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827 lines
23 KiB
Plaintext
827 lines
23 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c94240f5",
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"metadata": {
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"id": "c94240f5"
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},
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"source": [
|
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"# ArangoDB\n",
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"\n",
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"[](https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Langchain.ipynb)\n",
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"\n",
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">[ArangoDB](https://github.com/arangodb/arangodb) is a scalable graph database system to drive value from\n",
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">connected data, faster. Native graphs, an integrated search engine, and JSON support, via\n",
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">a single query language. `ArangoDB` runs on-prem or in the cloud.\n",
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"\n",
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"This notebook shows how to use LLMs to provide a natural language interface to an [ArangoDB](https://github.com/arangodb/arangodb#readme) database."
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]
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},
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{
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"cell_type": "markdown",
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"id": "dbc0ee68",
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"metadata": {
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"id": "dbc0ee68"
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},
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"source": [
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"## Setting up\n",
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"\n",
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"You can get a local `ArangoDB` instance running via the [ArangoDB Docker image](https://hub.docker.com/_/arangodb): \n",
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"\n",
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"```\n",
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"docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodb\n",
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"```\n",
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"\n",
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"An alternative is to use the [ArangoDB Cloud Connector package](https://github.com/arangodb/adb-cloud-connector#readme) to get a temporary cloud instance running:"
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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": 1,
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"id": "izi6YoFC8KRH",
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"metadata": {
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"id": "izi6YoFC8KRH"
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"%pip install --upgrade --quiet python-arango # The ArangoDB Python Driver\n",
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"%pip install --upgrade --quiet adb-cloud-connector # The ArangoDB Cloud Instance provisioner\n",
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"%pip install --upgrade --quiet langchain-openai\n",
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"%pip install --upgrade --quiet 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": 2,
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"id": "62812aad",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "62812aad",
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"outputId": "f7ed8346-d88b-40d1-eaff-68e97e0e157e"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Log: requesting new credentials...\n",
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"Succcess: new credentials acquired\n",
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"{\n",
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" \"dbName\": \"TUT3sp29s3pjf1io0h4cfdsq\",\n",
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" \"username\": \"TUTo6nkwgzkizej3kysgdyeo8\",\n",
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" \"password\": \"TUT9vx0qjqt42i9bq8uik4v9\",\n",
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" \"hostname\": \"tutorials.arangodb.cloud\",\n",
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" \"port\": 8529,\n",
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" \"url\": \"https://tutorials.arangodb.cloud:8529\"\n",
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"}\n"
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]
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}
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],
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"source": [
|
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"# Instantiate ArangoDB Database\n",
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"import json\n",
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"\n",
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"from adb_cloud_connector import get_temp_credentials\n",
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"from arango import ArangoClient\n",
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"\n",
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"con = get_temp_credentials()\n",
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"\n",
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"db = ArangoClient(hosts=con[\"url\"]).db(\n",
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" con[\"dbName\"], con[\"username\"], con[\"password\"], verify=True\n",
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")\n",
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"\n",
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"print(json.dumps(con, indent=2))"
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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": 3,
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"id": "0928915d",
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"metadata": {
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"id": "0928915d"
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},
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"outputs": [],
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"source": [
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"# Instantiate the ArangoDB-LangChain Graph\n",
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"from langchain_community.graphs import ArangoGraph\n",
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"\n",
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"graph = ArangoGraph(db)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "995ea9b9",
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"metadata": {
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"id": "995ea9b9"
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},
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"source": [
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"## Populating database\n",
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"\n",
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"We will rely on the `Python Driver` to import our [GameOfThrones](https://github.com/arangodb/example-datasets/tree/master/GameOfThrones) data into our database."
