mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-27 14:26:48 +00:00
huggingface[major]: upgrade pydantic (#26048)
This commit is contained in:
ccurme
GitHub
@@ -29,10 +29,11 @@ from langchain_core.messages import (
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ToolMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.runnables import Runnable
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from langchain_core.tools import BaseTool
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from pydantic import model_validator
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from typing_extensions import Self
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from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint
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from langchain_huggingface.llms.huggingface_pipeline import HuggingFacePipeline
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@@ -265,7 +266,7 @@ class ChatHuggingFace(BaseChatModel):
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Tool calling:
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.. code-block:: python
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from langchain_core.pydantic_v1 import BaseModel, Field
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from pydantic import BaseModel, Field
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class GetWeather(BaseModel):
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'''Get the current weather in a given location'''
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@@ -325,20 +326,20 @@ class ChatHuggingFace(BaseChatModel):
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else self.tokenizer
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)
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@root_validator(pre=False, skip_on_failure=True)
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def validate_llm(cls, values: dict) -> dict:
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@model_validator(mode="after")
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def validate_llm(self) -> Self:
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if (
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not _is_huggingface_hub(values["llm"])
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and not _is_huggingface_textgen_inference(values["llm"])
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and not _is_huggingface_endpoint(values["llm"])
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and not _is_huggingface_pipeline(values["llm"])
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not _is_huggingface_hub(self.llm)
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and not _is_huggingface_textgen_inference(self.llm)
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and not _is_huggingface_endpoint(self.llm)
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and not _is_huggingface_pipeline(self.llm)
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):
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raise TypeError(
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"Expected llm to be one of HuggingFaceTextGenInference, "
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"HuggingFaceEndpoint, HuggingFaceHub, HuggingFacePipeline "
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f"received {type(values['llm'])}"
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f"received {type(self.llm)}"
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)
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return values
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return self
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def _create_chat_result(self, response: TGI_RESPONSE) -> ChatResult:
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generations = []
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@@ -1,7 +1,7 @@
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from typing import Any, Dict, List, Optional # type: ignore[import-not-found]
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, Field
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from pydantic import BaseModel, ConfigDict, Field
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DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
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@@ -62,10 +62,10 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
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self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
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)
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class Config:
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"""Configuration for this pydantic object."""
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extra = "forbid"
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model_config = ConfigDict(
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extra="forbid",
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protected_namespaces=(),
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)
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a HuggingFace transformer model.
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@@ -1,9 +1,11 @@
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import json
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from typing import Any, Dict, List, Optional
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import os
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from typing import Any, List, Optional
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_core.utils import from_env
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from pydantic import BaseModel, ConfigDict, Field, model_validator
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from typing_extensions import Self
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DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
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VALID_TASKS = ("feature-extraction",)
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@@ -39,22 +41,20 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
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model_kwargs: Optional[dict] = None
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"""Keyword arguments to pass to the model."""
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huggingfacehub_api_token: Optional[str] = None
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huggingfacehub_api_token: Optional[str] = Field(
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default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
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)
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class Config:
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"""Configuration for this pydantic object."""
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model_config = ConfigDict(
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extra="forbid",
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protected_namespaces=(),
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)
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extra = "forbid"
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@root_validator(pre=False, skip_on_failure=True)
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def validate_environment(cls, values: Dict) -> Dict:
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that api key and python package exists in environment."""
