From bf79f9c29b98bfa9a6a17a85fba7016a88d19d49 Mon Sep 17 00:00:00 2001 From: YeonwooSung Date: Sun, 25 Jun 2023 21:43:30 +0900 Subject: [PATCH] feat: add notebooks for LLMOps --- LLMs/src/Neo4jOpenAIApoc.ipynb | 553 +++++++++++ LLMs/src/generic_cypher_gpt4.ipynb | 1109 +++++++++++++++++++++++ LLMs/src/langchain_fulltext_agent.ipynb | 496 ++++++++++ LLMs/src/langchain_neo4j.ipynb | 541 +++++++++++ LLMs/src/langchain_neo4j_tips.ipynb | 859 ++++++++++++++++++ LLMs/src/langchain_neo4j_vertexai.ipynb | 582 ++++++++++++ 6 files changed, 4140 insertions(+) create mode 100644 LLMs/src/Neo4jOpenAIApoc.ipynb create mode 100644 LLMs/src/generic_cypher_gpt4.ipynb create mode 100644 LLMs/src/langchain_fulltext_agent.ipynb create mode 100644 LLMs/src/langchain_neo4j.ipynb create mode 100644 LLMs/src/langchain_neo4j_tips.ipynb create mode 100644 LLMs/src/langchain_neo4j_vertexai.ipynb diff --git a/LLMs/src/Neo4jOpenAIApoc.ipynb b/LLMs/src/Neo4jOpenAIApoc.ipynb new file mode 100644 index 0000000..bab44e5 --- /dev/null +++ b/LLMs/src/Neo4jOpenAIApoc.ipynb @@ -0,0 +1,553 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyNtGurXJvdCqbMNGCM35Inr", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EehUBcly6lRg", + "outputId": "a764076b-f9dc-4439-f521-c6fb1d533135" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: neo4j in /usr/local/lib/python3.10/dist-packages (5.9.0)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from neo4j) (2022.7.1)\n" + ] + } + ], + "source": [ + "!pip install neo4j" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Integrate LLM workflows with Knowledge Graph using Neo4j and APOC\n", + "## OpenAI and VertexAI endpoints are now available as APOC Extended procedures\n", + "Probably a day doesn't go by that you don't hear about new and exciting things happening in the Large Language Model (LLM) space. There are so many opportunities and use cases for any company to utilize the power of LLMs to enhance their productivity, transform or manipulate their data, and be used in conversational AI and QA systems.\n", + "To make it easier for you to integrate LLMs with Knowledge Graphs, the team at Neo4j has begun the journey of adding support for LLM integrations. The integrations are available as APOC Extended procedures. At the moment, OpenAI and VertexAI endpoints are supported, but we plan to add support for many more.\n", + "When I was brainstorming what would be a cool use case to demonstrate the newly added APOC procedures, my friend Michael Hunger suggested an exciting idea. What if we used graph context, or the neighborhood of a node, to enrich the information stored in text embeddings? That way, the vector similarity search could produce better results due to the increased richness of embedded information. The idea is simple but compelling and could be helpful in many use cases.\n", + "\n", + "## Neo4j environment setup\n", + "In this example, we will use both the APOC and Graph Data Science libraries. Luckily, Neo4j Sandbox projects have both libraries installed and additionally come with a prepopulated database. Therefore, you can set up the environment with a couple of clicks. We will use [the small Movie project](https://sandbox.neo4j.com/?usecase=movies) to avoid incurring a more considerable LLM API cost." + ], + "metadata": { + "id": "Dz238wQH1UEd" + } + }, + { + "cell_type": "code", + "source": [ + "# Define Neo4j connections\n", + "from neo4j import GraphDatabase\n", + "host = 'bolt://44.215.124.63:7687'\n", + "user = 'neo4j'\n", + "password = 'steel-orders-reproduction'\n", + "driver = GraphDatabase.driver(host,auth=(user, password))\n", + "\n", + "def run_query(query, params={}):\n", + " with driver.session() as session:\n", + " result = session.run(query, params)\n", + " return result.to_df()" + ], + "metadata": { + "id": "QM4VE2Q_6n0D" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + 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)\n", + "\n", + "The dataset contains Movie and Person nodes. There are only 38 movies, so we are dealing with a tiny dataset. The information provides a movie's title and tagline, when it was released, and who acted in or directed it.\n", + "## Constructing text embedding values\n", + "\n", + "We will be using the OpenAI API endpoints. Therefore, you will end to create an OpenAI account if you haven't already.\n", + "\n", + "As mentioned, the idea is to use the neighborhood of a node to construct its text embedding representation. Since the graph model is simple, we don't have a lot of creative freedom. We will create text embedding representations of movies by using their properties and neighbor information. In this instance, the neighbor information is only about its actors and directors. However, I believe that this concept can be applied to more complex graph schema and be used to improve your vector similarity search applications.\n", + "\n" + ], + "metadata": { + "id": "CgJdlP841fj4" + } + }, + { + "cell_type": "markdown", + "source": [ + 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)\n", + "\n", + "The typical approach we see nowadays, where we simply chunk and embed documents, might fail when looking for information that spans multiple documents. This problem is also known as multi-hop question answering. However, the multi-hop QA problem can be solved using knowledge graphs. One way to look at a knowledge graph is as condensed information storage. For example, an information extraction pipeline can be used to extract relevant information from various records. Using knowledge graphs, you can represent highly-connected information that spans multiple documents as relationships between various entities.\n", + "\n", + "One solution is to use LLMs to generate a Cypher statement that can be used to retrieve connected information from the database. Another solution, which we will use here, is to use the connection information to enrich the text embedding representations. Additionally, the enhanced information can be retrieved at query time to provide additional context to the LLM from which it can base its response.\n", + "\n", + "The following Cypher query can be used to retrieve all the relevant information about the movie nodes from their neighbors." + ], + "metadata": { + "id": "yz60G2IR1sX3" + } + }, + { + "cell_type": "code", + "source": [ + "print(run_query(\"\"\"\n", + "MATCH (m:Movie)\n", + "MATCH (m)-[r:ACTED_IN|DIRECTED]-(t)\n", + "WITH m, type(r) as type, collect(t.name) as names\n", + "WITH m, type+\": \"+reduce(s=\"\", n IN names | s + n + \", \") as types\n", + "WITH m, collect(types) as contexts\n", + "WITH m, \"Movie title: \"+ m.title + \" year: \"+coalesce(m.released,\"\") +\" plot: \"+ coalesce(m.tagline,\"\")+\"\\n\" +\n", + " reduce(s=\"\", c in contexts | s + substring(c, 0, size(c)-2) +\"\\n\") as context\n", + "RETURN context LIMIT 1\n", + "\"\"\")['context'][0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M7_NaY4S7HTa", + "outputId": "962b21fb-0e90-4799-ad6d-8d2715c11524" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Movie title: The Matrix year: 1999 plot: Welcome to the Real World\n", + "ACTED_IN: Emil Eifrem, Hugo Weaving, Laurence Fishburne, Carrie-Anne Moss, Keanu Reeves\n", + "DIRECTED: Lana Wachowski, Lilly Wachowski\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Depending on your domain, you might also use custom queries that retrieve information more than one hop away or sometimes want to aggregate some results.\n", + "\n", + "We will now use OpenAI's embedding endpoint to generate text embeddings representing the movies and their context and store them as node properties." + ], + "metadata": { + "id": "qsjWWgtP10Dg" + } + }, + { + "cell_type": "code", + "source": [ + "openai_api_key = \"OPENAI_API_KEY\"" + ], + "metadata": { + "id": "HQuFAs7d_Yl6" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "run_query(\"\"\"\n", + "CALL apoc.periodic.iterate(\n", + " 'MATCH (m:Movie) RETURN id(m) AS id',\n", + " 'MATCH (m:Movie)\n", + " WHERE id(m) = id\n", + " MATCH (m)-[r:ACTED_IN|DIRECTED]-(t)\n", + " WITH m, type(r) as type, collect(t.name) as names\n", + " WITH m, type+\": \"+reduce(s=\"\", n IN names | s + n + \", \") as types\n", + " WITH m, collect(types) as contexts\n", + " WITH m, \"Movie title: \"+ m.title + \" year: \"+coalesce(m.released,\"\") +\" plot: \"+ coalesce(m.tagline,\"\")+\"\\n\" +\n", + " reduce(s=\"\", c in contexts | s + substring(c, 0, size(c)-2) +\"\\n\") as context\n", + " CALL apoc.ml.openai.embedding([context], $apiKey) YIELD embedding\n", + " SET m.embedding = embedding',\n", + " {batchSize:1, retries:3, params: {apiKey: $apiKey}})\n", + "\"\"\", {'apiKey': openai_api_key})['errorMessages'][0]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wzeKmQnT9Mqv", + "outputId": "8ee5f12c-828d-4cb9-aa35-d6be9a832e47" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{}" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The newly added apoc.ml.openai.embeddingprocedures make generating text embeddings very easy using OpenAI's API. We wrap the API call with apoc.periodic.iterate to batch the transactions and introduce the retry policy.\n", + "\n", + "# Retrieval-augmented LLMs\n", + "\n", + "It looks like the mainstream trend is to provide LLMs with external information at query time. We can even find OpenAI's guides how to provide relevant information as part of the prompt to generate the answer.\n", + "\n", + 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)\n", + "\n", + "Here, we will use vector similarity search to find relevant movies given the user input. The workflow is the following:\n", + "We embed the user question with the same text embedding model we used to embed node context information\n", + "We use the cosine similarity to find the top 3 most relevant nodes and return their information to the LLM\n", + "The LLM constructs the final answer based on the provided information\n", + "\n", + "Since we will be using the gpt-3.5-turbo model to generate the final answer, it is a good practice to define the system prompt. To make it more readable, we will define the system prompt as Python variable and then use query parameters when executing Cypher statements.\n" + ], + "metadata": { + "id": "h6FbeBKO12H4" + } + }, + { + "cell_type": "code", + "source": [ + "system_prompt = \"\"\"\n", + "You are an assistant that helps to generate text to form nice and human understandable answers based.\n", + "The latest prompt contains the information, and you need to generate a human readable response based on the given information.\n", + "Make the answer sound as a response to the question. Do not mention that you based the result on the given information.\n", + "Do not add any additional information that is not explicitly provided in the latest prompt.\n", + "I repeat, do not add any information that is not explicitly given.\n", + "\"\"\"" + ], + "metadata": { + "id": "KR8qsmX72AQ_" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Next, we will define a function that constructs a user prompt based on the user question and the provided context from the database." + ], + "metadata": { + "id": "vK2Xj7ky2BGr" + } + }, + { + "cell_type": "code", + "source": [ + "def generate_user_prompt(question, context):\n", + " return f\"\"\"\n", + " The question is {question}\n", + " Answer the question by using the provided information:\n", + " {context}\n", + " \"\"\"" + ], + "metadata": { + "id": "tGHG1dOi2DlI" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Before asking the LLM to generate answers, we must define the intelligent search tool that will provide relevant context information based on the vector similarity search. As mentioned, we need to embed the user input and then use the cosine similarity to identify relevant nodes. With graphs, you can decide the type of information you want to retrieve and provide as context. In this example, we will return the same context information that was used to generate text embeddings along with similar movie information." + ], + "metadata": { + "id": "GXrNjboh2EWH" + } + }, + { + "cell_type": "code", + "source": [ + "def retrieve_context(question, k=3):\n", + " data = run_query(\n", + " \"\"\"\n", + " // retrieve the embedding of the question\n", + " CALL apoc.ml.openai.embedding([$question], $apiKey) YIELD embedding\n", + " // match relevant movies\n", + " MATCH (m:Movie)\n", + " WITH m, gds.similarity.cosine(embedding, m.embedding) AS score\n", + " ORDER BY score DESC\n", + " // limit the number of relevant documents\n", + " LIMIT toInteger($k)\n", + " // retrieve graph context\n", + " MATCH (m)--()--(m1:Movie)\n", + " WITH m,m1, count(*) AS count\n", + " ORDER BY count DESC\n", + " WITH m, apoc.text.join(collect(m1.title)[..3], \", \") AS similarMovies\n", + " MATCH (m)-[r:ACTED_IN|DIRECTED]-(t)\n", + " WITH m, similarMovies, type(r) as type, collect(t.name) as names\n", + " WITH m, similarMovies, type+\": \"+reduce(s=\"\", n IN names | s + n + \", \") as types\n", + " WITH m, similarMovies, collect(types) as contexts\n", + " WITH m, \"Movie title: \"+ m.title + \" year: \"+coalesce(m.released,\"\") +\" plot: \"+ coalesce(m.tagline,\"\")+\"\\n\" +\n", + " reduce(s=\"\", c in contexts | s + substring(c, 0, size(c)-2) +\"\\n\") + \"similar movies:\" + similarMovies + \"\\n\" as context\n", + " RETURN context\n", + " \"\"\",\n", + " {\"question\": question, \"k\": k, \"apiKey\": openai_api_key},\n", + " )\n", + " return data[\"context\"].to_list()" + ], + "metadata": { + "id": "E4EGxJuLAm1y" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "At the moment, you need to use the gds.similarity.cosine function to calculate the cosine similarity between the question and relevant nodes. After identifying the relevant nodes, we retrieve the context using two additional MATCHclauses. You can check out Neo4j's GraphAcademy to learn more about Cypher query language.