Utilities for managing multi-shot conversations and structured data handling in LLM applications
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Project description
mightydatainc-gpt-conversation
Utilities for managing multi-shot LLM conversations and structured JSON responses with OpenAI's Responses API.
Installation
pip install mightydatainc-gpt-conversation
Quick Start
gpt_submit
from openai import OpenAI
from gpt_conversation import gpt_submit
client = OpenAI()
reply = gpt_submit(
messages=[{"role": "user", "content": "Say hello."}],
openai_client=client,
)
print(reply)
GptConversation
from openai import OpenAI
from gpt_conversation import GptConversation
client = OpenAI()
conversation = GptConversation(openai_client=client)
reply = conversation.submit_user_message("Give me three project name ideas.")
print(reply)
JSON Response Mode
from openai import OpenAI
from gpt_conversation import gpt_submit
client = OpenAI()
result = gpt_submit(
messages=[{"role": "user", "content": "Return JSON with keys a and b."}],
openai_client=client,
json_response=True,
)
print(type(result)) # dict or list
print(result)
JSONSchemaFormat
from openai import OpenAI
from gpt_conversation import gpt_submit, json_schema_format, JSON_INTEGER
client = OpenAI()
response_format = json_schema_format(
"answer_payload",
{
"answer": "The final answer",
"confidence": ["Confidence score", [0, 100], []],
"rank": JSON_INTEGER,
},
"Structured answer payload",
)
result = gpt_submit(
messages=[{"role": "user", "content": "Return answer as structured JSON."}],
openai_client=client,
json_response=response_format,
)
print(result)
Local dev (Windows)
From packages/python-gpt-conversation, activate the package venv and run tests:
.\.venv\Scripts\Activate.ps1
python -c "import sys; print(sys.executable)"
python -m unittest discover -v -s tests
Live integration tests (real API) require OPENAI_API_KEY in your environment.
Notes
- Package name for
pip installisgpt-conversation. - Python import package is
gpt_conversation. gpt_submitsupports optional warning reporting viawarning_callback.
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