From 1124e9a2257817609c1f7535101a68c918623f4b Mon Sep 17 00:00:00 2001 From: Max Jakob Date: Mon, 13 May 2024 14:54:29 +0200 Subject: [PATCH] update `Elasticsearch Vector Store` doc notebook --- .../ElasticsearchIndexDemo.ipynb | 391 +++++++++++------- 1 file changed, 231 insertions(+), 160 deletions(-) diff --git a/docs/docs/examples/vector_stores/ElasticsearchIndexDemo.ipynb b/docs/docs/examples/vector_stores/ElasticsearchIndexDemo.ipynb index c8edb9b0db8e80..ff009fbd300a81 100644 --- a/docs/docs/examples/vector_stores/ElasticsearchIndexDemo.ipynb +++ b/docs/docs/examples/vector_stores/ElasticsearchIndexDemo.ipynb @@ -24,22 +24,13 @@ "id": "67837810", "metadata": {}, "source": [ - "Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and keyword search. It is built on top of the Apache Lucene library.\n", + "Elasticsearch is a distributed, RESTful search and analytics engine built on top of Apache Lucene. It offers different retrieval options including dense vector retrieval, sparse vector retrieval, keyword search and hybrid search.\n", "\n", - "[Signup](https://cloud.elastic.co/registration?utm_source=llama-index&utm_content=documentation) for a free trial.\n", + "[Sign up](https://cloud.elastic.co/registration?utm_source=llama-index&utm_content=documentation) for a free trial of Elastic Cloud or run a local server like described below.\n", "\n", "Requires Elasticsearch 8.9.0 or higher and AIOHTTP." ] }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6531510e", - "metadata": {}, - "source": [ - "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙." - ] - }, { "cell_type": "code", "execution_count": null, @@ -47,17 +38,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index-vector-stores-elasticsearch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5bfe9ac3", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install llama-index" + "%pip install -qU llama-index-vector-stores-elasticsearc llama-index openai" ] }, { @@ -67,17 +48,12 @@ "metadata": {}, "outputs": [], "source": [ - "import logging\n", - "import sys\n", + "import getpass\n", "import os\n", "\n", - "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", - "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n", - "\n", - "import getpass\n", + "import openai\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", - "import openai\n", "\n", "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" ] @@ -92,7 +68,7 @@ "Two ways to setup an Elasticsearch instance for use with:\n", "\n", "### Elastic Cloud\n", - "Elastic Cloud is a managed Elasticsearch service. [Signup](https://cloud.elastic.co/registration?utm_source=llama-index&utm_content=documentation) for a free trial.\n", + "Elastic Cloud is a managed Elasticsearch service. [Sign up](https://cloud.elastic.co/registration?utm_source=llama-index&utm_content=documentation) for a free trial.\n", "\n", "### Locally\n", "Get started with Elasticsearch by running it locally. The easiest way is to use the official Elasticsearch Docker image. See the Elasticsearch Docker documentation for more information.\n", @@ -101,9 +77,8 @@ "docker run -p 9200:9200 \\\n", " -e \"discovery.type=single-node\" \\\n", " -e \"xpack.security.enabled=false\" \\\n", - " -e \"xpack.security.http.ssl.enabled=false\" \\\n", " -e \"xpack.license.self_generated.type=trial\" \\\n", - " docker.elastic.co/elasticsearch/elasticsearch:8.9.0\n", + " docker.elastic.co/elasticsearch/elasticsearch:8.13.2\n", "```\n", "\n", "## Configuring ElasticsearchStore\n", @@ -154,7 +129,7 @@ "es = ElasticsearchStore(\n", " index_name=\"my_index\",\n", " es_cloud_id=\"\", # found within the deployment page\n", - " es_api_key=\"\" # Create an API key within Kibana (Security -> API Keys)\n", + " es_api_key=\"\" # create an API key within Kibana (Security -> API Keys)\n", ")\n", "```\n" ] @@ -162,135 +137,291 @@ { "attachments": {}, "cell_type": "markdown", - "id": "8ee4473a-094f-4d0a-a825-e1213db07240", + "id": "7d7904a2", + "metadata": {}, + "source": [ + "#### Example data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4102ca7e", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.schema import TextNode\n", + "\n", + "movies = [\n", + " TextNode(\n", + " text=\"The lives of two mob hitmen, a boxer, a gangster and his wife, and a pair of diner bandits intertwine in four tales of violence and redemption.