\n", + "\n", + "Finally, we can define the function that takes in the user question and returns an answer." + ], + "metadata": { + "id": "PlPZeC3k2JML" + } + }, + { + "cell_type": "code", + "source": [ + "def generate_answer(question):\n", + " # Retrieve context\n", + " context = retrieve_context(question)\n", + " # Print context\n", + " for c in context:\n", + " print(c)\n", + " # Generate answer\n", + " response = run_query(\n", + " \"\"\"\n", + " CALL apoc.ml.openai.chat([{role:'system', content: $system},\n", + " {role: 'user', content: $user}], $apiKey) YIELD value\n", + " RETURN value.choices[0].message.content AS answer\n", + " \"\"\",\n", + " {\n", + " \"system\": system_prompt,\n", + " \"user\": generate_user_prompt(question, context),\n", + " \"apiKey\": openai_api_key,\n", + " },\n", + " )\n", + " return response[\"answer\"][0]" + ], + "metadata": { + "id": "AWL1XQh62Jax" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Let's test our retrieval-augmented LLM workflow." + ], + "metadata": { + "id": "pfUcyFnG2NW9" + } + }, + { + "cell_type": "code", + "source": [ + "generate_answer(\"Who played in the Matrix?\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "MSiHagee_70-", + "outputId": "398d15be-da08-4a6f-bcb5-d00b9dd1a0f1" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Movie title: The Matrix year: 1999 plot: Welcome to the Real World\n", + "ACTED_IN: Emil Eifrem, Hugo Weaving, Laurence Fishburne, Carrie-Anne Moss, Keanu Reeves\n", + "DIRECTED: Lana Wachowski, Lilly Wachowski\n", + "similar movies:The Matrix Revolutions, The Matrix Reloaded, V for Vendetta\n", + "\n", + "Movie title: The Matrix Reloaded year: 2003 plot: Free your mind\n", + "DIRECTED: Lana Wachowski, Lilly Wachowski\n", + "ACTED_IN: Hugo Weaving, Laurence Fishburne, Carrie-Anne Moss, Keanu Reeves\n", + "similar movies:The Matrix Revolutions, The Matrix, V for Vendetta\n", + "\n", + "Movie title: The Matrix Revolutions year: 2003 plot: Everything that has a beginning has an end\n", + "DIRECTED: Lana Wachowski, Lilly Wachowski\n", + "ACTED_IN: Hugo Weaving, Laurence Fishburne, Carrie-Anne Moss, Keanu Reeves\n", + "similar movies:The Matrix Reloaded, The Matrix, V for Vendetta\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The actors who played in The Matrix are Emil Eifrem, Hugo Weaving, Laurence Fishburne, Carrie-Anne Moss and Keanu Reeves.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "generate_answer(\"Recommend a movie with Jack Nicholson?\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 + }, + "id": "XepSm2miD7ac", + "outputId": "b86d6d0c-cea7-49bc-95d6-299c4fbd84ec" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Movie title: Something's Gotta Give year: 2003 plot: \n", + "ACTED_IN: Keanu Reeves, Diane Keaton, Jack Nicholson\n", + "DIRECTED: Nancy Meyers\n", + "similar movies:Something's Gotta Give, The Replacements, Johnny Mnemonic\n", + "\n", + "Movie title: One Flew Over the Cuckoo's Nest year: 1975 plot: If he's crazy, what does that make you?\n", + "ACTED_IN: Danny DeVito, Jack Nicholson\n", + "DIRECTED: Milos Forman\n", + "similar movies:Hoffa, As Good as It Gets, Something's Gotta Give\n", + "\n", + "Movie title: As Good as It Gets year: 1997 plot: A comedy from the heart that goes for the throat.\n", + "ACTED_IN: Helen Hunt, Jack Nicholson, Cuba Gooding Jr., Greg Kinnear\n", + "DIRECTED: James L. Brooks\n", + "similar movies:A Few Good Men, Cast Away, Twister\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'If you\\'re looking for a movie recommendation featuring Jack Nicholson, I\\'d suggest checking out \"One Flew Over the Cuckoo\\'s Nest\" from 1975. The movie stars Danny DeVito and Jack Nicholson, and was directed by Milos Forman. It\\'s a classic drama that portrays the struggles of patients in a mental institution.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "code", + "source": [ + "generate_answer(\"What are similar movies to As Good as It Gets?\")" + ], + "metadata": { + "id": "DtWRYPGnEgv9", + "outputId": "33ab60fd-4fdc-4adf-df15-ef8d8ae0e80e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 + } + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Movie title: Something's Gotta Give year: 2003 plot: \n", + "ACTED_IN: Keanu Reeves, Diane Keaton, Jack Nicholson\n", + "DIRECTED: Nancy Meyers\n", + "similar movies:Something's Gotta Give, The Replacements, Johnny Mnemonic\n", + "\n", + "Movie title: As Good as It Gets year: 1997 plot: A comedy from the heart that goes for the throat.\n", + "ACTED_IN: Helen Hunt, Jack Nicholson, Cuba Gooding Jr., Greg Kinnear\n", + "DIRECTED: James L. Brooks\n", + "similar movies:A Few Good Men, Cast Away, Twister\n", + "\n", + "Movie title: The Devil's Advocate year: 1997 plot: Evil has its winning ways\n", + "DIRECTED: Taylor Hackford\n", + "ACTED_IN: Al Pacino, Charlize Theron, Keanu Reeves\n", + "similar movies:That Thing You Do, Something's Gotta Give, The Replacements\n", + "\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'There are a few similar movies to \"As Good as It Gets\" that you may enjoy. If you liked the combination of comedy and drama in the plot, you may also enjoy \"A Few Good Men\" and \"Cast Away\". If you enjoyed the acting of Jack Nicholson, you might also like \"The Replacements\" and \"Johnny Mnemonic\", both of which he had a role in.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Summary\n", + "And there you have it, a glimpse into the fascinating world of integrating Large Language Models with Knowledge Graphs. As the field continues to evolve, so too will the tools and techniques at our disposal. With Neo4j and APOC's continued advancements, we can expect even greater innovation in how we handle and process data." + ], + "metadata": { + "id": "uvT3ajut2Q9z" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "-1pwS5zJx28Y" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/LLMs/src/generic_cypher_gpt4.ipynb b/LLMs/src/generic_cypher_gpt4.ipynb new file mode 100644 index 0000000..853e236 --- /dev/null +++ b/LLMs/src/generic_cypher_gpt4.ipynb @@ -0,0 +1,1109 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMV47wMTNlwFnmIA1EX1mSX", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JUDi5el-l8d8", + "outputId": "9104694a-4f9e-4c3d-a66d-7c013ad333c6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: openai in /usr/local/lib/python3.9/dist-packages (0.27.4)\n", + "Requirement already satisfied: neo4j in /usr/local/lib/python3.9/dist-packages (5.7.0)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.9/dist-packages (from openai) (3.8.4)\n", + "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.9/dist-packages (from openai) (2.27.1)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from openai) (4.65.0)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.9/dist-packages (from neo4j) (2022.7.1)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests>=2.20->openai) (1.26.15)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests>=2.20->openai) (2022.12.7)\n", + "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests>=2.20->openai) (2.0.12)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.20->openai) (3.4)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.9/dist-packages (from aiohttp->openai) (4.0.2)\n", + "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->openai) (1.9.2)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->openai) (23.1.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp->openai) (1.3.3)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp->openai) (6.0.4)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->openai) (1.3.1)\n" + ] + } + ], + "source": [ + "!pip install openai neo4j" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Generating Cypher queries with GPT-4 on any graph schema\n", + "\n", + "Large language models have a great potential to translate natural language into query language. For example, some people like to use GPT models to translate text to SQL, while others want to use GPT models to construct SPARQL queries. Personally, I prefer exploring how to translate natural language to Cypher query language.\n", + "In my experiments, I have noticed there are two approaches to developing an LLM flow that constructs Cypher statements. One option is to provide example queries in the prompt or use the examples to finetune an LLM model. However, the limitation of this approach is that it requires some work to produce the Cypher examples. Therefore, the example Cypher queries must be generated for each graph schema. On the other hand, we can provide an LLM directly with schema information and let it construct Cypher statements based on graph schema information alone. Using the second approach, we could develop a generic Cypher statement model to produce Cypher statements for any input graph schema, as we eliminate the need for any additional work like generating example Cypher statements.\n", + "This blog post aims to show you how to implement a Cypher statement-generating model by providing only the graph schema information. We will evaluate the model's Cypher construction capabilities on three graphs with different graph schemas. Currently, the only model I recommend to generate Cypher statements based on only the provided graph schema is GPT-4. Other models like GPT-3.5-turbo or text-davinci-003 aren't that great, and I have yet to find an open-source LLM model that would be good at following instructions in the prompt as well as GPT-4.\n", + "## Experiment Setup\n", + "I have implemented a Python class that connects to a Neo4j instance and fetches the schema information when initialized. The graph schema information can then be used as input to GPT-4 model." + ], + "metadata": { + "id": "NYILquGHZrd7" + } + }, + { + "cell_type": "code", + "source": [ + "node_properties_query = \"\"\"\n", + "CALL apoc.meta.data()\n", + "YIELD label, other, elementType, type, property\n", + "WHERE NOT type = \"RELATIONSHIP\" AND elementType = \"node\"\n", + "WITH label AS nodeLabels, collect(property) AS properties\n", + "RETURN {labels: nodeLabels, properties: properties} AS output\n", + "\n", + "\"\"\"\n", + "\n", + "rel_properties_query = \"\"\"\n", + "CALL apoc.meta.data()\n", + "YIELD label, other, elementType, type, property\n", + "WHERE NOT type = \"RELATIONSHIP\" AND elementType = \"relationship\"\n", + "WITH label AS nodeLabels, collect(property) AS properties\n", + "RETURN {type: nodeLabels, properties: properties} AS output\n", + "\"\"\"\n", + "\n", + "rel_query = \"\"\"\n", + "CALL apoc.meta.data()\n", + "YIELD label, other, elementType, type, property\n", + "WHERE type = \"RELATIONSHIP\" AND elementType = \"node\"\n", + "RETURN {source: label, relationship: property, target: other} AS output\n", + "\"\"\"" + ], + "metadata": { + "id": "67N5Q5-CmuG8" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from neo4j import GraphDatabase\n", + "from neo4j.exceptions import CypherSyntaxError\n", + "import openai\n", + "\n", + "\n", + "def schema_text(node_props, rel_props, rels):\n", + " return f\"\"\"\n", + " This is the schema representation of the Neo4j database.\n", + " Node properties are the following:\n", + " {node_props}\n", + " Relationship properties are the following:\n", + " {rel_props}\n", + " Relationship point from source to target nodes\n", + " {rels}\n", + " Make sure to respect relationship types and directions\n", + " \"\"\"\n", + "\n", + "\n", + "class Neo4jGPTQuery:\n", + " def __init__(self, url, user, password, openai_api_key):\n", + " self.driver = GraphDatabase.driver(url, auth=(user, password))\n", + " openai.api_key = openai_api_key\n", + " # construct schema\n", + " self.schema = self.generate_schema()\n", + "\n", + "\n", + " def generate_schema(self):\n", + " node_props = self.query_database(node_properties_query)\n", + " rel_props = self.query_database(rel_properties_query)\n", + " rels = self.query_database(rel_query)\n", + " return schema_text(node_props, rel_props, rels)\n", + "\n", + " def refresh_schema(self):\n", + " self.schema = self.generate_schema()\n", + "\n", + " def get_system_message(self):\n", + " return f\"\"\"\n", + " Task: Generate Cypher queries to query a Neo4j graph database based on the provided schema definition.\n", + " Instructions:\n", + " Use only the provided relationship types and properties.\n", + " Do not use any other relationship types or properties that are not provided.\n", + " If you cannot generate a Cypher statement based on the provided schema, explain the reason to the user.\n", + " Schema:\n", + " {self.schema}\n", + "\n", + " Note: Do not include any explanations or apologies in your responses.\n", + " \"\"\"\n", + "\n", + " def query_database(self, neo4j_query, params={}):\n", + " with self.driver.session() as session:\n", + " result = session.run(neo4j_query, params)\n", + " output = [r.values() for r in result]\n", + " output.insert(0, result.keys())\n", + " return output\n", + "\n", + " def construct_cypher(self, question, history=None):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": self.get_system_message()},\n", + " {\"role\": \"user\", \"content\": question},\n", + " ]\n", + " # Used for Cypher healing flows\n", + " if history:\n", + " messages.extend(history)\n", + "\n", + " completions = openai.ChatCompletion.create(\n", + " model=\"gpt-4\",\n", + " temperature=0.0,\n", + " max_tokens=1000,\n", + " messages=messages\n", + " )\n", + " return completions.choices[0].message.content\n", + "\n", + " def run(self, question, history=None, retry=True):\n", + " # Construct Cypher statement\n", + " cypher = self.construct_cypher(question, history)\n", + " print(cypher)\n", + " try:\n", + " return self.query_database(cypher)\n", + " # Self-healing flow\n", + " except CypherSyntaxError as e:\n", + " # If out of retries\n", + " if not retry:\n", + " return \"Invalid Cypher syntax\"\n", + " # Self-healing Cypher flow by\n", + " # providing specific error to GPT-4\n", + " print(\"Retrying\")\n", + " return self.run(\n", + " question,\n", + " [\n", + " {\"role\": \"assistant\", \"content\": cypher},\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": f\"\"\"This query returns an error: {str(e)} \n", + " Give me a improved query that works without any explanations or apologies\"\"\",\n", + " },\n", + " ],\n", + " retry=False\n", + " )\n" + ], + "metadata": { + "id": "IHY0Kt2-mFFq" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "It's interesting how I ended with the final system message to get GPT-4 following my instructions. At first, I wrote my directions as plain text and added some constraints. However, the model wasn't doing exactly what I wanted, so I opened ChatGPT in a web browser and asked it to rewrite my instructions in a manner that GPT-4 would understand. Finally, ChatGPT seems to understand what works best as GPT-4 prompts, as the model behaved much better with this new prompt structure.