mirror of https://github.com/hwchase17/langchain
couchbase: Add the initial version of Couchbase partner package (#22087)
Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev>pull/22691/head langchain-couchbase==0.0.1
Nithish Raghunandanan
4 months ago
committed by
GitHub
parent
6c07eb0c12
commit
f2f0e0e13d
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__pycache__
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# mypy
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.mypy_cache/
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MIT License
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Copyright (c) 2024 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
|
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furnished to do so, subject to the following conditions:
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|
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
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SOFTWARE.
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.PHONY: all format lint test tests integration_tests docker_tests help extended_tests
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# Default target executed when no arguments are given to make.
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all: help
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# Define a variable for the test file path.
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TEST_FILE ?= tests/unit_tests/
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integration_test integration_tests: TEST_FILE = tests/integration_tests/
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# unit tests are run with the --disable-socket flag to prevent network calls
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test tests:
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poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
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# integration tests are run without the --disable-socket flag to allow network calls
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integration_test integration_tests:
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poetry run pytest $(TEST_FILE)
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######################
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# LINTING AND FORMATTING
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######################
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# Define a variable for Python and notebook files.
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PYTHON_FILES=.
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MYPY_CACHE=.mypy_cache
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lint format: PYTHON_FILES=.
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lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/couchbase --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
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lint_package: PYTHON_FILES=langchain_couchbase
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lint_tests: PYTHON_FILES=tests
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lint_tests: MYPY_CACHE=.mypy_cache_test
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lint lint_diff lint_package lint_tests:
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poetry run ruff .
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poetry run ruff format $(PYTHON_FILES) --diff
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poetry run ruff --select I $(PYTHON_FILES)
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mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
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format format_diff:
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poetry run ruff format $(PYTHON_FILES)
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poetry run ruff --select I --fix $(PYTHON_FILES)
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spell_check:
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poetry run codespell --toml pyproject.toml
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spell_fix:
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poetry run codespell --toml pyproject.toml -w
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check_imports: $(shell find langchain_couchbase -name '*.py')
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poetry run python ./scripts/check_imports.py $^
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######################
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# HELP
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######################
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help:
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@echo '----'
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@echo 'check_imports - check imports'
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@echo 'format - run code formatters'
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@echo 'lint - run linters'
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@echo 'test - run unit tests'
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@echo 'tests - run unit tests'
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@echo 'test TEST_FILE=<test_file> - run all tests in file'
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@ -0,0 +1,42 @@
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# langchain-couchbase
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This package contains the LangChain integration with Couchbase
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## Installation
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```bash
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pip install -U langchain-couchbase
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```
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## Usage
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The `CouchbaseVectorStore` class exposes the connection to the Couchbase vector store.
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```python
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from langchain_couchbase.vectorstores import CouchbaseVectorStore
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from couchbase.cluster import Cluster
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from couchbase.auth import PasswordAuthenticator
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from couchbase.options import ClusterOptions
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from datetime import timedelta
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auth = PasswordAuthenticator(username, password)
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options = ClusterOptions(auth)
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connect_string = "couchbases://localhost"
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cluster = Cluster(connect_string, options)
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# Wait until the cluster is ready for use.
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cluster.wait_until_ready(timedelta(seconds=5))
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embeddings = OpenAIEmbeddings()
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vectorstore = CouchbaseVectorStore(
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cluster=cluster,
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bucket_name="",
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scope_name="",
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collection_name="",
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embedding=embeddings,
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index_name="vector-search-index",
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)
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```
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from langchain_couchbase.vectorstores import CouchbaseVectorStore
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__all__ = [
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"CouchbaseVectorStore",
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]
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"""Couchbase vector stores."""
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from __future__ import annotations
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import uuid
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from typing import (
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Any,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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Type,
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)
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import couchbase.search as search
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from couchbase.cluster import Cluster
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from couchbase.exceptions import DocumentExistsException, DocumentNotFoundException
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from couchbase.options import SearchOptions
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from couchbase.vector_search import VectorQuery, VectorSearch
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.vectorstores import VectorStore
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class CouchbaseVectorStore(VectorStore):
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"""Couchbase vector store.
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To use it, you need
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- a Couchbase database with a pre-defined Search index with support for
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vector fields
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Example:
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.. code-block:: python
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from langchain_couchbase import CouchbaseVectorStore
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from langchain_openai import OpenAIEmbeddings
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from couchbase.cluster import Cluster
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from couchbase.auth import PasswordAuthenticator
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from couchbase.options import ClusterOptions
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from datetime import timedelta
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auth = PasswordAuthenticator(username, password)
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options = ClusterOptions(auth)
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connect_string = "couchbases://localhost"
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cluster = Cluster(connect_string, options)
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# Wait until the cluster is ready for use.
