Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add HugeGraphQAChain to support gremlin generating chain #7132

Merged
merged 6 commits into from
Jul 4, 2023
Merged

Add HugeGraphQAChain to support gremlin generating chain #7132

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
62b5142
add HugeGraphQAChain support gremlin graph language
simon824 Jul 4, 2023
e398f27
fmt
simon824 Jul 4, 2023
3512425
fmt
baskaryan Jul 4, 2023
c0def0c
cr
baskaryan Jul 4, 2023
522a9d6
update
baskaryan Jul 4, 2023
f8f1d79
rm
baskaryan Jul 4, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
302 changes: 302 additions & 0 deletions docs/extras/modules/chains/additional/graph_hugegraph_qa.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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
}
2 changes: 2 additions & 0 deletions langchain/chains/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -69,6 +70,7 @@
"GraphQAChain",
"HypotheticalDocumentEmbedder",
"KuzuQAChain",
"HugeGraphQAChain",
"LLMBashChain",
"LLMChain",
"LLMCheckerChain",
Expand Down
Loading