Opensearch Vector Store
Source: llama_index

<a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/OpensearchDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a># Opensearch Vector StoreElasticsearch only supports Lucene indices, so only Opensearch is supported.**Note on setup**: We setup a local Opensearch instance through the following doc. https://opensearch.org/docs/1.0/If you run into SSL issues, try the following `docker run` command instead: ```docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" -e "plugins.security.disabled=true" opensearchproject/opensearch:1.0.1```Reference: https://github.com/opensearch-project/OpenSearch/issues/1598Download Data```python%pip install llama-index-readers-elasticsearch%pip install llama-index-vector-stores-opensearch%pip install llama-index-embeddings-ollama``````python!mkdir -p 'data/paul_graham/'!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'``````pythonfrom os import getenvfrom llama_index.core import SimpleDirectoryReaderfrom llama_index.vector_stores.opensearch import (    OpensearchVectorStore,    OpensearchVectorClient,)from llama_index.core import VectorStoreIndex, StorageContext# http endpoint for your cluster (opensearch required for vector index usage)endpoint = getenv("OPENSEARCH_ENDPOINT", "http://localhost:9200")# index to demonstrate the VectorStore implidx = getenv("OPENSEARCH_INDEX", "gpt-index-demo")# load some sample datadocuments = SimpleDirectoryReader("./data/paul_graham/").load_data()```    /Users/jerryliu/Programming/gpt_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html      from .autonotebook import tqdm as notebook_tqdm```python# OpensearchVectorClient stores text in this field by defaulttext_field = "content"# OpensearchVectorClient stores embeddings in this field by defaultembedding_field = "embedding"# OpensearchVectorClient encapsulates logic for a# single opensearch index with vector search enabledclient = OpensearchVectorClient(    endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field)# initialize vector storevector_store = OpensearchVectorStore(client)storage_context = StorageContext.from_defaults(vector_store=vector_store)# initialize an index using our sample data and the client we just createdindex = VectorStoreIndex.from_documents(    documents=documents, storage_context=storage_context)``````python# run queryquery_engine = index.as_query_engine()res = query_engine.query("What did the author do growing up?")res.response```    INFO:root:> [query] Total LLM token usage: 29628 tokens    INFO:root:> [query] Total embedding token usage: 8 tokens    '\n\nThe author grew up writing short stories, programming on an IBM 1401, and building a computer kit from Heathkit. They also wrote programs for a TRS-80, such as games, a program to predict model rocket flight, and a word processor. After years of nagging, they convinced their father to buy a TRS-80, and they wrote simple games, a program to predict how high their model rockets would fly, and a word processor that their father used to write at least one book. In college, they studied philosophy and AI, and wrote a book about Lisp hacking. They also took art classes and applied to art schools, and experimented with computer graphics and animation, exploring the use of algorithms to create art. Additionally, they experimented with machine learning algorithms, such as using neural networks to generate art, and exploring the use of numerical values to create art. They also took classes in fundamental subjects like drawing, color, and design, and applied to two art schools, RISD in the US, and the Accademia di Belli Arti in Florence. They were accepted to RISD, and while waiting to hear back from the Accademia, they learned Italian and took the entrance exam in Florence. They eventually graduated from RISD'The OpenSearch vector store supports [filter-context queries](https://opensearch.org/docs/latest/query-dsl/query-filter-context/).```pythonfrom llama_index.core import Documentfrom llama_index.core.vector_stores import MetadataFilters, ExactMatchFilterimport regex as re``````python# Split the text into paragraphs.text_chunks = documents[0].text.split("\n\n")# Create a document for each footnotefootnotes = [    Document(        text=chunk,        id=documents[0].doc_id,        metadata={"is_footnote": bool(re.search(r"^\s*\[\d+\]\s*", chunk))},    )    for chunk in text_chunks    if bool(re.search(r"^\s*\[\d+\]\s*", chunk))]``````python# Insert the footnotes into the indexfor f in footnotes:    index.insert(f)``````python# Create a query engine that only searches certain footnotes.footnote_query_engine = index.as_query_engine(    filters=MetadataFilters(        filters=[            ExactMatchFilter(                key="term", value='{"metadata.is_footnote": "true"}'            ),            ExactMatchFilter(                key="query_string",                value='{"query": "content: space AND content: lisp"}',            ),        ]    ))res = footnote_query_engine.query(    "What did the author about space aliens and lisp?")res.response```    "The author believes that any sufficiently advanced alien civilization would know about the Pythagorean theorem and possibly also about Lisp in McCarthy's 1960 paper."## Use reader to check out what VectorStoreIndex just created in our index.Reader works with Elasticsearch too as it just uses the basic search features.