Tony20100/langchain_full huggingface dataset:
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: Optional[str] = 'annoy'¶
param metric: str = 'angular'¶
param password: Optional[str] = None¶
param port: int = 8123¶
param secure: bool = False¶
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
Parameters
_fields_set (Optional[SetStr]) – 
values (Any) – 
Return type
Model
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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
Parameters
obj (Any) – 
Return type
Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
Parameters
b (Union[str, bytes]) – 
content_type (unicode) – 
encoding (unicode) – 
proto (Protocol) – 
allow_pickle (bool) – 
Return type
Model
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
Return type
DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
Parameters
by_alias (bool) – 
ref_template (unicode) – 
dumps_kwargs (Any) – 
Return type
unicode
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) – 
Return type
None
classmethod validate(value: Any) → Model¶
Parameters
value (Any) – 
Return type
Model
Examples using ClickhouseSettings¶
ClickHouse
langchain_community.vectorstores.tiledb.TileDB¶
class langchain_community.vectorstores.tiledb.TileDB(embedding: Embeddings, index_uri: str, metric: str, *, vector_index_uri: str = '', docs_array_uri: str = '', config: Optional[Mapping[str, Any]] = None, timestamp: Optional[Any] = None, allow_dangerous_deserialization: bool = False, **kwargs: Any)[source]¶
TileDB vector store.
To use, you should have the tiledb-vector-search python package installed.
Example
from langchain_community import TileDB
embeddings = OpenAIEmbeddings()
db = TileDB(embeddings, index_uri, metric)
Initialize with necessary components.
Parameters
allow_dangerous_deserialization (bool) – whether to allow deserialization
of the data which involves loading data using pickle.
data can be modified by malicious actors to deliver a
malicious payload that results in execution of
arbitrary code on your machine.
embedding (Embeddings) – 
index_uri (str) – 
metric (str) – 
vector_index_uri (str) – 
docs_array_uri (str) – 
config (Optional[Mapping[str, Any]]) – 
timestamp (Any) – 
kwargs (Any) – 
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, index_uri, metric, *[, ...])
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.
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, timestamp])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
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 and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
consolidate_updates(**kwargs)
create(index_uri, index_type, dimensions, ...)
delete([ids, timestamp])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding, ...)
Construct TileDB index from embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct a TileDB index from raw documents.
Construct a TileDB index from raw documents.
load(index_uri, embedding, *[, metric, ...])
Load a TileDB index from a URI.
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
process_index_results(ids, scores, *[, k, ...])
Turns TileDB results into a list of documents and scores.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
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(...)
Return docs most similar to query.
__init__(embedding: Embeddings, index_uri: str, metric: str, *, vector_index_uri: str = '', docs_array_uri: str = '', config: Optional[Mapping[str, Any]] = None, timestamp: Optional[Any] = None, allow_dangerous_deserialization: bool = False, **kwargs: Any)[source]¶
Initialize with necessary components.
Parameters
allow_dangerous_deserialization (bool) – whether to allow deserialization
of the data which involves loading data using pickle.
data can be modified by malicious actors to deliver a
data can be modified by malicious actors to deliver a
malicious payload that results in execution of
arbitrary code on your machine.
embedding (Embeddings) – 
index_uri (str) – 
metric (str) – 
vector_index_uri (str) – 
docs_array_uri (str) – 
config (Optional[Mapping[str, Any]]) – 
timestamp (Optional[Any]) – 
kwargs (Any) – 
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
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.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
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, timestamp: int = 0, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
ids (Optional[List[str]]) – Optional ids of each text object.
timestamp (int) – Optional timestamp to write new texts with.
kwargs (Any) – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
async asimilarity_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 (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
consolidate_updates(**kwargs: Any) → None[source]¶
Parameters
kwargs (Any) – 
Return type
None
classmethod create(index_uri: str, index_type: str, dimensions: int, vector_type: dtype, *, metadatas: bool = True, config: Optional[Mapping[str, Any]] = None) → None[source]¶
Parameters
index_uri (str) –
Parameters
index_uri (str) – 
index_type (str) – 
dimensions (int) – 
vector_type (dtype) – 
metadatas (bool) – 
config (Optional[Mapping[str, Any]]) – 
Return type
None
delete(ids: Optional[List[str]] = None, timestamp: int = 0, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
timestamp (int) – Optional timestamp to delete with.
**kwargs (Any) – 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.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, index_uri: str, *, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, metric: str = 'euclidean', index_type: str = 'FLAT', config: Optional[Mapping[str, Any]] = None, index_timestamp: int = 0, **kwargs: Any) → TileDB[source]¶
Construct TileDB index from embeddings.
