Hippo#

class langchain_community.vectorstores.hippo.Hippo(embedding_function: Embeddings, table_name: str = 'test', database_name: str = 'default', number_of_shards: int = 1, number_of_replicas: int = 1, connection_args: Dict[str, Any] | None = None, index_params: dict | None = None, drop_old: bool | None = False)[source]#

Hippo vector store.

You need to install hippo-api and run Hippo.

Please visit our official website for how to run a Hippo instance: https://www.transwarp.cn/starwarp

Parameters:

  • embedding_function (Embeddings) – Function used to embed the text.

  • table_name (str) – Which Hippo table to use. Defaults to “test”.

  • database_name (str) – Which Hippo database to use. Defaults to “default”.

  • number_of_shards (int) – The number of shards for the Hippo table.Defaults to 1.

  • number_of_replicas (int) – The number of replicas for the Hippo table.Defaults to 1.

  • connection_args (Optional[dict[str, any]]) – The connection args used for this class comes in the form of a dict.

  • index_params (Optional[dict]) – Which index params to use. Defaults to IVF_FLAT.

  • drop_old (Optional[bool]) – Whether to drop the current collection. Defaults to False.

  • primary_field (str) – Name of the primary key field. Defaults to “pk”.

  • text_field (str) – Name of the text field. Defaults to “text”.

  • vector_field (str) – Name of the vector field. Defaults to “vector”.

The connection args used for this class comes in the form of a dict, here are a few of the options:

host (str): The host of Hippo instance. Default at “localhost”. port (str/int): The port of Hippo instance. Default at 7788. user (str): Use which user to connect to Hippo instance. If user and

password are provided, we will add related header in every RPC call.

password (str): Required when user is provided. The password

corresponding to the user.

Example

from langchain_community.vectorstores import Hippo from langchain_community.embeddings import OpenAIEmbeddings

embedding = OpenAIEmbeddings() # Connect to a hippo instance on localhost vector_store = Hippo.from_documents(

docs, embedding=embeddings, table_name=”langchain_test”, connection_args=HIPPO_CONNECTION

)

Raises:

ValueError – If the hippo-api python package is not installed.

Parameters:

  • embedding_function (Embeddings) –

  • table_name (str) –

  • database_name (str) –

  • number_of_shards (int) –

  • number_of_replicas (int) –

  • connection_args (Optional[Dict[str, Any]]) –

  • index_params (Optional[dict]) –

  • drop_old (Optional[bool]) –

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding_function[, table_name, ...])

aadd_documents(documents, **kwargs)

Async run more documents through the embeddings and add to the vectorstore.

aadd_texts(texts[, metadatas])

Async run more texts through the embeddings and add to the vectorstore.

add_documents(documents, **kwargs)

Add or update documents in the vectorstore.

add_texts(texts[, metadatas, timeout, ...])

Add text to the collection.

adelete([ids])

Async delete by vector ID or other criteria.

afrom_documents(documents, embedding, **kwargs)

Async return VectorStore initialized from documents and embeddings.

afrom_texts(texts, embedding[, metadatas])

Async return VectorStore initialized from texts and embeddings.

aget_by_ids(ids, /)

Async get documents by their IDs.

amax_marginal_relevance_search(query[, k, ...])

Async return docs selected using the maximal marginal relevance.

amax_marginal_relevance_search_by_vector(...)

Async return docs selected using the maximal marginal relevance.

as_retriever(**kwargs)

Return VectorStoreRetriever initialized from this VectorStore.

asearch(query, search_type, **kwargs)

Async return docs most similar to query using a specified search type.

asimilarity_search(query[, k])

Async return docs most similar to query.

asimilarity_search_by_vector(embedding[, k])

Async return docs most similar to embedding vector.

asimilarity_search_with_relevance_scores(query)

Async return docs and relevance scores in the range [0, 1].

asimilarity_search_with_score(*args, **kwargs)

Async run similarity search with distance.

delete([ids])

Delete by vector ID or other criteria.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_texts(texts, embedding[, metadatas, ...])

Creates an instance of the VST class from the given texts.

get_by_ids(ids, /)

Get documents by their IDs.

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 a specified search type.

similarity_search(query[, k, param, expr, ...])

Perform a similarity search on the query string.

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, ...])

Performs a search on the query string and returns results with scores.

similarity_search_with_score_by_vector(embedding)

Performs a search on the query string and returns results with scores.
__init__(embedding_function: Embeddings, table_name: str = 'test', database_name: str = 'default', number_of_shards: int = 1, number_of_replicas: int = 1, connection_args: Dict[str, Any] | None = None, index_params: dict | None = None, drop_old: bool | None = False)[source]#
Parameters:

  • embedding_function (Embeddings) –

  • table_name (str) –

  • database_name (str) –

  • number_of_shards (int) –

  • number_of_replicas (int) –

  • connection_args (Dict[str, Any] | None) –

  • index_params (dict | None) –

  • drop_old (bool | None) –

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

  • documents (List[Document]) – Documents to add to the vectorstore.

  • kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

List[str]

async aadd_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

  • texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

  • metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.

  • **kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

  • ValueError – If the number of metadatas does not match the number of texts.

  • ValueError – If the number of ids does not match the number of texts.

Return type:

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]#

Add or update documents in the vectorstore.

Parameters:

  • documents (List[Document]) – Documents to add to the vectorstore.

  • kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

List[str]

add_texts(texts: Iterable[str], metadatas: List[dict] | None = None, timeout: int | None = None, batch_size: int = 1000, **kwargs: Any) → List[str][source]#

Add text to the collection.

Parameters:

  • texts (Iterable[str]) – An iterable that contains the text to be added.

  • metadatas (List[dict] | None) – An optional list of dictionaries,

  • text. (each dictionary contains the metadata associated with a) –

  • timeout (int | None) – Optional timeout, in seconds.

  • batch_size (int) – The number of texts inserted in each batch, defaults to 1000.

  • **kwargs (Any) – Other optional parameters.

Returns:

A list of strings, containing the unique identifiers of the inserted texts.

Return type:

List[str]

Note

If the collection has not yet been created, this method will create a new collection.

async adelete(ids: List[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

  • ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.

  • **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#

Async 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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, **kwargs: Any) → VST#

Async return VectorStore initialized from texts and embeddings.

Parameters:

  • texts (List[str]) – Texts to add to the vectorstore.

  • embedding (Embeddings) – Embedding function to use.

  • metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.

  • kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

ids (Sequence[str]) – List of ids to retrieve.

Returns:

List of Documents.

Return type:

List[Document]

New in version 0.2.11.

Async 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. Default is 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.

  • kwargs (Any) –

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]#

Async 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. Default is 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.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: 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

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]#

Async return docs most similar to query using a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

Async return docs most similar to query.

Parameters:

  • query (str) – Input text.

  • k (int) – Number of Documents to return. Defaults to 4.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]#

Async 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) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query vector.

Return type:

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]#

Async 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]]

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]#

Async run similarity search with distance.

Parameters:

  • *args (Any) – Arguments to pass to the search method.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

delete(ids: List[str] | None = None, **kwargs: Any) → bool | None#

Delete by vector ID or other criteria.

Parameters:

  • ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.

  • **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]

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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: List[dict] | None = None, table_name: str = 'test', database_name: str = 'default', connection_args: Dict[str, Any] = {'host': 'localhost', 'password': 'admin', 'port': '7788', 'username': 'admin'}, index_params: Dict[Any, Any] | None = None, search_params: Dict[str, Any] | None = None, drop_old: bool = False, **kwargs: Any) → Hippo[source]#

Creates an instance of the VST class from the given texts.

Parameters:

  • texts (List[str]) – List of texts to be added.

  • embedding (Embeddings) – Embedding model for the texts.

  • metadatas (List[dict], optional) –

  • None. (List of metadata dictionaries for each text.Defaults to) –

  • table_name (str) – Name of the table. Defaults to “test”.

  • database_name (str) – Name of the database. Defaults to “default”.

  • connection_args (dict[str, Any]) – Connection parameters.

  • DEFAULT_HIPPO_CONNECTION. (Defaults to) –

  • index_params (dict) – Indexing parameters. Defaults to None.

  • search_params (dict) – Search parameters. Defaults to an empty dictionary.

  • drop_old (bool) – Whether to drop the old collection. Defaults to False.

  • kwargs (Any) – Other arguments.

Returns:

An instance of the VST class.

Return type:

Hippo

get_by_ids(ids: Sequence[str], /) → List[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:

ids (Sequence[str]) – List of ids to retrieve.

Returns:

List of Documents.

Return type:

List[Document]

New in version 0.2.11.

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. Default is 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.

  • **kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]#

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

  • embedding (List[float]) – Embedding to look up documents similar to.

  • k (int) – Number of Documents to return. Defaults to 4.

  • fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm. Default is 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.

  • **kwargs (Any) – Arguments to pass to the search method.

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 a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

Perform a similarity search on the query string.

Parameters:

  • query (str) – The text to search for.

  • k (int, optional) – The number of results to return. Default is 4.

  • param (dict, optional) – Specifies the search parameters for the index.

  • None. (Defaults to) –

  • expr (str, optional) – Filtering expression. Defaults to None.

  • timeout (int, optional) – Time to wait before a timeout error.

  • None. –

  • kwargs (Any) – Keyword arguments for Collection.search().

Returns:

The document results of the search.

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) – Arguments to pass to the search method.

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:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

similarity_search_with_score(query: str, k: int = 4, param: dict | None = None, expr: str | None = None, timeout: int | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Performs a search on the query string and returns results with scores.

Parameters:

  • query (str) – The text being searched.

  • k (int, optional) – The number of results to return.

  • 4. (Default is) –

  • param (dict) – Specifies the search parameters for the index.

  • None. (Default is) –

  • expr (str, optional) – Filtering expression. Default is None.

  • timeout (int, optional) – The waiting time before a timeout error.

  • None. –

  • kwargs (Any) – Keyword arguments for Collection.search().

Return type:

List[float], List[Tuple[Document, any, any]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, param: dict | None = None, expr: str | None = None, timeout: int | None = None, **kwargs: Any) → List[Tuple[Document, float]][source]#

Performs a search on the query string and returns results with scores.

Parameters:

  • embedding (List[float]) – The embedding vector being searched.

  • k (int, optional) – The number of results to return.

  • 4. (Default is) –

  • param (dict) – Specifies the search parameters for the index.

  • None. (Default is) –

  • expr (str, optional) – Filtering expression. Default is None.

  • timeout (int, optional) – The waiting time before a timeout error.

  • None. –

  • kwargs (Any) – Keyword arguments for Collection.search().

Returns:

Resulting documents and scores.

Return type:

List[Tuple[Document, float]]

Examples using Hippo

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