CouchbaseSemanticCache#

class langchain_couchbase.cache.CouchbaseSemanticCache(cluster: Cluster, embedding: Embeddings, bucket_name: str, scope_name: str, collection_name: str, index_name: str, score_threshold: float | None = None)[source]#

Couchbase Semantic Cache Cache backed by a Couchbase Server with Vector Store support

Initialize the Couchbase LLM Cache :param cluster: couchbase cluster object with active connection. :type cluster: Cluster :param embedding: embedding model to use. :type embedding: Embeddings :param bucket_name: name of the bucket to store documents in. :type bucket_name: str :param scope_name: name of the scope in bucket to store documents in. :type scope_name: str :param collection_name: name of the collection in the scope to store

documents in.

Parameters:

index_name (str) – name of the Search index to use.
score_threshold (float) – score threshold to use for filtering results.
cluster (Cluster) –
embedding (Embeddings) –
bucket_name (str) –
scope_name (str) –
collection_name (str) –

Attributes

`DEFAULT_BATCH_SIZE`
`LLM`
`RETURN_VAL`
`embeddings`	Return the query embedding object.

Methods

`__init__`(cluster, embedding, bucket_name, ...)	Initialize the Couchbase LLM Cache :param cluster: couchbase cluster object with active connection. :type cluster: Cluster :param embedding: embedding model to use. :type embedding: Embeddings :param bucket_name: name of the bucket to store documents in. :type bucket_name: str :param scope_name: name of the scope in bucket to store documents in. :type scope_name: str :param collection_name: name of the collection in the scope to store documents in. :type collection_name: str :param index_name: name of the Search index to use. :type index_name: str :param score_threshold: score threshold to use for filtering results. :type score_threshold: float.
`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.
`aclear`(**kwargs)	Async clear cache that can take additional keyword arguments.
`add_documents`(documents, **kwargs)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas, ids, batch_size])	Run texts through the embeddings and persist in vectorstore.
`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.
`alookup`(prompt, llm_string)	Async look up based on prompt and llm_string.
`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.
`aupdate`(prompt, llm_string, return_val)	Async update cache based on prompt and llm_string.
`clear`(**kwargs)	Clear the cache.
`delete`([ids])	Delete documents from the vector store by ids.
`from_documents`(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
`from_texts`(texts, embedding[, metadatas])	Construct a Couchbase vector store from a list of texts.
`get_by_ids`(ids, /)	Get documents by their IDs.
`lookup`(prompt, llm_string)	Look up from cache based on the semantic similarity of the prompt
`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, search_options])	Return documents most similar to embedding vector with their scores.
`similarity_search_by_vector`(embedding[, k, ...])	Return documents that are most similar to the vector embedding.
`similarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1].
`similarity_search_with_score`(query[, k, ...])	Return documents that are most similar to the query with their scores.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to embedding vector with their scores.
`update`(prompt, llm_string, return_val)	Update cache based on the prompt and llm_string

__init__(cluster: Cluster, embedding: Embeddings, bucket_name: str, scope_name: str, collection_name: str, index_name: str, score_threshold: float | None = None) → None[source]#

Initialize the Couchbase LLM Cache :param cluster: couchbase cluster object with active connection. :type cluster: Cluster :param embedding: embedding model to use. :type embedding: Embeddings :param bucket_name: name of the bucket to store documents in. :type bucket_name: str :param scope_name: name of the scope in bucket to store documents in. :type scope_name: str :param collection_name: name of the collection in the scope to store

documents in.

Parameters:

index_name (str) – name of the Search index to use.
score_threshold (float) – score threshold to use for filtering results.
cluster (Cluster) –
embedding (Embeddings) –
bucket_name (str) –
scope_name (str) –
collection_name (str) –

Return type:

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]

async aclear(**kwargs: Any) → None#

Async clear cache that can take additional keyword arguments.

Parameters:: kwargs (Any) –
Return type:: None

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, ids: List[str] | None = None, batch_size: int | None = None, **kwargs: Any) → List[str]#

Run texts through the embeddings and persist in vectorstore.

If the document IDs are passed, the existing documents (if any) will be overwritten with the new ones.

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 associated with the texts. IDs have to be unique strings across the collection. If it is not specified uuids are generated and used as ids.
batch_size (Optional[int]) – Optional batch size for bulk insertions. Default is 100.
kwargs (Any) –

Returns:

List of ids from adding the texts into the vectorstore.

Return type:

List[str]

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 alookup(prompt: str, llm_string: str) → Sequence[Generation] | None#

Async look up based on prompt and llm_string.

