Tony20100/langchain_full huggingface dataset:
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filters])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(embedding)
Return docs most similar to embedding vector, 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, filters])
Return docs most similar to query, along with scores.
__init__(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)[source]¶
Parameters
index (VectorSearchIndex) – 
embedding (Optional[Embeddings]) – 
text_column (Optional[str]) – 
columns (Optional[List[str]]) – 
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]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[Any]] = None, **kwargs: Any) → List[str][source]¶
Add texts to the index.
Only support direct-access index.
Parameters
texts (Iterable[str]) – List of texts to add.
metadatas (Optional[List[dict]]) – List of metadata for each text. Defaults to None.
ids (Optional[List[Any]]) – List of ids for each text. Defaults to None.
If not provided, a random uuid will be generated for each text.
kwargs (Any) – 
Returns
List of ids from adding the texts into the index.
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
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[Any]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete documents from the index.
Only support direct-access index.
Parameters
ids (Optional[List[Any]]) – List of ids of documents to delete.
kwargs (Any) – 
Returns
True if successful.
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_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filters: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
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, filters: Optional[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 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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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, filters: Optional[Any] = None, **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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filters: Optional[Any] = None, **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.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding.
Return type
List[Document]
Returns
List of Documents most similar to the embedding.
Return type
List[Document]
similarity_search_by_vector_with_score(embedding: List[float], k: int = 4, filters: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector, along with scores.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding and score for each.
Return type
List[Tuple[Document, float]]
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, filters: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query, along with scores.
Parameters
query (str) – Text to look up documents similar to.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filters (Optional[Any]) – Filters to apply to the query. Defaults to None.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding and score for each.
Return type
List[Tuple[Document, float]]
Examples using DatabricksVectorSearch¶
Databricks Vector Search
langchain_community.vectorstores.utils.filter_complex_metadata¶
langchain_community.vectorstores.utils.filter_complex_metadata(documents: ~typing.List[~langchain_core.documents.base.Document], *, allowed_types: ~typing.Tuple[~typing.Type, ...] = (<class 'str'>, <class 'bool'>, <class 'int'>, <class 'float'>)) → List[Document][source]¶
Filter out metadata types that are not supported for a vector store.
Parameters
documents (List[Document]) – 
allowed_types (Tuple[Type, ...]) – 
Return type
List[Document]
langchain_community.vectorstores.qdrant.Qdrant¶
class langchain_community.vectorstores.qdrant.Qdrant(client: Any, collection_name: str, embeddings: Optional[Embeddings] = None, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', distance_strategy: str = 'COSINE', vector_name: Optional[str] = None, async_client: Optional[Any] = None, embedding_function: Optional[Callable] = None)[source]¶
[Deprecated] Qdrant vector store.
To use you should have the qdrant-client package installed.
Example
from qdrant_client import QdrantClient
from langchain_community.vectorstores import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
Notes
Deprecated since version 0.0.37.
Initialize with necessary components.
Attributes
CONTENT_KEY
METADATA_KEY
VECTOR_NAME
embeddings
Access the query embedding object if available.
Methods
__init__(client, collection_name[, ...])
Initialize with necessary components.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas, ids, batch_size])
Run more texts through the embeddings and add to the vectorstore.
aconstruct_instance(texts, embedding[, ...])
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, batch_size])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
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, ...])
Construct Qdrant wrapper from a list of texts.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :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. :param filter: Filter by metadata. Defaults to None. :param search_params: Additional search params :param score_threshold: Define a minimal score threshold for the result. If defined, less similar results will not be returned. Score of the returned result might be higher or smaller than the                         threshold depending on the Distance function used. E.g. for cosine similarity only higher scores will be returned. :param consistency: Read consistency of the search. Defines how many replicas should be                     queried before returning the result. Values:                     - int - number of replicas to query, values should present in all                             queried replicas                     - 'majority' - query all replicas, but return values present in the                                    majority of replicas                     - 'quorum' - query the majority of replicas, return values present in                                  all of them                     - 'all' - query all replicas, and return values present in all replicas :param **kwargs: Any other named arguments to pass through to                    AsyncQdrantClient.Search().
amax_marginal_relevance_search_with_score_by_vector(...)
amax_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. :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.
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, filter])
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(query[, k, ...])
Return docs most similar to query.
asimilarity_search_with_score_by_vector(...)
Return docs most similar to embedding vector.
construct_instance(texts, embedding[, ...])
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_existing_collection(embedding, path, ...)
Get instance of an existing Qdrant collection.
from_texts(texts, embedding[, metadatas, ...])
Construct Qdrant wrapper from a list of texts.
