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https://github.com/hwchase17/langchain.git
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Add OpenLM LLM multi-provider (#4993)
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP. It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added code. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This commit is contained in:
Matt Rickard
GitHub
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69de33e024
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de6a401a22
133
docs/modules/models/llms/integrations/openlm.ipynb
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133
docs/modules/models/llms/integrations/openlm.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# OpenLM\n",
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"[OpenLM](https://github.com/r2d4/openlm) is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP. \n",
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"\n",
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"\n",
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"It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added code.\n",
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"\n",
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"This examples goes over how to use LangChain to interact with both OpenAI and HuggingFace. You'll need API keys from both."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Setup\n",
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"Install dependencies and set API keys."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Uncomment to install openlm and openai if you haven't already\n",
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"\n",
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"# !pip install openlm\n",
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"# !pip install openai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from getpass import getpass\n",
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"import os\n",
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"import subprocess\n",
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"\n",
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"\n",
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"# Check if OPENAI_API_KEY environment variable is set\n",
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"if \"OPENAI_API_KEY\" not in os.environ:\n",
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" print(\"Enter your OpenAI API key:\")\n",
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" os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
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"\n",
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"# Check if HF_API_TOKEN environment variable is set\n",
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"if \"HF_API_TOKEN\" not in os.environ:\n",
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" print(\"Enter your HuggingFace Hub API key:\")\n",
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" os.environ[\"HF_API_TOKEN\"] = getpass()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Using LangChain with OpenLM\n",
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"\n",
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"Here we're going to call two models in an LLMChain, `text-davinci-003` from OpenAI and `gpt2` on HuggingFace."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenLM\n",
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"from langchain import PromptTemplate, LLMChain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: text-davinci-003\n",
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"Result: France is a country in Europe. The capital of France is Paris.\n",
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"Model: huggingface.co/gpt2\n",
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"Result: Question: What is the capital of France?\n",
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"\n",
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"Answer: Let's think step by step. I am not going to lie, this is a complicated issue, and I don't see any solutions to all this, but it is still far more\n"
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]
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}
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],
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"source": [
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"question = \"What is the capital of France?\"\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"\n",
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"for model in [\"text-davinci-003\", \"huggingface.co/gpt2\"]:\n",
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" llm = OpenLM(model=model)\n",
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" llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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" result = llm_chain.run(question)\n",
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" print(\"\"\"Model: {}\n",
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"Result: {}\"\"\".format(model, result))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -24,6 +24,7 @@ from langchain.llms.llamacpp import LlamaCpp
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from langchain.llms.modal import Modal
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from langchain.llms.nlpcloud import NLPCloud
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from langchain.llms.openai import AzureOpenAI, OpenAI, OpenAIChat
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from langchain.llms.openlm import OpenLM
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from langchain.llms.petals import Petals
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from langchain.llms.pipelineai import PipelineAI
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from langchain.llms.predictionguard import PredictionGuard
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@ -53,6 +54,7 @@ __all__ = [
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"NLPCloud",
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"OpenAI",
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"OpenAIChat",
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"OpenLM",
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"Petals",
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"PipelineAI",
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"HuggingFaceEndpoint",
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@ -96,6 +98,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
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"nlpcloud": NLPCloud,
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"human-input": HumanInputLLM,
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"openai": OpenAI,
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"openlm": OpenLM,
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"petals": Petals,
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"pipelineai": PipelineAI,
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"huggingface_pipeline": HuggingFacePipeline,
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26
langchain/llms/openlm.py
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langchain/llms/openlm.py
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from typing import Any, Dict
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from pydantic import root_validator
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from langchain.llms.openai import BaseOpenAI
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class OpenLM(BaseOpenAI):
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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return {**{"model": self.model_name}, **super()._invocation_params}
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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try:
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import openlm
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values["client"] = openlm.Completion
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except ImportError:
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raise ValueError(
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"Could not import openlm python package. "
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"Please install it with `pip install openlm`."
