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community: Deprecate Amazon Neptune resources in langchain-community (#29191)
Related: https://github.com/langchain-ai/langchain-aws/pull/322 The legacy `NeptuneOpenCypherQAChain` and `NeptuneSparqlQAChain` classes are being replaced by the new LCEL format chains `create_neptune_opencypher_qa_chain` and `create_neptune_sparql_qa_chain`, respectively, in the `langchain_aws` package. This PR adds deprecation warnings to all Neptune classes and functions that have been migrated to `langchain_aws`. All relevant documentation has also been updated to replace `langchain_community` usage with the new `langchain_aws` implementations. --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
This commit is contained in:
Michael Chin
GitHub
parent
c55af44711
commit
d9b856abad
@ -15,13 +15,14 @@
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">[openCypher](https://opencypher.org/) is an open-source implementation of Cypher.# Neptune Open Cypher QA Chain\n",
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"This QA chain queries Amazon Neptune using openCypher and returns human readable response\n",
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"\n",
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"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",
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"\n",
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"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 `create_neptune_opencypher_qa_chain`.\n",
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"\n",
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"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",
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"\n",
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"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",
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"\n",
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"\n",
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"\n",
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"## Using Neptune Database"
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]
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},
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@ -31,7 +32,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.graphs import NeptuneGraph\n",
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"from langchain_aws.graphs import NeptuneGraph\n",
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"\n",
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"host = \"<neptune-host>\"\n",
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"port = 8182\n",
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@ -53,7 +54,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.graphs import NeptuneAnalyticsGraph\n",
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"from langchain_aws.graphs import NeptuneAnalyticsGraph\n",
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"\n",
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"graph = NeptuneAnalyticsGraph(graph_identifier=\"<neptune-analytics-graph-id>\")"
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]
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@ -62,36 +63,35 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using NeptuneOpenCypherQAChain\n",
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"## Using the Neptune openCypher QA Chain\n",
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"\n",
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"This QA chain queries Neptune graph database using openCypher and returns human readable response."
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"This QA chain queries the Neptune graph database using openCypher and returns a human-readable response."
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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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"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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"'The Austin airport has 98 outgoing routes.'"
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]
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},
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"execution_count": 3,
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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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"outputs": [],
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"source": [
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"from langchain.chains import NeptuneOpenCypherQAChain\n",
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"from langchain_openai import ChatOpenAI\n",
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"from langchain_aws import ChatBedrockConverse\n",
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"from langchain_aws.chains import create_neptune_opencypher_qa_chain\n",
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"\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-4\")\n",
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"MODEL_ID = \"anthropic.claude-3-5-sonnet-20241022-v2:0\"\n",
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"llm = ChatBedrockConverse(\n",
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" model=MODEL_ID,\n",
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" temperature=0,\n",
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")\n",
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"\n",
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"chain = NeptuneOpenCypherQAChain.from_llm(llm=llm, graph=graph)\n",
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"chain = create_neptune_opencypher_qa_chain(\n",
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" llm=llm,\n",
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" graph=graph,\n",
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")\n",
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"\n",
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"chain.invoke(\"how many outgoing routes does the Austin airport have?\")"
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"result = chain.invoke(\n",
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" {\"query\": \"How many outgoing routes does the Austin airport have?\"}\n",
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")\n",
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"print(result[\"result\"].content)"
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]
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}
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],
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@ -15,7 +15,7 @@
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"\n",
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"\n",
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"This example uses a `NeptuneRdfGraph` class that connects with the Neptune database and loads its schema. \n",
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"The `NeptuneSparqlQAChain` is used to connect the graph and LLM to ask natural language questions.\n",
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"The `create_neptune_sparql_qa_chain` is used to connect the graph and LLM to ask natural language questions.\n",
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"\n",
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"This notebook demonstrates an example using organizational data.\n",
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"\n",
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@ -60,6 +60,11 @@
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"STAGE_BUCKET = \"<bucket-name>\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": ""
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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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@ -118,7 +123,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install --upgrade --quiet langchain langchain-community langchain-aws"
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"!pip install --upgrade --quiet langchain-aws"
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]
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},
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{
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@ -238,48 +243,37 @@
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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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"metadata": {},
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"source": "### Create the Neptune Database RDF Graph"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import boto3\n",