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async classmethod aconstruct_instance(texts: List[str], embedding: Embeddings, location: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: Optional[bool] = None, api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[float] = None, host: Optional[str] = None, path: Optional[str] = None, collection_name: Optional[str] = None, distance_func: str = 'Cosine', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: Optional[str] = None, shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, on_disk_payload: Optional[bool] = None, hnsw_config: Optional[common_types.HnswConfigDiff] = None, optimizers_config: Optional[common_types.OptimizersConfigDiff] = None, wal_config: Optional[common_types.WalConfigDiff] = None, quantization_config: Optional[common_types.QuantizationConfig] = None, init_from: Optional[common_types.InitFrom] = None, on_disk: Optional[bool] = None, force_recreate: bool = False, **kwargs: Any) → Qdrant[source]¶
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
location (Optional[str]) – 
url (Optional[str]) – 
port (Optional[int]) – 
grpc_port (int) – 
prefer_grpc (bool) – 
https (Optional[bool]) –
prefer_grpc (bool) – 
https (Optional[bool]) – 
api_key (Optional[str]) – 
prefix (Optional[str]) – 
timeout (Optional[float]) – 
host (Optional[str]) – 
path (Optional[str]) – 
collection_name (Optional[str]) – 
distance_func (str) – 
content_payload_key (str) – 
metadata_payload_key (str) – 
vector_name (Optional[str]) – 
shard_number (Optional[int]) – 
replication_factor (Optional[int]) – 
write_consistency_factor (Optional[int]) – 
on_disk_payload (Optional[bool]) – 
hnsw_config (Optional[common_types.HnswConfigDiff]) – 
optimizers_config (Optional[common_types.OptimizersConfigDiff]) – 
wal_config (Optional[common_types.WalConfigDiff]) – 
quantization_config (Optional[common_types.QuantizationConfig]) – 
init_from (Optional[common_types.InitFrom]) – 
on_disk (Optional[bool]) – 
force_recreate (bool) – 
kwargs (Any) – 
Return type
Qdrant
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, ids: Optional[Sequence[str]] = None, batch_size: int = 64, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[Sequence[str]] = None, location: Optional[str] = None, url: Optional[str] = None, port: Optional[int] = 6333, grpc_port: int = 6334, prefer_grpc: bool = False, https: Optional[bool] = None, api_key: Optional[str] = None, prefix: Optional[str] = None, timeout: Optional[float] = None, host: Optional[str] = None, path: Optional[str] = None, collection_name: Optional[str] = None, distance_func: str = 'Cosine', content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', vector_name: Optional[str] = None, batch_size: int = 64, shard_number: Optional[int] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, on_disk_payload: Optional[bool] = None, hnsw_config: Optional[common_types.HnswConfigDiff] = None, optimizers_config: Optional[common_types.OptimizersConfigDiff] = None, wal_config: Optional[common_types.WalConfigDiff] = None, quantization_config: Optional[common_types.QuantizationConfig] = None, init_from: Optional[common_types.InitFrom] = None, on_disk: Optional[bool] = None, force_recreate: bool = False, **kwargs: Any) → Qdrant[source]¶
Construct Qdrant wrapper from a list of texts.
Parameters
texts (List[str]) – A list of texts to be indexed in Qdrant.
texts (List[str]) – A list of texts to be indexed in Qdrant.
embedding (Embeddings) – A subclass of Embeddings, responsible for text vectorization.
metadatas (Optional[List[dict]]) – An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids (Optional[Sequence[str]]) – Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location (Optional[str]) – If :memory: - use in-memory Qdrant instance.
If str - use it as a url parameter.
If None - fallback to relying on host and port parameters.
url (Optional[str]) – either host or str of “Optional[scheme], host, Optional[port],
Optional[prefix]”. Default: None
port (Optional[int]) – Port of the REST API interface. Default: 6333
grpc_port (int) – Port of the gRPC interface. Default: 6334
prefer_grpc (bool) – If true - use gPRC interface whenever possible in custom methods.
Default: False
https (Optional[bool]) – If true - use HTTPS(SSL) protocol. Default: None
api_key (Optional[str]) – API key for authentication in Qdrant Cloud. Default: None
prefix (Optional[str]) – If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout (Optional[float]) – Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host (Optional[str]) – Host name of Qdrant service. If url and host are None, set to
‘localhost’. Default: None
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config (Optional[common_types.HnswConfigDiff]) – Params for HNSW index
optimizers_config (Optional[common_types.OptimizersConfigDiff]) – Params for optimizer
wal_config (Optional[common_types.WalConfigDiff]) – Params for Write-Ahead-Log
quantization_config (Optional[common_types.QuantizationConfig]) – Params for quantization, if None - quantization will be disabled
init_from (Optional[common_types.InitFrom]) – Use data stored in another collection to initialize this collection
force_recreate (bool) – Force recreating the collection
**kwargs (Any) – Additional arguments passed directly into REST client initialization
on_disk (Optional[bool]) – 
Return type
Qdrant
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example
from langchain_community.vectorstores import Qdrant
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[common_types.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[common_types.ReadConsistency] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (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.
Defaults to 20.
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[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[common_types.SearchParams]) – Additional search params
score_threshold (Optional[float]) – Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency (Optional[common_types.ReadConsistency]) – Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all