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Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
332 lines
9.6 KiB
Plaintext
332 lines
9.6 KiB
Plaintext
{
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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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"# Predibase\n",
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"\n",
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"[Predibase](https://predibase.com/) allows you to train, fine-tune, and deploy any ML model—from linear regression to large language model. \n",
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"\n",
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"This example demonstrates using Langchain with models deployed on Predibase"
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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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"\n",
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"To run this notebook, you'll need a [Predibase account](https://predibase.com/free-trial/?utm_source=langchain) and an [API key](https://docs.predibase.com/sdk-guide/intro).\n",
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"\n",
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"You'll also need to install the Predibase Python package:"
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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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"%pip install --upgrade --quiet predibase\n",
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"import os\n",
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"\n",
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"os.environ[\"PREDIBASE_API_TOKEN\"] = \"{PREDIBASE_API_TOKEN}\""
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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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"## Initial Call"
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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_community.llms import Predibase\n",
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"\n",
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"model = Predibase(\n",
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" model=\"mistral-7b\",\n",
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" predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\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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"from langchain_community.llms import Predibase\n",
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"\n",
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"# With a fine-tuned adapter hosted at Predibase (adapter_version must be specified).\n",
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"model = Predibase(\n",
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" model=\"mistral-7b\",\n",
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" predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
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" predibase_sdk_version=None, # optional parameter (defaults to the latest Predibase SDK version if omitted)\n",
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" adapter_id=\"e2e_nlg\",\n",
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" adapter_version=1,\n",
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" **{\n",
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" \"api_token\": os.environ.get(\"HUGGING_FACE_HUB_TOKEN\"),\n",
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" \"max_new_tokens\": 5, # default is 256\n",
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" },\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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"from langchain_community.llms import Predibase\n",
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"\n",
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"# With a fine-tuned adapter hosted at HuggingFace (adapter_version does not apply and will be ignored).\n",
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"model = Predibase(\n",
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" model=\"mistral-7b\",\n",
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" predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
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" predibase_sdk_version=None, # optional parameter (defaults to the latest Predibase SDK version if omitted)\n",
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" adapter_id=\"predibase/e2e_nlg\",\n",
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" **{\n",
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" \"api_token\": os.environ.get(\"HUGGING_FACE_HUB_TOKEN\"),\n",
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" \"max_new_tokens\": 5, # default is 256\n",
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" },\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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"# Optionally use `kwargs` to dynamically overwrite \"generate()\" settings.\n",
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"response = model.invoke(\n",
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" \"Can you recommend me a nice dry wine?\",\n",
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" **{\"temperature\": 0.5, \"max_new_tokens\": 1024},\n",
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")\n",
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"print(response)"
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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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"vscode": {
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"languageId": "plaintext"
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}
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},
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"source": [
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"## Chain Call Setup"
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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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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"from langchain_community.llms import Predibase\n",
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"\n",
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"model = Predibase(\n",
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" model=\"mistral-7b\",\n",
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" predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
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" predibase_sdk_version=None, # optional parameter (defaults to the latest Predibase SDK version if omitted)\n",
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" **{\n",
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" \"api_token\": os.environ.get(\"HUGGING_FACE_HUB_TOKEN\"),\n",
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" \"max_new_tokens\": 5, # default is 256\n",
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" },\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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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"# With a fine-tuned adapter hosted at Predibase (adapter_version must be specified).\n",
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"model = Predibase(\n",
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" model=\"mistral-7b\",\n",
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" predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
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" predibase_sdk_version=None, # optional parameter (defaults to the latest Predibase SDK version if omitted)\n",
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" adapter_id=\"e2e_nlg\",\n",
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" adapter_version=1,\n",
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" **{\n",
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" \"api_token\": os.environ.get(\"HUGGING_FACE_HUB_TOKEN\"),\n",
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" \"max_new_tokens\": 5, # default is 256\n",
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" },\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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"# With a fine-tuned adapter hosted at HuggingFace (adapter_version does not apply and will be ignored).\n",
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"llm = Predibase(\n",
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" model=\"mistral-7b\",\n",
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" predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\"),\n",
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" predibase_sdk_version=None, # optional parameter (defaults to the latest Predibase SDK version if omitted)\n",
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" adapter_id=\"predibase/e2e_nlg\",\n",
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" **{\n",
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" \"api_token\": os.environ.get(\"HUGGING_FACE_HUB_TOKEN\"),\n",
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" \"max_new_tokens\": 5, # default is 256\n",
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" },\n",
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")"
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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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"## SequentialChain"
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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.chains import LLMChain\n",
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"from langchain_core.prompts import PromptTemplate"
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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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"# This is an LLMChain to write a synopsis given a title of a play.\n",
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"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
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"\n",
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"Title: {title}\n",
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"Playwright: This is a synopsis for the above play:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
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"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)"
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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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"# This is an LLMChain to write a review of a play given a synopsis.\n",
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"template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
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"\n",
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"Play Synopsis:\n",
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"{synopsis}\n",
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"Review from a New York Times play critic of the above play:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)\n",
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"review_chain = LLMChain(llm=llm, prompt=prompt_template)"
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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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"# This is the overall chain where we run these two chains in sequence.\n",
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"from langchain.chains import SimpleSequentialChain\n",
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"\n",
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"overall_chain = SimpleSequentialChain(\n",
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" chains=[synopsis_chain, review_chain], verbose=True\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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"review = overall_chain.run(\"Tragedy at sunset on the beach\")"
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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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"## Fine-tuned LLM (Use your own fine-tuned LLM from Predibase)"
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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_community.llms import Predibase\n",
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"\n",
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"model = Predibase(\n",
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" model=\"my-base-LLM\",\n",
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" predibase_api_key=os.environ.get(\n",
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" \"PREDIBASE_API_TOKEN\"\n",
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" ), # Adapter argument is optional.\n",
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" predibase_sdk_version=None, # optional parameter (defaults to the latest Predibase SDK version if omitted)\n",
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" adapter_id=\"my-finetuned-adapter-id\", # Supports both, Predibase-hosted and HuggingFace-hosted adapter repositories.\n",
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" adapter_version=1, # required for Predibase-hosted adapters (ignored for HuggingFace-hosted adapters)\n",
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" **{\n",
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" \"api_token\": os.environ.get(\"HUGGING_FACE_HUB_TOKEN\"),\n",
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" \"max_new_tokens\": 5, # default is 256\n",
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" },\n",
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")\n",
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"# replace my-base-LLM with the name of your choice of a serverless base model in Predibase"
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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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"# Optionally use `kwargs` to dynamically overwrite \"generate()\" settings.\n",
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"# response = model.invoke(\"Can you help categorize the following emails into positive, negative, and neutral?\", **{\"temperature\": 0.5, \"max_new_tokens\": 1024})"
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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 3.8.9 64-bit",
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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": "3.8.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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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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