id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
76872486d2b2-4 | 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.analyticdb.AnalyticDB.html |
76872486d2b2-5 | 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.analyticdb.AnalyticDB.html |
76872486d2b2-6 | Either pass it as a parameter
or set the PG_CONNECTION_STRING environment variable.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, embedding_dimension: int = 1536, collection_name: str = 'langchain_document', ids: Optional[List[str]] = None, pre_delete_collection... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
76872486d2b2-7 | Return docs selected using the maximal marginal relevance.
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 pa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
76872486d2b2-8 | Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(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.
Parameters
query – input text
k – Number of Documents to re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
a547c6fdfb16-0 | langchain.vectorstores.sklearn.BaseSerializer¶
class langchain.vectorstores.sklearn.BaseSerializer(persist_path: str)[source]¶
Base class for serializing data.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.BaseSerializer.html |
86db094df847-0 | langchain.vectorstores.redis.schema.RedisModel¶
class langchain.vectorstores.redis.schema.RedisModel[source]¶
Bases: BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param content_key: str = 'conten... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.RedisModel.html |
86db094df847-1 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.RedisModel.html |
86db094df847-2 | get_fields() → List[RedisField][source]¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.RedisModel.html |
86db094df847-3 | property is_empty: bool¶
property metadata_keys: List[str]¶
property vector_dtype: numpy.dtype¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.RedisModel.html |
fc156e228899-0 | langchain.vectorstores.hologres.HologresWrapper¶
class langchain.vectorstores.hologres.HologresWrapper(connection_string: str, ndims: int, table_name: str)[source]¶
Hologres API wrapper.
Initialize the wrapper.
Parameters
connection_string – Hologres connection string.
ndims – Number of dimensions of the embedding outp... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.HologresWrapper.html |
fd64d174dbee-0 | langchain.vectorstores.redis.schema.FlatVectorField¶
class langchain.vectorstores.redis.schema.FlatVectorField[source]¶
Bases: RedisVectorField
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param algorithm... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.FlatVectorField.html |
fd64d174dbee-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.FlatVectorField.html |
fd64d174dbee-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.FlatVectorField.html |
b119e9effe6d-0 | langchain.vectorstores.supabase.SupabaseVectorStore¶
class langchain.vectorstores.supabase.SupabaseVectorStore(client: supabase.client.Client, embedding: Embeddings, table_name: str, query_name: Union[str, None] = None)[source]¶
Supabase Postgres vector store.
It assumes you have the pgvector
extension installed and a ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
b119e9effe6d-1 | from supabase.client import create_client
embeddings = OpenAIEmbeddings()
supabase_client = create_client("my_supabase_url", "my_supabase_key")
vector_store = SupabaseVectorStore(
client=supabase_client,
embedding=embeddings,
table_name="documents",
query_name="match_documents",
)
Initialize with supaba... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
b119e9effe6d-2 | 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 by vector IDs.
from_documents(documents, embedding, **kwarg... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
b119e9effe6d-3 | 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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
b119e9effe6d-4 | 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.supabase.SupabaseVectorStore.html |
b119e9effe6d-5 | )
# 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.supabase.SupabaseVectorStore.html |
b119e9effe6d-6 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[supabase.client.Client] = None, table_name: Optional[str] = 'documents', query_name: Union[str, None] = 'match_documents', ids: Optional[... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
b119e9effe6d-7 | embedding vector(1536),
similarity float)
LANGUAGE plpgsql
AS $$
# variable_conflict use_column
BEGINRETURN query
SELECT
id,
content,
metadata,
embedding,
1 -(docstore.embedding <=> query_embedding) AS similarity
FROMdocstore
ORDER BYdocstore.embedding <=> query_embedding
LIMIT match_count;
END;
$$;
```
max_marginal_re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
b119e9effe6d-8 | 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_by_vector_returning_embeddings(query: List[float], k: int, filter: Optional[Dict... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.supabase.SupabaseVectorStore.html |
60685ec9d2ac-0 | langchain.vectorstores.pgvector.DistanceStrategy¶
class langchain.vectorstores.pgvector.DistanceStrategy(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies.
