id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
252c3ae2a6d6-0 | Source code for langchain.vectorstores.cassandra
from __future__ import annotations
import typing
import uuid
from typing import (
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
)
import numpy as np
if typing.TYPE_CHECKING:
from cassandra.cluster ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-1 | return filter_dict
def _get_embedding_dimension(self) -> int:
if self._embedding_dimension is None:
self._embedding_dimension = len(
self.embedding.embed_query("This is a sample sentence.")
)
return self._embedding_dimension
[docs] def __init__(
sel... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-2 | so here the final score transformation is not reversing the interval:
"""
return self._dont_flip_the_cos_score
[docs] def delete_collection(self) -> None:
"""
Just an alias for `clear`
(to better align with other VectorStore implementations).
"""
self.clear()
[... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-3 | ids (Optional[List[str]], optional): Optional list of IDs.
batch_size (int): Number of concurrent requests to send to the server.
ttl_seconds (Optional[int], optional): Optional time-to-live
for the added texts.
Returns:
List[str]: List of IDs of the added tex... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-4 | ) -> List[Tuple[Document, float, str]]:
"""Return docs most similar to embedding vector.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
Returns:
List of (Document, score, id), the most s... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-5 | self,
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-6 | self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
) -> List[Tuple[Document, float]]:
embedding_vector = self.embedding.embed_query(query)
return self.similarity_search_with_score_by_vector(
embedding_vector,
k,
filter=f... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-7 | mmrChosenIndices = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
[pfHit["embedding_vector"] for pfHit in prefetchHits],
k=k,
lambda_mult=lambda_mult,
)
mmrHits = [
pfHit
for pfIndex, pfHit in enumerate(prefetchH... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-8 | embedding_vector,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter,
)
[docs] @classmethod
def from_texts(
cls: Type[CVST],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
batch_size:... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
252c3ae2a6d6-9 | table_name: str = kwargs["table_name"]
return cls.from_texts(
texts=texts,
metadatas=metadatas,
embedding=embedding,
session=session,
keyspace=keyspace,
table_name=table_name,
) | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/cassandra.html |
bb31951a288f-0 | Source code for langchain.vectorstores.baiducloud_vector_search
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Union,
)
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embed... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-1 | ) -> None:
self.embedding = embedding
self.index_name = index_name
self.query_field = kwargs.get("query_field", "text")
self.vector_query_field = kwargs.get("vector_query_field", "vector")
self.space_type = kwargs.get("space_type", "cosine")
self.index_type = kwargs.get("... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-2 | """Create the index if it doesn't already exist.
Args:
dims_length: Length of the embedding vectors.
"""
if self.client.indices.exists(index=self.index_name):
logger.info(f"Index {self.index_name} already exists. Skipping creation.")
else:
if dims_leng... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-3 | "type": "bpack_vector",
"dims": dims_length,
"index_type": "hnsw",
"space_type": self.space_type,
"parameters": {
"ef_construction": self.index_params.get(
"hnsw_ef_construction", 200
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-4 | return True
except BulkIndexError as e:
logger.error(f"Error deleting texts: {e}")
raise e
else:
logger.info("No documents to delete")
return False
def _query_body(
self,
query_vector: Union[List[float], None],
filte... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-5 | Returns:
List of Documents most similar to the query and score for each
"""
if self.embedding and query is not None:
query_vector = self.embedding.embed_query(query)
query_body = self._query_body(
query_vector=query_vector, filter=filter, search_params=search_... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-6 | """
results = self.similarity_search_with_score(
query=query, k=k, filter=filter, **kwargs
)
return [doc for doc, _ in results]
[docs] def similarity_search_with_score(
self, query: str, k: int, filter: Optional[dict] = None, **kwargs: Any
) -> List[Tuple[Document, flo... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-7 | # Encode the provided texts and add them to the newly created index.
vectorStore.add_documents(documents)
return vectorStore
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[Dict[str, Any]]]... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-8 | except ImportError:
raise ImportError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
embeddings = []
create_index_if_not_exists = kwargs.get("create_index_if_not_exists", True)
ids... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
bb31951a288f-9 | self.client, requests, stats_only=True, refresh=refresh_indices
)
logger.debug(
f"Added {success} and failed to add {failed} texts to index"
)
logger.debug(f"added texts {ids} to index")
return ids
except Bul... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/baiducloud_vector_search.html |
c53dda550dc3-0 | Source code for langchain.vectorstores.clickhouse
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, Union
from langchain.docstore.document import Document
from langchain.pydantic_v1 import Ba... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-1 | Defaults to 'vector_table'.
metric (str) : Metric to compute distance,
supported are ('angular', 'euclidean', 'manhattan', 'hamming',
'dot'). Defaults to 'angular'.
https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-2 | return getattr(self, item)
class Config:
env_file = ".env"
env_prefix = "clickhouse_"
env_file_encoding = "utf-8"
[docs]class Clickhouse(VectorStore):
"""`ClickHouse VectorSearch` vector store.