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cluster.wait_until_ready(timedelta(seconds=5))
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embeddings = OpenAIEmbeddings()
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vectorstore = CouchbaseVectorStore(
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cluster=cluster,
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bucket_name="",
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scope_name="",
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collection_name="",
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embedding=embeddings,
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index_name="vector-index",
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)
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vectorstore.add_texts(["hello", "world"])
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results = vectorstore.similarity_search("ola", k=1)
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"""
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# Default batch size
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DEFAULT_BATCH_SIZE = 100
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_metadata_key = "metadata"
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_default_text_key = "text"
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_default_embedding_key = "embedding"
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def _check_bucket_exists(self) -> bool:
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"""Check if the bucket exists in the linked Couchbase cluster"""
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bucket_manager = self._cluster.buckets()
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try:
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bucket_manager.get_bucket(self._bucket_name)
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return True
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except Exception:
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return False
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def _check_scope_and_collection_exists(self) -> bool:
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"""Check if the scope and collection exists in the linked Couchbase bucket
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Raises a ValueError if either is not found"""
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scope_collection_map: Dict[str, Any] = {}
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# Get a list of all scopes in the bucket
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for scope in self._bucket.collections().get_all_scopes():
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scope_collection_map[scope.name] = []
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# Get a list of all the collections in the scope
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for collection in scope.collections:
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scope_collection_map[scope.name].append(collection.name)
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# Check if the scope exists
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if self._scope_name not in scope_collection_map.keys():
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raise ValueError(
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f"Scope {self._scope_name} not found in Couchbase "
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f"bucket {self._bucket_name}"
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)
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# Check if the collection exists in the scope
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if self._collection_name not in scope_collection_map[self._scope_name]:
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raise ValueError(
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f"Collection {self._collection_name} not found in scope "
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f"{self._scope_name} in Couchbase bucket {self._bucket_name}"
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)
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return True
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def _check_index_exists(self) -> bool:
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"""Check if the Search index exists in the linked Couchbase cluster
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Raises a ValueError if the index does not exist"""
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if self._scoped_index:
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all_indexes = [
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index.name for index in self._scope.search_indexes().get_all_indexes()
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]
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if self._index_name not in all_indexes:
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raise ValueError(
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f"Index {self._index_name} does not exist. "
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" Please create the index before searching."
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)
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else:
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all_indexes = [
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index.name for index in self._cluster.search_indexes().get_all_indexes()
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]
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if self._index_name not in all_indexes:
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raise ValueError(
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f"Index {self._index_name} does not exist. "
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" Please create the index before searching."
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)
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return True
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def __init__(
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self,
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cluster: Cluster,
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bucket_name: str,
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scope_name: str,
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collection_name: str,
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embedding: Embeddings,
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index_name: str,
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*,
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text_key: Optional[str] = _default_text_key,
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embedding_key: Optional[str] = _default_embedding_key,
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scoped_index: bool = True,
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) -> None:
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"""
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Initialize the Couchbase Vector Store.
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Args:
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cluster (Cluster): couchbase cluster object with active connection.
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bucket_name (str): name of bucket to store documents in.
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scope_name (str): name of scope in the bucket to store documents in.
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collection_name (str): name of collection in the scope to store documents in
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embedding (Embeddings): embedding function to use.
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index_name (str): name of the Search index to use.
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text_key (optional[str]): key in document to use as text.
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Set to text by default.
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embedding_key (optional[str]): key in document to use for the embeddings.
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Set to embedding by default.
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scoped_index (optional[bool]): specify whether the index is a scoped index.
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Set to True by default.
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"""
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if not isinstance(cluster, Cluster):
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raise ValueError(
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f"cluster should be an instance of couchbase.Cluster, "
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f"got {type(cluster)}"
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)
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self._cluster = cluster
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if not embedding:
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raise ValueError("Embeddings instance must be provided.")
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if not bucket_name:
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raise ValueError("bucket_name must be provided.")
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if not scope_name:
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raise ValueError("scope_name must be provided.")
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if not collection_name:
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raise ValueError("collection_name must be provided.")
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if not index_name:
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raise ValueError("index_name must be provided.")
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self._bucket_name = bucket_name
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self._scope_name = scope_name
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self._collection_name = collection_name
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self._embedding_function = embedding
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self._text_key = text_key
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self._embedding_key = embedding_key
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self._index_name = index_name
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self._scoped_index = scoped_index
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# Check if the bucket exists
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if not self._check_bucket_exists():
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raise ValueError(
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f"Bucket {self._bucket_name} does not exist. "
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" Please create the bucket before searching."
