OpenSearchEmbeddingRetriever
An embedding-based Retriever compatible with the OpenSearch Document Store.
| Name | OpenSearchEmbeddingRetriever |
| Source | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/opensearch |
| Most common position in a pipeline | 1. After a Text Embedder and before a PromptBuilder in a RAG pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an ExtractiveReader in an extractive QA pipeline |
| Mandatory input variables | “query_embedding”: A list of floats |
| Output variables | “documents”: A list of documents |
Overview
The OpenSearchEmbeddingRetriever is an embedding-based Retriever compatible with the OpenSearchDocumentStore. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the OpenSearchDocumentStore based on the outcome.
When using the OpenSearchEmbeddingRetriever in your NLP system, make sure it has the query and Document embeddings available. You can do so by adding a Document Embedder to your indexing pipeline and a Text Embedder to your query pipeline.
In addition to the query_embedding, the OpenSearchEmbeddingRetriever accepts other optional parameters, including top_k (the maximum number of Documents to retrieve) and filters to narrow down the search space.
The embedding_dim for storing and retrieving embeddings must be defined when the corresponding OpenSearchDocumentStore is initialized.
Setup and installation
Install and run an OpenSearch instance.
If you have Docker set up, we recommend pulling the Docker image and running it.
docker pull opensearchproject/opensearch:2.11.0
docker run -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" -e "ES_JAVA_OPTS=-Xms1024m -Xmx1024m" opensearchproject/opensearch:2.11.0
As an alternative, you can go to OpenSearch integration GitHub and start a Docker container running OpenSearch using the provided docker-compose.yml:
docker compose up
Once you have a running OpenSearch instance, install the opensearch-haystack integration:
pip install opensearch-haystack
Usage
In a pipeline
Use this Retriever in a query Pipeline like this:
from haystack_integrations.components.retrievers.opensearch import OpenSearchEmbeddingRetriever
from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore
from haystack.document_stores.types import DuplicatePolicy
from haystack import Document
from haystack import Pipeline
from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder
document_store = OpenSearchDocumentStore(hosts="http://localhost:9200", use_ssl=True,
verify_certs=False, http_auth=("admin", "admin"))
model = "sentence-transformers/all-mpnet-base-v2"
documents = [Document(content="There are over 7,000 languages spoken around the world today."),
Document(content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors."),
Document(content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves.")]
document_embedder = SentenceTransformersDocumentEmbedder(model=model)
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)
document_store.write_documents(documents_with_embeddings.get("documents"), policy=DuplicatePolicy.SKIP)
query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder(model=model))
query_pipeline.add_component("retriever", OpenSearchEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
query = "How many languages are there?"
result = query_pipeline.run({"text_embedder": {"text": query}})
print(result['retriever']['documents'][0])
The example output would be:
Document(id=cfe93bc1c274908801e6670440bf2bbba54fad792770d57421f85ffa2a4fcc94, content: 'There are over 7,000 languages spoken around the world today.', score: 0.70026743, embedding: vector of size 768)
Updated 6 months ago