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lucene_filter: the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering. (deprecated, use efficient_filter)
efficient_filter: the Lucene Engine or Faiss Engine decides whether to
perform an exact k-NN search with pre-filtering or an approximate search
with modified post-filtering.
Optional Args for Script Scoring Search:search_type: “script_scoring”; default: “approximate_search”
space_type: “l2”, “l1”, “linf”, “cosinesimil”, “innerproduct”,
“hammingbit”; default: “l2”
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {“match_all”: {}}
Optional Args for Painless Scripting Search:search_type: “painless_scripting”; default: “approximate_search”
space_type: “l2Squared”, “l1Norm”, “cosineSimilarity”; default: “l2Squared”
pre_filter: script_score query to pre-filter documents before identifying
nearest neighbors; default: {“match_all”: {}}
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
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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 resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and it’s scores most similar to query.
By default, supports Approximate Search.
Also supports Script Scoring and Painless Scripting.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents along with its scores most similar to the query.
Optional Args:same as similarity_search
Examples using OpenSearchVectorSearch¶
OpenSearch
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langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch¶
class langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding')[source]¶
Wrapper around MongoDB Atlas Vector Search.
To use, you should have both:
- the pymongo python package installed
- a connection string associated with a MongoDB Atlas Cluster having deployed an
Atlas Search index
Example
from langchain.vectorstores import MongoDBAtlasVectorSearch
from langchain.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient
mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
collection = mongo_client["<db_name>"]["<collection_name>"]
embeddings = OpenAIEmbeddings()
vectorstore = MongoDBAtlasVectorSearch(collection, embeddings)
Parameters
collection – MongoDB collection to add the texts to.
embedding – Text embedding model to use.
text_key – MongoDB field that will contain the text for each
document.
embedding_key – MongoDB field that will contain the embedding for
each document.
index_name – Name of the Atlas Search index.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(collection, embedding, *[, ...])
param collection
MongoDB collection to add the texts to.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
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add_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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_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.
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 ID or other criteria.
from_connection_string(connection_string, ...)
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct MongoDBAtlasVectorSearch wrapper from raw documents.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, pre_filter, ...])
Return MongoDB documents most similar to query.
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Return MongoDB documents most similar to query.
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, ...])
Return MongoDB documents most similar to query, along with scores.
__init__(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding')[source]¶
Parameters
collection – MongoDB collection to add the texts to.
embedding – Text embedding model to use.
text_key – MongoDB field that will contain the text for each
document.
embedding_key – MongoDB field that will contain the embedding for
each document.
index_name – Name of the Atlas Search index.
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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
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 texts.
Return type
List[str]
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Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, **kwargs: Any) → List[source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
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: 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_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[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) → VectorStoreRetriever¶
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”.
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“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 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 VectorStore.
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 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_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
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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 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 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 implemented.
Return type
Optional[bool]
classmethod from_connection_string(connection_string: str, namespace: str, embedding: Embeddings, **kwargs: Any) → MongoDBAtlasVectorSearch[source]¶
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: Embeddings, metadatas: Optional[List[dict]] = None, collection: Optional[Collection[MongoDBDocumentType]] = None, **kwargs: Any) → MongoDBAtlasVectorSearch[source]¶
Construct MongoDBAtlasVectorSearch wrapper from raw documents.
This is a user-friendly interface that:
Embeds documents.
Adds the documents to a provided MongoDB Atlas Vector Search index(Lucene)
This is intended to be a quick way to get started.
Example
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This is intended to be a quick way to get started.
Example
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, pre_filter: Optional[dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document][source]¶
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 – Optional Number of Documents to return. Defaults to 4.
fetch_k – Optional Number of Documents to fetch before passing to MMR
algorithm. Defaults to 20.
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.
pre_filter – Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
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 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 that determines the degree
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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(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, pre_filter: Optional[dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document][source]¶
Return MongoDB documents most similar to query.
Use the knnBeta Operator available in MongoDB Atlas Search
This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
early access users. It is not recommended for production deployments as we may
introduce breaking changes.
For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
Parameters
query – Text to look up documents similar to.
k – Optional Number of Documents to return. Defaults to 4.
pre_filter – Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns
List of Documents most similar to the query and score for each
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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.
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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 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 resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, *, k: int = 4, pre_filter: Optional[dict] = None, post_filter_pipeline: Optional[List[Dict]] = None) → List[Tuple[Document, float]][source]¶
Return MongoDB documents most similar to query, along with scores.
Use the knnBeta Operator available in MongoDB Atlas Search
This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
early access users. It is not recommended for production deployments as we
may introduce breaking changes.
For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
Parameters
query – Text to look up documents similar to.
k – Optional Number of Documents to return. Defaults to 4.
pre_filter – Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns
List of Documents most similar to the query and score for each
Examples using MongoDBAtlasVectorSearch¶
MongoDB Atlas
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langchain.vectorstores.starrocks.has_mul_sub_str¶
langchain.vectorstores.starrocks.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string has multiple substrings.
