id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
a228a16b6200-1 | """
[docs] def __init__(
self,
connection_string: str,
embedding_function: Embeddings,
embedding_dimension: int = _LANGCHAIN_DEFAULT_EMBEDDING_DIM,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
pre_delete_collection: bool = False,
logger: Optional[... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-2 | Table(
self.collection_name,
Base.metadata,
Column("id", TEXT, primary_key=True, default=uuid.uuid4),
Column("embedding", ARRAY(REAL)),
Column("document", String, nullable=True),
Column("metadata", JSON, nullable=True),
extend_existing=... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-3 | metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
batch_size: int = 500,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-4 | conn.execute(insert(chunks_table).values(chunks_table_data))
# Clear the chunks_table_data list for the next batch
chunks_table_data.clear()
# Insert any remaining records that didn't make up a full batch
if chunks_table_data:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-5 | List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function.embed_query(query)
docs = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return docs
[docs] def similarity_search_with_sco... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-6 | Document(
page_content=result.document,
metadata=result.metadata,
),
result.distance if self.embedding_function is not None else None,
)
for result in results
]
return documents_with_scores
[docs] def simi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-7 | )
try:
with self.engine.connect() as conn:
with conn.begin():
delete_condition = chunks_table.c.id.in_(ids)
conn.execute(chunks_table.delete().where(delete_condition))
return True
except Exception as e:
p... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-8 | data=kwargs,
key="connection_string",
env_key="PG_CONNECTION_STRING",
)
if not connection_string:
raise ValueError(
"Postgres connection string is required"
"Either pass it as a parameter"
"or set the PG_CONNECTION_STRIN... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
a228a16b6200-9 | cls,
driver: str,
host: str,
port: int,
database: str,
user: str,
password: str,
) -> str:
"""Return connection string from database parameters."""
return f"postgresql+{driver}://{user}:{password}@{host}:{port}/{database}" | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
df97436ddf64-0 | Source code for langchain.vectorstores.usearch
"""Wrapper around USearch vector database."""
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Tuple
import numpy as np
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
df97436ddf64-1 | Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if not isinstance(se... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
df97436ddf64-2 | matches = self.index.search(np.array(query_embedding), k)
docs_with_scores: List[Tuple[Document, float]] = []
for id, score in zip(matches.keys, matches.distances):
doc = self.docstore.search(str(id))
if not isinstance(doc, Document):
raise ValueError(f"Could not ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
df97436ddf64-3 | This is a user friendly interface that:
1. Embeds documents.
2. Creates an in memory docstore
3. Initializes the USearch database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain.vectorstores... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/usearch.html |
261e3a672496-0 | Source code for langchain.vectorstores.pgembedding
"""VectorStore wrapper around a Postgres database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
import sqlalchemy
from sqlalchemy import func
from sqlalchemy.dialects.postgresql imp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-1 | """
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmetadata=cmetadata)
session.add(collection)
session.commit()
created = True
return collection, created
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-2 | The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
- `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN)
- `EUCLIDEAN` is the euclidean distance.
- `pre_delete_collection` if True, wi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-3 | statement = sqlalchemy.text("CREATE EXTENSION IF NOT EXISTS embedding")
session.execute(statement)
session.commit()
except Exception as e:
self.logger.exception(e)
[docs] def create_tables_if_not_exists(self) -> None:
with self._conn.begin():
Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-4 | session.commit()
print("HNSW extension and index created successfully.")
