Tony20100/langchain_full huggingface dataset:
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.
Parameters
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.
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
filter (Optional[MetadataFilter]) – 
search_params (Optional[models.SearchParams]) – 
score_threshold (Optional[float]) – 
consistency (Optional[models.ReadConsistency]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance and distance for
each.
Return type
List[Tuple[Document, float]]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
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 maximum. (Default: 0.5)
filter: Filter by document metadata
kwargs (Any) – 
Returns
filter: Filter by document metadata
kwargs (Any) – 
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    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(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(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]
Return type
List[Document]
async asimilarity_search(query: str, k: int = 4, filter: Optional[MetadataFilter] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param filter: Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
Parameters
query (str) – 
k (int) – 
filter (Optional[MetadataFilter]) – 
kwargs (Any) – 
Return type
List[Document]
async asimilarity_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.
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
AsyncQdrantClient.Search().
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
Return type
List[Tuple[Document, float]]
Return type
List[Tuple[Document, float]]
async asimilarity_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.
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
AsyncQdrantClient.Search().
AsyncQdrantClient.Search().
Returns
List of documents most similar to the query text and distance for each.
Return type
List[Tuple[Document, float]]
async asimilarity_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
’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
AsyncQdrantClient.Search().
Returns
List of documents most similar to the query text and distance for each.
Return type
List[Tuple[Document, float]]
classmethod construct_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[int] = 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[HnswConfigDiff] = None, optimizers_config: Optional[OptimizersConfigDiff] = None, wal_config: Optional[WalConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, init_from: Optional[InitFrom] = None, on_disk: Optional[bool] = None, force_recreate: bool = False, **kwargs: Any) → Qdrant[source]¶
Parameters
texts (List[str]) –
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
location (Optional[str]) – 
url (Optional[str]) – 
port (Optional[int]) – 
grpc_port (int) – 
prefer_grpc (bool) – 
https (Optional[bool]) – 
api_key (Optional[str]) – 
prefix (Optional[str]) – 
timeout (Optional[int]) – 
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[HnswConfigDiff]) – 
optimizers_config (Optional[OptimizersConfigDiff]) – 
wal_config (Optional[WalConfigDiff]) – 
quantization_config (Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]]) – 
init_from (Optional[InitFrom]) – 
on_disk (Optional[bool]) – 
force_recreate (bool) – 
kwargs (Any) – 
Return type
Qdrant
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
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.
Return type
Optional[bool]
Returns
True if deletion is successful, False otherwise.
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_existing_collection(embedding: Embeddings, path: Optional[str] = None, collection_name: Optional[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[int] = None, host: Optional[str] = None, content_payload_key: str = 'page_content', metadata_payload_key: str = 'metadata', distance_strategy: str = 'COSINE', vector_name: Optional[str] = None, **kwargs: Any) → Qdrant[source]¶
Get instance of an existing Qdrant collection.
This method will return the instance of the store without inserting any new
embeddings
Parameters
embedding (Embeddings) – 
path (Optional[str]) – 
collection_name (Optional[str]) – 
location (Optional[str]) – 
url (Optional[str]) – 
port (Optional[int]) – 
grpc_port (int) – 
prefer_grpc (bool) – 
https (Optional[bool]) – 
api_key (Optional[str]) – 
prefix (Optional[str]) – 
timeout (Optional[int]) –
kwargs (Any) – 
Return type
Qdrant
classmethod from_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[int] = 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[HnswConfigDiff] = None, optimizers_config: Optional[OptimizersConfigDiff] = None, wal_config: Optional[WalConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, init_from: Optional[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.
embedding (Embeddings) – A subclass of Embeddings, responsible for text vectorization.
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[HnswConfigDiff]) – Params for HNSW index
optimizers_config (Optional[OptimizersConfigDiff]) – Params for optimizer
wal_config (Optional[WalConfigDiff]) – Params for Write-Ahead-Log
quantization_config (Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]]) – Params for quantization, if None - quantization will be disabled
init_from (Optional[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_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.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[models.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[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 selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.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
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.
filter (Optional[MetadataFilter]) – Filter by metadata. Defaults to None.
search_params (Optional[models.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.
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 selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[MetadataFilter] = None, search_params: Optional[models.SearchParams] = None, score_threshold: Optional[float] = None, consistency: Optional[models.ReadConsistency] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
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.
Parameters
lambda_mult (float) – Number between 0 and 1 that determines the degree
Parameters
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[models.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[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()
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
Returns
List of Documents selected by maximal marginal relevance and distance for
each.
Return type
List[Tuple[Document, float]]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,