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community[minor]: add neptune analytics graph (#20047)

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Replacement for PR
[#19772](https://github.com/langchain-ai/langchain/pull/19772).

---------

Co-authored-by: Dave Bechberger <dbechbe@amazon.com>
Co-authored-by: bechbd <bechbd@users.noreply.github.com>
This commit is contained in:
Piyush Jain
2024-04-09 07:20:59 -07:00
committed by GitHub
parent ad9750403b
commit cd7abc495a
5 changed files with 302 additions and 117 deletions
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46
docs/docs/integrations/graphs/amazon_neptune_open_cypher.ipynb
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@@ -12,12 +12,23 @@
">\n",
">[Cypher](https://en.wikipedia.org/wiki/Cypher_(query_language)) is a declarative graph query language that allows for expressive and efficient data querying in a property graph.\n",
">\n",
">[openCypher](https://opencypher.org/) is an open-source implementation of Cypher."
">[openCypher](https://opencypher.org/) is an open-source implementation of Cypher.",
"# Neptune Open Cypher QA Chain\n",
"This QA chain queries Amazon Neptune using openCypher and returns human readable response\n",
"\n",
"LangChain supports both [Neptune Database](https://docs.aws.amazon.com/neptune/latest/userguide/intro.html) and [Neptune Analytics](https://docs.aws.amazon.com/neptune-analytics/latest/userguide/what-is-neptune-analytics.html) with `NeptuneOpenCypherQAChain` \n",
"\n",
"\n",
"Neptune Database is a serverless graph database designed for optimal scalability and availability. It provides a solution for graph database workloads that need to scale to 100,000 queries per second, Multi-AZ high availability, and multi-Region deployments. You can use Neptune Database for social networking, fraud alerting, and Customer 360 applications.\n",
"\n",
"Neptune Analytics is an analytics database engine that can quickly analyze large amounts of graph data in memory to get insights and find trends. Neptune Analytics is a solution for quickly analyzing existing graph databases or graph datasets stored in a data lake. It uses popular graph analytic algorithms and low-latency analytic queries.\n",
"\n",
"## Using Neptune Database"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -30,9 +41,36 @@
"graph = NeptuneGraph(host=host, port=port, use_https=use_https)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using Neptune Analytics"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.graphs import NeptuneAnalyticsGraph\n",
"\n",
"graph = NeptuneAnalyticsGraph(graph_identifier=\"<neptune-analytics-graph-id>\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using NeptuneOpenCypherQAChain\n",
"\n",
"This QA chain queries Neptune graph database using openCypher and returns human readable response."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
@@ -54,7 +92,7 @@
"\n",
"chain = NeptuneOpenCypherQAChain.from_llm(llm=llm, graph=graph)\n",
"\n",
"chain.run(\"how many outgoing routes does the Austin airport have?\")"
"chain.invoke(\"how many outgoing routes does the Austin airport have?\")"
]
}
],