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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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345
libs/community/langchain_community/embeddings/localai.py
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345
libs/community/langchain_community/embeddings/localai.py
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from __future__ import annotations
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import logging
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import warnings
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Literal,
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Optional,
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Sequence,
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Set,
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Tuple,
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Union,
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)
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
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from tenacity import (
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AsyncRetrying,
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before_sleep_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_exponential,
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)
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logger = logging.getLogger(__name__)
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def _create_retry_decorator(embeddings: LocalAIEmbeddings) -> Callable[[Any], Any]:
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import openai
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min_seconds = 4
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max_seconds = 10
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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return retry(
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reraise=True,
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stop=stop_after_attempt(embeddings.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def _async_retry_decorator(embeddings: LocalAIEmbeddings) -> Any:
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import openai
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min_seconds = 4
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max_seconds = 10
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# Wait 2^x * 1 second between each retry starting with
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# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
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async_retrying = AsyncRetrying(
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reraise=True,
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stop=stop_after_attempt(embeddings.max_retries),
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wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
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retry=(
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retry_if_exception_type(openai.error.Timeout)
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| retry_if_exception_type(openai.error.APIError)
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| retry_if_exception_type(openai.error.APIConnectionError)
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| retry_if_exception_type(openai.error.RateLimitError)
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| retry_if_exception_type(openai.error.ServiceUnavailableError)
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),
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before_sleep=before_sleep_log(logger, logging.WARNING),
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)
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def wrap(func: Callable) -> Callable:
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async def wrapped_f(*args: Any, **kwargs: Any) -> Callable:
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async for _ in async_retrying:
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return await func(*args, **kwargs)
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raise AssertionError("this is unreachable")
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return wrapped_f
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return wrap
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# https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
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def _check_response(response: dict) -> dict:
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if any(len(d["embedding"]) == 1 for d in response["data"]):
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import openai
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raise openai.error.APIError("LocalAI API returned an empty embedding")
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return response
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def embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any:
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"""Use tenacity to retry the embedding call."""
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retry_decorator = _create_retry_decorator(embeddings)
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@retry_decorator
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def _embed_with_retry(**kwargs: Any) -> Any:
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response = embeddings.client.create(**kwargs)
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return _check_response(response)
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return _embed_with_retry(**kwargs)
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async def async_embed_with_retry(embeddings: LocalAIEmbeddings, **kwargs: Any) -> Any:
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"""Use tenacity to retry the embedding call."""
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@_async_retry_decorator(embeddings)
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async def _async_embed_with_retry(**kwargs: Any) -> Any:
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response = await embeddings.client.acreate(**kwargs)
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return _check_response(response)
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return await _async_embed_with_retry(**kwargs)
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class LocalAIEmbeddings(BaseModel, Embeddings):
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"""LocalAI embedding models.
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Since LocalAI and OpenAI have 1:1 compatibility between APIs, this class
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uses the ``openai`` Python package's ``openai.Embedding`` as its client.
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Thus, you should have the ``openai`` python package installed, and defeat
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the environment variable ``OPENAI_API_KEY`` by setting to a random string.
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You also need to specify ``OPENAI_API_BASE`` to point to your LocalAI
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service endpoint.
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Example:
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.. code-block:: python
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from langchain_community.embeddings import LocalAIEmbeddings
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openai = LocalAIEmbeddings(
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openai_api_key="random-string",
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openai_api_base="http://localhost:8080"
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)
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"""
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client: Any #: :meta private:
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model: str = "text-embedding-ada-002"
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deployment: str = model
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openai_api_version: Optional[str] = None
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openai_api_base: Optional[str] = None
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# to support explicit proxy for LocalAI
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openai_proxy: Optional[str] = None
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embedding_ctx_length: int = 8191
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"""The maximum number of tokens to embed at once."""
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openai_api_key: Optional[str] = None
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openai_organization: Optional[str] = None
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allowed_special: Union[Literal["all"], Set[str]] = set()
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disallowed_special: Union[Literal["all"], Set[str], Sequence[str]] = "all"
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chunk_size: int = 1000
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"""Maximum number of texts to embed in each batch"""
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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"""Timeout in seconds for the LocalAI request."""
