mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
d5819a7ca7
Based on the inspiration from the SQL chain, the following three parameters are added to Graph Cypher Chain. - top_k: Limited the number of results from the database to be used as context - return_direct: Return database results without transforming them to natural language - return_intermediate_steps: Return intermediate steps
401 lines
10 KiB
Plaintext
401 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c94240f5",
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"metadata": {},
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"source": [
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"# GraphCypherQAChain\n",
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"\n",
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"This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language."
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]
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},
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{
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"cell_type": "markdown",
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"id": "dbc0ee68",
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"metadata": {},
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"source": [
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"You will need to have a running Neo4j instance. One option is to create a [free Neo4j database instance in their Aura cloud service](https://neo4j.com/cloud/platform/aura-graph-database/). You can also run the database locally using the [Neo4j Desktop application](https://neo4j.com/download/), or running a docker container.\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 neo4j \\\n",
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" -p 7474:7474 -p 7687:7687 \\\n",
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" -d \\\n",
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" -e NEO4J_AUTH=neo4j/pleaseletmein \\\n",
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" -e NEO4J_PLUGINS=\\[\\\"apoc\\\"\\] \\\n",
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" neo4j:latest\n",
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"```\n",
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"\n",
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"If you are using the docker container, you need to wait a couple of second for the database to start."
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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": 1,
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"id": "62812aad",
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"metadata": {},
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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 GraphCypherQAChain\n",
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"from langchain.graphs import Neo4jGraph"
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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": 2,
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"id": "0928915d",
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"metadata": {},
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"outputs": [],
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"source": [
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"graph = Neo4jGraph(\n",
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" url=\"bolt://localhost:7687\", username=\"neo4j\", password=\"pleaseletmein\"\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": "995ea9b9",
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"metadata": {},
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"source": [
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"## Seeding the database\n",
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"\n",
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"Assuming your database is empty, you can populate it using Cypher query language. The following Cypher statement is idempotent, which means the database information will be the same if you run it one or multiple times."
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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": 3,
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"id": "fedd26b9",
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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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"[]"
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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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"source": [
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"graph.query(\n",
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" \"\"\"\n",
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"MERGE (m:Movie {name:\"Top Gun\"})\n",
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"WITH m\n",
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"UNWIND [\"Tom Cruise\", \"Val Kilmer\", \"Anthony Edwards\", \"Meg Ryan\"] AS actor\n",
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"MERGE (a:Actor {name:actor})\n",
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"MERGE (a)-[:ACTED_IN]->(m)\n",
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"\"\"\"\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": "58c1a8ea",
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"metadata": {},
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"source": [
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"## Refresh graph schema information\n",
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"If the schema of database changes, you can refresh the schema information needed to generate Cypher 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": 4,
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"id": "4e3de44f",
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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": 5,
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"id": "1fe76ccd",
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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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" Node properties are the following:\n",
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" [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}]\n",
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" Relationship properties are the following:\n",
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" []\n",
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" The relationships are the following:\n",
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" ['(:Actor)-[:ACTED_IN]->(: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": "68a3c677",
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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 cypher 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": 6,
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"id": "7476ce98",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = GraphCypherQAChain.from_llm(\n",
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" ChatOpenAI(temperature=0), graph=graph, verbose=True\n",
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")"
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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": 7,
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"id": "ef8ee27b",
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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 GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]\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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"'Val Kilmer, Anthony Edwards, Meg Ryan, and Tom Cruise played in Top Gun.'"
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]
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},
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"execution_count": 7,
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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 Top Gun?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2d28c4df",
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"metadata": {},
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"source": [
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"## Limit the number of results\n",
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"You can limit the number of results from the Cypher QA Chain using the `top_k` parameter.\n",
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"The default is 10."
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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": 8,
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"id": "df230946",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = GraphCypherQAChain.from_llm(\n",
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" ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2\n",
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")"
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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": 9,
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"id": "3f1600ee",
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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 GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}]\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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"'Val Kilmer and Anthony Edwards played in Top Gun.'"
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]
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},
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"execution_count": 9,
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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 Top Gun?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "88c16206",
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"metadata": {},
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"source": [
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"## Return intermediate results\n",
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"You can return intermediate steps from the Cypher QA Chain using the `return_intermediate_steps` parameter"
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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": 10,
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"id": "e412f36b",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = GraphCypherQAChain.from_llm(\n",
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" ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True\n",
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")"
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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": 11,
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"id": "4f4699dc",
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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 GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
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"RETURN a.name\u001b[0m\n",
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"Full Context:\n",
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"\u001b[32;1m\u001b[1;3m[{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"Intermediate steps: [{'query': \"MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\\nRETURN a.name\"}, {'context': [{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}, {'a.name': 'Tom Cruise'}]}]\n",
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"Final answer: Val Kilmer, Anthony Edwards, Meg Ryan, and Tom Cruise played in Top Gun.\n"
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]
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}
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],
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"source": [
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"result = chain(\"Who played in Top Gun?\")\n",
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"print(f\"Intermediate steps: {result['intermediate_steps']}\")\n",
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"print(f\"Final answer: {result['result']}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d6e1b054",
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"metadata": {},
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"source": [
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"## Return direct results\n",
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"You can return direct results from the Cypher QA Chain using the `return_direct` parameter"
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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": 12,
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"id": "2d3acf10",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = GraphCypherQAChain.from_llm(\n",
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" ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True\n",
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")"
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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": "b0a9d143",
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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 GraphCypherQAChain chain...\u001b[0m\n",
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"Generated Cypher:\n",
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"\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\n",
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"RETURN a.name\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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"[{'a.name': 'Val Kilmer'},\n",
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" {'a.name': 'Anthony Edwards'},\n",
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" {'a.name': 'Meg Ryan'},\n",
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" {'a.name': 'Tom Cruise'}]"
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]
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},
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"execution_count": 13,
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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 Top Gun?\")"
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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": "74d0a36f",
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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": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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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": "3.8.8"
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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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