id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
50fa38b2bdcb-6 | database (str, optional) – Database name.
the (Additional optional arguments provide further customization over) –
connection –
pure_python (bool, optional) – Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, optional) – Allows local file uploads.
charset (str, optional) – Specifi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-7 | )
Advanced Usage:
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE,
host="127.0.0.1",
port=3306,
user="user",
password="password",
database... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-8 | Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → List[str][source]¶
Add more texts to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings/text to add ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-9 | Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-10 | docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-11 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, table_name: str = 'embeddings', content_field: str = 'content', metadata_field: str = ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-12 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(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 marginal relevan... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
50fa38b2bdcb-13 | Return docs most similar to embedding vector.
Parameters
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.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Docume... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
bfeaaf090136-0 | langchain.vectorstores.myscale.MyScaleSettings¶
class langchain.vectorstores.myscale.MyScaleSettings[source]¶
Bases: BaseSettings
MyScale Client Configuration
Attribute:
myscale_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
myscale_port (int) : URL port to connect with HTTP. Defaults to 8443.
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScaleSettings.html |
bfeaaf090136-1 | param index_param: Optional[Dict[str, str]] = None¶
param index_type: str = 'IVFFLAT'¶
param metric: str = 'cosine'¶
param password: Optional[str] = None¶
param port: int = 8443¶
param table: str = 'langchain'¶
param username: Optional[str] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: A... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScaleSettings.html |
bfeaaf090136-2 | Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScaleSettings.html |
7f8a61dc6611-0 | langchain.vectorstores.pinecone.Pinecone¶
class langchain.vectorstores.pinecone.Pinecone(index: Any, embedding_function: Callable, text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE)[source]¶
Wrapper around Pinecone vector database.
To use, you should... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-1 | amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-2 | Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return pinecone documents most similar to query, along with scores.
__init__(index: Any, embedding_function: Callable, text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = Dista... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-3 | metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of ids to associate with the texts.
namespace – Optional pinecone namespace to add the texts to.
Returns
List of ids from adding the texts into the vectorstore.
async classmethod afrom_documents(documents: List[Document], embedding: E... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-4 | score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for Vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-5 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-6 | Example
from langchain import Pinecone
from langchain.embeddings import OpenAIEmbeddings
import pinecone
# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-7 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7f8a61dc6611-8 | Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
09757a8c2b51-0 | langchain.vectorstores.starrocks.debug_output¶
langchain.vectorstores.starrocks.debug_output(s: Any) → None[source]¶
Print a debug message if DEBUG is True.
:param s: The message to print | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.debug_output.html |
6f7121690a88-0 | langchain.vectorstores.clickhouse.Clickhouse¶
class langchain.vectorstores.clickhouse.Clickhouse(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any)[source]¶
Wrapper around ClickHouse vector database
You need a clickhouse-connect python package, and a valid account
to connect to ClickHous... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-1 | amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-2 | ClickHouse Wrapper to LangChain
embedding_function (Embeddings):
config (ClickHouseSettings): Configuration to ClickHouse Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.clickhouse.com/)
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents throug... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-3 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-4 | Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-5 | Return docs most similar to query.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not im... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-6 | Returns
ClickHouse Index
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Par... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-7 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with ClickHouse
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
6f7121690a88-8 | Perform a similarity search with ClickHouse
Parameters
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
of SQL injection. When deal... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
352c93eddeb0-0 | langchain.vectorstores.qdrant.Qdrant¶
class langchain.vectorstores.qdrant.Qdrant(client: Any, collection_name: str, embeddings: Optional[Embeddings] = None, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', distance_strategy: str = 'COSINE', vector_name: Optional[str] = None, embedding_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-1 | Construct Qdrant wrapper from a list of texts.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-2 | Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
asimilarity_search_with_score(query[, k, ...])
Return docs most similar to query.
asimilarity_search_with_score_by_vector(...)
Return docs most similar to embedding vector.
delete([ids])
Dele... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-3 | Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to embedding vect... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-4 | Default: 64
Returns
List of ids from adding the texts into the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-5 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[Sequence[str]] = None, location: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-6 | embedding – A subclass of Embeddings, responsible for text vectorization.
metadatas – An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location – If :memory: - use in-memory Qdrant inst... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-7 | Default: “Cosine”
content_payload_key – A payload key used to store the content of the document.
Default: “page_content”
metadata_payload_key – A payload key used to store the metadata of the document.
Default: “metadata”
vector_name – Name of the vector to be used internally in Qdrant.
