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feat: improve pinecone tests (#2806)

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Improve the integration tests for Pinecone by adding an `.env.example`
file for local testing. Additionally, add some dev dependencies
specifically for integration tests.

This change also helps me understand how Pinecone deals with certain
things, see related issues
https://github.com/hwchase17/langchain/issues/2484
https://github.com/hwchase17/langchain/issues/2816
This commit is contained in:
sergerdn 2023-04-13 21:49:31 -07:00 committed by GitHub
parent 016738e676
commit 04c458a270
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11 changed files with 1985 additions and 239 deletions
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@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry and should not be changed by hand.
# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
[[package]]
name = "absl-py"
@ -743,7 +743,7 @@ name = "cachetools"
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version = "0.1.9"
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@ -2954,14 +2954,14 @@ websockets = ["websockets"]
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@ -5188,14 +5188,14 @@ testing = ["pytest", "pytest-benchmark"]
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requests = "*"
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@ -6228,14 +6244,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
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cuda11x = ["cupy-cuda11x (>=11.0.0,<12.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4,<12.0.0)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4,<12.0.0)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4,<12.0.0)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4,<12.0.0)"]
cuda = ["cupy (>=5.0.0b4,<13.0.0)"]
cuda-autodetect = ["cupy-wheel (>=11.0.0,<13.0.0)"]
cuda100 = ["cupy-cuda100 (>=5.0.0b4,<13.0.0)"]
cuda101 = ["cupy-cuda101 (>=5.0.0b4,<13.0.0)"]
cuda102 = ["cupy-cuda102 (>=5.0.0b4,<13.0.0)"]
cuda110 = ["cupy-cuda110 (>=5.0.0b4,<13.0.0)"]
cuda111 = ["cupy-cuda111 (>=5.0.0b4,<13.0.0)"]
cuda112 = ["cupy-cuda112 (>=5.0.0b4,<13.0.0)"]
cuda113 = ["cupy-cuda113 (>=5.0.0b4,<13.0.0)"]
cuda114 = ["cupy-cuda114 (>=5.0.0b4,<13.0.0)"]
cuda115 = ["cupy-cuda115 (>=5.0.0b4,<13.0.0)"]
cuda116 = ["cupy-cuda116 (>=5.0.0b4,<13.0.0)"]
cuda117 = ["cupy-cuda117 (>=5.0.0b4,<13.0.0)"]
cuda11x = ["cupy-cuda11x (>=11.0.0,<13.0.0)"]
cuda80 = ["cupy-cuda80 (>=5.0.0b4,<13.0.0)"]
cuda90 = ["cupy-cuda90 (>=5.0.0b4,<13.0.0)"]
cuda91 = ["cupy-cuda91 (>=5.0.0b4,<13.0.0)"]
cuda92 = ["cupy-cuda92 (>=5.0.0b4,<13.0.0)"]
ja = ["sudachidict-core (>=20211220)", "sudachipy (>=0.5.2,!=0.6.1)"]
ko = ["natto-py (>=0.9.0)"]
lookups = ["spacy-lookups-data (>=1.0.3,<1.1.0)"]
@ -7234,7 +7258,7 @@ files = [
]
[package.dependencies]
greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and platform_machine == \"aarch64\" or python_version >= \"3\" and platform_machine == \"ppc64le\" or python_version >= \"3\" and platform_machine == \"x86_64\" or python_version >= \"3\" and platform_machine == \"amd64\" or python_version >= \"3\" and platform_machine == \"AMD64\" or python_version >= \"3\" and platform_machine == \"win32\" or python_version >= \"3\" and platform_machine == \"WIN32\""}
[package.extras]
aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
@ -8096,14 +8120,14 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.
