id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
fa0e262c586c-2 | the new model: you should trust this data
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[boo... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.ClickhouseSettings.html |
fa0e262c586c-3 | 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.clickhouse.ClickhouseSettings.html |
ced8b726304d-0 | langchain.vectorstores.sklearn.SKLearnVectorStore¶
class langchain.vectorstores.sklearn.SKLearnVectorStore(embedding: Embeddings, *, persist_path: Optional[str] = None, serializer: Literal['json', 'bson', 'parquet'] = 'json', metric: str = 'cosine', **kwargs: Any)[source]¶
A simple in-memory vector store based on the s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
ced8b726304d-1 | 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_documents(documents, e... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
ced8b726304d-2 | persist()
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 d... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
ced8b726304d-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
ced8b726304d-4 | score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for Vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
ced8b726304d-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.sklearn.SKLearnVectorStore.html |
ced8b726304d-6 | :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.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal releva... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
ced8b726304d-7 | 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 ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStore.html |
84638146dd47-0 | langchain.vectorstores.tair.Tair¶
class langchain.vectorstores.tair.Tair(embedding_function: Embeddings, url: str, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', search_params: Optional[dict] = None, **kwargs: Any)[source]¶
Wrapper around Tair Vector store.
Attributes
embeddings
Access t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
84638146dd47-1 | Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
create_index_if_not_exist(dim, ...)
delete([ids])
Delete by vector ID or other criteria.
drop_index([index_name])
Drop an existing index.
from_documents(documents, embedding[, ...])
Return Ve... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
84638146dd47-2 | Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
84638146dd47-3 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
84638146dd47-4 | 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.tair.Tair.html |
84638146dd47-5 | Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
static drop_index(index_name: str = 'langchain', **kwargs: Any) → bool[source]¶
Drop an existing index.
Parameters
index_name (str) – Name of the index to drop.
Returns
True if the index is dropped successfully.... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
84638146dd47-6 | 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.tair.Tair.html |
84638146dd47-7 | Return type
List[Document]
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 sim... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
106702130be1-0 | langchain.vectorstores.milvus.Milvus¶
class langchain.vectorstores.milvus.Milvus(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-1 | to False.
The connection args used for this class comes in the form of a dict,
here are a few of the options:
address (str): The actual address of Milvusinstance. Example address: “localhost:19530”
uri (str): The uri of Milvus instance. Example uri:“http://randomwebsite:19530”,
“tcp:foobarsite:19530”,
“https://ok.s3.so... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-2 | embedding = OpenAIEmbeddings()
# Connect to a milvus instance on localhost
milvus_store = Milvus(
embedding_function = Embeddings,
collection_name = “LangChainCollection”,
drop_old = True,
)
Raises
ValueError – If the pymilvus python package is not installed.
Initialize the Milvus vector store.
Attributes
embeddings
Ac... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-3 | Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-4 | Initialize the Milvus vector store.
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.milvus.Milvus.html |
106702130be1-5 | the texts. Defaults to None.
timeout (Optional[int]) – Timeout for each batch insert. Defaults
to None.
batch_size (int, optional) – Batch size to use for insertion.
Defaults to 1000.
Raises
MilvusException – Failure to add texts
Returns
The resulting keys for each inserted element.
Return type
List[str]
async classmet... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-6 | 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.milvus.Milvus.html |
106702130be1-7 | 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.milvus.Milvus.html |
106702130be1-8 | Parameters
texts (List[str]) – Text data.
embedding (Embeddings) – Embedding function.
metadatas (Optional[List[dict]]) – Metadata for each text if it exists.
Defaults to None.
collection_name (str, optional) – Collection name to use. Defaults to
“LangChainCollection”.
connection_args (dict[str, Any], optional) – Conne... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-9 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5
param (dict, optional) – The search params for the specified index.
Defaults to None.
expr (str, optional) – Filtering expression. Defaults to None.
timeout (int, optional) – How long to wait before timeout error.
