diff --git a/docs/docs/integrations/vectorstores/couchbase.ipynb b/docs/docs/integrations/vectorstores/couchbase.ipynb index 8035376aeb..a92629fc62 100644 --- a/docs/docs/integrations/vectorstores/couchbase.ipynb +++ b/docs/docs/integrations/vectorstores/couchbase.ipynb @@ -23,17 +23,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "bec8d532-fec7-4dc7-9be3-020aa7bdb01f", "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet langchain langchain-openai langchain-community couchbase" + "%pip install --upgrade --quiet langchain langchain-openai langchain-couchbase" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "4a972cbc-bf59-46eb-9b50-e5dc3a69dcf0", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_community.vectorstores import CouchbaseVectorStore\n", + "from langchain_couchbase.vectorstores import CouchbaseVectorStore\n", "from langchain_openai import OpenAIEmbeddings" ] }, diff --git a/libs/cli/langchain_cli/integration_template/Makefile b/libs/cli/langchain_cli/integration_template/Makefile index ff29edb3d6..aeb3a5754c 100644 --- a/libs/cli/langchain_cli/integration_template/Makefile +++ b/libs/cli/langchain_cli/integration_template/Makefile @@ -10,7 +10,7 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ # unit tests are run with the --disable-socket flag to prevent network calls test tests: - poetry run pytest --disable-socket --allow-unit-socket $(TEST_FILE) + poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) # integration tests are run without the --disable-socket flag to allow network calls integration_test integration_tests: diff --git a/libs/community/langchain_community/vectorstores/couchbase.py b/libs/community/langchain_community/vectorstores/couchbase.py index 881d31b70f..d002bfc479 100644 --- a/libs/community/langchain_community/vectorstores/couchbase.py +++ b/libs/community/langchain_community/vectorstores/couchbase.py @@ -3,6 +3,7 @@ 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 @@ -11,6 +12,11 @@ if TYPE_CHECKING: from couchbase.cluster import Cluster +@deprecated( + since="0.2.4", + removal="0.3.0", + alternative_import="langchain_couchbase.CouchbaseVectorStore", +) class CouchbaseVectorStore(VectorStore): """`Couchbase Vector Store` vector store. diff --git a/libs/partners/couchbase/.gitignore b/libs/partners/couchbase/.gitignore new file mode 100644 index 0000000000..53fd65cf3d --- /dev/null +++ b/libs/partners/couchbase/.gitignore @@ -0,0 +1,3 @@ +__pycache__ +# mypy +.mypy_cache/ diff --git a/libs/partners/couchbase/LICENSE b/libs/partners/couchbase/LICENSE new file mode 100644 index 0000000000..fc0602feec --- /dev/null +++ b/libs/partners/couchbase/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/couchbase/Makefile b/libs/partners/couchbase/Makefile new file mode 100644 index 0000000000..4b85a26db8 --- /dev/null +++ b/libs/partners/couchbase/Makefile @@ -0,0 +1,62 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE = tests/integration_tests/ + + +# unit tests are run with the --disable-socket flag to prevent network calls +test tests: + poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) + +# integration tests are run without the --disable-socket flag to allow network calls +integration_test integration_tests: + poetry run pytest $(TEST_FILE) + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/couchbase --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_couchbase +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_couchbase -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/couchbase/README.md b/libs/partners/couchbase/README.md new file mode 100644 index 0000000000..006de243ab --- /dev/null +++ b/libs/partners/couchbase/README.md @@ -0,0 +1,42 @@ +# langchain-couchbase + +This package contains the LangChain integration with Couchbase + +## Installation + +```bash +pip install -U langchain-couchbase +``` + +## Usage + +The `CouchbaseVectorStore` class exposes the connection to the Couchbase vector store. + +```python +from langchain_couchbase.vectorstores import CouchbaseVectorStore + +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-search-index", +) + +``` diff --git a/libs/partners/couchbase/langchain_couchbase/__init__.py b/libs/partners/couchbase/langchain_couchbase/__init__.py new file mode 100644 index 0000000000..2f84db440e --- /dev/null +++ b/libs/partners/couchbase/langchain_couchbase/__init__.py @@ -0,0 +1,5 @@ +from langchain_couchbase.vectorstores import CouchbaseVectorStore + +__all__ = [ + "CouchbaseVectorStore", +] diff --git a/libs/partners/couchbase/langchain_couchbase/py.typed b/libs/partners/couchbase/langchain_couchbase/py.typed new file mode 100644 index 0000000000..e69de29bb2 diff --git a/libs/partners/couchbase/langchain_couchbase/vectorstores.py b/libs/partners/couchbase/langchain_couchbase/vectorstores.py new file mode 100644 index 0000000000..6553abb5e7 --- /dev/null +++ b/libs/partners/couchbase/langchain_couchbase/vectorstores.py @@ -0,0 +1,615 @@ +"""Couchbase vector stores.""" + +from __future__ import annotations + +import uuid +from typing import ( + Any, + Dict, + Iterable, + List, + Optional, + Tuple, + Type, +) + +import couchbase.search as search +from couchbase.cluster import Cluster +from couchbase.exceptions import DocumentExistsException, DocumentNotFoundException +from couchbase.options import SearchOptions +from couchbase.vector_search import VectorQuery, VectorSearch +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + + +class CouchbaseVectorStore(VectorStore): + """Couchbase vector store. + + To use it, you need + - a Couchbase database with a pre-defined Search index with support for + vector fields + + Example: + .. code-block:: python + + 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() + + 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 = 100 + _metadata_key = "metadata" + _default_text_key = "text" + _default_embedding_key = "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 + + 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. + """ + 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 + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = 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. + """ + + 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 + + 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. + + """ + + 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 + + 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] + + 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. + """ + + 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( + self._index_name, + search_req, + 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 + + 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 + + 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, + ) + + @classmethod + def from_texts( + cls: Type[CouchbaseVectorStore], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> CouchbaseVectorStore: + """Construct a Couchbase vector store from a list of texts. + + Example: + .. code-block:: python + + 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", + ) + + 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 diff --git a/libs/partners/couchbase/poetry.lock b/libs/partners/couchbase/poetry.lock new file mode 100644 index 0000000000..5182aaf4c8 --- /dev/null +++ b/libs/partners/couchbase/poetry.lock @@ -0,0 +1,771 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} + +[[package]] +name = "certifi" +version = "2024.2.2" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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+[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/couchbase" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain-core = ">=0.2.0,<0.3" +couchbase = "^4.2.1" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.4.3" +pytest-asyncio = "^0.23.2" +pytest-socket = "^0.7.0" +langchain-core = {path = "../../core", develop = true} +syrupy = "^4.0.2" + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.6" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.8" + +[tool.poetry.group.typing.dependencies] +mypy = "^1.7.1" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = {path = "../../core", develop = true} + +[tool.ruff.lint] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print +] + +[tool.mypy] +disallow_untyped_defs = "True" +ignore_missing_imports = "True" + +[tool.coverage.run] +omit = [ + "tests/*", +] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/couchbase/scripts/check_imports.py b/libs/partners/couchbase/scripts/check_imports.py new file mode 100644 index 0000000000..365f5fa118 --- /dev/null +++ b/libs/partners/couchbase/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) # noqa: T201 + traceback.print_exc() + print() # noqa: T201 + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/couchbase/scripts/check_pydantic.sh b/libs/partners/couchbase/scripts/check_pydantic.sh new file mode 100755 index 0000000000..06b5bb81ae --- /dev/null +++ b/libs/partners/couchbase/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/couchbase/scripts/lint_imports.sh b/libs/partners/couchbase/scripts/lint_imports.sh new file mode 100755 index 0000000000..19ccec1480 --- /dev/null +++ b/libs/partners/couchbase/scripts/lint_imports.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain, langchain_experimental, or langchain_community +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/couchbase/tests/__init__.py b/libs/partners/couchbase/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/libs/partners/couchbase/tests/integration_tests/__init__.py b/libs/partners/couchbase/tests/integration_tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/libs/partners/couchbase/tests/integration_tests/test_compile.py b/libs/partners/couchbase/tests/integration_tests/test_compile.py new file mode 100644 index 0000000000..33ecccdfa0 --- /dev/null +++ b/libs/partners/couchbase/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/couchbase/tests/integration_tests/test_vector_store.py b/libs/partners/couchbase/tests/integration_tests/test_vector_store.py new file mode 100644 index 0000000000..4cad73481d --- /dev/null +++ b/libs/partners/couchbase/tests/integration_tests/test_vector_store.py @@ -0,0 +1,366 @@ +"""Test Couchbase Vector Store functionality""" + +import os +import time +from typing import Any + +import pytest +from langchain_core.documents import Document + +from langchain_couchbase import