diff --git a/.github/workflows/docs-integration-tests.yml b/.github/workflows/docs-integration-tests.yml index ce30f83bb..4418f0b12 100644 --- a/.github/workflows/docs-integration-tests.yml +++ b/.github/workflows/docs-integration-tests.yml @@ -124,6 +124,8 @@ jobs: ZENROWS_API_KEY: ${{ secrets.INTEG_ZENROWS_API_KEY }} QDRANT_CLUSTER_ENDPOINT: ${{ secrets.INTEG_QDRANT_CLUSTER_ENDPOINT }} QDRANT_CLUSTER_API_KEY: ${{ secrets.INTEG_QDRANT_CLUSTER_API_KEY }} + ASTRA_DB_API_ENDPOINT: ${{ secrets.INTEG_ASTRA_DB_API_ENDPOINT }} + ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.INTEG_ASTRA_DB_APPLICATION_TOKEN }} services: postgres: image: ankane/pgvector:v0.5.0 diff --git a/CHANGELOG.md b/CHANGELOG.md index c591ec7f3..efe70904d 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## Unreleased +### Added +- `AstraDbVectorStoreDriver` to support DataStax Astra DB as a vector store. + ## [0.29.0] - 2024-07-30 ### Added diff --git a/docs/examples/query-webpage-astra-db.md b/docs/examples/query-webpage-astra-db.md new file mode 100644 index 000000000..46989c2ba --- /dev/null +++ b/docs/examples/query-webpage-astra-db.md @@ -0,0 +1,78 @@ +The following example script ingests a Web page (a blog post), +stores its chunked contents on Astra DB through the Astra DB vector store driver, +and finally runs a RAG process to answer a question specific to the topic of the +Web page. + +This script requires that a vector collection has been created in the Astra database +(with name `"griptape_test_collection"` and vector dimension matching the embedding being used, i.e. 1536 in this case). + +_Note:_ Besides the [Astra DB](../griptape-framework/drivers/vector-store-drivers.md#astra-db) extra, +this example requires the `drivers-web-scraper-trafilatura` +Griptape extra to be installed as well. + + +```python +import os + +from griptape.drivers import ( + AstraDbVectorStoreDriver, + OpenAiChatPromptDriver, + OpenAiEmbeddingDriver, +) +from griptape.engines.rag import RagEngine +from griptape.engines.rag.modules import ( + PromptResponseRagModule, + VectorStoreRetrievalRagModule, +) +from griptape.engines.rag.stages import ResponseRagStage, RetrievalRagStage +from griptape.loaders import WebLoader +from griptape.structures import Agent +from griptape.tools import RagClient, TaskMemoryClient + + +namespace = "datastax_blog" +input_blogpost = ( + "www.datastax.com/blog/indexing-all-of-wikipedia-on-a-laptop" +) + +vector_store_driver = AstraDbVectorStoreDriver( + embedding_driver=OpenAiEmbeddingDriver(), + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + collection_name="griptape_test_collection", + astra_db_namespace=os.environ.get("ASTRA_DB_KEYSPACE"), +) + +engine = RagEngine( + retrieval_stage=RetrievalRagStage( + retrieval_modules=[ + VectorStoreRetrievalRagModule( + vector_store_driver=vector_store_driver, + query_params={ + "count": 2, + "namespace": namespace, + }, + ) + ] + ), + response_stage=ResponseRagStage( + response_module=PromptResponseRagModule( + prompt_driver=OpenAiChatPromptDriver(model="gpt-4o") + ) + ) +) + +vector_store_driver.upsert_text_artifacts( + {namespace: WebLoader(max_tokens=256).load(input_blogpost)} +) + +vector_store_tool = RagClient( + description="A DataStax blog post", + rag_engine=engine, +) +agent = Agent(tools=[vector_store_tool, TaskMemoryClient(off_prompt=False)]) +agent.run( + "What engine made possible to index such an amount of data, " + "and what kind of tuning was required?" +) +``` diff --git a/docs/griptape-framework/drivers/vector-store-drivers.md b/docs/griptape-framework/drivers/vector-store-drivers.md index 2b76119b3..ad826d0aa 100644 --- a/docs/griptape-framework/drivers/vector-store-drivers.md +++ b/docs/griptape-framework/drivers/vector-store-drivers.md @@ -482,3 +482,49 @@ values = [r.to_artifact().value for r in results] print("\n\n".join(values)) ``` + +### Astra DB + +!!! info + This Driver requires the `drivers-vector-astra-db` [extra](../index.md#extras). + +The AstraDbVectorStoreDriver supports [DataStax Astra DB](https://www.datastax.com/products/datastax-astra). + +The following example shows how to store vector entries and query the information using the driver: + +```python +import os +from griptape.drivers import AstraDbVectorStoreDriver, OpenAiEmbeddingDriver +from griptape.loaders import WebLoader + +# Astra DB secrets and connection parameters +api_endpoint = os.environ["ASTRA_DB_API_ENDPOINT"] +token = os.environ["ASTRA_DB_APPLICATION_TOKEN"] +astra_db_namespace = os.environ.get("ASTRA_DB_KEYSPACE") # optional + +# Initialize an Embedding Driver. +embedding_driver = OpenAiEmbeddingDriver(api_key=os.environ["OPENAI_API_KEY"]) + +vector_store_driver = AstraDbVectorStoreDriver( + embedding_driver=embedding_driver, + api_endpoint=api_endpoint, + token=token, + collection_name="griptape_test_collection", + astra_db_namespace=astra_db_namespace, # optional +) + +# Load Artifacts from the web +artifacts = WebLoader().load("https://www.griptape.ai") + +# Upsert Artifacts into the Vector Store Driver +[ + vector_store_driver.upsert_text_artifact(a, namespace="griptape") + for a in artifacts +] + +results = vector_store_driver.query(query="What is griptape?") + +values = [r.to_artifact().value for r in results] + +print("\n\n".join(values)) +``` diff --git a/griptape/drivers/__init__.py b/griptape/drivers/__init__.py index f948f1be1..9e1790b01 100644 --- a/griptape/drivers/__init__.py +++ b/griptape/drivers/__init__.py @@ -41,6 +41,7 @@ from .vector.azure_mongodb_vector_store_driver import AzureMongoDbVectorStoreDriver from .vector.dummy_vector_store_driver import DummyVectorStoreDriver from .vector.qdrant_vector_store_driver import QdrantVectorStoreDriver +from .vector.astradb_vector_store_driver import AstraDbVectorStoreDriver from .vector.griptape_cloud_knowledge_base_vector_store_driver import GriptapeCloudKnowledgeBaseVectorStoreDriver from .sql.base_sql_driver import BaseSqlDriver @@ -171,6 +172,7 @@ "AmazonOpenSearchVectorStoreDriver", "PgVectorVectorStoreDriver", "QdrantVectorStoreDriver", + "AstraDbVectorStoreDriver", "DummyVectorStoreDriver", "GriptapeCloudKnowledgeBaseVectorStoreDriver", "BaseSqlDriver", diff --git a/griptape/drivers/vector/astradb_vector_store_driver.py b/griptape/drivers/vector/astradb_vector_store_driver.py new file mode 100644 index 000000000..029fa382d --- /dev/null +++ b/griptape/drivers/vector/astradb_vector_store_driver.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Optional + +from attrs import define, field + +from griptape.drivers import BaseVectorStoreDriver +from griptape.utils import import_optional_dependency + +if TYPE_CHECKING: + from astrapy import Collection + from astrapy.authentication import TokenProvider + + +@define +class AstraDbVectorStoreDriver(BaseVectorStoreDriver): + """A Vector Store Driver for Astra DB. + + Attributes: + embedding_driver: a `griptape.drivers.BaseEmbeddingDriver` for embedding computations within the store + api_endpoint: the "API Endpoint" for the Astra DB instance. + token: a Database Token ("AstraCS:...") secret to access Astra DB. An instance of `astrapy.authentication.TokenProvider` is also accepted. + collection_name: the name of the collection on Astra DB. The collection must have been created beforehand, + and support vectors with a vector dimension matching the embeddings being used by this driver. + environment: the environment ("prod", "hcd", ...) hosting the target Data API. + It can be omitted for production Astra DB targets. See `astrapy.constants.Environment` for allowed values. + astra_db_namespace: optional specification of the namespace (in the Astra database) for the data. + *Note*: not to be confused with the "namespace" mentioned elsewhere, which is a grouping within this vector store. + """ + + api_endpoint: str = field(kw_only=True, metadata={"serializable": True}) + token: Optional[str | TokenProvider] = field(kw_only=True, default=None, metadata={"serializable": False}) + collection_name: str = field(kw_only=True, metadata={"serializable": True}) + environment: Optional[str] = field(kw_only=True, default=None, metadata={"serializable": True}) + astra_db_namespace: Optional[str] = field(default=None, kw_only=True, metadata={"serializable": True}) + + collection: Collection = field(init=False) + + def __attrs_post_init__(self) -> None: + astrapy = import_optional_dependency("astrapy") + self.collection = ( + astrapy.DataAPIClient( + caller_name="griptape", + environment=self.environment, + ) + .get_database( + self.api_endpoint, + token=self.token, + namespace=self.astra_db_namespace, + ) + .get_collection( + name=self.collection_name, + ) + ) + + def delete_vector(self, vector_id: str) -> None: + """Delete a vector from Astra DB store. + + The method succeeds regardless of whether a vector with the provided ID + was actually stored or not in the first place. + + Args: + vector_id: ID of the vector to delete. + """ + self.collection.delete_one({"_id": vector_id}) + + def upsert_vector( + self, + vector: list[float], + *, + vector_id: Optional[str] = None, + namespace: Optional[str] = None, + meta: Optional[dict] = None, + **kwargs: Any, + ) -> str: + """Write a vector to the Astra DB store. + + In case the provided ID exists already, an overwrite will take place. + + Args: + vector: the vector to be upserted. + vector_id: the ID for the vector to store. If omitted, a server-provided new ID will be employed. + namespace: a namespace (a grouping within the vector store) to assign the vector to. + meta: a metadata dictionary associated to the vector. + kwargs: additional keyword arguments. Currently none is used: if they are passed, they will be ignored with a warning. + + Returns: + the ID of the written vector (str). + """ + document = { + k: v + for k, v in {"$vector": vector, "_id": vector_id, "namespace": namespace, "meta": meta}.items() + if v is not None + } + if vector_id is not None: + self.collection.find_one_and_replace({"_id": vector_id}, document, upsert=True) + return vector_id + else: + insert_result = self.collection.insert_one(document) + return insert_result.inserted_id + + def load_entry(self, vector_id: str, *, namespace: Optional[str] = None) -> Optional[BaseVectorStoreDriver.Entry]: + """Load a single vector entry from the Astra DB store given its ID. + + Args: + vector_id: the ID of the required vector. + namespace: a namespace, within the vector store, to constrain the search. + + Returns: + The vector entry (a `BaseVectorStoreDriver.Entry`) if found, otherwise None. + """ + find_filter = {k: v for k, v in {"_id": vector_id, "namespace": namespace}.items() if v is not None} + match = self.collection.find_one(filter=find_filter, projection={"*": 1}) + if match is not None: + return BaseVectorStoreDriver.Entry( + id=match["_id"], vector=match.get("$vector"), meta=match.get("meta"), namespace=match.get("namespace") + ) + else: + return None + + def load_entries(self, *, namespace: Optional[str] = None) -> list[BaseVectorStoreDriver.Entry]: + """Load entries from the Astra DB store. + + Args: + namespace: a namespace, within the vector store, to constrain the search. + + Returns: + A list of vector (`BaseVectorStoreDriver.Entry`) entries. + """ + find_filter: dict[str, str] = {} if namespace is None else {"namespace": namespace} + return [ + BaseVectorStoreDriver.Entry( + id=match["_id"], vector=match.get("$vector"), meta=match.get("meta"), namespace=match.get("namespace") + ) + for match in self.collection.find(filter=find_filter, projection={"*": 1}) + ] + + def query( + self, + query: str, + *, + count: Optional[int] = None, + namespace: Optional[str] = None, + include_vectors: bool = False, + **kwargs: Any, + ) -> list[BaseVectorStoreDriver.Entry]: + """Run a similarity search on the Astra DB store, based