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docs/extras/use_cases/graph/graph_hugegraph_qa.ipynb
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308
docs/extras/use_cases/graph/graph_hugegraph_qa.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d2777010",
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"metadata": {},
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"source": [
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"# HugeGraph QA Chain\n",
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"\n",
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"This notebook shows how to use LLMs to provide a natural language interface to [HugeGraph](https://hugegraph.apache.org/cn/) 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": "f26dcbe4",
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"metadata": {},
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"source": [
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"You will need to have a running HugeGraph instance.\n",
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"You can run a local docker container by running the executing the following script:\n",
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"\n",
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"```\n",
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"docker run \\\n",
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" --name=graph \\\n",
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" -itd \\\n",
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" -p 8080:8080 \\\n",
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" hugegraph/hugegraph\n",
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"```\n",
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"\n",
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"If we want to connect HugeGraph in the application, we need to install python sdk:\n",
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"\n",
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"```\n",
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"pip3 install hugegraph-python\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d64a29f1",
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"metadata": {},
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"source": [
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"If you are using the docker container, you need to wait a couple of second for the database to start, and then we need create schema and write graph data for the 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": 13,
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"id": "e53ab93e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from hugegraph.connection import PyHugeGraph\n",
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"\n",
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"client = PyHugeGraph(\"localhost\", \"8080\", user=\"admin\", pwd=\"admin\", graph=\"hugegraph\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b7c3a50e",
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"metadata": {},
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"source": [
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"First, we create the schema for a simple movie 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": "ef5372a8",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'create EdgeLabel success, Detail: \"b\\'{\"id\":1,\"name\":\"ActedIn\",\"source_label\":\"Person\",\"target_label\":\"Movie\",\"frequency\":\"SINGLE\",\"sort_keys\":[],\"nullable_keys\":[],\"index_labels\":[],\"properties\":[],\"status\":\"CREATED\",\"ttl\":0,\"enable_label_index\":true,\"user_data\":{\"~create_time\":\"2023-07-04 10:48:47.908\"}}\\'\"'"
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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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"\"\"\"schema\"\"\"\n",
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"schema = client.schema()\n",
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"schema.propertyKey(\"name\").asText().ifNotExist().create()\n",
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"schema.propertyKey(\"birthDate\").asText().ifNotExist().create()\n",
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"schema.vertexLabel(\"Person\").properties(\n",
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" \"name\", \"birthDate\"\n",
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").usePrimaryKeyId().primaryKeys(\"name\").ifNotExist().create()\n",
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"schema.vertexLabel(\"Movie\").properties(\"name\").usePrimaryKeyId().primaryKeys(\n",
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" \"name\"\n",
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").ifNotExist().create()\n",
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"schema.edgeLabel(\"ActedIn\").sourceLabel(\"Person\").targetLabel(\n",
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" \"Movie\"\n",
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").ifNotExist().create()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "016f7989",
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"metadata": {},
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"source": [
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"Then we can insert some data."
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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": 26,
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"id": "b7f4c370",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1:Robert De Niro--ActedIn-->2:The Godfather Part II"
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]
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},
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"execution_count": 26,
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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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"\"\"\"graph\"\"\"\n",
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"g = client.graph()\n",
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"g.addVertex(\"Person\", {\"name\": \"Al Pacino\", \"birthDate\": \"1940-04-25\"})\n",
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"g.addVertex(\"Person\", {\"name\": \"Robert De Niro\", \"birthDate\": \"1943-08-17\"})\n",
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"g.addVertex(\"Movie\", {\"name\": \"The Godfather\"})\n",
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"g.addVertex(\"Movie\", {\"name\": \"The Godfather Part II\"})\n",
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"g.addVertex(\"Movie\", {\"name\": \"The Godfather Coda The Death of Michael Corleone\"})\n",
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"\n",
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"g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather\", {})\n",
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"g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Part II\", {})\n",
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"g.addEdge(\n",
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" \"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Coda The Death of Michael Corleone\", {}\n",
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")\n",
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"g.addEdge(\"ActedIn\", \"1:Robert De Niro\", \"2:The Godfather Part II\", {})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5b8f7788",
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"metadata": {},
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"source": [
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"## Creating `HugeGraphQAChain`\n",
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"\n",
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"We can now create the `HugeGraph` and `HugeGraphQAChain`. To create the `HugeGraph` we simply need to pass the database object to the `HugeGraph` constructor."
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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": 27,
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"id": "f1f68fcf",
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"metadata": {
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"is_executing": true
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.chains import HugeGraphQAChain\n",
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"from langchain.graphs import HugeGraph"
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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": 28,
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"id": "b86ebfa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"graph = HugeGraph(\n",
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" username=\"admin\",\n",
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" password=\"admin\",\n",
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" address=\"localhost\",\n",
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" port=8080,\n",
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" graph=\"hugegraph\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e262540b",
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"metadata": {},
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"source": [
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"## Refresh graph schema information\n",
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"\n",
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"If the schema of database changes, you can refresh the schema information needed to generate Gremlin statements."
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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": 29,
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"id": "134dd8d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# graph.refresh_schema()"
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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": 30,
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"id": "e78b8e72",
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"metadata": {
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"ExecuteTime": {}
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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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"Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']]\n",
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"Edge properties: [name: ActedIn, properties: []]\n",
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"Relationships: ['Person--ActedIn-->Movie']\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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"print(graph.get_schema)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5c27e813",
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"metadata": {},
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"source": [
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"## Querying the graph\n",
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"\n",
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"We can now use the graph Gremlin QA chain to ask question of the graph"
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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": 31,
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"id": "3b23dead",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)"
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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": 32,
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"id": "76aecc93",
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"metadata": {},
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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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"\n",
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"\n",
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"\u001b[1m> Entering new chain...\u001b[0m\n",
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"Generated gremlin:\n",
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"\u001b[32;1m\u001b[1;3mg.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true)\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}]\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Al Pacino played in The Godfather.'"
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]
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},
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"execution_count": 32,
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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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"chain.run(\"Who played in The Godfather?\")"
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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": "869f0258",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "venv",
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"language": "python",
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"name": "venv"
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},
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"language_info": {
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"codemirror_mode": {
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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": "3.11.3"
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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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