:param s: The string to check
:param *args: The substrings to check for in the string
Returns
True if all substrings are present in the string, False otherwise
Return type
bool
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.has_mul_sub_str.html
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langchain.vectorstores.utils.DistanceStrategy¶
class langchain.vectorstores.utils.DistanceStrategy(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies for calculating distances
between vectors.
EUCLIDEAN_DISTANCE = 'EUCLIDEAN_DISTANCE'¶
MAX_INNER_PRODUCT = 'MAX_INNER_PRODUCT'¶
DOT_PRODUCT = 'DOT_PRODUCT'¶
JACCARD = 'JACCARD'¶
COSINE = 'COSINE'¶
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langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings¶
class langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearchSettings(endpoint: str, instance_id: str, username: str, password: str, datasource_name: str, embedding_index_name: str, field_name_mapping: Dict[str, str])[source]¶
Opensearch Client Configuration
Attribute:
endpoint (str) : The endpoint of opensearch instance, You can find it
from the console of Alibaba Cloud OpenSearch.
instance_id (str) : The identify of opensearch instance, You can find
it from the console of Alibaba Cloud OpenSearch.
datasource_name (str): The name of the data source specified when creating it.
username (str) : The username specified when purchasing the instance.
password (str) : The password specified when purchasing the instance.
embedding_index_name (str) : The name of the vector attribute specified
when configuring the instance attributes.
field_name_mapping (Dict) : Using field name mapping between opensearch
vector store and opensearch instance configuration table field names:
{
‘id’: ‘The id field name map of index document.’,
‘document’: ‘The text field name map of index document.’,
‘embedding’: ‘In the embedding field of the opensearch instance,
the values must be in float16 multivalue type and separated by commas.’,
‘metadata_field_x’: ‘Metadata field mapping includes the mapped
field name and operator in the mapping value, separated by a comma
between the mapped field name and the operator.’,
}
Attributes
field_name_mapping
endpoint
instance_id
username
password
datasource_name
embedding_index_name
Methods
__init__(endpoint, instance_id, username, ...)
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Methods
__init__(endpoint, instance_id, username, ...)
__init__(endpoint: str, instance_id: str, username: str, password: str, datasource_name: str, embedding_index_name: str, field_name_mapping: Dict[str, str]) → None[source]¶
Examples using AlibabaCloudOpenSearchSettings¶
Alibaba Cloud OpenSearch
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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]¶
Interface implemented by AwaDB vector stores.
Initialize with AwaDB client.If table_name is not specified,
a random table name of _DEFAULT_TABLE_NAME + last segment of uuid
would be created automatically.
Parameters
table_name – Name of the table created, default _DEFAULT_TABLE_NAME.
embedding – Optional Embeddings initially set.
log_and_data_dir – Optional the root directory of log and data.
client – Optional AwaDB client.
kwargs – Any possible extend parameters in the future.
Returns
None.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([table_name, embedding, ...])
Initialize with AwaDB client.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, is_duplicate_texts])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
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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_type, **kwargs)
Return docs most similar to query using specified search type.
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.
create_table(table_name, **kwargs)
Create a new table.
delete([ids])
Delete the documents which have the specified ids.
from_documents(documents[, embedding, ...])
Create an AwaDB vectorstore from a list of documents.
from_texts(texts[, embedding, metadatas, ...])
Create an AwaDB vectorstore from a raw documents.
get([ids, text_in_page_content, ...])
Return docs according ids.
get_current_table(**kwargs)
Get the current table.
list_tables(**kwargs)
List all the tables created by the client.
load_local(table_name, **kwargs)
Load the local specified table.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, ...])
Return docs most similar to query.
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similarity_search(query[, k, ...])
Return docs most similar to query.
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, texts[, metadatas])
Update the documents which have the specified ids.
use(table_name, **kwargs)
Use the specified table.
__init__(table_name: str = 'langchain_awadb', embedding: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs: Any) → None[source]¶
Initialize with AwaDB client.If table_name is not specified,
a random table name of _DEFAULT_TABLE_NAME + last segment of uuid
would be created automatically.
Parameters
table_name – Name of the table created, default _DEFAULT_TABLE_NAME.
embedding – Optional Embeddings initially set.
log_and_data_dir – Optional the root directory of log and data.
client – Optional AwaDB client.
kwargs – Any possible extend parameters in the future.
Returns
None.
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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
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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 texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, is_duplicate_texts: Optional[bool] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param is_duplicate_texts: Optional whether to duplicate texts. Defaults to True.
:param kwargs: any possible extend parameters in the future.
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: 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_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
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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) → VectorStoreRetriever¶
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. 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 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 VectorStore.
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 top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
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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(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
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 similar to query.
create_table(table_name: str, **kwargs: Any) → bool[source]¶
Create a new table.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete the documents which have the specified ids.
Parameters
ids – The ids of the embedding vectors.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful.
False otherwise, None if not implemented.
Return type
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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: Any) → AwaDB[source]¶
Create an AwaDB vectorstore from a list of documents.
If a log_and_data_dir specified, the table will be persisted there.
Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
table_name (str) – Name of the table to create.
log_and_data_dir (Optional[str]) – Directory to persist the table.
client (Optional[awadb.Client]) – AwaDB client.