Defaults to None.
kwargs – Collection.sea... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-10 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search against the query string.
Parameters
query (str) – The te... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-11 | Returns
Document results for search.
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 – input text
k – Number of Documents t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
106702130be1-12 | Return type
List[float], List[Tuple[Document, any, any]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a search on a query string and retur... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.milvus.Milvus.html |
34a6e981c996-0 | langchain.vectorstores.scann.dependable_scann_import¶
langchain.vectorstores.scann.dependable_scann_import() → Any[source]¶
Import scann if available, otherwise raise error. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.dependable_scann_import.html |
fe9cae3f63f0-0 | langchain.vectorstores.azuresearch.AzureSearch¶
class langchain.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Callable, search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, semantic_query_language: str = 'en-us', f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-1 | 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 mo... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-2 | similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
vector_search(query[, k])
Returns the most similar indexed documents to the query text.
vector_search_with_score(query[, k, filters])
Return docs most similar to query.
__init__(azure_search_endpoint: str, azure_search_key: str, index_na... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-3 | 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]¶
Add texts data to an existing index.
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-4 | score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for Vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-5 | 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.azuresearch.AzureSearch.html |
fe9cae3f63f0-6 | Return type
List[Document]
hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query with an hybrid query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
Li... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-7 | 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.azuresearch.AzureSearch.html |
fe9cae3f63f0-8 | 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 ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
fe9cae3f63f0-9 | Returns
List of Documents most similar to the query and score for each
Examples using AzureSearch¶
Azure Cognitive Search | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1c7e8c70440a-0 | langchain.vectorstores.pgembedding.EmbeddingStore¶
class langchain.vectorstores.pgembedding.EmbeddingStore(**kwargs)[source]¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the ins... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgembedding.EmbeddingStore.html |
8ee4bb0baf06-0 | langchain.vectorstores.myscale.has_mul_sub_str¶
langchain.vectorstores.myscale.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.myscale.has_mul_sub_str.html |
77cb157ff06b-0 | langchain.vectorstores.pgembedding.BaseModel¶
class langchain.vectorstores.pgembedding.BaseModel(**kwargs: Any)[source]¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgembedding.BaseModel.html |
d11e92f7aecd-0 | langchain.vectorstores.sklearn.BaseSerializer¶
class langchain.vectorstores.sklearn.BaseSerializer(persist_path: str)[source]¶
Abstract base class for saving and loading data.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.BaseSerializer.html |
1130af3d0cd1-0 | langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch¶
class langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
Wrapper around in-memory storage for exact search.
To use it, you should have the docarray package with version >=0.32.0 insta... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-1 | 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_documents(documents, e... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-2 | 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.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-3 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-4 | 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.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-5 | Return VectorStore initialized from documents and embeddings.
classmethod from_params(embedding: Embeddings, metric: Literal['cosine_sim', 'euclidian_dist', 'sgeuclidean_dist'] = 'cosine_sim', **kwargs: Any) → DocArrayInMemorySearch[source]¶
Initialize DocArrayInMemorySearch store.
Parameters
embedding (Embeddings) – E... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-6 | 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 max... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
1130af3d0cd1-7 | 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
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
d5fd1bd5e458-0 | langchain.vectorstores.singlestoredb.SingleStoreDBRetriever¶
class langchain.vectorstores.singlestoredb.SingleStoreDBRetriever[source]¶
Bases: VectorStoreRetriever
Retriever for SingleStoreDB vector stores.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the inp... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
d5fd1bd5e458-1 | add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Add documents to vectorstore.
