diff --git a/docs/core_docs/docs/use_cases/graph/quickstart.ipynb b/docs/core_docs/docs/use_cases/graph/quickstart.ipynb
index 87d6932d7da7..6cb4aa12f45c 100644
--- a/docs/core_docs/docs/use_cases/graph/quickstart.ipynb
+++ b/docs/core_docs/docs/use_cases/graph/quickstart.ipynb
@@ -1,284 +1,258 @@
{
"cells": [
- {
- "cell_type": "raw",
- "metadata": {},
- "source": [
- "---\n",
- "sidebar_position: 0\n",
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Quickstart\n",
- "\n",
- "In this guide we'll go over the basic ways to create a Q&A chain over a graph database. These systems will allow us to ask a question about the data in a graph database and get back a natural language answer.\n",
- "\n",
- "## ⚠️ Security note ⚠️\n",
- "\n",
- "Building Q&A systems of graph databases requires executing model-generated graph queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your chain/agent's needs. This will mitigate though not eliminate the risks of building a model-driven system. For more on general security best practices, [see here](/docs/security).\n",
- "\n",
- "## Architecture\n",
- "\n",
- "At a high-level, the steps of most graph chains are:\n",
- "\n",
- "1. **Convert question to a graph database query**: Model converts user input to a graph database query (e.g. Cypher).\n",
- "2. **Execute graph database query**: Execute the graph database query.\n",
- "3. **Answer the question**: Model responds to user input using the query results.\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "## Setup\n",
- "\n",
- "First, get required packages and set environment variables.\n",
- "In this example, we will be using Neo4j graph database."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup\n",
- "#### Install dependencies\n",
- "\n",
- "```{=mdx}\n",
- "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
- "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
- "\n",
- "\n",
- "\n",
- "\n",
- " langchain @langchain/community @langchain/openai neo4j-driver\n",
- "\n",
- "```\n",
- "\n",
- "#### Set environment variables\n",
- "\n",
- "We'll use OpenAI in this example:\n",
- "\n",
- "```env\n",
- "OPENAI_API_KEY=your-api-key\n",
- "\n",
- "# Optional, use LangSmith for best-in-class observability\n",
- "LANGSMITH_API_KEY=your-api-key\n",
- "LANGCHAIN_TRACING_V2=true\n",
- "```\n",
- "\n",
- "Next, we need to define Neo4j credentials.\n",
- "Follow [these installation steps](https://neo4j.com/docs/operations-manual/current/installation/) to set up a Neo4j database.\n",
- "\n",
- "```env\n",
- "NEO4J_URI=\"bolt://localhost:7687\"\n",
- "NEO4J_USERNAME=\"neo4j\"\n",
- "NEO4J_PASSWORD=\"password\"\n",
- "```"
+ {
+ "cell_type": "raw",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "sidebar_position: 0\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Quickstart\n",
+ "\n",
+ "In this guide we'll go over the basic ways to create a Q&A chain over a graph database. These systems will allow us to ask a question about the data in a graph database and get back a natural language answer.\n",
+ "\n",
+ "## ⚠️ Security note ⚠️\n",
+ "\n",
+ "Building Q&A systems of graph databases requires executing model-generated graph queries. There are inherent risks in doing this. Make sure that your database connection permissions are always scoped as narrowly as possible for your chain/agent's needs. This will mitigate though not eliminate the risks of building a model-driven system. For more on general security best practices, [see here](/docs/security).\n",
+ "\n",
+ "## Architecture\n",
+ "\n",
+ "At a high-level, the steps of most graph chains are:\n",
+ "\n",