Any – Any possible parameters in the future
Returns
AwaDB vectorstore.
Return type
AwaDB
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, table_name: str = 'langchain_awadb', log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None, **kwargs: Any) → AwaDB[source]¶
Create an AwaDB vectorstore from a raw documents.
Parameters
texts (List[str]) – List of texts to add to the table.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
metadatas (Optional[List[dict]]) – List of metadatas. Defaults to None.
table_name (str) – Name of the table to create.
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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] = None, not_include_fields: Optional[Set[str]] = None, limit: Optional[int] = None, **kwargs: Any) → Dict[str, Document][source]¶
Return docs according ids.
Parameters
ids – The ids of the embedding vectors.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter – Filter by any metadata of the document.
not_include_fields – Not pack the specified fields of each document.
limit – The number of documents to return. Defaults to 5. Optional.
Returns
Documents which satisfy the input conditions.
get_current_table(**kwargs: Any) → str[source]¶
Get the current table.
list_tables(**kwargs: Any) → List[str][source]¶
List all the tables created by the client.
load_local(table_name: str, **kwargs: Any) → bool[source]¶
Load the local specified table.
Parameters
table_name – Table name
kwargs – Any possible extend parameters in the future.
Returns
Success or failure of loading the local specified table
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
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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 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.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.
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, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
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 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.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.
Returns
List of Documents selected by maximal marginal relevance.
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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, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query – Text query.
k – The maximum number of documents to return.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter (Optional[dict]) – Filter by metadata. Defaults to None.
`{"color" (E.g.) – ”red”, “price”: 4.20}`. Optional.
`{"max_price" (E.g.) – 15.66, “min_price”: 4.20}`
field (price is the metadata) –
filter (means range) –
`{"maxe_price" (E.g.) – 15.66, “mine_price”: 4.20}`
field –
filter –
kwargs – Any possible extend parameters in the future.
Returns
Returns the k most similar documents to the specified text query.
similarity_search_by_vector(embedding: Optional[List[float]] = None, k: int = 4, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = None, not_include_fields_in_metadata: Optional[Set[str]] = None, **kwargs: Any) → List[Document][source]¶
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.
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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.
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 similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, text_in_page_content: Optional[str] = None, meta_filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
The most k similar documents and scores of the specified query.
Parameters
query – Text query.
k – The k most similar documents to the text query.
text_in_page_content – Filter by the text in page_content of Document.
meta_filter – Filter by metadata. Defaults to None.
kwargs – Any possible extend parameters in the future.
Returns
The k most similar documents to the specified text query.
0 is dissimilar, 1 is the most similar.
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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.
metadatas – The metadatas of the updating documents.
Returns
the ids of the updated documents.
use(table_name: str, **kwargs: Any) → bool[source]¶
Use the specified table. Don’t know the tables, please invoke list_tables.
Examples using AwaDB¶
AwaDB
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langchain.vectorstores.usearch.USearch¶
class langchain.vectorstores.usearch.USearch(embedding: Embeddings, index: Any, docstore: Docstore, ids: List[str])[source]¶
Wrapper around USearch vector database.
To use, you should have the usearch python package installed.
Initialize with necessary components.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, index, docstore, ids)
Initialize with necessary components.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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_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.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
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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[, metadatas, ...])
Construct USearch wrapper from raw documents. This is a user friendly interface that: 1. Embeds documents. 2. Creates an in memory docstore 3. Initializes the USearch database This is intended to be a quick way to get started.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Return docs most similar to query.
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])
Return docs most similar to query.
__init__(embedding: Embeddings, index: Any, docstore: Docstore, ids: List[str])[source]¶
Initialize with necessary components.
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]
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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 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 texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict]] = None, ids: Optional[ndarray] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
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: 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_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
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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) → VectorStoreRetriever¶
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. 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 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 VectorStore.
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 top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
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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(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
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 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 implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
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Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict]] = None, ids: Optional[ndarray] = None, metric: str = 'cos', **kwargs: Any) → USearch[source]¶
Construct USearch wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the USearch database
This is intended to be a quick way to get started.
Example
from langchain.vectorstores import USearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
usearch = USearch.from_texts(texts, embeddings)
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.
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 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.
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.
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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 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(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, **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.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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[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.
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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 resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4) → List[Tuple[Document, float]][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.
Returns
List of documents most similar to the query with distance.
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langchain.vectorstores.cassandra.Cassandra¶
class langchain.vectorstores.cassandra.Cassandra(embedding: Embeddings, session: Session, keyspace: str, table_name: str, ttl_seconds: Optional[int] = None)[source]¶
Wrapper around Cassandra embeddings platform.
There is no notion of a default table name, since each embedding
function implies its own vector dimension, which is part of the schema.
Example
from langchain.vectorstores import Cassandra
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
session = ...
keyspace = 'my_keyspace'
vectorstore = Cassandra(embeddings, session, keyspace, 'my_doc_archive')
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, session, keyspace, ...)
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, ...])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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_retriever(**kwargs)
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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.
