v1

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/

README.md

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+# LangChain gpt4free
+
+LangChain gpt4free is an open-source project that assists in building applications using LLM (Large Language Models) and provides free access to GPT4/3.5.
+
+## Installation
+
+To install langchain_g4f, run the following command:
+
+```shell
+pip install git+https://github.com/MIDORIBIN/langchain-gpt4free.git
+```
+
+This command will install langchain_g4f.
+
+## Usage
+
+Here is an example of how to use langchain_g4f
+
+```python
+from g4f import Provider, Model
+from langchain.llms.base import LLM
+
+from langchain_g4f import G4FLLM
+
+
+def main():
+    llm: LLM = G4FLLM(
+        model=Model.gpt_35_turbo,
+        provider=Provider.Aichat,
+    )
+
+    res = llm('hello')
+    print(res)  # Hello! How can I assist you today?
+
+
+if __name__ == '__main__':
+    main()
+```
+
+The above sample code demonstrates the basic usage of langchain_g4f. Choose the appropriate model and provider, initialize the LLM, and then pass input text to the LLM object to obtain the result.
+
+## Support and Bug Reports
+
+For support and bug reports, please use the GitHub Issues page. 
+
+Access the GitHub Issues page and create a new issue. Select the appropriate label and provide detailed information.
+
+## Contributing
+
+To contribute to langchain_g4f, follow these steps to submit a pull request:
+
+1. Fork the project repository.
+2. Clone the forked repository to your local machine.
+3. Make the necessary changes.
+4. Commit the changes and push to your forked repository.
+5. Create a pull request towards the original project repository.

langchain_g4f/G4FLLM.py

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+from types import ModuleType
+from typing import Optional, List, Any, Mapping, Union
+
+import g4f
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+
+
+class G4FLLM(LLM):
+    # Model.model or str
+    model: Union[type, str]
+    # Provider.Provider
+    provider: Optional[ModuleType] = None
+    auth: Optional[Union[str, bool]] = None
+    create_kwargs: Optional[dict] = None
+
+    @property
+    def _llm_type(self) -> str:
+        return 'custom'
+
+    def _call(self, prompt: str, stop: Optional[List[str]] = None,
+              run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> str:
+
+        create_kwargs = {} if self.create_kwargs is None else self.create_kwargs.copy()
+        if self.model is not None:
+            create_kwargs['model'] = self.model
+        if self.provider is not None:
+            create_kwargs['provider'] = self.provider
+        if self.auth is not None:
+            create_kwargs['auth'] = self.auth
+
+        text = g4f.ChatCompletion.create(
+            messages=[{'role': 'user', 'content': prompt}],
+            **create_kwargs,
+        )
+        if stop is not None:
+            text = enforce_stop_tokens(text, stop)
+        return text
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            'model': self.model,
+            'provider': self.provider,
+            'auth': self.auth,
+            'create_kwargs': self.create_kwargs,
+        }

langchain_g4f/__init__.py

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+from .G4FLLM import G4FLLM

pyproject.toml

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+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "langchain_g4f"
+version = "1.0.0"
+description = ""
+authors = []
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "langchain>=0.0.225",
+    "gpt4free @ git+https://github.com/xtekky/gpt4free.git",
+]
+
+[tool.setuptools.packages.find]
+include = ["langchain_g4f"]

sample.py

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+from g4f import Provider, Model
+from langchain.llms.base import LLM
+
+from langchain_g4f import G4FLLM
+
+
+def main():
+    llm: LLM = G4FLLM(
+        model=Model.gpt_35_turbo,
+        provider=Provider.Aichat,
+    )
+
+    res = llm('hello')
+    print(res)  # Hello! How can I assist you today?
+
+
+if __name__ == '__main__':
+    main()