id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
79acd5ad3a21-4 | constraints and even sub-queries.
For more information, please visit
[myscale official site](https://docs.myscale.com/en/overview/)
"""
def __init__(
self,
embedding: Embeddings,
config: Optional[MyScaleSettings] = None,
**kwargs: Any,
) -> None:
"""MyScal... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-5 | try:
from tqdm import tqdm
self.pgbar = tqdm
except ImportError:
# Just in case if tqdm is not installed
self.pgbar = lambda x: x
super().__init__()
if config is not None:
self.config = config
else:
self.config = MyS... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-6 | index_params = (
", " + ",".join([f"'{k}={v}'" for k, v in self.config.index_param.items()])
if self.config.index_param
else ""
)
schema_ = f"""
CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}(
{self.config.column_map[... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-7 | ) ENGINE = MergeTree ORDER BY {self.config.column_map['id']}
"""
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding.embed_query
self.dist_order = "ASC" if self.config.metric in ["cosine", "l2"] else "DESC"
# Create a c... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-8 | def _build_istr(self, transac: Iterable, column_names: Iterable[str]) -> str:
ks = ",".join(column_names)
_data = []
for n in transac:
n = ",".join([f"'{self.escape_str(str(_n))}'" for _n in n])
_data.append(f"({n})")
i_str = f"""
INSERT INTO TABLE... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-9 | texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
batch_size: int = 32,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-10 | column_names = {
colmap_["id"]: ids,
colmap_["text"]: texts,
colmap_["vector"]: map(self.embedding_function, texts),
}
metadatas = metadatas or [{} for _ in texts]
column_names[colmap_["metadata"]] = map(json.dumps, metadatas)
assert len(set(colmap_) -... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-11 | if t:
t.join()
t = Thread(target=self._insert, args=[transac, keys])
t.start()
transac = []
if len(transac) > 0:
if t:
t.join()
self._insert(transac, keys)
retu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-12 | text_ids: Optional[Iterable[str]] = None,
batch_size: int = 32,
**kwargs: Any,
) -> MyScale:
"""Create Myscale wrapper with existing texts
Args:
embedding_function (Embeddings): Function to extract text embedding
texts (Iterable[str]): List or tuple of strings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-13 | Returns:
MyScale Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas)
return ctx
def __repr__(self) -> str:
"""Text representation for myscale, prints backends, username and schemas.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-14 | _repr += "-" * 51 + "\n"
for r in self.client.query(
f"DESC {self.config.database}.{self.config.table}"
).named_results():
_repr += (
f"|\033[94m{r['name']:24s}\033[0m|\033[96m{r['type']:24s}\033[0m|\n"
)
_repr += "-" * 51 + "\n"
return... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-15 | q_str = f"""
SELECT {self.config.column_map['text']},
{self.config.column_map['metadata']}, dist
FROM {self.config.database}.{self.config.table}
{where_str}
ORDER BY distance({self.config.column_map['vector']}, [{q_emb_str}])
AS dist {sel... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-16 | Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
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`.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-17 | query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
Defaults to None.
NOTE: Please do not let end-user to fill this and always be aware
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-18 | ]
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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-19 | use `{self.metadata_column}.attribute` instead of `attribute`
alone. The default name for it is `metadata`.
Returns:
List[Document]: List of documents most similar to the query text
and cosine distance in float for each.
Lower score represents more similarit... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
79acd5ad3a21-20 | ]
except Exception as e:
logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
return []
[docs] def drop(self) -> None:
"""
Helper function: Drop data
"""
self.client.command(
f"DROP TABLE IF EXISTS {self.config.database}.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/myscale.html |
58087099bdc0-0 | 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 |
58087099bdc0-1 | We integrated deeplake's similarity search and filtering for fast prototyping,
Now, it supports Tensor Query Language (TQL) for production use cases
over billion rows.
Why Deep Lake?
- Not only stores embeddings, but also the original data with version control.
