id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 117 |
|---|---|---|
0f22ef235fe1-8 | maximal_marginal_relevance: Whether to use maximal marginal relevance.
Defaults to False.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
return_score: Whether to return the score. Defaults to False.
Returns:
Lis... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
0f22ef235fe1-9 | filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
Returns:
List[Tuple[Document, float]]: List of documents most similar to the query
text with distance in float.
"""
return self._search_helper(
query=query,
k=k,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
0f22ef235fe1-10 | self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
amon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
0f22ef235fe1-11 | ) -> DeepLake:
"""Create a Deep Lake dataset from a raw documents.
If a dataset_path is specified, the dataset will be persisted in that location,
otherwise by default at `./deeplake`
Args:
path (str, pathlib.Path): - The full path to the dataset. Can be:
- De... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
0f22ef235fe1-12 | dataset_path=dataset_path, embedding_function=embedding, **kwargs
)
deeplake_dataset.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return deeplake_dataset
[docs] def delete(
self,
ids: Any[List[str], None] = None,
filter: Any[Dict[str, str], None] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
0f22ef235fe1-13 | try:
import deeplake
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"Please install it with `pip install deeplake`."
)
deeplake.delete(path, large_ok=True, force=True)
[docs] def delete_dataset(sel... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
0a4f735a1e47-0 | Source code for langchain.vectorstores.weaviate
"""Wrapper around weaviate vector database."""
from __future__ import annotations
import datetime
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type
from uuid import uuid4
import numpy as np
from langchain.docstore.document import Document
from ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-1 | if weaviate_api_key is not None
else None
)
client = weaviate.Client(weaviate_url, auth_client_secret=auth)
return client
def _default_score_normalizer(val: float) -> float:
return 1 - 1 / (1 + np.exp(val))
def _json_serializable(value: Any) -> Any:
if isinstance(value, datetime.datetime):
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-2 | )
if not isinstance(client, weaviate.Client):
raise ValueError(
f"client should be an instance of weaviate.Client, got {type(client)}"
)
self._client = client
self._index_name = index_name
self._embedding = embedding
self._text_key = text_k... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-3 | class_name=self._index_name,
uuid=_id,
vector=vector,
)
ids.append(_id)
return ids
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-4 | if kwargs.get("where_filter"):
query_obj = query_obj.with_where(kwargs.get("where_filter"))
if kwargs.get("additional"):
query_obj = query_obj.with_additional(kwargs.get("additional"))
result = query_obj.with_near_text(content).with_limit(k).do()
if "errors" in result:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-5 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-6 | Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-7 | raise ValueError(
"_embedding cannot be None for similarity_search_with_score"
)
content: Dict[str, Any] = {"concepts": [query]}
if kwargs.get("search_distance"):
content["certainty"] = kwargs.get("search_distance")
query_obj = self._client.query.get(self.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-8 | """
if self._relevance_score_fn is None:
raise ValueError(
"relevance_score_fn must be provided to"
" Weaviate constructor to normalize scores"
)
docs_and_scores = self.similarity_search_with_score(query, k=k, **kwargs)
return [
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-9 | text_key = "text"
schema = _default_schema(index_name)
attributes = list(metadatas[0].keys()) if metadatas else None
# check whether the index already exists
if not client.schema.contains(schema):
client.schema.create_class(schema)
with client.batch as batch:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
0a4f735a1e47-10 | relevance_score_fn=relevance_score_fn,
by_text=by_text,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/weaviate.html |
23d0298bdaff-0 | Source code for langchain.vectorstores.typesense
"""Wrapper around Typesense vector search"""
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
fro... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
23d0298bdaff-1 | *,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
23d0298bdaff-2 | ]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_dim": num_dim},
{"name": f"{self._text_key}", "type": "string"},
{"name": ".*", "type": "auto"},
]
self._typesense_client.collections.create(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
23d0298bdaff-3 | self,
query: str,
k: int = 4,
filter: Optional[str] = "",
) -> List[Tuple[Document, float]]:
"""Return typesense documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. De... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
23d0298bdaff-4 | k: Number of Documents to return. Defaults to 4.
filter: typesense filter_by expression to filter documents on
Returns:
List of Documents most similar to the query and score for each
"""
docs_and_score = self.similarity_search_with_score(query, k=k, filter=filter)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
23d0298bdaff-5 | }
typesense_api_key = typesense_api_key or get_from_env(
"typesense_api_key", "TYPESENSE_API_KEY"
)
client_config = {
"nodes": [node],
"api_key": typesense_api_key,
"connection_timeout_seconds": connection_timeout_seconds,
}
return ... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
04e04b46f0cc-0 | Source code for langchain.vectorstores.analyticdb
"""VectorStore wrapper around a Postgres/PGVector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Dict, Iterable, List, Optional, Tuple
import sqlalchemy
from sqlalchemy import REAL, Index
from sqlalchemy.dialects.postg... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-1 | """
Get or create a collection.