EUCLIDEAN = 'l2'¶
COSINE = 'cosine'¶
MAX_INNER_PRODUCT = 'inner'¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.DistanceStrategy.html |
f79125706173-0 | langchain.vectorstores.awadb.AwaDB¶
class langchain.vectorstores.awadb.AwaDB(table_name: str = 'langchain_awadb', embedding: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs: Any)[source]¶
AwaDB vector store.
Initialize with AwaDB client.If table_name ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-1 | 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_type, **kwargs)
Return docs most similar to quer... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-2 | similarity_search_by_vector([embedding, k, ...])
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, ...])
The most k similar documents and scores of the specified query.
update(ids, te... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-3 | Run more texts through the embeddings and add to 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 added text... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-4 | 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.awadb.AwaDB.html |
f79125706173-5 | 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.awadb.AwaDB.html |
f79125706173-6 | True if deletion is successful.
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, table_name: str = 'langchain_awadb', log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs:... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-7 | table_name (str) – Name of the table to create.
log_and_data_dir (Optional[str]) – Directory of logging and persistence.
client (Optional[awadb.Client]) – AwaDB client
Returns
AwaDB vectorstore.
Return type
AwaDB
get(ids: Optional[List[str]] = None, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-8 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-9 | Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = None,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-10 | k – Number of Documents to return. Defaults to 4.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter – Filter by metadata. Defaults to None.
not_incude_fields_in_metadata – Not include meta fields of each document.
Returns
List of Documents which are the most similar to the query vector.... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f79125706173-11 | 0 is dissimilar, 1 is the most similar.
update(ids: List[str], texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Update the documents which have the specified ids.
Parameters
ids – The id list of the updating embedding vector.
texts – The texts of the updating documents.
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
3fd9214daf50-0 | langchain.vectorstores.tencentvectordb.ConnectionParams¶
class langchain.vectorstores.tencentvectordb.ConnectionParams(url: str, key: str, username: str = 'root', timeout: int = 10)[source]¶
Tencent vector DB Connection params.
See the following documentation for details:
https://cloud.tencent.com/document/product/1709... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tencentvectordb.ConnectionParams.html |
3eaa275e784e-0 | langchain.vectorstores.vald.Vald¶
class langchain.vectorstores.vald.Vald(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)))[source]¶
Wrapper around Vald vector database.
To use, you should have the vald-clien... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vald.Vald.html |
3eaa275e784e-1 | 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_type, **kwargs)
Return docs most similar to quer... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vald.Vald.html |
3eaa275e784e-2 | Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
__init__(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)))[source]¶
async aadd_documents(documents: List[Document], **kw... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vald.Vald.html |
3eaa275e784e-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.vald.Vald.html |
3eaa275e784e-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.vald.Vald.html |
3eaa275e784e-5 | Return docs most similar to query.
delete(ids: Optional[List[str]] = None, skip_strict_exist_check: bool = False, **kwargs: Any) → Optional[bool][source]¶
Parameters
skip_strict_exist_check – Deprecated. This is not used basically.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: A... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vald.Vald.html |
3eaa275e784e-6 | 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, radius: float = - 1.0, epsilon: float = 0.01, timeout: int = 3000000000, **kwargs: Any) → List[Document][source]¶
Re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vald.Vald.html |
3eaa275e784e-7 | 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.vald.Vald.html |
6b37dc11e311-0 | langchain.vectorstores.redis.base.Redis¶
class langchain.vectorstores.redis.base.Redis(redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, str], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-1 | redis_url="redis://localhost:6379",
)
Initialize, create index, and load Documents with metadatards = Redis.from_texts(
texts, # a list of strings
metadata, # a list of metadata dicts
embeddings, # an Embeddings object
redis_url="redis://localhost:6379",
)
Initialize, create index, and load Documents wi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-2 | metadata, # a list of metadata dicts
embeddings, # an Embeddings object
vector_schema=vector_schema,
redis_url="redis://localhost:6379",
)
Custom index schema can be supplied to change the way that the
metadata is indexed. This is useful for you would like to use the
hybrid querying (filtering) capability o... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-3 | Otherwise, the schema for newly added samples will be incorrect and metadata
will not be returned.