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.
clear()
Empty the collection.
delete([ids])
Delete by vector IDs.
delete_by_document_id(document_id)
delete_collection()
Just an alias for clear (to better align with other VectorStore implementations).
from_documents(documents, embedding[, ...])
Create a Cassandra vectorstore from a document list.
from_texts(texts, embedding[, metadatas, ...])
Create a Cassandra vectorstore from raw texts.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance. 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. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param 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. Optional.
max_marginal_relevance_search_by_vector(...)
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max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param 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.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Return docs most similar to query.
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])
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to embedding vector.
similarity_search_with_score_id(query[, k])
similarity_search_with_score_id_by_vector(...)
Return docs most similar to embedding vector.
__init__(embedding: Embeddings, session: Session, keyspace: str, table_name: str, ttl_seconds: Optional[int] = None) → None[source]¶
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]
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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 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 texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 16, ttl_seconds: Optional[int] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
ids (Optional[List[str]], optional) – Optional list of IDs.
batch_size (int) – Number of concurrent requests to send to the server.
ttl_seconds (Optional[int], optional) – Optional time-to-live
for the added texts.
Returns
List of IDs of the added texts.
Return type
List[str]
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: Optional[List[dict]] = None, **kwargs: Any) → VST¶
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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[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) → VectorStoreRetriever¶
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. 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 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 VectorStore.
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}
)
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)
# 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_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
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 similar to query.
clear() → None[source]¶
Empty the collection.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector IDs.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
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Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
delete_by_document_id(document_id: str) → None[source]¶
delete_collection() → None[source]¶
Just an alias for clear
(to better align with other VectorStore implementations).
classmethod from_documents(documents: List[Document], embedding: Embeddings, batch_size: int = 16, **kwargs: Any) → CVST[source]¶
Create a Cassandra vectorstore from a document list.
No support for specifying text IDs
Returns
a Cassandra vectorstore.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, batch_size: int = 16, **kwargs: Any) → CVST[source]¶
Create a Cassandra vectorstore from raw texts.
No support for specifying text IDs
Returns
a Cassandra vectorstore.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
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.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param 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.
Optional.
Returns
List of Documents selected by maximal marginal relevance.
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Optional.
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][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param embedding: Embedding to look up documents similar to.
:param k: Number of Documents to return.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param 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.
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, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
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[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
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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 resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector.
No support for filter query (on metadata) along with vector search.
Parameters
embedding (str) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
Returns
List of (Document, score), the most similar to the query vector.
similarity_search_with_score_id(query: str, k: int = 4) → List[Tuple[Document, float, str]][source]¶
similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4) → List[Tuple[Document, float, str]][source]¶
Return docs most similar to embedding vector.
No support for filter query (on metadata) along with vector search.
Parameters
embedding (str) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
Returns
List of (Document, score, id), the most similar to the query vector.
Examples using Cassandra¶
Cassandra
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langchain.vectorstores.starrocks.get_named_result¶
langchain.vectorstores.starrocks.get_named_result(connection: Any, query: str) → List[dict[str, Any]][source]¶
Get a named result from a query.
:param connection: The connection to the database
:param query: The query to execute
Returns
The result of the query
Return type
List[dict[str, Any]]
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langchain.vectorstores.matching_engine.MatchingEngine¶
class langchain.vectorstores.matching_engine.MatchingEngine(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Optional[Credentials] = None)[source]¶
Vertex Matching Engine implementation of the vector store.
While the embeddings are stored in the Matching Engine, the embedded
documents will be stored in GCS.
An existing Index and corresponding Endpoint are preconditions for
using this module.
See usage in docs/modules/indexes/vectorstores/examples/matchingengine.ipynb
Note that this implementation is mostly meant for reading if you are
planning to do a real time implementation. While reading is a real time
operation, updating the index takes close to one hour.
Vertex Matching Engine implementation of the vector store.
While the embeddings are stored in the Matching Engine, the embedded
documents will be stored in GCS.
An existing Index and corresponding Endpoint are preconditions for
using this module.
See usage in
docs/modules/indexes/vectorstores/examples/matchingengine.ipynb.
Note that this implementation is mostly meant for reading if you are
planning to do a real time implementation. While reading is a real time
operation, updating the index takes close to one hour.
project_id¶
The GCS project id.
index¶
The created index class. See
~:func:MatchingEngine.from_components.
endpoint¶
The created endpoint class. See
~:func:MatchingEngine.from_components.
embedding¶
A Embeddings that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
gcs_client¶
The GCS client.
gcs_bucket_name¶
The GCS bucket name.
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The GCS client.
gcs_bucket_name¶
The GCS bucket name.
credentials¶
Created GCP credentials.
Type
Optional
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(project_id, index, endpoint, ...[, ...])
Vertex Matching Engine implementation of the vector store.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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_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.
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 ID or other criteria.
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delete([ids])
Delete by vector ID or other criteria.
from_components(project_id, region, ...[, ...])
Takes the object creation out of the constructor.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Use from components instead.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Return docs most similar to query.
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(*args, **kwargs)
Run similarity search with distance.
