forked from Archives/langchain
docs: `ecosystem/integrations` update 5 (#5752)
- added missed integration to `docs/ecosystem/integrations/` - updated notebooks to consistent format: changed titles, file names; added descriptions #### Who can review? @hwchase17 @dev2049searx_updates
Leonid Ganeline
12 months ago
committed by
GitHub
parent
aea090045b
commit
92a5f00ffb
@ -0,0 +1,26 @@
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# Anthropic
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>[Anthropic](https://en.wikipedia.org/wiki/Anthropic) is an American artificial intelligence (AI) startup and
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> public-benefit corporation, founded by former members of OpenAI. `Anthropic` specializes in developing general AI
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> systems and language models, with a company ethos of responsible AI usage.
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> `Anthropic` develops a chatbot, named `Claude`. Similar to `ChatGPT`, `Claude` uses a messaging
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> interface where users can submit questions or requests and receive highly detailed and relevant responses.
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## Installation and Setup
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```bash
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pip install anthropic
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```
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See the [setup documentation](https://console.anthropic.com/docs/access).
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## Chat Models
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See a [usage example](../modules/models/chat/integrations/anthropic.ipynb)
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```python
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from langchain.chat_models import ChatAnthropic
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```
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@ -0,0 +1,24 @@
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# Google Vertex AI
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>[Vertex AI](https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform) is a machine learning (ML)
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> platform that lets you train and deploy ML models and AI applications.
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> `Vertex AI` combines data engineering, data science, and ML engineering workflows, enabling your teams to
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> collaborate using a common toolset.
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## Installation and Setup
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```bash
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pip install google-cloud-aiplatform
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```
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See the [setup instructions](../modules/models/chat/integrations/google_vertex_ai_palm.ipynb)
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## Chat Models
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See a [usage example](../modules/models/chat/integrations/google_vertex_ai_palm.ipynb)
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```python
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from langchain.chat_models import ChatVertexAI
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```
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@ -0,0 +1,22 @@
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# Tensorflow Hub
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>[TensorFlow Hub](https://www.tensorflow.org/hub) is a repository of trained machine learning models ready for fine-tuning and deployable anywhere.
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>[TensorFlow Hub](https://tfhub.dev/) lets you search and discover hundreds of trained, ready-to-deploy machine learning models in one place.
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## Installation and Setup
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```bash
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pip install tensorflow-hub
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pip install tensorflow_text
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```
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## Text Embedding Models
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See a [usage example](../modules/models/text_embedding/examples/tensorflowhub.ipynb)
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```python
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from langchain.embeddings import TensorflowHubEmbeddings
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```
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@ -1,222 +1,233 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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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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"id": "3RqWPav7AtKL"
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},
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"outputs": [],
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"source": [
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"! pip install predictionguard langchain"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import os\n",
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"\n",
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"import predictionguard as pg\n",
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"from langchain.llms import PredictionGuard\n",
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"from langchain import PromptTemplate, LLMChain"
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],
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"metadata": {
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"id": "2xe8JEUwA7_y"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Basic LLM usage\n",
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"\n"
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],
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"metadata": {
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"id": "mesCTyhnJkNS"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Optional, add your OpenAI API Key. This is optional, as Prediction Guard allows\n",
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"# you to access all the latest open access models (see https://docs.predictionguard.com)\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"<your OpenAI api key>\"\n",
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"\n",
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"# Your Prediction Guard API key. Get one at predictionguard.com\n",
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"os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"<your Prediction Guard access token>\""
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],
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"metadata": {
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"id": "kp_Ymnx1SnDG"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
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],
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"metadata": {
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"id": "Ua7Mw1N4HcER"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"pgllm(\"Tell me a joke\")"
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],
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"metadata": {
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"id": "Qo2p5flLHxrB"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Control the output structure/ type of LLMs"
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],
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"metadata": {
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"id": "EyBYaP_xTMXH"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"template = \"\"\"Respond to the following query based on the context.\n",
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"\n",
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"Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦\n",
|
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"Exclusive Candle Box - $80 \n",
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"Monthly Candle Box - $45 (NEW!)\n",
|
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"Scent of The Month Box - $28 (NEW!)\n",
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"Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉\n",
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"\n",
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"Query: {query}\n",
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"\n",
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"Result: \"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
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],
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"metadata": {
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"id": "55uxzhQSTPqF"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Without \"guarding\" or controlling the output of the LLM.\n",
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"pgllm(prompt.format(query=\"What kind of post is this?\"))"
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],
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"metadata": {
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"id": "yersskWbTaxU"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# With \"guarding\" or controlling the output of the LLM. See the \n",
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"# Prediction Guard docs (https://docs.predictionguard.com) to learn how to \n",
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"# control the output with integer, float, boolean, JSON, and other types and\n",
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"# structures.\n",
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\", \n",
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" output={\n",
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" \"type\": \"categorical\",\n",
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" \"categories\": [\n",
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" \"product announcement\", \n",
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" \"apology\", \n",
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" \"relational\"\n",
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" ]\n",
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" })\n",
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"pgllm(prompt.format(query=\"What kind of post is this?\"))"
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],
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"metadata": {
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"id": "PzxSbYwqTm2w"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Chaining"
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],
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"metadata": {
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"id": "v3MzIUItJ8kV"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
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],
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"metadata": {
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"id": "pPegEZExILrT"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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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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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
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"\n",
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.predict(question=question)"
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],
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"metadata": {
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"id": "suxw62y-J-bg"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
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"\n",
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"llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"
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],
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"metadata": {
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"id": "l2bc26KHKr7n"
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},
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"execution_count": null,
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"outputs": []
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||||
},
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{
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"cell_type": "code",
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"source": [],
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||||
"metadata": {
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||||
"id": "I--eSa2PLGqq"
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||||
},
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||||
"execution_count": null,
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||||
"outputs": []
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||||
}
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||||
]
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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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"id": "mesCTyhnJkNS"
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},
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"source": [
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"# Prediction Guard\n",
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"\n",
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">[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."
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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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||||
"id": "3RqWPav7AtKL"
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},
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"outputs": [],
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"source": [
|
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"! pip install predictionguard langchain"
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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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||||
"id": "2xe8JEUwA7_y"
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||||
},
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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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"import predictionguard as pg\n",
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"from langchain.llms import PredictionGuard\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,
|
||||
"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",
|
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"\n",
|
||||
"# Your Prediction Guard API key. Get one at predictionguard.com\n",
|
||||
"os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"<your Prediction Guard access token>\""
|
||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "Ua7Mw1N4HcER"
|
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},
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"outputs": [],
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"source": [
|
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"pgllm = PredictionGuard(model=\"OpenAI-text-davinci-003\")"
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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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||||
"id": "Qo2p5flLHxrB"
|
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},
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"outputs": [],
|
||||
"source": [
|
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"pgllm(\"Tell me a joke\")"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "EyBYaP_xTMXH"
|
||||
},
|
||||
"source": [
|
||||
"# Control the output structure/ type of LLMs"
|
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]
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},
|
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{
|
||||
"cell_type": "code",
|
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"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",
|
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"\n",
|
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"Result: \"\"\"\n",
|
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"prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
|
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]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue