forked from Archives/langchain
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>searx_updates
Matt Rickard
12 months ago
committed by
GitHub
parent
69de33e024
commit
de6a401a22
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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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@ -0,0 +1,26 @@
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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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@ -0,0 +1,8 @@
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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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