[[package]]
name = "types-pyopenssl"
version = "23.1.0.1"
version = "23.1.0.2"
description = "Typing stubs for pyOpenSSL"
category = "dev"
optional = false
python-versions = "*"
files = [
{file = "types-pyOpenSSL-23.1.0.1.tar.gz", hash = "sha256:59044283c475eaa5a29b36a903c123d52bdf4a7e012f0a1ca0e41115b99216da"},
{file = "types_pyOpenSSL-23.1.0.1-py3-none-any.whl", hash = "sha256:ac7fbc240930c2f9a1cbd2d04f9cb14ad0f15b0ad8d6528732a83747b1b2086e"},
{file = "types-pyOpenSSL-23.1.0.2.tar.gz", hash = "sha256:20b80971b86240e8432a1832bd8124cea49c3088c7bfc77dfd23be27ffe4a517"},
{file = "types_pyOpenSSL-23.1.0.2-py3-none-any.whl", hash = "sha256:b050641aeff6dfebf231ad719bdac12d53b8ee818d4afb67b886333484629957"},
]
[package.dependencies]
@ -9002,13 +9026,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
cffi = ["cffi (>=1.11)"]
[extras]
all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "pyowm"]
all = ["aleph-alpha-client", "anthropic", "beautifulsoup4", "cohere", "deeplake", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "huggingface_hub", "jina", "jinja2", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pyowm", "pypdf", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
cohere = ["cohere"]
llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"]
openai = ["openai"]
qdrant = ["qdrant-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "26b1bbfbc3a228b892b2466af3561b799238a6d379853d325dc3c798776df0d8"
content-hash = "3c8488864a754852fdec3e56dd5630ed73852ec2120a94cfe22537c075901b24"

3
pyproject.toml
View File

@ -104,6 +104,9 @@ deeplake = "^3.2.21"
torch = "^1.0.0"
chromadb = "^0.3.21"
tiktoken = "^0.3.3"
python-dotenv = "^1.0.0"
gptcache = "^0.1.9"
promptlayer = "^0.1.80"
[tool.poetry.group.lint.dependencies]
ruff = "^0.0.249"

7
tests/README.md
View File

@ -39,6 +39,11 @@ cd tests/integration_tests/vectorstores/docker-compose
docker-compose -f elasticsearch.yml up
```
### Prepare environment variables for local testing:
- copy `tests/.env.example` to `tests/.env`
- set variables in `tests/.env` file, e.g `OPENAI_API_KEY`
Additionally, it's important to note that some integration tests may require certain
environment variables to be set, such as `OPENAI_API_KEY`. Be sure to set any required
environment variables before running the tests to ensure they run correctly.
@ -54,7 +59,9 @@ cassettes. You can use the --vcr-record=none command-line option to disable reco
new cassettes. Here's an example:
```bash
pytest --log-cli-level=10 tests/integration_tests/vectorstores/test_pinecone.py --vcr-record=none
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --vcr-record=none
```
### Run some tests with coverage:

9
tests/integration_tests/.env.example Normal file
View File

@ -0,0 +1,9 @@
# openai
# your api key from https://platform.openai.com/account/api-keys
OPENAI_API_KEY=
# pinecone
# your api key from left menu "API Keys" in https://app.pinecone.io
PINECONE_API_KEY=your_pinecone_api_key_here
# your pinecone environment from left menu "API Keys" in https://app.pinecone.io
PINECONE_ENVIRONMENT=us-west4-gcp

36
tests/integration_tests/conftest.py
View File

@ -1,10 +1,31 @@
import os
from pathlib import Path
import pytest
# Getting the absolute path of the current file's directory
ABS_PATH = os.path.dirname(os.path.abspath(__file__))
# Getting the absolute path of the project's root directory
PROJECT_DIR = os.path.abspath(os.path.join(ABS_PATH, os.pardir, os.pardir))
# Loading the .env file if it exists
def _load_env() -> None:
dotenv_path = os.path.join(PROJECT_DIR, "tests", "integration_tests", ".env")
if os.path.exists(dotenv_path):
from dotenv import load_dotenv
load_dotenv(dotenv_path)
_load_env()
@pytest.fixture(scope="module")
def test_dir() -> Path:
return Path(os.path.join(PROJECT_DIR, "tests", "integration_tests"))
# This fixture returns a string containing the path to the cassette directory for the
# current module
@ -15,18 +36,3 @@ def vcr_cassette_dir(request: pytest.FixtureRequest) -> str:
"cassettes",
os.path.basename(request.module.__file__).replace(".py", ""),
)