__init__(project_id: str, index: MatchingEngineIndex, endpoint: MatchingEngineIndexEndpoint, embedding: Embeddings, gcs_client: storage.Client, gcs_bucket_name: str, credentials: Optional[Credentials] = None)[source]¶
Vertex Matching Engine implementation of the vector store.
While the embeddings are stored in the Matching Engine, the embedded
documents will be stored in GCS.
An existing Index and corresponding Endpoint are preconditions for
using this module.
See usage in
docs/modules/indexes/vectorstores/examples/matchingengine.ipynb.
Note that this implementation is mostly meant for reading if you are
planning to do a real time implementation. While reading is a real time
operation, updating the index takes close to one hour.
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operation, updating the index takes close to one hour.
project_id¶
The GCS project id.
index¶
The created index class. See
~:func:MatchingEngine.from_components.
endpoint¶
The created endpoint class. See
~:func:MatchingEngine.from_components.
embedding¶
A Embeddings that will be used for
embedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
gcs_client¶
The GCS client.
gcs_bucket_name¶
The GCS bucket name.
credentials¶
Created GCP credentials.
Type
Optional
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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
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 texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
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Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters.
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: 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_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[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) → VectorStoreRetriever¶
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. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
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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 VectorStore.
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 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_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
Return docs most similar to query.
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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 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 implemented.
Return type
Optional[bool]
classmethod from_components(project_id: str, region: str, gcs_bucket_name: str, index_id: str, endpoint_id: str, credentials_path: Optional[str] = None, embedding: Optional[Embeddings] = None) → MatchingEngine[source]¶
Takes the object creation out of the constructor.
Parameters
project_id – The GCP project id.
region – The default location making the API calls. It must have
regional. (the same location as the GCS bucket and must be) –
gcs_bucket_name – The location where the vectors will be stored in
created. (order for the index to be) –
index_id – The id of the created index.
endpoint_id – The id of the created endpoint.
credentials_path – (Optional) The path of the Google credentials on
system. (the local file) –
embedding – The Embeddings that will be used for
texts. (embedding the) –
Returns
A configured MatchingEngine with the texts added to the index.
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Returns
A configured MatchingEngine with the texts added to the index.
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: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → MatchingEngine[source]¶
Use from components instead.
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.
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 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.
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 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.
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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(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, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query – The string that will be used to search for similar documents.
k – The amount of neighbors that will be retrieved.
Returns
A list of k matching documents.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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[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 similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
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Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using MatchingEngine¶
MatchingEngine
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langchain.vectorstores.elastic_vector_search.ElasticKnnSearch¶
class langchain.vectorstores.elastic_vector_search.ElasticKnnSearch(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = 'vector', query_field: Optional[str] = 'text')[source]¶
ElasticKnnSearch is a class for performing k-nearest neighbor
(k-NN) searches on text data using Elasticsearch.
This class is used to create an Elasticsearch index of text data that
can be searched using k-NN search. The text data is transformed into
vector embeddings using a provided embedding model, and these embeddings
are stored in the Elasticsearch index.
index_name¶
The name of the Elasticsearch index.
Type
str
embedding¶
The embedding model to use for transforming text data
into vector embeddings.
Type
Embeddings
es_connection¶
An existing Elasticsearch connection.
Type
Elasticsearch, optional
es_cloud_id¶
The Cloud ID of your Elasticsearch Service
deployment.
Type
str, optional
es_user¶
The username for your Elasticsearch Service deployment.
Type
str, optional
es_password¶
The password for your Elasticsearch Service
deployment.
Type
str, optional
vector_query_field¶
The name of the field in the Elasticsearch
index that contains the vector embeddings.
Type
str, optional
query_field¶
The name of the field in the Elasticsearch index
that contains the original text data.
Type
str, optional
Usage:>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
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>>> 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!', metadata={}), 0.9)]
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(index_name, embedding[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, model_id, ...])
Add a list of texts to the Elasticsearch index.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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_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.
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.
create_knn_index(mapping)
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Return docs most similar to query.
create_knn_index(mapping)
Create a new k-NN index in Elasticsearch.
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[, metadatas])
Create a new ElasticKnnSearch instance and add a list of texts to the
knn_hybrid_search([query, k, query_vector, ...])
Perform a hybrid k-NN and text search on the Elasticsearch index.
knn_search([query, k, query_vector, ...])
Perform a k-NN search on the Elasticsearch index.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Pass through to knn_search
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])
Pass through to knn_search including score
__init__(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = 'vector', query_field: Optional[str] = 'text')[source]¶
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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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
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 texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[Any, Any]]] = None, model_id: Optional[str] = None, refresh_indices: bool = False, **kwargs: Any) → List[str][source]¶
Add a list of texts to the Elasticsearch index.
Parameters
texts (Iterable[str]) – The texts to add to the index.
metadatas (List[Dict[Any, Any]], optional) – A list of metadata dictionaries
to associate with the texts.
model_id (str, optional) – The ID of the model to use for transforming the
texts into vectors.
refresh_indices (bool, optional) – Whether to refresh the Elasticsearch
indices after adding the texts.