\",\n", + " metadata={\"title\": \"Pulp Fiction\"},\n", + " ),\n", + " TextNode(\n", + " text=\"When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.\",\n", + " metadata={\"title\": \"The Dark Knight\"},\n", + " ),\n", + " TextNode(\n", + " text=\"An insomniac office worker and a devil-may-care soapmaker form an underground fight club that evolves into something much, much more.\",\n", + " metadata={\"title\": \"Fight Club\"},\n", + " ),\n", + " TextNode(\n", + " text=\"A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.\",\n", + " metadata={\"title\": \"Inception\"},\n", + " ),\n", + " TextNode(\n", + " text=\"A computer hacker learns from mysterious rebels about the true nature of his reality and his role in the war against its controllers.\",\n", + " metadata={\"title\": \"The Matrix\"},\n", + " ),\n", + " TextNode(\n", + " text=\"Two detectives, a rookie and a veteran, hunt a serial killer who uses the seven deadly sins as his motives.\",\n", + " metadata={\"title\": \"Se7en\"},\n", + " ),\n", + " TextNode(\n", + " text=\"An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.\",\n", + " metadata={\"title\": \"The Godfather\", \"theme\": \"Mafia\"},\n", + " ),\n", + "]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "04304299-fc3e-40a0-8600-f50c3292767e", + "metadata": {}, + "source": [ + "## Retrieval Examples\n", + "\n", + "This section shows the different retrieval options available through the `ElasticsearchStore` and make use of them via a VectorStoreIndex." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7717be18", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import StorageContext, VectorStoreIndex\n", + "from llama_index.vector_stores.elasticsearch import ElasticsearchStore" + ] + }, + { + "cell_type": "markdown", + "id": "34419b98", + "metadata": {}, + "source": [ + "We first define a helper function to retrieve and print results for user query input:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf62fb19", "metadata": {}, + "outputs": [], "source": [ - "#### Load documents, build VectorStoreIndex with Elasticsearch" + "def print_results(results):\n", + " for rank, result in enumerate(results, 1):\n", + " print(\n", + " f\"{rank}. title={result.metadata['title']} score={result.get_score()} text={result.get_text()}\"\n", + " )\n", + "\n", + "\n", + "def search(\n", + " vector_store: ElasticsearchStore, nodes: list[TextNode], query: str\n", + "):\n", + " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + " index = VectorStoreIndex(nodes, storage_context=storage_context)\n", + "\n", + " print(\">>> Documents:\")\n", + " retriever = index.as_retriever()\n", + " results = retriever.retrieve(query)\n", + " print_results(results)\n", + "\n", + " print(\"\\n>>> Answer:\")\n", + " query_engine = index.as_query_engine()\n", + " response = query_engine.query(query)\n", + " print(response)" ] }, { "cell_type": "code", "execution_count": null, - "id": "0a2bcc07", + "id": "d3392918", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "Note: NumExpr detected 10 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n", - "NumExpr defaulting to 8 threads.\n" + ">>> Documents:\n", + "1. title=Inception score=1.0 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.\n", + "\n", + ">>> Answer:\n", + "Inception\n" ] } ], "source": [ - "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", - "from llama_index.vector_stores.elasticsearch import ElasticsearchStore" + "from llama_index.vector_stores.elasticsearch import AsyncDenseVectorStrategy\n", + "\n", + "dense_vector_store = ElasticsearchStore(\n", + " es_url=\"http://localhost:9200\", # for Elastic Cloud authentication see above\n", + " index_name=\"movies_dense\",\n", + " retrieval_strategy=AsyncDenseVectorStrategy(),\n", + ")\n", + "\n", + "search(dense_vector_store, movies, \"which movie involves dreaming?\")" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "7d7904a2", + "id": "d72f1c40", "metadata": {}, "source": [ - "Download Data" + "This is also the default retrieval strategy:" ] }, { "cell_type": "code", "execution_count": null, - "id": "f28ce261", + "id": "ba1558b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Documents:\n", + "1. title=Inception score=1.0 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.\n", + "\n", + ">>> Answer:\n", + "Inception\n" + ] + } + ], + "source": [ + "default_store = ElasticsearchStore(\n", + " es_url=\"http://localhost:9200\", # for Elastic Cloud authentication see above\n", + " index_name=\"movies_default\",\n", + ")\n", + "\n", + "search(default_store, movies, \"which movie involves dreaming?\")" + ] + }, + { + "cell_type": "markdown", + "id": "3a1adf49", "metadata": {}, - "outputs": [], "source": [ - "!mkdir -p 'data/paul_graham/'\n", - "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'" + "### Sparse retrieval" ] }, { "cell_type": "code", "execution_count": null, - "id": "68cbd239-880e-41a3-98d8-dbb3fab55431", + "id": "7d9863de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>> Documents:\n", + "1. title=Inception score=1.0 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.\n", + "\n", + ">>> Answer:\n", + "Inception\n" + ] + } + ], "source": [ - "# load documents\n", - "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()" + "from llama_index.vector_stores.elasticsearch import AsyncSparseVectorStrategy\n", + "\n", + "sparse_vector_store = ElasticsearchStore(\n", + " es_url=\"http://localhost:9200\", # for Elastic Cloud authentication see above\n", + " index_name=\"movies_sparse\",\n", + " retrieval_strategy=AsyncSparseVectorStrategy(model_id=\".elser_model_2\"),\n", + ")\n", + "\n", + "search(sparse_vector_store, movies, \"which movie involves dreaming?\")" + ] + }, + { + "cell_type": "markdown", + "id": "d3f6c6e3", + "metadata": {}, + "source": [ + "### Keyword retrieval" ] }, { "cell_type": "code", "execution_count": null, - "id": "ba1558b3", + "id": "e2531fab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:elastic_transport.transport:GET http://localhost:9200/ [status:200 duration:0.024s]\n", - "GET http://localhost:9200/ [status:200 duration:0.024s]\n", - "INFO:elastic_transport.transport:HEAD http://localhost:9200/paul_graham [status:200 duration:0.011s]\n", - "HEAD http://localhost:9200/paul_graham [status:200 duration:0.011s]\n", - "INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.115s]\n", - "PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.115s]\n", - "INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.083s]\n", - "PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.083s]\n" + ">>> Documents:\n", + "1. title=The Dark Knight score=1.0 text=When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.\n", + "\n", + ">>> Answer:\n", + "The Joker is a menacing character who wreaks havoc and chaos on the people of Gotham, posing a significant challenge for Batman to combat injustice.\n" ] } ], "source": [ - "# initialize without metadata filter\n", - "from llama_index.core import StorageContext\n", + "from llama_index.vector_stores.elasticsearch import AsyncBM25Strategy\n", "\n", - "vector_store = ElasticsearchStore(\n", - " es_url=\"http://localhost:9200\",\n", - " # Or with Elastic Cloud\n", - " # es_cloud_id=\"my_cloud_id\",\n", - " # es_user=\"elastic\",\n", - " # es_password=\"my_password\",\n", - " index_name=\"paul_graham\",\n", + "bm25_store = ElasticsearchStore(\n", + " es_url=\"http://localhost:9200\", # for Elastic Cloud authentication see above\n", + " index_name=\"movies_bm25\",\n", + " retrieval_strategy=AsyncBM25Strategy(),\n", ")\n", - "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", - "index = VectorStoreIndex.from_documents(\n", - " documents, storage_context=storage_context\n", - ")" + "\n", + "search(bm25_store, movies, \"joker\")" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "04304299-fc3e-40a0-8600-f50c3292767e", + "id": "b4e8f643", "metadata": {}, "source": [ - "## Basic Example\n", - "We are going to ask the query engine a question about the data we just indexed." + "### Hybrid retrieval" ] }, { "cell_type": "code", "execution_count": null, - "id": "35369eda", + "id": "b4c6707a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "INFO:elastic_transport.transport:POST http://localhost:9200/paul_graham/_search [status:200 duration:0.030s]\n", - "POST http://localhost:9200/paul_graham/_search [status:200 duration:0.030s]\n", - "He invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%.\n" + ">>> Documents:\n", + "1. title=Inception score=0.36787944117144233 text=A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into thed of a C.E.O.\n", + "\n", + ">>> Answer:\n", + "\"Inception\" is the movie that involves dreaming.\n" ] } ], "source": [ - "# set Logging to DEBUG for more detailed outputs\n", - "query_engine = index.as_query_engine()\n", - "response = query_engine.query(\"what were his investments in Y Combinator?\")\n", - "print(response)" + "from llama_index.vector_stores.elasticsearch import AsyncDenseVectorStrategy\n", + "\n", + "hybrid_store = ElasticsearchStore(\n", + " es_url=\"http://localhost:9200\", # for Elastic Cloud authentication see above\n", + " index_name=\"movies_hybrid\",\n", + " retrieval_strategy=AsyncDenseVectorStrategy(hybrid=True),\n", + ")\n", + "\n", + "search(hybrid_store, movies, \"which movie involves dreaming?\")" ] }, { @@ -299,7 +430,7 @@ "id": "056d6a4a", "metadata": {}, "source": [ - "## Metadata Filters\n", + "### Metadata Filters\n", "Here we are going to index a few documents with metadata so that we can apply filters to the query engine." ] }, @@ -313,84 +444,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:elastic_transport.transport:GET http://localhost:9200/ [status:200 duration:0.012s]\n", - "GET http://localhost:9200/ [status:200 duration:0.012s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:elastic_transport.transport:HEAD http://localhost:9200/movies_metadata_example [status:404 duration:0.022s]\n", - "HEAD http://localhost:9200/movies_metadata_example [status:404 duration:0.022s]\n", - "INFO:elastic_transport.transport:PUT http://localhost:9200/movies_metadata_example [status:200 duration:0.099s]\n", - "PUT http://localhost:9200/movies_metadata_example [status:200 duration:0.099s]\n", - "INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.053s]\n", - "PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.053s]\n", - "INFO:elastic_transport.transport:PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.023s]\n", - "PUT http://localhost:9200/_bulk?refresh=true [status:200 duration:0.023s]\n", - "INFO:elastic_transport.transport:POST http://localhost:9200/movies_metadata_example/_search [status:200 duration:0.034s]\n", - "POST http://localhost:9200/movies_metadata_example/_search [status:200 duration:0.034s]\n" + "1. title=The Godfather score=1.0 text=An organized crime dynasty's aging patriarch transfers control of his clandestine empire to his reluctant son.\n" ] - }, - { - "data": { - "text/plain": [ - "[NodeWithScore(node=TextNode(id_='3b47c6b6-f01b-44fe-8f88-2249aad2a615', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='81cf4b9e847ba42e83fc401e31af8e17d629f0d5cf9c0c320ec7ac69dd0257e1', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.88513875)]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "from llama_index.core.schema import TextNode\n", - "\n", - "nodes = [\n", - " TextNode(\n", - " text=\"The Shawshank Redemption\",\n", - " metadata={\n", - " \"author\": \"Stephen King\",\n", - " \"theme\": \"Friendship\",\n", - " },\n", - " ),\n", - " TextNode(\n", - " text=\"The Godfather\",\n", - " metadata={\n", - " \"director\": \"Francis Ford Coppola\",\n", - " \"theme\": \"Mafia\",\n", - " },\n", - " ),\n", - " TextNode(\n", - " text=\"Inception\",\n", - " metadata={\n", - " \"director\": \"Christopher Nolan\",\n", - " },\n", - " ),\n", - "]\n", + "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n", "\n", - "# initialize