except Exception as e:
print(f"Failed to create HNSW extension or index: {e}")
[docs] def delete_collection(self) -> None:
self.logger.debug("Trying to delete collection")
with Session(self._conn)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-5 | )
return store
[docs] def add_embeddings(
self,
texts: List[str],
embeddings: List[List[float]],
metadatas: List[dict],
ids: List[str],
**kwargs: Any,
) -> None:
with Session(self._conn) as session:
collection = self.get_collection(sessi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-6 | cmetadata=metadata,
custom_id=id,
)
collection.embeddings.append(embedding_store)
session.add(embedding_store)
session.commit()
return ids
[docs] def similarity_search(
self,
query: str,
k: int = 4,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-7 | filter_clauses = []
for key, value in filter.items():
IN = "in"
if isinstance(value, dict) and IN in map(str.lower, value):
value_case_insensitive = {
k.lower(): v for k, v in value.items()
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-8 | filter: Optional[dict] = None,
**kwargs: Any,
) -> List[Document]:
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter
)
return [doc for doc, _ in docs_and_scores]
[docs] @classmethod
def from_texts(
cls: T... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-9 | texts,
embeddings,
embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
pre_delete_collection=pre_delete_collection,
**kwargs,
)
[docs] @classmethod
def from_existing_index(
cls: Type[PGEmbedd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
261e3a672496-10 | texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
connection_string = cls.get_connection_string(kwargs)
kwargs["connection_string"] = connection_string
return cls.from_texts(
texts=texts,
pre_delete_collection=pre_delete_colle... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pgembedding.html |
1560d88cc7ae-0 | Source code for langchain.vectorstores.utils
"""Utility functions for working with vectors and vectorstores."""
from enum import Enum
from typing import List
import numpy as np
from langchain.utils.math import cosine_similarity
[docs]class DistanceStrategy(str, Enum):
"""Enumerator of the Distance strategies for ca... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/utils.html |
1560d88cc7ae-1 | lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idx... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/utils.html |
690d40f85fff-0 | Source code for langchain.vectorstores.deeplake
"""Wrapper around Activeloop Deep Lake."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
try:
import deeplake
from deeplake.core.fast_forwarding import version_co... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-1 | vectorstore = DeepLake("langchain_store", embeddings.embed_query)
"""
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
[docs] def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding: Optional[Embeddings] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-2 | either documents or query. Optional.
embedding_function (Embeddings, optional): Function to convert
either documents or query. Optional. Deprecated: keeping this
parameter for backwards compatibility.
read_only (bool): Open dataset in read-only mode. Default is Fa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-3 | """
self.ingestion_batch_size = ingestion_batch_size
self.num_workers = num_workers
self.verbose = verbose
if _DEEPLAKE_INSTALLED is False:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deep... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-4 | **kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Examples:
>>> ids = deeplake_vectorstore.add_texts(
... texts = <list_of_texts>,
... metadatas = <list_of_metadata_jsons>,
... ids = <list_o... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-5 | return self.vectorstore.add(
text=texts,
metadata=metadatas,
embedding_data=texts,
embedding_tensor="embedding",
embedding_function=self._embedding_function.embed_documents, # type: ignore
return_ids=True,
**kwargs,
)
def _... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-6 | """
result = self.vectorstore.search(
query=tql,
exec_option=exec_option,
)
metadatas = result["metadata"]
texts = result["text"]
docs = [
Document(
page_content=text,
metadata=metadata,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-7 | into embedding.
k (int): Number of Documents to return.
distance_metric (str): `L2` for Euclidean, `L1` for Nuclear, `max`
for L-infinity distance, `cos` for cosine similarity, 'dot' for dot
product.
filter (Union[Dict, Callable], optional): Additional... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-8 | if return_score True, return a tuple of (Document, score)
Raises:
ValueError: if both `embedding` and `embedding_function` are not specified.
"""
if kwargs.get("tql"):
return self._search_tql(
tql=kwargs["tql"],
exec_option=exec_option,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-9 | )
scores = result["score"]
embeddings = result["embedding"]
metadatas = result["metadata"]
texts = result["text"]
if use_maximal_marginal_relevance:
lambda_mult = kwargs.get("lambda_mult", 0.5)
indices = maximal_marginal_relevance( # type: ignore
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-10 | ... exec_option="compute_engine",
... )
Args:
k (int): Number of Documents to return. Defaults to 4.
query (str): Text to look up similar documents.
**kwargs: Additional keyword arguments include:
embedding (Callable): Embedding function to use... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-11 | k=k,
use_maximal_marginal_relevance=False,
return_score=False,
**kwargs,
)
[docs] def similarity_search_by_vector(
self,
embedding: Union[List[float], np.ndarray],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Retur... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-12 | - "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be
used with in-memory or local datasets.
