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docs: Google Cloud Documentation Cleanup (#12224)
- Move Document AI provider to the Google provider page - Change Vertex AI Matching Engine to Vector Search - Change references from GCP to Google Cloud - Add Gmail chat loader to Google provider page - Change Serper page title to "Serper - Google Search API" since it is not a Google product.
This commit is contained in:
Holt Skinner
GitHub
parent
286a29a49e
commit
e7e670805c
@ -5,7 +5,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GCP Vertex AI \n",
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"# Google Cloud Vertex AI \n",
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"\n",
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"Note: This is separate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
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"\n",
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@ -31,7 +31,7 @@
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},
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"outputs": [],
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"source": [
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"#!pip install langchain google-cloud-aiplatform"
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"#!pip install langchain google-cloud-aiplatform\n"
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]
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},
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{
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@ -41,7 +41,7 @@
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatVertexAI\n",
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"from langchain.prompts import ChatPromptTemplate"
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"from langchain.prompts import ChatPromptTemplate\n"
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]
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},
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{
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@ -50,7 +50,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chat = ChatVertexAI()"
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"chat = ChatVertexAI()\n"
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]
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},
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{
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@ -64,7 +64,7 @@
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [(\"system\", system), (\"human\", human)]\n",
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")\n",
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"messages = prompt.format_messages()"
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"messages = prompt.format_messages()\n"
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]
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},
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{
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@ -84,7 +84,7 @@
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}
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],
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"source": [
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"chat(messages)"
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"chat(messages)\n"
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]
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},
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{
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@ -104,7 +104,7 @@
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"human = \"{text}\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [(\"system\", system), (\"human\", human)]\n",
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")"
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")\n"
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]
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},
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{
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@ -127,7 +127,7 @@
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"chain = prompt | chat\n",
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"chain.invoke(\n",
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" {\"input_language\": \"English\", \"output_language\": \"Japanese\", \"text\": \"I love programming\"}\n",
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")"
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")\n"
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]
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},
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{
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@ -161,7 +161,7 @@
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" model_name=\"codechat-bison\",\n",
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" max_output_tokens=1000,\n",
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" temperature=0.5\n",
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")"
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")\n"
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]
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},
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{
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@ -189,7 +189,7 @@
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],
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"source": [
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"# For simple string in string out usage, we can use the `predict` method:\n",
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"print(chat.predict(\"Write a Python function to identify all prime numbers\"))"
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"print(chat.predict(\"Write a Python function to identify all prime numbers\"))\n"
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]
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},
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{
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@ -209,7 +209,7 @@
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"source": [
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"import asyncio\n",
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"# import nest_asyncio\n",
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"# nest_asyncio.apply()"
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"# nest_asyncio.apply()\n"
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]
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},
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{
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@ -237,7 +237,7 @@
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" top_k=40,\n",
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")\n",
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"\n",
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"asyncio.run(chat.agenerate([messages]))"
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"asyncio.run(chat.agenerate([messages]))\n"
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]
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},
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{
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@ -257,7 +257,7 @@
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}
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],
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"source": [
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"asyncio.run(chain.ainvoke({\"input_language\": \"English\", \"output_language\": \"Sanskrit\", \"text\": \"I love programming\"}))"
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"asyncio.run(chain.ainvoke({\"input_language\": \"English\", \"output_language\": \"Sanskrit\", \"text\": \"I love programming\"}))\n"
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]
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},
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{
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@ -275,7 +275,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys"
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"import sys\n"
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]
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},
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{
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@ -310,7 +310,7 @@
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"messages = prompt.format_messages()\n",
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"for chunk in chat.stream(messages):\n",
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" sys.stdout.write(chunk.content)\n",
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" sys.stdout.flush()"
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" sys.stdout.flush()\n"
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]
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}
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],
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@ -4,7 +4,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GCP Vertex AI\n",
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"# Google Cloud Vertex AI\n",
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"\n",
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"**Note:** This is separate from the `Google PaLM` integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`. \n"
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]
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@ -41,7 +41,7 @@
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},
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"outputs": [],
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"source": [
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"#!pip install langchain google-cloud-aiplatform"
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"#!pip install langchain google-cloud-aiplatform\n"
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]
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},
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{
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@ -50,7 +50,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import VertexAI"
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"from langchain.llms import VertexAI\n"
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]
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},