# This fixture returns a dictionary containing filter_headers options
# for replacing certain headers with dummy values during cassette playback
# Specifically, it replaces the authorization header with a dummy value to
# prevent sensitive data from being recorded in the cassette.
@pytest.fixture(scope="module")
def vcr_config() -> dict:
return {
"filter_headers": [
("authorization", "authorization-DUMMY"),
("X-OpenAI-Client-User-Agent", "X-OpenAI-Client-User-Agent-DUMMY"),
("User-Agent", "User-Agent-DUMMY"),
],
}

489
tests/integration_tests/vectorstores/cassettes/test_pinecone/TestPinecone.test_from_texts.yaml Normal file
View File

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x-ratelimit-limit-requests:
- '3000'
x-ratelimit-remaining-requests:
- '2999'
x-ratelimit-reset-requests:
- 20ms
x-request-id:
- 029a9d93fe8a07946d39f3e957b33c11
status:
code: 200
message: OK
version: 1

53
tests/integration_tests/vectorstores/conftest.py
View File

@ -1,15 +1,49 @@
import os
from typing import Generator, List
from typing import Generator, List, Union
import pytest
from vcr.request import Request
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema import Document
from langchain.text_splitter import CharacterTextSplitter
# Define a fixture that yields a generator object returning a list of documents
# This fixture returns a dictionary containing filter_headers options
# for replacing certain headers with dummy values during cassette playback
# Specifically, it replaces the authorization header with a dummy value to
# prevent sensitive data from being recorded in the cassette.
# It also filters request to certain hosts (specified in the `ignored_hosts` list)
# to prevent data from being recorded in the cassette.
@pytest.fixture(scope="module")
def vcr_config() -> dict:
skipped_host = ["pinecone.io"]
def before_record_response(response: dict) -> Union[dict, None]:
return response
def before_record_request(request: Request) -> Union[Request, None]:
for host in skipped_host:
if request.host.startswith(host) or request.host.endswith(host):
return None
return request
return {
"before_record_request": before_record_request,
"before_record_response": before_record_response,
"filter_headers": [
("authorization", "authorization-DUMMY"),
("X-OpenAI-Client-User-Agent", "X-OpenAI-Client-User-Agent-DUMMY"),
("Api-Key", "Api-Key-DUMMY"),
("User-Agent", "User-Agent-DUMMY"),
],
"ignore_localhost": True,
}
# Define a fixture that yields a generator object returning a list of documents
@pytest.fixture(scope="function")
def documents() -> Generator[List[Document], None, None]:
"""Return a generator that yields a list of documents."""
@ -23,3 +57,18 @@ def documents() -> Generator[List[Document], None, None]:
# Yield the documents split into chunks
yield text_splitter.split_documents(documents)
@pytest.fixture(scope="function")
def texts() -> Generator[List[str], None, None]:
# Load the documents from a file located in the fixtures directory
documents = TextLoader(
os.path.join(os.path.dirname(__file__), "fixtures", "sharks.txt")
).load()
yield [doc.page_content for doc in documents]
@pytest.fixture(scope="module")
def embedding_openai() -> OpenAIEmbeddings:
return OpenAIEmbeddings()

38
tests/integration_tests/vectorstores/test_elasticsearch.py
View File

@ -21,6 +21,11 @@ docker-compose -f elasticsearch.yml up
class TestElasticsearch:
@classmethod
def setup_class(cls) -> None:
if not os.getenv("OPENAI_API_KEY"):
raise ValueError("OPENAI_API_KEY environment variable is not set")
@pytest.fixture(scope="class", autouse=True)
def elasticsearch_url(self) -> Union[str, Generator[str, None, None]]:
"""Return the elasticsearch url."""
@ -34,15 +39,6 @@ class TestElasticsearch:
# print(index_name)
es.indices.delete(index=index_name)
@pytest.fixture(scope="class", autouse=True)
def openai_api_key(self) -> Union[str, Generator[str, None, None]]:
"""Return the OpenAI API key."""
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
raise ValueError("OPENAI_API_KEY environment variable is not set")
yield openai_api_key
def test_similarity_search_without_metadata(self, elasticsearch_url: str) -> None:
"""Test end to end construction and search without metadata."""
texts = ["foo", "bar", "baz"]
@ -67,15 +63,17 @@ class TestElasticsearch:
@pytest.mark.vcr(ignore_localhost=True)
def test_default_index_from_documents(
self, documents: List[Document], openai_api_key: str, elasticsearch_url: str
self,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
elasticsearch_url: str,
) -> None:
"""This test checks the construction of a default
ElasticSearch index using the 'from_documents'."""