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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": 4,
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"id": "fedd26b9",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "fedd26b9",
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"outputId": "fc7d9067-e4f5-495e-cd0c-79135bc16fc2"
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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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"{'error': False,\n",
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" 'created': 4,\n",
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" 'errors': 0,\n",
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" 'empty': 0,\n",
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" 'updated': 0,\n",
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" 'ignored': 0,\n",
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" 'details': []}"
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]
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},
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"execution_count": 4,
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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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"if db.has_graph(\"GameOfThrones\"):\n",
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" db.delete_graph(\"GameOfThrones\", drop_collections=True)\n",
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"\n",
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"db.create_graph(\n",
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" \"GameOfThrones\",\n",
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" edge_definitions=[\n",
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" {\n",
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" \"edge_collection\": \"ChildOf\",\n",
|
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" \"from_vertex_collections\": [\"Characters\"],\n",
|
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" \"to_vertex_collections\": [\"Characters\"],\n",
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" },\n",
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" ],\n",
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")\n",
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"\n",
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"documents = [\n",
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" {\n",
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" \"_key\": \"NedStark\",\n",
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" \"name\": \"Ned\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": True,\n",
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" \"age\": 41,\n",
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" \"gender\": \"male\",\n",
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" },\n",
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" {\n",
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" \"_key\": \"CatelynStark\",\n",
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" \"name\": \"Catelyn\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": False,\n",
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" \"age\": 40,\n",
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" \"gender\": \"female\",\n",
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" },\n",
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" {\n",
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" \"_key\": \"AryaStark\",\n",
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" \"name\": \"Arya\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": True,\n",
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" \"age\": 11,\n",
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" \"gender\": \"female\",\n",
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" },\n",
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" {\n",
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" \"_key\": \"BranStark\",\n",
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" \"name\": \"Bran\",\n",
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" \"surname\": \"Stark\",\n",
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" \"alive\": True,\n",
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" \"age\": 10,\n",
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" \"gender\": \"male\",\n",
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" },\n",
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"]\n",
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"\n",
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"edges = [\n",
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" {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/AryaStark\"},\n",
|
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" {\"_to\": \"Characters/NedStark\", \"_from\": \"Characters/BranStark\"},\n",
|
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" {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/AryaStark\"},\n",
|
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" {\"_to\": \"Characters/CatelynStark\", \"_from\": \"Characters/BranStark\"},\n",
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"]\n",
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"\n",
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"db.collection(\"Characters\").import_bulk(documents)\n",
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"db.collection(\"ChildOf\").import_bulk(edges)"
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]
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},
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{
|
|
"cell_type": "markdown",
|
|
"id": "58c1a8ea",
|
|
"metadata": {
|
|
"id": "58c1a8ea"
|
|
},
|
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"source": [
|
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"## Getting and setting the ArangoDB schema\n",
|
|
"\n",
|
