[](https://www.factset.com)
# Vector Data client library for Python
[](https://developer.factset.com/api-catalog/vector-data-api)
[](https://pypi.org/project/fds.sdk.VectorData/v/0.0.3)
[](https://www.apache.org/licenses/LICENSE-2.0)
The Vector Data API provides streamlined access to vector data through its defined endpoints. It supports functionalities such as:
Retrieving detailed vector data based on user-defined parameters.
Efficiently processing associated text data for enhanced performance.
This API is designed to enable developers to integrate vector data into their applications, ensuring flexibility and performance while leveraging the specified endpoint functionalities.
This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:
- API version: 0.2.0
- SDK version: 0.0.3
- Build package: org.openapitools.codegen.languages.PythonClientCodegen
For more information, please visit [https://developer.factset.com/contact](https://developer.factset.com/contact)
## Requirements
* Python >= 3.7
## Installation
### Poetry
```shell
poetry add fds.sdk.utils fds.sdk.VectorData==0.0.3
```
### pip
```shell
pip install fds.sdk.utils fds.sdk.VectorData==0.0.3
```
## Usage
1. [Generate authentication credentials](../../../../README.md#authentication).
2. Setup Python environment.
1. Install and activate python 3.7+. If you're using [pyenv](https://github.com/pyenv/pyenv):
```sh
pyenv install 3.9.7
pyenv shell 3.9.7
```
2. (optional) [Install poetry](https://python-poetry.org/docs/#installation).
3. [Install dependencies](#installation).
4. Run the following:
> [!IMPORTANT]
> The parameter variables defined below are just examples and may potentially contain non valid values. Please replace them with valid values.
### Example Code
```python
from fds.sdk.utils.authentication import ConfidentialClient
import fds.sdk.VectorData
from fds.sdk.VectorData.api import meta_api
from fds.sdk.VectorData.models import *
from dateutil.parser import parse as dateutil_parser
from pprint import pprint
# See configuration.py for a list of all supported configuration parameters.
# Examples for each supported authentication method are below,
# choose one that satisfies your use case.
# (Preferred) OAuth 2.0: FactSetOAuth2
# See https://github.com/FactSet/enterprise-sdk#oauth-20
# for information on how to create the app-config.json file
#
# The confidential client instance should be reused in production environments.
# See https://github.com/FactSet/enterprise-sdk-utils-python#authentication
# for more information on using the ConfidentialClient class
configuration = fds.sdk.VectorData.Configuration(
fds_oauth_client=ConfidentialClient('/path/to/app-config.json')
)
# Basic authentication: FactSetApiKey
# See https://github.com/FactSet/enterprise-sdk#api-key
# for information how to create an API key
# configuration = fds.sdk.VectorData.Configuration(
# username='USERNAME-SERIAL',
# password='API-KEY'
# )
# Enter a context with an instance of the API client
with fds.sdk.VectorData.ApiClient(configuration) as api_client:
# Create an instance of the API class
api_instance = meta_api.MetaApi(api_client)
try:
# Returns the document types.
# example, this endpoint has no required or optional parameters
api_response = api_instance.get_document_types()
pprint(api_response)
except fds.sdk.VectorData.ApiException as e:
print("Exception when calling MetaApi->get_document_types: %s\n" % e)
# # Get response, http status code and response headers
# try:
# # Returns the document types.
# # example, this endpoint has no required or optional parameters
# api_response, http_status_code, response_headers = api_instance.get_document_types_with_http_info()
# pprint(api_response)
# pprint(http_status_code)
# pprint(response_headers)
# except fds.sdk.VectorData.ApiException as e:
# print("Exception when calling MetaApi->get_document_types: %s\n" % e)
# # Get response asynchronous
# try:
# # Returns the document types.
# # example, this endpoint has no required or optional parameters
# async_result = api_instance.get_document_types_async()
# api_response = async_result.get()
# pprint(api_response)
# except fds.sdk.VectorData.ApiException as e:
# print("Exception when calling MetaApi->get_document_types: %s\n" % e)
# # Get response, http status code and response headers asynchronous
# try:
# # Returns the document types.
# # example, this endpoint has no required or optional parameters
# async_result = api_instance.get_document_types_with_http_info_async()
# api_response, http_status_code, response_headers = async_result.get()
# pprint(api_response)
# pprint(http_status_code)
# pprint(response_headers)
# except fds.sdk.VectorData.ApiException as e:
# print("Exception when calling MetaApi->get_document_types: %s\n" % e)
```
### Using Pandas
To convert an API response to a Pandas DataFrame, it is necessary to transform it first to a dictionary.