Initialize with necessary components.
Attributes
DEFAULT_VECTOR_SCHEMA
embeddings
Access the query embedding object if available.
schema
Return the schema of the index.
Methods
__init__(redis_url, index_name, embedding[, ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-4 | Return docs most similar to query.
delete([ids])
Delete a Redis entry.
drop_index(index_name, delete_documents, ...)
Drop a Redis search index.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_existing_index(embedding, index_name, ...)
Connect to an exist... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-5 | write_schema(path)
Write the schema to a yaml file.
__init__(redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, str], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, **kwargs: A... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-6 | metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional) – Optional pre-generated
embeddings. Defaults to None.
keys (List[str]) or ids (List[str]) – Identifiers of entries.
Defaults to None.
batch_size (int, optional) – Batch size to ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-7 | 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_threshold
fetch_k: Amount of ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-8 | 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]¶
Return docs most similar to q... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-9 | Return VectorStore initialized from documents and embeddings.
classmethod from_existing_index(embedding: Embeddings, index_name: str, schema: Union[Dict[str, str], str, PathLike], **kwargs: Any) → Redis[source]¶
Connect to an existing Redis index.
Example
from langchain.vectorstores import Redis
from langchain.embeddin... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-10 | Adds the documents to the newly created Redis index.
This method will generate schema based on the metadata passed in
if the index_schema is not defined. If the index_schema is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
me... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-11 | Returns
Redis VectorStore instance.
Return type
Redis
Raises
ValueError – If the number of metadatas does not match the number of texts.
ImportError – If the redis python package is not installed.
classmethod from_texts_return_keys(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_n... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-12 | Parameters
texts (List[str]) – List of texts to add to the vectorstore.
embedding (Embeddings) – Embeddings to use for the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadata
dicts to add to the vectorstore. Defaults to None.
index_name (Optional[str], optional) – Optional name of the in... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-13 | lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (RedisFilterExpression, optional) – Optional metadata filter.
Defaults to None.
return_metadata (bool, optional) – Whether ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-14 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[RedisFilterExpression] = None, return_metadata: bool = True, distance_threshold: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search
Parameters
query (str) – The q... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-15 | Return type
List[Document]
similarity_search_limit_score(query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any) → List[Document][source]¶
[Deprecated] Returns the most similar indexed documents to the query text within the
score_threshold range.
Deprecated: Use similarity_search with distance_threshold i... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
6b37dc11e311-16 | similarity_search_with_relevance_scores(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.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to simil... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.base.Redis.html |
1f625eda9869-0 | langchain.vectorstores.neo4j_vector.sort_by_index_name¶
langchain.vectorstores.neo4j_vector.sort_by_index_name(lst: List[Dict[str, Any]], index_name: str) → List[Dict[str, Any]][source]¶
Sort first element to match the index_name if exists | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.neo4j_vector.sort_by_index_name.html |
e958b7277936-0 | langchain.vectorstores.scann.normalize¶
langchain.vectorstores.scann.normalize(x: ndarray) → ndarray[source]¶
Normalize vectors to unit length. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.normalize.html |
1eef1a004677-0 | langchain.vectorstores.starrocks.StarRocks¶
class langchain.vectorstores.starrocks.StarRocks(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any)[source]¶
StarRocks vector store.
You need a pymysql python package, and a valid account
to connect to StarRocks.