**kwargs – Arbitrary keyword arguments.
Returns
A list of IDs for the added texts.
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
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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_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[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) → VectorStoreRetriever¶
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. 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 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 VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
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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 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_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
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 similar to query.
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Return docs most similar to query.
create_knn_index(mapping: Dict) → None[source]¶
Create a new k-NN index in Elasticsearch.
Parameters
mapping (Dict) – The mapping to use for the new index.
Returns
None
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 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: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, **kwargs: Any) → ElasticKnnSearch[source]¶
Create a new ElasticKnnSearch instance and add a list of texts to theElasticsearch index.
Parameters
texts (List[str]) – The texts to add to the index.
embedding (Embeddings) – The embedding model to use for transforming the
texts into vectors.
metadatas (List[Dict[Any, Any]], optional) – A list of metadata dictionaries
to associate with the texts.
**kwargs – Arbitrary keyword arguments.
Returns
A new ElasticKnnSearch instance.
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**kwargs – Arbitrary keyword arguments.
Returns
A new ElasticKnnSearch instance.
knn_hybrid_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, knn_boost: Optional[float] = 0.9, query_boost: Optional[float] = 0.1, fields: Optional[Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...]]] = None, page_content: Optional[str] = 'text') → List[Tuple[Document, float]][source]¶
Perform a hybrid k-NN and text search on the Elasticsearch index.
Parameters
query (str, optional) – The query text to search for.
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 search results to return.
source (bool, optional) – Whether to return the source of the search results.
knn_boost (float, optional) – The boost value to apply to the k-NN search
results.
query_boost (float, optional) – The boost value to apply to the text search
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 object and a score.
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Returns
A list of tuples, where each tuple contains a Document object and a score.
knn_search(query: Optional[str] = None, k: Optional[int] = 10, query_vector: Optional[List[float]] = None, model_id: Optional[str] = None, size: Optional[int] = 10, source: Optional[bool] = True, fields: Optional[Union[List[Mapping[str, Any]], Tuple[Mapping[str, Any], ...]]] = None, page_content: Optional[str] = 'text') → List[Tuple[Document, float]][source]¶
Perform a k-NN search on the Elasticsearch index.
Parameters
query (str, optional) – The query text to search for.
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 search results to return.
source (bool, optional) – Whether to return the source of the search 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 object and a score.
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.
Parameters
query – Text to look up documents similar to.
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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 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.
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 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 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(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, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Pass through to knn_search
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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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[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 similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 10, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Pass through to knn_search including score
Examples using ElasticKnnSearch¶
ElasticSearch
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langchain.vectorstores.usearch.dependable_usearch_import¶
langchain.vectorstores.usearch.dependable_usearch_import() → Any[source]¶
Import usearch if available, otherwise raise error.
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.dependable_usearch_import.html
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langchain.vectorstores.qdrant.QdrantException¶
class langchain.vectorstores.qdrant.QdrantException[source]¶
Base class for all the Qdrant related exceptions
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https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.QdrantException.html
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langchain.vectorstores.alibabacloud_opensearch.create_metadata¶
langchain.vectorstores.alibabacloud_opensearch.create_metadata(fields: Dict[str, Any]) → Dict[str, Any][source]¶
Create metadata from fields.
Parameters
fields – The fields of the document. The fields must be a dict.
Returns
The metadata of the document. The metadata must be a dict.
Return type
metadata
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langchain.vectorstores.pgembedding.PGEmbedding¶
class langchain.vectorstores.pgembedding.PGEmbedding(connection_string: str, embedding_function: Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None)[source]¶
VectorStore implementation using Postgres and the pg_embedding extension.
pg_embedding uses sequential scan by default. but you can create a HNSW index
using the create_hnsw_index method.
- connection_string is a postgres connection string.
- embedding_function any embedding function implementing
langchain.embeddings.base.Embeddings interface.
collection_name is the name of the collection to use. (default: langchain)
NOTE: This is not the name of the table, but the name of the collection.The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy is the distance strategy to use. (default: EUCLIDEAN)
EUCLIDEAN is the euclidean distance.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(connection_string, embedding_function)
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_embeddings(texts, embeddings, metadatas, ...)
add_texts(texts[, metadatas, ids])
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add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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_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.
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.
connect()
create_collection()
create_hnsw_extension()
create_hnsw_index([max_elements, dims, m, ...])
create_tables_if_not_exists()
delete([ids])
Delete by vector ID or other criteria.
delete_collection()
drop_tables()
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
from_existing_index(embedding[, ...])
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_collection(session)
get_connection_string(kwargs)
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
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Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Return docs most similar to query.
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, filter])
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
__init__(connection_string: str, embedding_function: Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None) → None[source]¶
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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
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 texts.
Return type
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Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(texts: List[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None[source]¶
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.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
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: 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_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[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) → VectorStoreRetriever¶
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as_retriever(**kwargs: Any) → VectorStoreRetriever¶
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. 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 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 VectorStore.
