flush() | def on_llm_new_token(self, token: str, **kwargs: Any) ->None:
"""Run on new LLM token. Only available when streaming is enabled."""
sys.stdout.write(token)
sys.stdout.flush() | Run on new LLM token. Only available when streaming is enabled. |
can_cast_to_float | """Check if a string can be cast to a float."""
try:
float(string)
return True
except ValueError:
return False | def can_cast_to_float(string: str) ->bool:
"""Check if a string can be cast to a float."""
try:
float(string)
return True
except ValueError:
return False | Check if a string can be cast to a float. |
_import_starrocks | from langchain_community.vectorstores.starrocks import StarRocks
return StarRocks | def _import_starrocks() ->Any:
from langchain_community.vectorstores.starrocks import StarRocks
return StarRocks | null |
sample_data_frame | import polars as pl
data = {'text': ['Hello', 'World'], 'author': ['Alice', 'Bob'], 'date': [
'2022-01-01', '2022-01-02']}
return pl.DataFrame(data) | @pytest.fixture
def sample_data_frame() ->pl.DataFrame:
import polars as pl
data = {'text': ['Hello', 'World'], 'author': ['Alice', 'Bob'], 'date':
['2022-01-01', '2022-01-02']}
return pl.DataFrame(data) | null |
test_golang_code_splitter | splitter = RecursiveCharacterTextSplitter.from_language(Language.GO,
chunk_size=CHUNK_SIZE, chunk_overlap=0)
code = """
package main
import "fmt"
func helloWorld() {
fmt.Println("Hello, World!")
}
func main() {
helloWorld()
}
"""
chunks = splitter.split_text(code)
assert chunks == ['package main', 'import "fmt"', 'func', 'helloWorld() {',
'fmt.Println("He', 'llo,', 'World!")', '}', 'func main() {',
'helloWorld()', '}'] | def test_golang_code_splitter() ->None:
splitter = RecursiveCharacterTextSplitter.from_language(Language.GO,
chunk_size=CHUNK_SIZE, chunk_overlap=0)
code = """
package main
import "fmt"
func helloWorld() {
fmt.Println("Hello, World!")
}
func main() {
helloWorld()
}
"""
chunks = splitter.split_text(code)
assert chunks == ['package main', 'import "fmt"', 'func',
'helloWorld() {', 'fmt.Println("He', 'llo,', 'World!")', '}',
'func main() {', 'helloWorld()', '}'] | null |
Subsets and Splits