Right now StarRocks has only imp... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
1eef1a004677-1 | 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.starrocks.StarRocks.html |
1eef1a004677-2 | similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any) → None[source]¶
StarRocks Wrapper to LangChain
embedding_function (Embeddings):
config (StarRocksSettings): Configuration to StarRocks Client
asyn... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
1eef1a004677-3 | 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.starrocks.StarRocks.html |
1eef1a004677-4 | 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.starrocks.StarRocks.html |
1eef1a004677-5 | 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.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameter... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
1eef1a004677-6 | Returns
StarRocks 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.
Para... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
1eef1a004677-7 | 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 StarRocks
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
1eef1a004677-8 | Perform a similarity search with StarRocks
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 deali... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
20ee238ba585-0 | langchain.vectorstores.tigris.Tigris¶
class langchain.vectorstores.tigris.Tigris(client: TigrisClient, embeddings: Embeddings, index_name: str)[source]¶
Tigris vector store.
Initialize Tigris vector store.
Attributes
embeddings
Access the query embedding object if available.
search_index
Methods
__init__(client, embedd... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
20ee238ba585-1 | Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
20ee238ba585-2 | Run more texts through the embeddings and add to 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 added text... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
20ee238ba585-3 | 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.tigris.Tigris.html |
20ee238ba585-4 | 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.tigris.Tigris.html |
20ee238ba585-5 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, client: Optional[TigrisClient] = None, index_name: Optional[str] = None, **kwargs: Any) → Tigris[source]¶
Return VectorSt... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
20ee238ba585-6 | fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
20ee238ba585-7 | Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[TigrisFilter] = None) → List[Tuple[Document, float]][source]¶
Run similarity search with Chroma with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return.... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
f167b8b3366e-0 | langchain.vectorstores.redis.schema.RedisDistanceMetric¶
class langchain.vectorstores.redis.schema.RedisDistanceMetric(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
l2 = 'L2'¶
cosine = 'COSINE'¶
ip = 'IP'¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.RedisDistanceMetric.html |
bccf3596f04d-0 | langchain.vectorstores.hologres.Hologres¶
class langchain.vectorstores.hologres.Hologres(connection_string: str, embedding_function: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, logger: Optional[Logger] = None)[source]¶
Hologres API vector store.
connection_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
bccf3596f04d-1 | afrom_texts(texts, embedding[, metadatas])
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_retr... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
bccf3596f04d-2 | search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with Hologres with distance.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_sco... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
bccf3596f04d-3 | Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None[source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
embeddings – List o... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
bccf3596f04d-4 | 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.hologres.Hologres.html |
bccf3596f04d-5 | 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.hologres.Hologres.html |
bccf3596f04d-6 | 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 implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embed... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
bccf3596f04d-7 | faiss = Hologres.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_existing_index(embedding: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Get intsance of an existing Hologres store.This method will
return t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
bccf3596f04d-8 | 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.hologres.Hologres.html |
bccf3596f04d-9 | 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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query vector.
similarity_search_with_r... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
40cd6a2530e6-0 | langchain.vectorstores.pgembedding.CollectionStore¶
class langchain.vectorstores.pgembedding.CollectionStore(**kwargs)[source]¶
Collection store.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
a... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgembedding.CollectionStore.html |
1a752bd7b4c4-0 | 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 |
951a95fa9e8d-0 | langchain.vectorstores.myscale.MyScale¶
class langchain.vectorstores.myscale.MyScale(embedding: Embeddings, config: Optional[MyScaleSettings] = None, **kwargs: Any)[source]¶
MyScale vector store.
You need a clickhouse-connect python package, and a valid account
to connect to MyScale.
MyScale can not only search with si... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
951a95fa9e8d-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 |
951a95fa9e8d-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 |
951a95fa9e8d-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 |
951a95fa9e8d-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 |
951a95fa9e8d-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 |
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