- "tens... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-13 | ... )
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
**kwargs: Additional keyword arguments. Some of these arguments are:
distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-14 | text with distance in float."""
return self._search(
query=query,
k=k,
return_score=True,
**kwargs,
)
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-15 | option with big datasets is discouraged due to potential
memory issues.
- "compute_engine" - Performant C++ implementation of the Deep
Lake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-16 | ... embedding_function = <embedding_function_for_query>,
... k = <number_of_items_to_return>,
... exec_option = <preferred_exec_option>,
... )
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-17 | "For MMR search, you must specify an embedding function on"
" `creation` or during add call."
)
return self._search(
query=query,
k=k,
fetch_k=fetch_k,
use_maximal_marginal_relevance=True,
lambda_mult=lambda_mult,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-18 | (use 'activeloop login' from command line)
- AWS S3 path of the form ``s3://bucketname/path/to/dataset``.
Credentials are required in either the environment
- Google Cloud Storage path of the form
``gcs://bucketname/path/to/dataset`` Credentials ar... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
690d40f85fff-19 | Defaults to None.
**kwargs: Other keyword arguments that subclasses might use.
- filter (Optional[Dict[str, str]], optional): The filter to delete by.
- delete_all (Optional[bool], optional): Whether to drop the dataset.
Returns:
bool: Whether the delete o... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
6d17fcab96b4-0 | Source code for langchain.vectorstores.docarray.in_memory
"""Wrapper around in-memory storage."""
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
6d17fcab96b4-1 | [docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> DocArrayInMemorySearch:
"""Create an DocArrayInMemorySearch store and insert data.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
010a2af88363-0 | Source code for langchain.vectorstores.docarray.base
from abc import ABC
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type
import numpy as np
from pydantic import Field
from langchain.embeddings.base import Embeddings
from langchain.schema import Document
from langchain.vectorstores import Ve... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
010a2af88363-1 | from docarray import BaseDoc
from docarray.typing import NdArray
class DocArrayDoc(BaseDoc):
text: Optional[str]
embedding: Optional[NdArray] = Field(**embeddings_params)
metadata: Optional[dict]
return DocArrayDoc
@property
def doc_cls(self) -> Type["... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
010a2af88363-2 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of documents most similar to the query text and
cosine distance in float for each.
Lower score represents more similarity.
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
010a2af88363-3 | """
raise NotImplementedError()
[docs] def similarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
010a2af88363-4 | Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self.embedding.embed_query(query)
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
docs = self.doc_index.find(
query_doc, search_field="embedding", limit=fe... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/base.html |
23ed39564530-0 | Source code for langchain.vectorstores.docarray.hnsw
"""Wrapper around Hnswlib store."""
from __future__ import annotations
from typing import Any, List, Literal, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,
)... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
23ed39564530-1 | "cosine", "ip", and "l2". Defaults to "cosine".
max_elements (int): Maximum number of vectors that can be stored.
Defaults to 1024.
index (bool): Whether an index should be built for this field.
Defaults to True.
ef_construction (int): defines a constr... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
23ed39564530-2 | work_dir: Optional[str] = None,
n_dim: Optional[int] = None,
**kwargs: Any,
) -> DocArrayHnswSearch:
"""Create an DocArrayHnswSearch store and insert data.
Args:
texts (List[str]): Text data.
embedding (Embeddings): Embedding function.
metadatas (O... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
2e87736f77b4-0 | Source code for langchain.storage.in_memory
"""In memory store that is not thread safe and has no eviction policy.
This is a simple implementation of the BaseStore using a dictionary that is useful
primarily for unit testing purposes.
"""
from typing import Any, Dict, Iterator, List, Optional, Sequence, Tuple
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/in_memory.html |
2e87736f77b4-1 | """
return [self.store.get(key) for key in keys]
[docs] def mset(self, key_value_pairs: Sequence[Tuple[str, Any]]) -> None:
"""Set the values for the given keys.
Args:
key_value_pairs (Sequence[Tuple[str, V]]): A sequence of key-value pairs.
