langchain_community.vectorstores.hippo.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: Optional[Dict[str, Any]] = None, index_params: Optional[dict] = None, drop_old: Optional[bool] = False)

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.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding_function[, table_name, ...])
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, timeout, ...])	Add text to the collection.
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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)	Run similarity search with distance asynchronously.
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.
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, 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: Optional[Dict[str, Any]] = None, index_params: Optional[dict] = None, drop_old: Optional[bool] = False)

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

Run more documents through the embeddings and add to the vectorstore.

Parameters

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

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.

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

Run more documents through the embeddings and add to the vectorstore.

Parameters

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

Returns

List of IDs of the added texts.

Return type

List[str]

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

Add text to the collection.

Parameters

• texts – An iterable that contains the text to be added.

• metadatas – An optional list of dictionaries,

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

• timeout – Optional timeout, in seconds.

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

• **kwargs – Other optional parameters.

Returns

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

Note

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

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

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

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

Return VectorStore initialized from texts and embeddings.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

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]

Return docs selected using the maximal marginal relevance.

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

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.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

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

Return docs most similar to embedding vector.

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], asynchronously.

0 is dissimilar, 1 is most similar.

Parameters

• query – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  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)

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

Run similarity search with distance asynchronously.

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

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – 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.

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

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 – Other arguments.

Returns

An instance of the VST class.

Return type

Hippo

max_marginal_relevance_search(query: str, 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

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

Returns

List of Documents selected by maximal marginal relevance.

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 – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.

Returns

List of Documents selected by maximal marginal relevance.

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

similarity_search(query: str, k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → 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 – 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 – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query vector.

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 – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  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)

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

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 – 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: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Tuple[Document, float]]

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 – Keyword arguments for Collection.search().

Returns

Resulting documents and scores.

Return type

List[Tuple[Document, float]]