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)
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try:
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self._bucket = self._cluster.bucket(self._bucket_name)
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self._scope = self._bucket.scope(self._scope_name)
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self._collection = self._scope.collection(self._collection_name)
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except Exception as e:
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raise ValueError(
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"Error connecting to couchbase. "
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"Please check the connection and credentials."
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) from e
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# Check if the scope and collection exists. Throws ValueError if they don't
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try:
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self._check_scope_and_collection_exists()
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except Exception as e:
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raise e
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# Check if the index exists. Throws ValueError if it doesn't
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try:
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self._check_index_exists()
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except Exception as e:
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raise e
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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batch_size: Optional[int] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run texts through the embeddings and persist in vectorstore.
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If the document IDs are passed, the existing documents (if any) will be
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overwritten with the new ones.
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Args:
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texts (Iterable[str]): Iterable of strings to add to the vectorstore.
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metadatas (Optional[List[Dict]]): Optional list of metadatas associated
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with the texts.
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ids (Optional[List[str]]): Optional list of ids associated with the texts.
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IDs have to be unique strings across the collection.
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If it is not specified uuids are generated and used as ids.
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batch_size (Optional[int]): Optional batch size for bulk insertions.
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Default is 100.
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Returns:
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List[str]:List of ids from adding the texts into the vectorstore.
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"""
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if not batch_size:
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batch_size = self.DEFAULT_BATCH_SIZE
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doc_ids: List[str] = []
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if ids is None:
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ids = [uuid.uuid4().hex for _ in texts]
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if metadatas is None:
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metadatas = [{} for _ in texts]
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embedded_texts = self._embedding_function.embed_documents(list(texts))
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documents_to_insert = [
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{
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id: {
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self._text_key: text,
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self._embedding_key: vector,
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self._metadata_key: metadata,
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}
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for id, text, vector, metadata in zip(
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ids, texts, embedded_texts, metadatas
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)
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}
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]
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# Insert in batches
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for i in range(0, len(documents_to_insert), batch_size):
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batch = documents_to_insert[i : i + batch_size]
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try:
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result = self._collection.upsert_multi(batch[0])
|
||||
if result.all_ok:
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||||
doc_ids.extend(batch[0].keys())
|
||||
except DocumentExistsException as e:
|
||||
raise ValueError(f"Document already exists: {e}")
|
||||
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||||
return doc_ids
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def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
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"""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
|
||||
@ -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. Open, modern and actively maintained alternative to Chardet."
|
||||
optional = false
|
||||
python-versions = ">=3.7.0"
|
||||
files = [
|
||||
{file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
|
||||
{file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
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||||
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|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
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||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"},
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||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
|
||||
{file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"},
