mirror of
https://github.com/hwchase17/langchain.git
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update golden-query notebook and fix typo in golden docs (#8253)
updating the documentation to be consistent for Golden query tool and have a better introduction to the tool
This commit is contained in:
Emory Petermann
GitHub
parent
c14571ab37
commit
7734a2b5ab
@ -1,6 +1,6 @@
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# Golden
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# Golden
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>[Golden](https://golden.com) provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: `Products from OpenAI`, `Generative ai companies with series a funding`, and `rappers who invest` can be used to retrieve relevant structured data about relevant entities.
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>[Golden](https://golden.com) provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: `Products from OpenAI`, `Generative ai companies with series a funding`, and `rappers who invest` can be used to retrieve structured data about relevant entities.
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>
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>
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>The `golden-query` langchain tool is a wrapper on top of the [Golden Query API](https://docs.golden.com/reference/query-api) which enables programmatic access to these results.
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>The `golden-query` langchain tool is a wrapper on top of the [Golden Query API](https://docs.golden.com/reference/query-api) which enables programmatic access to these results.
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>See the [Golden Query API docs](https://docs.golden.com/reference/query-api) for more information.
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>See the [Golden Query API docs](https://docs.golden.com/reference/query-api) for more information.
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@ -1,142 +1,160 @@
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{
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{
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"cells": [
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "245a954a",
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"metadata": {},
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"source": [
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"# Golden Query\n",
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"\n",
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"This notebook goes over how to use the golden-query tool.\n",
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"\n",
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"- Go to the [Golden API docs](https://docs.golden.com/) to get an overview about the Golden API.\n",
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"- Create a Golden account if you don't have one on the [Golden Website](golden.com).\n",
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"- Get your API key from the [Golden API Settings](https://golden.com/settings/api) page.\n",
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"- Save your API key into GOLDEN_API_KEY env variable"
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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": 5,
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"id": "34bb5968",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"GOLDEN_API_KEY\"] = \"\""
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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": 6,
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"id": "ac4910f8",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.utilities.golden_query import GoldenQueryAPIWrapper"
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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": 7,
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"id": "84b8f773",
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"metadata": {},
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"outputs": [],
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"source": [
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"golden_query = GoldenQueryAPIWrapper()"
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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": 9,
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"id": "068991a6",
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"metadata": {},
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"outputs": [
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{
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{
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"data": {
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"cell_type": "markdown",
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"text/plain": [
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"id": "245a954a",
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"{'results': [{'id': 4673886,\n",
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"metadata": {
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" 'latestVersionId': 60276991,\n",
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"id": "245a954a"
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" 'properties': [{'predicateId': 'name',\n",
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},
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" 'instances': [{'value': 'Samsung', 'citations': []}]}]},\n",
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"source": [
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" {'id': 7008,\n",
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"# Golden Query\n",
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" 'latestVersionId': 61087416,\n",
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"\n",
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" 'properties': [{'predicateId': 'name',\n",
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">[Golden](https://golden.com) provides a set of natural language APIs for querying and enrichment using the Golden Knowledge Graph e.g. queries such as: `Products from OpenAI`, `Generative ai companies with series a funding`, and `rappers who invest` can be used to retrieve structured data about relevant entities.\n",
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" 'instances': [{'value': 'Intel', 'citations': []}]}]},\n",
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">\n",
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" {'id': 24193,\n",
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">The `golden-query` langchain tool is a wrapper on top of the [Golden Query API](https://docs.golden.com/reference/query-api) which enables programmatic access to these results.\n",
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" 'latestVersionId': 60274482,\n",
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">See the [Golden Query API docs](https://docs.golden.com/reference/query-api) for more information.\n",
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" 'properties': [{'predicateId': 'name',\n",
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"\n",
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" 'instances': [{'value': 'Texas Instruments', 'citations': []}]}]},\n",
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"\n",
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" {'id': 1142,\n",
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"This notebook goes over how to use the `golden-query` tool.\n",
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" 'latestVersionId': 61406205,\n",
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"\n",
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" 'properties': [{'predicateId': 'name',\n",
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"- Go to the [Golden API docs](https://docs.golden.com/) to get an overview about the Golden API.\n",
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" 'instances': [{'value': 'Advanced Micro Devices', 'citations': []}]}]},\n",
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"- Get your API key from the [Golden API Settings](https://golden.com/settings/api) page.\n",
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" {'id': 193948,\n",
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"- Save your API key into GOLDEN_API_KEY env variable"
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" 'latestVersionId': 58326582,\n",
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]
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" 'properties': [{'predicateId': 'name',\n",
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},
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" 'instances': [{'value': 'Freescale Semiconductor', 'citations': []}]}]},\n",
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{
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" {'id': 91316,\n",
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"cell_type": "code",
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" 'latestVersionId': 60387380,\n",
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"execution_count": null,
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" 'properties': [{'predicateId': 'name',\n",
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"id": "34bb5968",
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" 'instances': [{'value': 'Agilent Technologies', 'citations': []}]}]},\n",
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"metadata": {
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" {'id': 90014,\n",
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"id": "34bb5968"
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" 'latestVersionId': 60388078,\n",
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},
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" 'properties': [{'predicateId': 'name',\n",
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"outputs": [],
