From 1be6ccadb89e99a36973054eed73e7ffb80a4542 Mon Sep 17 00:00:00 2001 From: Alon Parag Date: Wed, 24 Apr 2024 13:12:44 +0300 Subject: [PATCH 1/2] async_documentdb_vectorstore --- cookbook/async_rag_with_aws_documentdb.ipynb | 241 + .../vectorstores/async_documentdb.py | 420 ++ libs/community/poetry.lock | 4384 ++++++++--------- libs/community/pyproject.toml | 3 +- .../vectorstores/test_async_documentdb.py | 392 ++ 5 files changed, 3222 insertions(+), 2218 deletions(-) create mode 100644 cookbook/async_rag_with_aws_documentdb.ipynb create mode 100644 libs/community/langchain_community/vectorstores/async_documentdb.py create mode 100644 libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py diff --git a/cookbook/async_rag_with_aws_documentdb.ipynb b/cookbook/async_rag_with_aws_documentdb.ipynb new file mode 100644 index 0000000000000..e66d932165732 --- /dev/null +++ b/cookbook/async_rag_with_aws_documentdb.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install nest-asyncio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "env_data = {\n", + " \"OPENAI_API_KEY\":\"\",\n", + " \"DOCUMENTDB_CONNECTION_STRING\":r\"\"\n", + " }\n", + "\n", + "os.environ.update(env_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Assume that you are passionate about baking cakes, and you are looking for some recipes for your next cake. Fortunately you have a large database of cakes. Which cake would be best suited for you?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.documents import Document\n", + "recipes = [\n", + " Document(page_content=\"In order to make the famous langchain chocolate cake Blend dark chocolate and cocoa powder for a rich chocolate cake base.\", metadata={\"recipeId\": \"101\"}),\n", + " Document(page_content=\"In order to make the famous langchain cheese cake Combine cream cheese, sugar, and eggs to prepare the cheesecake batter.\", metadata={\"recipeId\": \"102\"}),\n", + " Document(page_content=\"In order to make the famous langchain orange cake Stir together flour, fresh orange zest, and orange juice for a zesty orange cake.\", metadata={\"recipeId\": \"103\"}),\n", + " Document(page_content=\"In order to make the famous langchain banan cake Mix mashed bananas with cinnamon and nutmeg for a spiced banana cake.\", metadata={\"recipeId\": \"104\"}),\n", + " Document(page_content=\"In order to make the famous langchain cardmom cake Whisk eggs, almond flour, and ground cardamom for a fragrant cardamom cake.\", metadata={\"recipeId\": \"105\"})\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# You can create the collection using afrom_documents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.vectorstores.async_documentdb import AsyncDocumentDBVectorSearch\n", + "from langchain_community.embeddings.openai import OpenAIEmbeddings\n", + "from motor.motor_asyncio import AsyncIOMotorClient\n", + "import os\n", + "\n", + "connection_string = os.environ.get(\"DOCUMENTDB_CONNECTION_STRING\")\n", + "\n", + "namespace = \"cookbook.cakes\"\n", + "db_name, collection_name = namespace.split(\".\")\n", + "motor_client = AsyncIOMotorClient(connection_string)\n", + "collection = motor_client[db_name][collection_name]\n", + "if collection_name in await motor_client[db_name].list_collection_names():\n", + " await collection.drop()\n", + "index_name = \"cakes\"\n", + "\n", + "embedding_model = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n", + "\n", + "# Vector dimension for the index similarity\n", + "dimensions = 1536\n", + "vectorstore = await AsyncDocumentDBVectorSearch.afrom_documents(\n", + " recipes,\n", + " embedding_model,\n", + " collection=collection,\n", + " index_name=index_name\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Lets create the index ove the documents\n", + "from langchain_community.vectorstores.async_documentdb import DocumentDBSimilarityType\n", + "\n", + "\n", + "await vectorstore.acreate_index(dimensions, DocumentDBSimilarityType.COS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Lets create a chain that can call the vectorstore\n", + "from langchain_community.chat_models.openai import ChatOpenAI\n", + "from langchain_core.prompts.chat import ChatPromptTemplate\n", + "from langchain_core.runnables.passthrough import RunnablePassthrough\n", + "\n", + "# We define a prompt here, `recipe` is the placeholder for the most suitable recipe\n", + "prompt = \"\"\"\n", + "You are an expert conditor, Please recommend a suitable cake recipe from langchain.