Default: None
batch_size – How m... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-8 | This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example
from langchain import ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-9 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
Parameters
lambda_mult –... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-10 | the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-11 | search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, filter: Optional[MetadataFilter] = None, **kwargs: Any) → List... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-12 | 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:
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return va... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-13 | 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:
- int - number of replicas to query, values should present in all
queried repl... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-14 | consistency – Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the maj... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-15 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[Sequence[str]] = None, location: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, prefer_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-16 | embedding – A subclass of Embeddings, responsible for text vectorization.
metadatas – An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location – If :memory: - use in-memory Qdrant inst... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-17 | Default: “Cosine”
content_payload_key – A payload key used to store the content of the document.
Default: “page_content”
metadata_payload_key – A payload key used to store the metadata of the document.
Default: “metadata”
vector_name – Name of the vector to be used internally in Qdrant.
Default: None
batch_size – How m... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-18 | This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example
from langchain import ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-19 | among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 correspondi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-20 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[MetadataFilter] = None, search_params: Optional[common_types.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[common_types.ReadConsistency] = Non... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-21 | Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[MetadataFilter] = None, search_params: Optional[common_types.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[common_types.ReadConsis... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-22 | Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-23 | ’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
Returns
List of documents most similar to the query text and distance for each.
simil... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
352c93eddeb0-24 | ’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
Returns
List of documents most similar to the query text and distance for each.
Examples using Qdrant¶
Qdrant
Qdrant self-querying | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
d45550c66a1b-0 | langchain.vectorstores.sklearn.ParquetSerializer¶
class langchain.vectorstores.sklearn.ParquetSerializer(persist_path: str)[source]¶
Serializes data in Apache Parquet format using the pyarrow package.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.ParquetSerializer.html |
0c69cf23b2e1-0 | langchain.vectorstores.elastic_vector_search.ElasticVectorSearch¶
class langchain.vectorstores.elastic_vector_search.ElasticVectorSearch(elasticsearch_url: str, index_name: str, embedding: Embeddings, *, ssl_verify: Optional[Dict[str, Any]] = None)[source]¶
Wrapper around Elasticsearch as a vector database.
To connect ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-1 | Example
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-2 | Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-3 | similarity_search_with_score(query[, k, filter])
Return docs most similar to query.
__init__(elasticsearch_url: str, index_name: str, embedding: Embeddings, *, ssl_verify: Optional[Dict[str, Any]] = None)[source]¶
Initialize with necessary components.
async aadd_documents(documents: List[Document], **kwargs: Any) → Lis... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-4 | Returns
List of ids from adding the texts into the vectorstore.
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Option... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-5 | lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar d... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-6 | Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
client_search(client: Any, index_name: str, script_query: Dict, size: int) → Any[source]¶
create_index(client: Any, index... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-7 | embeddings,
elasticsearch_url="http://localhost:9200"
)
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND dive... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-8 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0c69cf23b2e1-9 | :param k: Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
Examples using ElasticVectorSearch¶
ElasticSearch
How to add memory to a Multi-Input Chain | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
36cb1b48bb72-0 | langchain.vectorstores.sklearn.BsonSerializer¶
class langchain.vectorstores.sklearn.BsonSerializer(persist_path: str)[source]¶
Serializes data in binary json using the bson python package.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data fro... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.BsonSerializer.html |
df60de688f7a-0 | langchain.vectorstores.myscale.MyScale¶
class langchain.vectorstores.myscale.MyScale(embedding: Embeddings, config: Optional[MyScaleSettings] = None, **kwargs: Any)[source]¶
Wrapper around MyScale vector database
You need a clickhouse-connect python package, and a valid account
to connect to MyScale.
MyScale can not on... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-1 | amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-2 | MyScale Wrapper to LangChain
embedding (Embeddings):
config (MyScaleSettings): Configuration to MyScale Client
Other keyword arguments will pass into
[clickhouse-connect](https://docs.myscale.com/)
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-3 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-4 | Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-5 | Return docs most similar to query.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not im... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-6 | Returns
MyScale Index
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parame... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-7 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with MyScale
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
df60de688f7a-8 | Perform a similarity search with MyScale
Parameters
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
of SQL injection. When dealing... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
cd15b20e4ffb-0 | langchain.vectorstores.deeplake.DeepLake¶
class langchain.vectorstores.deeplake.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding: Optional[Embeddings] = None, embedding_function: Optional[Embeddings] = None, read_only: bool = False, ingestion_batch_size: int = 1000, num_workers: int = ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-1 | ... path = "hub://org_id/dataset_name",
... exec_option = "tensor_db",
... )
Parameters
dataset_path (str) – Path to existing dataset or where to create
a new one. Defaults to _LANGCHAIN_DEFAULT_DEEPLAKE_PATH.
token (str, optional) – Activeloop token, for fetching credentials
to the dataset at path if it ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-2 | or connected to Deep Lake. Not for in-memory or local datasets.
tensor_db - Hosted Managed Tensor Database that isresponsible for storage and query execution. Only for data stored in
the Deep Lake Managed Database. Use runtime = {“db_engine”: True}
during dataset creation.
