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Add MosaicML inference endpoints (#4607)
# Add MosaicML inference endpoints This PR adds support in langchain for MosaicML inference endpoints. We both serve a select few open source models, and allow customers to deploy their own models using our inference service. Docs are here (https://docs.mosaicml.com/en/latest/inference.html), and sign up form is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not intimately familiar with the details of langchain, or the contribution process, so please let me know if there is anything that needs fixing or this is the wrong way to submit a new integration, thanks! I'm also not sure what the procedure is for integration tests. I have tested locally with my api key. ## Who can review? @hwchase17 --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
Daniel King
GitHub
105
docs/modules/models/llms/integrations/mosaicml.ipynb
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105
docs/modules/models/llms/integrations/mosaicml.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# MosaicML\n",
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"\n",
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"[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
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"\n",
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"This example goes over how to use LangChain to interact with MosaicML Inference for text completion."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"MOSAICML_API_TOKEN = getpass()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"MOSAICML_API_TOKEN\"] = MOSAICML_API_TOKEN"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import MosaicML\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = MosaicML(inject_instruction_format=True, model_kwargs={'do_sample': False})"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What is one good reason why you should train a large language model on domain specific data?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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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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}
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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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109
docs/modules/models/text_embedding/examples/mosaicml.ipynb
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109
docs/modules/models/text_embedding/examples/mosaicml.ipynb
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# MosaicML embeddings\n",
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"\n",
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"[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open source models, or deploy your own.\n",
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"\n",
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"This example goes over how to use LangChain to interact with MosaicML Inference for text embedding."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"MOSAICML_API_TOKEN = getpass()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"MOSAICML_API_TOKEN\"] = MOSAICML_API_TOKEN"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import MosaicMLInstructorEmbeddings"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = MosaicMLInstructorEmbeddings(\n",
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" query_instruction=\"Represent the query for retrieval: \"\n",
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")"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query_text = \"This is a test query.\"\n",
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"query_result = embeddings.embed_query(query_text)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"document_text = \"This is a test document.\"\n",
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"document_result = embeddings.embed_documents([document_text])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"query_numpy = np.array(query_result)\n",
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"document_numpy = np.array(document_result[0])\n",
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"similarity = np.dot(query_numpy, document_numpy) / (np.linalg.norm(query_numpy)*np.linalg.norm(document_numpy))\n",
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"print(f\"Cosine similarity between document and query: {similarity}\")"
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]
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}
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],
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"metadata": {
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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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}
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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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