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values["huggingfacehub_api_token"] = get_from_dict_or_env(
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values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN", None
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)
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huggingfacehub_api_token = get_from_dict_or_env(
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values, "huggingfacehub_api_token", "HF_TOKEN", None
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huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv(
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"HF_TOKEN"
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)
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try:
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@@ -63,38 +63,38 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
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InferenceClient,
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)
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if values["model"]:
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values["repo_id"] = values["model"]
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elif values["repo_id"]:
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values["model"] = values["repo_id"]
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if self.model:
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self.repo_id = self.model
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elif self.repo_id:
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self.model = self.repo_id
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else:
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values["model"] = DEFAULT_MODEL
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values["repo_id"] = DEFAULT_MODEL
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self.model = DEFAULT_MODEL
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self.repo_id = DEFAULT_MODEL
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client = InferenceClient(
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model=values["model"],
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model=self.model,
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token=huggingfacehub_api_token,
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)
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async_client = AsyncInferenceClient(
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model=values["model"],
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model=self.model,
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token=huggingfacehub_api_token,
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)
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if values["task"] not in VALID_TASKS:
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if self.task not in VALID_TASKS:
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raise ValueError(
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f"Got invalid task {values['task']}, "
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f"Got invalid task {self.task}, "
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f"currently only {VALID_TASKS} are supported"
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)
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values["client"] = client
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values["async_client"] = async_client
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self.client = client
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self.async_client = async_client
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except ImportError:
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raise ImportError(
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"Could not import huggingface_hub python package. "
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"Please install it with `pip install huggingface_hub`."
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)
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return values
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return self
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
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@@ -9,8 +9,9 @@ from langchain_core.callbacks import (
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)
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from langchain_core.language_models.llms import LLM
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from langchain_core.outputs import GenerationChunk
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from langchain_core.pydantic_v1 import 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 langchain_core.utils import from_env, get_pydantic_field_names
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from pydantic import ConfigDict, Field, model_validator
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from typing_extensions import Self
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logger = logging.getLogger(__name__)
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@@ -71,7 +72,9 @@ class HuggingFaceEndpoint(LLM):
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should be pass as env variable in `HF_INFERENCE_ENDPOINT`"""
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repo_id: Optional[str] = None
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"""Repo to use. If endpoint_url is not specified then this needs to given"""
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huggingfacehub_api_token: Optional[str] = None
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huggingfacehub_api_token: Optional[str] = Field(
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default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
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)
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max_new_tokens: int = 512
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"""Maximum number of generated tokens"""
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top_k: Optional[int] = None
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@@ -118,13 +121,13 @@ class HuggingFaceEndpoint(LLM):
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"""Task to call the model with.
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Should be a task that returns `generated_text` or `summary_text`."""
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class Config:
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"""Configuration for this pydantic object."""
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model_config = ConfigDict(
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extra="forbid",
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)
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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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@model_validator(mode="before")
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@classmethod
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def build_extra(cls, values: Dict[str, Any]) -> 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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@@ -182,8 +185,8 @@ class HuggingFaceEndpoint(LLM):
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)
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return values
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@root_validator(pre=False, skip_on_failure=True)
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def validate_environment(cls, values: Dict) -> Dict:
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that package is installed and that the API token is valid."""
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try:
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from huggingface_hub import login # type: ignore[import]
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@@ -194,12 +197,8 @@ class HuggingFaceEndpoint(LLM):
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"Please install it with `pip install huggingface_hub`."