\n", + "{recipe}\n", + "\"\"\"\n", + "\n", + "prompt_template = ChatPromptTemplate.from_messages(\n", + " [('system', prompt),\n", + " (\"human\",\"{message}\")]\n", + ")\n", + "\n", + "# Vectorstores can also create a retriever object which could be used inside a chain to retrieve relevant objects.\n", + "# Additionaly, you can add aggregation commands such as $match to this call to filter on specific fields.\n", + "# for example\n", + "# retriever = vectorstore.as_retriever([{\n", + " # \"$match\": {\n", + " # \"recipeId\": 123\n", + " # }\n", + " # }])\n", + "\n", + "retriever = vectorstore.as_retriever()\n", + "\n", + "# We would need a small helper function to turn the results of the search to a string\n", + "def format_docs(docs) -> str:\n", + " \"\"\"Helper function to format docs as input for RAG chaing\n", + " Args:\n", + " docs (list): list of Document objects retrieved from vector store\n", + " \"\"\"\n", + " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", + " \n", + "# We need a function to extract the message as string from the input object, I recommend you to check how the input looks like by printing it. You can also write this expression as a lambda function\n", + "def input_formatter(_input) -> str:\n", + " return _input[\"message\"]\n", + "\n", + "# Let's create the chain\n", + "llm = ChatOpenAI()\n", + "chain = (\n", + " RunnablePassthrough.assign(\n", + " recipe=input_formatter | retriever | format_docs\n", + " )\n", + " | prompt_template\n", + " | llm\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Now Let's ask our chain to find us a recipe with cheescake\n", + "message = dict(\n", + " message=\"Recommend me a good cheescake\"\n", + ")\n", + "res = await chain.ainvoke(input=message)\n", + "print(res.content)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">>> I recommend trying the famous Langchain cheesecake recipe. To make it, combine cream cheese, sugar, and eggs to prepare the cheesecake batter. You can also add a hint of vanilla extract for extra flavor. Bake the cheesecake until it is set and then chill it in the fridge before serving. Enjoy this delicious and creamy dessert!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "message = dict(\n", + " message=\"Recommend me a recipe with banana\"\n", + ")\n", + "res = await chain.ainvoke(input=message)\n", + "print(res.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ">>> I recommend trying the famous langchain banana cake recipe! To make this delicious spiced banana cake, mix mashed bananas with cinnamon and nutmeg for a flavorful twist. It's a perfect treat for any occasion and is sure to be a crowd-pleaser. Enjoy baking!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/libs/community/langchain_community/vectorstores/async_documentdb.py b/libs/community/langchain_community/vectorstores/async_documentdb.py new file mode 100644 index 0000000000000..d6ca7215857a0 --- /dev/null +++ b/libs/community/langchain_community/vectorstores/async_documentdb.py @@ -0,0 +1,420 @@ +from __future__ import annotations + +import logging +from enum import Enum +from typing import ( + TYPE_CHECKING, + Any, + Dict, + Generator, + Iterable, + List, + Optional, + TypeVar, + Union, +) + +from langchain_core.documents import Document +from langchain_core.vectorstores import VectorStore + +if TYPE_CHECKING: + from langchain_core.embeddings import Embeddings + from motor.core import AgnosticClient, AgnosticCollection # noqa: F401 + + +# Before Python 3.11 native StrEnum is not available +class DocumentDBSimilarityType(str, Enum): + """DocumentDB Similarity Type as enumerator.""" + + COS = "cosine" + """Cosine similarity.""" + DOT = "dotProduct" + """Dot product.""" + EUC = "euclidean" + """Euclidean distance.""" + + +DocumentDBDocumentType = TypeVar("DocumentDBDocumentType", bound=Dict[str, Any]) + +logger = logging.getLogger(__name__) + +DEFAULT_INSERT_BATCH_SIZE = 128 + + +class AsyncDocumentDBVectorSearch(VectorStore): + """`Amazon DocumentDB (with MongoDB compatibility)` async vector store built with `motor`. + Please refer to the official Vector Search documentation for more details: + https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html + + To use, you should have both: + - the `motor` python package installed + - a connection string and credentials associated with a DocumentDB cluster + + Example: + . code-block:: python + + from langchain_community.vectorstores import DocumentDBVectorSearch + from langchain_community.embeddings.openai import OpenAIEmbeddings + from motor.motor_asyncio import AsyncIOMotorClient + + mongo_client = await AsyncIOMotorClient("") + collection = await mongo_client[""][""] + embeddings = OpenAIEmbeddings() + vectorstore = DocumentDBVectorSearch(collection, embeddings) + """ + + def __init__( + self, + collection: AgnosticCollection[DocumentDBDocumentType], + embedding: Embeddings, + *, + index_name: str = "vectorSearchIndex", + text_key: str = "textContent", + embedding_key: str = "vectorContent", + similarity_type: DocumentDBSimilarityType = DocumentDBSimilarityType.COS, + ): + """Constructor for DocumentDBVectorSearch. + + Args: + collection: MongoDB collection to add the texts to. + embedding: Text embedding model to use. + index_name: Name of the Vector Search index. + text_key: MongoDB field that will contain the text + for each document. + embedding_key: MongoDB field that will contain the embedding + for each document. + similarity_type: Method for comparing vector similarity + """ + self._collection = collection + self._embedding = embedding + self._index_name = index_name + self._text_key = text_key + self._embedding_key = embedding_key + self._similarity_type = similarity_type + + @property + def embeddings(self) -> Embeddings: + return self._embedding + + def get_index_name(self) -> str: + """Returns the index name. + + Returns: + Returns the index name + """ + return self._index_name + + @classmethod + def from_connection_string( + cls, + connection_string: str, + namespace: str, + embedding: Embeddings, + **kwargs: Any, + ) -> "AsyncDocumentDBVectorSearch": + """Creates an Instance of DocumentDBVectorSearch from a Connection + String. + + Args: + connection_string: The DocumentDB cluster endpoint connection string + namespace: The namespace (database.collection) + embedding: The embedding utility + **kwargs: Dynamic keyword arguments + + Returns: + an instance of the vector store + """ + try: + from motor.motor_asyncio import AsyncIOMotorClient + from motor.core import AgnosticClient # noqa: F811 + except ImportError: + raise ImportError( + "Could not import motor, please install it with " "`pip install motor`." + ) + client: AgnosticClient = AsyncIOMotorClient(connection_string) + db_name, collection_name = namespace.split(".") + collection = client[db_name][collection_name] + return cls(collection, embedding, **kwargs) + + async def aindex_exists(self) -> bool: + """Verifies if the specified index name during instance construction + exists on the collection. + + Returns: + Returns True on success and False if no such index exists + on the collection + """ + cursor = self._collection.list_indexes() + index_name = self._index_name + + async for res in cursor: + current_index_name = res.pop("name") + if current_index_name == index_name: + return True + + return False + + async def adelete_index(self) -> None: + """Deletes the index specified during instance construction if it + exists.""" + if self.aindex_exists(): + await self._collection.drop_index(self._index_name) + # Raises OperationFailure on an error (e.g. trying to drop + # an index that does not exist) + + async def acreate_index( + self, + dimensions: int = 1536, + similarity: DocumentDBSimilarityType = DocumentDBSimilarityType.COS, + m: int = 16, + ef_construction: int = 64, + ) -> dict[str, Any]: + """Creates an index using the index name specified at instance + construction. + + Args: + dimensions: Number of dimensions for vector similarity. + The maximum number of supported dimensions is 2000 + + similarity: Similarity algorithm to use with the HNSW index. + + m: Specifies the max number of connections for an HNSW index. + Large impact on memory consumption. + + ef_construction: Specifies the size of the dynamic candidate list + for constructing the graph for HNSW index. Higher values lead + to more accurate results but slower indexing speed. + + Possible options are: + - DocumentDBSimilarityType.COS (cosine distance), + - DocumentDBSimilarityType.EUC (Euclidean distance), and + - DocumentDBSimilarityType.DOT (dot product). + + Returns: + An object describing the created index + """ + self._similarity_type = similarity + + # prepare the command + create_index_commands = { + "createIndexes": self._collection.name, + "indexes": [ + { + "name": self._index_name, + "key": {self._embedding_key: "vector"}, + "vectorOptions": { + "type": "hnsw", + "similarity": similarity, + "dimensions": dimensions, + "m": m, + "efConstruction": ef_construction, + }, + } + ], + } + + # retrieve the database object + current_database = self._collection.database + + # invoke the command from the database object + create_index_responses: dict[str, Any] = await current_database.command( + create_index_commands + ) + + return create_index_responses + + async def aadd_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[Dict[str, Any]]] = None, + **kwargs: Any, + ) -> List: + batch_size = kwargs.get("batch_size", DEFAULT_INSERT_BATCH_SIZE) + _metadatas: Union[List, Generator] = metadatas or ({} for _ in texts) + texts_batch = [] + metadatas_batch = [] + result_ids = [] + for i, (text, metadata) in enumerate(zip(texts, _metadatas)): + texts_batch.append(text) + metadatas_batch.append(metadata) + if (i + 1) % batch_size == 0: + new_result_ids = await self._insert_texts(texts_batch, metadatas_batch) + result_ids.extend(new_result_ids) + texts_batch = [] + metadatas_batch = [] + if texts_batch: + new_result_ids = await self._insert_texts(texts_batch, metadatas_batch) + result_ids.extend(new_result_ids) + return result_ids + + async def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[Dict[str, Any]]] = None, + **kwargs: Any, + ) -> List: + return await self.aadd_texts(texts, metadatas, **kwargs) + + async def _insert_texts( + self, texts: List[str], metadatas: List[Dict[str, Any]] + ) -> List: + """Used to Load Documents into the collection. + + Args: + texts: The list of documents strings to load + metadatas: The list of metadata objects associated with each document + + Returns: + """ + # If the text is empty, then exit early + if not texts: + return [] + + # Embed and create the documents + embeddings = self._embedding.embed_documents(texts) + to_insert = [ + {self._text_key: t, self._embedding_key: embedding, **m} + for t, m, embedding in zip(texts, metadatas, embeddings) + ] + # insert the documents in DocumentDB + insert_result = await self._collection.insert_many(to_insert) # type: ignore + return insert_result.inserted_ids + + @classmethod + async def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + collection: Optional[AgnosticCollection[DocumentDBDocumentType]] = None, + **kwargs: Any, + ) -> AsyncDocumentDBVectorSearch: + return await cls.afrom_texts( + texts, + embedding, + metadatas, + collection, + **kwargs, + ) + + @classmethod + async def afrom_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + collection: Optional[AgnosticCollection[DocumentDBDocumentType]] = None, + **kwargs: Any, + ) -> AsyncDocumentDBVectorSearch: + if collection is None: + raise ValueError("Must provide 'collection' named parameter.") + vectorstore = cls(collection, embedding, **kwargs) + await vectorstore.aadd_texts(texts, metadatas=metadatas) + return vectorstore + + async def adelete( + self, ids: Optional[List[str]] = None, **kwargs: Any + ) -> Optional[bool]: + if ids is None: + raise ValueError("No document ids provided to delete.") + + for document_id in ids: + await self.adelete_document_by_id(document_id) + return True + + async def delete( + self, ids: Optional[List[str]] = None, **kwargs: Any + ) -> Optional[bool]: + await self.adelete(ids, **kwargs) + + async def adelete_document_by_id(self, document_id: Optional[str] = None) -> None: + """Removes a Specific Document by Id. + + Args: + document_id: The document identifier + """ + try: + from bson.objectid import ObjectId + except ImportError as e: + raise ImportError( + "Unable to import bson, please install with `pip install bson`." + ) from e + if document_id is None: + raise ValueError("No document id provided to delete.") + + await self._collection.delete_one({"_id": ObjectId(document_id)}) + + async def _asimilarity_search_without_score( + self, + embeddings: List[float], + k: int = 4, + ef_search: int = 40, + agg_operators: List[Dict[str, Any]] | None = None, + ) -> List[Document]: + """Returns a list of documents. + + Args: + embeddings: The query vector + k: the number of documents to return + ef_search: Specifies the size of the dynamic candidate list + that HNSW index uses during search. A higher value of + efSearch provides better recall at cost of speed. + agg_operators: Any additional mongodb aggregation operators + + Returns: + A list of documents closest to the query vector + """ + pipeline: List[dict[str, Any]] = [ + { + "$search": { + "vectorSearch": { + "vector": embeddings, + "path": self._embedding_key, + "similarity": self._similarity_type, + "k": k, + "efSearch": ef_search, + } + } + } + ] + if agg_operators: + pipeline = agg_operators + pipeline + cursor = self._collection.aggregate(pipeline) + + docs = [] + + async for res in cursor: + text = res.pop(self._text_key) + docs.append(Document(page_content=text, metadata=res)) + + return docs + + async def similarity_search( + self, + query: str, + k: int = 4, + ef_search: int = 40, + agg_operators: List[Dict[str, Any]] | None = None, + **kwargs: Any, + ) -> List[Document]: + return await self.asimilarity_search( + query, + k, + ef_search, + agg_operators, + **kwargs, + ) + + async def asimilarity_search( + self, + query: str, + k: int = 4, + ef_search: int = 40, + agg_operators: List[Dict[str, Any]] | None = None, + **kwargs: Any, + ) -> List[Document]: + embeddings = self._embedding.embed_query(query) + docs = await self._asimilarity_search_without_score( + embeddings=embeddings, k=k, ef_search=ef_search, agg_operators=agg_operators + ) + return [doc for doc in docs] diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index cfd7fd4580966..24079ee87a97e 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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"pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] cli = ["typer"] @@ -9233,5 +9183,5 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "48ea73a94d06ae90f8f089017ae1bbcf9d37b2cc9957a44fb617785be0fe3236" +python-versions = ">=3.9,<4.0" +content-hash = "d35076e1e15175e6d69904430e1e71d1701782be64fc4aee5f41996b7db850b4" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 43a4c185691ad..58150d66bc0da 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -8,7 +8,7 @@ readme = "README.md" repository = "https://github.com/langchain-ai/langchain" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = "^0.1.45" SQLAlchemy = ">=1.4,<3" requests = "^2" @@ -100,6 +100,7 @@ premai = {version = "^0.3.25", optional = true} vdms = {version = "^0.0.20", optional = true} httpx-sse = {version = "^0.4.0", optional = true} pyjwt = {version = "^2.8.0", optional = true} +pymongo = {version = "^4.6.3", optional = true} [tool.poetry.group.test] optional = true diff --git a/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py b/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py new file mode 100644 index 0000000000000..6a684fa033dcc --- /dev/null +++ b/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py @@ -0,0 +1,392 @@ +"""Test DocumentDBVectorSearch functionality.""" +import logging +import os +# from time import sleep +from asyncio import sleep +from typing import Any, Optional + +import pytest +from langchain_core.documents import Document + +from langchain_community.embeddings import OpenAIEmbeddings +from langchain_community.vectorstores.async_documentdb import ( + DocumentDBSimilarityType, + AsyncDocumentDBVectorSearch, +) + +logging.basicConfig(level=logging.DEBUG) + +model_deployment = os.getenv( + "OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada" +) +model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002") + +INDEX_NAME = "langchain-test-index" +NAMESPACE = "langchain_test_db.langchain_test_collection" +CONNECTION_STRING = os.getenv("DOCUMENTDB_URI", "") +DB_NAME, COLLECTION_NAME = NAMESPACE.split(".") + +dimensions = 1536 +similarity_algorithm = DocumentDBSimilarityType.COS + + +def prepare_collection() -> Any: + from motor.motor_asyncio import AsyncIOMotorClient + from motor.core import AgnosticClient + + test_client: AgnosticClient = AsyncIOMotorClient(CONNECTION_STRING) + return test_client[DB_NAME][COLLECTION_NAME] + + +@pytest.fixture() +def collection() -> Any: + return