from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
from couchbase.cluster import Cluster
[docs]@deprecated(
since="0.2.4",
removal="1.0",
alternative_import="langchain_couchbase.CouchbaseVectorStore",
)
class CouchbaseVectorStore(VectorStore):
"""`Couchbase Vector Store` vector store.
To use it, you need
- a recent installation of the `couchbase` library
- a Couchbase database with a pre-defined Search index with support for
vector fields
Example:
.. code-block:: python
from langchain_community.vectorstores 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()
vectorstore = CouchbaseVectorStore(
cluster=cluster,
bucket_name="",
scope_name="",
collection_name="",
embedding=embeddings,
index_name="vector-index",
)
vectorstore.add_texts(["hello", "world"])
results = vectorstore.similarity_search("ola", k=1)
"""
# Default batch size
DEFAULT_BATCH_SIZE: int = 100
_metadata_key: str = "metadata"
_default_text_key: str = "text"
_default_embedding_key: str = "embedding"
def _check_bucket_exists(self) -> bool:
"""Check if the bucket exists in the linked Couchbase cluster"""
bucket_manager = self._cluster.buckets()
try:
bucket_manager.get_bucket(self._bucket_name)
return True
except Exception:
return False
def _check_scope_and_collection_exists(self) -> bool:
"""Check if the scope and collection exists in the linked Couchbase bucket
Raises a ValueError if either is not found"""
scope_collection_map: Dict[str, Any] = {}
# Get a list of all scopes in the bucket
for scope in self._bucket.collections().get_all_scopes():
scope_collection_map[scope.name] = []
# Get a list of all the collections in the scope
for collection in scope.collections:
scope_collection_map[scope.name].append(collection.name)
# Check if the scope exists
if self._scope_name not in scope_collection_map.keys():
raise ValueError(
f"Scope {self._scope_name} not found in Couchbase "
f"bucket {self._bucket_name}"
)
# Check if the collection exists in the scope
if self._collection_name not in scope_collection_map[self._scope_name]:
raise ValueError(
f"Collection {self._collection_name} not found in scope "
f"{self._scope_name} in Couchbase bucket {self._bucket_name}"
)
return True
def _check_index_exists(self) -> bool:
"""Check if the Search index exists in the linked Couchbase cluster
Raises a ValueError if the index does not exist"""
if self._scoped_index:
all_indexes = [
index.name for index in self._scope.search_indexes().get_all_indexes()
]
if self._index_name not in all_indexes:
raise ValueError(
f"Index {self._index_name} does not exist. "
" Please create the index before searching."
)
else:
all_indexes = [
index.name for index in self._cluster.search_indexes().get_all_indexes()
]
if self._index_name not in all_indexes:
raise ValueError(
f"Index {self._index_name} does not exist. "
" Please create the index before searching."
)
return True
[docs] def __init__(
self,
cluster: Cluster,
bucket_name: str,
scope_name: str,
collection_name: str,
embedding: Embeddings,
index_name: str,
*,
text_key: Optional[str] = _default_text_key,
embedding_key: Optional[str] = _default_embedding_key,
scoped_index: bool = True,
) -> None:
"""
Initialize the Couchbase Vector Store.
Args:
cluster (Cluster): couchbase cluster object with active connection.
bucket_name (str): name of bucket to store documents in.
scope_name (str): name of scope in the bucket to store documents in.
collection_name (str): name of collection in the scope to store documents in
embedding (Embeddings): embedding function to use.
index_name (str): name of the Search index to use.
text_key (optional[str]): key in document to use as text.
Set to text by default.
embedding_key (optional[str]): key in document to use for the embeddings.
Set to embedding by default.
scoped_index (optional[bool]): specify whether the index is a scoped index.
Set to True by default.
"""
try:
from couchbase.cluster import Cluster
except ImportError as e:
raise ImportError(
"Could not import couchbase python package. "
"Please install couchbase SDK with `pip install couchbase`."