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)
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values["huggingfacehub_api_token"] = get_from_dict_or_env(
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values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN", None
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)
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huggingfacehub_api_token = get_from_dict_or_env(
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values, "huggingfacehub_api_token", "HF_TOKEN", None
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huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv(
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"HF_TOKEN"
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)
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if huggingfacehub_api_token is not None:
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@@ -213,20 +212,20 @@ class HuggingFaceEndpoint(LLM):
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from huggingface_hub import AsyncInferenceClient, InferenceClient
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values["client"] = InferenceClient(
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model=values["model"],
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timeout=values["timeout"],
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self.client = InferenceClient(
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model=self.model,
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timeout=self.timeout,
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token=huggingfacehub_api_token,
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**values["server_kwargs"],
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**self.server_kwargs,
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)
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values["async_client"] = AsyncInferenceClient(
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model=values["model"],
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timeout=values["timeout"],
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self.async_client = AsyncInferenceClient(
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model=self.model,
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timeout=self.timeout,
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token=huggingfacehub_api_token,
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**values["server_kwargs"],
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**self.server_kwargs,
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)
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return values
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return self
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@property
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def _default_params(self) -> Dict[str, Any]:
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@@ -7,6 +7,7 @@ from typing import Any, Iterator, List, Mapping, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import BaseLLM
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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from pydantic import ConfigDict
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DEFAULT_MODEL_ID = "gpt2"
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DEFAULT_TASK = "text-generation"
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@@ -63,10 +64,9 @@ class HuggingFacePipeline(BaseLLM):
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batch_size: int = DEFAULT_BATCH_SIZE
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"""Batch size to use when passing multiple documents to generate."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = "forbid"
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model_config = ConfigDict(
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extra="forbid",
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)
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@classmethod
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def from_model_id(
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104
libs/partners/huggingface/poetry.lock
generated
104
libs/partners/huggingface/poetry.lock
generated
@@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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[[package]]
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name = "aiohttp"
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@@ -121,9 +121,6 @@ files = [
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{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
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]
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[package.dependencies]
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typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
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[[package]]
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name = "anyio"
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version = "4.4.0"
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@@ -1066,19 +1063,19 @@ test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"
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[[package]]
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name = "langchain"
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version = "0.2.6"
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version = "0.2.16"
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description = "Building applications with LLMs through composability"
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optional = false
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python-versions = "<4.0,>=3.8.1"
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files = [
|
||||
{file = "langchain-0.2.6-py3-none-any.whl", hash = "sha256:f86e8a7afd3e56f8eb5ba47f01dd00144fb9fc2f1db9873bd197347be2857aa4"},
|
||||
{file = "langchain-0.2.6.tar.gz", hash = "sha256:867f6add370c1e3911b0e87d3dd0e36aec1e8f513bf06131340fe8f151d89dc5"},
|
||||
{file = "langchain-0.2.16-py3-none-any.whl", hash = "sha256:8f59ee8b45f268df4b924ea3b9c63e49286efa756d16b3f6a9de5c6e502c36e1"},
|
||||
{file = "langchain-0.2.16.tar.gz", hash = "sha256:ffb426a76a703b73ac69abad77cd16eaf03dda76b42cff55572f592d74944166"},
|
||||
]
|
||||
|
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[package.dependencies]
|
||||
aiohttp = ">=3.8.3,<4.0.0"
|
||||
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
|
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langchain-core = ">=0.2.10,<0.3.0"
|
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langchain-core = ">=0.2.38,<0.3.0"
|
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langchain-text-splitters = ">=0.2.0,<0.3.0"
|
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langsmith = ">=0.1.17,<0.2.0"
|
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numpy = [
|
||||
@@ -1093,7 +1090,7 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
|
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|
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[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.2.6"
|
||||
version = "0.2.16"
|
||||
description = "Community contributed LangChain integrations."
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@@ -1103,8 +1100,8 @@ develop = true
|
||||
[package.dependencies]
|
||||
aiohttp = "^3.8.3"
|
||||
dataclasses-json = ">= 0.5.7, < 0.7"
|
||||
langchain = "^0.2.6"
|
||||
langchain-core = "^0.2.10"
|
||||
langchain = "^0.2.16"
|
||||
langchain-core = "^0.2.38"
|
||||
langsmith = "^0.1.0"
|
||||
numpy = [
|
||||
{version = ">=1,<2", markers = "python_version < \"3.12\""},
|
||||
@@ -1121,10 +1118,10 @@ url = "../../community"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.11"
|
||||
version = "0.2.38"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
files = []
|
||||
develop = true
|
||||
|
||||
@@ -1138,6 +1135,7 @@ pydantic = [
|
||||
]
|
||||
PyYAML = ">=5.3"
|
||||
tenacity = "^8.1.0,!=8.4.0"
|
||||
typing-extensions = ">=4.7"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
@@ -1517,43 +1515,6 @@ doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-
|
||||
extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
|
||||
test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "1.24.4"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64"},
|
||||
{file = "numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1"},
|
||||
{file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4"},
|
||||
{file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6"},
|
||||
{file = "numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc"},
|
||||
{file = "numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e"},
|
||||
{file = "numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810"},
|
||||
{file = "numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254"},
|
||||
{file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7"},
|
||||
{file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5"},
|
||||
{file = "numpy-1.24.4-cp311-cp311-win32.whl", hash = "sha256:4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d"},
|
||||
{file = "numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694"},
|
||||
{file = "numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61"},
|
||||
{file = "numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f"},
|
||||
{file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e"},
|
||||
{file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc"},
|
||||
{file = "numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2"},
|
||||
{file = "numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706"},
|
||||
{file = "numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400"},
|
||||
{file = "numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f"},
|
||||
{file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9"},
|
||||
{file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d"},
|
||||
{file = "numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835"},
|
||||
{file = "numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8"},
|
||||
{file = "numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef"},
|
||||
{file = "numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a"},
|
||||
{file = "numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2"},
|
||||
{file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "1.26.4"
|
||||
@@ -1726,7 +1687,6 @@ description = "Nvidia JIT LTO Library"
|
||||
optional = false
|
||||
python-versions = ">=3"
|
||||
files = [
|
||||
{file = "nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_aarch64.whl", hash = "sha256:004186d5ea6a57758fd6d57052a123c73a4815adf365eb8dd6a85c9eaa7535ff"},
|
||||
{file = "nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d9714f27c1d0f0895cd8915c07a87a1d0029a0aa36acaf9156952ec2a8a12189"},
|
||||
{file = "nvidia_nvjitlink_cu12-12.5.40-py3-none-win_amd64.whl", hash = "sha256:c3401dc8543b52d3a8158007a0c1ab4e9c768fcbd24153a48c86972102197ddd"},
|
||||
]
|
||||
@@ -2709,44 +2669,6 @@ docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)"
|
||||
examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"]
|
||||
tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.0.272)", "scikit-image (>=0.16.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.9.3"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "scipy-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0"},
|
||||
{file = "scipy-1.9.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:83b89e9586c62e787f5012e8475fbb12185bafb996a03257e9675cd73d3736dd"},
|
||||
{file = "scipy-1.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a72d885fa44247f92743fc20732ae55564ff2a519e8302fb7e18717c5355a8b"},
|
||||
{file = "scipy-1.9.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d01e1dd7b15bd2449c8bfc6b7cc67d630700ed655654f0dfcf121600bad205c9"},
|
||||
{file = "scipy-1.9.3-cp310-cp310-win_amd64.whl", hash = "sha256:68239b6aa6f9c593da8be1509a05cb7f9efe98b80f43a5861cd24c7557e98523"},
