Tony20100/langchain_full huggingface dataset:
langchain_community.vectorstores.dingo.Dingo¶
class langchain_community.vectorstores.dingo.Dingo(embedding: Embeddings, text_key: str, *, client: Optional[Any] = None, index_name: Optional[str] = None, dimension: int = 1024, host: Optional[List[str]] = None, user: str = 'root', password: str = '123123', self_id: bool = False)[source]¶
Dingo vector store.
To use, you should have the dingodb python package installed.
Example
from langchain_community.vectorstores import Dingo
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
dingo = Dingo(embeddings, "text")
Initialize with Dingo client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, text_key, *[, client, ...])
Initialize with Dingo 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, ids, text_key, ...])
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, ...])
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 by vector IDs or filter.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct Dingo wrapper from raw documents.
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)
Return docs most similar to query using specified search type.
similarity_search(query[, k, search_params, ...])
Return Dingo documents most similar to query, along with scores.
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].
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return Dingo documents most similar to query, along with scores.
Parameters
embedding (Embeddings) – 
text_key (str) – 
client (Any) – 
index_name (Optional[str]) – 
dimension (int) – 
host (Optional[List[str]]) – 
user (str) – 
password (str) – 
self_id (bool) – 
__init__(embedding: Embeddings, text_key: str, *, client: Optional[Any] = None, index_name: Optional[str] = None, dimension: int = 1024, host: Optional[List[str]] = None, user: str = 'root', password: str = '123123', self_id: bool = False)[source]¶
Initialize with Dingo client.
Parameters
embedding (Embeddings) – 
text_key (str) – 
client (Optional[Any]) – 
index_name (Optional[str]) – 
dimension (int) – 
host (Optional[List[str]]) – 
user (str) – 
password (str) – 
self_id (bool) – 
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
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[str]] = None, text_key: str = 'text', batch_size: int = 500, **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.
ids (Optional[List[str]]) – Optional list of ids to associate with the texts.
text_key (str) – 
batch_size (int) – 
kwargs (Any) – 
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]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Any[source]¶
Delete by vector IDs or filter.
:param ids: List of ids to delete.
Parameters
ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
Any
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, ids: Optional[List[str]] = None, text_key: str = 'text', index_name: Optional[str] = None, dimension: int = 1024, client: Optional[Any] = None, host: List[str] = ['172.20.31.10:13000'], user: str = 'root', password: str = '123123', batch_size: int = 500, **kwargs: Any) → Dingo[source]¶
Construct Dingo wrapper from raw documents.
This is a user friendly interface that:
Embeds documents.
Adds the documents to a provided Dingo index
This is intended to be a quick way to get started.
Example:from langchain_community.vectorstores import Dingo
from langchain_community.embeddings import OpenAIEmbeddings
import dingodb
sssembeddings = OpenAIEmbeddings()
dingo = Dingo.from_texts(
texts,
embeddings,
index_name=”langchain-demo”
)
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
text_key (str) – 
index_name (Optional[str]) – 
dimension (int) – 
client (Optional[Any]) – 
host (List[str]) – 
user (str) – 
password (str) – 
batch_size (int) – 
kwargs (Any) – 
Return type
Dingo
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, search_params: Optional[dict] = 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.
lambda_mult (float) – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
search_params (Optional[dict]) – 
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, search_params: Optional[dict] = 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.
search_params (Optional[dict]) – 
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]
Return type
List[Document]
similarity_search(query: str, k: int = 4, search_params: Optional[dict] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document][source]¶
Return Dingo documents most similar to query, along with scores.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_params (Optional[dict]) – Dictionary of argument(s) to filter on metadata
timeout (Optional[int]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
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
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, search_params: Optional[dict] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return Dingo documents most similar to query, along with scores.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_params (Optional[dict]) – Dictionary of argument(s) to filter on metadata
timeout (Optional[int]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
Examples using Dingo¶
DingoDB
langchain_community.vectorstores.sklearn.JsonSerializer¶
class langchain_community.vectorstores.sklearn.JsonSerializer(persist_path: str)[source]¶
Serialize data in JSON using the json package from python standard library.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)
Saves the data to the persist_path
Parameters
persist_path (str) – 
__init__(persist_path: str) → None¶
Parameters
persist_path (str) – 
Return type
None
classmethod extension() → str[source]¶
The file extension suggested by this serializer (without dot).