Returns [Collection, bool] where the bool is True if the collection was created.
"""
created = False
collection = cls.get_by_name(session, name)
if collection:
return collection, created
collection = cls(name=name, cmeta... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-2 | """
VectorStore implementation using AnalyticDB.
AnalyticDB is a distributed full PostgresSQL syntax cloud-native database.
- `connection_string` is a postgres connection string.
- `embedding_function` any embedding function implementing
`langchain.embeddings.base.Embeddings` interface.
- `c... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-3 | engine = sqlalchemy.create_engine(self.connection_string)
conn = engine.connect()
return conn
[docs] def create_tables_if_not_exists(self) -> None:
Base.metadata.create_all(self._conn)
[docs] def drop_tables(self) -> None:
Base.metadata.drop_all(self._conn)
[docs] def create_col... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-4 | """
if ids is None:
ids = [str(uuid.uuid1()) for _ in texts]
embeddings = self.embedding_function.embed_documents(list(texts))
if not metadatas:
metadatas = [{} for _ in texts]
with Session(self._conn) as session:
collection = self.get_collection(sessi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-5 | k: int = 4,
filter: Optional[dict] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]): Filte... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-6 | .order_by(EmbeddingStore.embedding.op("<->")(embedding))
.join(
CollectionStore,
EmbeddingStore.collection_id == CollectionStore.uuid,
)
.limit(k)
.all()
)
docs = [
(
Document(
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-7 | **kwargs: Any,
) -> AnalyticDB:
"""
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
"""
connection_string = cls.get_conne... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
04e04b46f0cc-8 | """
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_... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
8466eb6284f5-0 | Source code for langchain.vectorstores.elastic_vector_search
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from abc import ABC
from typing import Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.bas... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
8466eb6284f5-1 | # and attributes.
[docs]class ElasticVectorSearch(VectorStore, ABC):
"""Wrapper around Elasticsearch as a vector database.
To connect to an Elasticsearch instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.
Ex... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
8466eb6284f5-2 | Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:pass... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
8466eb6284f5-3 | except ValueError as e:
raise ValueError(
f"Your elasticsearch client string is mis-formatted. Got error: {e} "
)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
refresh_indices: bool = True,
**k... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
8466eb6284f5-4 | request = {
"_op_type": "index",
"_index": self.index_name,
"vector": embeddings[i],
"text": text,
"metadata": metadata,
"_id": _id,
}
ids.append(_id)
requests.append(request)
bulk... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
8466eb6284f5-5 | response = self.client.search(index=self.index_name, query=script_query, size=k)
hits = [hit for hit in response["hits"]["hits"]]
docs_and_scores = [
(
Document(
page_content=hit["_source"]["text"],
metadata=hit["_source"]["metadata"],
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
8466eb6284f5-6 | )
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
vectorsearch.add_texts(
texts, metadatas=metadatas, refresh_indices=refresh_indices
)
return vectorsearch
By Harrison Chase
© Copyright 2023... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
9d69c3bb5bdc-0 | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
9d69c3bb5bdc-1 | f"got {type(index)}"
)
self._index = index
self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Opt... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
9d69c3bb5bdc-2 | k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to r... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
9d69c3bb5bdc-3 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documen... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
9d69c3bb5bdc-4 | pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecone
except ImportError:
raise ValueError(
"Could not import pinecone python pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
9d69c3bb5bdc-5 | for j, line in enumerate(lines_batch):
metadata[j][text_key] = line
to_upsert = zip(ids_batch, embeds, metadata)
# upsert to Pinecone
index.upsert(vectors=list(to_upsert), namespace=namespace)
return cls(index, embedding.embed_query, text_key, namespace)
[docs... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
9fe057b59278-0 | Source code for langchain.vectorstores.myscale
"""Wrapper around MyScale vector database."""