\n", + "\n", + "\n", + "The GPT-4 model uses the ChatCompletion endpoint, which uses a combination of system, user, and optional assistant messages when we want to ask follow-up questions. So, we always start with only the system and user message. However, if the generated Cypher statement has any syntax error, the self-healing flow will be started, where we include the error in the follow-up question so that GPT-4 can fix the query. Therefore, we have included the optional history parameter for Cypher self-healing flow.\n", + "\n", + "The run function starts by generating a Cypher statement. Then, the generated Cypher statement is used to query the Neo4j database. If the Cypher syntax is valid, the query results are returned. However, suppose there is a Cypher syntax error. In that case, we do a single follow-up to GPT-4, provide the generated Cypher statement it constructed in the previous call, and include the error from the Neo4j database. GPT-4 is quite good at fixing a Cypher statement when provided with the error.\n", + "\n", + "The self-healing Cypher flow was inspired by others who have used similar flows for Python and other code. However, I have limited the follow-up Cypher healing to only a single iteration. If the follow-up doesn't provide a valid Cypher statement, the function returns the \"Invalid Cypher syntax response\".\n", + "Let's now test the capabilities of GPT-4 to construct Cypher statements based on the provided graph schema only." + ], + "metadata": { + "id": "LXk1Uy-vZ7cm" + } + }, + { + "cell_type": "markdown", + "source": [ + "We will begin with a simple airport route graph, which is available [as the GDS project in Neo4j Sandbox](https://sandbox.neo4j.com/?usecase=graph-data-science2).\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "CGCS37mZushD" + } + }, + { + "cell_type": "markdown", + "source": [ + "![Screenshot from 2023-04-26 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)" + ], + "metadata": { + "id": "Lc7d99b6azdm" + } + }, + { + "cell_type": "markdown", + "source": [ + "This graph schema is relatively simple. The graph contains information about airports and their routes. Additionally, information about the airport's city, region, country, and continent is stored as separate nodes.\n", + "\n", + "We can instantiate the Python class used to query the airport graph with the following Python code:" + ], + "metadata": { + "id": "qxzVgleva2dp" + } + }, + { + "cell_type": "code", + "source": [ + "openai_key = \"INSERT_OPENAI_API_KEY\"" + ], + "metadata": { + "id": "7hg23hmtqIvD" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "gds_db = Neo4jGPTQuery(\n", + " url=\"bolt://18.207.187.166:7687\",\n", + " user=\"neo4j\",\n", + " password=\"preferences-accomplishments-vent\",\n", + " openai_api_key=openai_key,\n", + ")\n" + ], + "metadata": { + "id": "NZTTW3TkpKFY" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Now we can begin our experiment. First, we will begin with a simple question." + ], + "metadata": { + "id": "aD4z9PW7a8i-" + } + }, + { + "cell_type": "code", + "source": [ + "gds_db.run(\"\"\"\n", + "What is the city with the most airports?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TyBNV92QqeUn", + "outputId": "fca146ab-ac24-4abc-9d6b-3db3db0d4f8d" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (a:Airport)-[:IN_CITY]->(c:City)\n", + "RETURN c.name AS City, COUNT(a) AS NumberOfAirports\n", + "ORDER BY NumberOfAirports DESC\n", + "LIMIT 1\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['City', 'NumberOfAirports'], ['London', 6]]" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Great start. The Cypher statement was correctly generated, and we found that London has six airports. Next, let's try something more complex." + ], + "metadata": { + "id": "Qm67jPVaa-u4" + } + }, + { + "cell_type": "code", + "source": [ + "gds_db.run(\"\"\"\n", + "calculate the minimum, maximum, average, and standard deviation of the number of flights out of each airport.\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QLbBT4zVrkgS", + "outputId": "c9a4e5b8-7761-43fe-df29-7829a14d9783" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (a:Airport)-[r:HAS_ROUTE]->(:Airport)\n", + "WITH a, count(r) as num_flights\n", + "RETURN min(num_flights) as min_flights, max(num_flights) as max_flights, avg(num_flights) as avg_flights, stDev(num_flights) as stddev_flights\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['min_flights', 'max_flights', 'avg_flights', 'stddev_flights'],\n", + " [1, 307, 20.905362776025285, 38.28730861505158]]" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Quite nice. The GPT-4 model correctly assumed that flights relate to the HAS_ROUTE relationship. Additionally, it accurately aggregates flights per airport, then calculates the specified metrics.\n", + "\n", + "Let's now throw it a curveball. We will ask the model to calculate the variance since Cypher doesn't have any built-in function to calculate the variance." + ], + "metadata": { + "id": "Ci3zSR8ZbBN9" + } + }, + { + "cell_type": "code", + "source": [ + "gds_db.run(\"\"\"\n", + "calculate the variance of the number of flights out of each airport.\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 111 + }, + "id": "OpnkI_laEMda", + "outputId": "8ecb6490-5e61-4d94-f803-2d10df74776b" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The provided schema does not have information about the number of flights for each airport. Therefore, it is not possible to calculate the variance of the number of flights out of each airport using the given schema.\n", + "Retrying\n", + "As mentioned earlier, the provided schema does not have information about the number of flights for each airport. Therefore, it is not possible to create a query to calculate the variance of the number of flights out of each airport using the given schema.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Invalid Cypher syntax'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "First of all, GPT-4 provided explanations when explicitly told not to. Secondly, neither Cypher statements make any sense. In this example, even the self-healing flow didn't succeed since we are not dealing with a Cypher syntax error but a GPT-4 system malfunction.\n", + "\n", + "I have noticed that GPT-4 struggles when it needs to perform multiple aggregations using different grouping keys in a single Cypher statement. Here it wanted to split the statement into two parts (which don't work either), but in other cases it wants to borrow syntax from SQL.\n", + "\n", + "However, GPT-4 is quite obedient and provides the specified results from the database as instructed by the user." + ], + "metadata": { + "id": "PHk4gjdebFEg" + } + }, + { + "cell_type": "code", + "source": [ + "gds_db.run(\"\"\"\n", + "Find the shortest route between ATL and IAH airports\n", + "and return only the iata and runways property of the nodes as a map object\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h_IxUkoIykhd", + "outputId": "f6e60bb7-7a0c-4d5a-f7a7-448adda75d26" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (atl:Airport {iata: \"ATL\"}), (iah:Airport {iata: \"IAH\"}), path = shortestPath((atl)-[:HAS_ROUTE*]-(iah))\n", + "WITH nodes(path) AS airports\n", + "UNWIND airports AS airport\n", + "RETURN {iata: airport.iata, runways: airport.runways} AS airportInfo\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['airportInfo'],\n", + " [{'iata': 'ATL', 'runways': 5}],\n", + " [{'iata': 'IAH', 'runways': 5}]]" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here is where the power of GPT-4 shines. The more specific we are in what we want to find and how we want the results to be structured, the better it works.\n", + "We can also test if it knows how to use the GDS library." + ], + "metadata": { + "id": "e2VDhhXxbJyR" + } + }, + { + "cell_type": "code", + "source": [ + "print(gds_db.construct_cypher(\"\"\"\n", + "Calculate the betweenness centrality of airports using the Graph Data Science library\n", + "\"\"\"))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7S6rkSYzufWM", + "outputId": "b5d75083-5198-4ca6-b873-e7718f735f73" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CALL gds.betweenness.stream({\n", + " nodeProjection: 'Airport',\n", + " relationshipProjection: {\n", + " HAS_ROUTE: {\n", + " type: 'HAS_ROUTE',\n", + " orientation: 'UNDIRECTED'\n", + " }\n", + " }\n", + "})\n", + "YIELD nodeId, score\n", + "RETURN gds.util.asNode(nodeId).id AS airportId, score\n", + "ORDER BY score DESC\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Well, the constructed Cypher statement looks fine. However, there is only one problem. The generated Cypher statement uses the anonymous graph projection, which was deprecated and removed in GDS v2. Here we see some issues arising from GPT-4's knowledge cutoff date. Unfortunately, it looks like GDS v2 was released after the knowledge cutoff date, and therefore the new syntax is not baked into GPT-4. Therefore, at the moment, the GPT-4 model doesn't provide valid GDS procedures.\n", + "\n", + "If you pay attention, you will also notice that GPT-4 never uses the Cypher subquery syntax, which is again another syntax change that was added after the knowledge cutoff date.\n", + "\n", + "Interestingly, if you calculate any of the values from graph algorithms and store them as node property, the GPT-4 has no problem retrieving that." + ], + "metadata": { + "id": "55cxuibdbNhl" + } + }, + { + "cell_type": "code", + "source": [ + "gds_db.run(\"\"\"\n", + "Use PageRank to find the five most important airports and return their descr and pagerank value\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6DPu-MyYwFa7", + "outputId": "b3829b72-16a5-40d9-ac95-61d9d11ce17b" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (a:Airport)\n", + "RETURN a.descr, a.pagerank\n", + "ORDER BY a.pagerank DESC\n", + "LIMIT 5\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['a.descr', 'a.pagerank'],\n", + " ['Dallas/Fort Worth International Airport', 11.97978260670334],\n", + " [\"Chicago O'Hare International Airport\", 11.162988178920267],\n", + " ['Denver International Airport', 10.997299338126387],\n", + " ['Hartsfield - Jackson Atlanta International Airport', 10.389948350302957],\n", + " ['Istanbul International Airport', 8.42580121770578]]" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "It looks like Dallas and Chicago have the highest PageRank scores.\n", + "## Healthcare sandbox\n", + "You might say that the airport sandbox might have been part of the training data of GPT-4. That is definitely a possibility. Therefore, let's test GPT-4 ability to construct Cypher statements on the latest Neo4j Sandbox project dealing with healthcare data, published between December 2022 and January 2023. That should be after the GPT-4 knowledge cutoff date.\n", + "\n", + "![Screenshot from 2023-04-26 22-10-30.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "PtyWWubP4xLh" + } + }, + { + "cell_type": "markdown", + "source": [ + "The healthcare graph schema revolves around adverse drug event cases. Therefore, each case is related to relevant drugs. In addition, other information is available such as the age group, outcome, and reaction. Here, I took the examples from the sandbox guide as I am not familiar with the adverse drug events domain." + ], + "metadata": { + "id": "D2U9HoJobWBi" + } + }, + { + "cell_type": "code", + "source": [ + "hc_db = Neo4jGPTQuery(\n", + " url=\"bolt://3.216.123.73:7687\",\n", + " user=\"neo4j\",\n", + " password=\"reenlistment-superstructures-shafts\",\n", + " openai_api_key=openai_key,\n", + ")" + ], + "metadata": { + "id": "UsKiPnx1wy8Z" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "hc_db.run(\"\"\"\n", + "What are the top 5 side effects reported?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ch0QpKzb491V", + "outputId": "14eecb9d-cc15-44a6-bca5-34cefdb02a1a" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (c:Case)-[:HAS_REACTION]->(r:Reaction)\n", + "RETURN r.description as SideEffect, COUNT(*) as Frequency\n", + "ORDER BY Frequency DESC\n", + "LIMIT 5\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['SideEffect', 'Frequency'],\n", + " ['Fatigue', 303],\n", + " ['Product dose omission issue', 285],\n", + " ['Headache', 272],\n", + " ['Nausea', 256],\n", + " ['Pain', 253]]" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "It would be interesting to learn how did GPT-4 know that side effects can be found as the Reaction nodes. Even I couldn't find that without any details about the graph. Are there graph out there with similar schema, or is the knowledge cutoff date of GPT-4 not that accurate? Or does it only have great intuition to find relevant data based on node labels and their properties.\n", + "\n", + "Let's try something more complex." + ], + "metadata": { + "id": "Tapjpf0HbaO6" + } + }, + { + "cell_type": "code", + "source": [ + "hc_db.run(\"\"\"\n", + "What are the top 3 manufacturing companies with the most reported side effects?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bMEOMI0n5aT5", + "outputId": "7d91e1a7-69ca-4d9c-c9e1-71401113f74c" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (m:Manufacturer)-[:REGISTERED]->(c:Case)-[:HAS_REACTION]->(r:Reaction)\n", + "RETURN m.manufacturerName, COUNT(r) as sideEffectsCount\n", + "ORDER BY sideEffectsCount DESC\n", + "LIMIT 3\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['m.manufacturerName', 'sideEffectsCount'],\n", + " ['TAKEDA', 5058],\n", + " ['PFIZER', 3219],\n", + " ['NOVARTIS', 1823]]" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Here, we can see that GPT-4 is very specific in our request. Since we are asking for the count of reported side effects, it expands to Reaction nodes and counts them. On the other hand, we could request only the number of cases." + ], + "metadata": { + "id": "7-uxtXJWbeGH" + } + }, + { + "cell_type": "code", + "source": [ + "hc_db.run(\"\"\"\n", + "What are the top 3 manufacturing companies with the most reported cases?