on a query string. + + Args: + query: the query string. + count: the maximum number of results to return. If omitted, defaults will apply. + namespace: the namespace to filter results by. + include_vectors: whether to include vector data in the results. + kwargs: additional keyword arguments. Currently only the free-form dict `filter` + is recognized (and goes straight to the Data API query); + others will generate a warning and be ignored. + + Returns: + A list of vector (`BaseVectorStoreDriver.Entry`) entries, + with their `score` attribute set to the vector similarity to the query. + """ + query_filter: Optional[dict[str, Any]] = kwargs.get("filter") + find_filter_ns: dict[str, Any] = {} if namespace is None else {"namespace": namespace} + find_filter = {**(query_filter or {}), **find_filter_ns} + find_projection: Optional[dict[str, int]] = {"*": 1} if include_vectors else None + vector = self.embedding_driver.embed_string(query) + ann_limit = count or BaseVectorStoreDriver.DEFAULT_QUERY_COUNT + matches = self.collection.find( + filter=find_filter, + sort={"$vector": vector}, + limit=ann_limit, + projection=find_projection, + include_similarity=True, + ) + return [ + BaseVectorStoreDriver.Entry( + id=match["_id"], + vector=match.get("$vector"), + score=match["$similarity"], + meta=match.get("meta"), + namespace=match.get("namespace"), + ) + for match in matches + ] diff --git a/mkdocs.yml b/mkdocs.yml index 911301f4e..6c9bffa13 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -170,5 +170,6 @@ nav: - Load and Query Pinecone: "examples/load-and-query-pinecone.md" - Load and Query Marqo: "examples/load-query-and-chat-marqo.md" - Query a Webpage: "examples/query-webpage.md" + - RAG with Astra DB vector store: "examples/query-webpage-astra-db.md" - Reference Guide: "reference/" - Trade School: "https://learn.griptape.ai" diff --git a/poetry.lock b/poetry.lock index 6d34873c6..26cc36f06 100644 --- a/poetry.lock +++ b/poetry.lock @@ -31,92 +31,104 @@ test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "py test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"] testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", 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"psycopg2-binary", "pusher", "pymongo", "pypdf", "qdrant-client", "redis", "sentencepiece", "snowflake-sqlalchemy", "sqlalchemy", "torch", "trafilatura", "transformers", "voyageai"] +all = ["accelerate", "anthropic", "astrapy", "beautifulsoup4", "boto3", "cohere", "diffusers", "duckduckgo-search", "elevenlabs", "filetype", "google-generativeai", "mail-parser", "markdownify", "marqo", "ollama", "opensearch-py", "opentelemetry-api", "opentelemetry-exporter-otlp-proto-http", "opentelemetry-instrumentation", "opentelemetry-instrumentation-threading", "opentelemetry-sdk", "pandas", "pgvector", "pillow", "pinecone-client", "playwright", "psycopg2-binary", "pusher", "pymongo", "pypdf", "qdrant-client", "redis", "sentencepiece", "snowflake-sqlalchemy", "sqlalchemy", "torch", "trafilatura", "transformers", "voyageai"] drivers-embedding-amazon-bedrock = ["boto3"] drivers-embedding-amazon-sagemaker = ["boto3"] drivers-embedding-cohere = ["cohere"] @@ -6808,6 +6935,7 @@ drivers-sql = ["sqlalchemy"] drivers-sql-amazon-redshift = ["boto3"] drivers-sql-snowflake = ["snowflake-sqlalchemy", "sqlalchemy"] drivers-vector-amazon-opensearch = ["boto3", "opensearch-py"] +drivers-vector-astra-db = ["astrapy"] drivers-vector-marqo = ["marqo"] drivers-vector-mongodb = ["pymongo"] drivers-vector-opensearch = ["opensearch-py"] @@ -6828,4 +6956,4 @@ loaders-sql = ["sqlalchemy"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "0f9ce9192af9125e85a27ba269786ee49254e2f139965d37aa2599e8ae8e2dca" +content-hash = "7ce0070dd09732677f5647c616acfd7de130dfda269c9fdd47f375883dda60c9" diff --git a/pyproject.toml b/pyproject.toml index 764ba4f25..74c16fdc4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,6 +50,7 @@ markdownify = {version = "^0.11.6", optional = true} voyageai = {version = "^0.2.1", optional = true} elevenlabs = {version = "^1.1.2", optional = true} qdrant-client = { version = "^1.10.1", optional = true } +astrapy = { version = "^1.3", optional = true } pusher = {version = "^3.3.2", optional = true} ollama = {version = "^0.3.0", optional = true} duckduckgo-search = {version = "^6.1.12", optional = true} @@ -96,6 +97,7 @@ drivers-vector-opensearch = ["opensearch-py"] drivers-vector-amazon-opensearch = ["opensearch-py", "boto3"] drivers-vector-pgvector = ["sqlalchemy", "pgvector", "psycopg2-binary"] drivers-vector-qdrant = ["qdrant-client"] +drivers-vector-astra-db = ["astrapy"] drivers-embedding-amazon-bedrock = ["boto3"] drivers-embedding-amazon-sagemaker = ["boto3"] @@ -166,6 +168,7 @@ all = [ "marqo", "pinecone-client", "qdrant-client", + "astrapy", "pymongo", "redis", "opensearch-py", diff --git a/tests/integration/drivers/vector/test_astra_db_vector_store_driver.py b/tests/integration/drivers/vector/test_astra_db_vector_store_driver.py new file mode 100644 index 000000000..94dbb8570 --- /dev/null +++ b/tests/integration/drivers/vector/test_astra_db_vector_store_driver.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import json +import math +import os + +import pytest + +from griptape.drivers import AstraDbVectorStoreDriver, BaseVectorStoreDriver +from tests.mocks.mock_embedding_driver import MockEmbeddingDriver + +TEST_COLLECTION_NAME = "gt_int_test" +TEST_COLLECTION_NAME_METRIC = "gt_int_test_dot" + + +class TestAstraDbVectorStoreDriver: + @pytest.fixture() + def embedding_driver(self): + def circle_fraction_string_to_vector(chunk: str) -> list[float]: + try: + fraction = float(json.loads(chunk)) + angle = fraction * math.pi * 2 + return [math.cos(angle), math.sin(angle)] + except Exception: + return [0.0, 0.0] + + return MockEmbeddingDriver(mock_output=circle_fraction_string_to_vector) + + @pytest.fixture() + def vector_store_collection(self): + import astrapy + + database = astrapy.DataAPIClient().get_database( + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + namespace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + collection = database.create_collection( + name=TEST_COLLECTION_NAME, + dimension=2, + metric="cosine", + ) + yield collection + collection.drop() + + @pytest.fixture() + def vector_store_driver(self, embedding_driver, vector_store_collection): + driver = AstraDbVectorStoreDriver( + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + collection_name=vector_store_collection.name, + astra_db_namespace=os.environ.get("ASTRA_DB_KEYSPACE"), + embedding_driver=embedding_driver, + ) + return driver + + def test_vector_crud(self, vector_store_driver, vector_store_collection, embedding_driver): + """Test basic vector CRUD, various call patterns.""" + vector_store_collection.delete_many({}) + + vec1 = embedding_driver.embed_string("0.012") + id1 = vector_store_driver.upsert_vector(vec1, vector_id="v1") + + vec2 = embedding_driver.embed_string("0.024") + id2 = vector_store_driver.upsert_vector(vec2, vector_id="v2", namespace="ns") + + vec3 = embedding_driver.embed_string("0.036") + id3 = vector_store_driver.upsert_vector(vec3) + + vec4 = embedding_driver.embed_string("0.048") + id4 = vector_store_driver.upsert_vector(vec4, vector_id="v4", meta={"i": 4}, namespace="ns") + + assert id1 == "v1" + assert id2 == "v2" + assert isinstance(id3, str) + assert id4 == "v4" + + # retrieve by id + e1 = vector_store_driver.load_entry(id1) + e1_n = vector_store_driver.load_entry(id1, namespace="false_ns") + e2 = vector_store_driver.load_entry(id2, namespace="ns") + e3 = vector_store_driver.load_entry(id3) + e4 = vector_store_driver.load_entry(id4) + assert e1 == BaseVectorStoreDriver.Entry( + id=id1, + vector=vec1, + ) + assert e1_n is None + assert e2 == BaseVectorStoreDriver.Entry( + id=id2, + vector=vec2, + namespace="ns", + ) + assert e3 == BaseVectorStoreDriver.Entry( + id=id3, + vector=vec3, + ) + assert e4 == BaseVectorStoreDriver.Entry( + id=id4, + vector=vec4, + meta={"i": 4}, + namespace="ns", + ) + + # retrieve multiple entries + es_ns = vector_store_driver.load_entries(namespace="ns") + es_all = vector_store_driver.load_entries() + assert len(es_ns) == 2 + assert any(e == e2 for e in es_ns) + assert any(e == e4 for e in es_ns) + assert len(es_all) == 4 + assert any(e == e1 for e in es_all) + assert any(e == e2 for e in es_all) + assert any(e == e3 for e in es_all) + assert any(e == e4 for e in es_all) + + # delete and recheck + vector_store_driver.delete_vector("fake_id") + vector_store_driver.delete_vector(id4) + es_ns_postdel = vector_store_driver.load_entries(namespace="ns") + assert len(es_ns_postdel) == 1 + assert es_ns_postdel[0] == e2 + + # queries + query_2 = vector_store_driver.query("0.060", count=2, include_vectors=True) + query_all = vector_store_driver.query("0.060", include_vectors=True) + query_2_novectors = vector_store_driver.query("0.060", count=2) + query_all_ns = vector_store_driver.query("0.060", include_vectors=True, namespace="ns") + # + d_query_2 = [self._descore_entry(ent) for ent in query_2] + assert d_query_2 == [e3, e2] + + d_query_all = [self._descore_entry(ent) for ent in query_all] + assert d_query_all == [e3, e2, e1] + d_query_2_novectors = [self._descore_entry(ent) for ent in query_2_novectors] + assert d_query_2_novectors == [ + BaseVectorStoreDriver.Entry( + id=id3, + ), + BaseVectorStoreDriver.Entry( + id=id2, + namespace="ns", + ), + ] + d_query_all_ns = [self._descore_entry(ent) for ent in query_all_ns] + assert d_query_all_ns == [e2] + + def _descore_entry(self, entry: BaseVectorStoreDriver.Entry) -> BaseVectorStoreDriver.Entry: + return BaseVectorStoreDriver.Entry.from_dict({k: v for k, v in entry.__dict__.items() if k != "score"}) diff --git a/tests/mocks/mock_embedding_driver.py b/tests/mocks/mock_embedding_driver.py index 46d9bf515..6fe415195 100644 --- a/tests/mocks/mock_embedding_driver.py +++ b/tests/mocks/mock_embedding_driver.py @@ -1,5 +1,7 @@ from __future__ import annotations +from typing import Callable + from attrs import define, field from griptape.drivers import BaseEmbeddingDriver @@ -12,6 +14,7 @@ class MockEmbeddingDriver(BaseEmbeddingDriver): dimensions: int = field(default=42, kw_only=True) max_attempts: int = field(default=1, kw_only=True) tokenizer: MockTokenizer = field(factory=lambda: MockTokenizer(model="foo bar"), kw_only=True) + mock_output: Callable[[str], list[float]] = field(default=lambda chunk: [0, 1], kw_only=True) def try_embed_chunk(self, chunk: str) -> list[float]: - return [0, 1] + return self.mock_output(chunk) diff --git a/tests/unit/drivers/vector/test_astra_db_vector_store_driver.py b/tests/unit/drivers/vector/test_astra_db_vector_store_driver.py new file mode 100644 index 000000000..16e6530b3 --- /dev/null +++ b/tests/unit/drivers/vector/test_astra_db_vector_store_driver.py @@ -0,0 +1,139 @@ +from unittest.mock import MagicMock + +import pytest + +from griptape.drivers import AstraDbVectorStoreDriver, BaseVectorStoreDriver +from tests.mocks.mock_embedding_driver import MockEmbeddingDriver + + +class TestAstraDbVectorStoreDriver: + @pytest.fixture(autouse=True) + def base_mock_collection(self, mocker): + mock_get_collection = mocker.patch( + "astrapy.DataAPIClient" + ).return_value.get_database.return_value.get_collection + return mock_get_collection + + @pytest.fixture() + def mock_collection(self, base_mock_collection, one_document): + """Augmented with specific response to certain method calls.""" + # insert_one with server-side provided ID + mock_insert_one_return_value = MagicMock() + mock_insert_one_return_value.inserted_id = "insert_one_server_side_id" + base_mock_collection.return_value.insert_one.return_value = mock_insert_one_return_value + # find_one + base_mock_collection.return_value.find_one.return_value = one_document + # find + base_mock_collection.return_value.find.return_value = [one_document] + # + return base_mock_collection + + @pytest.fixture() + def mock_collection_findnothing(self, base_mock_collection): + """`find` and `find_one` return nothing.""" + base_mock_collection.return_value.find_one.return_value = None + base_mock_collection.return_value.find.return_value = [] + return base_mock_collection + + @pytest.fixture() + def driver(self, mock_collection): + return AstraDbVectorStoreDriver( + api_endpoint="ep", + token="to", + collection_name="co", + astra_db_namespace="ns", + embedding_driver=MockEmbeddingDriver(dimensions=3), + ) + + @pytest.fixture() + def one_document( + self, + ): + return { + "_id": "doc_id", + "$vector": [3.0, 2.0, 1.0], + "meta": "doc_meta", + "namespace": "doc_namespace", + "$similarity": 10, + } + + @pytest.fixture() + def one_entry(self, one_document): + return BaseVectorStoreDriver.Entry( + id=one_document["_id"], + vector=one_document["$vector"], + meta=one_document["meta"], + namespace=one_document["namespace"], + ) + + @pytest.fixture() + def one_query_entry(self, one_document): + return BaseVectorStoreDriver.Entry( + id=one_document["_id"], + vector=one_document["$vector"], + meta=one_document["meta"], + namespace=one_document["namespace"], + score=one_document["$similarity"], + ) + + def test_delete_vector(self, driver, mock_collection): + driver.delete_vector("deletee_id") + mock_collection.return_value.delete_one.assert_called_once() + + def test_upsert_vector_with_id(self, driver, mock_collection): + upserted_id = driver.upsert_vector([1.0, 2.0, 3.0], vector_id="some_vector_id", namespace="some_namespace") + assert upserted_id == "some_vector_id" + mock_collection.return_value.find_one_and_replace.assert_called_once() + + def test_upsert_vector_no_id(self, driver, mock_collection): + upserted_id = driver.upsert_vector([1.0, 2.0, 3.0], namespace="some_namespace") + assert upserted_id == "insert_one_server_side_id" + mock_collection.return_value.insert_one.assert_called_once() + + def test_load_entry(self, driver, mock_collection, one_entry): + entry = driver.load_entry("vector_id", namespace="some_namespace") + assert entry == one_entry + mock_collection.return_value.find_one.assert_called_once_with( + filter={"_id": "vector_id", "namespace": "some_namespace"}, + projection={"*": 1}, + ) + + def test_load_entry_empty(self, driver, mock_collection_findnothing): + entry = driver.load_entry("vector_id", namespace="some_namespace") + assert entry is None + mock_collection_findnothing.return_value.find_one.assert_called_once_with( + filter={"_id": "vector_id", "namespace": "some_namespace"}, + projection={"*": 1}, + ) + + def test_load_entries(self, driver, mock_collection, one_entry): + entries = driver.load_entries(namespace="some_namespace") + assert entries == [one_entry] + mock_collection.return_value.find.assert_called_once_with( + filter={"namespace": "some_namespace"}, + projection={"*": 1}, + ) + + def test_query_allparams(self, driver, mock_collection, one_query_entry): + entries1 = driver.query("some query", count=999, namespace="some_namespace", include_vectors=True) + assert entries1 == [one_query_entry] + query_vector = driver.embedding_driver.embed_string("some query") + mock_collection.return_value.find.assert_called_once_with( + filter={"namespace": "some_namespace"}, + sort={"$vector": query_vector}, + limit=999, + projection={"*": 1}, + include_similarity=True, + ) + + def test_query_minparams(self, driver, mock_collection, one_query_entry): + entries0 = driver.query("some query") + assert entries0 == [one_query_entry] + query_vector = driver.embedding_driver.embed_string("some query") + mock_collection.return_value.find.assert_called_once_with( + filter={}, + sort={"$vector": query_vector}, + limit=BaseVectorStoreDriver.DEFAULT_QUERY_COUNT, + projection=None, + include_similarity=True, + )