from __future__ import annotations
import json
import logging
from hashlib import sha1
from threading import Thread
from typing import Any, Dict, Iterable, List, Optional, Tuple
from pydantic import BaseSettings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-1 | .. code-block:: python
{
'id': 'text_id',
'vector': 'text_embedding',
'text': 'text_plain',
'metadata': 'metadata_dictionary_in_json',
}... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-2 | config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScale Wrapper to LangChain
embedding_function (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-3 | CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map['id']} String,
{self.config.column_map['text']} String,
{self.config.column_map['vector']} Array(Float32),
{self.config.column_map['metadata']} JSON,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-4 | _data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE
{self.config.database}.{self.config.table}({ks})
VALUES
{','.join(_data)}
"""
return i_str
def _insert(self, transac: Iterable, column_names: Iterable[str]) -> N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-5 | column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) - set(column_names)) >= 0
keys, values = zip(*column_names.items())
try:
t = None
for v in self.pgbar(
zip(*values), desc="Inserting data...", total=len(metadatas)
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-6 | texts (Iterable[str]): List or tuple of strings to be added
config (MyScaleSettings, Optional): Myscale configuration
text_ids (Optional[Iterable], optional): IDs for the texts.
Defaults to None.
batch_size (int, optional): Batchsi... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-7 | ).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return _repr
def _build_qstr(
self, q_emb: List[float], topk: int, where_str: Optional[str] = None
) -> str:
q_emb... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-8 | of SQL injection. When dealing with metadatas, remember to
use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of Documents
"""
return self.similarity_search_by_v... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-9 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def similarity_search_with_relevance_scores(
self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any
) -> List[Tuple[Document, float]]:
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
9fe057b59278-10 | return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}"
)
@property
def metadata_column(self) -> str:
return self.config.column_map["metadata... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
16942036c972-0 | Source code for langchain.vectorstores.tair
"""Wrapper around Tair Vector."""
from __future__ import annotations
import json
import logging
import uuid
from typing import Any, Iterable, List, Optional, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
16942036c972-1 | data_type: str,
**kwargs: Any,
) -> bool:
index = self.client.tvs_get_index(self.index_name)
if index is not None:
logger.info("Index already exists")
return False
self.client.tvs_create_index(
self.index_name,
dim,
distance... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
16942036c972-2 | Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
Returns:
List[Document]: A list of documents that are most similar to the query text.
"""
# Creates embedding vector from user quer... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
16942036c972-3 | if "tair_url" in kwargs:
kwargs.pop("tair_url")
distance_type = tairvector.DistanceMetric.InnerProduct
if "distance_type" in kwargs:
distance_type = kwargs.pop("distance_typ")
index_type = tairvector.IndexType.HNSW
if "index_type" in kwargs:
index_type... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
16942036c972-4 | cls,
documents: List[Document],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadata",
**kwargs: Any,
) -> Tair:
texts = [d.page_content for d in docum... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
16942036c972-5 | # index not exist
logger.info("Index does not exist")
return False
return True
[docs] @classmethod
def from_existing_index(
cls,
embedding: Embeddings,
index_name: str = "langchain",
content_key: str = "content",
metadata_key: str = "metadat... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/tair.html |
c96311448f84-0 | 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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
c96311448f84-1 | "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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
c96311448f84-2 | 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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/hnsw.html |
f930bd92d905-0 | 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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
f930bd92d905-1 | [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://python.langchain.com/en/latest/_modules/langchain/vectorstores/docarray/in_memory.html |
9bcdccd5e806-0 | Source code for langchain.output_parsers.regex
from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex: str
output_keys: List[str]
... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex.html |
b9068afa51ec-0 | Source code for langchain.output_parsers.retry
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.prompt import PromptTemplate
from lang... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b9068afa51ec-1 | chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except OutputParserException:
new_completio... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
b9068afa51ec-2 | ) -> RetryWithErrorOutputParser[T]:
chain = LLMChain(llm=llm, prompt=prompt)
return cls(parser=parser, retry_chain=chain)
[docs] def parse_with_prompt(self, completion: str, prompt_value: PromptValue) -> T:
try:
parsed_completion = self.parser.parse(completion)
except Outp... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/retry.html |
322c3e94dcd0-0 | Source code for langchain.output_parsers.rail_parser
from __future__ import annotations
from typing import Any, Dict
from langchain.schema import BaseOutputParser
[docs]class GuardrailsOutputParser(BaseOutputParser):
guard: Any
@property
def _type(self) -> str:
return "guardrails"
[docs] @classme... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/rail_parser.html |
cd0f6b77400d-0 | Source code for langchain.output_parsers.list
from __future__ import annotations
from abc import abstractmethod
from typing import List
from langchain.schema import BaseOutputParser
[docs]class ListOutputParser(BaseOutputParser):
"""Class to parse the output of an LLM call to a list."""
