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)[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.
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.
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.
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.
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.
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)[source]¶
- 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] [source]¶
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
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 [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 – Other arguments.
- Returns
An instance of the VST class.
- Return type
- 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] [source]¶
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]] [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 – 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]] [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 – Keyword arguments for Collection.search().
- Returns
Resulting documents and scores.
- Return type
List[Tuple[Document, float]]