prepare_collection() + + +@pytest.fixture() +def embedding_openai() -> Any: + openai_embeddings: OpenAIEmbeddings = OpenAIEmbeddings( + deployment=model_deployment, model=model_name, chunk_size=1 + ) + return openai_embeddings + + +""" +This is how to run the integration tests: + +cd libs/community +make test TEST_FILE=tests/integration_tests/vectorstores/test_async_documentdb.py + +NOTE: You will first need to follow the contributor setup steps: +https://python.langchain.com/docs/contributing/code. You will also need to install +`motor` via `poetry`. You can also run the test directly using `pytest`, but please +make sure to install all dependencies. +""" + + +class TestDocumentDBVectorSearch: + @classmethod + def setup_class(cls) -> None: + if not os.getenv("OPENAI_API_KEY"): + raise ValueError("OPENAI_API_KEY environment variable is not set") + + # insure the test collection is empty + collection = prepare_collection() + assert collection.count_documents({}) == 0 # type: ignore[index] # noqa: E501 + + @classmethod + def teardown_class(cls) -> None: + collection = prepare_collection() + # delete all the documents in the collection + collection.delete_many({}) # type: ignore[index] + collection.drop_indexes() + + @pytest.fixture(autouse=True) + def setup(self) -> None: + collection = prepare_collection() + # delete all the documents in the collection + collection.delete_many({}) # type: ignore[index] + collection.drop_indexes() + + async def test_from_documents_cosine_distance( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + """Test end to end construction and search.""" + documents = [ + Document(page_content="Dogs are tough.", metadata={"a": 1}), + Document(page_content="Cats have fluff.", metadata={"b": 1}), + Document(page_content="What is a sandwich?", metadata={"c": 1}), + Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}), + ] + + vectorstore = await AsyncDocumentDBVectorSearch.afrom_documents( + documents, + embedding_openai, + collection=collection, + index_name=INDEX_NAME, + ) + await sleep(1) # waits for DocumentDB to save contents to the collection + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, similarity_algorithm) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1) + + assert output + assert output[0].page_content == "What is a sandwich?" + assert output[0].metadata["c"] == 1 + await vectorstore.adelete_index() + + async def test_from_documents_inner_product( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + """Test end to end construction and search.""" + documents = [ + Document(page_content="Dogs are tough.", metadata={"a": 1}), + Document(page_content="Cats have fluff.", metadata={"b": 1}), + Document(page_content="What is a sandwich?", metadata={"c": 1}), + Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}), + ] + + vectorstore = await AsyncDocumentDBVectorSearch.afrom_documents( + documents, + embedding_openai, + collection=collection, + index_name=INDEX_NAME, + ) + await sleep(1) # waits for DocumentDB to save contents to the collection + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, DocumentDBSimilarityType.DOT) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1, ef_search=100) + + assert output + assert output[0].page_content == "What is a sandwich?" + assert output[0].metadata["c"] == 1 + await vectorstore.adelete_index() + + async def test_from_texts_cosine_distance( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + texts = [ + "Dogs are tough.", + "Cats have fluff.", + "What is a sandwich?", + "That fence is purple.", + ] + vectorstore = await AsyncDocumentDBVectorSearch.afrom_texts( + texts, + embedding_openai, + collection=collection, + index_name=INDEX_NAME, + ) + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, similarity_algorithm) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1) + + assert output[0].page_content == "What is a sandwich?" + await vectorstore.adelete_index() + + async def test_from_texts_with_metadatas_cosine_distance( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + texts = [ + "Dogs are tough.", + "Cats have fluff.", + "What is a sandwich?", + "The fence is purple.", + ] + metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}] + vectorstore = await