```python
import pandas as pd
response_dict = api_response.to_dict()['data']
simple_json_response = pd.DataFrame(response_dict)
nested_json_response = pd.json_normalize(response_dict)
```
### Debugging
The SDK uses the standard library [`logging`](https://docs.python.org/3/library/logging.html#module-logging) module.
Setting `debug` to `True` on an instance of the `Configuration` class sets the log-level of related packages to `DEBUG`
and enables additional logging in Pythons [HTTP Client](https://docs.python.org/3/library/http.client.html).
**Note**: This prints out sensitive information (e.g. the full request and response). Use with care.
```python
import logging
import fds.sdk.VectorData
logging.basicConfig(level=logging.DEBUG)
configuration = fds.sdk.VectorData.Configuration(...)
configuration.debug = True
```
### Configure a Proxy
You can pass proxy settings to the Configuration class:
* `proxy`: The URL of the proxy to use.
* `proxy_headers`: a dictionary to pass additional headers to the proxy (e.g. `Proxy-Authorization`).
```python
import fds.sdk.VectorData
configuration = fds.sdk.VectorData.Configuration(
# ...
proxy="http://secret:password@localhost:5050",
proxy_headers={
"Custom-Proxy-Header": "Custom-Proxy-Header-Value"
}
)
```
### Custom SSL Certificate
TLS/SSL certificate verification can be configured with the following Configuration parameters:
* `ssl_ca_cert`: a path to the certificate to use for verification in `PEM` format.
* `verify_ssl`: setting this to `False` disables the verification of certificates.
Disabling the verification is not recommended, but it might be useful during
local development or testing.
```python
import fds.sdk.VectorData
configuration = fds.sdk.VectorData.Configuration(
# ...
ssl_ca_cert='/path/to/ca.pem'
)
```
### Request Retries
In case the request retry behaviour should be customized, it is possible to pass a `urllib3.Retry` object to the `retry` property of the Configuration.
```python
from urllib3 import Retry
import fds.sdk.VectorData
configuration = fds.sdk.VectorData.Configuration(
# ...
)
configuration.retries = Retry(total=3, status_forcelist=[500, 502, 503, 504])
```
## Documentation for API Endpoints
All URIs are relative to *https://api.factset.com/content/vector/v0*
Class | Method | HTTP request | Description
------------ | ------------- | ------------- | -------------
*MetaApi* | [**get_document_types**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#get_document_types) | **GET** /meta/document-types | Returns the document types.
*MetaApi* | [**get_sources**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#get_sources) | **GET** /meta/sources | Returns the sources.
*MetaApi* | [**get_themes**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#get_themes) | **GET** /meta/themes | Returns the themes.
*MetaApi* | [**getschemas**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#getschemas) | **GET** /meta/schemas | Returns the schemas.
*VectorApi* | [**get_count**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorApi.md#get_count) | **GET** /chunk-text | Returns chunked text for the given vectorId.