- Serverless, doesn't require another ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-2 | """
_LANGCHAIN_DEFAULT_DEEPLAKE_PATH = "./deeplake/"
def __init__(
self,
dataset_path: str = _LANGCHAIN_DEFAULT_DEEPLAKE_PATH,
token: Optional[str] = None,
embedding_function: Optional[Embeddings] = None,
read_only: bool = False,
ingestion_batch_size: int = 1000,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-3 | ... path = <path_for_storing_Data>,
... )
>>>
>>> # Create a vector store in the Deep Lake Managed Tensor Database
>>> data = DeepLake(
... path = "hub://org_id/dataset_name",
... exec_option = "tensor_db",
... )
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-4 | ingestion_batch_size (int): During data ingestion, data is divided
into batches. Batch size is the size of each batch.
Default is 1000.
num_workers (int): Number of workers to use during data ingestion.
Default is 0.
verbose (bool): Print dataset s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-5 | or connected to Deep Lake. Not for in-memory or local datasets.
- ``tensor_db`` - Hosted Managed Tensor Database that is
responsible for storage and query execution. Only for data stored in
the Deep Lake Managed Database. Use runtime = {"db_engine": True} during
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-6 | raise ValueError(
"deeplake version should be >= 3.6.3, but you've installed"
f" {deeplake.__version__}. Consider upgrading deeplake version \
pip install --upgrade deeplake."
)
self.dataset_path = dataset_path
self.vectorstore = DeepLakeVe... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-7 | ids: Optional[List[str]] = None,
**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_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-8 | kwargs = {}
if ids:
if self._id_tensor_name == "ids": # for backwards compatibility
kwargs["ids"] = ids
else:
kwargs["id"] = ids
if metadatas is None:
metadatas = [{}] * len(list(texts))
return self.vectorstore.add(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-9 | return_score: bool = False,
) -> Any[List[Document], List[Tuple[Document, float]]]:
"""Function for performing tql_search.
Args:
tql_query (str): TQL Query string for direct evaluation.
Available only for `compute_engine` and `tensor_db`.
exec_option (str, opt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-10 | Use runtime = {"db_engine": True} during dataset creation.
return_score (bool): Return score with document. Default is False.
Returns:
List[Document] - A list of documents
Raises:
ValueError: If return_score is True but some condition is not met.
"""
r... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-11 | def _search(
self,
query: Optional[str] = None,
embedding: Optional[Union[List[float], np.ndarray]] = None,
embedding_function: Optional[Callable] = None,
k: int = 4,
distance_metric: str = "L2",
use_maximal_marginal_relevance: bool = False,
fetch_k: Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-12 | embedding (Union[List[float], np.ndarray], optional): Query's embedding.
embedding_function (Callable, optional): Function to convert `query`
into embedding.
k (int): Number of Documents to return.
distance_metric (str): `L2` for Euclidean, `L1` for Nuclear, `max`
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-13 | - ``Function`` - Any function compatible with `deeplake.filter`.
use_maximal_marginal_relevance (bool): Use maximal marginal relevance.
fetch_k (int): Number of Documents for MMR algorithm.
return_score (bool): Return the score.
exec_option (str, optional): Supports 3 way... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-14 | **kwargs: Additional keyword arguments.
Returns:
List of Documents by the specified distance metric,
if return_score True, return a tuple of (Document, score)
Raises:
ValueError: if both `embedding` and `embedding_function` are not specified.
"""
if kw... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-15 | if embedding is None:
if _embedding_function is None:
raise ValueError(
"Either `embedding` or `embedding_function` needs to be"
" specified."
)
embedding = _embedding_function(query) if query else None
if isinstance... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-16 | 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
embedding, # type: ignore
embeddings,
k=min... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-17 | ]
if return_score:
return [(doc, score) for doc, score in zip(docs, scores)]
return docs
[docs] def similarity_search(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Return docs most similar to query.
Examp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-18 | ... )
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. Defaults to None.
distance_metri... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-19 | Defaults to None.
exec_option (str): Supports 3 ways to perform searching.
'python', 'compute_engine', or 'tensor_db'. Defaults to 'python'.
- 'python': Pure-python implementation for the client.
