id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
a3995490ca87-2 | 4. Click "Reset password"
5. Follow the prompts to reset the password
The format for Elastic Cloud URLs is
https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.
Example:
.. code-block:: python
from langchain import ElasticVectorSearch
from langchain.embeddi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-3 | )
self.embedding = embedding
self.index_name = index_name
_ssl_verify = ssl_verify or {}
try:
self.client = elasticsearch.Elasticsearch(elasticsearch_url, **_ssl_verify)
except ValueError as e:
raise ValueError(
f"Your elasticsearch client ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-4 | # check to see if the index already exists
try:
self.client.indices.get(index=self.index_name)
except NotFoundError:
# TODO would be nice to create index before embedding,
# just to save expensive steps for last
self.create_index(self.client, self.index_na... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-5 | """Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
embedding = self.embedding.embed_query(query)
sc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-6 | embeddings = OpenAIEmbeddings()
elastic_vector_search = ElasticVectorSearch.from_texts(
texts,
embeddings,
elasticsearch_url="http://localhost:9200"
)
"""
elasticsearch_url = get_from_dict_or_env(
kwa... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-7 | )
return response
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
"""
if ids is None:
raise ValueError("No ids provided to delete.")
# TODO: Check if thi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-8 | >>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_content='Hello world!', met... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-9 | raise ValueError(
"""Either provide a pre-existing Elasticsearch connection, \
or valid credentials for creating a new connection."""
)
@staticmethod
def _default_knn_mapping(
dims: int, similarity: Optional[str] = "dot_product"
) -> Dict:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-10 | }
}
else:
raise ValueError(
"Either `query_vector` or `model_id` must be provided, but not both."
)
return knn
[docs] def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-11 | k (int, optional): The number of nearest neighbors to return.
query_vector (List[float], optional): The query vector to search for.
model_id (str, optional): The ID of the model to use for transforming the
query text into a vector.
size (int, optional): The number of ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-12 | metadata=hit["fields"] if fields else {},
),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] def knn_hybrid_search(
self,
query: Optional[str] = None,
k: Optional[int] = 10,
query_vector: Optional... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-13 | results.
fields (List[Mapping[str, Any]], optional): The fields to return in the
search results.
page_content (str, optional): The name of the field that contains the page
content.
Returns:
A list of tuples, where each tuple contains a Document... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-14 | ),
hit["_score"],
)
for hit in hits
]
return docs_and_scores
[docs] def create_knn_index(self, mapping: Dict) -> None:
"""
Create a new k-NN index in Elasticsearch.
Args:
mapping (Dict): The mapping to use for the new index.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-15 | optional_args = {}
if similarity is not None:
optional_args["similarity"] = similarity
mapping = self._default_knn_mapping(dims=dims, **optional_args)
self.create_knn_index(mapping)
embeddings = self.embedding.embed_documents(list(texts))
# body = []
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
a3995490ca87-16 | Returns:
A new ElasticKnnSearch instance.
"""
index_name = kwargs.get("index_name", str(uuid.uuid4()))
es_connection = kwargs.get("es_connection")
es_cloud_id = kwargs.get("es_cloud_id")
es_user = kwargs.get("es_user")
es_password = kwargs.get("es_password")
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/elastic_vector_search.html |
9f60b6d53214-0 | Source code for langchain.vectorstores.redis
"""Wrapper around Redis vector database."""
from __future__ import annotations
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Mapping,
Optional,
Tuple,
Type,... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-1 | ) >= int(module["ver"]):
return
# otherwise raise error
error_message = (
"Redis cannot be used as a vector database without RediSearch >=2.4"
"Please head to https://redis.io/docs/stack/search/quick_start/"
"to know more about installing the RediSearch module within Redis St... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-2 | embedding_function=embeddings.embed_query,
)
To use a redis replication setup with multiple redis server and redis sentinels
set "redis_url" to "redis+sentinel://" scheme. With this url format a path is
needed holding the name of the redis service within the sentinels to get the
correct redi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-3 | except ValueError as e:
raise ValueError(f"Redis failed to connect: {e}")
self.client = redis_client
self.content_key = content_key
self.metadata_key = metadata_key
self.vector_key = vector_key
self.distance_metric = distance_metric
self.relevance_score_fn = r... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-4 | "DISTANCE_METRIC": self.distance_metric,
},
),
)
prefix = _redis_prefix(self.index_name)
# Create Redis Index
self.client.ft(self.index_name).create_index(
fields=schema,
definition=IndexDefinition(prefix... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-5 | # Use provided values by default or fallback
key = keys_or_ids[i] if keys_or_ids else _redis_key(prefix)
metadata = metadatas[i] if metadatas else {}
embedding = embeddings[i] if embeddings else self.embedding_function(text)
pipeline.hset(
key,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-6 | Args:
query (str): The query text for which to find similar documents.
k (int): The number of documents to return. Default is 4.
score_threshold (float): The minimum matching score required for a document
to be considered a match. Defaults to 0.2.