+ "1. **Convert question to a graph database query**: Model converts user input to a graph database query (e.g. Cypher).\n",
+ "2. **Execute graph database query**: Execute the graph database query.\n",
+ "3. **Answer the question**: Model responds to user input using the query results.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "## Setup\n",
+ "\n",
+ "First, get required packages and set environment variables.\n",
+ "In this example, we will be using Neo4j graph database."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "#### Install dependencies\n",
+ "\n",
+ "```{=mdx}\n",
+ "import IntegrationInstallTooltip from \"@mdx_components/integration_install_tooltip.mdx\";\n",
+ "import Npm2Yarn from \"@theme/Npm2Yarn\";\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " langchain @langchain/community @langchain/openai neo4j-driver\n",
+ "\n",
+ "```\n",
+ "\n",
+ "#### Set environment variables\n",
+ "\n",
+ "We'll use OpenAI in this example:\n",
+ "\n",
+ "```env\n",
+ "OPENAI_API_KEY=your-api-key\n",
+ "\n",
+ "# Optional, use LangSmith for best-in-class observability\n",
+ "LANGSMITH_API_KEY=your-api-key\n",
+ "LANGCHAIN_TRACING_V2=true\n",
+ "```\n",
+ "\n",
+ "Next, we need to define Neo4j credentials.\n",
+ "Follow [these installation steps](https://neo4j.com/docs/operations-manual/current/installation/) to set up a Neo4j database.\n",
+ "\n",
+ "```env\n",
+ "NEO4J_URI=\"bolt://localhost:7687\"\n",
+ "NEO4J_USERNAME=\"neo4j\"\n",
+ "NEO4J_PASSWORD=\"password\"\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The below example will create a connection with a Neo4j database and will populate it with example data about movies and their actors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Schema refreshed successfully.\n"
]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The below example will create a connection with a Neo4j database and will populate it with example data about movies and their actors."
- ]
- },
- {
- "cell_type": "code",
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
"execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Schema refreshed successfully.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import \"neo4j-driver\";\n",
- "import { Neo4jGraph } from \"@langchain/community/graphs/neo4j_graph\";\n",
- "\n",
- "const url = Deno.env.get(\"NEO4J_URI\");\n",
- "const username = Deno.env.get(\"NEO4J_USER\");\n",
- "const password = Deno.env.get(\"NEO4J_PASSWORD\");\n",
- "const graph = await Neo4jGraph.initialize({ url, username, password });\n",
- "\n",
- "// Import movie information\n",
- "const moviesQuery = `LOAD CSV WITH HEADERS FROM \n",
- "'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv'\n",
- "AS row\n",
- "MERGE (m:Movie {id:row.movieId})\n",
- "SET m.released = date(row.released),\n",
- " m.title = row.title,\n",
- " m.imdbRating = toFloat(row.imdbRating)\n",
- "FOREACH (director in split(row.director, '|') | \n",
- " MERGE (p:Person {name:trim(director)})\n",
- " MERGE (p)-[:DIRECTED]->(m))\n",
- "FOREACH (actor in split(row.actors, '|') | \n",
- " MERGE (p:Person {name:trim(actor)})\n",
- " MERGE (p)-[:ACTED_IN]->(m))\n",
- "FOREACH (genre in split(row.genres, '|') | \n",
- " MERGE (g:Genre {name:trim(genre)})\n",
- " MERGE (m)-[:IN_GENRE]->(g))`\n",
- "\n",
- "await graph.query(moviesQuery);"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Graph schema\n",
- "\n",
- "In order for an LLM to be able to generate a Cypher statement, it needs information about the graph schema. When you instantiate a graph object, it retrieves the information about the graph schema. If you later make any changes to the graph, you can run the `refreshSchema` method to refresh the schema information."
