Tony20100/langchain_full huggingface dataset:
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[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = 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.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.SearchParams]) – Additional search params
offset (int) – Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
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[models.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
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
**kwargs (Any) – Any other named arguments to pass through to QdrantClient.search()
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[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.SearchParams]) – Additional search params
offset (int) – Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
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[models.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
queried replicas
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
**kwargs (Any) – Any other named arguments to pass through to QdrantClient.search()
Returns
List of Documents most similar to the query.
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[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **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.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.SearchParams]) – Additional search params
offset (int) – Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
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[models.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
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
**kwargs (Any) – Any other named arguments to pass through to QdrantClient.search()
Returns
List of documents most similar to the query text and distance for each.
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, offset: int = 0, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.SearchParams]) – Additional search params
offset (int) – Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
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[models.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
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas, and return values present in all replicas
**kwargs (Any) – Any other named arguments to pass through to QdrantClient.search()
Returns
List of documents most similar to the query text and distance for each.
Return type
List[Tuple[Document, float]]
Examples using Qdrant¶
Qdrant
langchain_community.vectorstores.lantern.Lantern¶
class langchain_community.vectorstores.lantern.Lantern(connection_string: str, embedding_function: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None)[source]¶
Postgres with the lantern extension as a vector store.
lantern uses sequential scan by default. but you can create a HNSW index
using the create_hnsw_index method.
- connection_string is a postgres connection string.
- embedding_function any embedding function implementing
langchain.embeddings.base.Embeddings interface.
collection_name is the name of the collection to use. (default: langchain)
NOTE: This is the name of the table in which embedding data will be storedThe table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy is the distance strategy to use. (default: EUCLIDEAN)
EUCLIDEAN is the euclidean distance.
COSINE is the cosine distance.
HAMMING is the hamming distance.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
Attributes
distance_function
distance_strategy
embeddings
Access the query embedding object if available.
Methods
__init__(connection_string, embedding_function)
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)
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_embeddings(texts, embeddings, metadatas, ...)
add_texts(texts[, metadatas, ids])
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.
connect()
connection_string_from_db_params(driver, ...)
Return connection string from database parameters.
create_collection()
create_hnsw_extension()
create_hnsw_index([dims, m, ...])
Create HNSW index on collection.
create_tables_if_not_exists()
delete([ids])
Delete vectors by ids or uuids.
delete_collection()
drop_index()
Delete vectors by ids or uuids.
delete_collection()
drop_index()
drop_table()
drop_tables()
from_documents(documents, embedding[, ...])
Initialize a vector store with a set of documents.
from_embeddings(text_embeddings, embedding)
Construct Lantern wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ...])
Get instance of an existing Lantern store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])
Initialize Lantern vectorstore from list of texts.
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(query)
Return docs selected using the maximal marginal relevance with score.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance with score
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
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, filter])
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
distance_strategy (DistanceStrategy) – 
collection_name (str) – 
collection_metadata (Optional[dict]) –
collection_name (str) – 
collection_metadata (Optional[dict]) – 
pre_delete_collection (bool) – 
logger (Optional[logging.Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
__init__(connection_string: str, embedding_function: Embeddings, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None) → None[source]¶
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
distance_strategy (DistanceStrategy) – 
collection_name (str) – 
collection_metadata (Optional[dict]) – 
pre_delete_collection (bool) – 
logger (Optional[Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
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.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
**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_embeddings(texts: List[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None[source]¶
Parameters
texts (List[str]) – 
embeddings (List[List[float]]) – 
metadatas (List[dict]) – 
ids (List[str]) – 
kwargs (Any) – 
Return type
None
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = 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.
ids (Optional[List[str]]) – 
**kwargs – 
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.
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]]
connect() → Connection[source]¶
Return type
Connection
classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
Parameters
driver (str) – 
host (str) – 
port (int) – 
database (str) – 
user (str) – 
password (str) – 
Return type
str
create_collection() → None[source]¶
Return type
None
create_hnsw_extension() → None[source]¶
Return type
None
create_hnsw_index(dims: int = 1536, m: int = 16, ef_construction: int = 64, ef_search: int = 64, **_kwargs: Any) → None[source]¶
Create HNSW index on collection.