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gUBIsss9PXLd", + "outputId": "3fdf29a7-e4fb-4985-bc57-ece657f4762b" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (m:Manufacturer)-[:REGISTERED]->(c:Case)\n", + "RETURN m.manufacturerName, COUNT(c) as case_count\n", + "ORDER BY case_count DESC\n", + "LIMIT 3\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['m.manufacturerName', 'case_count'],\n", + " ['TAKEDA', 617],\n", + " ['CELGENE', 572],\n", + " ['PFIZER', 513]]" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, lets do something where GPT-4 has to do both filtering and aggregating." + ], + "metadata": { + "id": "qjwYZA2GbgOk" + } + }, + { + "cell_type": "code", + "source": [ + "hc_db.run(\"\"\"\n", + "What are the top 5 drugs whose side effects resulted in Death of patients as an outcome?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0SVUT8l95sVo", + "outputId": "1f6d09a5-b3d4-47bc-d48d-042b398a81ca" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (d:Drug)-[:IS_PRIMARY_SUSPECT|:IS_SECONDARY_SUSPECT|:IS_CONCOMITANT|:IS_INTERACTING]->(c:Case)-[:RESULTED_IN]->(o:Outcome)\n", + "WHERE o.outcome = \"Death\"\n", + "RETURN d.name, COUNT(*) as count\n", + "ORDER BY count DESC\n", + "LIMIT 5\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['d.name', 'count']]" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Something that happens sometimes is that GPT-4 messes up the relationship direction. For example, the relationships from the Drug to the Case node should have a reverse direction. Additionally, the Sandbox guide uses only the IS_PRIMARY_SUSPECT relationship type, but we can't blame the GPT-4 model due to the question's ambiguity.\n", + "\n", + "Note that GPT-4 is not deterministic. Therefore, it may return correct relationship directions and sometimes not. For me, it worked correctly one day and not the other. However, I got consistent results within the same day, so who knows what is happening behind the scenes.\n", + "\n", + "What I found interesting is that the GPT-4 model knew that the outcome property contains information about the death of patients. But more than that, it knew that the death value should be capitalized, which makes me think the model saw this dataset in one form or another.\n", + "\n", + "## Custom astronomical dataset\n", + "I have decided to construct a custom astronomical dataset that the model definitely hasn't seen during its training since it didn't exist until I started writing this post. It is very tiny, but good enough to test out GPT-4 generalization ability. I have created a [blank project on Neo4j Sandbox](https://sandbox.neo4j.com/?usecase=blank-sandbox) and then seeded the database with the following script." + ], + "metadata": { + "id": "EdFQGZADTTCW" + } + }, + { + "cell_type": "code", + "source": [ + "astro_db = Neo4jGPTQuery(\n", + " url=\"bolt://35.171.160.87:7687\",\n", + " user=\"neo4j\",\n", + " password=\"discontinuance-fifths-sports\",\n", + " openai_api_key=openai_key,\n", + ")" + ], + "metadata": { + "id": "3Ceh2OEh6JMR" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "url = \"https://gist.githubusercontent.com/tomasonjo/52b2da916ef5cd1c2adf0ad62cc71a26/raw/a3a8716f7b28f3a82ce59e6e7df28389e3cb33cb/astro.cql\"\n", + "astro_db.query_database(\"CALL apoc.cypher.runFile($url)\", {'url':url})\n", + "astro_db.refresh_schema()" + ], + "metadata": { + "id": "w8GjNAPnTd8k" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The constructed graph has the following schema.\n", + "\n", + "![Screenshot from 2023-04-26 22-43-17.png](data:image/png;base64,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)\n", + "\n", + "The database contains planets within our solar system that orbit the sun. Additionally, satellites like ISS, the moon, and Hubble Telescope are included." + ], + "metadata": { + "id": "pRsVNCkxbt2Z" + } + }, + { + "cell_type": "code", + "source": [ + "astro_db.run(\"\"\"\n", + "What orbits the Earth?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "i-nFUrbfT7Zp", + "outputId": "8aeace90-e748-46c4-e5a1-0fba731e8b96" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (ao:AstronomicalObject {name: \"Earth\"})<-[:ORBITS]-(o)\n", + "RETURN o.name, labels(o)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['o.name', 'labels(o)'],\n", + " ['Hubble Space Telescope', ['Satellite']],\n", + " ['ISS', ['Satellite']],\n", + " ['Moon', ['Satellite']]]" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Remember, the GPT-4 only know that there are satellites and astronomical objects in the database. Astronomical objects orbit other astronomical objects, while satellites can only orbit objects. It looks like it used its knowledge to assume that only a satellite would orbit the Earth, which is impressive. We can observe that GPT-4 probably makes a lot of assumption based on its baked knowledge to help us with our queries.\n", + "\n", + "Let's dig deeper." + ], + "metadata": { + "id": "YQXN8CRNb1j1" + } + }, + { + "cell_type": "code", + "source": [ + "astro_db.run(\"\"\"\n", + "Does ISS orbits the Sun?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hU7agLmMUUOa", + "outputId": "f842f49c-c764-49ac-cc11-39e0720ac178" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH (s:Satellite {name: \"ISS\"})-[:ORBITS]->(a:AstronomicalObject {name: \"Sun\"})\n", + "RETURN s.name as Satellite, a.name as AstronomicalObject\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['Satellite', 'AstronomicalObject']]" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "So, the ISS doesn't directly orbit the sun. We can rephrase our question." + ], + "metadata": { + "id": "XYizIkKHb4JP" + } + }, + { + "cell_type": "code", + "source": [ + "astro_db.run(\"\"\"\n", + "Does ISS orbits the Sun? Find any path between them\n", + "and return names of nodes in the path\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l6RY0-1jXyyK", + "outputId": "b244c722-09b4-41d8-960b-f28bd785c3bf" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MATCH path = (iss:Satellite {name: \"ISS\"})-[:ORBITS*]->(sun:AstronomicalObject {name: \"Sun\"})\n", + "RETURN [node in nodes(path) | node.name] AS path_names\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[['path_names'], [['ISS', 'Earth', 'Sun']]]" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now, it uses a variable-length path pattern to find if ISS orbits the sun by proxy. Of course, we gave it a hint to use that, but it is still remarkable. For the final example, let's observe how good GPT-4 is at guessing never-seen-before property values." + ], + "metadata": { + "id": "iIwY3cBtb7X3" + } + }, + { + "cell_type": "code", + "source": [ + "astro_db.run(\"\"\"\n", + "What's the altitude difference between ISS and Hubble telescope\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 239 + }, + "id": "_gdrePY_b_wz", + "outputId": "66b2469c-0848-49b9-de21-942e214bf0e3" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "To find the altitude difference between ISS and Hubble telescope, you can use the following Cypher query:\n", + "\n", + "```cypher\n", + "MATCH (s1:Satellite {name: \"ISS\"}), (s2:Satellite {name: \"Hubble Telescope\"})\n", + "RETURN abs(s1.altitude - s2.altitude) as altitude_difference\n", + "```\n", + "Retrying\n", + "```cypher\n", + "MATCH (s1:Satellite {name: \"ISS\"}), (s2:Satellite {name: \"Hubble Telescope\"})\n", + "RETURN abs(s1.altitude - s2.altitude) as altitude_difference\n", + "```\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Invalid Cypher syntax'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "To tell you the truth, I am kind of relieved GPT-4 didn't guess correctly that Hubble is stored in the database as \"Hubble Space Telescope\". Other than that, the generated Cypher statement is perfectly valid.\n", + "Summary\n", + "GPT-4 has a great potential to generate Cypher statements based on only the provided graph schema. My opinion is that it has seen a lot of datasets and graph models during its training, so it is kind of good at guessing which properties to use and sometimes even their values. However, you can always provide the model with instructions about which properties to use and specify the exact values if the model isn't performing well on your specific graph model. The limitations I have observed during this experiment are the following:\n", + "* Multiple aggregations with different grouping keys are a problem\n", + "* Version two of the Graph Data Science library is beyond the knowledge cutoff date\n", + "* Sometimes it messes up the relationship direction (not frequently, though)\n", + "* The non-deterministic nature of GPT-4 makes it feel like you are dealing with a horoscope-based model, where identical queries work in the morning but not in the afternoon\n", + "* Sometimes the model bypasses system instructions and provides explanations for queries\n", + "\n", + "Using the schema-only approach to GPT-4 can be used for experimental setups to help developers or researchers that don't have malicious intents to interact with the graph database. On the other hand, if you want to build something more production-ready, I would recommend using the approach of providing examples of Cypher statements." + ], + "metadata": { + "id": "Gq1a80BHb-Oc" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "U_WPbddjWVEc" + }, + "execution_count": 22, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/LLMs/src/langchain_fulltext_agent.ipynb b/LLMs/src/langchain_fulltext_agent.ipynb new file mode 100644 index 0000000..1b8b448 --- /dev/null +++ b/LLMs/src/langchain_fulltext_agent.ipynb @@ -0,0 +1,496 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyPEI+4M9KTa9FUFAUClVHNd", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gH7LglHfbQfQ", + "outputId": "5a7b1c49-f5d5-4be6-aaca-8fa544121e64" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: neo4j in /usr/local/lib/python3.10/dist-packages (5.9.0)\n", + "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (0.27.8)\n", + "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.0.205)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from neo4j) (2022.7.1)\n", + "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.27.1)\n", + "Requirement already satisfied: tqdm in 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"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.3)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n", + "Requirement already satisfied: marshmallow<4.0.0,>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.6.0,>=0.5.7->langchain) (3.19.0)\n", + "Requirement already satisfied: marshmallow-enum<2.0.0,>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.6.0,>=0.5.7->langchain) (1.5.1)\n", + "Requirement already satisfied: typing-inspect>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from dataclasses-json<0.6.0,>=0.5.7->langchain) (0.9.0)\n", + "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2,>=1->langchain) (4.5.0)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (1.26.15)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2022.12.7)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.4)\n", + "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain) (2.0.2)\n", + "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from marshmallow<4.0.0,>=3.3.0->dataclasses-json<0.6.0,>=0.5.7->langchain) (23.1)\n", + "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect>=0.4.0->dataclasses-json<0.6.0,>=0.5.7->langchain) (1.0.0)\n" + ] + } + ], + "source": [ + "!pip install neo4j openai langchain" + ] + }, + { + "cell_type": "code", + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.chains import GraphCypherQAChain\n", + "from langchain.graphs import Neo4jGraph\n", + "\n", + "graph = Neo4jGraph(\n", + " url=\"bolt://3.238.222.252:7687\",\n", + " username=\"neo4j\",\n", + " password=\"bushels-harpoon-deduction\"\n", + ")" + ], + "metadata": { + "id": "ad1yMWkjbUTj" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "os.environ['OPENAI_API_KEY'] = \"sk-\"\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " ChatOpenAI(temperature=0, model_name=\"gpt-4-0613\"), graph=graph, verbose=True,\n", + " return_direct=True\n", + ")" + ], + "metadata": { + "id": "jhE0tzoXbiGs" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"Who played in the Matrix?\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zkWN8VX6bngj", + "outputId": "281973da-fcae-4588-d02f-ae9e5f232a0e" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (a:Actor)-[:ACTED_IN]->(m:Movie) WHERE m.title = 'Matrix' RETURN a.name\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "graph.query(\"\"\"CREATE FULLTEXT INDEX entities IF NOT EXISTS\n", + "FOR (n:Movie|Person)\n", + "ON EACH [n.name, n.title]\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SmS2Zu_bcZKQ", + "outputId": "dbd210fe-ab29-4c76-f5ea-ce3c5ed53468" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "source": [ + "graph.query(\"\"\"\n", + "CALL db.index.fulltext.queryNodes(\"entities\", \"Matrix\") YIELD node, score\n", + "RETURN node.title, score\n", + "LIMIT 3\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AE3-hCuVccdL", + "outputId": "67a78c3c-ac92-4985-b028-fc7d5a4261ac" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'node.title': 'Matrix, The', 'score': 4.259097099304199},\n", + " {'node.title': 'Matrix Revolutions, The', 'score': 3.7098255157470703},\n", + " {'node.title': 'Matrix Reloaded, The', 'score': 3.7098255157470703}]" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from __future__ import annotations\n", + "from langchain.chains.base import Chain\n", + "\n", + "from typing import Any, Dict, List\n", + "\n", + "from pydantic import Field\n", + "\n", + "\n", + "fulltext_search = \"\"\"\n", + "CALL db.index.fulltext.queryNodes(\"entities\", $query)\n", + "YIELD node, score\n", + "RETURN coalesce(node.title, node.name) AS option, labels(node)[0] AS type LIMIT 3\n", + "\"\"\"\n", + "\n", + "\n", + "class Neo4jFulltextGraphChain(Chain):\n", + " \"\"\"Chain for keyword question-answering against a graph.\"\"\"\n", + "\n", + " graph: Neo4jGraph = Field(exclude=True)\n", + " input_key: str = \"query\" #: :meta private:\n", + " output_key: str = \"result\" #: :meta private:\n", + "\n", + " @property\n", + " def input_keys(self) -> List[str]:\n", + " \"\"\"Return the input keys.\n", + " :meta private:\n", + " \"\"\"\n", + " return [self.input_key]\n", + "\n", + " @property\n", + " def output_keys(self) -> List[str]:\n", + " \"\"\"Return the output keys.