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"from langchain_aws import ChatBedrock\n",
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"from langchain_community.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain\n",
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"from langchain_community.graphs import NeptuneRdfGraph\n",
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"from langchain_aws.graphs import NeptuneRdfGraph\n",
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"\n",
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"host = \"<your host>\"\n",
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"port = 8182 # change if different\n",
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"region = \"us-east-1\" # change if different\n",
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"graph = NeptuneRdfGraph(host=host, port=port, use_iam_auth=True, region_name=region)\n",
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"\n",
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"# Optionally change the schema\n",
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"# Optionally, change the schema\n",
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"# elems = graph.get_schema_elements\n",
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"# change elems ...\n",
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"# graph.load_schema(elems)\n",
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"\n",
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"MODEL_ID = \"anthropic.claude-v2\"\n",
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"bedrock_client = boto3.client(\"bedrock-runtime\")\n",
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"llm = ChatBedrock(model_id=MODEL_ID, client=bedrock_client)\n",
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"\n",
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"chain = NeptuneSparqlQAChain.from_llm(\n",
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" llm=llm,\n",
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" graph=graph,\n",
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" examples=EXAMPLES,\n",
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" verbose=True,\n",
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" top_K=10,\n",
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" return_intermediate_steps=True,\n",
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" return_direct=False,\n",
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")"
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"# graph.load_schema(elems)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Ask questions\n",
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"Depends on the data we ingested above"
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"## Using the Neptune SPARQL QA Chain\n",
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"\n",
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"This QA chain queries the Neptune graph database using SPARQL and returns a human-readable response."
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]
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},
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{
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@ -288,7 +282,31 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"How many organizations are in the graph\"\"\")"
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"from langchain_aws import ChatBedrockConverse\n",
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"from langchain_aws.chains import create_neptune_sparql_qa_chain\n",
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"\n",
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"MODEL_ID = \"anthropic.claude-3-5-sonnet-20241022-v2:0\"\n",
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"llm = ChatBedrockConverse(\n",
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" model_id=MODEL_ID,\n",
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" temperature=0,\n",
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")\n",
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"\n",
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"chain = create_neptune_sparql_qa_chain(\n",
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" llm=llm,\n",
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" graph=graph,\n",
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" examples=EXAMPLES,\n",
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")\n",
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"\n",
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"result = chain.invoke({\"query\": \"How many organizations are in the graph?\"})\n",
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"print(result[\"result\"].content)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Extra questions\n",
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"Here are a few more prompts to try on the graph data that was ingested.\n"
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]
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},
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{
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@ -297,7 +315,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Are there any mergers or acquisitions\"\"\")"
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"chain.invoke({\"query\": \"Are there any mergers or acquisitions?\"})"
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]
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},
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{
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@ -306,7 +324,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find organizations\"\"\")"
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"chain.invoke({\"query\": \"Find organizations.\"})"
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]
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},
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{
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@ -315,7 +333,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find sites of MegaSystems or MegaFinancial\"\"\")"
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"chain.invoke({\"query\": \"Find sites of MegaSystems or MegaFinancial.\"})"
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]
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},
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{
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@ -324,7 +342,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find a member who is manager of one or more members.\"\"\")"
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"chain.invoke({\"query\": \"Find a member who is a manager of one or more members.\"})"
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]
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},
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{
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@ -333,7 +351,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"chain.invoke(\"\"\"Find five members and who their manager is.\"\"\")"
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"chain.invoke({\"query\": \"Find five members and their managers.\"})"
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]
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},
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{
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@ -343,7 +361,9 @@
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"outputs": [],
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"source": [
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"chain.invoke(\n",
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" \"\"\"Find org units or suborganizations of The Mega Group. What are the sites of those units?\"\"\"\n",
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" {\n",
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" \"query\": \"Find org units or suborganizations of The Mega Group. What are the sites of those units?\"\n",
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" }\n",
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")"
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]
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}
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@ -310,14 +310,25 @@ from langchain_community.chat_message_histories import DynamoDBChatMessageHistor
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## Graphs
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### Amazon Neptune
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>[Amazon Neptune](https://aws.amazon.com/neptune/)
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> is a high-performance graph analytics and serverless database for superior scalability and availability.