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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
d5fd1bd5e458-2 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
d5fd1bd5e458-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
d5fd1bd5e458-4 | 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() → SerializedN... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
2c079cde2172-0 | langchain.vectorstores.pgvector.BaseModel¶
class langchain.vectorstores.pgvector.BaseModel(**kwargs: Any)[source]¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s cla... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.BaseModel.html |
2dc8ee668fc2-0 | All modules for which code is available
langchain._api.deprecation
langchain.agents.agent
langchain.agents.agent_iterator
langchain.agents.agent_toolkits.amadeus.toolkit
langchain.agents.agent_toolkits.azure_cognitive_services
langchain.agents.agent_toolkits.base
langchain.agents.agent_toolkits.conversational_retrieval... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-1 | langchain.agents.agent_toolkits.vectorstore.base
langchain.agents.agent_toolkits.vectorstore.toolkit
langchain.agents.agent_toolkits.xorbits.base
langchain.agents.agent_toolkits.zapier.toolkit
langchain.agents.agent_types
langchain.agents.chat.base
langchain.agents.chat.output_parser
langchain.agents.conversational.bas... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-2 | langchain.callbacks.stdout
langchain.callbacks.streaming_aiter
langchain.callbacks.streaming_aiter_final_only
langchain.callbacks.streaming_stdout
langchain.callbacks.streaming_stdout_final_only
langchain.callbacks.streamlit.mutable_expander
langchain.callbacks.streamlit.streamlit_callback_handler
langchain.callbacks.t... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-3 | langchain.chains.graph_qa.sparql
langchain.chains.hyde.base
langchain.chains.llm
langchain.chains.llm_bash.base
langchain.chains.llm_bash.prompt
langchain.chains.llm_checker.base
langchain.chains.llm_math.base
langchain.chains.llm_requests
langchain.chains.llm_summarization_checker.base
langchain.chains.llm_symbolic_ma... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-4 | langchain.chat_models.anyscale
langchain.chat_models.azure_openai
langchain.chat_models.azureml_endpoint
langchain.chat_models.base
langchain.chat_models.fake
langchain.chat_models.google_palm
langchain.chat_models.human
langchain.chat_models.jinachat
langchain.chat_models.mlflow_ai_gateway
langchain.chat_models.openai... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-5 | langchain.document_loaders.diffbot
langchain.document_loaders.directory
langchain.document_loaders.discord
langchain.document_loaders.docugami
langchain.document_loaders.dropbox
langchain.document_loaders.duckdb_loader
langchain.document_loaders.email
langchain.document_loaders.embaas
langchain.document_loaders.epub
la... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-6 | langchain.document_loaders.notebook
langchain.document_loaders.notion
langchain.document_loaders.notiondb
langchain.document_loaders.nuclia
langchain.document_loaders.obs_directory
langchain.document_loaders.obs_file
langchain.document_loaders.obsidian
langchain.document_loaders.odt
langchain.document_loaders.onedrive
... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-7 | langchain.document_loaders.stripe
langchain.document_loaders.telegram
langchain.document_loaders.tencent_cos_directory
langchain.document_loaders.tencent_cos_file
langchain.document_loaders.tensorflow_datasets
langchain.document_loaders.text
langchain.document_loaders.tomarkdown
langchain.document_loaders.toml
langchai... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-8 | langchain.embeddings.huggingface_hub
langchain.embeddings.jina
langchain.embeddings.llamacpp
langchain.embeddings.localai
langchain.embeddings.minimax
langchain.embeddings.mlflow_gateway
langchain.embeddings.modelscope_hub
langchain.embeddings.mosaicml
langchain.embeddings.nlpcloud
langchain.embeddings.octoai_embedding... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-9 | langchain.llms.beam
langchain.llms.bedrock
langchain.llms.cerebriumai
langchain.llms.chatglm
langchain.llms.clarifai
langchain.llms.cohere
langchain.llms.ctransformers
langchain.llms.databricks
langchain.llms.deepinfra
langchain.llms.edenai
langchain.llms.fake
langchain.llms.fireworks
langchain.llms.forefrontai