Parameters
text_embeddings (List[Tuple[str, List[float]]]) – List of tuples of (text, embedding)
embedding (Embeddings) – Embedding function to use.
embedding (Embeddings) – Embedding function to use.
index_uri (str) – The URI to write the TileDB arrays
metadatas (Optional[List[dict]]) – List of metadata dictionaries to associate with documents.
metric (str) – Optional, Metric to use for indexing. Defaults to “euclidean”.
index_type (str) – Optional, Vector index type (“FLAT”, IVF_FLAT”)
config (Optional[Mapping[str, Any]]) – Optional, TileDB config
index_timestamp (int) – Optional, timestamp to write new texts with.
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
TileDB
Example
from langchain_community import TileDB
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
db = TileDB.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, metric: str = 'euclidean', index_uri: str = '/tmp/tiledb_array', index_type: str = 'FLAT', config: Optional[Mapping[str, Any]] = None, index_timestamp: int = 0, **kwargs: Any) → TileDB[source]¶
Construct a TileDB index from raw documents.
Parameters
texts (List[str]) – List of documents to index.
embedding (Embeddings) – Embedding function to use.
metadatas (Optional[List[dict]]) – List of metadata dictionaries to associate with documents.
ids (Optional[List[str]]) – Optional ids of each text object.
metric (str) – Metric to use for indexing. Defaults to “euclidean”.
metric (str) – Metric to use for indexing. Defaults to “euclidean”.
index_uri (str) – The URI to write the TileDB arrays
index_type (str) – Optional,  Vector index type (“FLAT”, IVF_FLAT”)
config (Optional[Mapping[str, Any]]) – Optional, TileDB config
index_timestamp (int) – Optional, timestamp to write new texts with.
kwargs (Any) – 
Return type
TileDB
Example
from langchain_community import TileDB
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
index = TileDB.from_texts(texts, embeddings)
classmethod load(index_uri: str, embedding: Embeddings, *, metric: str = 'euclidean', config: Optional[Mapping[str, Any]] = None, timestamp: Optional[Any] = None, **kwargs: Any) → TileDB[source]¶
Load a TileDB index from a URI.
Parameters
index_uri (str) – The URI of the TileDB vector index.
embedding (Embeddings) – Embeddings to use when generating queries.
metric (str) – Optional, Metric to use for indexing. Defaults to “euclidean”.
config (Optional[Mapping[str, Any]]) – Optional, TileDB config
timestamp (Optional[Any]) – Optional, timestamp to use for opening the arrays.
kwargs (Any) – 
Return type
TileDB
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
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch before filtering (if needed) to
pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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 maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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, **kwargs: Any) → 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 (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Returns
List of Documents and similarity scores selected by maximal marginalrelevance and score for each.
Return type
List[Tuple[Document, float]]
process_index_results(ids: List[int], scores: List[float], *, k: int = 4, filter: Optional[Dict[str, Any]] = None, score_threshold: float = 1.7976931348623157e+308) → List[Tuple[Document, float]][source]¶
Turns TileDB results into a list of documents and scores.
Parameters
ids (List[int]) – List of indices of the documents in the index.
scores (List[float]) – List of distances of the documents in the index.
scores (List[float]) – List of distances of the documents in the index.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.
score_threshold (float) – Optional, a floating point value to filter the
resulting set of retrieved docs
Returns
List of Documents and scores.
Return type
List[Tuple[Document, float]]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
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 (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
Returns
List of Documents most similar to the query.
Return type
List[Document]
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 (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding.
Return type
List[Document]
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 (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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)
Return type
List[Tuple[Document, float]]
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 (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of documents most similar to the query text with
Distance as float. Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
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 (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]) – Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
**kwargs (Any) – kwargs to be passed to similarity search. Can include:
nprobe: Optional, number of partitions to check if using IVF_FLAT index
score_threshold: Optional, a floating point value to filter the
resulting set of retrieved docs
Returns
List of documents most similar to the query text and distance
in float for each. Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
Examples using TileDB¶
TileDB
langchain_milvus.vectorstores.milvus.maximal_marginal_relevance¶
langchain_milvus.vectorstores.milvus.maximal_marginal_relevance(query_embedding: ndarray, embedding_list: list, lambda_mult: float = 0.5, k: int = 4) → List[int][source]¶
Calculate maximal marginal relevance.
Parameters
query_embedding (ndarray) – 
embedding_list (list) – 
lambda_mult (float) – 
k (int) – 
Return type
List[int]
langchain_community.vectorstores.oraclevs.create_index¶
langchain_community.vectorstores.oraclevs.create_index(client: Connection, vector_store: OracleVS, params: Optional[dict[str, Any]] = None) → None[source]¶
Parameters
client (Connection) – 
vector_store (OracleVS) – 
params (Optional[dict[str, Any]]) – 
Return type
None