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{
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@ -74,7 +74,7 @@
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],
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"source": [
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"llm = VertexAI()\n",
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"print(llm(\"What are some of the pros and cons of Python as a programming language?\"))"
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"print(llm(\"What are some of the pros and cons of Python as a programming language?\"))\n"
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]
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},
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{
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@ -90,7 +90,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import PromptTemplate"
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"from langchain.prompts import PromptTemplate\n"
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]
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},
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{
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@ -102,7 +102,7 @@
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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.from_template(template)"
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"prompt = PromptTemplate.from_template(template)\n"
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]
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},
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{
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@ -111,7 +111,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = prompt | llm"
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"chain = prompt | llm\n"
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]
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},
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{
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@ -130,7 +130,7 @@
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],
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"source": [
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"question = \"Who was the president in the year Justin Beiber was born?\"\n",
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"print(chain.invoke({\"question\": question}))"
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"print(chain.invoke({\"question\": question}))\n"
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]
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},
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{
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@ -159,7 +159,7 @@
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},
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"outputs": [],
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"source": [
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"llm = VertexAI(model_name=\"code-bison\", max_output_tokens=1000, temperature=0.3)"
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"llm = VertexAI(model_name=\"code-bison\", max_output_tokens=1000, temperature=0.3)\n"
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]
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},
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{
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@ -168,7 +168,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"Write a python function that checks if a string is a valid email address\""
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"question = \"Write a python function that checks if a string is a valid email address\"\n"
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]
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},
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{
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@ -193,7 +193,7 @@
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}
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],
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"source": [
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"print(llm(question))"
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"print(llm(question))\n"
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]
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},
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{
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@ -223,7 +223,7 @@
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],
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"source": [
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"result = llm.generate([question])\n",
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"result.generations"
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"result.generations\n"
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]
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},
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{
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@ -243,7 +243,7 @@
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"source": [
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"# If running in a Jupyter notebook you'll need to install nest_asyncio\n",
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"\n",
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"# !pip install nest_asyncio"
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"# !pip install nest_asyncio\n"
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]
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},
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{
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@ -254,7 +254,7 @@
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"source": [
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"import asyncio\n",
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"# import nest_asyncio\n",
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"# nest_asyncio.apply()"
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"# nest_asyncio.apply()\n"
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]
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},
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{
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@ -274,7 +274,7 @@
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}
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],
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"source": [
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"asyncio.run(llm.agenerate([question]))"
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"asyncio.run(llm.agenerate([question]))\n"
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]
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},
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{
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@ -292,7 +292,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys"
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"import sys\n"
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]
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},
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{
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@ -337,7 +337,7 @@
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"source": [
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"for chunk in llm.stream(question):\n",
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" sys.stdout.write(chunk)\n",
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" sys.stdout.flush()"
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" sys.stdout.flush()\n"
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]
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},
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{
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@ -360,7 +360,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import VertexAIModelGarden"
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"from langchain.llms import VertexAIModelGarden\n"
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]
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},
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{
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@ -372,7 +372,7 @@
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"llm = VertexAIModelGarden(\n",
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" project=\"YOUR PROJECT\",\n",
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" endpoint_id=\"YOUR ENDPOINT_ID\"\n",
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")"
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")\n"
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]
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},
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{
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@ -381,7 +381,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print(llm(\"What is the meaning of life?\"))"
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"print(llm(\"What is the meaning of life?\"))\n"
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]
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},
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{
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@ -397,7 +397,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")"
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"prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")\n"
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]
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},
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{
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@ -407,7 +407,7 @@
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"outputs": [],
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"source": [
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"chian = prompt | llm\n",
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"print(chain.invoke({\"thing\": \"life\"}))"
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"print(chain.invoke({\"thing\": \"life\"}))\n"
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]
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}
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],
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@ -30,7 +30,6 @@ Access PaLM chat models like `chat-bison` and `codechat-bison` via Google Cloud.
