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openai[patch]: Explicitly support embedding dimensions (#16596)

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300
docs/docs/integrations/text_embedding/openai.ipynb
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@ -10,9 +10,42 @@
"Let's load the OpenAI Embedding class."
]
},
{
"cell_type": "markdown",
"id": "40ff98ff-58e9-4716-8788-227a5c3f473d",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"First we install langchain-openai and set the required env vars"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "c66c4613-6c67-40ca-b3b1-c026750d1742",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e3710e-55a0-44fb-ba51-2f1d520dfc38",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0be1af71",
"metadata": {},
"outputs": [],
@ -22,17 +55,17 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 5,
"id": "2c66e5da",
"metadata": {},
"outputs": [],
"source": [
"embeddings = OpenAIEmbeddings()"
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 6,
"id": "01370375",
"metadata": {},
"outputs": [],
@ -40,33 +73,50 @@
"text = \"This is a test document.\""
]
},
{
"cell_type": "markdown",
"id": "f012c222-3fa9-470a-935c-758b2048d9af",
"metadata": {},
"source": [
"## Usage\n",
"### Embed query"
]
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 7,
"id": "bfb6142c",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning: model not found. Using cl100k_base encoding.\n"
]
}
],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 8,
"id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.003186025367556387,\n",
" 0.011071979803637493,\n",
" -0.004020420763285827,\n",
" -0.011658221276953042,\n",
" -0.0010534035786864363]"
"[-0.014380056377383358,\n",
" -0.027191711627651764,\n",
" -0.020042716111860304,\n",
" 0.057301379620345545,\n",
" -0.022267658631828974]"
]
},
"execution_count": 32,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@ -75,33 +125,49 @@
"query_result[:5]"
]
},
{
"cell_type": "markdown",
"id": "6b733391-1e23-438b-a6bc-0d77eed9426e",
"metadata": {},
"source": [
"## Embed documents"
]
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 9,
"id": "0356c3b7",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning: model not found. Using cl100k_base encoding.\n"
]
}
],
"source": [
"doc_result = embeddings.embed_documents([text])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 10,
"id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-0.003186025367556387,\n",
" 0.011071979803637493,\n",
" -0.004020420763285827,\n",
" -0.011658221276953042,\n",
" -0.0010534035786864363]"
"[-0.014380056377383358,\n",
" -0.027191711627651764,\n",
" -0.020042716111860304,\n",
" 0.057301379620345545,\n",
" -0.022267658631828974]"
]
},
"execution_count": 34,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@ -112,131 +178,87 @@
},
{
"cell_type": "markdown",
"id": "bb61bbeb",
"id": "e7dc464a-6fa2-4cff-ab2e-49a0566d819b",
"metadata": {},
"source": [
"Let's load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see [here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c0b072cc",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "a56b70f5",
"metadata": {},
"outputs": [],
"source": [
"embeddings = OpenAIEmbeddings(model=\"text-embedding-ada-002\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "14aefb64",
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "3c39ed33",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "2ee7ce9f-d506-4810-8897-e44334412714",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.004452846988523035,\n",
" 0.034550655976098514,\n",
" -0.015029939040690051,\n",
" 0.03827273883655212,\n",
" 0.005785414075152477]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query_result[:5]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "e3221db6",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "a0865409-3a6d-468f-939f-abde17c7cac3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.004452846988523035,\n",
" 0.034550655976098514,\n",
" -0.015029939040690051,\n",
" 0.03827273883655212,\n",
" 0.005785414075152477]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc_result[0][:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aaad49f8",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"## Specify dimensions\n",
"\n",
"# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through\n",
"os.environ[\"OPENAI_PROXY\"] = \"http://proxy.yourcompany.com:8080\""
"With the `text-embedding-3` class of models, you can specify the size of the embeddings you want returned. For example by default `text-embedding-3-large` returned embeddings of dimension 3072:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f7be1e7b-54c6-4893-b8ad-b872e6705735",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3072"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(doc_result[0])"
]
},
{
"cell_type": "markdown",
"id": "33287142-0835-4958-962f-385ae4447431",
"metadata": {},
"source": [
"But by passing in `dimensions=1024` we can reduce the size of our embeddings to 1024:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "854ee772-2de9-4a83-84e0-908033d98e4e",
"metadata": {},
"outputs": [],
"source": [
"embeddings_1024 = OpenAIEmbeddings(model=\"text-embedding-3-large\", dimensions=1024)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3b464396-8d94-478b-8329-849b56e1ae23",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning: model not found. Using cl100k_base encoding.\n"
]
},
{
"data": {
"text/plain": [
"1024"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(embeddings_1024.embed_documents([text])[0])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "poetry-venv",
"language": "python",
"name": "python3"
"name": "poetry-venv"
},
"language_info": {
"codemirror_mode": {
@ -248,7 +270,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.9.1"
},
"vscode": {
"interpreter": {

1
libs/community/langchain_community/llms/openai.py
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@ -770,6 +770,7 @@ class AzureOpenAI(BaseOpenAI):
.. code-block:: python
from langchain_community.llms import AzureOpenAI
openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
"""