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"An initial `ArangoDB Schema` is generated upon instantiating the `ArangoDBGraph` object. Below are the schema's getter & setter methods should you be interested in viewing or modifying the schema:"
|
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]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "4e3de44f",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "4e3de44f",
|
|
"outputId": "6102f0c6-4a94-4e00-b93e-eb8de2f7d9d5"
|
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},
|
|
"outputs": [
|
|
{
|
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"name": "stdout",
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|
"output_type": "stream",
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"text": [
|
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"{\n",
|
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" \"Graph Schema\": [],\n",
|
|
" \"Collection Schema\": []\n",
|
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"}\n"
|
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]
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}
|
|
],
|
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"source": [
|
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"# The schema should be empty here,\n",
|
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"# since `graph` was initialized prior to ArangoDB Data ingestion (see above).\n",
|
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"\n",
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"import json\n",
|
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"\n",
|
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"print(json.dumps(graph.schema, indent=4))"
|
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]
|
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "1fe76ccd",
|
|
"metadata": {
|
|
"id": "1fe76ccd"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"graph.set_schema()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "mZ679anj_-Er",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "mZ679anj_-Er",
|
|
"outputId": "e05229c7-bc61-4803-d720-47e3e9b2b350"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{\n",
|
|
" \"Graph Schema\": [\n",
|
|
" {\n",
|
|
" \"graph_name\": \"GameOfThrones\",\n",
|
|
" \"edge_definitions\": [\n",
|
|
" {\n",
|
|
" \"edge_collection\": \"ChildOf\",\n",
|
|
" \"from_vertex_collections\": [\n",
|
|
" \"Characters\"\n",
|
|
" ],\n",
|
|
" \"to_vertex_collections\": [\n",
|
|
" \"Characters\"\n",
|
|
" ]\n",
|
|
" }\n",
|
|
" ]\n",
|
|
" }\n",
|
|
" ],\n",
|
|
" \"Collection Schema\": [\n",
|
|
" {\n",
|
|
" \"collection_name\": \"ChildOf\",\n",
|
|
" \"collection_type\": \"edge\",\n",
|
|
" \"edge_properties\": [\n",
|
|
" {\n",
|
|
" \"name\": \"_key\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"_id\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"_from\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"_to\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"_rev\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" }\n",
|
|
" ],\n",
|
|
" \"example_edge\": {\n",
|
|
" \"_key\": \"266218884025\",\n",
|
|
" \"_id\": \"ChildOf/266218884025\",\n",
|
|
" \"_from\": \"Characters/AryaStark\",\n",
|
|
" \"_to\": \"Characters/NedStark\",\n",
|
|
" \"_rev\": \"_gVPKGSq---\"\n",
|
|
" }\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"collection_name\": \"Characters\",\n",
|
|
" \"collection_type\": \"document\",\n",
|
|
" \"document_properties\": [\n",
|
|
" {\n",
|
|
" \"name\": \"_key\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"_id\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"_rev\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"name\",\n",
|
|
" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"surname\",\n",
|
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" \"type\": \"str\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"alive\",\n",
|
|
" \"type\": \"bool\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"name\": \"age\",\n",
|
|
" \"type\": \"int\"\n",
|
|
" },\n",
|
|
" {\n",
|
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" \"name\": \"gender\",\n",
|
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" \"type\": \"str\"\n",
|
|
" }\n",
|
|
" ],\n",
|
|
" \"example_document\": {\n",
|
|
" \"_key\": \"NedStark\",\n",
|
|
" \"_id\": \"Characters/NedStark\",\n",
|
|
" \"_rev\": \"_gVPKGPi---\",\n",
|
|
" \"name\": \"Ned\",\n",
|
|
" \"surname\": \"Stark\",\n",
|
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" \"alive\": true,\n",
|
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" \"age\": 41,\n",
|
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" \"gender\": \"male\"\n",
|
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" }\n",
|
|
" }\n",
|
|
" ]\n",
|
|
"}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# We can now view the generated schema\n",
|
|
"\n",
|
|
"import json\n",
|
|
"\n",
|
|
"print(json.dumps(graph.schema, indent=4))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "68a3c677",
|
|
"metadata": {
|
|
"id": "68a3c677"
|
|
},
|
|
"source": [
|
|
"## Querying the ArangoDB database\n",
|
|
"\n",
|
|
"We can now use the `ArangoDB Graph` QA Chain to inquire about our data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "635c4018",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import os\n",
|
|
"\n",
|
|
"os.environ[\"OPENAI_API_KEY\"] = \"your-key-here\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "7476ce98",
|
|
"metadata": {
|
|
"id": "7476ce98"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.chains import ArangoGraphQAChain\n",
|
|
"from langchain_openai import ChatOpenAI\n",
|
|
"\n",
|
|
"chain = ArangoGraphQAChain.from_llm(\n",
|
|
" ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "ef8ee27b",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 261
|
|
},
|
|
"id": "ef8ee27b",
|
|
"outputId": "6008ee92-a3ec-4968-d48d-6a5b66403959"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
|
"WITH Characters\n",
|
|
"FOR character IN Characters\n",
|
|