*VectorApi* | [**post_vector**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorApi.md#post_vector) | **POST** /data | Return vector information based on the input parameters below
## Documentation For Models
- [ChunkTextResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResponse.md)
- [ChunkTextResponseMeta](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResponseMeta.md)
- [ChunkTextResponseMetaPagination](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResponseMetaPagination.md)
- [ChunkTextResult](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResult.md)
- [DocumentTypes](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/DocumentTypes.md)
- [DocumentTypesResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/DocumentTypesResponse.md)
- [ErrorObject](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ErrorObject.md)
- [ErrorResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ErrorResponse.md)
- [Meta](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Meta.md)
- [Schemas](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Schemas.md)
- [SchemasResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/SchemasResponse.md)
- [Source](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Source.md)
- [SourceResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/SourceResponse.md)
- [Themes](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Themes.md)
- [ThemesResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ThemesResponse.md)
- [VectorDataRequest](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataRequest.md)
- [VectorDataRequestData](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataRequestData.md)
- [VectorDataResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataResponse.md)
- [VectorDataResponseMeta](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataResponseMeta.md)
- [VectorDataResult](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataResult.md)
## Documentation For Authorization
## FactSetApiKey
- **Type**: HTTP basic authentication
## FactSetOAuth2
- **Type**: OAuth
- **Flow**: application
- **Authorization URL**:
- **Scopes**: N/A
## Notes for Large OpenAPI documents
If the OpenAPI document is large, imports in fds.sdk.VectorData.apis and fds.sdk.VectorData.models may fail with a
RecursionError indicating the maximum recursion limit has been exceeded. In that case, there are a couple of solutions:
Solution 1:
Use specific imports for apis and models like:
- `from fds.sdk.VectorData.api.default_api import DefaultApi`
- `from fds.sdk.VectorData.model.pet import Pet`
Solution 2:
Before importing the package, adjust the maximum recursion limit as shown below:
```
import sys
sys.setrecursionlimit(1500)
import fds.sdk.VectorData
from fds.sdk.VectorData.apis import *
from fds.sdk.VectorData.models import *
```
## Contributing
Please refer to the [contributing guide](../../../../CONTRIBUTING.md).
## Copyright
Copyright 2025 FactSet Research Systems Inc
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Raw data
{
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"name": "fds.sdk.VectorData",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "FactSet, API, SDK",
"author": "FactSet Research Systems",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/2d/8e/3aded3a6bee2b09cf3e85fcbec763187749d4d69012e8fa854321f99ecaa/fds_sdk_vectordata-0.0.3.tar.gz",
"platform": null,
"description": "[](https://www.factset.com)\n\n# Vector Data client library for Python\n\n[](https://developer.factset.com/api-catalog/vector-data-api)\n[](https://pypi.org/project/fds.sdk.VectorData/v/0.0.3)\n[](https://www.apache.org/licenses/LICENSE-2.0)\n\nThe Vector Data API provides streamlined access to vector data through its defined endpoints. It supports functionalities such as:\nRetrieving detailed vector data based on user-defined parameters.\nEfficiently processing associated text data for enhanced performance.\nThis API is designed to enable developers to integrate vector data into their applications, ensuring flexibility and performance while leveraging the specified endpoint functionalities.\n\nThis Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:\n\n- API version: 0.2.0\n- SDK version: 0.0.3\n- Build package: org.openapitools.codegen.languages.PythonClientCodegen\n\nFor more information, please visit [https://developer.factset.com/contact](https://developer.factset.com/contact)\n\n## Requirements\n\n* Python >= 3.7\n\n## Installation\n\n### Poetry\n\n```shell\npoetry add fds.sdk.utils fds.sdk.VectorData==0.0.3\n```\n\n### pip\n\n```shell\npip install fds.sdk.utils fds.sdk.VectorData==0.0.3\n```\n\n## Usage\n\n1. [Generate authentication credentials](../../../../README.md#authentication).\n2. Setup Python environment.\n 1. Install and activate python 3.7+. If you're using [pyenv](https://github.com/pyenv/pyenv):\n\n ```sh\n pyenv install 3.9.7\n pyenv shell 3.9.7\n ```\n\n 2. (optional) [Install poetry](https://python-poetry.org/docs/#installation).