divyam-llm-interop 0.2.0

A lightweight, provider-neutral library for translating LLM requests and responses across model APIs.

Divyam LLM Interop

A minimal, provider‑agnostic library for interoperable AI model requests and responses. Divyam LLM Interop provides a unified interface for interacting with models across providers while maintaining consistent request and response semantics.

Installation

# Install from PyPI
pip install divyam-llm-interop

See PyPI

Usage

The primary API for text based chat request and response conversion is ChatTranslator.

Translate a chat request

from divyam_llm_interop.translate.chat.api_types import ModelApiType
from divyam_llm_interop.translate.chat.translate import ChatTranslator
from divyam_llm_interop.translate.chat.types import ChatRequest, ChatResponse, Model

# Translate gemini-1.5-pro Chat Completions API request to a gpt-4.1
# Responses API request
translator = ChatTranslator()
chat_request = ChatRequest(
    body={
        "model": "gemini-1.5-pro",
        "messages": [
            {
                "role": "system",
                "content": (
                    "You are a highly knowledgeable trivia assistant. "
                    "Provide clear, accurate answers across history, geography, "
                    "science, pop culture, and general knowledge. "
                    "When explaining, keep it concise unless asked otherwise."
                ),
            },
            {"role": "user", "content": "What is the capital of India?"},
        ],
        "temperature": 0.7,
        "top_p": 1.0,
        "max_tokens": 100000,
        "presence_penalty": 0.5,
    }
)
source = Model(name="gemini-1.5-pro", api_type=ModelApiType.COMPLETIONS)
target = Model(name="gpt-4.1", api_type=ModelApiType.RESPONSES)
translated = translator.translate_request(chat_request, source, target)

Translate chat response

from divyam_llm_interop.translate.chat.api_types import ModelApiType
from divyam_llm_interop.translate.chat.translate import ChatTranslator
from divyam_llm_interop.translate.chat.types import ChatResponse, Model

# Translate Responses API response to Chat Completions API Response.
translator = ChatTranslator()

# Response body most likely obtained from a LLM call.
chat_response = ChatResponse(
    body={
        "id": "resp_abc123",
        "object": "response",
        "model": "gpt-4.1",
        "created": 1733400000,
        "output": [
            {
                "role": "assistant",
                "content": [
                    {
                        "type": "output_text",
                        "text": "The capital of India is New Delhi.",
                    }
                ],
            }
        ],
        "usage": {"input_tokens": 35, "output_tokens": 10, "total_tokens": 45},
        "metadata": {"temperature": 0.7, "top_p": 1.0, "presence_penalty": 0.5},
    }
)

source = Model(name="gpt-4.1", api_type=ModelApiType.RESPONSES)
target = Model(name="gpt-4.1", api_type=ModelApiType.COMPLETIONS)
translated = translator.translate_response(chat_response, source, target)

Model Name Resolution and Fallback

When a request model name is resolved against the catalog, matching happens in this order:

1. Exact normalized name match (provider/model-name and case differences are normalized).
2. Explicit catalog override via name_match.regex in model YAML.
3. Generic best-effort fallback in code:
   • strips punctuation (-, _, .) for comparison,
   • matches runtime names that extend a known catalog name's canonical form (longest match wins).

Runtime names that include -instruct in the segment you care about (for example llama-3.2-3b-instruct-ft-v1) align with the *-instruct catalog entry; a name like llama-3.2-3b-experiment_2026 aligns with the non-instruct base if both exist. Use name_match.regex if you need a different mapping.

This means fine-tuned/runtime names like gemini-2.0-flash-001, llama-3.2-3b-instruct-ft-custom-v1, or qwen-3-8b-adapter_x can resolve without adding model-specific regex in config.

Adding New Models

To add a new model family, start with canonical names only in: src/divyam_llm_interop/config/translate/chat/models/*.yaml.

Example:

- name: mymodel-4b
- name: mymodel-4b-instruct

In most cases, this is enough because fallback matching handles runtime suffixes. Add name_match.regex only when you need an explicit override or a non-standard alias.

Example override:

- name: mymodel-4b-instruct
  name_match:
    regex:
      - "^vendor-special-4b-v\\d+$"

Use override regex when:

• naming does not share a stable base with catalog names,
• multiple catalog names could match and you must force one,
• you need provider-specific alias behavior.

Development Environment Setup

This project uses uv to manage Python, the virtual environment, and all dependencies. You do not need to install Python or create a virtual environment manually — uv handles all of that.

Quick start

./scripts/setup-dev.sh

This will install uv (if not present), find or install a compatible Python (>=3.10), sync all dependency groups (dev, test, lint), and create a .venv in the project root.

To upgrade all dependencies and regenerate the lock file:

./scripts/setup-dev.sh --upgrade

After setup, point your IDE's Python interpreter at:

<project-root>/.venv/bin/python

Manage dependencies via uv add / uv remove or by editing pyproject.toml and running uv sync --all-groups — do not use your IDE's built-in package manager.

Contributing

We welcome contributions to improve the library!

How to contribute

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-improvement
3. Make your changes
4. Run tests and linters (see below)
5. Submit a pull request

Contribution guidelines

• Follow existing code style
• Write clear commit messages
• Include tests when adding features or fixing bugs
• Ensure documentation reflects changes

If you're unsure about a change, feel free to open a discussion or draft PR.

Code Quality Checks

Before submitting your PR, make sure the code passes all checks.

For in-editor linting, formatting, and type checking, open the repo in VS Code or Cursor and install the recommended extensions (.vscode/extensions.json). Settings use pyproject.toml (ruff) and pyrightconfig.json (types).

Agent instructions for any AI tool: see AGENTS.md.

Run all checks at once

./scripts/lint.sh

Auto-fix formatting and lint issues

./scripts/lint.sh --fix

Run individual checks

# Format code
uv run ruff format .

# Check formatting (without modifying files)
uv run ruff format --check .

# Lint code
uv run ruff check .

# Auto-fix linting issues (where possible)
uv run ruff check --fix .

# Type check
uv run pyright .

Running Tests

./scripts/test.sh

With coverage report:

./scripts/test.sh --coverage

Or manually:

uv run pytest
uv run pytest --cov=src --cov-report=term-missing

Publishing to PyPI

Note: Publishing should typically be done through CI/build scripts.

rm -rf dist/
uv build
uv publish

License

This project is licensed under the Apache License, Version 2.0. You may obtain a copy of the License at:

https://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 file for the full license text.

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