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import \"neo4j-driver\";\n",
+ "import { Neo4jGraph } from \"@langchain/community/graphs/neo4j_graph\";\n",
+ "\n",
+ "const url = Deno.env.get(\"NEO4J_URI\");\n",
+ "const username = Deno.env.get(\"NEO4J_USER\");\n",
+ "const password = Deno.env.get(\"NEO4J_PASSWORD\");\n",
+ "const graph = await Neo4jGraph.initialize({ url, username, password });\n",
+ "\n",
+ "// Import movie information\n",
+ "const moviesQuery = `LOAD CSV WITH HEADERS FROM \n",
+ "'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies_small.csv'\n",
+ "AS row\n",
+ "MERGE (m:Movie {id:row.movieId})\n",
+ "SET m.released = date(row.released),\n",
+ " m.title = row.title,\n",
+ " m.imdbRating = toFloat(row.imdbRating)\n",
+ "FOREACH (director in split(row.director, '|') | \n",
+ " MERGE (p:Person {name:trim(director)})\n",
+ " MERGE (p)-[:DIRECTED]->(m))\n",
+ "FOREACH (actor in split(row.actors, '|') | \n",
+ " MERGE (p:Person {name:trim(actor)})\n",
+ " MERGE (p)-[:ACTED_IN]->(m))\n",
+ "FOREACH (genre in split(row.genres, '|') | \n",
+ " MERGE (g:Genre {name:trim(genre)})\n",
+ " MERGE (m)-[:IN_GENRE]->(g))`\n",
+ "\n",
+ "await graph.query(moviesQuery);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Graph schema\n",
+ "\n",
+ "In order for an LLM to be able to generate a Cypher statement, it needs information about the graph schema. When you instantiate a graph object, it retrieves the information about the graph schema. If you later make any changes to the graph, you can run the `refreshSchema` method to refresh the schema information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Node properties are the following:\n",
+ "Movie {imdbRating: FLOAT, id: STRING, released: DATE, title: STRING}, Person {name: STRING}, Genre {name: STRING}\n",
+ "Relationship properties are the following:\n",
+ "\n",
+ "The relationships are the following:\n",
+ "(:Movie)-[:IN_GENRE]->(:Genre), (:Person)-[:DIRECTED]->(:Movie), (:Person)-[:ACTED_IN]->(:Movie)\n"
]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Node properties are the following:\n",
- "Movie {imdbRating: FLOAT, id: STRING, released: DATE, title: STRING}, Person {name: STRING}, Genre {name: STRING}\n",
- "Relationship properties are the following:\n",
- "\n",
- "The relationships are the following:\n",
- "(:Movie)-[:IN_GENRE]->(:Genre), (:Person)-[:DIRECTED]->(:Movie), (:Person)-[:ACTED_IN]->(:Movie)\n"
- ]
- }
- ],
- "source": [
- "await graph.refreshSchema()\n",
- "console.log(graph.schema)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Great! We've got a graph database that we can query. Now let's try hooking it up to an LLM.\n",
- "\n",
- "## Chain\n",
- "\n",
- "Let's use a simple chain that takes a question, turns it into a Cypher query, executes the query, and uses the result to answer the original question.\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "LangChain comes with a built-in chain for this workflow that is designed to work with Neo4j: [GraphCypherQAChain](https://python.langchain.com/docs/use_cases/graph/graph_cypher_qa)"
- ]
- },
- {
- "cell_type": "code",
+ }
+ ],
+ "source": [
+ "await graph.refreshSchema()\n",
+ "console.log(graph.schema)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Great! We've got a graph database that we can query. Now let's try hooking it up to an LLM.\n",
+ "\n",
+ "## Chain\n",
+ "\n",
+ "Let's use a simple chain that takes a question, turns it into a Cypher query, executes the query, and uses the result to answer the original question.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "LangChain comes with a built-in chain for this workflow that is designed to work with Neo4j: [GraphCypherQAChain](https://python.langchain.com/docs/use_cases/graph/graph_cypher_qa)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{ result: \u001b[32m\"James Woods, Joe Pesci, Robert De Niro, Sharon Stone\"\u001b[39m }"
+ ]
+ },
"execution_count": 5,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{ result: \u001b[32m\"James Woods, Joe Pesci, Robert De Niro, Sharon Stone\"\u001b[39m }"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import { GraphCypherQAChain } from \"langchain/chains/graph_qa/cypher\";\n",
- "import { ChatOpenAI } from \"@langchain/openai\";\n",
- "\n",
- "const llm = new ChatOpenAI({ model: \"gpt-3.5-turbo\", temperature: 0 })\n",
- "const chain = GraphCypherQAChain.fromLLM({\n",
- " llm,\n",
- " graph,\n",
- "});\n",
- "const response = await chain.invoke({ query: \"What was the cast of the Casino?\" })\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Next steps\n",
- "\n",
- "For more complex query-generation, we may want to create few-shot prompts or add query-checking steps. For advanced techniques like this and more check out:\n",
- "\n",
- "* [Prompting strategies](/docs/use_cases/graph/prompting): Advanced prompt engineering techniques.\n",
- "* [Mapping values](/docs/use_cases/graph/mapping): Techniques for mapping values from questions to database.\n",
- "* [Semantic layer](/docs/use_cases/graph/semantic): Techniques for working implementing semantic layers."