You need a `clickhouse-connect` python package, and a valid account
to connect to Clic... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-3 | assert self.config
assert self.config.host and self.config.port
assert (
self.config.column_map
and self.config.database
and self.config.table
and self.config.metric
)
for k in ["id", "embedding", "document", "metadata", "uuid"]:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-4 | """
self.dim = dim
self.BS = "\\"
self.must_escape = ("\\", "'")
self.embedding_function = embedding
self.dist_order = "ASC" # Only support ConsingDistance and L2Distance
# Create a connection to clickhouse
self.client = get_client(
host=self.config.h... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-5 | self.client.command(_insert_query)
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
batch_size: int = 32,
ids: Optional[Iterable[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Insert more texts through the embedding... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-6 | )
transac.append(v)
if len(transac) == batch_size:
if t:
t.join()
t = Thread(target=self._insert, args=[transac, keys])
t.start()
transac = []
if len(transac) > 0:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-7 | Other keyword arguments will pass into
[clickhouse-connect](https://clickhouse.com/docs/en/integrations/python#clickhouse-connect-driver-api)
Returns:
ClickHouse Index
"""
ctx = cls(embedding, config, **kwargs)
ctx.add_texts(texts, ids=text_ids, batch_size=bat... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-8 | if where_str:
where_str = f"PREWHERE {where_str}"
else:
where_str = ""
settings_strs = []
if self.config.index_query_params:
for k in self.config.index_query_params:
settings_strs.append(f"SETTING {k}={self.config.index_query_params[k]}")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-9 | self.embedding_function.embed_query(query), k, where_str, **kwargs
)
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
where_str: Optional[str] = None,
**kwargs: Any,
) -> List[Document]:
"""Perform a similarity search with C... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
c53dda550dc3-10 | ) -> List[Tuple[Document, float]]:
"""Perform a similarity search with ClickHouse
Args:
query (str): query string
k (int, optional): Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional): where condition string.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/clickhouse.html |
30088a248d2c-0 | Source code for langchain.vectorstores.zep
from __future__ import annotations
import logging
import warnings
from dataclasses import asdict, dataclass
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.schema.embeddings import Emb... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-1 | Args:
api_url (str): The URL of the Zep API.
collection_name (str): The name of the collection in the Zep store.
api_key (Optional[str]): The API key for the Zep API.
config (Optional[CollectionConfig]): The configuration for the collection.
Required if the collection does no... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-2 | @property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
return self._embedding
def _load_collection(self) -> DocumentCollection:
"""
Load the collection from the Zep backend.
"""
from zep_python import NotFound... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-3 | embeddings = self._embedding.embed_documents(list(texts))
if self._collection and self._collection.embedding_dimensions != len(
embeddings[0]
):
raise ValueError(
"The embedding dimensions of the collection and the embedding"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-4 | "collection should be an instance of a Zep DocumentCollection"
)
documents = self._generate_documents_to_add(texts, metadatas, document_ids)
uuids = self._collection.add_documents(documents)
return uuids
[docs] async def aadd_texts(
self,
texts: Iterable[str],
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-5 | "search_type to be 'similarity' or 'mmr'."
)
[docs] async def asearch(
self,
query: str,
search_type: str,
metadata: Optional[Dict[str, Any]] = None,
k: int = 3,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query using spec... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-6 | **kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
return self._similarity_search_with_relevance_scores(
query, k=k, metadata=metadata, **kwargs
)
def _similarity_search_with_relevance_scores(
self,
query: str,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-7 | )
return [
(
Document(
page_content=doc.content,
metadata=doc.metadata,
),
doc.score or 0.0,
)
for doc in results
]
[docs] async def asimilarity_search_with_relevance_scores(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-8 | results = await self.asimilarity_search_with_relevance_scores(
query, k, metadata=metadata, **kwargs
)
return [doc for doc, _ in results]
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
metadata: Optional[Dict[str, Any]] = ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-9 | embedding=embedding, limit=k, metadata=metadata, **kwargs
)
return [
Document(
page_content=doc.content,
metadata=doc.metadata,
)
for doc in results
]
[docs] def max_marginal_relevance_search(
self,
query: str... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-10 | embedding=query_vector,
limit=k,
metadata=metadata,
search_type="mmr",
mmr_lambda=lambda_mult,
**kwargs,
)
else:
results, query_vector = self._collection.search_return_query_vector(
query,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-11 | mmr_lambda=lambda_mult,
**kwargs,
)
return [Document(page_content=d.content, metadata=d.metadata) for d in results]
[docs] def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult:... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-12 | return [Document(page_content=d.content, metadata=d.metadata) for d in results]
[docs] async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
metadata: Optional[Dict[str, Any]] = None,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
30088a248d2c-13 | embedding (Optional[Embeddings]): Optional embedding function to use to
embed the texts.