langcha... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-10 | langchain.llms.textgen
langchain.llms.tongyi
langchain.llms.utils
langchain.llms.vertexai
langchain.llms.vllm
langchain.llms.writer
langchain.llms.xinference
langchain.load.dump
langchain.load.load
langchain.load.serializable
langchain.memory.buffer
langchain.memory.buffer_window
langchain.memory.chat_memory
langchain.... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-11 | langchain.output_parsers.rail_parser
langchain.output_parsers.regex
langchain.output_parsers.regex_dict
langchain.output_parsers.retry
langchain.output_parsers.structured
langchain.prompts.base
langchain.prompts.chat
langchain.prompts.example_selector.base
langchain.prompts.example_selector.length_based
langchain.promp... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-12 | langchain.retrievers.re_phraser
langchain.retrievers.remote_retriever
langchain.retrievers.self_query.base
langchain.retrievers.self_query.chroma
langchain.retrievers.self_query.deeplake
langchain.retrievers.self_query.myscale
langchain.retrievers.self_query.pinecone
langchain.retrievers.self_query.qdrant
langchain.ret... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-13 | langchain.tools.azure_cognitive_services.text2speech
langchain.tools.azure_cognitive_services.utils
langchain.tools.base
langchain.tools.bing_search.tool
langchain.tools.brave_search.tool
langchain.tools.convert_to_openai
langchain.tools.dataforseo_api_search.tool
langchain.tools.ddg_search.tool
langchain.tools.file_ma... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-14 | langchain.tools.playwright.extract_text
langchain.tools.playwright.get_elements
langchain.tools.playwright.navigate
langchain.tools.playwright.navigate_back
langchain.tools.playwright.utils
langchain.tools.plugin
langchain.tools.powerbi.tool
langchain.tools.pubmed.tool
langchain.tools.python.tool
langchain.tools.reques... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-15 | langchain.utilities.twilio
langchain.utilities.vertexai
langchain.utilities.wikipedia
langchain.utilities.wolfram_alpha
langchain.utilities.zapier
langchain.utils.env
langchain.utils.formatting
langchain.utils.input
langchain.utils.math
langchain.utils.strings
langchain.utils.utils
langchain.vectorstores.alibabacloud_o... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-16 | langchain.vectorstores.utils
langchain.vectorstores.vectara
langchain.vectorstores.weaviate
langchain.vectorstores.xata
langchain.vectorstores.zilliz
langchain_experimental.autonomous_agents.autogpt.agent
langchain_experimental.autonomous_agents.autogpt.memory
langchain_experimental.autonomous_agents.autogpt.output_par... | https://api.python.langchain.com/en/latest/_modules/index.html |
2dc8ee668fc2-17 | langchain_experimental.tot.checker
langchain_experimental.tot.controller
langchain_experimental.tot.memory
langchain_experimental.tot.prompts
langchain_experimental.tot.thought
langchain_experimental.tot.thought_generation
pydantic.main | https://api.python.langchain.com/en/latest/_modules/index.html |
92c94153315f-0 | Source code for langchain.server
"""Script to run langchain-server locally using docker-compose."""
import subprocess
from pathlib import Path
from langsmith.cli.main import get_docker_compose_command
[docs]def main() -> None:
"""Run the langchain server locally."""
p = Path(__file__).absolute().parent / "docke... | https://api.python.langchain.com/en/latest/_modules/langchain/server.html |
3e17c05b1921-0 | Source code for langchain.text_splitter
"""**Text Splitters** are classes for splitting text.
**Class hierarchy:**
.. code-block::
BaseDocumentTransformer --> TextSplitter --> <name>TextSplitter # Example: CharacterTextSplitter
RecursiveCharacterTextSplitter --> <n... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-1 | sentencizer = spacy.load(pipeline, exclude=["ner", "tagger"])
return sentencizer
def _split_text_with_regex(
text: str, separator: str, keep_separator: bool
) -> List[str]:
# Now that we have the separator, split the text
if separator:
if keep_separator:
# The parentheses in the patt... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-2 | add_start_index: If `True`, includes chunk's start index in metadata
"""
if chunk_overlap > chunk_size:
raise ValueError(
f"Got a larger chunk overlap ({chunk_overlap}) than chunk size "
f"({chunk_size}), should be smaller."