\n3. [Install dependencies](#installation).\n4. Run the following:\n\n> [!IMPORTANT]\n> The parameter variables defined below are just examples and may potentially contain non valid values. Please replace them with valid values.\n\n### Example Code\n\n```python\nfrom fds.sdk.utils.authentication import ConfidentialClient\n\nimport fds.sdk.VectorData\nfrom fds.sdk.VectorData.api import meta_api\nfrom fds.sdk.VectorData.models import *\nfrom dateutil.parser import parse as dateutil_parser\nfrom pprint import pprint\n\n# See configuration.py for a list of all supported configuration parameters.\n\n# Examples for each supported authentication method are below,\n# choose one that satisfies your use case.\n\n# (Preferred) OAuth 2.0: FactSetOAuth2\n# See https://github.com/FactSet/enterprise-sdk#oauth-20\n# for information on how to create the app-config.json file\n#\n# The confidential client instance should be reused in production environments.\n# See https://github.com/FactSet/enterprise-sdk-utils-python#authentication\n# for more information on using the ConfidentialClient class\nconfiguration = fds.sdk.VectorData.Configuration(\n fds_oauth_client=ConfidentialClient('/path/to/app-config.json')\n)\n\n# Basic authentication: FactSetApiKey\n# See https://github.com/FactSet/enterprise-sdk#api-key\n# for information how to create an API key\n# configuration = fds.sdk.VectorData.Configuration(\n# username='USERNAME-SERIAL',\n# password='API-KEY'\n# )\n\n# Enter a context with an instance of the API client\nwith fds.sdk.VectorData.ApiClient(configuration) as api_client:\n # Create an instance of the API class\n api_instance = meta_api.MetaApi(api_client)\n\n try:\n # Returns the document types.\n # example, this endpoint has no required or optional parameters\n api_response = api_instance.get_document_types()\n\n pprint(api_response)\n except fds.sdk.VectorData.ApiException as e:\n print(\"Exception when calling MetaApi->get_document_types: %s\\n\" % e)\n\n # # Get response, http status code and response headers\n # try:\n # # Returns the document types.\n # # example, this endpoint has no required or optional parameters\n # api_response, http_status_code, response_headers = api_instance.get_document_types_with_http_info()\n\n\n # pprint(api_response)\n # pprint(http_status_code)\n # pprint(response_headers)\n # except fds.sdk.VectorData.ApiException as e:\n # print(\"Exception when calling MetaApi->get_document_types: %s\\n\" % e)\n\n # # Get response asynchronous\n # try:\n # # Returns the document types.\n # # example, this endpoint has no required or optional parameters\n # async_result = api_instance.get_document_types_async()\n # api_response = async_result.get()\n\n\n # pprint(api_response)\n # except fds.sdk.VectorData.ApiException as e:\n # print(\"Exception when calling MetaApi->get_document_types: %s\\n\" % e)\n\n # # Get response, http status code and response headers asynchronous\n # try:\n # # Returns the document types.\n # # example, this endpoint has no required or optional parameters\n # async_result = api_instance.get_document_types_with_http_info_async()\n # api_response, http_status_code, response_headers = async_result.get()\n\n\n # pprint(api_response)\n # pprint(http_status_code)\n # pprint(response_headers)\n # except fds.sdk.VectorData.ApiException as e:\n # print(\"Exception when calling MetaApi->get_document_types: %s\\n\" % e)\n\n```\n\n### Using Pandas\n\nTo convert an API response to a Pandas DataFrame, it is necessary to transform it first to a dictionary.\n```python\nimport pandas as pd\n\nresponse_dict = api_response.to_dict()['data']\n\nsimple_json_response = pd.DataFrame(response_dict)\nnested_json_response = pd.json_normalize(response_dict)\n```\n\n### Debugging\n\nThe SDK uses the standard library [`logging`](https://docs.python.org/3/library/logging.html#module-logging) module.\n\nSetting `debug` to `True` on an instance of the `Configuration` class sets the log-level of related packages to `DEBUG`\nand enables additional logging in Pythons [HTTP Client](https://docs.python.org/3/library/http.client.html).\n\n**Note**: This prints out sensitive information (e.g. the full request and response). Use with care.\n\n```python\nimport logging\nimport fds.sdk.VectorData\n\nlogging.basicConfig(level=logging.DEBUG)\n\nconfiguration = fds.sdk.VectorData.Configuration(...)\nconfiguration.debug = True\n```\n\n### Configure a Proxy\n\nYou can pass proxy settings to the Configuration class:\n\n* `proxy`: The URL of the proxy to use.\n* `proxy_headers`: a dictionary to pass additional headers to the proxy (e.g. `Proxy-Authorization`).\n\n```python\nimport fds.sdk.VectorData\n\nconfiguration = fds.sdk.VectorData.Configuration(\n # ...\n proxy=\"http://secret:password@localhost:5050\",\n proxy_headers={\n \"Custom-Proxy-Header\": \"Custom-Proxy-Header-Value\"\n }\n)\n```\n\n### Custom SSL Certificate\n\nTLS/SSL certificate verification can be configured with the following Configuration parameters:\n\n* `ssl_ca_cert`: a path to the certificate to use for verification in `PEM` format.