WARNING: not recommended for big datasets.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-20 | **kwargs,
)
[docs] def similarity_search_by_vector(
self,
embedding: Union[List[float], np.ndarray],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""
Return docs most similar to embedding vector.
Examples:
>>> # Search using an embedd... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-21 | Additional filter before embedding search.
- ``Dict`` - Key-value search on tensors of htype json. True
if all key-value filters are satisfied.
Dict = {"tensor_name_1": {"key": value},
"tensor_name_2": {"key": value}}
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-22 | any data stored in or connected to Deep Lake. It cannot be
used with in-memory or local datasets.
- "tensor_db" - Performant, fully-hosted Managed Tensor Database.
Responsible for storage and query execution. Only available
for ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-23 | return_score=False,
**kwargs,
)
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Run similarity search with Deep Lake with distance returned.
Examples:
>... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-24 | distance_metric: `L2` for Euclidean, `L1` for Nuclear, `max` L-infinity
distance, `cos` for cosine similarity, 'dot' for dot product.
Defaults to `L2`.
filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
embedding_function ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-25 | 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.
- "tensor_db" - Performant, fully-hosted Managed Tensor Database.
Resp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-26 | embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
exec_option: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""
Return docs selected using the maximal marginal relevance. Maximal marginal
relevance optimizes ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-27 | fetch_k: Number of Documents to fetch for MMR algorithm.
lambda_mult: Number between 0 and 1 determining the degree of diversity.
0 corresponds to max diversity and 1 to min diversity. Defaults to 0.5.
exec_option (str): DeepLakeVectorStore supports 3 ways for searching.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-28 | with in-memory or local datasets.
- "tensor_db" - Performant, fully-hosted Managed Tensor Database.
Responsible for storage and query execution. Only available for
data stored in the Deep Lake Managed Database. To store datasets
in this databas... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-29 | k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
exec_option: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-30 | fetch_k: Number of Documents for MMR algorithm.
lambda_mult: Value between 0 and 1. 0 corresponds
to maximum diversity and 1 to minimum.
Defaults to 0.5.
exec_option (str): Supports 3 ways to perform searching.
- "python" - Pure-pyt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-31 | for data stored in the Deep Lake Managed Database. To store
datasets in this database, specify
`runtime = {"db_engine": True}` during dataset creation.
**kwargs: Additional keyword arguments
Returns:
List of Documents selected by maximal ma... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-32 | exec_option=exec_option,
embedding_function=embedding_function, # type: ignore
**kwargs,
)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optio... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-33 | ... texts = <the_texts_that_you_want_to_embed>,
... embedding_function = <embedding_function_for_query>,
... k = <number_of_items_to_return>,
... exec_option = <preferred_exec_option>,
... )
Args:
dataset_path (str): - The full path to the ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-34 | in either the environment
- Local file system path of the form ``./path/to/dataset`` or
``~/path/to/dataset`` or ``path/to/dataset``.
- In-memory path of the form ``mem://path/to/dataset`` which doesn't
save the dataset, but keeps it in memory inst... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-35 | Raises:
ValueError: If 'embedding' is provided in kwargs. This is deprecated,
please use `embedding_function` instead.
"""
if kwargs.get("embedding"):
raise ValueError(
"using embedding as embedidng_functions is deprecated. "
"Pleas... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-36 | delete_all: Any[bool, None] = None,
) -> bool:
"""Delete the entities in the dataset.
Args:
ids (Optional[List[str]], optional): The document_ids to delete.
Defaults to None.
filter (Optional[Dict[str, str]], optional): The filter to delete by.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
58087099bdc0-37 | Args:
path (str): path of the dataset to delete.
Raises:
ValueError: if deeplake is not installed.
"""
try:
import deeplake
except ImportError:
raise ValueError(
"Could not import deeplake python package. "
"... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/deeplake.html |
04321d39b384-0 | Source code for langchain.vectorstores.annoy
"""Wrapper around Annoy vector database."""
from __future__ import annotations
import os
import pickle
import uuid
from configparser import ConfigParser
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-1 | try:
import annoy
except ImportError:
raise ValueError(
"Could not import annoy python package. "
"Please install it with `pip install --user annoy` "
)
return annoy
[docs]class Annoy(VectorStore):
"""Wrapper around Annoy vector database.
