id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
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971c4a934c21-0 | langchain.vectorstores.redis.schema.TextFieldSchema¶
class langchain.vectorstores.redis.schema.TextFieldSchema[source]¶
Bases: RedisField
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 name: str [Requ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.TextFieldSchema.html |
971c4a934c21-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.TextFieldSchema.html |
971c4a934c21-2 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.TextFieldSchema.html |
b9b90a7a5d31-0 | langchain.vectorstores.llm_rails.LLMRails¶
class langchain.vectorstores.llm_rails.LLMRails(datastore_id: Optional[str] = None, api_key: Optional[str] = None)[source]¶
Implementation of Vector Store using LLMRails (https://llmrails.com/).
.. rubric:: Example
from langchain.vectorstores import LLMRails
vectorstore = LLMR... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-1 | 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])
Del... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-2 | 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 documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-3 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → LLMRailsRet... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-4 | search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-5 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, **kwargs: Any) → LLMRails[source]¶
Construct LLMRails wrapper from raw documents.
This is intended to be a quick way to get started.
.. r... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-6 | among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 correspondi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
b9b90a7a5d31-7 | **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 = 5) → List[Tuple[Document, float]][source... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRails.html |
0c465b961455-0 | 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)[sourc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-1 | 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 mor... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-2 | 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 component... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-3 | 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 non... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-4 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-5 | score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for Vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-6 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-7 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-8 | fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0c465b961455-9 | 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¶
Google Vertex AI MatchingEngine | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
b69adba194bc-0 | langchain.vectorstores.elasticsearch.BaseRetrievalStrategy¶
class langchain.vectorstores.elasticsearch.BaseRetrievalStrategy[source]¶
Base class for Elasticsearch retrieval strategies.
Methods
__init__()
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_fi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.BaseRetrievalStrategy.html |
b69adba194bc-1 | Executes when a search is performed on the store.
Parameters
query_vector – The query vector,
or None if not using vector-based query.
query – The text query, or None if not using text-based query.
k – The total number of results to retrieve.
fetch_k – The number of results to fetch initially.
vector_query_field – The ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.BaseRetrievalStrategy.html |
61e04b4db801-0 | langchain.vectorstores.typesense.Typesense¶
class langchain.vectorstores.typesense.Typesense(typesense_client: Client, embedding: Embeddings, *, typesense_collection_name: Optional[str] = None, text_key: str = 'text')[source]¶
Typesense vector store.
To use, you should have the typesense python package installed.
Examp... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-1 | Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids])
Run more texts through the embedding and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-2 | search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Return typesense documents most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(qu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-3 | Run more texts through the embedding 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 ids to associate with the texts.
Returns
List of ids from adding the texts into the vectorstore.
asy... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-4 | 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 doc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-5 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-6 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, typesense_client: Optional[Client] = None, typesense_client_params: Optional[dict] = None, typesense_collection_name: Opt... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-7 | 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 Documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
61e04b4db801-8 | 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_wit... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
2d4bcfdd97a5-0 | langchain.vectorstores.marqo.Marqo¶
class langchain.vectorstores.marqo.Marqo(client: marqo.Client, index_name: str, add_documents_settings: Optional[Dict[str, Any]] = None, searchable_attributes: Optional[List[str]] = None, page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = None)[source]¶
Marqo vector st... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-1 | add_texts(texts[, metadatas])
Upload texts with metadata (properties) to Marqo.
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_searc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-2 | get_number_of_documents()
Helper to see the number of documents in the index
marqo_bulk_similarity_search(queries[, k])
Return documents from Marqo using a bulk search, exposes Marqo's output directly
marqo_similarity_search(query[, k])
Return documents from Marqo exposing Marqo's output directly
max_marginal_relevance... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-3 | 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 documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-4 | Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-5 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-6 | queries.
Parameters
queries (Iterable[Union[str, Dict[str, float]]]) – An iterable of queries to
bulk (execute in) –
of (queries in the list can be strings or dictionaries) –
queries. (weighted) –
k (int, optional) – The number of documents to return for each query.
