chains.query_constructor.parser.v_args(...)
Dummy decorator for when lark is not installed.
chains.sql_database.query.create_sql_query_chain(llm, db)
Create a chain that generates SQL queries.
langchain.embeddings¶
Embedding models  are wrappers around embedding models
from different APIs and services.
Embedding models can be LLMs or not.
Class hierarchy:
Embeddings --> <name>Embeddings  # Examples: OpenAIEmbeddings, HuggingFaceEmbeddings
Classes¶
embeddings.cache.CacheBackedEmbeddings(...)
Interface for caching results from embedding models.
Functions¶
langchain.evaluation¶
Evaluation chains for grading LLM and Chain outputs.
This module contains off-the-shelf evaluation chains for grading the output of
LangChain primitives such as language models and chains.
Loading an evaluator
To load an evaluator, you can use the load_evaluators or
load_evaluator functions with the
names of the evaluators to load.
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("qa")
evaluator.evaluate_strings(
    prediction="We sold more than 40,000 units last week",
    input="How many units did we sell last week?",
    reference="We sold 32,378 units",
)
The evaluator must be one of EvaluatorType.
Datasets
To load one of the LangChain HuggingFace datasets, you can use the load_dataset function with the
name of the dataset to load.
from langchain.evaluation import load_dataset
ds = load_dataset("llm-math")
Some common use cases for evaluation include:
Grading the accuracy of a response against ground truth answers: QAEvalChain
Comparing the output of two models: PairwiseStringEvalChain or LabeledPairwiseStringEvalChain when there is additionally a reference label.