Returns:
None
... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/in_memory.html |
4575267863d3-0 | Source code for langchain.storage.file_system
import re
from pathlib import Path
from typing import Iterator, List, Optional, Sequence, Tuple, Union
from langchain.schema import BaseStore
from langchain.storage.exceptions import InvalidKeyException
[docs]class LocalFileStore(BaseStore[str, bytes]):
"""BaseStore int... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/file_system.html |
4575267863d3-1 | Returns:
Path: The full path for the given key.
"""
if not re.match(r"^[a-zA-Z0-9_.\-/]+$", key):
raise InvalidKeyException(f"Invalid characters in key: {key}")
return self.root_path / key
[docs] def mget(self, keys: Sequence[str]) -> List[Optional[bytes]]:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/file_system.html |
4575267863d3-2 | for key in keys:
full_path = self._get_full_path(key)
if full_path.exists():
full_path.unlink()
[docs] def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]:
"""Get an iterator over keys that match the given prefix.
Args:
prefix (Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/file_system.html |
6083b6367f0b-0 | Source code for langchain.storage.encoder_backed
from typing import (
Any,
Callable,
Iterator,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
from langchain.schema import BaseStore
K = TypeVar("K")
V = TypeVar("V")
[docs]class EncoderBackedStore(BaseStore[K, V]):
"""Wraps a s... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/encoder_backed.html |
6083b6367f0b-1 | value_serializer: Callable[[V], bytes],
value_deserializer: Callable[[Any], V],
) -> None:
"""Initialize an EncodedStore."""
self.store = store
self.key_encoder = key_encoder
self.value_serializer = value_serializer
self.value_deserializer = value_deserializer
[docs] ... | https://api.python.langchain.com/en/latest/_modules/langchain/storage/encoder_backed.html |
5bab5632d843-0 | Source code for langchain.storage.exceptions
from langchain.schema import LangChainException
[docs]class InvalidKeyException(LangChainException):
"""Raised when a key is invalid; e.g., uses incorrect characters.""" | https://api.python.langchain.com/en/latest/_modules/langchain/storage/exceptions.html |
4a28b03e9de4-0 | Source code for langchain.evaluation.loading
"""Loading datasets and evaluators."""
from typing import Any, Dict, List, Optional, Sequence, Type, Union
from langchain.chains.base import Chain
from langchain.chat_models.openai import ChatOpenAI
from langchain.evaluation.agents.trajectory_eval_chain import TrajectoryEval... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
4a28b03e9de4-1 | """ # noqa: E501
try:
from datasets import load_dataset
except ImportError:
raise ImportError(
"load_dataset requires the `datasets` package."
" Please install with `pip install datasets`"
)
dataset = load_dataset(f"LangChainDatasets/{uri}")
return [d for... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
4a28b03e9de4-2 | ----------
evaluator : EvaluatorType
The type of evaluator to load.
llm : BaseLanguageModel, optional
The language model to use for evaluation, by default None
**kwargs : Any
Additional keyword arguments to pass to the evaluator.
Returns
-------
Chain
The loaded e... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
4a28b03e9de4-3 | config : dict, optional
A dictionary mapping evaluator types to additional keyword arguments,
by default None
**kwargs : Any
Additional keyword arguments to pass to all evaluators.
Returns
-------
List[Chain]
The loaded evaluators.
Examples
--------
>>> from l... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/loading.html |
12c6aef45733-0 | Source code for langchain.evaluation.schema
"""Interfaces to be implemented by general evaluators."""
from __future__ import annotations
import logging
from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, Optional, Sequence, Tuple
from warnings import warn
from langchain.chains.base import ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-1 | STRING_DISTANCE = "string_distance"
"""Compare predictions to a reference answer using string edit distances."""
PAIRWISE_STRING_DISTANCE = "pairwise_string_distance"
"""Compare predictions based on string edit distances."""
EMBEDDING_DISTANCE = "embedding_distance"
"""Compare a prediction to a refe... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-2 | input: Optional[str] = None,
) -> None:
"""Check if the evaluation arguments are valid.