metadatas (Optional[List[Dict[str, Any]]]): Optional list of metadata
associated with the texts.
collection_name (str): The name of the collection in the Zep store.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/zep.html |
4bae1c8108af-0 | Source code for langchain.vectorstores.vespa
from __future__ import annotations
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.vectorstores.base import VectorStore, VectorStoreR... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
4bae1c8108af-1 | input_field: Optional[str] = None,
metadata_fields: Optional[List[str]] = None,
) -> None:
"""
Initialize with a PyVespa client.
"""
try:
from vespa.application import Vespa
except ImportError:
raise ImportError(
"Could not impo... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
4bae1c8108af-2 | if ids is None:
ids = [str(f"{i+1}") for i, _ in enumerate(texts)]
batch = []
for i, text in enumerate(texts):
fields: Dict[str, Union[str, List[float]]] = {}
if self._page_content_field is not None:
fields[self._page_content_field] = text
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
4bae1c8108af-3 | ) -> Dict:
hits = k
doc_embedding_field = self._embedding_field
input_embedding_field = self._input_field
ranking_function = kwargs["ranking"] if "ranking" in kwargs else "default"
filter = kwargs["filter"] if "filter" in kwargs else None
approximate = kwargs["approximate... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
4bae1c8108af-4 | query = self._create_query(query_embedding, k, **kwargs)
try:
response = self._vespa_app.query(body=query)
except Exception as e:
raise RuntimeError(
f"Could not retrieve data from Vespa: "
f"{e.args[0][0]['summary']}. "
f"Error: {e... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
4bae1c8108af-5 | [docs] def similarity_search_with_score(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Tuple[Document, float]]:
query_emb = []
if self._embedding_function is not None:
query_emb = self._embedding_function.embed_query(query)
return self.similarity_search_by_vect... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
4bae1c8108af-6 | ids: Optional[List[str]] = None,
**kwargs: Any,
) -> VespaStore:
vespa = cls(embedding_function=embedding, **kwargs)
vespa.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return vespa
[docs] def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
return super()... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/vespa.html |
005c33cdfcc1-0 | Source code for langchain.vectorstores.semadb
from typing import Any, Iterable, List, Optional, Tuple
from uuid import uuid4
import numpy as np
import requests
from langchain.schema.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import VectorStore
from lang... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/semadb.html |
005c33cdfcc1-1 | }
def _get_internal_distance_strategy(self) -> str:
"""Return the internal distance strategy."""
if self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE:
return "euclidean"
elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT:
raise ValueError("M... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/semadb.html |
005c33cdfcc1-2 | **kwargs: Any,
) -> List[str]:
"""Add texts to the vector store."""
if not isinstance(texts, list):
texts = list(texts)
embeddings = self._embedding.embed_documents(texts)
# Check dimensions
if len(embeddings[0]) != self.vector_size:
raise ValueError(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/semadb.html |
005c33cdfcc1-3 | batch = points[i : i + batch_size]
response = requests.post(
SemaDB.BASE_URL + f"/collections/{self.collection_name}/points",
json={"points": batch},
headers=self.headers,
)
if response.status_code != 200:
print("HERE--"... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/semadb.html |
005c33cdfcc1-4 | vec = np.array(embedding)
vec = vec / np.linalg.norm(vec)
embedding = vec.tolist()
# Perform search request
payload = {
"vector": embedding,
"limit": k,
}
response = requests.post(
SemaDB.BASE_URL + f"/collections/{self.collecti... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/semadb.html |
005c33cdfcc1-5 | Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query vector.
"""
points = self._search_points(embedding, k=k)
return [
Document(pag... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/semadb.html |
b84a692fa8ab-0 | Source code for langchain.vectorstores.hippo
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import VectorS... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-1 | to False.
primary_field (str): Name of the primary key field. Defaults to "pk".
text_field (str): Name of the text field. Defaults to "text".
vector_field (str): Name of the vector field. Defaults to "vector".