Return type
str
load() → Any[source]¶
Loads the data from the persist_path
Return type
Any
save(data: Any) → None[source]¶
Saves the data to the persist_path
Parameters
data (Any) – 
Return type
None
langchain_elasticsearch.vectorstores.SparseRetrievalStrategy¶
class langchain_elasticsearch.vectorstores.SparseRetrievalStrategy(model_id: Optional[str] = None)[source]¶
[Deprecated] Sparse retrieval strategy using the text_expansion processor.
Notes
Deprecated since version 0.2.0: Use SparseVectorStrategy instead.
Methods
__init__([model_id])
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_field, ...)
Executes when the index is created.
query(query_vector, query, k, fetch_k, ...)
Executes when a search is performed on the store.
require_inference()
Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
Parameters
model_id (Optional[str]) – 
__init__(model_id: Optional[str] = None)[source]¶
Parameters
model_id (Optional[str]) – 
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None[source]¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client (Elasticsearch) – The Elasticsearch client.
text_field (str) – The field containing the text data in the index.
vector_query_field (str) – The field containing the vector
representations in the index.
Return type
None
index(dims_length: Optional[int], vector_query_field: str, text_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when the index is created.
Parameters
dims_length (Optional[int]) – Numeric length of the embedding vectors,
or None if not using vector-based query.
or None if not using vector-based query.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use,
or None if not using one.
Returns
The Elasticsearch settings and mappings for the strategy.
Return type
Dict
query(query_vector: Optional[List[float]], query: Optional[str], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector (Optional[List[float]]) – The query vector,
or None if not using vector-based query.
query (Optional[str]) – The text query, or None if not using text-based query.
k (int) – The total number of results to retrieve.
fetch_k (int) – The number of results to fetch initially.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
filter (List[dict]) – List of filter clauses to apply to the query.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use, or None if not using one.
Returns
The Elasticsearch query body.
Return type
Dict
require_inference() → bool[source]¶
Returns whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Returns
Whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Return type
bool
langchain_community.vectorstores.documentdb.DocumentDBVectorSearch¶
class langchain_community.vectorstores.documentdb.DocumentDBVectorSearch(collection: Collection[DocumentDBDocumentType], embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent')[source]¶
Amazon DocumentDB (with MongoDB compatibility) vector store.
Please refer to the official Vector Search documentation for more details:
https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html
To use, you should have both:
- the pymongo python package installed
- a connection string and credentials associated with a DocumentDB cluster
Example
. code-block:: python
from langchain_community.vectorstores import DocumentDBVectorSearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient
mongo_client = MongoClient(“<YOUR-CONNECTION-STRING>”)
collection = mongo_client[“<db_name>”][“<collection_name>”]
embeddings = OpenAIEmbeddings()
vectorstore = DocumentDBVectorSearch(collection, embeddings)
Constructor for DocumentDBVectorSearch
Parameters
collection (Collection[DocumentDBDocumentType]) – MongoDB collection to add the texts to.
embedding (Embeddings) – Text embedding model to use.
index_name (str) – Name of the Vector Search index.
text_key (str) – MongoDB field that will contain the text
for each document.
embedding_key (str) – MongoDB field that will contain the embedding
for each document.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(collection, embedding, *[, ...])
Constructor for DocumentDBVectorSearch
aadd_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.
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.
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([dimensions, similarity, m, ...])
Creates an index using the index name specified at
delete([ids])
Delete by vector ID or other criteria.
delete_document_by_id([document_id])
Removes a Specific Document by Id
delete_index()
Removes a Specific Document by Id
delete_index()
Deletes the index specified during instance construction if it exists
from_connection_string(connection_string, ...)
Creates an Instance of DocumentDBVectorSearch from a Connection String
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_index_name()
Returns the index name
index_exists()
Verifies if the specified index name during instance
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)
Return docs most similar to query using specified search type.
similarity_search(query[, k, ef_search])
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(*args, **kwargs)
Run similarity search with distance.
__init__(collection: Collection[DocumentDBDocumentType], embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent')[source]¶
Constructor for DocumentDBVectorSearch
Parameters
collection (Collection[DocumentDBDocumentType]) – MongoDB collection to add the texts to.
embedding (Embeddings) – Text embedding model to use.
index_name (str) – Name of the Vector Search index.
text_key (str) – MongoDB field that will contain the text
for each document.
text_key (str) – MongoDB field that will contain the text
for each document.
embedding_key (str) – MongoDB field that will contain the embedding
for each document.
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[str, Any]]] = None, **kwargs: Any) → List[source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.