\n", + " :meta private:\n", + " \"\"\"\n", + " _output_keys = [self.output_key]\n", + " return _output_keys\n", + "\n", + " def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:\n", + " \"\"\"Extract entities, look up info and answer question.\"\"\"\n", + " question = inputs[self.input_key]\n", + " context = self.graph.query(\n", + " fulltext_search, {'query': question})\n", + " return {self.output_key: context}\n" + ], + "metadata": { + "id": "gUbIKwm_cfVy" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fulltext = Neo4jFulltextGraphChain(graph=graph)\n", + "fulltext.run(\"Matrix\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jAGQ0Y4CiAPw", + "outputId": "aa99af7d-49ce-4803-d1d8-60e332b63733" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'option': 'Matrix, The', 'type': 'Movie'},\n", + " {'option': 'Matrix Revolutions, The', 'type': 'Movie'},\n", + " {'option': 'Matrix Reloaded, The', 'type': 'Movie'}]" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "source": [ + "fulltext.run(\"Keanu\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N9_SWhELiF5l", + "outputId": "040582cd-d741-48da-eae6-ca2f972c24f0" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'option': 'Keanu', 'type': 'Movie'},\n", + " {'option': 'Keanu Reeves', 'type': 'Actor'}]" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from langchain.agents import initialize_agent, Tool\n", + "from langchain.agents import AgentType\n", + "\n", + "llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n", + "tools = [\n", + " Tool(\n", + " name=\"Movies\",\n", + " func=chain.run,\n", + " description=\"\"\"useful for when you need to information about movies, actors, directors, and so on.\n", + " Input must be full question.\n", + " Always make sure to use the entity search first to validate the names of movies and person.\"\"\",\n", + " ),\n", + " Tool(\n", + " name=\"Entity search\",\n", + " func=fulltext.run,\n", + " description=\"\"\"useful for when you need find exact values of names of people and movies. The tool returns three options and you have to select the best one.\n", + " Input must be a single entity.\"\"\",\n", + " ),\n", + "\n", + "]" + ], + "metadata": { + "id": "brcvHFabiNcu" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "graph_agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)" + ], + "metadata": { + "id": "XNbJEch5j4Zg" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "graph_agent.run(\"Who played in the Matrix?\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "RslNeUUEj-Y4", + "outputId": "1e705c06-2356-46d3-8c52-b61c0def267e" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3mThought: I should use the Movies tool to get information about the movie \"The Matrix\" and its cast.\n", + "Action:\n", + "```\n", + "{\n", + " \"action\": \"Movies\",\n", + " \"action_input\": \"cast of The Matrix\"\n", + "}\n", + "```\n", + "\u001b[0m\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (m:Movie {title: 'The Matrix'})-[:ACTED_IN]-(a:Actor)\n", + "RETURN a.name\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "Observation: \u001b[36;1m\u001b[1;3m[]\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3mThe observation should not be an empty list. Let me try again.\n", + "Action:\n", + "```\n", + "{\n", + " \"action\": \"Movies\",\n", + " \"action_input\": \"The Matrix\"\n", + "}\n", + "```\n", + "\u001b[0m\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (m:Movie) WHERE m.title = 'The Matrix' RETURN m\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "Observation: \u001b[36;1m\u001b[1;3m[]\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3mI need to check the exact name of the movie using the Entity search tool first.\n", + "Action:\n", + "```\n", + "{\n", + " \"action\": \"Entity search\",\n", + " \"action_input\": \"The Matrix\"\n", + "}\n", + "```\n", + "\n", + "\u001b[0m\n", + "Observation: \u001b[33;1m\u001b[1;3m[{'option': 'Matrix, The', 'type': 'Movie'}, {'option': 'Matrix Reloaded, The', 'type': 'Movie'}, {'option': 'Matrix Revolutions, The', 'type': 'Movie'}]\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3mI can now use the correct name of the movie to get the cast using the Movies tool.\n", + "Action:\n", + "```\n", + "{\n", + " \"action\": \"Movies\",\n", + " \"action_input\": \"cast of Matrix, The\"\n", + "}\n", + "```\n", + "\n", + "\u001b[0m\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (m:Movie {title: \"Matrix, The\"})-[:ACTED_IN]-(a:Actor)\n", + "RETURN a.name\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "Observation: \u001b[36;1m\u001b[1;3m[{'a.name': 'Hugo Weaving'}, {'a.name': 'Laurence Fishburne'}, {'a.name': 'Keanu Reeves'}, {'a.name': 'Carrie-Anne Moss'}]\u001b[0m\n", + "Thought:" + ] + }, + { + "output_type": "error", + "ename": "OutputParserException", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/chat/output_parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"```\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mOutputParserException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgraph_agent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Who played in the Matrix?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, *args, **kwargs)\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 272\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 273\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_output_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m 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144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[0;31m# We now enter the agent loop (until it returns something).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_continue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterations\u001b[0m\u001b[0;34m,\u001b[0m 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run_manager)\u001b[0m\n\u001b[1;32m 760\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 761\u001b[0m \u001b[0;31m# Call the LLM to see what to do.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 762\u001b[0;31m output = self.agent.plan(\n\u001b[0m\u001b[1;32m 763\u001b[0m \u001b[0mintermediate_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_child\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_manager\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/agent.py\u001b[0m in \u001b[0;36mplan\u001b[0;34m(self, intermediate_steps, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0mfull_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_full_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintermediate_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0mfull_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllm_chain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfull_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 446\u001b[0m async def aplan(\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/agents/chat/output_parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mincludes_answer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mOutputParserException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Could not parse LLM output: {text}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 31\u001b[0m return AgentFinish(\n\u001b[1;32m 32\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"output\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFINAL_ANSWER_ACTION\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mOutputParserException\u001b[0m: Could not parse LLM output: I have the list of actors who played in The Matrix." + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "mdBxM7R_kDJf" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/LLMs/src/langchain_neo4j.ipynb b/LLMs/src/langchain_neo4j.ipynb new file mode 100644 index 0000000..b2eb565 --- /dev/null +++ b/LLMs/src/langchain_neo4j.ipynb @@ -0,0 +1,541 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyOcXBcR86rPwNdXV2pzMJOf", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lqp9wLWW-UFE", + "outputId": "156a39ce-bcaf-4f78-d07e-62a343da4f81" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, 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langchain\n", + "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 dataclasses-json-0.5.7 frozenlist-1.3.3 langchain-0.0.180 marshmallow-3.19.0 marshmallow-enum-1.5.1 multidict-6.0.4 mypy-extensions-1.0.0 neo4j-5.8.1 openai-0.27.7 openapi-schema-pydantic-1.2.4 typing-inspect-0.9.0 yarl-1.9.2\n" + ] + } + ], + "source": [ + "!pip install neo4j openai langchain" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# LangChain has added Cypher Search\n", + "## With the LangChain library, you can conveniently generate Cypher queries, enabling an efficient retrieval of information from Neo4j.\n", + "\n", + "If you have developed or plan to implement any solution that uses Large Language Models, you have most likely heard of the LangChain library. LangChain library is the most widely known Python library used to develop applications that use LLMs in one or another capabilities. It is designed to be modular, allowing us to use any LLM in any available modules, such as chains, tools, memory, or agents.\n", + "\n", + "A month ago, I spent a week researching and implementing a solution allowing anyone to retrieve information from Neo4j directly from the LangChain library and use it in their LLM applications. I learned quite a lot about the internals of LangChain library and wrote up my experience in a blog post.\n", + "\n", + "A colleague of mine showed me a LangChain feature request, where the user requested that my work of having the option to retrieve information from the Neo4j database would be added as a module directly to the LangChain library so that no additional code or external modules would be needed to integrate Neo4j into LangChain applications. Since I was already familiar with LangChain internals, I decided to try and implement Cypher searching capabilities myself. I spent a weekend researching and coding the solution and ensuring it would conform to the contribution standards for it to be added to the library. Luckily, the maintainers of LangChain are very responsive and open to new ideas, and the Cypher Search has been added in the latest release of the LangChain library. Thanks to Harrison Chase for maintaining such a great library and also being very responsive to new ideas.\n", + "\n", + "In tutorial, I will show you how you can use the newly added Cypher Search in the LangChain library to retrieve information from a Neo4j database.\n", + "\n", + "## What is a knowledge graph\n", + "LangChain has already integrations with Vector and SQL databases, so why do we need an integration with a Graph Database like Neo4j?\n", + "Knowledge graphs are ideal for storing heterogeneous and highly connected data. For instance, the above image contains information about people, organizations, movies, websites, etc. While the ability to intuitively model and store diverse sets of data is incredible, I think the main benefit of using graphs is the ability to analyze data points through their relationships. Graphs enable us to uncover connections and correlations that might otherwise remain unnoticed with traditional database and analytics approaches as they often overlook the context surrounding individual data points.\n", + "\n", + "The power of graph databases truly shines when dealing with complex systems where interdependencies and interactions are vital in understanding the system.\n", + "They enable us to see beyond individual data points and delve into the intricate relationships that define their context. This provides a deeper, more holistic view of the data, facilitating better decision-making and knowledge discovery.\n", + "\n", + "## Setting up Neo4j environment\n", + "\n", + "If you have an existing Neo4j database, you can use it to try the newly added Cypher Search. The Cypher Search module uses graph schema information to generate Cypher statements, meaning you can plug it into any Neo4j database.\n", + "If you don't have any Neo4j database yet, you can use [Neo4j Sandbox](https://neo4j.com/sandbox/), which offers a free cloud instance of a Neo4j database. You need to register and instantiate any of the available pre-populated databases. I will be using the [ICIJ Paradise Papers dataset](https://sandbox.neo4j.com/?usecase=icij-paradise-papers) in this blog post, but you can use any other if you want. The dataset has been made available by International Consortium of Investigative Journalists as part of their Offshore Leaks Database.\n", + "\n", + "The graph contains four types of nodes:\n", + "* Entity - The offshore legal entity. This could be a company, trust, foundation, or other legal entity created in a low-tax jurisdiction.\n", + "* Officer - A person or company who plays a role in an offshore entity, such as beneficiary, director, or shareholder. The relationships shown in the diagram are just a sample of all the existing ones.\n", + "* Intermediary - A go-between for someone seeking an offshore corporation and an offshore service provider - usually a law-firm or a middleman that asks an offshore service provider to create an offshore firm.\n", + "* Address - The registered address as it appears in the original databases obtained by ICIJ.\n", + "\n", + "## Knowledge Graph Cypher Search\n", + "The name Cypher Search comes from Cypher, which is a query language used to interact with graph databases like Neo4j.\n", + "\n", + "![langchain.drawio.png](data:image/png;base64,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)\n", + "\n", + "In order to allow LangChain to retrieve information from graph databases, I implemented a module that can convert the natural language to a Cypher statement, use it to retrieve data from Neo4j and return the retrieved information to the user in a natural language form. This two-way conversion process between natural language and database language not only enhances the overall accessibility of data retrieval but also greatly improves the user experience.