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)
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if values["streaming"]:
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raise ValueError("Streaming not supported with openlm")
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return values
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21
poetry.lock
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21
poetry.lock
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@ -5277,6 +5277,21 @@ files = [
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[package.dependencies]
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pydantic = ">=1.8.2"
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[[package]]
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name = "openlm"
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version = "0.0.5"
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description = "Drop-in OpenAI-compatible that can call LLMs from other providers"
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category = "main"
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optional = true
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python-versions = ">=3.8.1,<4.0"
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files = [
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{file = "openlm-0.0.5-py3-none-any.whl", hash = "sha256:9fcbbc575d2869e2a6c0b00827f9be2189c067c2de4bf03ef3cbdf488367ae93"},
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{file = "openlm-0.0.5.tar.gz", hash = "sha256:0eb3fd7a9e4f7b4248931ff2f0dc91c525d990b99956886861a1b3f9868bc451"},
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]
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[package.dependencies]
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requests = ">=2,<3"
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[[package]]
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name = "opensearch-py"
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version = "2.2.0"
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@ -10445,13 +10460,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
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cffi = ["cffi (>=1.11)"]
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[extras]
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all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "lark", "lxml", "manifest-ml", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "requests-toolbelt", "sentence-transformers", "spacy", "steamship", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
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all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "lark", "lxml", "manifest-ml", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "requests-toolbelt", "sentence-transformers", "spacy", "steamship", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
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azure = ["azure-core", "azure-cosmos", "azure-identity", "openai"]
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cohere = ["cohere"]
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docarray = ["docarray"]
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embeddings = ["sentence-transformers"]
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extended-testing = ["atlassian-python-api", "beautifulsoup4", "beautifulsoup4", "chardet", "gql", "html2text", "jq", "lxml", "pandas", "pdfminer-six", "psychicapi", "pymupdf", "pypdf", "pypdfium2", "requests-toolbelt", "telethon", "tqdm", "zep-python"]
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llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"]
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llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"]
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openai = ["openai", "tiktoken"]
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qdrant = ["qdrant-client"]
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text-helpers = ["chardet"]
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@ -10459,4 +10474,4 @@ text-helpers = ["chardet"]
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[metadata]
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lock-version = "2.0"
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python-versions = ">=3.8.1,<4.0"
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content-hash = "52fa365939f4bf1a9f5a93c9dfc8f0fe77a5e7989ff2c1caf0392044b72e08dc"
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content-hash = "cba33c7d2dc43649ad0ededc7b29f0bfeb9cbba1b2bbbc439b06cb608e678b9c"
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@ -91,6 +91,7 @@ psychicapi = {version = "^0.2", optional = true}
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zep-python = {version="^0.25", optional=true}
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chardet = {version="^5.1.0", optional=true}
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requests-toolbelt = {version = "^1.0.0", optional = true}
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openlm = {version = "^0.0.5", optional = true}
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[tool.poetry.group.docs.dependencies]
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autodoc_pydantic = "^1.8.0"
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@ -175,7 +176,7 @@ playwright = "^1.28.0"
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setuptools = "^67.6.1"
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[tool.poetry.extras]
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llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
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llms = ["anthropic", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
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qdrant = ["qdrant-client"]
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openai = ["openai", "tiktoken"]
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text_helpers = ["chardet"]
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@ -241,6 +242,7 @@ all = [
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"lxml",
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"requests-toolbelt",
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"neo4j",
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"openlm"
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]
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# An extra used to be able to add extended testing.
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8
tests/integration_tests/llms/test_openlm.py
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8
tests/integration_tests/llms/test_openlm.py
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from langchain.llms.openlm import OpenLM
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def test_openlm_call() -> None:
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"""Test valid call to openlm."""
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llm = OpenLM(model_name="dolly-v2-7b", max_tokens=10)
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output = llm(prompt="Say foo:")
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assert isinstance(output, str)
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