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headers: Any = None
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show_progress_bar: bool = False
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"""Whether to show a progress bar when embedding."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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if field_name not in all_required_field_names:
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warnings.warn(
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f"""WARNING! {field_name} is not default parameter.
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{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
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if invalid_model_kwargs:
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raise ValueError(
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f"Parameters {invalid_model_kwargs} should be specified explicitly. "
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f"Instead they were passed in as part of `model_kwargs` parameter."
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)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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values["openai_api_key"] = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY"
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)
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values["openai_api_base"] = get_from_dict_or_env(
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values,
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"openai_api_base",
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"OPENAI_API_BASE",
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default="",
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)
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values["openai_proxy"] = get_from_dict_or_env(
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values,
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"openai_proxy",
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"OPENAI_PROXY",
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default="",
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)
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default_api_version = ""
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values["openai_api_version"] = get_from_dict_or_env(
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values,
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"openai_api_version",
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"OPENAI_API_VERSION",
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default=default_api_version,
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)
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values["openai_organization"] = get_from_dict_or_env(
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values,
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"openai_organization",
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"OPENAI_ORGANIZATION",
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default="",
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)
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try:
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import openai
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values["client"] = openai.Embedding
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except ImportError:
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raise ImportError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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return values
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@property
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def _invocation_params(self) -> Dict:
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openai_args = {
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"model": self.model,
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"request_timeout": self.request_timeout,
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"headers": self.headers,
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"api_key": self.openai_api_key,
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"organization": self.openai_organization,
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"api_base": self.openai_api_base,
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"api_version": self.openai_api_version,
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**self.model_kwargs,
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}
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if self.openai_proxy:
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import openai
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openai.proxy = {
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"http": self.openai_proxy,
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"https": self.openai_proxy,
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} # type: ignore[assignment] # noqa: E501
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return openai_args
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def _embedding_func(self, text: str, *, engine: str) -> List[float]:
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"""Call out to LocalAI's embedding endpoint."""
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# handle large input text
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if self.model.endswith("001"):
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# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
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# replace newlines, which can negatively affect performance.
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text = text.replace("\n", " ")
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return embed_with_retry(
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self,
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input=[text],
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**self._invocation_params,
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)["data"][0]["embedding"]
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async def _aembedding_func(self, text: str, *, engine: str) -> List[float]:
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"""Call out to LocalAI's embedding endpoint."""
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# handle large input text
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if self.model.endswith("001"):
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# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
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# replace newlines, which can negatively affect performance.
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text = text.replace("\n", " ")
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return (
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await async_embed_with_retry(
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self,
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input=[text],
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**self._invocation_params,
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)
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)["data"][0]["embedding"]
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def embed_documents(
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self, texts: List[str], chunk_size: Optional[int] = 0
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) -> List[List[float]]:
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"""Call out to LocalAI's embedding endpoint for embedding search docs.
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Args:
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texts: The list of texts to embed.
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chunk_size: The chunk size of embeddings. If None, will use the chunk size
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specified by the class.
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Returns:
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List of embeddings, one for each text.
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"""
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# call _embedding_func for each text
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return [self._embedding_func(text, engine=self.deployment) for text in texts]
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async def aembed_documents(
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self, texts: List[str], chunk_size: Optional[int] = 0
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) -> List[List[float]]:
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"""Call out to LocalAI's embedding endpoint async for embedding search docs.
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Args:
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texts: The list of texts to embed.
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chunk_size: The chunk size of embeddings. If None, will use the chunk size
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specified by the class.
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Returns:
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List of embeddings, one for each text.
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"""
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embeddings = []
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for text in texts:
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response = await self._aembedding_func(text, engine=self.deployment)
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embeddings.append(response)
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return embeddings
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def embed_query(self, text: str) -> List[float]:
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"""Call out to LocalAI's embedding endpoint for embedding query text.
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Args:
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text: The text to embed.
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Returns:
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Embedding for the text.
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"""
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embedding = self._embedding_func(text, engine=self.deployment)
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return embedding
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async def aembed_query(self, text: str) -> List[float]:
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"""Call out to LocalAI's embedding endpoint async for embedding query text.
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Args:
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text: The text to embed.
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Returns:
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Embedding for the text.
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"""
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embedding = await self._aembedding_func(text, engine=self.deployment)
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return embedding
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