4. (Defaults to) –
Returns
A list of results for ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-7 | Return VectorStore initialized from documents. Note that Marqo does not
need embeddings, we retain the parameter to adhere to the Liskov substitution
principle.
Parameters
documents (List[Document]) – Input documents
embedding (Any, optional) – Embeddings (not required). Defaults to None.
Returns
A Marqo vectorstore
Re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-8 | texts (List[str]) – A list of texts to index into marqo upon creation.
embedding (Any, optional) – Embeddings (not required). Defaults to None.
index_name (str, optional) – The name of the index to use, if none is
None. (accompany the texts. Defaults to) –
url (str, optional) – The URL for Marqo. Defaults to “http://l... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-9 | List[Dict[str, str]]
get_number_of_documents() → int[source]¶
Helper to see the number of documents in the index
Returns
The number of documents
Return type
int
marqo_bulk_similarity_search(queries: Iterable[Union[str, Dict[str, float]]], k: int = 4) → Dict[str, List[Dict[str, List[Dict[str, str]]]]][source]¶
Return do... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-10 | 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 Documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-11 | query. (as a string or a weighted) –
k (int, optional) – The number of documents to return. Defaults to 4.
Returns
k documents ordered from best to worst match.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedd... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
2d4bcfdd97a5-12 | ordered by descending score.
Return type
List[Tuple[Document, float]]
Examples using Marqo¶
Marqo | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.marqo.Marqo.html |
72c67befc536-0 | langchain.vectorstores.annoy.dependable_annoy_import¶
langchain.vectorstores.annoy.dependable_annoy_import() → Any[source]¶
Import annoy if available, otherwise raise error. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.annoy.dependable_annoy_import.html |
f9c4294d6272-0 | langchain.vectorstores.llm_rails.ModelChoices¶
class langchain.vectorstores.llm_rails.ModelChoices(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
embedding_english_v1 = 'embedding-english-v1'¶
embedding_multi_v1 = 'embedding-multi-v1'¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.ModelChoices.html |
bfb80cfcb25e-0 | 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. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.has_mul_sub_str.html |
83a9f7c70144-0 | langchain.vectorstores.rocksetdb.Rockset¶
class langchain.vectorstores.rocksetdb.Rockset(client: Any, embeddings: Embeddings, collection_name: str, text_key: str, embedding_key: str, workspace: str = 'commons')[source]¶
Rockset vector store.
To use, you should have the rockset python package installed. Note that to use... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-1 | Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(client, embeddings, ...[, workspace])
Initialize with Rockset client. :param client: Rockset client object :param collection: Rockset collection to insert docs / query :param embeddings: Langchain Embeddings object to use to generate... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-2 | asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete([ids])
Delete by vector ID or other criteria.
delete_texts(ids)
Delete a ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-3 | Initialize with Rockset client.
:param client: Rockset client object
:param collection: Rockset collection to insert docs / query
:param embeddings: Langchain Embeddings object to use to generate
embedding for given text.
Parameters
text_key – column in Rockset collection to use to store the text
embedding_key – column... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-4 | ids: Optional list of ids to associate with the texts.
batch_size: Send documents in batches to rockset.
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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-5 | 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 documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-6 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-7 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-8 | Same as similarity_search_with_relevance_scores but
doesn’t return the scores.
similarity_search_by_vector(embedding: List[float], k: int = 4, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Accepts a query_embedding (vector), and ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
83a9f7c70144-9 | List of documents with their relevance score
Return type
List[Tuple[Document, float]]
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using Rockset¶
Rockset | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
2cc9c88256e8-0 | 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 = DistanceStrate... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-1 | Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-2 | save_local(folder_path[, index_name])
Save FAISS index, docstore, and index_to_docstore_id to disk.