\n", + "\n", + "\n", + "The beauty of the LangChain library is in its simplicity. We only need a couple of lines of code and we can retrieve information from Neo4j using natural language." + ], + "metadata": { + "id": "2wg51pF7LVVi" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.chains import GraphCypherQAChain\n", + "from langchain.graphs import Neo4jGraph\n", + "\n", + "graph = Neo4jGraph(\n", + " url=\"bolt://3.239.187.136:7687\", \n", + " username=\"neo4j\", \n", + " password=\"cushions-haul-revolution\"\n", + ")" + ], + "metadata": { + "id": "hTz9REHt-XxO" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "os.environ['OPENAI_API_KEY'] = \"sk-\"\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " ChatOpenAI(temperature=0), graph=graph, verbose=True,\n", + ")" + ], + "metadata": { + "id": "DkEz1NTBFrXu" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Here, we are using the gpt-3.5-turbo model from OpenAI to generate Cypher statements. The Cypher statements are generated based on the graph schema, which means that, in theory, you can plug the Cypher chain into any Neo4j instance, and it should be able to answer natural language answers. Unfortunately, I haven't yet tested other LLM providers in their ability to generate Cypher statements since I don't have access to any of them. Still, I would love to hear your evaluation of other LLMs generating Cypher statements if you will give it a go. Of course, if you want to break the dependency on LLM cloud providers, you can always [fine-tune an open-source LLM to generate Cypher statements](https://towardsdatascience.com/fine-tuning-an-llm-model-with-h2o-llm-studio-to-generate-cypher-statements-3f34822ad5).\n", + "\n", + "Let's start with a simple test." + ], + "metadata": { + "id": "UW50UoMUMRSg" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "Which intermediary is connected to most entites?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 + }, + "id": "nblHLc6TJnLT", + "outputId": "5d54fa86-d442-4361-f24e-2ec068de16e2" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (i:Intermediary)-[:CONNECTED_TO]->(e:Entity)\n", + "RETURN i.name, COUNT(e) AS num_entities\n", + "ORDER BY num_entities DESC\n", + "LIMIT 1\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'i.name': 'Group - SUN Capital Partner Group', 'num_entities': 115}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Based on the provided information, the intermediary that is connected to the most entities is the Group - SUN Capital Partner Group, with a total of 115 entities.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can observe the generated Cypher statement and the retrieved information from Neo4j used to form the answer. That is as easy a setup as it gets. Let's move on to the next example." + ], + "metadata": { + "id": "QMHK94FyMYnV" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "Who are the officers of ZZZ-MILI COMPANY LTD.?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 222 + }, + "id": "_ia3wgn7JDg3", + "outputId": "d2fada74-3351-49a1-c2ad-ed4fbc9d2d8b" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (o:Officer)-[:OFFICER_OF]->(e:Entity{name:'ZZZ-MILI COMPANY LTD.'}) RETURN o.name\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'o.name': 'Whitson - Iva Marie'}, {'o.name': 'Whitson - Claud S.'}, {'o.name': 'Whitson - Claud S.'}, {'o.name': 'FINSBURY NOMINEES LTD'}, {'o.name': 'EURO NOMINEES LTD.'}, {'o.name': 'EURO SECURITIES LTD.'}, {'o.name': 'COMPANY DIRECTORS LTD.'}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The officers of ZZZ-MILI COMPANY LTD. are Whitson - Iva Marie, Whitson - Claud S., FINSBURY NOMINEES LTD, EURO NOMINEES LTD., EURO SECURITIES LTD., and COMPANY DIRECTORS LTD.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Since we are using a graph, let's construct a question that would utilize the power of graph databases." + ], + "metadata": { + "id": "X04jYeaeMhZe" + } + }, + { + "cell_type": "code", + "source": [ + "#chain_improved.run(\"\"\"\n", + "#How are entities SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited related?\n", + "#\"\"\")" + ], + "metadata": { + "id": "IvGS4_c1CHQg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The generated Cypher statement looks fine at first glance. However, there is a problem as the Cypher statements used the variable-length path finding syntax and also treated relationships as undirected. As a result, this type of query is highly unoptimized and would explode in the number of rows.\n", + "\n", + "The nice thing about gpt-3.5-turbo is that it follows hints and instructions we drop in the input. For example, we can ask it to find only the shortest path." + ], + "metadata": { + "id": "W1Cqo3KrMoU1" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "How are entities SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited connected?\n", + "Find a shortest path.\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "ZVQPdna0GaLP", + "outputId": "2e44963e-652d-44ce-ba6a-f33a2ab5b95e" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (e1:Entity {name: 'SOUTHWEST LAND DEVELOPMENT LTD.'}), (e2:Entity {name: 'Dragon Capital Markets Limited'})\n", + "MATCH p=shortestPath((e1)-[*]-(e2))\n", + "RETURN p\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'p': [{'sourceID': 'Paradise Papers - Appleby', 'jurisdiction': 'KY', 'service_provider': 'Appleby', 'countries': 'Cayman Islands', 'jurisdiction_description': 'Cayman Islands', 'type': 'CE', 'valid_until': 'Appleby data is current through 2014', 'ibcRUC': '50843', 'labels(n)': '[\"Entity\"]', 'name': 'SOUTHWEST LAND DEVELOPMENT LTD.', 'country_codes': 'CYM', 'incorporation_date': '1993-Oct-05', 'node_id': '82011899'}, 'REGISTERED_ADDRESS', {'sourceID': 'Paradise Papers - Appleby', 'valid_until': 'Appleby data is current through 2014', 'address': 'Clifton House', 'labels(n)': '[\"Address\"]', 'name': 'Clifton House; 75 Fort Street; Grand Cayman KY1-1108; Cayman Islands', 'country_codes': 'CYM', 'countries': 'Cayman Islands', 'node_id': '81027146'}, 'REGISTERED_ADDRESS', {'sourceID': 'Paradise Papers - Appleby', 'jurisdiction': 'KY', 'service_provider': 'Appleby', 'countries': 'British Virgin Islands;Cayman Islands', 'jurisdiction_description': 'Cayman Islands', 'type': 'CE', 'valid_until': 'Appleby data is current through 2014', 'ibcRUC': '251645', 'labels(n)': '[\"Entity\"]', 'name': 'Dragon Capital Markets Limited', 'country_codes': 'VGB;CYM', 'incorporation_date': '1996-May-02', 'node_id': '82014099'}]}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited are connected through the fact that they were both serviced by Appleby in the Cayman Islands jurisdiction. A shortest path between them would be to follow the service provider Appleby and the jurisdiction of Cayman Islands.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now that we dropped a hint that only the shortest path should be retrieved, we don't run into cardinality explosion troubles anymore. However, one thing I noticed is that the LLM sometimes doesn't provide the best results if a path object is returned. However, we can also fix that by instructing the model what information to use." + ], + "metadata": { + "id": "dKYeXyqnMrJm" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "How are entities SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited connected?\n", + "Find a shortest path.\n", + "Return only name properties of nodes and relationship types\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 239 + }, + "id": "jpUGmHgzIF4i", + "outputId": "dfb66d71-7eca-4595-cc69-2fab4d033b62" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH path = shortestPath((e1:Entity {name: 'SOUTHWEST LAND DEVELOPMENT LTD.'})-[*]-(e2:Entity {name: 'Dragon Capital Markets Limited'}))\n", + "RETURN [n IN nodes(path) | n.name] AS Names, [r IN relationships(path) | type(r)] AS Relationships\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'Names': ['SOUTHWEST LAND DEVELOPMENT LTD.', 'Clifton House; 75 Fort Street; Grand Cayman KY1-1108; Cayman Islands', 'Dragon Capital Markets Limited'], 'Relationships': ['REGISTERED_ADDRESS', 'REGISTERED_ADDRESS']}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The entities SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited are connected through the relationship types REGISTERED_ADDRESS and REGISTERED_ADDRESS. A shortest path between them can be found by following these relationships. The name properties of the nodes along this path are SOUTHWEST LAND DEVELOPMENT LTD., Clifton House; 75 Fort Street; Grand Cayman KY1-1108; Cayman Islands, and Dragon Capital Markets Limited.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now we can a better response and more appropriate response. The more hints you drop to an LLM, the better results you can expect. For example, you can also instruct it which relationships it can traverse." + ], + "metadata": { + "id": "i1ISIYQvMxEF" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "How are entities SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited connected?\n", + "Find a shortest path and use only officer, intermediary, and connected relationships.\n", + "Return only name properties of nodes and relationship types\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "Sxhj6n7NT6bd", + "outputId": "6b431f95-986e-41bf-d13e-f5da5e2b8694" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (e1:Entity {name: 'SOUTHWEST LAND DEVELOPMENT LTD.'}), (e2:Entity {name: 'Dragon Capital Markets Limited'})\n", + "MATCH p=shortestPath((e1)-[:OFFICER_OF|INTERMEDIARY_OF|CONNECTED_TO*]-(e2))\n", + "RETURN [n IN nodes(p) | n.name] AS Names, [r IN relationships(p) | type(r)] AS Relationships\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'Names': ['SOUTHWEST LAND DEVELOPMENT LTD.', 'Appleby Trust (Cayman) Ltd.', 'Dragon Capital Clean Development Investments Ltd.', 'Group - Dragon Capital', 'Dragon Capital Markets Limited'], 'Relationships': ['INTERMEDIARY_OF', 'INTERMEDIARY_OF', 'CONNECTED_TO', 'CONNECTED_TO']}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'SOUTHWEST LAND DEVELOPMENT LTD. and Dragon Capital Markets Limited are connected through intermediary and connected relationships. The shortest path between them is as follows: SOUTHWEST LAND DEVELOPMENT LTD. (INTERMEDIARY_OF) -> Appleby Trust (Cayman) Ltd. (INTERMEDIARY_OF) -> Dragon Capital Clean Development Investments Ltd. (CONNECTED_TO) -> Group - Dragon Capital (CONNECTED_TO) -> Dragon Capital Markets Limited.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Summary\n", + "Graph databases are an excellent tool for retrieving or analyzing the connections between various entities like people and organizations. In this blog post, we looked at a simple shortest path use case, where the number of relationships and the sequence of relationship types is unknown beforehand. These types of queries are virtually impossible in a vector database and could also be quite complicated in a SQL database.\n", + "I am thrilled about the addition of Cypher Search to the LangChain library. Please test it out, and let me know how it works for you, especially if you are testing it on other LLM models or have exciting use cases." + ], + "metadata": { + "id": "myeTTU1zM0b-" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "5mmBVoJiIr9K" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/LLMs/src/langchain_neo4j_tips.ipynb b/LLMs/src/langchain_neo4j_tips.ipynb new file mode 100644 index 0000000..4e64ad3 --- /dev/null +++ b/LLMs/src/langchain_neo4j_tips.ipynb @@ -0,0 +1,859 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyM4xOyaU8TfeD/8PDfwIi4J", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SgGtmyZxDfCI", + "outputId": "1a5f5dcf-7052-4a6a-a604-a507bebe7159" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: neo4j in /usr/local/lib/python3.10/dist-packages (5.9.0)\n", + "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (0.27.7)\n", + "Requirement already satisfied: langchain in /usr/local/lib/python3.10/dist-packages (0.0.183)\n", + "Requirement already satisfied: pytz in /usr/local/lib/python3.10/dist-packages (from neo4j) (2022.7.1)\n", + "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.27.1)\n", + "Requirement already satisfied: tqdm in 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your LLM applications\n", + "\n", + "Last time, we looked at how to get started with [Cypher Search in the LangChain](https://towardsdatascience.com/langchain-has-added-cypher-search-cb9d821120d5) library and why you would want to use knowledge graphs in your LLM applications. In this blog post, we will continue to explore various use cases for integrating knowledge graphs into LLM and LangChain applications. Along the way, you will learn how to improve prompts to produce better and more accurate Cypher statements.\n", + "\n", + "Specifically, we will look at how to use the few-shot capabilities of LLMs by providing a couple of Cypher statement examples, which can be used to specify which Cypher statements the LLM should produce, what the results should look like, and more. Additionally, you will learn how you can integrate graph algorithms from the Neo4j Graph Data Science library into your LangChain applications.\n", + "\n", + "## Neo4j environment setup\n", + "\n", + "In this blog post, we will be using the [Twitch dataset that is available in Neo4j Sandbox](https://sandbox.neo4j.com/?usecase=twitch).\n", + "\n", + "![graph-model.png](data:image/png;base64,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)\n", + "\n", + "The Twitch social network composes of users. A small percentage of those users broadcast their gameplay or activities through live streams. In the graph model, users who do live streams are tagged with a secondary label Stream. Additional information about which teams they belong to, which games they play on stream, and in which language they present their content is present. We also know how many followers they had at the moment of scraping, the all-time historical view count, and when they created their accounts. The most relevant information for network analysis is knowing which users engaged in the streamer's chat. You can distinguish if the user who chatted in the stream was a regular user (CHATTER relationship), a moderator of the stream (MODERATOR relationship), or a stream VIP.\n", + "The network information was scraped between the 7th and the 10th of May 2021. Therefore, the dataset has outdated information.