Tony20100/langchain_full huggingface dataset:
Perform a similarity search with StarRocks by vectors
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) – where condition string.
Defaults to None.
NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
embedding (List[float]) – 
kwargs (Any) – 
Returns
List of (Document, similarity)
Return type
List[Document]
similarity_search_with_relevance_scores(query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a similarity search with StarRocks
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
where_str (Optional[str], optional) – where condition string.
Defaults to None.
NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
kwargs (Any) – 
Returns
List of documents
Return type
List[Document]
similarity_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]]
langchain_community.vectorstores.redis.base.RedisVectorStoreRetriever¶
class langchain_community.vectorstores.redis.base.RedisVectorStoreRetriever[source]¶
Bases: VectorStoreRetriever
Retriever for Redis VectorStore.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param search_kwargs: Dict[str, Any] = {'distance_threshold': None, 'k': 4, 'score_threshold': 0.9}¶
Default search kwargs.
param search_type: str = 'similarity'¶
Type of search to perform. Can be either
‘similarity’,
‘similarity_distance_threshold’,
‘similarity_score_threshold’
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a retriever with its
use case.
param vectorstore: Redis [Required]¶
Redis VectorStore.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str][source]¶
Add documents to vectorstore.
Parameters
documents (List[Document]) – 
kwargs (Any) – 
Return type
List[str]
kwargs (Any) – 
Return type
List[str]
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
Parameters
inputs (List[Input]) – 
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
List[Output]
async abatch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[Output, Exception]]]¶
Run ainvoke in parallel on a list of inputs,
yielding results as they complete.
Parameters
inputs (Sequence[Input]) – 
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 
return_exceptions (bool) – 
kwargs (Optional[Any]) – 
Return type
AsyncIterator[Tuple[int, Union[Output, Exception]]]
add_documents(documents: List[Document], **kwargs: Any) → List[str][source]¶
Add documents to vectorstore.
Parameters
documents (List[Document]) – 
kwargs (Any) – 
Return type
List[str]
Return type
Runnable[Input, Output]
with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]¶
Bind input and output types to a Runnable, returning a new Runnable.
Parameters
input_type (Optional[Type[Input]]) – 
output_type (Optional[Type[Output]]) – 
Return type
Runnable[Input, Output]
property InputType: Type[Input]¶
The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[Output]¶
The type of output this runnable produces specified as a type annotation.
allowed_search_types: ClassVar[Collection[str]] = ['similarity', 'similarity_distance_threshold', 'similarity_score_threshold', 'mmr']¶
Allowed search types.
property config_specs: List[ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[BaseModel]¶
The type of input this runnable accepts specified as a pydantic model.
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
name: Optional[str] = None¶
The name of the runnable. Used for debugging and tracing.
property output_schema: Type[BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
langchain_community.vectorstores.yellowbrick.Yellowbrick¶
class langchain_community.vectorstores.yellowbrick.Yellowbrick(embedding: Embeddings, connection_string: str, table: str, *, schema: Optional[str] = None, logger: Optional[Logger] = None, drop: bool = False)[source]¶
Yellowbrick as a vector database.
.. rubric:: Example
Initialize with yellowbrick client.
:param embedding: Embedding operator
:param connection_string: Format ‘postgres://username:password@host:port/database’
:param table: Table used to store / retrieve embeddings from
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, connection_string, table, *)
Initialize with yellowbrick client.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
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.
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.
create_index(index_params)
Create index from existing vectors
delete([ids, delete_all])
Delete vectors by uuids.
drop(table[, schema, cursor])
Helper function: Drop data.
drop_index(index_params)
Drop an index
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Add texts to the vectorstore index.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
migrate_schema_v1_to_v2()
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Perform a similarity search with Yellowbrick
similarity_search_by_vector(embedding[, k])
Perform a similarity search with Yellowbrick by vectors
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
Perform a similarity search with Yellowbrick
similarity_search_with_score(query[, k])
Perform a similarity search with Yellowbrick
similarity_search_with_score_by_vector(embedding)
Perform a similarity search with Yellowbrick with vector
Parameters
embedding (Embeddings) – 
connection_string (str) – 
table (str) – 
schema (Optional[str]) – 
logger (Optional[logging.Logger]) – 
drop (bool) – 
__init__(embedding: Embeddings, connection_string: str, table: str, *, schema: Optional[str] = None, logger: Optional[Logger] = None, drop: bool = False) → None[source]¶
Initialize with yellowbrick client.
:param embedding: Embedding operator
:param connection_string: Format ‘postgres://username:password@host:port/database’
:param table: Table used to store / retrieve embeddings from
Parameters
embedding (Embeddings) – 
connection_string (str) – 
table (str) – 
schema (Optional[str]) – 
logger (Optional[Logger]) – 
drop (bool) – 
Return type
None
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.
