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docs: ecosystem/integrations update 5 (#5752)

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- added missed integration to `docs/ecosystem/integrations/`
- updated notebooks to consistent format: changed titles, file names;
added descriptions

#### Who can review?
 @hwchase17 
 @dev2049
This commit is contained in:
Leonid Ganeline
2023-06-05 16:08:55 -07:00
committed by GitHub
parent aea090045b
commit 92a5f00ffb
27 changed files with 431 additions and 309 deletions
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10
docs/modules/models/llms/integrations/beam.ipynb
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@@ -6,7 +6,11 @@
"id": "J-yvaDTmTTza"
},
"source": [
"# Beam integration for langchain\n",
"# Beam\n",
"\n",
">[Beam](https://docs.beam.cloud/introduction) makes it easy to run code on GPUs, deploy scalable web APIs, \n",
"> schedule cron jobs, and run massively parallel workloads — without managing any infrastructure.\n",
"\n",
"\n",
"Calls the Beam API wrapper to deploy and make subsequent calls to an instance of the gpt2 LLM in a cloud deployment. Requires installation of the Beam library and registration of Beam Client ID and Client Secret. By calling the wrapper an instance of the model is created and run, with returned text relating to the prompt. Additional calls can then be made by directly calling the Beam API.\n",
"\n",
@@ -151,9 +155,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}

6
docs/modules/models/llms/integrations/huggingface_pipelines.ipynb
View File

@@ -5,9 +5,9 @@
"id": "959300d4",
"metadata": {},
"source": [
"# Hugging Face Local Pipelines\n",
"# Hugging Face Pipeline\n",
"\n",
"Hugging Face models can be run locally through the `HuggingFacePipeline` class.\n",
"`Hugging Face` models can be run locally through the `HuggingFacePipeline` class.\n",
"\n",
"The [Hugging Face Model Hub](https://huggingface.co/models) hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
"\n",
@@ -137,7 +137,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
"version": "3.10.6"
}
},
"nbformat": 4,

6
docs/modules/models/llms/integrations/jsonformer_experimental.ipynb
View File

@@ -5,9 +5,9 @@
"id": "fdd7864c-93e6-4eb4-a923-b80d2ae4377d",
"metadata": {},
"source": [
"# Structured Decoding with JSONFormer\n",
"# Jsonformer\n",
"\n",
"[JSONFormer](https://github.com/1rgs/jsonformer) is a library that wraps local HuggingFace pipeline models for structured decoding of a subset of the JSON Schema.\n",
"[Jsonformer](https://github.com/1rgs/jsonformer) is a library that wraps local `HuggingFace pipeline` models for structured decoding of a subset of the JSON Schema.\n",
"\n",
"It works by filling in the structure tokens and then sampling the content tokens from the model.\n",
"\n",
@@ -272,7 +272,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
"version": "3.10.6"
}
},
"nbformat": 4,

