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base.py — langchain Source File

Architecture documentation for base.py, a python file in the langchain codebase. 20 imports, 0 dependents.

File python CoreAbstractions Serialization 20 imports 3 functions 4 classes

Entity Profile

Dependency Diagram

graph LR
  8d2afa68_e16d_c06a_fbe2_08321c12e529["base.py"]
  c990f2d7_9509_7cea_ca95_51ad57dbe5c6["functools"]
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  2a7f66a7_8738_3d47_375b_70fcaa6ac169["logging"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> 2a7f66a7_8738_3d47_375b_70fcaa6ac169
  b188e880_71c6_b93e_127d_c22666293d37["enum"]
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  3888b2bf_bffe_7c16_770f_a406d400119c["importlib"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> 3888b2bf_bffe_7c16_770f_a406d400119c
  8e2034b7_ceb8_963f_29fc_2ea6b50ef9b3["typing"]
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  f3bc7443_c889_119d_0744_aacc3620d8d2["langchain_core.callbacks"]
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  e8ec017e_6c91_4b34_675f_2a96c5aa9be6["langchain_core.callbacks.manager"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> e8ec017e_6c91_4b34_675f_2a96c5aa9be6
  bc46b61d_cfdf_3f6b_a9dd_ac2a328d84b3["langchain_core.embeddings"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> bc46b61d_cfdf_3f6b_a9dd_ac2a328d84b3
  f4d905c6_a2b2_eb8f_be9b_7808b72f6a16["langchain_core.utils"]
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  6e58aaea_f08e_c099_3cc7_f9567bfb1ae7["pydantic"]
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  91721f45_4909_e489_8c1f_084f8bd87145["typing_extensions"]
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  01158a5b_b299_f45d_92e9_2a7433a1a91a["langchain_classic.chains.base"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> 01158a5b_b299_f45d_92e9_2a7433a1a91a
  538b302b_528d_b6e6_cf56_04147780d18b["langchain_classic.evaluation.schema"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> 538b302b_528d_b6e6_cf56_04147780d18b
  52a02ed3_b44b_45aa_e71c_064994c739be["langchain_classic.schema"]
  8d2afa68_e16d_c06a_fbe2_08321c12e529 --> 52a02ed3_b44b_45aa_e71c_064994c739be
  style 8d2afa68_e16d_c06a_fbe2_08321c12e529 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

"""A chain for comparing the output of two models using embeddings."""

import functools
import logging
from enum import Enum
from importlib import util
from typing import Any

from langchain_core.callbacks import Callbacks
from langchain_core.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain_core.embeddings import Embeddings
from langchain_core.utils import pre_init
from pydantic import ConfigDict, Field
from typing_extensions import override

from langchain_classic.chains.base import Chain
from langchain_classic.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
from langchain_classic.schema import RUN_KEY


def _import_numpy() -> Any:
    try:
        import numpy as np
    except ImportError as e:
        msg = "Could not import numpy, please install with `pip install numpy`."
        raise ImportError(msg) from e
    return np


logger = logging.getLogger(__name__)


@functools.lru_cache(maxsize=1)
def _check_numpy() -> bool:
    if bool(util.find_spec("numpy")):
        return True
    logger.warning(
        "NumPy not found in the current Python environment. "
        "langchain will use a pure Python implementation for embedding distance "
        "operations, which may significantly impact performance, especially for large "
        "datasets. For optimal speed and efficiency, consider installing NumPy: "
        "pip install numpy",
    )
    return False


def _embedding_factory() -> Embeddings:
    """Create an `Embeddings` object.

    Returns:
        The created `Embeddings` object.
    """
    # Here for backwards compatibility.
    # Generally, we do not want to be seeing imports from langchain community
    # or partner packages in langchain.
    try:
        from langchain_openai import OpenAIEmbeddings
// ... (598 more lines)

Domain

Subdomains

Functions

Classes

Dependencies

  • enum
  • functools
  • importlib
  • langchain_classic.chains.base
  • langchain_classic.evaluation.schema
  • langchain_classic.schema
  • langchain_community.embeddings.openai
  • langchain_core.callbacks
  • langchain_core.callbacks.manager
  • langchain_core.embeddings
  • langchain_core.utils
  • langchain_core.vectorstores.utils
  • langchain_openai
  • logging
  • numpy
  • pydantic
  • scipy.spatial.distance
  • tiktoken
  • typing
  • typing_extensions

Source

Frequently Asked Questions

What does base.py do?
base.py is a source file in the langchain codebase, written in python. It belongs to the CoreAbstractions domain, Serialization subdomain.
What functions are defined in base.py?
base.py defines 3 function(s): _check_numpy, _embedding_factory, _import_numpy.
What does base.py depend on?
base.py imports 20 module(s): enum, functools, importlib, langchain_classic.chains.base, langchain_classic.evaluation.schema, langchain_classic.schema, langchain_community.embeddings.openai, langchain_core.callbacks, and 12 more.
Where is base.py in the architecture?
base.py is located at libs/langchain/langchain_classic/evaluation/embedding_distance/base.py (domain: CoreAbstractions, subdomain: Serialization, directory: libs/langchain/langchain_classic/evaluation/embedding_distance).

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