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 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_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
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docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
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 similar to query.
connect() → Connection[source]¶
create_collection() → None[source]¶
create_hnsw_extension() → None[source]¶
create_hnsw_index(max_elements: int = 10000, dims: int = 1536, m: int = 8, ef_construction: int = 16, ef_search: int = 16) → None[source]¶
create_tables_if_not_exists() → None[source]¶
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 implemented.
Return type
Optional[bool]
delete_collection() → None[source]¶
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Return type
Optional[bool]
delete_collection() → None[source]¶
drop_tables() → None[source]¶
classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Return VectorStore initialized from documents and embeddings.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Return VectorStore initialized from texts and embeddings.
get_collection(session: Session) → Optional[CollectionStore][source]¶
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
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.
Parameters
query – Text to look up documents similar to.
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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 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.
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 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 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(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, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
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Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
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[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 similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Examples using PGEmbedding¶
pg_embedding
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langchain.vectorstores.utils.maximal_marginal_relevance¶
langchain.vectorstores.utils.maximal_marginal_relevance(query_embedding: ndarray, embedding_list: list, lambda_mult: float = 0.5, k: int = 4) → List[int][source]¶
Calculate maximal marginal relevance.
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langchain.vectorstores.vectara.VectaraRetriever¶
class langchain.vectorstores.vectara.VectaraRetriever[source]¶
Bases: VectorStoreRetriever
Retriever class for Vectara.
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 metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param search_kwargs: dict [Optional]¶
Search params.
k: Number of Documents to return. Defaults to 5.
lambda_val: lexical match parameter for hybrid search.
filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be “doc.rating > 3.0 and part.lang = ‘deu’”} see
https://docs.vectara.com/docs/search-apis/sql/filter-overview
for more details.
n_sentence_context: number of sentences before/after the matching segment to add
param search_type: str = 'similarity'¶
Type of search to perform. Defaults to “similarity”.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param vectorstore: Vectara [Required]¶
Vectara vectorstore.
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param vectorstore: Vectara [Required]¶
Vectara vectorstore.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Add documents to vectorstore.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Add documents to vectorstore.
add_texts(texts: List[str], metadatas: Optional[List[dict]] = None, doc_metadata: Optional[dict] = {}) → None[source]¶
Add text to the Vectara vectorstore.
Parameters
texts (List[str]) – The text
metadatas (List[dict]) – Metadata dicts, must line up with existing store
async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document]¶
Asynchronously get documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
:param tags: Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
Parameters
metadata – Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
Returns
List of relevant documents
async ainvoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]¶
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async astream(input: Input, config: Optional[RunnableConfig] = None) → AsyncIterator[Output]¶
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
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 choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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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, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
get_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document]¶
Retrieve documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
:param tags: Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
Parameters
metadata – Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
Returns
List of relevant documents
invoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]¶
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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_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
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¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
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classmethod validate(value: Any) → Model¶
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.Runnable[~langchain.schema.runnable.Input, ~langchain.schema.runnable.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException]] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
allowed_search_types: ClassVar[Collection[str]] = ('similarity', 'similarity_score_threshold', 'mmr')¶
property lc_attributes: Dict¶
Return a list of attribute names that should be included in the
serialized kwargs. These attributes must be accepted by the
constructor.
property lc_namespace: List[str]¶
Return the namespace of the langchain object.
eg. [“langchain”, “llms”, “openai”]
property lc_secrets: Dict[str, str]¶
Return a map of constructor argument names to secret ids.
eg. {“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
Return whether or not the class is serializable.
Examples using VectaraRetriever¶
Chat Over Documents with Vectara
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langchain.vectorstores.clickhouse.has_mul_sub_str¶
langchain.vectorstores.clickhouse.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string contains multiple substrings.
:param s: string to check.
:param *args: substrings to check.
Returns
True if all substrings are in the string, False otherwise.
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langchain.vectorstores.faiss.FAISS¶
class langchain.vectorstores.faiss.FAISS(embedding_function: Callable, index: Any, docstore: Docstore, index_to_docstore_id: Dict[int, str], relevance_score_fn: Optional[Callable[[float], float]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
Wrapper around FAISS vector database.
To use, you should have the faiss python package installed.
Example
from langchain import FAISS
faiss = FAISS(embedding_function, index, docstore, index_to_docstore_id)
Initialize with necessary components.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding_function, index, ...[, ...])
Initialize with necessary components.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_embeddings(text_embeddings[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
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.
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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 query using specified search type.
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 ID.
deserialize_from_bytes(serialized, ...)
Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct FAISS wrapper from raw documents.
from_texts(texts, embedding[, metadatas, ids])
Construct FAISS wrapper from raw documents.
load_local(folder_path, embeddings[, index_name])
Load FAISS index, docstore, and index_to_docstore_id from disk.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs and their similarity scores selected using the maximal marginal
merge_from(target)
Merge another FAISS object with the current one.
save_local(folder_path[, index_name])
Save FAISS index, docstore, and index_to_docstore_id to disk.
search(query, search_type, **kwargs)
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search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
serialize_to_bytes()
Serialize FAISS index, docstore, and index_to_docstore_id to bytes.
similarity_search(query[, k, filter, fetch_k])
Return docs most similar to query.
