Add HugeGraphQAChain to support gremlin generating chain (#7132)

[Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a convenient, efficient, and adaptable graph database, compatible with the Apache TinkerPop3 framework and the Gremlin query language. In this PR, the HugeGraph and HugeGraphQAChain provide the same functionality as the existing integration with Neo4j and enables query generation and question answering over HugeGraph database. The difference is that the graph query language supported by HugeGraph is not cypher but another very popular graph query language [Gremlin](https://tinkerpop.apache.org/gremlin.html). A notebook example and a simple test case have also been added. --------- Co-authored-by: Bagatur <[email protected]>
langchain-ai · Jul 4, 2023 · 81eebc4 · 81eebc4
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diff --git a/docs/extras/modules/chains/additional/graph_hugegraph_qa.ipynb b/docs/extras/modules/chains/additional/graph_hugegraph_qa.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d2777010",
+   "metadata": {},
+   "source": [
+    "# HugeGraph QA Chain\n",
+    "\n",
+    "This notebook shows how to use LLMs to provide a natural language interface to [HugeGraph](https://hugegraph.apache.org/cn/) database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f26dcbe4",
+   "metadata": {},
+   "source": [
+    "You will need to have a running HugeGraph instance.\n",
+    "You can run a local docker container by running the executing the following script:\n",
+    "\n",
+    "```\n",
+    "docker run \\\n",
+    "    --name=graph \\\n",
+    "    -itd \\\n",
+    "    -p 8080:8080 \\\n",
+    "    hugegraph/hugegraph\n",
+    "```\n",
+    "\n",
+    "If we want to connect HugeGraph in the application, we need to install python sdk:\n",
+    "\n",
+    "```\n",
+    "pip3 install hugegraph-python\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d64a29f1",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e53ab93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hugegraph.connection import PyHugeGraph\n",
+    "\n",
+    "client = PyHugeGraph(\"localhost\", \"8080\", user=\"admin\", pwd=\"admin\", graph=\"hugegraph\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c3a50e",
+   "metadata": {},
+   "source": [
+    "First, we create the schema for a simple movie database:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ef5372a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'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\"}}\\'\"'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"schema\"\"\"\n",
+    "schema = client.schema()\n",
+    "schema.propertyKey(\"name\").asText().ifNotExist().create()\n",
+    "schema.propertyKey(\"birthDate\").asText().ifNotExist().create()\n",
+    "schema.vertexLabel(\"Person\").properties(\"name\", \"birthDate\").usePrimaryKeyId().primaryKeys(\"name\").ifNotExist().create()\n",
+    "schema.vertexLabel(\"Movie\").properties(\"name\").usePrimaryKeyId().primaryKeys(\"name\").ifNotExist().create()\n",
+    "schema.edgeLabel(\"ActedIn\").sourceLabel(\"Person\").targetLabel(\"Movie\").ifNotExist().create()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "016f7989",
+   "metadata": {},
+   "source": [
+    "Then we can insert some data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b7f4c370",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1:Robert De Niro--ActedIn-->2:The Godfather Part II"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"graph\"\"\"\n",
+    "g = client.graph()\n",
+    "g.addVertex(\"Person\", {\"name\": \"Al Pacino\", \"birthDate\": \"1940-04-25\"})\n",
+    "g.addVertex(\"Person\", {\"name\": \"Robert De Niro\", \"birthDate\": \"1943-08-17\"})\n",
+    "g.addVertex(\"Movie\", {\"name\": \"The Godfather\"})\n",
+    "g.addVertex(\"Movie\", {\"name\": \"The Godfather Part II\"})\n",
+    "g.addVertex(\"Movie\", {\"name\": \"The Godfather Coda The Death of Michael Corleone\"})\n",
+    "\n",
+    "g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather\", {})\n",
+    "g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Part II\", {})\n",
+    "g.addEdge(\"ActedIn\", \"1:Al Pacino\", \"2:The Godfather Coda The Death of Michael Corleone\", {})\n",
+    "g.addEdge(\"ActedIn\", \"1:Robert De Niro\", \"2:The Godfather Part II\", {})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b8f7788",
+   "metadata": {},
+   "source": [
+    "## Creating `HugeGraphQAChain`\n",
+    "\n",
+    "We can now create the `HugeGraph` and `HugeGraphQAChain`. To create the `HugeGraph` we simply need to pass the database object to the `HugeGraph` constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f1f68fcf",
+   "metadata": {
+    "is_executing": true
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.chains import HugeGraphQAChain\n",
+    "from langchain.graphs import HugeGraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b86ebfa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph = HugeGraph(\n",
+    "    username=\"admin\",\n",
+    "    password=\"admin\",\n",
+    "    address=\"localhost\",\n",
+    "    port=8080,\n",
+    "    graph=\"hugegraph\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e262540b",
+   "metadata": {},
+   "source": [
+    "## Refresh graph schema information\n",
+    "\n",
+    "If the schema of database changes, you can refresh the schema information needed to generate Gremlin statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "134dd8d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# graph.refresh_schema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e78b8e72",
+   "metadata": {
+    "ExecuteTime": {}
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']]\n",
+      "Edge properties: [name: ActedIn, properties: []]\n",
+      "Relationships: ['Person--ActedIn-->Movie']\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(graph.get_schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c27e813",
+   "metadata": {},
+   "source": [
+    "## Querying the graph\n",
+    "\n",
+    "We can now use the graph Gremlin QA chain to ask question of the graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "3b23dead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = HugeGraphQAChain.from_llm(\n",
+    "    ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "76aecc93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new  chain...\u001b[0m\n",
+      "Generated gremlin:\n",
+      "\u001b[32;1m\u001b[1;3mg.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true)\u001b[0m\n",
+      "Full Context:\n",
+      "\u001b[32;1m\u001b[1;3m[{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}]\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Al Pacino played in The Godfather.'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.run(\"Who played in The Godfather?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "869f0258",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/langchain/chains/__init__.py b/langchain/chains/__init__.py
@@ -15,6 +15,7 @@
 from langchain.chains.flare.base import FlareChain
 from langchain.chains.graph_qa.base import GraphQAChain
 from langchain.chains.graph_qa.cypher import GraphCypherQAChain
+from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain
 from langchain.chains.graph_qa.kuzu import KuzuQAChain
 from langchain.chains.graph_qa.nebulagraph import NebulaGraphQAChain
 from langchain.chains.hyde.base import HypotheticalDocumentEmbedder
@@ -69,6 +70,7 @@
     "GraphQAChain",
     "HypotheticalDocumentEmbedder",
     "KuzuQAChain",
+    "HugeGraphQAChain",
     "LLMBashChain",
     "LLMChain",
     "LLMCheckerChain",