Optional Keyword Args for HNSW Index:engine: “nmslib”, “faiss”, “lucene”; default: “nmslib”
ef: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 64
lead to more accurate but slower searches; default: 64
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 64
m: Number of bidirectional links created for each new element. Large impact
on memory consumption. Between 2 and 100; default: 16
dims: Dimensions of the vectors in collection. default: 1536
Parameters
dims (int) – 
m (int) – 
ef_construction (int) – 
ef_search (int) – 
_kwargs (Any) – 
Return type
None
create_tables_if_not_exists() → None[source]¶
Return type
None
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete vectors by ids or uuids.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
kwargs (Any) – 
Return type
None
delete_collection() → None[source]¶
Return type
None
drop_index() → None[source]¶
Return type
None
drop_table() → None[source]¶
Return type
None
drop_tables() → None[source]¶
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Lantern[source]¶
Initialize a vector store with a set of documents.
Postgres connection string is required
“Either pass it as connection_string parameter
or set the LANTERN_CONNECTION_STRING environment variable.
connection_string is a postgres connection string.
documents is list of Document to initialize the vector store with
documents is list of Document to initialize the vector store with
embedding is Embeddings that will be used forembedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
collection_name is the name of the collection to use. (default: langchain)
NOTE: This is the name of the table in which embedding data will be storedThe table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy is the distance strategy to use. (default: EUCLIDEAN)
EUCLIDEAN is the euclidean distance.
COSINE is the cosine distance.
HAMMING is the hamming distance.
ids row ids to insert into collection.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
Lantern
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, **kwargs: Any) → Lantern[source]¶
Construct Lantern wrapper from raw documents and pre-
generated embeddings.
Postgres connection string is required
“Either pass it as connection_string parameter
or set the LANTERN_CONNECTION_STRING environment variable.
or set the LANTERN_CONNECTION_STRING environment variable.
Order of elements for lists ids, text_embeddings, metadatas should match,
so each row will be associated with correct values.
connection_string is fully populated connection string for postgres database
text_embeddings is array with tuples (text, embedding)to insert into collection.
embedding is Embeddings that will be used forembedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
metadatas row metadata to insert into collection.
collection_name is the name of the collection to use. (default: langchain)
NOTE: This is the name of the table in which embedding data will be storedThe table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
ids row ids to insert into collection.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
distance_strategy is the distance strategy to use. (default: EUCLIDEAN)
EUCLIDEAN is the euclidean distance.
COSINE is the cosine distance.
HAMMING is the hamming distance.
Parameters
text_embeddings (List[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
collection_name (str) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
distance_strategy (DistanceStrategy) – 
kwargs (Any) – 
Return type
Lantern
classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', pre_delete_collection: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, **kwargs: Any) → Lantern[source]¶
Get instance of an existing Lantern store.This method will
return the instance of the store without inserting any new
embeddings
Postgres connection string is required
“Either pass it as connection_string parameter
or set the LANTERN_CONNECTION_STRING environment variable.
connection_string is a postgres connection string.
embedding is Embeddings that will be used forembedding the text sent. If none is sent, then the
multilingual Tensorflow Universal Sentence Encoder will be used.
collection_name is the name of the collection to use. (default: langchain)
NOTE: This is the name of the table in which embedding data will be storedThe table will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
ids row ids to insert into collection.
pre_delete_collection if True, will delete the collection if it exists.(default: False)
- Useful for testing.
distance_strategy is the distance strategy to use. (default: EUCLIDEAN)
EUCLIDEAN is the euclidean distance.
COSINE is the cosine distance.
HAMMING is the hamming distance.
Parameters
embedding (Embeddings) – 
collection_name (str) – 
pre_delete_collection (bool) – 
distance_strategy (DistanceStrategy) – 
kwargs (Any) – 
Return type
Lantern
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → Lantern[source]¶
Initialize Lantern vectorstore from list of texts.
The embeddings will be generated using embedding class provided.