Beca... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-7 | self, query: str, k: int = 4
) -> 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.
Returns:
List of Documents most similar to the query a... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-8 | **kwargs: Any,
) -> Tuple[Redis, List[str]]:
"""Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
1. Embeds documents.
2. Creates a new index for the embeddings in Redis.
3. Adds the documents to the newly created Redis index.
4. R... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-9 | return instance, keys
[docs] @classmethod
def from_texts(
cls: Type[Redis],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
index_name: Optional[str] = None,
content_key: str = "content",
metadata_key: str = "metadata",
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-10 | ) -> bool:
"""
Delete a Redis entry.
Args:
ids: List of ids (keys) to delete.
Returns:
bool: Whether or not the deletions were successful.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
if ids is None:
ra... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-11 | Returns:
bool: Whether or not the drop was successful.
"""
redis_url = get_from_dict_or_env(kwargs, "redis_url", "REDIS_URL")
try:
import redis # noqa: F401
except ImportError:
raise ValueError(
"Could not import redis python package. ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-12 | "Please install it with `pip install redis`."
)
try:
# We need to first remove redis_url from kwargs,
# otherwise passing it to Redis will result in an error.
if "redis_url" in kwargs:
kwargs.pop("redis_url")
client = get_client(redis_u... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-13 | """Score threshold for similarity_limit search."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
9f60b6d53214-14 | ) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis.html |
7b8d04498303-0 | Source code for langchain.vectorstores.singlestoredb
"""Wrapper around SingleStore DB."""
from __future__ import annotations
import json
from typing import (
Any,
Callable,
ClassVar,
Collection,
Iterable,
List,
Optional,
Tuple,
Type,
)
from sqlalchemy.pool import QueuePool
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-1 | [docs] def __init__(
self,
embedding: Embeddings,
*,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
table_name: str = "embeddings",
content_field: str = "content",
metadata_field: str = "metadata",
vector_field: str = "vector",
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-2 | max_overflow (int, optional): Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, optional): Specifies the maximum wait time in seconds for
establishing a connection. Defaults to 30.
Following arguments pertai... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-3 | conv (dict[int, Callable], optional): A dictionary of data conversion
functions.
credential_type (str, optional): Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_SSO.
autocommit (bool, optional): Enables autocommits.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-4 | vectorstore = SingleStoreDB(OpenAIEmbeddings())
"""
self.embedding = embedding
self.distance_strategy = distance_strategy
self.table_name = table_name
self.content_field = content_field
self.metadata_field = metadata_field
self.vector_field = vector_field
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-5 | ),
)
finally:
cur.close()
finally:
conn.close()
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
embeddings: Optional[List[List[float]]] = None,
**kwargs: Any,
) -> Lis... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-6 | conn.close()
return []
[docs] def similarity_search(
self, query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any
) -> List[Document]:
"""Returns the most similar indexed documents to the query text.
Uses cosine similarity.
Args:
query (str): The ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-7 | filter: A dictionary of metadata fields and values to filter by.
Defaults to None.
Returns:
List of Documents most similar to the query and score for each
"""
# Creates embedding vector from user query
embedding = self.embedding.embed_query(query)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-8 | where_clause,
ORDERING_DIRECTIVE[self.distance_strategy],
),
("[{}]".format(",".join(map(str, embedding))),)
+ tuple(where_clause_values)
+ (k,),
)
for row in cur.fetchall():
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-9 | texts,
OpenAIEmbeddings(),
host="username:password@localhost:3306/database"
)
"""
instance = cls(
embedding,
distance_strategy=distance_strategy,
table_name=table_name,
content_field=content_field,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
7b8d04498303-10 | ) -> List[Document]:
raise NotImplementedError(
"SingleStoreDBVectorStoreRetriever does not support async"
) | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/singlestoredb.html |
f84046fabc8a-0 | Source code for langchain.vectorstores.azuresearch
"""Wrapper around Azure Cognitive Search."""