) from e
if not isinstance(cluster, Cluster):
raise ValueError(
f"cluster should be an instance of couchbase.Cluster, "
f"got {type(cluster)}"
)
self._cluster = cluster
if not embedding:
raise ValueError("Embeddings instance must be provided.")
if not bucket_name:
raise ValueError("bucket_name must be provided.")
if not scope_name:
raise ValueError("scope_name must be provided.")
if not collection_name:
raise ValueError("collection_name must be provided.")
if not index_name:
raise ValueError("index_name must be provided.")
self._bucket_name = bucket_name
self._scope_name = scope_name
self._collection_name = collection_name
self._embedding_function = embedding
self._text_key = text_key
self._embedding_key = embedding_key
self._index_name = index_name
self._scoped_index = scoped_index
# Check if the bucket exists
if not self._check_bucket_exists():
raise ValueError(
f"Bucket {self._bucket_name} does not exist. "
" Please create the bucket before searching."
)
try:
self._bucket = self._cluster.bucket(self._bucket_name)
self._scope = self._bucket.scope(self._scope_name)
self._collection = self._scope.collection(self._collection_name)
except Exception as e:
raise ValueError(
"Error connecting to couchbase. "
"Please check the connection and credentials."
) from e
# Check if the scope and collection exists. Throws ValueError if they don't
try:
self._check_scope_and_collection_exists()
except Exception as e:
raise e
# Check if the index exists. Throws ValueError if it doesn't
try:
self._check_index_exists()
except Exception as e:
raise e
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[Dict[str, Any]]] = None,
ids: Optional[List[str]] = None,
batch_size: Optional[int] = 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.
Args:
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.
Returns:
List[str]:List of ids from adding the texts into the vectorstore.
"""
from couchbase.exceptions import DocumentExistsException
if not batch_size:
batch_size = self.DEFAULT_BATCH_SIZE
doc_ids: List[str] = []
if ids is None:
ids = [uuid.uuid4().hex for _ in texts]
if metadatas is None:
metadatas = [{} for _ in texts]
embedded_texts = self._embedding_function.embed_documents(list(texts))
documents_to_insert = [
{
id: {
self._text_key: text,
self._embedding_key: vector,
self._metadata_key: metadata,
}
for id, text, vector, metadata in zip(
ids, texts, embedded_texts, metadatas
)
}
]
# Insert in batches
for i in range(0, len(documents_to_insert), batch_size):
batch = documents_to_insert[i : i + batch_size]
try:
result = self._collection.upsert_multi(batch[0])
if result.all_ok:
doc_ids.extend(batch[0].keys())
except DocumentExistsException as e:
raise ValueError(f"Document already exists: {e}")
return doc_ids
[docs] def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete documents from the vector store by ids.
Args:
ids (List[str]): List of IDs of the documents to delete.
batch_size (Optional[int]): Optional batch size for bulk deletions.
Returns:
bool: True if all the documents were deleted successfully, False otherwise.
"""
from couchbase.exceptions import DocumentNotFoundException
if ids is None:
raise ValueError("No document ids provided to delete.")
batch_size = kwargs.get("batch_size", self.DEFAULT_BATCH_SIZE)
deletion_status = True
# Delete in batches
for i in range(0, len(ids), batch_size):
batch = ids[i : i + batch_size]
try:
result = self._collection.remove_multi(batch)
except DocumentNotFoundException as e:
deletion_status = False
raise ValueError(f"Document not found: {e}")
deletion_status &= result.all_ok
return deletion_status
@property
def embeddings(self) -> Embeddings:
"""Return the query embedding object."""
return self._embedding_function
def _format_metadata(self, row_fields: Dict[str, Any]) -> Dict[str, Any]:
"""Helper method to format the metadata from the Couchbase Search API.
Args:
row_fields (Dict[str, Any]): The fields to format.
Returns:
Dict[str, Any]: The formatted metadata.
"""
metadata = {}
for key, value in row_fields.items():
# Couchbase Search returns the metadata key with a prefix
# `metadata.` We remove it to get the original metadata key
if key.startswith(self._metadata_key):
new_key = key.split(self._metadata_key + ".")[-1]
metadata[new_key] = value
else:
metadata[key] = value
return metadata
[docs] def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector with their scores.
Args:
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.
Returns:
List of (Document, score) that are the most similar to the query vector.