The connection args used for this class comes in the form of a dict,
here are ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-2 | index_params: Optional[dict] = None,
drop_old: Optional[bool] = False,
):
self.number_of_shards = number_of_shards
self.number_of_replicas = number_of_replicas
self.embedding_func = embedding_function
self.table_name = table_name
self.database_name = database_name
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-3 | except Exception as e:
logging.error(
f"An error occurred while getting the table " f"{self.table_name}: {e}"
)
raise
# Initialize the vector database
self._get_env()
def _create_connection_alias(self, connection_args: dict) -> HippoClient:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-4 | raise e
def _get_env(
self, embeddings: Optional[list] = None, metadatas: Optional[List[dict]] = None
) -> None:
logger.info("init ...")
if embeddings is not None:
logger.info("create collection")
self._create_collection(embeddings, metadatas)
self._extrac... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-5 | # # Infer the corresponding datatype of the metadata
if isinstance(value, list):
value_dim = len(value)
fields.append(
HippoField(
key,
False,
Hippo... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-6 | # vector type columns.
def _get_index(self) -> Optional[Dict[str, Any]]:
"""Return the vector index information if it exists"""
from transwarp_hippo_api.hippo_client import HippoTable
if isinstance(self.col, HippoTable):
table_info = self.hc.get_table_info(
self.t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-7 | self.index_params["index_type"],
self.index_params["metric_type"],
nlist=self.index_params["nlist"],
)
logger.debug(
self.col.activate_index(self.index_params["index_name"])
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-8 | self.index_params["index_type"]
]
self.col.create_index(
self._vector_field,
self.index_params["index_name"],
self.index_params["index_type"],
self.index_params... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-9 | ef_search=self.index_params.get("ef_search"),
)
logger.debug(
self.col.activate_index(self.index_params["index_name"])
)
else:
raise ValueError(
"In... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-10 | embeddings = self.embedding_func.embed_documents(texts)
except NotImplementedError:
embeddings = [self.embedding_func.embed_query(x) for x in texts]
if len(embeddings) == 0:
logger.debug("Nothing to insert, skipping.")
return []
logger.debug(f"[add_texts] len_... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-11 | try:
res = self.col.insert_rows(insert_list)
logger.info(f"05 [add_texts] insert {res}")
except Exception as e:
logger.error(
"Failed to insert batch starting at entity: %s/%s", i, total_count
)
raise e
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-12 | k: int = 4,
param: Optional[dict] = None,
expr: Optional[str] = None,
timeout: Optional[int] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Performs a search on the query string and returns results with scores.
Args:
query (str): The... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-13 | Performs a search on the query string and returns results with scores.
Args:
embedding (List[float]): The embedding vector being searched.
k (int, optional): The number of results to return.
Default is 4.
param (dict): Specifies the search parameters for the index... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-14 | for items in zip(*[res[0][field] for field in output_fields]):
meta = {field: value for field, value in zip(output_fields, items)}
doc = Document(page_content=meta.pop(self._text_field), metadata=meta)
logger.debug(
f"[similarity_search_with_score_by_vector] "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
b84a692fa8ab-15 | Defaults to DEFAULT_HIPPO_CONNECTION.
index_params (dict): Indexing parameters. Defaults to None.
search_params (dict): Search parameters. Defaults to an empty dictionary.
drop_old (bool): Whether to drop the old collection. Defaults to False.
kwargs: Other arguments.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/hippo.html |
c5eeed205c69-0 | Source code for langchain.vectorstores.tigris
from __future__ import annotations
import itertools
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple
from langchain.schema import Document
from langchain.schema.embeddings import Embeddings
from langchain.schema.vectorstore import VectorStore
if TYPE_C... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
c5eeed205c69-1 | """Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of ids for documents.
Ids will be autogenerated... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
c5eeed205c69-2 | text with distance in float.