**kwargs – Other optional keyword arguments.
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-3 | asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete([ids])
Delete the entities in the dataset.
delete_dataset()
Delete the co... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-4 | Creates an empty DeepLakeVectorStore or loads an existing one.
The DeepLakeVectorStore is located at the specified path.
Examples
>>> # Create a vector store with default tensors
>>> deeplake_vectorstore = DeepLake(
... path = <path_for_storing_Data>,
... )
>>>
>>> # Create a vector store in the Deep Lake Manage... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-5 | Default is None.
- auto- Selects the best execution method based on the storage
location of the Vector Store. It is the default option.
python - Pure-python implementation that runs on the client.WARNING: using this with big datasets can lead to memory
issues. Data can be stored anywhere.
compute_engine - C++ implement... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-6 | Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Examples
>>> ids = deeplake_vectorstore.add_texts(
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-7 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(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.
as_retriever(**kwargs: Any) → VectorStore... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-8 | search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-9 | - delete_all (Optional[bool], optional): Whether to drop the dataset.
Returns
Whether the delete operation was successful.
Return type
bool
delete_dataset() → None[source]¶
Delete the collection.
ds() → Any[source]¶
classmethod force_delete_by_path(path: str) → None[source]¶
Force delete dataset by path.
Parameters
pat... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-10 | (use ‘activeloop login’ from command line)
AWS S3 path of the form s3://bucketname/path/to/dataset.Credentials are required in either the environment
Google Cloud Storage path of the formgcs://bucketname/path/to/dataset Credentials are required
in either the environment
Local file system path of the form ./path/to/data... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-11 | … exec_option = <preferred_exec_option>,
… )
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
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_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-12 | relevance optimizes for similarity to query AND diversity among selected docs.
Examples:
>>> data = vector_store.max_marginal_relevance_search_by_vector(
… embedding=<your_embedding>,
… fetch_k=<elements_to_fetch_before_mmr_search>,
… k=<number_of_items_to_return>,
… exec_option=<preferred_e... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-13 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Examples
>>> # Search using an embedding
>>> data = vector_store.similarity_search(
... query=<your_query>,
... k=<num_items>,
... ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-14 | WARNING: not recommended for big datasets.
’compute_engine’: C++ implementation of the Compute Engine forthe client. Not for in-memory or local datasets.
’tensor_db’: Managed Tensor Database for storage and query.Only for data in Deep Lake Managed Database.
Use runtime = {“db_engine”: True} during dataset creation.
Ret... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-15 | option with big datasets is discouraged due to potential
memory issues.
”compute_engine” - Performant C++ implementation of the DeepLake 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, full... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-16 | … query=<your_query>,
… embedding=<your_embedding_function>
… k=<number_of_items_to_return>,
… exec_option=<preferred_exec_option>,
… )
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
**kwargs – Additional keyword arguments. Some of these argument... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
cd15b20e4ffb-17 | Examples using DeepLake¶
Deep Lake
Activeloop’s Deep Lake
Question answering over a group chat messages using Activeloop’s DeepLake
QA using Activeloop’s DeepLake
Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop’s Deep Lake
Use LangChain, GPT and Activeloop’s Deep Lake to work with code... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
56d932d3e8ab-0 | langchain.vectorstores.lancedb.LanceDB¶
class langchain.vectorstores.lancedb.LanceDB(connection: Any, embedding: Embeddings, vector_key: Optional[str] = 'vector', id_key: Optional[str] = 'id', text_key: Optional[str] = 'text')[source]¶
Wrapper around LanceDB vector database.
To use, you should have lancedb python packa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
56d932d3e8ab-1 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilar... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
56d932d3e8ab-2 | Initialize with Lance DB connection
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
56d932d3e8ab-3 | Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
56d932d3e8ab-4 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
56d932d3e8ab-5 | Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.