Args:
reference (Optional[str], optional): The reference label.
input (Optional[str], optional): The input string.
Raises:
ValueError: If the evaluator requires an inpu... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-3 | reference (Optional[str], optional): The reference label to evaluate against.
input (Optional[str], optional): The input to consider during evaluation.
**kwargs: Additional keyword arguments, including callbacks, tags, etc.
Returns:
dict: The evaluation results containing the... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-4 | )
[docs] def evaluate_strings(
self,
*,
prediction: str,
reference: Optional[str] = None,
input: Optional[str] = None,
**kwargs: Any,
) -> dict:
"""Evaluate Chain or LLM output, based on optional input and label.
Args:
prediction (str): ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-5 | self._check_evaluation_args(reference=reference, input=input)
return await self._aevaluate_strings(
prediction=prediction, reference=reference, input=input, **kwargs
)
[docs]class PairwiseStringEvaluator(_EvalArgsMixin, ABC):
"""Compare the output of two models (or two outputs of the sam... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-6 | input (Optional[str], optional): The input string.
**kwargs: Additional keyword arguments, such as callbacks and optional reference strings.
Returns:
dict: A dictionary containing the preference, scores, and/or other information.
""" # noqa: E501
raise NotImplementedErro... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-7 | **kwargs: Any,
) -> dict:
"""Asynchronously evaluate the output string pairs.
Args:
prediction (str): The output string from the first model.
prediction_b (str): The output string from the second model.
reference (Optional[str], optional): The expected output / re... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-8 | Returns:
dict: The evaluation result.
"""
async def _aevaluate_agent_trajectory(
self,
*,
prediction: str,
agent_trajectory: Sequence[Tuple[AgentAction, str]],
input: str,
reference: Optional[str] = None,
**kwargs: Any,
) -> dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
12c6aef45733-9 | prediction=prediction,
input=input,
agent_trajectory=agent_trajectory,
reference=reference,
**kwargs,
)
[docs] async def aevaluate_agent_trajectory(
self,
*,
prediction: str,
agent_trajectory: Sequence[Tuple[AgentAction, str]],
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/schema.html |
ca03e1c9c2a8-0 | Source code for langchain.evaluation.qa.eval_chain
"""LLM Chains for evaluating question answering."""
from __future__ import annotations
import re
from typing import Any, List, Optional, Sequence
from pydantic import Extra
from langchain import PromptTemplate
from langchain.callbacks.manager import Callbacks
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
ca03e1c9c2a8-1 | """LLM Chain for evaluating question answering."""
output_key: str = "results" #: :meta private:
class Config:
"""Configuration for the QAEvalChain."""
extra = Extra.ignore
@property
def evaluation_name(self) -> str:
return "correctness"
@property
def requires_reference(... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
ca03e1c9c2a8-2 | question_key: str = "query",
answer_key: str = "answer",
prediction_key: str = "result",
*,
callbacks: Callbacks = None,
) -> List[dict]:
"""Evaluate question answering examples and predictions."""
inputs = [
{
"query": example[question_key... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
ca03e1c9c2a8-3 | Returns:
dict: The evaluation results containing the score or value.
"""
result = self(
{
"query": input,
"answer": reference,
"result": prediction,
},
callbacks=callbacks,
include_run_info=includ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
ca03e1c9c2a8-4 | raise ValueError(
f"Input variables should be {expected_input_vars}, "
f"but got {prompt.input_variables}"
)
@property
def evaluation_name(self) -> str:
return "Contextual Accuracy"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguag... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
ca03e1c9c2a8-5 | }
for i, example in enumerate(examples)
]
return self.apply(inputs, callbacks=callbacks)
def _prepare_output(self, result: dict) -> dict:
parsed_result = _parse_string_eval_output(result[self.output_key])
if RUN_KEY in result:
parsed_result[RUN_KEY] = result[R... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
ca03e1c9c2a8-6 | @property
def evaluation_name(self) -> str:
return "COT Contextual Accuracy"
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
prompt: Optional[PromptTemplate] = None,
**kwargs: Any,
) -> CotQAEvalChain:
"""Load QA Eval Chain from LLM."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/eval_chain.html |
489e5445709b-0 | Source code for langchain.evaluation.qa.generate_chain
"""LLM Chain for generating examples for question answering."""