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
elastic_vector_search = ElasticVectorSearch.from_documents(
documents=documents,
embedding=embedding,
embedding=embedding_openai,
elasticsearch_url=elasticsearch_url,
)
@ -86,16 +84,18 @@ class TestElasticsearch:
@pytest.mark.vcr(ignore_localhost=True)
def test_custom_index_from_documents(
self, documents: List[Document], openai_api_key: str, elasticsearch_url: str
self,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
elasticsearch_url: str,
) -> None:
"""This test checks the construction of a custom
ElasticSearch index using the 'from_documents'."""
index_name = f"custom_index_{uuid.uuid4().hex}"
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
elastic_vector_search = ElasticVectorSearch.from_documents(
documents=documents,
embedding=embedding,
embedding=embedding_openai,
elasticsearch_url=elasticsearch_url,
index_name=index_name,
)
@ -110,15 +110,17 @@ class TestElasticsearch:
@pytest.mark.vcr(ignore_localhost=True)
def test_custom_index_add_documents(
self, documents: List[Document], openai_api_key: str, elasticsearch_url: str
self,
documents: List[Document],
embedding_openai: OpenAIEmbeddings,
elasticsearch_url: str,
) -> None:
"""This test checks the construction of a custom
ElasticSearch index using the 'add_documents'."""
index_name = f"custom_index_{uuid.uuid4().hex}"
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
elastic_vector_search = ElasticVectorSearch(
embedding=embedding,
embedding=embedding_openai,
elasticsearch_url=elasticsearch_url,
index_name=index_name,
)

215
tests/integration_tests/vectorstores/test_pinecone.py
View File

@ -1,97 +1,208 @@
"""Test Pinecone functionality."""
import importlib
import os
import uuid
from typing import List
import pinecone
import pytest
from langchain.docstore.document import Document
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.pinecone import Pinecone
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENV")
# if the index already exists, delete it
try:
pinecone.delete_index("langchain-demo")
except Exception:
pass
index = pinecone.Index("langchain-demo")
index_name = "langchain-test-index" # name of the index
namespace_name = "langchain-test-namespace" # name of the namespace
dimension = 1536 # dimension of the embeddings
def test_pinecone() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Pinecone.from_texts(
texts, FakeEmbeddings(), index_name="langchain-demo", namespace="test"
def reset_pinecone() -> None:
assert os.environ.get("PINECONE_API_KEY") is not None
assert os.environ.get("PINECONE_ENVIRONMENT") is not None
import pinecone
importlib.reload(pinecone)
pinecone.init(
api_key=os.environ.get("PINECONE_API_KEY"),
environment=os.environ.get("PINECONE_ENVIRONMENT"),
)
output = docsearch.similarity_search("foo", k=1, namespace="test")
assert output == [Document(page_content="foo")]
def test_pinecone_with_metadatas() -> None:
class TestPinecone:
index: pinecone.Index
@classmethod
def setup_class(cls) -> None:
reset_pinecone()
cls.index = pinecone.Index(index_name)
if index_name in pinecone.list_indexes():
index_stats = cls.index.describe_index_stats()
if index_stats["dimension"] == dimension:
# delete all the vectors in the index if the dimension is the same
# from all namespaces
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
else:
pinecone.delete_index(index_name)
pinecone.create_index(name=index_name, dimension=dimension)
else:
pinecone.create_index(name=index_name, dimension=dimension)
# insure the index is empty
index_stats = cls.index.describe_index_stats()
assert index_stats["dimension"] == dimension
if index_stats["namespaces"].get(namespace_name) is not None:
assert index_stats["namespaces"][namespace_name]["vector_count"] == 0
@classmethod
def teardown_class(cls) -> None:
index_stats = cls.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
cls.index.delete(delete_all=True, namespace=_namespace_name)
reset_pinecone()
@pytest.fixture(autouse=True)
def setup(self) -> None:
# delete all the vectors in the index
index_stats = self.index.describe_index_stats()
for _namespace_name in index_stats["namespaces"].keys():
self.index.delete(delete_all=True, namespace=_namespace_name)
reset_pinecone()
@pytest.mark.vcr()
def test_from_texts(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
docsearch = Pinecone.from_texts(
texts=texts,
embedding=embedding_openai,
index_name=index_name,
namespace=namespace_name,
)
output = docsearch.similarity_search(unique_id, k=1, namespace=namespace_name)
assert output == [Document(page_content=needs)]
@pytest.mark.vcr()
def test_from_texts_with_metadatas(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
"""Test end to end construction and search."""