A cache implementation is expected to generate a key from the 2-tuple of prompt and llm_string (e.g., by concatenating them with a delimiter).

Parameters:

prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.

Returns:

On a cache miss, return None. On a cache hit, return the cached value. The cached value is a list of Generations (or subclasses).

Return type:

Sequence[Generation] | None

async amax_marginal_relevance_search(query: str, 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:

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

async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None#

Async update cache based on prompt and llm_string.

The prompt and llm_string are used to generate a key for the cache. The key should match that of the look up method.

Parameters:

prompt (str) – a string representation of the prompt. In the case of a Chat model, the prompt is a non-trivial serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration. This is used to capture the invocation parameters of the LLM (e.g., model name, temperature, stop tokens, max tokens, etc.). These invocation parameters are serialized into a string representation.
return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations (or subclasses).

Return type:

None

clear(**kwargs: Any) → None[source]#

Clear the cache. This will delete all documents in the collection. This requires an index on the collection.

Parameters:: kwargs (Any) –
Return type:: None

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

Delete documents from the vector store by ids.

Parameters:

ids (List[str]) – List of IDs of the documents to delete.
batch_size (Optional[int]) – Optional batch size for bulk deletions.
kwargs (Any) –

Returns:

True if all the documents were deleted successfully, False otherwise.

Return type:

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, **kwargs: Any) → CouchbaseVectorStore#

Construct a Couchbase vector store from a list of texts.

Example

from langchain_couchbase import CouchbaseVectorStore from langchain_openai import OpenAIEmbeddings

from couchbase.cluster import Cluster from couchbase.auth import PasswordAuthenticator from couchbase.options import ClusterOptions from datetime import timedelta

auth = PasswordAuthenticator(username, password) options = ClusterOptions(auth) connect_string = “couchbases://localhost” cluster = Cluster(connect_string, options)

# Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5))

embeddings = OpenAIEmbeddings()

texts = [“hello”, “world”]

vectorstore = CouchbaseVectorStore.from_texts(: texts, embedding=embeddings, cluster=cluster, bucket_name=””, scope_name=””, collection_name=””, index_name=”vector-index”,

)

Parameters:

texts (List[str]) – list of texts to add to the vector store.
embedding (Embeddings) – embedding function to use.
metadatas (optional[List[Dict]) – list of metadatas to add to documents.
**kwargs – Keyword arguments used to initialize the vector store with and/or passed to add_texts method. Check the constructor and/or add_texts for the list of accepted arguments.

Returns:

A Couchbase vector store.

Return type:

CouchbaseVectorStore

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.

lookup(prompt: str, llm_string: str) → Sequence[Generation] | None[source]#

Look up from cache based on the semantic similarity of the prompt

Parameters:

prompt (str) –
llm_string (str) –

Return type:

Sequence[Generation] | None

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 (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]

similarity_search(query: str, k: int = 4, search_options: Dict[str, Any] | None = {}, **kwargs: Any) → List[Document]#

Return documents most similar to embedding vector with their scores.

Parameters:

query (str) – Query to look up for similar documents
k (int) – Number of Documents to return. Defaults to 4.
search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary
fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to all the fields stored in the index.
kwargs (Any) –

Returns:

List of Documents most similar to the query.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, search_options: Dict[str, Any] | None = {}, **kwargs: Any) → List[Document]#

Return documents that are most similar to the vector embedding.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary.
fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to document text and metadata fields.
kwargs (Any) –

Returns:

List of Documents most similar to the query.

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, search_options: Dict[str, Any] | None = {}, **kwargs: Any) → List[Tuple[Document, float]]#

Return documents that are most similar to the query with their scores.

Parameters:

query (str) – Query to look up for similar documents
k (int) – Number of Documents to return. Defaults to 4.
search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary.
fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to text and metadata fields.
kwargs (Any) –

Returns:

List of (Document, score) that are most similar to the query.

Return type:

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, search_options: Dict[str, Any] | None = {}, **kwargs: Any) → List[Tuple[Document, float]]#

Return docs most similar to embedding vector with their scores.

Parameters:

embedding (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary.
fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to all the fields stored in the index.
kwargs (Any) –

Returns:

List of (Document, score) that are the most similar to the query vector.

Return type:

List[Tuple[Document, float]]

update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]#

Update cache based on the prompt and llm_string

Parameters:

prompt (str) –
llm_string (str) –
return_val (Sequence[Generation]) –

Return type:

None