Conversational API Powered by FactSet Mercury client library for Python

OpenAI-compatible streaming endpoint for Conversational API.

Only the last user message is used; system prompts and chat history are ignored. Responses are always streamed as Server-Sent Events (text/event-stream) and terminate with data: [DONE].

This Python package is automatically generated by the OpenAPI Generator project:

• API version: 0.1.0
• SDK version: 0.11.0
• Build package: org.openapitools.codegen.languages.PythonClientCodegen

For more information, please visit https://developer.factset.com/contact

Requirements

• Python >= 3.7

Installation

Poetry

poetry add fds.sdk.utils fds.sdk.ConversationalAPIPoweredbyFactSetMercury==0.11.0

pip

pip install fds.sdk.utils fds.sdk.ConversationalAPIPoweredbyFactSetMercury==0.11.0

Usage

1. Generate authentication credentials.
2. Setup Python environment.

   1. Install and activate python 3.10+. If you're using pyenv:

      pyenv install 3.10.0
      pyenv shell 3.10.0
      

   2. (optional) Install poetry.

3. Install dependencies.
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

from fds.sdk.utils.authentication import ConfidentialClient

import fds.sdk.ConversationalAPIPoweredbyFactSetMercury
from fds.sdk.ConversationalAPIPoweredbyFactSetMercury.api import chat_api
from fds.sdk.ConversationalAPIPoweredbyFactSetMercury.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.ConversationalAPIPoweredbyFactSetMercury.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.ConversationalAPIPoweredbyFactSetMercury.Configuration(
#     username='USERNAME-SERIAL',
#     password='API-KEY'
# )

# Enter a context with an instance of the API client
with fds.sdk.ConversationalAPIPoweredbyFactSetMercury.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = chat_api.ChatApi(api_client)
    consumer = "developer-portal" # str | Identify the requestor
    chat_completion_request = ChatCompletionRequest(
        model="fds-answer-2.0",
        stream=True,
        messages=[
            ChatMessage(
                role="system",
                content="content_example",
            ),
        ],
    ) # ChatCompletionRequest | 

    try:
        # Stream a chat completion (OpenAI-compatible)
        # example passing only required values which don't have defaults set
        api_response = api_instance.chat_completion(consumer, chat_completion_request)

        pprint(api_response)
    except fds.sdk.ConversationalAPIPoweredbyFactSetMercury.ApiException as e:
        print("Exception when calling ChatApi->chat_completion: %s\n" % e)

    # # Get response, http status code and response headers
    # try:
    #     # Stream a chat completion (OpenAI-compatible)
    #     api_response, http_status_code, response_headers = api_instance.chat_completion_with_http_info(consumer, chat_completion_request)


    #     pprint(api_response)
    #     pprint(http_status_code)
    #     pprint(response_headers)
    # except fds.sdk.ConversationalAPIPoweredbyFactSetMercury.ApiException as e:
    #     print("Exception when calling ChatApi->chat_completion: %s\n" % e)

    # # Get response asynchronous
    # try:
    #     # Stream a chat completion (OpenAI-compatible)
    #     async_result = api_instance.chat_completion_async(consumer, chat_completion_request)
    #     api_response = async_result.get()


    #     pprint(api_response)
    # except fds.sdk.ConversationalAPIPoweredbyFactSetMercury.ApiException as e:
    #     print("Exception when calling ChatApi->chat_completion: %s\n" % e)

    # # Get response, http status code and response headers asynchronous
    # try:
    #     # Stream a chat completion (OpenAI-compatible)
    #     async_result = api_instance.chat_completion_with_http_info_async(consumer, chat_completion_request)
    #     api_response, http_status_code, response_headers = async_result.get()


    #     pprint(api_response)
    #     pprint(http_status_code)
    #     pprint(response_headers)
    # except fds.sdk.ConversationalAPIPoweredbyFactSetMercury.ApiException as e:
    #     print("Exception when calling ChatApi->chat_completion: %s\n" % e)

Using Pandas

To convert an API response to a Pandas DataFrame, it is necessary to transform it first to a dictionary.

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 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.

Note: This prints out sensitive information (e.g. the full request and response). Use with care.

import logging
import fds.sdk.ConversationalAPIPoweredbyFactSetMercury

logging.basicConfig(level=logging.DEBUG)

configuration = fds.sdk.ConversationalAPIPoweredbyFactSetMercury.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).

import fds.sdk.ConversationalAPIPoweredbyFactSetMercury

configuration = fds.sdk.ConversationalAPIPoweredbyFactSetMercury.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.

import fds.sdk.ConversationalAPIPoweredbyFactSetMercury

configuration = fds.sdk.ConversationalAPIPoweredbyFactSetMercury.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.

from urllib3 import Retry
import fds.sdk.ConversationalAPIPoweredbyFactSetMercury

configuration = fds.sdk.ConversationalAPIPoweredbyFactSetMercury.Configuration(
    # ...
)

configuration.retries = Retry(total=3, status_forcelist=[500, 502, 503, 504])

Documentation for API Endpoints

All URIs are relative to https://api.uat.factset.com/conversational/v0

Documentation For Models

• ChatCompletionChunk
• ChatCompletionRequest
• ChatMessage
• ErrorResponse
• ErrorResponseError
• Model
• ModelList
• ResponseChoices
• ResponseChoicesDelta

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.ConversationalAPIPoweredbyFactSetMercury.apis and fds.sdk.ConversationalAPIPoweredbyFactSetMercury.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.ConversationalAPIPoweredbyFactSetMercury.api.default_api import DefaultApi
• from fds.sdk.ConversationalAPIPoweredbyFactSetMercury.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.ConversationalAPIPoweredbyFactSetMercury
from fds.sdk.ConversationalAPIPoweredbyFactSetMercury.apis import *
from fds.sdk.ConversationalAPIPoweredbyFactSetMercury.models import *

Contributing

Please refer to the contributing guide.

Copyright

Copyright 2026 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.