"FILTER character.name == \"Ned\" AND character.surname == \"Stark\"\n",
|
|
"RETURN character.alive\n",
|
|
"\u001b[0m\n",
|
|
"AQL Result:\n",
|
|
"\u001b[32;1m\u001b[1;3m[True]\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "string"
|
|
},
|
|
"text/plain": [
|
|
"'Yes, Ned Stark is alive.'"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"Is Ned Stark alive?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "9CSig1BgA76q",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 261
|
|
},
|
|
"id": "9CSig1BgA76q",
|
|
"outputId": "3060cf15-68e0-4f8a-cdfd-68af3f0e5fbf"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
|
"WITH Characters\n",
|
|
"FOR character IN Characters\n",
|
|
"FILTER character.name == \"Arya\" && character.surname == \"Stark\"\n",
|
|
"RETURN character.age\n",
|
|
"\u001b[0m\n",
|
|
"AQL Result:\n",
|
|
"\u001b[32;1m\u001b[1;3m[11]\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "string"
|
|
},
|
|
"text/plain": [
|
|
"'Arya Stark is 11 years old.'"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"How old is Arya Stark?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "9Fzdic_pA_4y",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 298
|
|
},
|
|
"id": "9Fzdic_pA_4y",
|
|
"outputId": "9bd93580-964e-4c53-e273-6723dad5f375"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
|
"WITH Characters, ChildOf\n",
|
|
"FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf\n",
|
|
" FILTER p.vertices[-1]._key == 'NedStark'\n",
|
|
" RETURN p\n",
|
|
"\u001b[0m\n",
|
|
"AQL Result:\n",
|
|
"\u001b[32;1m\u001b[1;3m[{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}]\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "string"
|
|
},
|
|
"text/plain": [
|
|
"'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"Are Arya Stark and Ned Stark related?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "zq_oeDpAOXpF",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 261
|
|
},
|
|
"id": "zq_oeDpAOXpF",
|
|
"outputId": "a47f37b5-4d7b-41c6-fbc7-7b1abc25fa20"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
|
"WITH Characters, ChildOf\n",
|
|
"FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf\n",
|
|
"FILTER v.alive == false\n",
|
|
"RETURN e\n",
|
|
"\u001b[0m\n",
|
|
"AQL Result:\n",
|
|
"\u001b[32;1m\u001b[1;3m[{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}]\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "string"
|
|
},
|
|
"text/plain": [
|
|
"'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"Does Arya Stark have a dead parent?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "Ob_3aGauGd7d",
|
|
"metadata": {
|
|
"id": "Ob_3aGauGd7d"
|
|
},
|
|
"source": [
|
|
"## Chain modifiers"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3P490E2dGiBp",
|
|
"metadata": {
|
|
"id": "3P490E2dGiBp"
|
|
},
|
|
"source": [
|
|
"You can alter the values of the following `ArangoDBGraphQAChain` class variables to modify the behaviour of your chain results\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "1B9h3PvzJ41T",
|
|
"metadata": {
|
|
"id": "1B9h3PvzJ41T"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Specify the maximum number of AQL Query Results to return\n",
|
|
"chain.top_k = 10\n",
|
|
"\n",
|
|
"# Specify whether or not to return the AQL Query in the output dictionary\n",
|
|
"chain.return_aql_query = True\n",
|
|
"\n",
|
|
"# Specify whether or not to return the AQL JSON Result in the output dictionary\n",
|
|
"chain.return_aql_result = True\n",
|
|
"\n",
|
|
"# Specify the maximum amount of AQL Generation attempts that should be made\n",
|
|
"chain.max_aql_generation_attempts = 5\n",
|
|
"\n",
|
|
"# Specify a set of AQL Query Examples, which are passed to\n",
|
|
"# the AQL Generation Prompt Template to promote few-shot-learning.\n",
|
|
"# Defaults to an empty string.\n",
|
|
"chain.aql_examples = \"\"\"\n",
|
|
"# Is Ned Stark alive?\n",
|
|
"RETURN DOCUMENT('Characters/NedStark').alive\n",
|
|
"\n",
|
|
"# Is Arya Stark the child of Ned Stark?\n",
|
|
"FOR e IN ChildOf\n",
|
|
" FILTER e._from == \"Characters/AryaStark\" AND e._to == \"Characters/NedStark\"\n",
|
|
" RETURN e\n",
|
|
"\"\"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "49cnjYV-PUv3",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 209
|
|
},
|
|
"id": "49cnjYV-PUv3",
|
|
"outputId": "f05f0a86-f922-47d1-91b9-5380ef1f996d"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
|
"RETURN DOCUMENT('Characters/NedStark').alive\n",
|
|
"\u001b[0m\n",
|
|
"AQL Result:\n",
|
|
"\u001b[32;1m\u001b[1;3m[True]\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "string"
|
|
},
|
|
"text/plain": [
|
|
"'Yes, according to the information in the database, Ned Stark is alive.'"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"Is Ned Stark alive?\")\n",
|
|
"\n",
|
|
"# chain(\"Is Ned Stark alive?\") # Returns a dictionary with the AQL Query & AQL Result"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "nWfALJ8dPczE",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 244
|
|
},
|
|
"id": "nWfALJ8dPczE",
|
|
"outputId": "1235baae-f3f7-438e-ef24-658cd17f727d"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
"\n",
|
|
"\u001b[1m> Entering new ArangoGraphQAChain chain...\u001b[0m\n",
|
|
"AQL Query (1):\u001b[32;1m\u001b[1;3m\n",
|
|
"FOR e IN ChildOf\n",
|
|
" FILTER e._from == \"Characters/BranStark\" AND e._to == \"Characters/NedStark\"\n",
|
|
" RETURN e\n",
|
|
"\u001b[0m\n",
|
|
"AQL Result:\n",
|
|
"\u001b[32;1m\u001b[1;3m[{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}]\u001b[0m\n",
|
|
"\n",
|
|
"\u001b[1m> Finished chain.\u001b[0m\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.google.colaboratory.intrinsic+json": {
|
|
"type": "string"
|
|
},
|
|
"text/plain": [
|
|
"'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"chain.run(\"Is Bran Stark the child of Ned Stark?\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [
|
|
"995ea9b9",
|
|
"58c1a8ea",
|
|
"68a3c677",
|
|
"Ob_3aGauGd7d"
|
|
],
|
|
"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": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|