from __future__ import annotations
import base64
import json
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
)
im... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-1 | )
MAX_UPLOAD_BATCH_SIZE = 1000
def _get_search_client(
endpoint: str,
key: str,
index_name: str,
semantic_configuration_name: Optional[str] = None,
fields: Optional[List[SearchField]] = None,
vector_search: Optional[VectorSearch] = None,
semantic_settings: Optional[SemanticSettings] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-2 | missing_fields = {
key: mandatory_fields[key]
for key, value in set(mandatory_fields.items())
- set(fields_types.items())
}
if len(missing_fields) > 0:
fmt_err = lambda x: ( # noqa: E731
f"{x} current type: '{fi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-3 | prioritized_fields=PrioritizedFields(
prioritized_content_fields=[
SemanticField(field_name=FIELDS_CONTENT)
],
),
)
]
)
# Create the search index with t... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-4 | # Initialize base class
self.embedding_function = embedding_function
default_fields = [
SimpleField(
name=FIELDS_ID,
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-5 | ids = []
# Write data to index
data = []
for i, text in enumerate(texts):
# Use provided key otherwise use default key
key = keys[i] if keys else str(uuid.uuid4())
# Encoding key for Azure Search valid characters
key = base64.urlsafe_b64encode(byte... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-6 | return ids
else:
raise Exception(response)
[docs] def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
search_type = kwargs.get("search_type", self.search_type)
if search_type == "similarity":
docs = self.vector_search(que... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-7 | Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for each
"""
results = self.client.search(
search_text="",
vector=np.arra... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-8 | ) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with an hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query and score for... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-9 | [docs] def semantic_hybrid_search_with_score(
self, query: str, k: int = 4, filters: Optional[str] = None
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query with an hybrid query.
Args:
query: Text to look up documents similar to.
k: Number of D... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-10 | "text": result.get("@search.captions", [{}])[0].text,
"highlights": result.get("@search.captions", [{}])[
0
].highlights,
}
if result.get("@search.captions")
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
f84046fabc8a-11 | """Number of documents to return."""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
@root_validator()
def validate_search_type(cls, values: Dict) -> Dict:
"""Validate search type."""
if "search_type" in values:
search_ty... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/azuresearch.html |
3e0354471797-0 | Source code for langchain.vectorstores.marqo
"""Wrapper around weaviate vector database."""
from __future__ import annotations
import json
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
)
from langchain.docstore.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-1 | add_documents_settings: Optional[Dict[str, Any]] = None,
searchable_attributes: Optional[List[str]] = None,
page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = None,
):
"""Initialize with Marqo client."""
try:
import marqo
except ImportError:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-2 | order that matches the metadatas.
metadatas (Optional[List[dict]], optional): a list of metadatas.
Raises:
ValueError: if metadatas is provided and the number of metadatas differs
from the number of texts.
Returns:
List[str]: The list of ids that were adde... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-3 | ids += [item["_id"] for item in response["items"]]
return ids
[docs] def similarity_search(
self,
query: Union[str, Dict[str, float]],
k: int = 4,
**kwargs: Any,
) -> List[Document]:
"""Search the marqo index for the most similar documents.
Args:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-4 | return scored_documents
[docs] def bulk_similarity_search(
self,
queries: Iterable[Union[str, Dict[str, float]]],
k: int = 4,
**kwargs: Any,
) -> List[List[Document]]:
"""Search the marqo index for the most similar documents in bulk with multiple
queries.
A... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-5 | k (int, optional): The number of documents to return. Defaults to 4.
Returns:
List[Tuple[Document, float]]: A list of lists of the matching
documents and their scores for each query
"""
bulk_results = self.marqo_bulk_similarity_search(queries=queries, k=k)
bulk_do... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-6 | ) -> List[Document]:
"""Helper to convert Marqo results into documents.
Args:
results (List[dict]): A marqo results object with the 'hits'.
include_scores (bool, optional): Include scores alongside documents.
Defaults to False.