AsyncDocumentDBVectorSearch.afrom_texts( + texts, + embedding_openai, + metadatas=metadatas, + collection=collection, + index_name=INDEX_NAME, + ) + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, similarity_algorithm) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1) + + assert output + assert output[0].page_content == "What is a sandwich?" + assert output[0].metadata["c"] == 1 + + await vectorstore.adelete_index() + + async def test_from_texts_with_metadatas_delete_one( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + texts = [ + "Dogs are tough.", + "Cats have fluff.", + "What is a sandwich?", + "The fence is purple.", + ] + metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}] + vectorstore = await AsyncDocumentDBVectorSearch.afrom_texts( + texts, + embedding_openai, + metadatas=metadatas, + collection=collection, + index_name=INDEX_NAME, + ) + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, similarity_algorithm) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1) + + assert output + assert output[0].page_content == "What is a sandwich?" + assert output[0].metadata["c"] == 1 + + first_document_id_object = output[0].metadata["_id"] + first_document_id = str(first_document_id_object) + + await vectorstore.adelete_document_by_id(first_document_id) + await sleep(2) # waits for the index to be updated + + output2 = await vectorstore.asimilarity_search("Sandwich", k=1, ef_search=10) + assert output2 + assert output2[0].page_content != "What is a sandwich?" + + await vectorstore.adelete_index() + + async def test_from_texts_with_metadatas_delete_multiple( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + texts = [ + "Dogs are tough.", + "Cats have fluff.", + "What is a sandwich?", + "The fence is purple.", + ] + metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}] + vectorstore = await AsyncDocumentDBVectorSearch.afrom_texts( + texts, + embedding_openai, + metadatas=metadatas, + collection=collection, + index_name=INDEX_NAME, + ) + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, similarity_algorithm) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=5) + + first_document_id_object = output[0].metadata["_id"] + first_document_id = str(first_document_id_object) + + output[1].metadata["_id"] + second_document_id = output[1].metadata["_id"] + + output[2].metadata["_id"] + third_document_id = output[2].metadata["_id"] + + document_ids = [first_document_id, second_document_id, third_document_id] + await vectorstore.adelete(document_ids) + await sleep(2) # waits for the index to be updated + + output_2 = await vectorstore.asimilarity_search("Sandwich", k=5) + assert output + assert output_2 + + assert len(output) == 4 # we should see all the four documents + assert ( + len(output_2) == 1 + ) # we should see only one document left after three have been deleted + + await vectorstore.adelete_index() + + async def test_from_texts_with_metadatas_inner_product( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + texts = [ + "Dogs are tough.", + "Cats have fluff.", + "What is a sandwich?", + "The fence is purple.", + ] + metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}] + vectorstore = await AsyncDocumentDBVectorSearch.afrom_texts( + texts, + embedding_openai, + metadatas=metadatas, + collection=collection, + index_name=INDEX_NAME, + ) + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, DocumentDBSimilarityType.DOT) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1) + + assert output + assert output[0].page_content == "What is a sandwich?" + assert output[0].metadata["c"] == 1 + await vectorstore.adelete_index() + + async def test_from_texts_with_metadatas_euclidean_distance( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + texts = [ + "Dogs are tough.", + "Cats have fluff.", + "What is a sandwich?", + "The fence is purple.", + ] + metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}] + vectorstore = await AsyncDocumentDBVectorSearch.afrom_texts( + texts, + embedding_openai, + metadatas=metadatas, + collection=collection, + index_name=INDEX_NAME, + ) + + # Create the HNSW index that will be leveraged later for vector search + await vectorstore.acreate_index(dimensions, DocumentDBSimilarityType.EUC) + await sleep(2) # waits for the index to be set up + + output = await vectorstore.asimilarity_search("Sandwich", k=1) + + assert output + assert output[0].page_content == "What is a sandwich?" + assert output[0].metadata["c"] == 1 + await vectorstore.adelete_index() + + async def invoke_delete_with_no_args( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> Optional[bool]: + vectorstore: AsyncDocumentDBVectorSearch = ( + AsyncDocumentDBVectorSearch.from_connection_string( + CONNECTION_STRING, + NAMESPACE, + embedding_openai, + index_name=INDEX_NAME, + ) + ) + + return await vectorstore.adelete() + + async def invoke_delete_by_id_with_no_args( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + vectorstore: AsyncDocumentDBVectorSearch = ( + AsyncDocumentDBVectorSearch.from_connection_string( + CONNECTION_STRING, + NAMESPACE, + embedding_openai, + index_name=INDEX_NAME, + ) + ) + + await vectorstore.adelete_document_by_id() + + async def test_invalid_arguments_to_delete( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + with pytest.raises(ValueError) as exception_info: + await self.invoke_delete_with_no_args(embedding_openai, collection) + assert str(exception_info.value) == "No document ids provided to delete." + + async def test_no_arguments_to_delete_by_id( + self, embedding_openai: OpenAIEmbeddings, collection: Any + ) -> None: + with pytest.raises(Exception) as exception_info: + await self.invoke_delete_by_id_with_no_args(embedding_openai, collection) + assert str(exception_info.value) == "No document id provided to delete." From 6c89276077615e5e6d7548803e05f0469789e47c Mon Sep 17 00:00:00 2001 From: Alon Parag Date: Wed, 24 Apr 2024 14:44:55 +0300 Subject: [PATCH 2/2] Formatting and moving the notebook to the docs --- .../docs/integrations}/async_rag_with_aws_documentdb.ipynb | 0 .../langchain_community/vectorstores/async_documentdb.py | 5 +++-- .../integration_tests/vectorstores/test_async_documentdb.py | 5 +++-- 3 files changed, 6 insertions(+), 4 deletions(-) rename {cookbook => docs/docs/integrations}/async_rag_with_aws_documentdb.ipynb (100%) diff --git a/cookbook/async_rag_with_aws_documentdb.ipynb b/docs/docs/integrations/async_rag_with_aws_documentdb.ipynb similarity index 100% rename from cookbook/async_rag_with_aws_documentdb.ipynb rename to docs/docs/integrations/async_rag_with_aws_documentdb.ipynb diff --git a/libs/community/langchain_community/vectorstores/async_documentdb.py b/libs/community/langchain_community/vectorstores/async_documentdb.py index d6ca7215857a0..0011277bb51dd 100644 --- a/libs/community/langchain_community/vectorstores/async_documentdb.py +++ b/libs/community/langchain_community/vectorstores/async_documentdb.py @@ -42,7 +42,8 @@ class DocumentDBSimilarityType(str, Enum): class AsyncDocumentDBVectorSearch(VectorStore): - """`Amazon DocumentDB (with MongoDB compatibility)` async vector store built with `motor`. + """`Amazon DocumentDB (with MongoDB compatibility)` async vector store + built with `motor`. Please refer to the official Vector Search documentation for more details: https://docs.aws.amazon.com/documentdb/latest/developerguide/vector-search.html @@ -125,8 +126,8 @@ def from_connection_string( an instance of the vector store """ try: + from motor.core import AgnosticClient # noqa: F811 F401 from motor.motor_asyncio import AsyncIOMotorClient - from motor.core import AgnosticClient # noqa: F811 except ImportError: raise ImportError( "Could not import motor, please install it with " "`pip install motor`." diff --git a/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py b/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py index 6a684fa033dcc..f4ffa0131813d 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_async_documentdb.py @@ -1,6 +1,7 @@ """Test DocumentDBVectorSearch functionality.""" import logging import os + # from time import sleep from asyncio import sleep from typing import Any, Optional @@ -10,8 +11,8 @@ from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores.async_documentdb import ( - DocumentDBSimilarityType, AsyncDocumentDBVectorSearch, + DocumentDBSimilarityType, ) logging.basicConfig(level=logging.DEBUG) @@ -31,8 +32,8 @@ def prepare_collection() -> Any: - from motor.motor_asyncio import AsyncIOMotorClient from motor.core import AgnosticClient + from motor.motor_asyncio import AsyncIOMotorClient test_client: AgnosticClient = AsyncIOMotorClient(CONNECTION_STRING) return test_client[DB_NAME][COLLECTION_NAME]