2
libs/partners/openai/langchain_openai/chat_models/azure.py
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@ -35,6 +35,8 @@ class AzureChatOpenAI(ChatOpenAI):
.. code-block:: python
from langchain_openai import AzureChatOpenAI
AzureChatOpenAI(
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",

14
libs/partners/openai/langchain_openai/embeddings/azure.py
View File

@ -12,7 +12,19 @@ from langchain_openai.embeddings.base import OpenAIEmbeddings
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
"""`Azure OpenAI` Embeddings API."""
"""`Azure OpenAI` Embeddings API.
To use, you should have the
environment variable ``AZURE_OPENAI_API_KEY`` set with your API key or pass it
as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_openai import AzureOpenAIEmbeddings
openai = AzureOpenAIEmbeddings(model=""text-embedding-3-large")
"""
azure_endpoint: Union[str, None] = None
"""Your Azure endpoint, including the resource.

41
libs/partners/openai/langchain_openai/embeddings/base.py
View File

@ -38,41 +38,23 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
Example:
.. code-block:: python
from langchain_community.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
from langchain_openai import OpenAIEmbeddings
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and OPENAI_API_VERSION.
The OPENAI_API_TYPE must be set to 'azure' and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
openai = OpenAIEmbeddings(model=""text-embedding-3-large")
Example:
.. code-block:: python
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_PROXY"] = "http://your-corporate-proxy:8080"
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name",
openai_api_base="https://your-endpoint.openai.azure.com/",
openai_api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
In order to use the library with Microsoft Azure endpoints, use
AzureOpenAIEmbeddings.
"""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model: str = "text-embedding-ada-002"
dimensions: Optional[int] = None
"""The number of dimensions the resulting output embeddings should have.
Only supported in `text-embedding-3` and later models.
"""
# to support Azure OpenAI Service custom deployment names
deployment: Optional[str] = model
# TODO: Move to AzureOpenAIEmbeddings.
@ -231,7 +213,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
@property
def _invocation_params(self) -> Dict[str, Any]:
return {"model": self.model, **self.model_kwargs}
params: Dict = {"model": self.model, **self.model_kwargs}
if self.dimensions is not None:
params["dimensions"] = self.dimensions
return params
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb

3
libs/partners/openai/langchain_openai/llms/azure.py
View File

@ -32,7 +32,8 @@ class AzureOpenAI(BaseOpenAI):
Example:
.. code-block:: python
from langchain_community.llms import AzureOpenAI
from langchain_openai import AzureOpenAI
openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
"""

8
libs/partners/openai/poetry.lock generated
View File

@ -457,13 +457,13 @@ files = [
[[package]]
name = "openai"
version = "1.6.1"
version = "1.10.0"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
files = [
{file = "openai-1.6.1-py3-none-any.whl", hash = "sha256:bc9f774838d67ac29fb24cdeb2d58faf57de8b311085dcd1348f7aa02a96c7ee"},
{file = "openai-1.6.1.tar.gz", hash = "sha256:d553ca9dbf9486b08e75b09e8671e4f638462aaadccfced632bf490fc3d75fa2"},
{file = "openai-1.10.0-py3-none-any.whl", hash = "sha256:aa69e97d0223ace9835fbf9c997abe9ee95318f684fd2de6d02c870700c71ebc"},
{file = "openai-1.10.0.tar.gz", hash = "sha256:208886cb501b930dc63f48d51db9c15e5380380f80516d07332adad67c9f1053"},
]
[package.dependencies]
@ -1147,4 +1147,4 @@ watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "9f4b19ea531b89f5c5390782b0b205512317db0c7ec3e81c1143f1b9a146fb42"
content-hash = "689f74ee7854ade754369fd7b42f70a60ec167ee68161825b2e128324afbd90b"

2
libs/partners/openai/pyproject.toml
View File

@ -13,7 +13,7 @@ license = "MIT"
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
langchain-core = ">=0.1.16,<0.2"
openai = "^1.6.1"
openai = "^1.10.0"
numpy = "^1"
tiktoken = "^0.5.2"

13
libs/partners/openai/tests/integration_tests/embeddings/test_base.py
View File

@ -3,7 +3,7 @@ from langchain_openai.embeddings.base import OpenAIEmbeddings
def test_langchain_openai_embedding_documents() -> None:
"""Test cohere embeddings."""
"""Test openai embeddings."""
documents = ["foo bar"]
embedding = OpenAIEmbeddings()
output = embedding.embed_documents(documents)
@ -12,8 +12,17 @@ def test_langchain_openai_embedding_documents() -> None:
def test_langchain_openai_embedding_query() -> None:
"""Test cohere embeddings."""
"""Test openai embeddings."""
document = "foo bar"
embedding = OpenAIEmbeddings()
output = embedding.embed_query(document)
assert len(output) > 0
def test_langchain_openai_embeddings_dimensions() -> None:
"""Test openai embeddings."""
documents = ["foo bar"]
embedding = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=128)
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 128