\n", + "## Improving LangChain Cypher search\n", + "First, we have to setup the LangChain Cypher search." + ], + "metadata": { + "id": "2lXfT6Luz2oV" + } + }, + { + "cell_type": "code", + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.chains import GraphCypherQAChain\n", + "from langchain.graphs import Neo4jGraph\n", + "\n", + "graph = Neo4jGraph(\n", + " url=\"bolt://44.212.12.199:7687\", \n", + " username=\"neo4j\", \n", + " password=\"buoy-warehouse-subordinates\"\n", + ")" + ], + "metadata": { + "id": "x5oiK71aDhwm" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "os.environ['OPENAI_API_KEY'] = \"OPENAI_API_KEY\"\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " ChatOpenAI(temperature=0), graph=graph, verbose=True,\n", + ")" + ], + "metadata": { + "id": "Wliyn4YWDrj0" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "I really love how easy it is go setup the Cypher Search in the LangChain library. You only need to define the Neo4j and OpenAI credentials, and you are good to go. Under the hood, the graph objects inspects the graph schema model and passes it to the GraphCypherQAChain to construct accurate Cypher statements.\n", + "\n", + "Let's begin with a simple question." + ], + "metadata": { + "id": "rd2pXgxZ0Ovo" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "Which fortnite streamer has the most followers?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 258 + }, + "id": "oaKOhoyKDvzj", + "outputId": "2953b317-196e-455a-a836-ce4965f2b34d" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s:Stream)-[:PLAYS]->(:Game {name: 'Fortnite'})\n", + "RETURN s.name, s.followers\n", + "ORDER BY s.followers DESC\n", + "LIMIT 1\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'s.name': 'thegrefg', 's.followers': 7269018}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'According to the provided information, the Fortnite streamer with the most followers is thegrefg, with a total of 7,269,018 followers.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The Cypher chain constructed a relevant Cypher statement, used it to retrieve information from Neo4j, and provided the answer in natural language form.\n", + "\n", + "Now let's ask another question." + ], + "metadata": { + "id": "RH7heaDW0RJP" + } + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"\"\"\n", + "Which italian streamer has the most followers?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "id": "4Cdzdk29E77u", + "outputId": "0eef44d4-1d19-4b22-f901-08f68881fb1c" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:langchain.chat_models.openai:Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.._completion_with_retry in 1.0 seconds as it raised RateLimitError: That model is currently overloaded with other requests. You can retry your request, or contact us through our help center at help.openai.com if the error persists. (Please include the request ID ff0e12be427eab2e88d77430f44dc49a in your message.).\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s:Stream)-[:HAS_LANGUAGE]->(:Language {name: 'Italian'})\n", + "RETURN s.name, s.followers\n", + "ORDER BY s.followers DESC\n", + "LIMIT 1\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"I'm sorry, but I cannot provide an answer to your question as no information has been provided. Please provide more details or a specific name to assist you better.\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The generated Cypher statement looks valid, but unfortunately, we didn't get any results. The problem is that the language values are stored as two-character country codes, and the LLM is unaware of that. There are a few options we have to overcome this problem. First, we can utilize the few-shot capabilities of LLMs by providing examples of Cypher statements, which the model then imitates when generating Cypher statements. To add example Cypher statements in the prompt, we have to update the Cypher generating prompt. You can take a look at the default prompt used to generate Cypher statements to better understand the update we are going to do." + ], + "metadata": { + "id": "X1_7O7MD0TYr" + } + }, + { + "cell_type": "code", + "source": [ + "# https://github.com/hwchase17/langchain/blob/master/langchain/chains/graph_qa/prompts.py\n", + "from langchain.prompts.prompt import PromptTemplate\n", + "\n", + "\n", + "CYPHER_GENERATION_TEMPLATE = \"\"\"Task:Generate Cypher statement to query a graph database.\n", + "Instructions:\n", + "Use only the provided relationship types and properties in the schema.\n", + "Do not use any other relationship types or properties that are not provided.\n", + "Schema:\n", + "{schema}\n", + "Cypher examples:\n", + "# How many streamers are from Norway?\n", + "MATCH (s:Stream)-[:HAS_LANGUAGE]->(:Language {{name: 'no'}})\n", + "RETURN count(s) AS streamers\n", + "\n", + "Note: Do not include any explanations or apologies in your responses.\n", + "Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.\n", + "Do not include any text except the generated Cypher statement.\n", + "\n", + "The question is:\n", + "{question}\"\"\"\n", + "CYPHER_GENERATION_PROMPT = PromptTemplate(\n", + " input_variables=[\"schema\", \"question\"], template=CYPHER_GENERATION_TEMPLATE\n", + ")" + ], + "metadata": { + "id": "Wu8DkdjXF0Vf" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "If you compare the new Cypher generating prompt to the default one, you can observe we only added the Cypher examples section. We added an example where the model could observe that the language values are given as two-character country codes. Now we can test the improved Cypher chain to answer the question about the most followed Italian streamers." + ], + "metadata": { + "id": "2gR8nPcQ0VAM" + } + }, + { + "cell_type": "code", + "source": [ + "chain_language_example = GraphCypherQAChain.from_llm(\n", + " ChatOpenAI(temperature=0), graph=graph, verbose=True,\n", + " cypher_prompt=CYPHER_GENERATION_PROMPT\n", + ")\n", + "\n", + "chain_language_example.run(\"\"\"\n", + "Which italian streamer has the most followers?\n", + "\"\"\")\n" + ], + "metadata": { + "id": "uHqJDR43HFOf", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 276 + }, + "outputId": "1801e211-2250-4b57-b145-50aaba77edcb" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s:Stream)-[:HAS_LANGUAGE]->(:Language {name: 'it'})\n", + "WHERE s.followers IS NOT NULL\n", + "RETURN s.name AS streamer, s.followers AS followers\n", + "ORDER BY followers DESC\n", + "LIMIT 1\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'streamer': 'pow3rtv', 'followers': 1530428}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'According to the provided information, the streamer with the most followers is pow3rtv, with a total of 1530428 followers.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The model is now aware that the languages are given as two-character country codes and can now accurately answer questions that use the language information.\n", + "\n", + "## Using graph algorithms to answer questions\n", + "In the previous blog post, we looked at how integrating graph databases into LLM applications can answer questions like how entities are connected by finding the shortest or other paths between them. Today we will look at another use cases where graph databases can be used in LLM applications that other databases struggle with, specifically how we can use graph algorithms like PageRank to provide relevant answers. For example, we can use personalized PageRank to provide recommendations to an end user at query time.\n", + "\n", + "Take a look at the following example:" + ], + "metadata": { + "id": "sjqCT1x00XXF" + } + }, + { + "cell_type": "code", + "source": [ + "chain_language_example.run(\"\"\"\n", + "Which streamers should I also watch if I like pokimane?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 239 + }, + "id": "-fsAoE_MHvDq", + "outputId": "271397f0-c027-4e2c-a310-9ef39b1da9d1" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s1:Stream)-[:PLAYS]->(:Game {name: 'League of Legends'})<-[:PLAYS]-(s2:Stream)-[:CHATTER]->(s1)\n", + "WHERE s1.name = 'pokimane'\n", + "RETURN s2.name AS recommended_streamer\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Based on your interest in Pokimane, you may also enjoy watching other popular streamers such as Valkyrae, LilyPichu, and Fuslie. These streamers have similar content and personalities that may appeal to your interests.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Interestingly, every time we rerun this question, the model will generate a different Cypher statement. However, one thing is consistent. For some reason, every time the League of Legends is somehow included in the query.\n", + "\n", + "A bit more worrying fact is that the LLM model provided recommendations even though it wasn't provided with any suggestions in the prompt context. It's known that gpt-3.5-turbo sometimes doesn't follow the rules, especially if you do not repeat them more than once.\n", + "\n", + "Repeating the instruction three times can help gpt-3.5-turbo solve this problem. However, by repeating instructions, you are increasing the token count and consequently the cost of Cypher generation. Therefore, it would take some prompt engineering to get the best results using the lowest count of tokens.\n", + "\n", + "As mentioned, we will use Personalized PageRank to provide stream recommendations. But first, we need to project the in-memory graph and run the Node Similarity algorithm to prepare the graph to be able to give recommendations. Look at my [previous blog post](https://towardsdatascience.com/twitchverse-a-network-analysis-of-twitch-universe-using-neo4j-graph-data-science-d7218b4453ff) to learn more about how graph algorithms can be used to analyze the Twitch network." + ], + "metadata": { + "id": "Uui_hb0v0cCf" + } + }, + { + "cell_type": "code", + "source": [ + "# Project in-memory graph\n", + "graph.query(\"\"\"\n", + "CALL gds.graph.project('shared-audience',\n", + " ['User', 'Stream'],\n", + " {CHATTER: {orientation:'REVERSE'}})\n", + "\"\"\")\n", + "\n", + "# Run node similarity algorithm\n", + "graph.query(\"\"\"\n", + "CALL gds.nodeSimilarity.mutate('shared-audience',\n", + " {similarityMetric: 'Jaccard',similarityCutoff:0.05, topK:10, sudo:true,\n", + " mutateProperty:'score', mutateRelationshipType:'SHARED_AUDIENCE'})\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6NimpqImIXzU", + "outputId": "073caee8-2102-41cb-d5c9-caa4e6d4add1" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'preProcessingMillis': 0,\n", + " 'computeMillis': 109479,\n", + " 'mutateMillis': 39,\n", + " 'postProcessingMillis': -1,\n", + " 'nodesCompared': 4538,\n", + " 'relationshipsWritten': 23609,\n", + " 'similarityDistribution': {'p1': 0.05039477348327637,\n", + " 'max': 0.9291150569915771,\n", + " 'p5': 0.05223870277404785,\n", + " 'p90': 0.27272772789001465,\n", + " 'p50': 0.08695673942565918,\n", + " 'p95': 0.3424661159515381,\n", + " 'p10': 0.05494499206542969,\n", + " 'p75': 0.14691996574401855,\n", + " 'p99': 0.46153998374938965,\n", + " 'p25': 0.06399989128112793,\n", + " 'p100': 0.9291150569915771,\n", + " 'min': 0.04999995231628418,\n", + " 'mean': 0.1265612697302399,\n", + " 'stdDev': 0.09586148263128431},\n", + " 'configuration': {'topK': 10,\n", + " 'similarityMetric': 'JACCARD',\n", + " 'bottomK': 10,\n", + " 'bottomN': 0,\n", + " 'mutateRelationshipType': 'SHARED_AUDIENCE',\n", + " 'topN': 0,\n", + " 'concurrency': 4,\n", + " 'jobId': '78d599a8-ae8b-4a5d-8ccb-1471b7b6bbeb',\n", + " 'degreeCutoff': 1,\n", + " 'similarityCutoff': 0.05,\n", + " 'logProgress': True,\n", + " 'nodeLabels': ['*'],\n", + " 'sudo': True,\n", + " 'relationshipTypes': ['*'],\n", + " 'mutateProperty': 'score'}}]" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The node similarity algorithm will take about 30 seconds to complete as the database has almost five million users. The Cypher statement to provide recommendations using Personalized PageRank is the following:" + ], + "metadata": { + "id": "LFCvIpyW0o2J" + } + }, + { + "cell_type": "code", + "source": [ + "graph.query(\"\"\"\n", + "MATCH (s:Stream)\n", + "WHERE s.name = \"kimdoe\"\n", + "WITH collect(s) AS sourceNodes\n", + "CALL gds.pageRank.stream(\"shared-audience\", \n", + " {sourceNodes:sourceNodes, relationshipTypes:['SHARED_AUDIENCE'], \n", + " nodeLabels:['Stream']})\n", + "YIELD nodeId, score\n", + "WITH gds.util.asNode(nodeId) AS node, score\n", + "WHERE NOT node in sourceNodes\n", + "RETURN node.name AS streamer, score\n", + "ORDER BY score DESC LIMIT 3\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hmoSjlwsquPi", + "outputId": "a35e7e2b-9016-42f3-8e1d-ce97d0282e86" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'streamer': 'tranth', 'score': 0.13697276805472164},\n", + " {'streamer': 'jungtaejune', 'score': 0.13697276805472164},\n", + " {'streamer': 'hanryang1125', 'score': 0.1051181893540686}]" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The OpenAI LLMs could be better at using the Graph Data Science library as their knowledge cutoff is September 2021, and version 2 of the Graph Data Science library was released in April 2022. Therefore, we need to provide another example in the prompt to show the LLM show to use Personalized PageRank to give recommendations." + ], + "metadata": { + "id": "VlfPC50_0rBU" + } + }, + { + "cell_type": "code", + "source": [ + "# https://github.com/hwchase17/langchain/blob/master/langchain/chains/graph_qa/prompts.py\n", + "\n", + "CYPHER_RECOMMENDATION_TEMPLATE = \"\"\"Task:Generate Cypher statement to query a graph database.\n", + "Instructions:\n", + "Use only the provided relationship types and properties in the schema.\n", + "Do not use any other relationship types or properties that are not provided.\n", + "Schema:\n", + "{schema}\n", + "Cypher examples:\n", + "# How many streamers are from Norway?\n", + "MATCH (s:Stream)-[:HAS_LANGUAGE]->(:Language {{name: 'no'}})\n", + "RETURN count(s) AS streamers\n", + "# Which streamers do you recommend if I like kimdoe?\n", + "MATCH (s:Stream)\n", + "WHERE s.name = \"kimdoe\"\n", + "WITH collect(s) AS sourceNodes\n", + "CALL gds.pageRank.stream(\"shared-audience\", \n", + " {{sourceNodes:sourceNodes, relationshipTypes:['SHARED_AUDIENCE'], \n", + " nodeLabels:['Stream']}})\n", + "YIELD nodeId, score\n", + "WITH gds.util.asNode(nodeId) AS node, score\n", + "WHERE NOT node in sourceNodes\n", + "RETURN node.name AS streamer, score\n", + "ORDER BY score DESC LIMIT 3\n", + "\n", + "Note: Do not include any explanations or apologies in your responses.