the vector store\n", - "vector_store_metadata_example = ElasticsearchStore(\n", - " index_name=\"movies_metadata_example\",\n", - " es_url=\"http://localhost:9200\",\n", - ")\n", - "storage_context = StorageContext.from_defaults(\n", - " vector_store=vector_store_metadata_example\n", + "metadata_store = ElasticsearchStore(\n", + " es_url=\"http://localhost:9200\", # for Elastic Cloud authentication see above\n", + " index_name=\"movies_metadata\",\n", ")\n", - "index = VectorStoreIndex(nodes, storage_context=storage_context)\n", - "\n", + "storage_context = StorageContext.from_defaults(vector_store=metadata_store)\n", + "index = VectorStoreIndex(movies, storage_context=storage_context)\n", "\n", "# Metadata filter\n", - "from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n", - "\n", "filters = MetadataFilters(\n", " filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")]\n", ")\n", - "\n", "retriever = index.as_retriever(filters=filters)\n", "\n", - "retriever.retrieve(\"What is inception about?\")" + "results = retriever.retrieve(\"What is inception about?\")\n", + "print_results(results)" ] }, { @@ -413,10 +488,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "custom query {'knn': {'filter': [{'match': {'content': 'growing up'}}], 'field': 'embedding', 'query_vector': [0.002520269714295864, -0.03282919153571129, 0.016138022765517235, -0.029537975788116455, -0.006744919344782829, 0.01626248098909855, -0.03703309968113899, 0.002381983445957303, -0.003031929489225149, -0.003616189584136009, 0.032746221870183945, 0.030201751738786697, 0.011726687662303448, 0.005043996497988701, 0.0030665011145174503, 0.016207166016101837, 0.018115518614649773, -0.008539185859262943, 0.020825933665037155, -0.011595315299928188, -0.027754081413149834, -0.004622223321348429, -0.004750138148665428, -0.015363619662821293, -0.006496003828942776, 0.012860636226832867, 0.02331508882343769, -0.009368903934955597, -0.002686213469132781, 0.0029818005859851837, 0.032441992312669754, 0.0015107790241017938, -0.0023059258237481117, 0.02384057641029358, -0.029233746230602264, 0.003574703587219119, 0.0048296526074409485, 0.019401581957936287, 0.01830912008881569, -0.009375818073749542, 0.037724532186985016, 0.026274416595697403, -0.016746483743190765, -0.005078568123281002, -0.02065998874604702, -0.012846807017922401, -0.002015524310991168, -0.01924946717917919, -0.0026568276807665825, 0.01626248098909855, 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two-founder case was $12k, in return for 6%.\n" + "custom query {'knn': {'filter': [{'match': {'title': 'matrix'}}], 'field': 'embedding', 'k': 2, 'num_candidates': 20, 'query_vector': [0.00446691969409585, -0.038953110575675964, -0.023963095620274544, -0.024891795590519905, -0.016729693859815598, 0.017200583592057228, -0.002360992832109332, -0.012622482143342495, -0.009980263188481331, -0.026108263060450554, 0.02950914017856121, 0.018626336008310318, -0.016154160723090172, -0.012099270708858967, 0.03777588531374931, 0.006209868937730789, 0.03539527207612991, -0.011746102944016457, 0.0029888467397540808, -0.022066453471779823, -0.02290359139442444, -0.011752642691135406, -0.018744058907032013, -0.015251620672643185, 0.0034074161667376757, 0.00014756205200683326, 0.022955913096666336, -0.02264198660850525, 0.002032350515946746, -0.021778685972094536, 0.012164671905338764, -0.015055416151881218, 0.006543416064232588, -0.009509372524917126, -0.008632993325591087, -0.006814832333475351, 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-0.030634045600891113]}}\n" ] } ], @@ -428,12 +500,11 @@ "\n", "query_engine = index.as_query_engine(\n", " vector_store_kwargs={\n", - " \"es_filter\": [{\"match\": {\"content\": \"growing up\"}}],\n", + " \"es_filter\": [{\"match\": {\"title\": \"matrix\"}}],\n", " \"custom_query\": custom_query,\n", " }\n", ")\n", - "response = query_engine.query(\"what were his investments in Y Combinator?\")\n", - "print(response)" + "query_engine.query(\"what is this movie about?\")" ] } ],