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, ...])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
Return docs most similar to query.
__init__(embedding_function: Callable, index: Any, docstore: Docstore, index_to_docstore_id: Dict[int, str], relevance_score_fn: Optional[Callable[[float], float]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE)[source]¶
Initialize with necessary components.
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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
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
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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]
add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], 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.
Parameters
text_embeddings – Iterable pairs of string and embedding to
add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
Returns
List of ids from adding the texts into the vectorstore.
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.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of unique IDs.
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: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
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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[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) → VectorStoreRetriever¶
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. 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 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 VectorStore.
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}
)
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)
# 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_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
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]¶
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 similar to query.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by ID. These are the IDs in the vectorstore.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
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Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod deserialize_from_bytes(serialized: bytes, embeddings: Embeddings, **kwargs: Any) → FAISS[source]¶
Deserialize FAISS index, docstore, and index_to_docstore_id from bytes.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Creates an in memory docstore
Initializes the FAISS database
This is intended to be a quick way to get started.
Example
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
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from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
faiss = FAISS.from_texts(texts, embeddings)
classmethod load_local(folder_path: str, embeddings: Embeddings, index_name: str = 'index', **kwargs: Any) → FAISS[source]¶
Load FAISS index, docstore, and index_to_docstore_id from disk.
Parameters
folder_path – folder path to load index, docstore,
and index_to_docstore_id from.
embeddings – Embeddings to use when generating queries
index_name – for saving with a specific index file name
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
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 before filtering (if needed) 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.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
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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 before filtering 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.
max_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None) → List[Tuple[Document, float]][source]¶
Return docs and their similarity scores selected using the maximal marginalrelevance.
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 before filtering 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 and similarity scores selected by maximal marginalrelevance and score for each.
merge_from(target: FAISS) → None[source]¶
Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Parameters
target – FAISS object you wish to merge into the current one
Returns
None.
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target – FAISS object you wish to merge into the current one
Returns
None.
save_local(folder_path: str, index_name: str = 'index') → None[source]¶
Save FAISS index, docstore, and index_to_docstore_id to disk.
Parameters
folder_path – folder path to save index, docstore,
and index_to_docstore_id to.
index_name – for saving with a specific index file name
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
serialize_to_bytes() → bytes[source]¶
Serialize FAISS index, docstore, and index_to_docstore_id to bytes.
similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **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.
filter – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
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.
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k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
fetch_k – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns
List of Documents most similar to the embedding.
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 similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
fetch_k – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns
List of documents most similar to the query text with
L2 distance in float. Lower score represents more similarity.
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L2 distance in float. Lower score represents more similarity.
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
embedding – Embedding vector to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.
fetch_k – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
**kwargs – kwargs to be passed to similarity search. Can include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
Examples using FAISS¶
Cohere Reranker
Document Comparison
FAISS
Loading documents from a YouTube url
AutoGPT
BabyAGI User Guide
BabyAGI with Tools
!pip install bs4
Plug-and-Plai
Custom Agent with PlugIn Retrieval
Generative Agents in LangChain
Ensemble Retriever
Custom agent with tool retrieval
Select by maximal marginal relevance (MMR)
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langchain.vectorstores.clickhouse.ClickhouseSettings¶
class langchain.vectorstores.clickhouse.ClickhouseSettings[source]¶
Bases: BaseSettings
ClickHouse Client Configuration
Attribute:
clickhouse_host (str)An URL to connect to MyScale backend.Defaults to ‘localhost’.
clickhouse_port (int) : URL port to connect with HTTP. Defaults to 8443.
username (str) : Username to login. Defaults to None.
password (str) : Password to login. Defaults to None.
index_type (str): index type string.
index_param (list): index build parameter.
index_query_params(dict): index query parameters.
database (str) : Database name to find the table. Defaults to ‘default’.
table (str) : Table name to operate on.
Defaults to ‘vector_table’.
metric (str)Metric to compute distance,supported are (‘angular’, ‘euclidean’, ‘manhattan’, ‘hamming’,
‘dot’). Defaults to ‘angular’.
https://github.com/spotify/annoy/blob/main/src/annoymodule.cc#L149-L169
column_map (Dict)Column type map to project column name onto langchainsemantics. Must have keys: text, id, vector,
must be same size to number of columns. For example:
.. code-block:: python
{‘id’: ‘text_id’,
‘uuid’: ‘global_unique_id’
‘embedding’: ‘text_embedding’,
‘document’: ‘text_plain’,
‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
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.
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Raises ValidationError if the input data cannot be parsed to form a valid model.
param column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'id': 'id', 'metadata': 'metadata', 'uuid': 'uuid'}¶
param database: str = 'default'¶
param host: str = 'localhost'¶
param index_param: Optional[Union[List, Dict]] = ["'L2Distance'", 100]¶
param index_query_params: Dict[str, str] = {}¶
param index_type: str = 'annoy'¶
param metric: str = 'angular'¶
param password: Optional[str] = None¶
param port: int = 8123¶
param table: str = 'langchain'¶
param username: Optional[str] = None¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
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 choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
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