@property
def _type(... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/list.html |
4d9c918f70c3-0 | Source code for langchain.output_parsers.structured
from __future__ import annotations
from typing import Any, List
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import STRUCTURED_FORMAT_INSTRUCTIONS
from langchain.output_parsers.json import parse_and_check_json_markdown
from langchai... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/structured.html |
03fa06d6e4d2-0 | Source code for langchain.output_parsers.fix
from __future__ import annotations
from typing import TypeVar
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT
from langchain.prompts.base import BasePromptTemplate
f... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/fix.html |
143def13812c-0 | Source code for langchain.output_parsers.pydantic
import json
import re
from typing import Type, TypeVar
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS
from langchain.schema import BaseOutputParser, OutputParserException
T = TypeVar(... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
143def13812c-1 | @property
def _type(self) -> str:
return "pydantic"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/pydantic.html |
2d54b70c0344-0 | Source code for langchain.output_parsers.regex_dict
from __future__ import annotations
import re
from typing import Dict, Optional
from langchain.schema import BaseOutputParser
[docs]class RegexDictParser(BaseOutputParser):
"""Class to parse the output into a dictionary."""
regex_pattern: str = r"{}:\s?([^.'\n'... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/regex_dict.html |
7610c1b5670d-0 | Source code for langchain.output_parsers.datetime
import random
from datetime import datetime, timedelta
from typing import List
from langchain.schema import BaseOutputParser, OutputParserException
from langchain.utils import comma_list
def _generate_random_datetime_strings(
pattern: str,
n: int = 3,
start_... | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
7610c1b5670d-1 | ) from e
@property
def _type(self) -> str:
return "datetime"
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 29, 2023. | https://python.langchain.com/en/latest/_modules/langchain/output_parsers/datetime.html |
b241347a1a50-0 | Source code for langchain.docstore.wikipedia
"""Wrapper around wikipedia API."""
from typing import Union
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
[docs]class Wikipedia(Docstore):
"""Wrapper around wikipedia API."""
def __init__(self) -> None:
"""Chec... | https://python.langchain.com/en/latest/_modules/langchain/docstore/wikipedia.html |
8bc3a609d7fb-0 | Source code for langchain.docstore.in_memory
"""Simple in memory docstore in the form of a dict."""
from typing import Dict, Union
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
[docs]class InMemoryDocstore(Docstore, AddableMixin):
"""Simple in memory doc... | https://python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html |
f844aeb30e69-0 | Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
import yaml
from pydantic import BaseModel, Extra, Field, roo... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
f844aeb30e69-1 | "jinja2 not installed, which is needed to use the jinja2_formatter. "
"Please install it with `pip install jinja2`."
)
env = Environment()
ast = env.parse(template)
variables = meta.find_undeclared_variables(ast)
return variables
DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
f844aeb30e69-2 | """Base class for all prompt templates, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
output_parser: Optional[BaseOutputParser] = None
"""How to parse the output of calling an LLM on this formatted prompt."""
partial_variabl... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
f844aeb30e69-3 | prompt_dict["input_variables"] = list(
set(self.input_variables).difference(kwargs)
)
prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
return type(self)(**prompt_dict)
def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
# G... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
f844aeb30e69-4 | # Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
c754afbb2e16-0 | Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
)
from langcha... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
c754afbb2e16-1 | """Check that one and only one of examples/example_selector are provided."""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_select... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
c754afbb2e16-2 | # Get the examples to use.
examples = self._get_examples(**kwargs)
examples = [
{k: e[k] for k in self.example_prompt.input_variables} for e in examples
]
# Format the examples.
example_strings = [
self.example_prompt.format(**example) for example in examp... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
93d739e70e01-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
S... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
93d739e70e01-1 | """
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
@root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that template and input variables are consistent."""
if value... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
93d739e70e01-2 | [docs] @classmethod
def from_file(
cls, template_file: Union[str, Path], input_variables: List[str], **kwargs: Any
) -> PromptTemplate:
"""Load a prompt from a file.
Args:
template_file: The path to the file containing the prompt template.
input_variables: A li... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
83369d295909-0 | Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot i... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
83369d295909-1 | if template_path.suffix == ".txt":
with open(template_path) as f:
template = f.read()
else:
raise ValueError
# Set the template variable to the extracted variable.
config[var_name] = template
return config
def _load_examples(config: dict) -> dict:
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
83369d295909-2 | config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueError(
"Only one of example_prompt and example_prompt_path should "
"be specified."
)
config[... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
83369d295909-3 | with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
elif file_path.suffix == ".py":
spec = importlib.util.spec_from_loader(
"prompt", loader=None, origin=str(file_path)
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
15423f9eb833-0 | Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
from langchain.prompts.example_selec... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
15423f9eb833-1 | examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selecto... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
15423f9eb833-2 | kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the examples to use.
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
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