CouchbaseVectorStore +from tests.utils import ( + ConsistentFakeEmbeddings, +) + +CONNECTION_STRING = os.getenv("COUCHBASE_CONNECTION_STRING", "") +BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "") +SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "") +COLLECTION_NAME = os.getenv("COUCHBASE_COLLECTION_NAME", "") +USERNAME = os.getenv("COUCHBASE_USERNAME", "") +PASSWORD = os.getenv("COUCHBASE_PASSWORD", "") +INDEX_NAME = os.getenv("COUCHBASE_INDEX_NAME", "") +SLEEP_DURATION = 1 + + +def set_all_env_vars() -> bool: + return all( + [ + CONNECTION_STRING, + BUCKET_NAME, + SCOPE_NAME, + COLLECTION_NAME, + USERNAME, + PASSWORD, + INDEX_NAME, + ] + ) + + +def get_cluster() -> Any: + """Get a couchbase cluster object""" + from datetime import timedelta + + from couchbase.auth import PasswordAuthenticator + from couchbase.cluster import Cluster + from couchbase.options import ClusterOptions + + auth = PasswordAuthenticator(USERNAME, PASSWORD) + options = ClusterOptions(auth) + connect_string = CONNECTION_STRING + cluster = Cluster(connect_string, options) + + # Wait until the cluster is ready for use. + cluster.wait_until_ready(timedelta(seconds=5)) + + return cluster + + +@pytest.fixture() +def cluster() -> Any: + """Get a couchbase cluster object""" + return get_cluster() + + +def delete_documents( + cluster: Any, bucket_name: str, scope_name: str, collection_name: str +) -> None: + """Delete all the documents in the collection""" + query = f"DELETE FROM `{bucket_name}`.`{scope_name}`.`{collection_name}`" + cluster.query(query).execute() + + +@pytest.mark.skipif( + not set_all_env_vars(), reason="Missing Couchbase environment variables" +) +class TestCouchbaseVectorStore: + @classmethod + def setup_method(self) -> None: + cluster = get_cluster() + # Delete all the documents in the collection + delete_documents(cluster, BUCKET_NAME, SCOPE_NAME, COLLECTION_NAME) + + def test_from_documents(self, cluster: Any) -> None: + """Test end to end search using a list of documents.""" + + documents = [ + Document(page_content="foo", metadata={"page": 1}), + Document(page_content="bar", metadata={"page": 2}), + Document(page_content="baz", metadata={"page": 3}), + ] + + vectorstore = CouchbaseVectorStore.from_documents( + documents, + ConsistentFakeEmbeddings(), + cluster=cluster, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + index_name=INDEX_NAME, + ) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search("baz", k=1) + assert output[0].page_content == "baz" + assert output[0].metadata["page"] == 3 + + def test_from_texts(self, cluster: Any) -> None: + """Test end to end search using a list of texts.""" + + texts = [ + "foo", + "bar", + "baz", + ] + + vectorstore = CouchbaseVectorStore.from_texts( + texts, + ConsistentFakeEmbeddings(), + cluster=cluster, + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search("foo", k=1) + assert len(output) == 1 + assert output[0].page_content == "foo" + + def test_from_texts_with_metadatas(self, cluster: Any) -> None: + """Test end to end search using a list of texts and metadatas.""" + + texts = [ + "foo", + "bar", + "baz", + ] + + metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] + + vectorstore = CouchbaseVectorStore.from_texts( + texts, + ConsistentFakeEmbeddings(), + metadatas=metadatas, + cluster=cluster, + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search("baz", k=1) + assert output[0].page_content == "baz" + assert output[0].metadata["c"] == 3 + + def test_add_texts_with_ids_and_metadatas(self, cluster: Any) -> None: + """Test end to end search by adding a list of texts, ids and metadatas.""" + + texts = [ + "foo", + "bar", + "baz", + ] + + ids = ["a", "b", "c"] + + metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] + + vectorstore = CouchbaseVectorStore( + cluster=cluster, + embedding=ConsistentFakeEmbeddings(), + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + results = vectorstore.add_texts( + texts, + ids=ids, + metadatas=metadatas, + ) + assert results == ids + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search("foo", k=1) + assert output[0].page_content == "foo" + assert output[0].metadata["a"] == 1 + + def test_delete_texts_with_ids(self, cluster: Any) -> None: + """Test deletion of documents by ids.""" + texts = [ + "foo", + "bar", + "baz", + ] + + ids = ["a", "b", "c"] + + metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] + + vectorstore = CouchbaseVectorStore( + cluster=cluster, + embedding=ConsistentFakeEmbeddings(), + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + results = vectorstore.add_texts( + texts, + ids=ids, + metadatas=metadatas, + ) + assert results == ids + assert vectorstore.delete(ids) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search("foo", k=1) + assert len(output) == 0 + + def test_similarity_search_with_scores(self, cluster: Any) -> None: + """Test similarity search with scores.""" + + texts = ["foo", "bar", "baz"] + + metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] + + vectorstore = CouchbaseVectorStore( + cluster=cluster, + embedding=ConsistentFakeEmbeddings(), + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + vectorstore.add_texts(texts, metadatas=metadatas) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search_with_score("foo", k=2) + + assert len(output) == 2 + assert output[0][0].page_content == "foo" + + # check if the scores are sorted + assert output[0][0].metadata["a"] == 1 + assert output[0][1] > output[1][1] + + def test_similarity_search_by_vector(self, cluster: Any) -> None: + """Test similarity search by vector.""" + + texts = ["foo", "bar", "baz"] + + metadatas = [{"a": 1}, {"b": 2}, {"c": 3}] + + vectorstore = CouchbaseVectorStore( + cluster=cluster, + embedding=ConsistentFakeEmbeddings(), + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + vectorstore.add_texts(texts, metadatas=metadatas) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + vector = ConsistentFakeEmbeddings().embed_query("foo") + vector_output = vectorstore.similarity_search_by_vector(vector, k=1) + + assert vector_output[0].page_content == "foo" + + similarity_output = vectorstore.similarity_search("foo", k=1) + + assert similarity_output == vector_output + + def test_output_fields(self, cluster: Any) -> None: + """Test that output fields are set correctly.""" + + texts = [ + "foo", + "bar", + "baz", + ] + + metadatas = [{"page": 1, "a": 1}, {"page": 2, "b": 2}, {"page": 3, "c": 3}] + + vectorstore = CouchbaseVectorStore( + cluster=cluster, + embedding=ConsistentFakeEmbeddings(), + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + ids = vectorstore.add_texts(texts, metadatas) + assert len(ids) == len(texts) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + output = vectorstore.similarity_search("foo", k=1, fields=["metadata.page"]) + assert output[0].page_content == "foo" + assert output[0].metadata["page"] == 1 + assert "a" not in output[0].metadata + + def test_hybrid_search(self, cluster: Any) -> None: + """Test hybrid search.""" + + texts = [ + "foo", + "bar", + "baz", + ] + + metadatas = [ + {"section": "index"}, + {"section": "glossary"}, + {"section": "appendix"}, + ] + + vectorstore = CouchbaseVectorStore( + cluster=cluster, + embedding=ConsistentFakeEmbeddings(), + index_name=INDEX_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + collection_name=COLLECTION_NAME, + ) + + vectorstore.add_texts(texts, metadatas=metadatas) + + # Wait for the documents to be indexed + time.sleep(SLEEP_DURATION) + + result, score = vectorstore.similarity_search_with_score("foo", k=1)[0] + + # Wait for the documents to be indexed for hybrid search + time.sleep(SLEEP_DURATION) + + hybrid_result, hybrid_score = vectorstore.similarity_search_with_score( + "foo", + k=1, + search_options={"query": {"match": "index", "field": "metadata.section"}}, + )[0] + + assert result == hybrid_result + assert score <= hybrid_score diff --git a/libs/partners/couchbase/tests/unit_tests/__init__.py b/libs/partners/couchbase/tests/unit_tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/libs/partners/couchbase/tests/unit_tests/test_imports.py b/libs/partners/couchbase/tests/unit_tests/test_imports.py new file mode 100644 index 0000000000..ec771d0ba2 --- /dev/null +++ b/libs/partners/couchbase/tests/unit_tests/test_imports.py @@ -0,0 +1,9 @@ +from langchain_couchbase import __all__ + +EXPECTED_ALL = [ + "CouchbaseVectorStore", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py b/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/libs/partners/couchbase/tests/unit_tests/test_vectorstore.py @@ -0,0 +1 @@ + diff --git a/libs/partners/couchbase/tests/utils.py b/libs/partners/couchbase/tests/utils.py new file mode 100644 index 0000000000..d12df58d92 --- /dev/null +++ b/libs/partners/couchbase/tests/utils.py @@ -0,0 +1,55 @@ +"""Fake Embedding class for testing purposes.""" + +from typing import List + +from langchain_core.embeddings import Embeddings + +fake_texts = ["foo", "bar", "baz"] + + +class FakeEmbeddings(Embeddings): + """Fake embeddings functionality for testing.""" + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return simple embeddings. + Embeddings encode each text as its index.""" + return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + """Return constant query embeddings. + Embeddings are identical to embed_documents(texts)[0]. + Distance to each text will be that text's index, + as it was passed to embed_documents.""" + return [float(1.0)] * 9 + [float(0.0)] + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +class ConsistentFakeEmbeddings(FakeEmbeddings): + """Fake embeddings which remember all the texts seen so far to return consistent + vectors for the same texts.""" + + def __init__(self, dimensionality: int = 10) -> None: + self.known_texts: List[str] = [] + self.dimensionality = dimensionality + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return consistent embeddings for each text seen so far.""" + out_vectors = [] + for text in texts: + if text not in self.known_texts: + self.known_texts.append(text) + vector = [float(1.0)] * (self.dimensionality - 1) + [ + float(self.known_texts.index(text)) + ] + out_vectors.append(vector) + return out_vectors + + def embed_query(self, text: str) -> List[float]: + """Return consistent embeddings for the text, if seen before, or a constant + one if the text is unknown.""" + return self.embed_documents([text])[0]