- ]
- }
- ],
- "source": [
- "import { GraphCypherQAChain } from \"langchain/chains/graph_qa/cypher\";\n",
- "import { ChatOpenAI } from \"@langchain/openai\";\n",
- "\n",
- "const llm = new ChatOpenAI({ modelName: \"gpt-3.5-turbo\", temperature: 0 })\n",
- "const chain = GraphCypherQAChain.fromLLM({\n",
- " llm,\n",
- " graph,\n",
- "});\n",
- "const response = await chain.invoke({ query: \"What was the cast of the Casino?\" })\n",
- "response"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Next steps\n",
- "\n",
- "For more complex query-generation, we may want to create few-shot prompts or add query-checking steps. For advanced techniques like this and more check out:\n",
- "\n",
- "* [Prompting strategies](/docs/use_cases/graph/prompting): Advanced prompt engineering techniques.\n",
- "* [Mapping values](/docs/use_cases/graph/mapping): Techniques for mapping values from questions to database.\n",
- "* [Semantic layer](/docs/use_cases/graph/semantic): Techniques for working implementing semantic layers.\n",
- "* [Constructing graphs](/docs/use_cases/graph/construction): Techniques for constructing knowledge graphs.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Deno",
- "language": "typescript",
- "name": "deno"
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import { GraphCypherQAChain } from \"langchain/chains/graph_qa/cypher\";\n",
+ "import { ChatOpenAI } from \"@langchain/openai\";\n",
+ "\n",
+ "const llm = new ChatOpenAI({ model: \"gpt-3.5-turbo\", temperature: 0 })\n",
+ "const chain = GraphCypherQAChain.fromLLM({\n",
+ " llm,\n",
+ " graph,\n",
+ "});\n",
+ "const response = await chain.invoke({ query: \"What was the cast of the Casino?\" })\n",
+ "response"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Next steps\n",
+ "\n",
+ "For more complex query-generation, we may want to create few-shot prompts or add query-checking steps. For advanced techniques like this and more check out:\n",
+ "\n",
+ "* [Prompting strategies](/docs/use_cases/graph/prompting): Advanced prompt engineering techniques.\n",
+ "* [Mapping values](/docs/use_cases/graph/mapping): Techniques for mapping values from questions to database.\n",
+ "* [Semantic layer](/docs/use_cases/graph/semantic): Techniques for working implementing semantic layers.\n",
+ "* [Constructing graphs](/docs/use_cases/graph/construction): Techniques for constructing knowledge graphs.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Deno",
+ "language": "typescript",
+ "name": "deno"
+ },
+ "language_info": {
+ "file_extension": ".ts",
+ "mimetype": "text/x.typescript",
+ "name": "typescript",
+ "nb_converter": "script",
+ "pygments_lexer": "typescript",
+ "version": "5.4.3"
+ }
},
- "language_info": {
- "file_extension": ".ts",
- "mimetype": "text/x.typescript",
- "name": "typescript",
- "nb_converter": "script",
- "pygments_lexer": "typescript",
- "version": "5.4.3"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+ "nbformat": 4,
+ "nbformat_minor": 4
+ }
+
\ No newline at end of file