"""
import couchbase.search as search
from couchbase.options import SearchOptions
from couchbase.vector_search import VectorQuery, VectorSearch
fields = kwargs.get("fields", ["*"])
# Document text field needs to be returned from the search
if fields != ["*"] and self._text_key not in fields:
fields.append(self._text_key)
search_req = search.SearchRequest.create(
VectorSearch.from_vector_query(
VectorQuery(
self._embedding_key,
embedding,
k,
)
)
)
try:
if self._scoped_index:
search_iter = self._scope.search(
self._index_name,
search_req,
SearchOptions(
limit=k,
fields=fields,
raw=search_options,
),
)
else:
search_iter = self._cluster.search(
index=self._index_name,
request=search_req,
options=SearchOptions(limit=k, fields=fields, raw=search_options),
)
docs_with_score = []
# Parse the results
for row in search_iter.rows():
text = row.fields.pop(self._text_key, "")
# Format the metadata from Couchbase
metadata = self._format_metadata(row.fields)
score = row.score
doc = Document(page_content=text, metadata=metadata)
docs_with_score.append((doc, score))
except Exception as e:
raise ValueError(f"Search failed with error: {e}")
return docs_with_score
[docs] def similarity_search(
self,
query: str,
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Document]:
"""Return documents most similar to embedding vector with their scores.
Args:
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.
Returns:
List of Documents most similar to the query.
"""
query_embedding = self.embeddings.embed_query(query)
docs_with_scores = self.similarity_search_with_score_by_vector(
query_embedding, k, search_options, **kwargs
)
return [doc for doc, _ in docs_with_scores]
[docs] def similarity_search_with_score(
self,
query: str,
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return documents that are most similar to the query with their scores.
Args:
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.
Returns:
List of (Document, score) that are most similar to the query.
"""
query_embedding = self.embeddings.embed_query(query)
docs_with_score = self.similarity_search_with_score_by_vector(
query_embedding, k, search_options, **kwargs
)
return docs_with_score
[docs] def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
search_options: Optional[Dict[str, Any]] = {},
**kwargs: Any,
) -> List[Document]:
"""Return documents that are most similar to the vector embedding.
Args:
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.
Returns:
List of Documents most similar to the query.
"""
docs_with_score = self.similarity_search_with_score_by_vector(
embedding, k, search_options, **kwargs
)
return [doc for doc, _ in docs_with_score]
@classmethod
def _from_kwargs(
cls: Type[CouchbaseVectorStore],
embedding: Embeddings,
**kwargs: Any,
) -> CouchbaseVectorStore:
"""Initialize the Couchbase vector store from keyword arguments for the
vector store.
Args:
embedding: Embedding object to use to embed text.
**kwargs: Keyword arguments to initialize the vector store with.
Accepted arguments are:
- cluster
- bucket_name
- scope_name
- collection_name
- index_name
- text_key
- embedding_key
- scoped_index
"""
cluster = kwargs.get("cluster", None)
bucket_name = kwargs.get("bucket_name", None)
scope_name = kwargs.get("scope_name", None)
collection_name = kwargs.get("collection_name", None)
index_name = kwargs.get("index_name", None)
text_key = kwargs.get("text_key", cls._default_text_key)
embedding_key = kwargs.get("embedding_key", cls._default_embedding_key)
scoped_index = kwargs.get("scoped_index", True)
return cls(
embedding=embedding,
cluster=cluster,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
index_name=index_name,
text_key=text_key,
embedding_key=embedding_key,
scoped_index=scoped_index,
)
[docs] @classmethod
def from_texts(
cls: Type[CouchbaseVectorStore],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[Dict[Any, Any]]] = None,
**kwargs: Any,
) -> CouchbaseVectorStore:
"""Construct a Couchbase vector store from a list of texts.
Example:
.. code-block:: python
from langchain_community.vectorstores 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",
)
Args:
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.
"""
vector_store = cls._from_kwargs(embedding, **kwargs)
batch_size = kwargs.get("batch_size", vector_store.DEFAULT_BATCH_SIZE)
ids = kwargs.get("ids", None)
vector_store.add_texts(
texts, metadatas=metadatas, ids=ids, batch_size=batch_size
)
return vector_store