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from langchain.chat_models import ChatVertexAI
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```
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## Document Loader
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### Google BigQuery
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@ -51,7 +50,7 @@ from langchain.document_loaders import BigQueryLoader
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### Google Cloud Storage
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>[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.
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> [Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.
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First, we need to install `google-cloud-storage` python package.
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@ -74,11 +73,11 @@ from langchain.document_loaders import GCSFileLoader
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### Google Drive
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>[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.
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> [Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.
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Currently, only `Google Docs` are supported.
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First, we need to install several python package.
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First, we need to install several python packages.
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```bash
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pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
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@ -91,10 +90,11 @@ from langchain.document_loaders import GoogleDriveLoader
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```
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## Vector Store
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### Google Vertex AI MatchingEngine
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### Google Vertex AI Vector Search
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> [Google Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides
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> the industry's leading high-scale low latency vector database. These vector databases are commonly
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> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview),
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> formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale
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> low latency vector database. These vector databases are commonly
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> referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.
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We need to install several python packages.
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@ -181,14 +181,28 @@ There exists a `GoogleSearchAPIWrapper` utility which wraps this API. To import
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```python
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from langchain.utilities import GoogleSearchAPIWrapper
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```
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For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search.html).
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We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
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```python
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from langchain.agents import load_tools
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tools = load_tools(["google-search"])
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```
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### Google Places
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See a [usage example](/docs/integrations/tools/google_places).
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```
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pip install googlemaps
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```
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```python
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from langchain.tools import GooglePlacesTool
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```
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## Document Transformer
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### Google Document AI
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@ -216,3 +230,40 @@ See a [usage example](/docs/integrations/document_transformers/docai).
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from langchain.document_loaders.blob_loaders import Blob
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from langchain.document_loaders.parsers import DocAIParser
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```
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## Chat loaders
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### Gmail
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> [Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
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First, we need to install several python packages.
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```bash
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pip install --upgrade google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client
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```
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See a [usage example and authorizing instructions](/docs/integrations/chat_loaders/gmail).
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```python
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from langchain.chat_loaders.gmail import GMailLoader
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```
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## Agents and Toolkits
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### Gmail
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See a [usage example and authorizing instructions](/docs/integrations/toolkits/gmail).
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```python
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from langchain.agents.agent_toolkits import GmailToolkit
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toolkit = GmailToolkit()
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```
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### Google Drive
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See a [usage example and authorizing instructions](/docs/integrations/toolkits/google_drive).
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```python
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from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper
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from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool
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```
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@ -1,28 +0,0 @@
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# Google Document AI
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>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform`
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> service to transform unstructured data from documents into structured data, making it easier
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> to understand, analyze, and consume.
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## Installation and Setup
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You need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)
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The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`)
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and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
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You can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
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tab in the Google Cloud Console.
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```bash
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pip install google-cloud-documentai
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pip install google-cloud-documentai-toolbox
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```
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## Document Transformer
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See a [usage example](/docs/integrations/document_transformers/docai).
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```python
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from langchain.document_loaders.blob_loaders import Blob
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from langchain.document_loaders.parsers import DocAIParser
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```
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@ -1,4 +1,4 @@
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# Google Serper
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# Serper - Google Search API
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This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search.
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It is broken into two parts: setup, and then references to the specific Google Serper wrapper.