\n", + "Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.\n", + "Do not include any text except the generated Cypher statement.\n", + "\n", + "The question is:\n", + "{question}\"\"\"\n", + "CYPHER_RECOMMENDATION_PROMPT = PromptTemplate(\n", + " input_variables=[\"schema\", \"question\"], template=CYPHER_RECOMMENDATION_TEMPLATE\n", + ")" + ], + "metadata": { + "id": "ZzgTn65jJXvF" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We can now test the Personalized PageRank recommendations." + ], + "metadata": { + "id": "KaSQI5me0s5f" + } + }, + { + "cell_type": "code", + "source": [ + "chain_recommendation_example = GraphCypherQAChain.from_llm(\n", + " ChatOpenAI(temperature=0, model_name='gpt-4'), graph=graph, verbose=True,\n", + " cypher_prompt=CYPHER_RECOMMENDATION_PROMPT, \n", + ")\n", + "\n", + "chain_recommendation_example.run(\"\"\"\n", + "Which streamers do you recommend if I like pokimane?\n", + "\"\"\")" + ], + "metadata": { + "id": "r14bQBlaL03n", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405 + }, + "outputId": "25bef31a-21b2-44e6-b09a-0e60ea97bec6" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s:Stream)\n", + "WHERE s.name = \"pokimane\"\n", + "WITH collect(s) AS sourceNodes\n", + "CALL gds.pageRank.stream(\"shared-audience\", \n", + " {sourceNodes:sourceNodes, relationshipTypes:['SHARED_AUDIENCE'], \n", + " nodeLabels:['Stream']})\n", + "YIELD nodeId, score\n", + "WITH gds.util.asNode(nodeId) AS node, score\n", + "WHERE NOT node in sourceNodes\n", + "RETURN node.name AS streamer, score\n", + "ORDER BY score DESC LIMIT 3\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'streamer': 'xchocobars', 'score': 0.2343657053097286}, {'streamer': 'ariasaki', 'score': 0.06485239618458194}, {'streamer': 'natsumiii', 'score': 0.05969369486512491}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "\"Based on the information provided, I recommend checking out the following streamers if you like Pokimane:\\n\\n1. xchocobars with a score of 0.2344\\n2. ariasaki with a score of 0.0649\\n3. natsumiii with a score of 0.0597\\n\\nThese scores indicate their similarity to Pokimane's content and style. Enjoy watching!\"" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Unfortunately, here, we have to use the gpt-4 model as the gpt-3.5-turbo is stubborn and doesn't want to imitate the complex Personalized PageRank example.\n", + "\n", + "We can also test if the gpt-4 model will decide to generalize the Personalized PageRank recommendation in other use cases." + ], + "metadata": { + "id": "F6BOSdmG0u8W" + } + }, + { + "cell_type": "code", + "source": [ + "chain_recommendation_example.run(\"\"\"\n", + "Which streamers do you recommend to watch if I like Chess games?\n", + "\"\"\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 278 + }, + "id": "ZvWNlpFIMJwj", + "outputId": "15f6edae-2938-4ae2-aa9c-d1ad3d78ddbb" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s:Stream)-[:PLAYS]->(:Game {name: 'Chess'})\n", + "RETURN s.name AS streamer\n", + "ORDER BY s.followers DESC LIMIT 10\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'streamer': 'gmhikaru'}, {'streamer': 'thisisnotgeorgenotfound'}, {'streamer': 'gothamchess'}, {'streamer': 'mates'}, {'streamer': 'akanemsko'}, {'streamer': 'xntentacion'}, {'streamer': 'chessbrah'}, {'streamer': 'inet_saju'}, {'streamer': 'annacramling'}, {'streamer': 'michelleputtini'}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'I recommend the following chess streamers for you to watch: gmhikaru, thisisnotgeorgenotfound, gothamchess, mates, akanemsko, xntentacion, chessbrah, inet_saju, annacramling, and michelleputtini. Enjoy watching their chess games!'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The LLM decided to take a more straightforward route to provide recommendations and simply returned the three chess players with the highest follower count. We can't really blame it for choosing this option.\n", + "\n", + "However, LLMs are quite good at listening to hints:" + ], + "metadata": { + "id": "72O2RJqR0xtE" + } + }, + { + "cell_type": "code", + "source": [ + "chain_recommendation_example.run(\"\"\"\n", + "Which streamers do you recommend to watch if I like Chess games?\n", + "Use Personalized PageRank to provide recommendations.\n", + "Do not exclude sourceNodes in the answer\n", + "\"\"\")" + ], + "metadata": { + "id": "ys23tsgfMYRp", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368 + }, + "outputId": "672cd445-cd6e-464d-cb4e-557a81f638b5" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new GraphCypherQAChain chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3mMATCH (s:Stream)-[:PLAYS]->(:Game {name: 'Chess'})\n", + "WITH collect(s) AS sourceNodes\n", + "CALL gds.pageRank.stream(\"shared-audience\", \n", + " {sourceNodes:sourceNodes, relationshipTypes:['SHARED_AUDIENCE'], \n", + " nodeLabels:['Stream']})\n", + "YIELD nodeId, score\n", + "WITH gds.util.asNode(nodeId) AS node, score\n", + "RETURN node.name AS streamer, score\n", + "ORDER BY score DESC LIMIT 3\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'streamer': 'segonaye', 'score': 1.1104359051332637}, {'streamer': 'dafatw01', 'score': 0.978557815808113}, {'streamer': 'chessbrah', 'score': 0.9612404689154856}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'Based on the Personalized PageRank algorithm, I recommend the following streamers for watching Chess games:\\n\\n1. segonaye with a score of 1.1104359051332637\\n2. dafatw01 with a score of 0.978557815808113\\n3. chessbrah with a score of 0.9612404689154856\\n\\nThese streamers have been ranked according to their relevance to Chess games. Enjoy watching!'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Summary\n", + "In this blog post, we expanded on using knowledge graphs in LangChain applications, focusing on improving prompts for better Cypher statements. The main opportunity to improve the Cypher generation accuracy is to use the few-shot capabilities of LLMs, offering Cypher statement examples that dictate the type of statements an LLM should produce. Sometimes, the LLM model doesn't correctly guess the property values, while other times, it doesn't provide the Cypher statements we would like it to generate. Additionally, we have looked at how we can use graph algorithms like Personalized PageRank in LLM applications to provide better and more relevant answers." + ], + "metadata": { + "id": "vD3m_ovB01_3" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "zJvtuXlvuaon" + }, + "execution_count": 14, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/LLMs/src/langchain_neo4j_vertexai.ipynb b/LLMs/src/langchain_neo4j_vertexai.ipynb new file mode 100644 index 0000000..328ee8e --- /dev/null +++ b/LLMs/src/langchain_neo4j_vertexai.ipynb @@ -0,0 +1,582 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyNOOwWqANx97QWxWt3GRMrG", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ISol2e_-PGJK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a57d5819-205c-4841-d1d2-bd295016c581" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m321.3/321.3 kB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m78.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m68.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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"execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'status': 'ok', 'restart': True}" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from langchain.graphs import Neo4jGraph\n", + "\n", + "graph = Neo4jGraph(\n", + " url=\"bolt://34.239.133.123:7687\",\n", + " username=\"neo4j\",\n", + " password=\"traps-henry-milliliters\"\n", + ")" + ], + "metadata": { + "id": "WedomuMuPT91" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "\n", + "uploaded = files.upload()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 73 + }, + "id": "7zgoRpLPb1Kv", + "outputId": "45c6238f-b6b1-4305-8a1d-8a3c35d983c0" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving credentials.json to credentials.json\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "\n", + "from langchain.chat_models import ChatVertexAI\n", + "from langchain.chains import GraphCypherQAChain\n", + "\n", + "os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = \"credentials.json\"" + ], + "metadata": { + "id": "Ax6qjSxIPMy2" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from langchain.chains import GraphCypherQAChain\n", + "\n", + "chain = GraphCypherQAChain.from_llm(\n", + " ChatVertexAI(temperature=0, model_name=\"codechat-bison\"), graph=graph, verbose=True,\n", + ")\n", + "\n", + "chain.run(\"Who is the most followed fortnite streamer?\")" + ], + "metadata": { + "id": "8uYg8LhCQRU9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "outputId": "5b2a7617-c132-4402-b2cc-73a5a618ad29" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3m\n", + "MATCH (s:Stream)-[:PLAYS]->(g:Game)\n", + "WHERE g.name = \"Fortnite\"\n", + "RETURN s.followers ORDER BY s.followers DESC LIMIT 1\n", + "\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'s.followers': 7269018}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The most followed Fortnite streamer is Ninja.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"Please recommend a good chess stream?\")" + ], + "metadata": { + "id": "eDEYAakNe21i", + "outputId": "396acdbc-1a42-422d-ac9d-a901b58e9176", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + } + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3m\n", + "MATCH (s:Stream)-[:PLAYS]->(g:Game)\n", + "WHERE g.name = \"Chess\"\n", + "RETURN s\n", + "ORDER BY s.total_view_count DESC\n", + "LIMIT 1\n", + "\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'s': {'followers': 8772, 'name': 'imsatranc', 'url': 'https://www.twitch.tv/imsatranc'}}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'I recommend imsatranc. He has 8772 followers and his stream is at https://www.twitch.tv/imsatranc.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"Which are the top five games played by streamers?\")" + ], + "metadata": { + "id": "k_V1nduwfPfk", + "outputId": "b1591717-bd2a-448a-e6be-23160f269228", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 316 + } + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3m\n", + "MATCH (s:Stream)-[:PLAYS]->(g:Game)\n", + "RETURN g.name AS game,\n", + " COUNT(*) AS number_of_streams\n", + "ORDER BY number_of_streams DESC\n", + "LIMIT 5;\n", + "\u001b[0m\n", + "Full Context:\n", + "\u001b[32;1m\u001b[1;3m[{'game': 'Just Chatting', 'number_of_streams': 868}, {'game': 'Resident Evil Village', 'number_of_streams': 442}, {'game': 'Grand Theft Auto V', 'number_of_streams': 380}, {'game': 'League of Legends', 'number_of_streams': 279}, {'game': 'Fortnite', 'number_of_streams': 217}]\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'The top five games played by streamers are:\\n\\n1. Just Chatting\\n2. Resident Evil Village\\n3. Grand Theft Auto V\\n4. League of Legends\\n5. Fortnite'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "chain.run(\"Which streams are the most important based on PageRank?\")" + ], + "metadata": { + "id": "th3WtJwofgQU", + "outputId": "21ba5792-93a9-431b-aaa9-42201ffca095", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 814 + } + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new chain...\u001b[0m\n", + "Generated Cypher:\n", + "\u001b[32;1m\u001b[1;3m\n", + "MATCH (s:Stream)\n", + "WITH s AS stream\n", + "CALL pageRank(\n", + " startNode: stream,\n", + " maxIterations: 10,\n", + " dampingFactor: 0.85\n", + ")\n", + "RETURN stream,\n", + " rnk AS pageRank\n", + "ORDER BY rnk DESC\n", + "LIMIT 10\n", + "\u001b[0m\n" + ] + }, + { + "output_type": "error", + "ename": "ValueError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCypherSyntaxError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/graphs/neo4j_graph.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, params)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/neo4j/_sync/work/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, query, parameters, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m self._auto_result._run(\n\u001b[0m\u001b[1;32m 312\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m 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PageRank?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/base.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callbacks, tags, *args, **kwargs)\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"`run` supports only one positional argument.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m 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include_run_info)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m outputs = (\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnew_arg_supported\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/chains/graph_qa/cypher.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[0;31m# Retrieve and limit the number of results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m \u001b[0mcontext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerated_cypher\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtop_k\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_direct\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/graphs/neo4j_graph.py\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, params)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mCypherSyntaxError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Generated Cypher Statement is not valid\\n\"\u001b[0m \u001b[0;34mf\"{e}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrefresh_schema\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Generated Cypher Statement is not valid\n{code: Neo.ClientError.Statement.SyntaxError} {message: Invalid input '10': expected \"%\", \"(\", \"YIELD\" or an identifier (line 6, column 20 (offset: 92))\n\" maxIterations: 10,\"\n ^}" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "2kmzV4euf-oT" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No 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