451
docs/modules/models/llms/integrations/predictionguard.ipynb
View File

@@ -1,222 +1,233 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "mesCTyhnJkNS"
},
"source": [
"# Prediction Guard\n",
"\n",
">[Prediction Guard](https://docs.predictionguard.com/) gives a quick and easy access to state-of-the-art open and closed access LLMs, without needing to spend days and weeks figuring out all of the implementation details, managing a bunch of different API specs, and setting up the infrastructure for model deployments."
]
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3RqWPav7AtKL"
},
"outputs": [],
"source": [
"! pip install predictionguard langchain"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"\n",
"import predictionguard as pg\n",
"from langchain.llms import PredictionGuard\n",
"from langchain import PromptTemplate, LLMChain"
],
"metadata": {
"id": "2xe8JEUwA7_y"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Basic LLM usage\n",
"\n"
],
"metadata": {
"id": "mesCTyhnJkNS"
}
},
{
"cell_type": "code",
"source": [
"# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows\n",
"# you to access all the latest open access models (see https://docs.predictionguard.com)\n",
"os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI api key>\"\n",
"\n",
"# Your Prediction Guard API key. Get one at predictionguard.com\n",
"os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"<your Prediction Guard access token>\""
],
"metadata": {
"id": "kp_Ymnx1SnDG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
],
"metadata": {
"id": "Ua7Mw1N4HcER"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pgllm(\"Tell me a joke\")"
],
"metadata": {
"id": "Qo2p5flLHxrB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Control the output structure/ type of LLMs"
],
"metadata": {
"id": "EyBYaP_xTMXH"
}
},
{
"cell_type": "code",
"source": [
"template = \"\"\"Respond to the following query based on the context.\n",
"\n",
"Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦\n",
"Exclusive Candle Box - $80 \n",
"Monthly Candle Box - $45 (NEW!)\n",
"Scent of The Month Box - $28 (NEW!)\n",
"Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉\n",
"\n",
"Query: {query}\n",
"\n",
"Result: \"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
],
"metadata": {
"id": "55uxzhQSTPqF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Without \"guarding\" or controlling the output of the LLM.\n",
"pgllm(prompt.format(query=\"What kind of post is this?\"))"
],
"metadata": {
"id": "yersskWbTaxU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# With \"guarding\" or controlling the output of the LLM. See the \n",
"# Prediction Guard docs (https://docs.predictionguard.com) to learn how to \n",
"# control the output with integer, float, boolean, JSON, and other types and\n",
"# structures.\n",
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\", \n",
" output={\n",
" \"type\": \"categorical\",\n",
" \"categories\": [\n",
" \"product announcement\", \n",
" \"apology\", \n",
" \"relational\"\n",
" ]\n",
" })\n",
"pgllm(prompt.format(query=\"What kind of post is this?\"))"
],
"metadata": {
"id": "PzxSbYwqTm2w"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Chaining"
],
"metadata": {
"id": "v3MzIUItJ8kV"
}
},
{
"cell_type": "code",
"source": [
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
],
"metadata": {
"id": "pPegEZExILrT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
"\n",
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.predict(question=question)"
],
"metadata": {
"id": "suxw62y-J-bg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
"\n",
"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
],
"metadata": {
"id": "l2bc26KHKr7n"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "I--eSa2PLGqq"
},
"execution_count": null,
"outputs": []
}
]
}
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3RqWPav7AtKL"
},
"outputs": [],
"source": [
"! pip install predictionguard langchain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2xe8JEUwA7_y"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import predictionguard as pg\n",
"from langchain.llms import PredictionGuard\n",
"from langchain import PromptTemplate, LLMChain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kp_Ymnx1SnDG"
},
"outputs": [],
"source": [
"# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows\n",
"# you to access all the latest open access models (see https://docs.predictionguard.com)\n",
"os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI api key>\"\n",
"\n",
"# Your Prediction Guard API key. Get one at predictionguard.com\n",
"os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"<your Prediction Guard access token>\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ua7Mw1N4HcER"
},
"outputs": [],
"source": [
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Qo2p5flLHxrB"
},
"outputs": [],
"source": [
"pgllm(\"Tell me a joke\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EyBYaP_xTMXH"
},
"source": [
"# Control the output structure/ type of LLMs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "55uxzhQSTPqF"
},
"outputs": [],
"source": [
"template = \"\"\"Respond to the following query based on the context.\n",
"\n",
"Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦\n",
"Exclusive Candle Box - $80 \n",
"Monthly Candle Box - $45 (NEW!)\n",
"Scent of The Month Box - $28 (NEW!)\n",
"Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉\n",
"\n",
"Query: {query}\n",
"\n",
"Result: \"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yersskWbTaxU"
},
"outputs": [],
"source": [
"# Without \"guarding\" or controlling the output of the LLM.\n",
"pgllm(prompt.format(query=\"What kind of post is this?\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PzxSbYwqTm2w"
},
"outputs": [],
"source": [
"# With \"guarding\" or controlling the output of the LLM. See the \n",
"# Prediction Guard docs (https://docs.predictionguard.com) to learn how to \n",
"# control the output with integer, float, boolean, JSON, and other types and\n",
"# structures.\n",
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\", \n",
" output={\n",
" \"type\": \"categorical\",\n",
" \"categories\": [\n",
" \"product announcement\", \n",
" \"apology\", \n",
" \"relational\"\n",
" ]\n",
" })\n",
"pgllm(prompt.format(query=\"What kind of post is this?\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v3MzIUItJ8kV"
},
"source": [
"# Chaining"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pPegEZExILrT"
},
"outputs": [],
"source": [
"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "suxw62y-J-bg"
},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
"\n",
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
"\n",
"llm_chain.predict(question=question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "l2bc26KHKr7n"
},
"outputs": [],
"source": [
"template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
"\n",
"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "I--eSa2PLGqq"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

9
docs/modules/models/llms/integrations/rellm_experimental.ipynb
View File

@@ -5,11 +5,10 @@
"id": "fdd7864c-93e6-4eb4-a923-b80d2ae4377d",
"metadata": {},
"source": [
"# Structured Decoding with RELLM\n",
"# ReLLM\n",
"\n",
"[RELLM](https://github.com/r2d4/rellm) is a library that wraps local Hugging Face pipeline models for structured decoding.\n",
"\n",
"It works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression.\n",
">[ReLLM](https://github.com/r2d4/rellm) is a library that wraps local Hugging Face pipeline models for structured decoding.\n",
">It works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression.\n",
"\n",
"\n",
"**Warning - this module is still experimental**"
@@ -200,7 +199,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
"version": "3.10.6"
}
},
"nbformat": 4,

2
docs/modules/models/llms/integrations/sagemaker.ipynb
View File

@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# SageMakerEndpoint\n",
"# SageMaker Endpoint\n",
"\n",
"[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a system that can build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.\n",
"\n",