|
||||
{file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"},
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|
||||
{file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "requests"
|
||||
version = "2.32.2"
|
||||
description = "Python HTTP for Humans."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "requests-2.32.2-py3-none-any.whl", hash = "sha256:fc06670dd0ed212426dfeb94fc1b983d917c4f9847c863f313c9dfaaffb7c23c"},
|
||||
{file = "requests-2.32.2.tar.gz", hash = "sha256:dd951ff5ecf3e3b3aa26b40703ba77495dab41da839ae72ef3c8e5d8e2433289"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
certifi = ">=2017.4.17"
|
||||
charset-normalizer = ">=2,<4"
|
||||
idna = ">=2.5,<4"
|
||||
urllib3 = ">=1.21.1,<3"
|
||||
|
||||
[package.extras]
|
||||
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
||||
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.1.15"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:5fe8d54df166ecc24106db7dd6a68d44852d14eb0729ea4672bb4d96c320b7df"},
|
||||
{file = "ruff-0.1.15-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:6f0bfbb53c4b4de117ac4d6ddfd33aa5fc31beeaa21d23c45c6dd249faf9126f"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e0d432aec35bfc0d800d4f70eba26e23a352386be3a6cf157083d18f6f5881c8"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9405fa9ac0e97f35aaddf185a1be194a589424b8713e3b97b762336ec79ff807"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c66ec24fe36841636e814b8f90f572a8c0cb0e54d8b5c2d0e300d28a0d7bffec"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:6f8ad828f01e8dd32cc58bc28375150171d198491fc901f6f98d2a39ba8e3ff5"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:86811954eec63e9ea162af0ffa9f8d09088bab51b7438e8b6488b9401863c25e"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fd4025ac5e87d9b80e1f300207eb2fd099ff8200fa2320d7dc066a3f4622dc6b"},
|
||||
{file = "ruff-0.1.15-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b17b93c02cdb6aeb696effecea1095ac93f3884a49a554a9afa76bb125c114c1"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:ddb87643be40f034e97e97f5bc2ef7ce39de20e34608f3f829db727a93fb82c5"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:abf4822129ed3a5ce54383d5f0e964e7fef74a41e48eb1dfad404151efc130a2"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6c629cf64bacfd136c07c78ac10a54578ec9d1bd2a9d395efbee0935868bf852"},
|
||||
{file = "ruff-0.1.15-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:1bab866aafb53da39c2cadfb8e1c4550ac5340bb40300083eb8967ba25481447"},
|
||||
{file = "ruff-0.1.15-py3-none-win32.whl", hash = "sha256:2417e1cb6e2068389b07e6fa74c306b2810fe3ee3476d5b8a96616633f40d14f"},
|
||||
{file = "ruff-0.1.15-py3-none-win_amd64.whl", hash = "sha256:3837ac73d869efc4182d9036b1405ef4c73d9b1f88da2413875e34e0d6919587"},
|
||||
{file = "ruff-0.1.15-py3-none-win_arm64.whl", hash = "sha256:9a933dfb1c14ec7a33cceb1e49ec4a16b51ce3c20fd42663198746efc0427360"},
|
||||
{file = "ruff-0.1.15.tar.gz", hash = "sha256:f6dfa8c1b21c913c326919056c390966648b680966febcb796cc9d1aaab8564e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "syrupy"
|
||||
version = "4.6.1"
|
||||
description = "Pytest Snapshot Test Utility"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4"
|
||||
files = [
|
||||
{file = "syrupy-4.6.1-py3-none-any.whl", hash = "sha256:203e52f9cb9fa749cf683f29bd68f02c16c3bc7e7e5fe8f2fc59bdfe488ce133"},
|
||||
{file = "syrupy-4.6.1.tar.gz", hash = "sha256:37a835c9ce7857eeef86d62145885e10b3cb9615bc6abeb4ce404b3f18e1bb36"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pytest = ">=7.0.0,<9.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "tenacity"
|
||||
version = "8.3.0"
|
||||
description = "Retry code until it succeeds"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "tenacity-8.3.0-py3-none-any.whl", hash = "sha256:3649f6443dbc0d9b01b9d8020a9c4ec7a1ff5f6f3c6c8a036ef371f573fe9185"},
|
||||
{file = "tenacity-8.3.0.tar.gz", hash = "sha256:953d4e6ad24357bceffbc9707bc74349aca9d245f68eb65419cf0c249a1949a2"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
doc = ["reno", "sphinx"]
|
||||
test = ["pytest", "tornado (>=4.5)", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
description = "A lil' TOML parser"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.11.0"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
|
||||
{file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.1"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
|
||||
h2 = ["h2 (>=4,<5)"]
|
||||
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
|
||||
zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "d27ea82fa58fa4e03d47f03b6644b8da6a1b1792014e6b68abfe88cc9f45c9b3"
|
||||
@ -0,0 +1,92 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-couchbase"
|
||||
version = "0.0.1"
|
||||
description = "An integration package connecting Couchbase and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
repository = "https://github.com/langchain-ai/langchain"
|
||||
license = "MIT"
|
||||
|
||||
[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"
|
||||
@ -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)
|
||||
@ -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 "$result"
|
||||
echo "Please replace the code with an import from langchain_core.pydantic_v1."
|
||||
echo "For example, replace 'from pydantic import BaseModel'"
|
||||
echo "with 'from langchain_core.pydantic_v1 import BaseModel'"
|
||||
exit 1
|
||||
fi
|
||||
@ -0,0 +1,18 @@
|
||||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
# Initialize a variable to keep track of errors
|
||||
errors=0
|
||||
|
||||
# make sure not importing from langchain, langchain_experimental, or langchain_community
|
||||
git --no-pager grep '^from langchain\.' . && errors=$((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
|
||||
@ -0,0 +1,7 @@
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.mark.compile
|
||||
def test_placeholder() -> None:
|
||||
"""Used for compiling integration tests without running any real tests."""
|
||||
pass
|
||||
@ -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
|
||||
@ -0,0 +1,9 @@
|
||||
from langchain_couchbase import __all__
|
||||
|
||||
EXPECTED_ALL = [
|
||||
"CouchbaseVectorStore",
|
||||
]
|
||||
|
||||
|
||||
def test_all_imports() -> None:
|
||||
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
||||
@ -0,0 +1 @@
|
||||
|
||||
@ -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]
|
||||
Loading…
Reference in New Issue