\n* `verify_ssl`: setting this to `False` disables the verification of certificates.\n Disabling the verification is not recommended, but it might be useful during\n local development or testing.\n\n```python\nimport fds.sdk.VectorData\n\nconfiguration = fds.sdk.VectorData.Configuration(\n # ...\n ssl_ca_cert='/path/to/ca.pem'\n)\n```\n\n### Request Retries\n\nIn case the request retry behaviour should be customized, it is possible to pass a `urllib3.Retry` object to the `retry` property of the Configuration.\n\n```python\nfrom urllib3 import Retry\nimport fds.sdk.VectorData\n\nconfiguration = fds.sdk.VectorData.Configuration(\n # ...\n)\n\nconfiguration.retries = Retry(total=3, status_forcelist=[500, 502, 503, 504])\n```\n\n\n## Documentation for API Endpoints\n\nAll URIs are relative to *https://api.factset.com/content/vector/v0*\n\nClass | Method | HTTP request | Description\n------------ | ------------- | ------------- | -------------\n*MetaApi* | [**get_document_types**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#get_document_types) | **GET** /meta/document-types | Returns the document types.\n*MetaApi* | [**get_sources**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#get_sources) | **GET** /meta/sources | Returns the sources.\n*MetaApi* | [**get_themes**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#get_themes) | **GET** /meta/themes | Returns the themes.\n*MetaApi* | [**getschemas**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/MetaApi.md#getschemas) | **GET** /meta/schemas | Returns the schemas.\n*VectorApi* | [**get_count**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorApi.md#get_count) | **GET** /chunk-text | Returns chunked text for the given vectorId.\n*VectorApi* | [**post_vector**](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorApi.md#post_vector) | **POST** /data | Return vector information based on the input parameters below\n\n\n## Documentation For Models\n\n - [ChunkTextResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResponse.md)\n - [ChunkTextResponseMeta](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResponseMeta.md)\n - [ChunkTextResponseMetaPagination](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResponseMetaPagination.md)\n - [ChunkTextResult](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ChunkTextResult.md)\n - [DocumentTypes](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/DocumentTypes.md)\n - [DocumentTypesResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/DocumentTypesResponse.md)\n - [ErrorObject](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ErrorObject.md)\n - [ErrorResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ErrorResponse.md)\n - [Meta](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Meta.md)\n - [Schemas](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Schemas.md)\n - [SchemasResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/SchemasResponse.md)\n - [Source](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Source.md)\n - [SourceResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/SourceResponse.md)\n - [Themes](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/Themes.md)\n - [ThemesResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/ThemesResponse.md)\n - [VectorDataRequest](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataRequest.md)\n - [VectorDataRequestData](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataRequestData.md)\n - [VectorDataResponse](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataResponse.md)\n - [VectorDataResponseMeta](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataResponseMeta.md)\n - [VectorDataResult](https://github.com/FactSet/enterprise-sdk/tree/main/code/python/VectorData/v0/docs/VectorDataResult.md)\n\n\n## Documentation For Authorization\n\n\n## FactSetApiKey\n\n- **Type**: HTTP basic authentication\n\n\n## FactSetOAuth2\n\n- **Type**: OAuth\n- **Flow**: application\n- **Authorization URL**: \n- **Scopes**: N/A\n\n\n## Notes for Large OpenAPI documents\nIf the OpenAPI document is large, imports in fds.sdk.VectorData.apis and fds.sdk.VectorData.models may fail with a\nRecursionError indicating the maximum recursion limit has been exceeded. In that case, there are a couple of solutions:\n\nSolution 1:\nUse specific imports for apis and models like:\n- `from fds.sdk.VectorData.api.default_api import DefaultApi`\n- `from fds.sdk.VectorData.model.pet import Pet`\n\nSolution 2:\nBefore importing the package, adjust the maximum recursion limit as shown below:\n```\nimport sys\nsys.setrecursionlimit(1500)\nimport fds.sdk.VectorData\nfrom fds.sdk.VectorData.apis import *\nfrom fds.sdk.VectorData.models import *\n```\n\n## Contributing\n\nPlease refer to the [contributing guide](../../../../CONTRIBUTING.md).\n\n## Copyright\n\nCopyright 2025 FactSet Research Systems Inc\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n",
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