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" 'instances': [{'value': 'Novartis', 'citations': []}]}]},\n",
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"source": [
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" {'id': 237458,\n",
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"import os\n",
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" 'latestVersionId': 61406160,\n",
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"\n",
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" 'properties': [{'predicateId': 'name',\n",
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"os.environ[\"GOLDEN_API_KEY\"] = \"\""
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" 'instances': [{'value': 'Analog Devices', 'citations': []}]}]},\n",
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]
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" {'id': 3941943,\n",
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},
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" 'latestVersionId': 60382250,\n",
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{
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" 'properties': [{'predicateId': 'name',\n",
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"cell_type": "code",
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" 'instances': [{'value': 'AbbVie Inc.', 'citations': []}]}]},\n",
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"execution_count": null,
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" {'id': 4178762,\n",
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"id": "ac4910f8",
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" 'latestVersionId': 60542667,\n",
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"metadata": {
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" 'properties': [{'predicateId': 'name',\n",
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"id": "ac4910f8"
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" 'instances': [{'value': 'IBM', 'citations': []}]}]}],\n",
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},
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" 'next': 'https://golden.com/api/v2/public/queries/59044/results/?cursor=eyJwb3NpdGlvbiI6IFsxNzYxNiwgIklCTS04M1lQM1oiXX0%3D&pageSize=10',\n",
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"outputs": [],
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" 'previous': None}"
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"source": [
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"from langchain.utilities.golden_query import GoldenQueryAPIWrapper"
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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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"id": "84b8f773",
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"metadata": {
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"id": "84b8f773"
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},
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"outputs": [],
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"source": [
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"golden_query = GoldenQueryAPIWrapper()"
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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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"id": "068991a6",
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"metadata": {
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"id": "068991a6",
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"outputId": "c5cdc6ec-03cf-4084-cc6f-6ae792d91d39"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'results': [{'id': 4673886,\n",
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" 'latestVersionId': 60276991,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Samsung', 'citations': []}]}]},\n",
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" {'id': 7008,\n",
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" 'latestVersionId': 61087416,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Intel', 'citations': []}]}]},\n",
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" {'id': 24193,\n",
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" 'latestVersionId': 60274482,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Texas Instruments', 'citations': []}]}]},\n",
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" {'id': 1142,\n",
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" 'latestVersionId': 61406205,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Advanced Micro Devices', 'citations': []}]}]},\n",
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" {'id': 193948,\n",
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" 'latestVersionId': 58326582,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Freescale Semiconductor', 'citations': []}]}]},\n",
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" {'id': 91316,\n",
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" 'latestVersionId': 60387380,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Agilent Technologies', 'citations': []}]}]},\n",
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" {'id': 90014,\n",
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" 'latestVersionId': 60388078,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Novartis', 'citations': []}]}]},\n",
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" {'id': 237458,\n",
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" 'latestVersionId': 61406160,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'Analog Devices', 'citations': []}]}]},\n",
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" {'id': 3941943,\n",
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" 'latestVersionId': 60382250,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'AbbVie Inc.', 'citations': []}]}]},\n",
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" {'id': 4178762,\n",
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" 'latestVersionId': 60542667,\n",
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" 'properties': [{'predicateId': 'name',\n",
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" 'instances': [{'value': 'IBM', 'citations': []}]}]}],\n",
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" 'next': 'https://golden.com/api/v2/public/queries/59044/results/?cursor=eyJwb3NpdGlvbiI6IFsxNzYxNiwgIklCTS04M1lQM1oiXX0%3D&pageSize=10',\n",
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" 'previous': None}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import json\n",
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"\n",
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"json.loads(golden_query.run(\"companies in nanotech\"))"
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]
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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}
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],
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],
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"source": [
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"metadata": {
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"import json\n",
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"kernelspec": {
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"\n",
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"display_name": ".venv",
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"json.loads(golden_query.run(\"companies in nanotech\"))"
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"language": "python",
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]
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"name": "python3"
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}
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},
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],
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"language_info": {
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"metadata": {
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"codemirror_mode": {
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"kernelspec": {
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"name": "ipython",
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"display_name": ".venv",
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"version": 3
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"language": "python",
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},
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"name": "python3"
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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": "3.9.13"
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},
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"vscode": {
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"interpreter": {
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"hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
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}
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},
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"colab": {
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"provenance": []
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}
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},
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},
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"language_info": {
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"nbformat": 4,
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"codemirror_mode": {
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"nbformat_minor": 5
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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": "3.9.13"
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},
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"vscode": {
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"interpreter": {
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"hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}
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