|
||||
{file = "scipy-1.9.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b41bc822679ad1c9a5f023bc93f6d0543129ca0f37c1ce294dd9d386f0a21096"},
|
||||
{file = "scipy-1.9.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:90453d2b93ea82a9f434e4e1cba043e779ff67b92f7a0e85d05d286a3625df3c"},
|
||||
{file = "scipy-1.9.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:83c06e62a390a9167da60bedd4575a14c1f58ca9dfde59830fc42e5197283dab"},
|
||||
{file = "scipy-1.9.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:abaf921531b5aeaafced90157db505e10345e45038c39e5d9b6c7922d68085cb"},
|
||||
{file = "scipy-1.9.3-cp311-cp311-win_amd64.whl", hash = "sha256:06d2e1b4c491dc7d8eacea139a1b0b295f74e1a1a0f704c375028f8320d16e31"},
|
||||
{file = "scipy-1.9.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5a04cd7d0d3eff6ea4719371cbc44df31411862b9646db617c99718ff68d4840"},
|
||||
{file = "scipy-1.9.3-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:545c83ffb518094d8c9d83cce216c0c32f8c04aaf28b92cc8283eda0685162d5"},
|
||||
{file = "scipy-1.9.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d54222d7a3ba6022fdf5773931b5d7c56efe41ede7f7128c7b1637700409108"},
|
||||
{file = "scipy-1.9.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cff3a5295234037e39500d35316a4c5794739433528310e117b8a9a0c76d20fc"},
|
||||
{file = "scipy-1.9.3-cp38-cp38-win_amd64.whl", hash = "sha256:2318bef588acc7a574f5bfdff9c172d0b1bf2c8143d9582e05f878e580a3781e"},
|
||||
{file = "scipy-1.9.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:d644a64e174c16cb4b2e41dfea6af722053e83d066da7343f333a54dae9bc31c"},
|
||||
{file = "scipy-1.9.3-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:da8245491d73ed0a994ed9c2e380fd058ce2fa8a18da204681f2fe1f57f98f95"},
|
||||
{file = "scipy-1.9.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4db5b30849606a95dcf519763dd3ab6fe9bd91df49eba517359e450a7d80ce2e"},
|
||||
{file = "scipy-1.9.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c68db6b290cbd4049012990d7fe71a2abd9ffbe82c0056ebe0f01df8be5436b0"},
|
||||
{file = "scipy-1.9.3-cp39-cp39-win_amd64.whl", hash = "sha256:5b88e6d91ad9d59478fafe92a7c757d00c59e3bdc3331be8ada76a4f8d683f58"},
|
||||
{file = "scipy-1.9.3.tar.gz", hash = "sha256:fbc5c05c85c1a02be77b1ff591087c83bc44579c6d2bd9fb798bb64ea5e1a027"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.18.5,<1.26.0"
|
||||
|
||||
[package.extras]
|
||||
dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"]
|
||||
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"]
|
||||
test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
version = "1.13.1"
|
||||
@@ -3495,5 +3417,5 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "fa255346e4827837235f2a9c0a6379820d25f78ccf0f6a0a20294e18d0a4333a"
|
||||
python-versions = ">=3.9,<4.0"
|
||||
content-hash = "6dcaa0616c3cc282ff908e1734b87a6f5227d6db4ab78f985877134cce5ebc0b"
|
||||
|
||||
@@ -19,12 +19,13 @@ disallow_untyped_defs = "True"
|
||||
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-huggingface%3D%3D0%22&expanded=true"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
python = ">=3.9,<4.0"
|
||||
langchain-core = ">=0.1.52,<0.3"
|
||||
tokenizers = ">=0.19.1"
|
||||
transformers = ">=4.39.0"
|
||||
sentence-transformers = ">=2.6.0"
|
||||
huggingface-hub = ">=0.23.0"
|
||||
pydantic = ">=2,<3"
|
||||
|
||||
[tool.ruff.lint]
|
||||
select = [ "E", "F", "I", "T201",]
|
||||
|
||||
@@ -1,27 +0,0 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
@@ -226,7 +226,7 @@ class ChatOllama(BaseChatModel):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain_ollama import ChatOllama
|
||||
from langchain_core.pydantic_v1 import BaseModel, Field
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
class Multiply(BaseModel):
|
||||
a: int = Field(..., description="First integer")
|
||||
|
||||
@@ -1,27 +0,0 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# This script searches for lines starting with "import pydantic" or "from pydantic"
|
||||
# in tracked files within a Git repository.
|
||||
#
|
||||
# Usage: ./scripts/check_pydantic.sh /path/to/repository
|
||||
|
||||
# Check if a path argument is provided
|
||||
if [ $# -ne 1 ]; then
|
||||
echo "Usage: $0 /path/to/repository"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
repository_path="$1"
|
||||
|
||||
# Search for lines matching the pattern within the specified repository
|
||||
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
|
||||
|
||||
# Check if any matching lines were found
|
||||
if [ -n "$result" ]; then
|
||||
echo "ERROR: The following lines need to be updated:"
|
||||
echo "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
Reference in New Issue
Block a user