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@ -5,11 +5,11 @@
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"id": "655b8f55-2089-4733-8b09-35dea9580695",
|
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"metadata": {},
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"source": [
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"# Google Vertex AI MatchingEngine\n",
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"# Google Vertex AI Vector Search\n",
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"\n",
|
||||
"This notebook shows how to use functionality related to the `GCP Vertex AI MatchingEngine` vector database.\n",
|
||||
"This notebook shows how to use functionality related to the `Google Cloud Vertex AI Vector Search` vector database.\n",
|
||||
"\n",
|
||||
"> Vertex AI [Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n",
|
||||
"> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview), formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n",
|
||||
"\n",
|
||||
"**Note**: This module expects an endpoint and deployed index already created as the creation time takes close to one hour. To see how to create an index refer to the section [Create Index and deploy it to an Endpoint](#create-index-and-deploy-it-to-an-endpoint)"
|
||||
]
|
||||
@ -29,7 +29,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.vectorstores import MatchingEngine"
|
||||
"from langchain.vectorstores import MatchingEngine\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -61,7 +61,7 @@
|
||||
"\n",
|
||||
"vector_store.add_texts(texts=texts)\n",
|
||||
"\n",
|
||||
"vector_store.similarity_search(\"lunch\", k=2)"
|
||||
"vector_store.similarity_search(\"lunch\", k=2)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -93,7 +93,7 @@
|
||||
"!pip install tensorflow \\\n",
|
||||
" google-cloud-aiplatform \\\n",
|
||||
" tensorflow-hub \\\n",
|
||||
" tensorflow-text "
|
||||
" tensorflow-text \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -108,7 +108,7 @@
|
||||
"\n",
|
||||
"from google.cloud import aiplatform\n",
|
||||
"import tensorflow_hub as hub\n",
|
||||
"import tensorflow_text"
|
||||
"import tensorflow_text\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -137,7 +137,7 @@
|
||||
"VPC_NETWORK_FULL = f\"projects/{PROJECT_NUMBER}/global/networks/{VPC_NETWORK}\"\n",
|
||||
"\n",
|
||||
"# Change this if you need the VPC to be created.\n",
|
||||
"CREATE_VPC = False"
|
||||
"CREATE_VPC = False\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -148,7 +148,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set the project id\n",
|
||||
"! gcloud config set project {PROJECT_ID}"
|
||||
"! gcloud config set project {PROJECT_ID}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -177,7 +177,7 @@
|
||||
"\n",
|
||||
" # Set up peering with service networking\n",
|
||||
" # Your account must have the \"Compute Network Admin\" role to run the following.\n",
|
||||
" ! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={VPC_NETWORK} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}"
|
||||
" ! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={VPC_NETWORK} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -188,7 +188,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Creating bucket.\n",
|
||||
"! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
|
||||
"! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -208,7 +208,7 @@
|
||||
"source": [
|
||||
"# Load the Universal Sentence Encoder module\n",
|
||||
"module_url = \"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3\"\n",
|
||||
"model = hub.load(module_url)"
|
||||
"model = hub.load(module_url)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -219,7 +219,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Generate embeddings for each word\n",
|
||||
"embeddings = model([\"banana\"])"
|
||||
"embeddings = model([\"banana\"])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -245,7 +245,7 @@
|
||||
"with open(\"data.json\", \"w\") as f:\n",
|
||||
" json.dump(initial_config, f)\n",
|
||||
"\n",
|
||||
"!gsutil cp data.json {EMBEDDING_DIR}/file.json"
|
||||
"!gsutil cp data.json {EMBEDDING_DIR}/file.json\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -255,7 +255,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)"
|
||||
"aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -279,7 +279,7 @@
|
||||
" dimensions=DIMENSIONS,\n",
|
||||
" approximate_neighbors_count=150,\n",
|
||||
" distance_measure_type=\"DOT_PRODUCT_DISTANCE\",\n",
|
||||
")"
|
||||
")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -300,7 +300,7 @@
|
||||
"my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(\n",
|
||||
" display_name=f\"{DISPLAY_NAME}-endpoint\",\n",
|
||||
" network=VPC_NETWORK_FULL,\n",
|
||||
")"
|
||||
")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -322,7 +322,7 @@
|
||||
" index=my_index, deployed_index_id=DEPLOYED_INDEX_ID\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"my_index_endpoint.deployed_indexes"
|
||||
"my_index_endpoint.deployed_indexes\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@ -3850,6 +3850,10 @@
|
||||
{
|
||||
"source": "/docs/integrations/retrievers/google_cloud_enterprise_search",
|
||||
"destination": "/docs/integrations/retrievers/google_vertex_ai_search"
|
||||
},
|
||||
{
|
||||
"source": "/docs/integrations/providers/google_document_ai",
|
||||
"destination": "/docs/integrations/platforms/google#google-document-ai"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user