from __future__ import annotations
from typing import Any
from pydantic import Field
from langchain.chains.llm import LLMChain
from langchain.evaluation.qa.generate_prompt import PROMPT
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/qa/generate_chain.html |
9edb81f744bf-0 | Source code for langchain.evaluation.comparison.eval_chain
"""Base classes for comparing the output of two models."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
from pydantic import Extra, Field
from langchain.callbacks.manager import Callbacks
from langchain.chains.constitut... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-1 | Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?",
Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
}
[d... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-2 | if not criteria:
raise ValueError(
"Criteria cannot be empty. "
"Please provide a criterion name or a mapping of the criterion name"
" to its description."
)
criteria_ = dict(criteria)
return criteria_
[docs]class PairwiseStringResultOu... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-3 | score = {
"A": 1,
"B": 0,
None: 0.5,
}.get(verdict_)
return {
"reasoning": reasoning,
"value": verdict_,
"score": score,
}
[docs]class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain):
"""A chain ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-4 | # }
"""
output_key: str = "results" #: :meta private:
output_parser: BaseOutputParser = Field(
default_factory=PairwiseStringResultOutputParser
)
class Config:
"""Configuration for the PairwiseStringEvalChain."""
extra = Extra.ignore
@property
def requires_reference(... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-5 | Args:
llm (BaseLanguageModel): The LLM to use.
prompt (PromptTemplate, optional): The prompt to use.
**kwargs (Any): Additional keyword arguments.
Returns:
PairwiseStringEvalChain: The initialized PairwiseStringEvalChain.
Raises:
ValueError: If... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-6 | "input": input,
}
if self.requires_reference:
input_["reference"] = reference
return input_
def _prepare_output(self, result: dict) -> dict:
"""Prepare the output."""
parsed = result[self.output_key]
if RUN_KEY in result:
parsed[RUN_KEY] = resu... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-7 | result = self(
inputs=input_,
callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
async def _aevaluate_string_pairs(
self,
*,
prediction: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-8 | callbacks=callbacks,
tags=tags,
metadata=metadata,
include_run_info=include_run_info,
)
return self._prepare_output(result)
[docs]class LabeledPairwiseStringEvalChain(PairwiseStringEvalChain):
"""A chain for comparing two outputs, such as the outputs
of two m... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
9edb81f744bf-9 | expected_input_vars = {
"prediction",
"prediction_b",
"input",
"reference",
"criteria",
}
prompt_ = prompt or PROMPT_WITH_REFERENCE
if expected_input_vars != set(prompt_.input_variables):
raise ValueError(
f"... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/comparison/eval_chain.html |
94c9c3e1d429-0 | Source code for langchain.evaluation.string_distance.base
"""String distance evaluators based on the RapidFuzz library."""
from enum import Enum
from typing import Any, Callable, Dict, List, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForChainRun... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-1 | JARO = "jaro"
JARO_WINKLER = "jaro_winkler"
HAMMING = "hamming"
INDEL = "indel"
class _RapidFuzzChainMixin(Chain):
"""Shared methods for the rapidfuzz string distance evaluators."""
distance: StringDistance = Field(default=StringDistance.JARO_WINKLER)
normalize_score: bool = Field(default=True)
... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-2 | return result
@staticmethod
def _get_metric(distance: str, normalize_score: bool = False) -> Callable:
"""
Get the distance metric function based on the distance type.
Args:
distance (str): The distance type.
Returns:
Callable: The distance metric function... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
94c9c3e1d429-3 | Args:
a (str): The first string.
b (str): The second string.
Returns:
float: The distance between the two strings.
"""
return self.metric(a, b)
[docs]class StringDistanceEvalChain(StringEvaluator, _RapidFuzzChainMixin):
"""Compute string distances between ... | https://api.python.langchain.com/en/latest/_modules/langchain/evaluation/string_distance/base.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.