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]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[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]
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¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
create_index(index_params: IndexParams) → None[source]¶
Create index from existing vectors
Parameters
index_params (IndexParams) – 
Return type
None
delete(ids: Optional[List[str]] = None, delete_all: Optional[bool] = None, **kwargs: Any) → None[source]¶
Delete vectors by uuids.
Parameters
ids (Optional[List[str]]) – List of ids to delete, where each id is a uuid string.
delete_all (Optional[bool]) – 
kwargs (Any) – 
Return type
None
drop(table: str, schema: Optional[str] = None, cursor: Optional['PgCursor'] = None) → None[source]¶
Helper function: Drop data. If a cursor is provided, use it;
otherwise, obtain a new cursor for the operation.
Parameters
table (str) – 
schema (Optional[str]) – 
cursor (Optional['PgCursor']) – 
Return type
None
drop_index(index_params: IndexParams) → None[source]¶
Drop an index
Parameters
index_params (IndexParams) – 
Return type
None
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
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, connection_string: str = '', table: str = 'langchain', schema: str = 'public', drop: bool = False, **kwargs: Any) → Yellowbrick[source]¶
Add texts to the vectorstore index.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param connection_string: URI to Yellowbrick instance
:param embedding: Embedding function
:param table: table to store embeddings
:param kwargs: vectorstore specific parameters
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
connection_string (str) – 
table (str) – 
schema (str) – 
drop (bool) – 
kwargs (Any) – 
Return type
Yellowbrick
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (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 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.
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
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, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (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 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.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
migrate_schema_v1_to_v2() → None[source]¶
Return type
None
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, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with Yellowbrick
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
NOTE – Please do not let end-user fill this and always be aware
of SQL injection.
kwargs (Any) – 
Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with Yellowbrick by vectors
Parameters
embedding (List[float]) – query embedding
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
NOTE – Please do not let end-user fill this and always be aware
of SQL injection.
kwargs (Any) – 
Returns
List of documents
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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a similarity search with Yellowbrick
Parameters
Perform a similarity search with Yellowbrick
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
NOTE – Please do not let end-user fill this and always be aware
of SQL injection.
kwargs (Any) – 
Returns
List of (Document, similarity)
Return type
List[Document]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Perform a similarity search with Yellowbrick with vector
Parameters
embedding (List[float]) – query embedding
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
NOTE – Please do not let end-user fill this and always be aware
of SQL injection.
kwargs (Any) – 
Returns
List of Documents and scores
Return type
List[Document, float]
Examples using Yellowbrick¶
Yellowbrick
langchain_community.vectorstores.databricks_vector_search.DatabricksVectorSearch¶
class langchain_community.vectorstores.databricks_vector_search.DatabricksVectorSearch(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)[source]¶
Databricks Vector Search vector store.
To use, you should have the databricks-vectorsearch python package installed.
Example
from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
  endpoint_name="vs_endpoint",
  index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(vs_index)
Parameters
index (VectorSearchIndex) – A Databricks Vector Search index object.
embedding (Optional[Embeddings]) – The embedding model.
Required for direct-access index or delta-sync index
with self-managed embeddings.
text_column (Optional[str]) – The name of the text column to use for the embeddings.
Required for direct-access index or delta-sync index
with self-managed embeddings.
Make sure the text column specified is in the index.
columns (Optional[List[str]]) – The list of column names to get when doing the search.
Defaults to [primary_key, text_column].
Delta-sync index with Databricks-managed embeddings manages the ingestion, deletion,
and embedding for you.
Manually ingestion/deletion of the documents/texts is not supported for delta-sync
index.
If you want to use a delta-sync index with self-managed embeddings, you need to
provide the embedding model and text column name to use for the embeddings.
Example
from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
from databricks.vector_search.client import VectorSearchClient
from langchain_community.embeddings.openai import OpenAIEmbeddings
vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
  endpoint_name="vs_endpoint",
  index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(
  index=vs_index,
  embedding=OpenAIEmbeddings(),
  text_column="document_content"
)
If you want to manage the documents ingestion/deletion yourself, you can use a
direct-access index.
Example
from langchain_community.vectorstores import DatabricksVectorSearch
from databricks.vector_search.client import VectorSearchClient
from langchain_community.embeddings.openai import OpenAIEmbeddings
vs_client = VectorSearchClient()
vs_index = vs_client.get_index(
  endpoint_name="vs_endpoint",
  index_name="ml.llm.index"
)
vectorstore = DatabricksVectorSearch(
  index=vs_index,
  embedding=OpenAIEmbeddings(),
  text_column="document_content"
)
vectorstore.add_texts(
  texts=["text1", "text2"]
)
For more information on Databricks Vector Search, see `Databricks Vector Search
documentation: https://docs.databricks.com/en/generative-ai/vector-search.html.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(index, *[, embedding, text_column, ...])
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])
Add texts to the index.
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.
delete([ids])
Delete documents from the index.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)