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For the Cypher and SPARQL integrations below, we need to install the `langchain-aws` library.
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```bash
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pip install langchain-aws
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```
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### Amazon Neptune with Cypher
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See a [usage example](/docs/integrations/graphs/amazon_neptune_open_cypher).
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```python
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from langchain_community.graphs import NeptuneGraph
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from langchain_community.graphs import NeptuneAnalyticsGraph
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from langchain_community.chains.graph_qa.neptune_cypher import NeptuneOpenCypherQAChain
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from langchain_aws.graphs import NeptuneGraph
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from langchain_aws.graphs import NeptuneAnalyticsGraph
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from langchain_aws.chains import create_neptune_opencypher_qa_chain
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```
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### Amazon Neptune with SPARQL
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@ -325,8 +336,8 @@ from langchain_community.chains.graph_qa.neptune_cypher import NeptuneOpenCypher
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See a [usage example](/docs/integrations/graphs/amazon_neptune_sparql).
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```python
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from langchain_community.graphs import NeptuneRdfGraph
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from langchain_community.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain
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from langchain_aws.graphs import NeptuneRdfGraph
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from langchain_aws.chains import create_neptune_sparql_qa_chain
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```
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@ -6,6 +6,7 @@ from typing import Any, Dict, List, Optional
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.chains.prompt_selector import ConditionalPromptSelector
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts.base import BasePromptTemplate
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@ -82,6 +83,11 @@ PROMPT_SELECTOR = ConditionalPromptSelector(
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)
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@deprecated(
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since="0.3.15",
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removal="1.0",
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alternative_import="langchain_aws.create_neptune_opencypher_qa_chain",
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)
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class NeptuneOpenCypherQAChain(Chain):
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"""Chain for question-answering against a Neptune graph
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by generating openCypher statements.
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@ -8,6 +8,7 @@ from typing import Any, Dict, List, Optional
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks.manager import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts.base import BasePromptTemplate
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@ -75,6 +76,11 @@ def extract_sparql(query: str) -> str:
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return query
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@deprecated(
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since="0.3.15",
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removal="1.0",
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alternative_import="langchain_aws.create_neptune_sparql_qa_chain",
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)
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class NeptuneSparqlQAChain(Chain):
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"""Chain for question-answering against a Neptune graph
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by generating SPARQL statements.
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@ -2,6 +2,8 @@ import json
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Tuple, Union
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from langchain_core._api.deprecation import deprecated
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class NeptuneQueryException(Exception):
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"""Exception for the Neptune queries."""
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@ -139,6 +141,11 @@ class BaseNeptuneGraph(ABC):
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"""
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@deprecated(
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since="0.3.15",
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removal="1.0",
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alternative_import="langchain_aws.NeptuneAnalyticsGraph",
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)
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class NeptuneAnalyticsGraph(BaseNeptuneGraph):
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"""Neptune Analytics wrapper for graph operations.
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@ -269,6 +276,11 @@ class NeptuneAnalyticsGraph(BaseNeptuneGraph):
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return summary
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@deprecated(
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since="0.3.15",
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removal="1.0",
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alternative_import="langchain_aws.NeptuneGraph",
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)
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class NeptuneGraph(BaseNeptuneGraph):
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"""Neptune wrapper for graph operations.
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@ -3,6 +3,7 @@ from types import SimpleNamespace
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from typing import Any, Dict, Optional, Sequence
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import requests
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from langchain_core._api.deprecation import deprecated
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# Query to find OWL datatype properties
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DTPROP_QUERY = """
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@ -28,6 +29,11 @@ ELEM_TYPES = {
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}
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@deprecated(
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since="0.3.15",
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removal="1.0",
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alternative_import="langchain_aws.NeptuneRdfGraph",
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)
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class NeptuneRdfGraph:
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"""Neptune wrapper for RDF graph operations.
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