unique_id = uuid.uuid4().hex
needs = f"foobuu {unique_id} booo"
texts.insert(0, needs)
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace="test-metadata",
namespace=namespace_name,
)
output = docsearch.similarity_search("foo", k=1, namespace="test-metadata")
assert output == [Document(page_content="foo", metadata={"page": 0})]
output = docsearch.similarity_search(needs, k=1, namespace=namespace_name)
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}?
assert output == [Document(page_content=needs, metadata={"page": 0.0})]
def test_pinecone_with_scores() -> None:
@pytest.mark.vcr()
def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> None:
"""Test end to end construction and search with scores and IDs."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace="test-metadata-score",
namespace=namespace_name,
)
output = docsearch.similarity_search_with_score(
"foo", k=3, namespace="test-metadata-score"
"foo", k=3, namespace=namespace_name
)
docs = [o[0] for o in output]
scores = [o[1] for o in output]
assert docs == [
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="bar", metadata={"page": 1}),
Document(page_content="baz", metadata={"page": 2}),
sorted_documents = sorted(docs, key=lambda x: x.metadata["page"])
# TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc???
assert sorted_documents == [
Document(page_content="foo", metadata={"page": 0.0}),
Document(page_content="bar", metadata={"page": 1.0}),
Document(page_content="baz", metadata={"page": 2.0}),
]
assert scores[0] > scores[1] > scores[2]
def test_pinecone_with_namespaces() -> None:
"Test that namespaces are properly handled." ""
def test_from_existing_index_with_namespaces(
self, embedding_openai: OpenAIEmbeddings
) -> None:
"""Test that namespaces are properly handled."""
# Create two indexes with the same name but different namespaces
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
texts_1 = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts_1))]
Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
texts_1,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace="test-namespace",
namespace=f"{index_name}-1",
)
texts = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts))]
texts_2 = ["foo2", "bar2", "baz2"]
metadatas = [{"page": i} for i in range(len(texts_2))]
Pinecone.from_texts(
texts,
FakeEmbeddings(),
index_name="langchain-demo",
texts_2,
embedding_openai,
index_name=index_name,
metadatas=metadatas,
namespace="test-namespace2",
namespace=f"{index_name}-2",
)
# Search with namespace
docsearch = Pinecone.from_existing_index(
"langchain-demo", embedding=FakeEmbeddings(), namespace="test-namespace"
index_name=index_name,
embedding=embedding_openai,
namespace=f"{index_name}-1",
)
output = docsearch.similarity_search("foo", k=6)
output = docsearch.similarity_search("foo", k=20, namespace=f"{index_name}-1")
# check that we don't get results from the other namespace
page_contents = [o.page_content for o in output]
assert set(page_contents) == set(["foo", "bar", "baz"])
page_contents = sorted(set([o.page_content for o in output]))
assert all(content in ["foo", "bar", "baz"] for content in page_contents)
assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents)
def test_add_documents_with_ids(
self, texts: List[str], embedding_openai: OpenAIEmbeddings
) -> None:
ids = [uuid.uuid4().hex for _ in range(len(texts))]
Pinecone.from_texts(
texts=texts,
ids=ids,
embedding=embedding_openai,
index_name=index_name,
namespace=index_name,
)
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][index_name]["vector_count"] == len(texts)
ids_1 = [uuid.uuid4().hex for _ in range(len(texts))]
Pinecone.from_texts(
texts=texts,
ids=ids_1,
embedding=embedding_openai,
index_name=index_name,
namespace=index_name,
)
index_stats = self.index.describe_index_stats()
assert index_stats["namespaces"][index_name]["vector_count"] == len(texts) * 2