Returns:
Union[List[... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-7 | ) -> Dict[str, List[Dict[str, List[Dict[str, str]]]]]:
"""Return documents from Marqo using a bulk search, exposes Marqo's
output directly
Args:
queries (Iterable[Union[str, Dict[str, float]]]): A list of queries.
k (int, optional): The number of documents to return for e... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-8 | return cls.from_texts(texts, metadatas=metadatas, **kwargs)
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Any = None,
metadatas: Optional[List[dict]] = None,
index_name: str = "",
url: str = "http://localhost:8882",
api_key: str = ""... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-9 | embedding (Any, optional): Embeddings (not required). Defaults to None.
index_name (str, optional): The name of the index to use, if none is
provided then one will be created with a UUID. Defaults to None.
url (str, optional): The URL for Marqo. Defaults to "http://localhost:8882".
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
3e0354471797-10 | client.create_index(index_name, settings_dict=index_settings)
if verbose:
print(f"Created {index_name} successfully.")
except Exception:
if verbose:
print(f"Index {index_name} exists.")
instance: Marqo = cls(
client,
index_n... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/marqo.html |
13ba630d8980-0 | Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import asyncio
import functools
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-1 | except NotImplementedError:
# If the async method is not implemented, call the synchronous method
# by removing the first letter from the method name. For example,
# if the async method is called ``aaad_texts``, the synchronous method
# will be called ``aad_texts``.
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-2 | raise ValueError(
"Could not import qdrant-client python package. "
"Please install it with `pip install qdrant-client`."
)
if not isinstance(client, qdrant_client.QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_cl... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-3 | self.distance_strategy = distance_strategy.upper()
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embeddings
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
bat... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-4 | """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 to associate with the texts. Ids have to... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-5 | k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset val... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-6 | filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""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: Filter by metadata. Defaults to N... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-7 | threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-8 | k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset val... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-9 | filter: Optional[MetadataFilter] = None,
search_params: Optional[common_types.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[common_types.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-10 | embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
[docs] @sync_call_fallback
as... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-11 | - int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values pr... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-12 | Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
th... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-13 | search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-14 | threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-15 | filter=qdrant_filter,
params=search_params,
limit=k,
offset=offset,
with_payload=grpc.WithPayloadSelector(enable=True),
with_vectors=grpc.WithVectorsSelector(enable=False),
score_threshold=score_threshold,
re... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-16 | query_embedding, k, fetch_k, lambda_mult, **kwargs
)
[docs] @sync_call_fallback
async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return do... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-17 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
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 algor... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-18 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
results = await self.amax_marginal_relevance_search_with_score_by_vector(
emb... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-19 | query_vector=query_vector,
with_payload=True,
with_vectors=True,
limit=fetch_k,
)
embeddings = [
result.vector.get(self.vector_name) # type: ignore[index, union-attr]
if self.vector_name is not None
else result.vector
f... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-20 | Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
from qdrant_client import grpc # noqa
from qdrant_client.conversions.conversion import GrpcToRest
response = await self.client.async_grpc_points.... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-21 | Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
from qdrant_client.http import models as rest
result = self.client.delete(
collection_name=self.collection_name,
points_selector=ids,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-22 | hnsw_config: Optional[common_types.HnswConfigDiff] = None,
optimizers_config: Optional[common_types.OptimizersConfigDiff] = None,
wal_config: Optional[common_types.WalConfigDiff] = None,
quantization_config: Optional[common_types.QuantizationConfig] = None,
init_from: Optional[common_typ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-23 | https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Cloud. Default: None
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-24 | Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor:
Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-25 | Example:
.. code-block:: python
from langchain import Qdrant
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
"""
qdrant = cls._construc... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-26 | api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[float] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-27 | Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location:
If `:memory:` - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-28 | it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-29 | optimizers_config: Params for optimizer
wal_config: Params for Write-Ahead-Log
quantization_config:
Params for quantization, if None - quantization will be disabled
init_from:
Use data stored in another collection to initialize this collection
... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-30 | on_disk,
force_recreate,
**kwargs,
)
await qdrant.aadd_texts(texts, metadatas, ids, batch_size)
return qdrant
@classmethod
def _construct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] ... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-31 | init_from: Optional[common_types.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
try:
import qdrant_client
except ImportError:
raise ValueError(
"Could not import qdrant-client... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-32 | current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name}... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-33 | )
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size: # type: ignore[union-attr]
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vec... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-34 | collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_confi... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
13ba630d8980-35 | )
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text... | https://api.python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
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