"""
vector = self._embed_fn.embed_query(query)
result = self.search_index.similarity_search(
vector=vector, k=k, filter_by=filter
)
docs: List[Tuple[Document, float]] = []
for r in result:
docs.append(
(... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
c5eeed205c69-3 | for t, m, e, _id in itertools.zip_longest(
texts, metadatas or [], embeddings or [], ids or []
):
doc: TigrisDocument = {
"text": t,
"embeddings": e or [],
"metadata": m or {},
}
if _id:
doc["id"] = _... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/tigris.html |
0e0d6e5a9a5b-0 | Source code for langchain.vectorstores.bageldb
from __future__ import annotations
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
if TYPE_CHECKING:
import bagel
import bagel.config
from bagel.api.types import I... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-1 | client_settings: Optional[bagel.config.Settings] = None,
embedding_function: Optional[Embeddings] = None,
cluster_metadata: Optional[Dict] = None,
client: Optional[bagel.Client] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
) -> None:
"""Initialize wi... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-2 | **kwargs: Any,
) -> List[Document]:
"""Query the BagelDB cluster based on the provided parameters."""
try:
import bagel # noqa: F401
except ImportError:
raise ImportError("Please install bagel `pip install betabageldb`.")
return self._cluster.find(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-3 | if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids = []
for idx, metadata in enumerate(metadatas):
if metadata:
non_empty_ids.append(idx)
else:
empty_ids.appen... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-4 | )
return ids
[docs] def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
where: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""
Run a similarity search with BagelDB.
Args:
query (str): The query t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-5 | return _results_to_docs_and_scores(results)
[docs] @classmethod
def from_texts(
cls: Type[Bagel],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
cluster_name: str = _LANGCHAIN_DEFAU... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-6 | **kwargs,
)
_ = bagel_cluster.add_texts(
texts=texts, embeddings=text_embeddings, metadatas=metadatas, ids=ids
)
return bagel_cluster
[docs] def delete_cluster(self) -> None:
"""Delete the cluster."""
self._client.delete_cluster(self._cluster.name)
[docs] ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-7 | distance = "l2"
distance_key = "hnsw:space"
metadata = self._cluster.metadata
if metadata and distance_key in metadata:
distance = metadata[distance_key]
if distance == "cosine":
return self._cosine_relevance_score_fn
elif distance == "l2":
ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-8 | client (Optional[bagel.Client]): Bagel client instance.
cluster_metadata (Optional[Dict]): Metadata associated with the
Bagel cluster. Defaults to None.
Returns:
Bagel: Bagel vectorstore.
"""
texts = [doc.page_content for doc... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
0e0d6e5a9a5b-9 | "limit": limit,
"offset": offset,
"where_document": where_document,
}
if include is not None:
kwargs["include"] = include
return self._cluster.get(**kwargs)
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/bageldb.html |
38fdd588dd0f-0 | Source code for langchain.vectorstores.dashvector
from __future__ import annotations
import logging
import uuid
from typing import (
Any,
Iterable,
List,
Optional,
Tuple,
)
import numpy as np
from langchain.docstore.document import Document
from langchain.schema.embeddings import Embeddings
from lan... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-1 | )
self._collection = collection
self._embedding = embedding
self._text_field = text_field
def _similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Ret... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-2 | List of ids from adding the texts into the vectorstore.
"""
ids = ids or [str(uuid.uuid4().hex) for _ in texts]
text_list = list(texts)
for i in range(0, len(text_list), batch_size):
# batch end
end = min(i + batch_size, len(text_list))
batch_texts = t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-3 | **kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to search documents similar to.
k: Number of documents to return. Default to 4.
filter: Doc fields filter conditions that meet the SQL where clause
spec... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-4 | """Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Doc fields filter conditions that meet the SQL where clause
specification.
Returns... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-5 | return self.max_marginal_relevance_search_by_vector(
embedding, k, fetch_k, lambda_mult, filter
)
[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: Opti... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-6 | np.array(embedding), candidate_embeddings, lambda_mult, k
)
metadatas = [ret.output[i].fields for i in mmr_selected]
return [
Document(page_content=metadata.pop(self._text_field), metadata=metadata)
for metadata in metadatas
]
[docs] @classmethod
def from_t... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
38fdd588dd0f-7 | )
dashvector_client = dashvector.Client(api_key=dashvector_api_key)
dashvector_client.delete(collection_name)
collection = dashvector_client.get(collection_name)
if not collection:
dim = len(embedding.embed_query(texts[0]))
# create collection if not existed
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/dashvector.html |
7e2ddef7886f-0 | Source code for langchain.vectorstores.analyticdb
from __future__ import annotations
import logging
import uuid
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Type
from sqlalchemy import REAL, Column, String, Table, create_engine, insert, text
from sqlalchemy.dialects.postgresql impo... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
7e2ddef7886f-1 | 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[logging.Logger] = None,
engi... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
7e2ddef7886f-2 | 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=True,
)
with self.engine.connect() as conn:
with con... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
7e2ddef7886f-3 | 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.
metadatas: Optional list of metadatas associ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/analyticdb.html |
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