)
self._chunk_s... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-3 | return self.create_documents(texts, metadatas=metadatas)
def _join_docs(self, docs: List[str], separator: str) -> Optional[str]:
text = separator.join(docs)
text = text.strip()
if text == "":
return None
else:
return text
def _merge_splits(self, splits: It... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-4 | ):
total -= self._length_function(current_doc[0]) + (
separator_len if len(current_doc) > 1 else 0
)
current_doc = current_doc[1:]
current_doc.append(d)
total += _len + (separator_len if len(curre... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-5 | """Text splitter that uses tiktoken encoder to count length."""
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to calculate max_tokens_for_prompt. "
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-6 | """Splitting text that looks at characters."""
[docs] def __init__(
self, separator: str = "\n\n", is_separator_regex: bool = False, **kwargs: Any
) -> None:
"""Create a new TextSplitter."""
super().__init__(**kwargs)
self._separator = separator
self._is_separator_regex = ... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-7 | self.return_each_line = return_each_line
# Given the headers we want to split on,
# (e.g., "#, ##, etc") order by length
self.headers_to_split_on = sorted(
headers_to_split_on, key=lambda split: len(split[0]), reverse=True
)
[docs] def aggregate_lines_to_chunks(self, lines... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-8 | # Keep track of the nested header structure
# header_stack: List[Dict[str, Union[int, str]]] = []
header_stack: List[HeaderType] = []
initial_metadata: Dict[str, str] = {}
for line in lines:
stripped_line = line.strip()
# Check each line against each of the header... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-9 | }
header_stack.append(header)
# Update initial_metadata with the current header
initial_metadata[name] = header["data"]
# Add the previous line to the lines_with_metadata
# only if current_content is not empt... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-10 | encode: Callable[[str], List[int]]
[docs]def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]:
"""Split incoming text and return chunks using tokenizer."""
splits: List[str] = []
input_ids = tokenizer.encode(text)
start_idx = 0
cur_idx = min(start_idx + tokenizer.tokens_per_chun... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-11 | enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
[docs] def split_text(self, text: str) -> List[str]:
def _enco... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-12 | self.model_name = model_name
self._model = SentenceTransformer(self.model_name)
self.tokenizer = self._model.tokenizer
self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk)
def _initialize_chunk_configuration(
self, *, tokens_per_chunk: Optional[int]
) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-13 | token_ids_with_start_and_end_token_ids = self.tokenizer.encode(
text,
max_length=self._max_length_equal_32_bit_integer,
truncation="do_not_truncate",
)
return token_ids_with_start_and_end_token_ids
[docs]class Language(str, Enum):
"""Enum of the programming langua... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-14 | """Split incoming text and return chunks."""
final_chunks = []
# Get appropriate separator to use
separator = separators[-1]
new_separators = []
for i, _s in enumerate(separators):
_separator = _s if self._is_separator_regex else re.escape(_s)
if _s == "":... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-15 | [docs] @classmethod
def from_language(
cls, language: Language, **kwargs: Any
) -> RecursiveCharacterTextSplitter:
separators = cls.get_separators_for_language(language)
return cls(separators=separators, is_separator_regex=True, **kwargs)
[docs] @staticmethod
def get_separators... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-16 | "\nstatic ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-17 | # Split along import statements
"\nimport ",
# Split along syntax declarations
"\nsyntax ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == La... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-18 | # Split along control flow statements
"\nif ",
"\nwhile ",
"\nfor ",
"\nloop ",
"\nmatch ",
"\nconst ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-19 | # Heading level 2
# ---------------
# End of code block
"```\n",
# Horizontal lines
"\n\\*\\*\\*+\n",
"\n---+\n",
"\n___+\n",
# Note that this splitter doesn't handle horizontal lines defined
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-20 | "<h6",
"<span",
"<table",
"<tr",
"<td",
"<th",
"<ul",
"<ol",
"<header",
"<footer",
"<nav",
# Head
"<head",
"<sty... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
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