```python# create a reader to check out the index used in previous section.from llama_index.readers.elasticsearch import ElasticsearchReaderrdr = ElasticsearchReader(endpoint, idx)# set embedding_field optionally to read embedding data from the elasticsearch indexdocs = rdr.load_data(text_field, embedding_field=embedding_field)# docs have embeddings in themprint("embedding dimension:", len(docs[0].embedding))# full document is stored in metadataprint("all fields in index:", docs[0].metadata.keys())```    embedding dimension: 1536    all fields in index: dict_keys(['content', 'embedding'])```python# we can check out how the text was chunked by the `GPTOpensearchIndex`print("total number of chunks created:", len(docs))```    total number of chunks: 10```python# search index using standard elasticsearch query DSLdocs = rdr.load_data(text_field, {"query": {"match": {text_field: "Lisp"}}})print("chunks that mention Lisp:", len(docs))docs = rdr.load_data(text_field, {"query": {"match": {text_field: "Yahoo"}}})print("chunks that mention Yahoo:", len(docs))```    chunks that mention Lisp: 10    chunks that mention Yahoo: 8## Hybrid query for opensearch vector storeHybrid query has been supported since OpenSearch 2.10. It is a combination of vector search and text search. It is useful when you want to search for a specific text and also want to filter the results by vector similarity. You can find more details: https://opensearch.org/docs/latest/query-dsl/compound/hybrid/. ### Prepare Search PipelineCreate a new [search pipeline](https://opensearch.org/docs/latest/search-plugins/search-pipelines/creating-search-pipeline/) with [score normalization and weighted harmonic mean combination](https://opensearch.org/docs/latest/search-plugins/search-pipelines/normalization-processor/).```PUT /_search/pipeline/hybrid-search-pipeline{  "description": "Post processor for hybrid search",  "phase_results_processors": [    {      "normalization-processor": {        "normalization": {          "technique": "min_max"        },        "combination": {          "technique": "harmonic_mean",          "parameters": {            "weights": [              0.3,              0.7            ]          }        }      }    }  ]}```### Initialize a OpenSearch client and vector store supporting hybrid query with search pipeline details```pythonfrom os import getenvfrom llama_index.vector_stores.opensearch import (    OpensearchVectorStore,    OpensearchVectorClient,)# http endpoint for your cluster (opensearch required for vector index usage)endpoint = getenv("OPENSEARCH_ENDPOINT", "http://localhost:9200")# index to demonstrate the VectorStore implidx = getenv("OPENSEARCH_INDEX", "auto_retriever_movies")# OpensearchVectorClient stores text in this field by defaulttext_field = "content"# OpensearchVectorClient stores embeddings in this field by defaultembedding_field = "embedding"# OpensearchVectorClient encapsulates logic for a# single opensearch index with vector search enabled with hybrid search pipelineclient = OpensearchVectorClient(    endpoint,    idx,    4096,    embedding_field=embedding_field,    text_field=text_field,    search_pipeline="hybrid-search-pipeline",)from llama_index.embeddings.ollama import OllamaEmbeddingembed_model = OllamaEmbedding(model_name="llama2")# initialize vector storevector_store = OpensearchVectorStore(client)```### Prepare the index```pythonfrom llama_index.core.schema import TextNodefrom llama_index.core import VectorStoreIndex, StorageContextstorage_context = StorageContext.from_defaults(vector_store=vector_store)nodes = [    TextNode(        text="The Shawshank Redemption",        metadata={            "author": "Stephen King",            "theme": "Friendship",        },    ),    TextNode(        text="The Godfather",        metadata={            "director": "Francis Ford Coppola",            "theme": "Mafia",        },    ),    TextNode(        text="Inception",        metadata={            "director": "Christopher Nolan",        },    ),]index = VectorStoreIndex(    nodes, storage_context=storage_context, embed_model=embed_model)```    LLM is explicitly disabled. Using MockLLM.### Search the index with hybrid query by specifying the vector store query mode: VectorStoreQueryMode.HYBRID with filters```pythonfrom llama_index.core.vector_stores import ExactMatchFilter, MetadataFiltersfrom llama_index.core.vector_stores.types import VectorStoreQueryModefilters = MetadataFilters(    filters=[        ExactMatchFilter(            key="term", value='{"metadata.theme.keyword": "Mafia"}'        )    ])retriever = index.as_retriever(    filters=filters, vector_store_query_mode=VectorStoreQueryMode.HYBRID)result = retriever.retrieve("What is inception about?")print(result)```    query_strWhat is inception about?    query_modehybrid    {'size': 2, 'query': {'hybrid': {'queries': [{'bool': {'must': {'match': {'content': {'query': 'What is inception about?'}}}, 'filter': [{'term': {'metadata.theme.keyword': 'Mafia'}}]}}, {'script_score': {'query': {'bool': {'filter': [{'term': {'metadata.theme.keyword': 'Mafia'}}]}}, 'script': {'source': "1/(1.0 + l2Squared(params.query_value, doc['embedding']))", 'params': {'field': 'embedding', 'query_value': [0.41321834921836853, 0.18020285665988922, 2.5630273818969727, 1.490068793296814, -2.2188172340393066, 0.3613924980163574, 0.036182258278131485, 1.3815258741378784, -0.4603463411331177, 0.9783738851547241, 0.3667166233062744, -0.30677080154418945, -1.2893489599227905, -1.19036865234375, -1.4050743579864502, -2.200796365737915, 0.05992934852838516, 0.30156904458999634, 0.6115846633911133, -0.028691552579402924, 0.5112416744232178, -2.069373846054077, 0.6121743321418762, -0.05102552846074104, 1.8506423234939575, -1.293755292892456, -0.8149858117103577, 0.37656715512275696, 0.427949458360672, 0.43708929419517517, 3.2720835208892822, -1.9999115467071533, -2.374300241470337, 3.1277284622192383, 3.2631218433380127, -4.0594635009765625, -0.7985063195228577, 1.9719655513763428, -1.0863256454467773, -1.3689632415771484, -1.6202458143234253, -0.970841109752655, 0.4361116886138916, 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