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, fi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-3 | Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added text... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-4 | Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-5 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-6 | 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(do... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-7 | from langchain.vectorstores import FAISS
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, a... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-8 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the max... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-9 | 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 t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-10 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-11 | 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 ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
2cc9c88256e8-12 | SQL
Caching
Ensemble Retriever
Custom agent with tool retrieval
Select by maximal marginal relevance (MMR)
First we add a step to load memory | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
280a7b5b8c97-0 | langchain.vectorstores.pgembedding.QueryResult¶
class langchain.vectorstores.pgembedding.QueryResult[source]¶
Result from a query.
Attributes
EmbeddingStore
distance
Methods
__init__()
__init__()¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgembedding.QueryResult.html |
8474db3cc818-0 | langchain.vectorstores.pinecone.Pinecone¶
class langchain.vectorstores.pinecone.Pinecone(index: Any, embedding: Union[Embeddings, Callable], text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE)[source]¶
Pinecone vector store.
To use, you should have th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-1 | Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-2 | Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(...)
Return pinecone documents most similar to embedding, along with scores.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return pine... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-3 | Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This is done to avoid memory issues and optimize using HTTP based embeddings.
For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
embedding_chunk_size>... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-4 | 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
“s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-5 | )
# 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, **kwa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-6 | Load pinecone vectorstore from index name.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, batch_size: int = 32, text_key: str = 'text', namespace: Optional[str] = None, index_name: Optional[str] = None, upsert_kwargs: Optional[dic... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-7 | Returns
Pinecone Index instance.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, namespace: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevan... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-8 | 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, namespace: Optional[str] = None... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
8474db3cc818-9 | 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 o... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
122a5a31c361-0 | langchain.vectorstores.lancedb.LanceDB¶
class langchain.vectorstores.lancedb.LanceDB(connection: Any, embedding: Embeddings, vector_key: Optional[str] = 'vector', id_key: Optional[str] = 'id', text_key: Optional[str] = 'text')[source]¶
LanceDB vector store.
To use, you should have lancedb python package installed.
Exam... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-1 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilar... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-2 | Initialize with Lance DB connection
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-3 | Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-4 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-5 | Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-6 | Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
122a5a31c361-7 | **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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Ru... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
faceda03cab8-0 | langchain.vectorstores.qdrant.sync_call_fallback¶
langchain.vectorstores.qdrant.sync_call_fallback(method: Callable) → Callable[source]¶
Decorator to call the synchronous method of the class if the async method is not
implemented. This decorator might be only used for the methods that are defined
as async in the class. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.sync_call_fallback.html |
97e650375853-0 | langchain.vectorstores.utils.filter_complex_metadata¶
langchain.vectorstores.utils.filter_complex_metadata(documents: ~typing.List[~langchain.schema.document.Document], *, allowed_types: ~typing.Tuple[~typing.Type, ...] = (<class 'str'>, <class 'bool'>, <class 'int'>, <class 'float'>)) → List[Document][source]¶
Filter ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.utils.filter_complex_metadata.html |
8004b0899ce4-0 | langchain.vectorstores.chroma.Chroma¶
class langchain.vectorstores.chroma.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Optional[Dict] = None, client: O... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-1 | amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-2 | Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with Chroma with distance.
update_document(document_id, document)
Update a document in the collection.
update_documents(ids, documents)
Update a document in the collection.
__init__(collection_name:... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-3 | 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.
Returns
List of IDs of the added texts.
Return type
L... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-4 | 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 max... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-5 | 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 embeddin... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-6 | embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
client_settings (Optional[chromadb.config.Settings]) – Chroma client settings
collection_metadata (Optional[Dict]) – Collection configurations.
Defaults to None.
Returns
Chroma vectorstore.
Return type
Chroma
classmethod from_texts(texts: List[str... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
8004b0899ce4-7 | Defaults to None.
Returns
Chroma vectorstore.
Return type
Chroma
get(ids: Optional[OneOrMany[ID]] = None, where: Optional[Where] = None, limit: Optional[int] = None, offset: Optional[int] = None, where_document: Optional[WhereDocument] = None, include: Optional[List[str]] = None) → Dict[str, Any][source]¶
Gets the coll... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
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