To use, you shoul... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-2 | """Initialize with necessary components."""
self.embedding_function = embedding_function
self.index = index
self.metric = metric
self.docstore = docstore
self.index_to_docstore_id = index_to_docstore_id
[docs] def add_texts(
self,
texts: Iterable[str],
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-3 | dists: List of distances of the documents in the index.
Returns:
List of Documents and scores.
"""
docs = []
for idx, dist in zip(idxs, dists):
_id = self.index_to_docstore_id[idx]
doc = self.docstore.search(_id)
if not isinstance(doc, Docu... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-4 | k: Number of Documents to return. Defaults to 4.
search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
idxs, dists = self.index.get_nns_by_vector(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-5 | search_k: inspect up to search_k nodes which defaults
to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
idxs, dists = self.index.get_nns_by_item(
docstore_index, k, search_k=search_k, include_distanc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-6 | to n_trees * n if not provided
Returns:
List of Documents most similar to the query and score for each
"""
embedding = self.embedding_function(query)
docs = self.similarity_search_with_score_by_vector(embedding, k, search_k)
return docs
[docs] def similarity_search... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-7 | """
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search_by_index(
self, docstore_index: int, k: int = 4, search_k: int = -1, **kwargs: Any
) -> List[Document]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-8 | docstore_index, k, search_k
)
return [doc for doc, _ in docs_and_scores]
[docs] def similarity_search(
self, query: str, k: int = 4, search_k: int = -1, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up docume... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-9 | [docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
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 marg... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-10 | Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
idxs = self.index.get_nns_by_vector(
embedding, fetch_k, search_k=-1, include_distances=False
)
embeddings = [self.index.get_item_vector(i) for i in idxs]
mmr_s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-11 | if not isinstance(doc, Document):
raise ValueError(f"Could not find document for id {_id}, got {doc}")
docs.append(doc)
return docs
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-12 | lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-13 | **kwargs: Any,
) -> Annoy:
if metric not in INDEX_METRICS:
raise ValueError(
(
f"Unsupported distance metric: {metric}. "
f"Expected one of {list(INDEX_METRICS)}"
)
)
annoy = dependable_annoy_import()
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-14 | index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
docstore = InMemoryDocstore(
{index_to_id[i]: doc for i, doc in enumerate(documents)}
)
return cls(embedding.embed_query, index, metric, docstore, index_to_id)
[docs] @classmethod
def from_texts(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-15 | embedding: Embedding function to use.
metadatas: List of metadata dictionaries to associate with documents.
metric: Metric to use for indexing. Defaults to "angular".
trees: Number of trees to use for indexing. Defaults to 100.
n_jobs: Number of jobs to use for indexing. ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-16 | embeddings = embedding.embed_documents(texts)
return cls.__from(
texts, embeddings, embedding, metadatas, metric, trees, n_jobs, **kwargs
)
[docs] @classmethod
def from_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-17 | metric: Metric to use for indexing. Defaults to "angular".
trees: Number of trees to use for indexing. Defaults to 100.
n_jobs: Number of jobs to use for indexing. Defaults to -1
This is a user friendly interface that:
1. Creates an in memory docstore with provided embeddings... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-18 | embeddings = [t[1] for t in text_embeddings]
return cls.__from(
texts, embeddings, embedding, metadatas, metric, trees, n_jobs, **kwargs
)
[docs] def save_local(self, folder_path: str, prefault: bool = False) -> None:
"""Save Annoy index, docstore, and index_to_docstore_id to disk... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-19 | "f": self.index.f,
"metric": self.metric,
}
self.index.save(str(path / "index.annoy"), prefault=prefault)
with open(path / "index.pkl", "wb") as file:
pickle.dump((self.docstore, self.index_to_docstore_id, config_object), file)
[docs] @classmethod
def load_local(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
04321d39b384-20 | path = Path(folder_path)
# load index separately since it is not picklable
annoy = dependable_annoy_import()
# load docstore and index_to_docstore_id
with open(path / "index.pkl", "rb") as file:
docstore, index_to_docstore_id, config_object = pickle.load(file)
f = int... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/annoy.html |
f6eb15c794d4-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-1 | import typesense
node = {
"host": "localhost", # For Typesense Cloud use xxx.a1.typesense.net
"port": "8108", # For Typesense Cloud use 443
"protocol": "http" # For Typesense Cloud use https
}
typesense_client = typesense.Clie... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-2 | def __init__(
self,
typesense_client: Client,
embedding: Embeddings,
*,
typesense_collection_name: Optional[str] = None,
text_key: str = "text",
):
"""Initialize with Typesense client."""
try:
from typesense import Client
except Imp... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-3 | self._typesense_collection_name = (
typesense_collection_name or f"langchain-{str(uuid.uuid4())}"
)
self._text_key = text_key
@property
def _collection(self) -> Collection:
return self._typesense_client.collections[self._typesense_collection_name]
def _prep_texts(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-4 | return [
{"id": _id, "vec": vec, f"{self._text_key}": text, "metadata": metadata}
for _id, vec, text, metadata in zip(_ids, embedded_texts, texts, _metadatas)
]
def _create_collection(self, num_dim: int) -> None:
fields = [
{"name": "vec", "type": "float[]", "num_... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-5 | texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embedding 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/typesense.html |
f6eb15c794d4-6 | self._create_collection(len(docs[0]["vec"]))
self._collection.documents.import_(docs, {"action": "upsert"})
return [doc["id"] for doc in docs]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 10,
filter: Optional[str] = "",
) -> List[Tuple[Do... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-7 | """
embedded_query = [str(x) for x in self._embedding.embed_query(query)]
query_obj = {
"q": "*",
"vector_query": f'vec:([{",".join(embedded_query)}], k:{k})',
"filter_by": filter,
"collection": self._typesense_collection_name,
}
docs = []
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-8 | self,
query: str,
k: int = 10,
filter: Optional[str] = "",
**kwargs: Any,
) -> List[Document]:
"""Return typesense documents most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-9 | embedding: Embeddings,
*,
host: str = "localhost",
port: Union[str, int] = "8108",
protocol: str = "http",
typesense_api_key: Optional[str] = None,
connection_timeout_seconds: int = 2,
**kwargs: Any,
) -> Typesense:
"""Initialize Typesense directly fro... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-10 | )
"""
try:
from typesense import Client
except ImportError:
raise ValueError(
"Could not import typesense python package. "
"Please install it with `pip install typesense`."
)
node = {
"host": host,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-11 | texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
typesense_client: Optional[Client] = None,
typesense_client_params: Optional[dict] = None,
typesense_collection_name: Optional[str] = None,
text_key: ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
f6eb15c794d4-12 | )
vectorstore.add_texts(texts, metadatas=metadatas, ids=ids)
return vectorstore | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/typesense.html |
acf5728c5ee9-0 | 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
import numpy as np
from langchain.docstore.document import Document
from langchain.embeddings.bas... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-1 | # in your Pinecone console
pinecone.init(api_key="***", environment="...")
index = pinecone.Index("langchain-demo")
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone(index, embeddings.embed_query, "text")
"""
def __init__(
self,
index: Any,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-2 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-3 | namespace: Optional pinecone namespace to add the texts to.
Returns:
List of ids from adding the texts into the vectorstore.
"""
if namespace is None:
namespace = self._namespace
# Embed and create the documents
docs = []
ids = ids or [str(uuid.uui... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-4 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-5 | include_metadata=True,
namespace=namespace,
filter=filter,
)
for res in results["matches"]:
metadata = res["metadata"]
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
d... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-6 | 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://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-7 | [docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
""... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-8 | of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance.
"""
if namespace is None:
namespace = s... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-9 | return [
Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
for metadata in selected
]
[docs] def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
acf5728c5ee9-10 | 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 among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
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