PgvectorEmbeddingRetriever
An embedding-based Retriever compatible with the Pgvector Document Store.
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 init variables | "document_store": An instance of a PgvectorDocumentStore |
Mandatory run variables | “query_embedding”: A vector representing the query (a list of floats) |
Output variables | “documents”: A list of documents |
API reference | Pgvector |
GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector |
Overview
The PgvectorEmbeddingRetriever
is an embedding-based Retriever compatible with the PgvectorDocumentStore
. It compares the query and Document embeddings and fetches the Documents most relevant to the query from the PgvectorDocumentStore
based on the outcome.
When using the PgvectorEmbeddingRetriever
in your Pipeline, 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 PgvectorEmbeddingRetriever
accepts other optional parameters, including top_k
(the maximum number of Documents to retrieve) and filters
to narrow down the search space.
Some relevant parameters that impact the embedding retrieval must be defined when the corresponding PgvectorDocumentStore
is initialized: these include embedding dimension, vector function, and some others related to the search strategy (exact nearest neighbor or HNSW).
Installation
To quickly set up a PostgreSQL database with pgvector, you can use Docker:
docker run -d -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres -e POSTGRES_DB=postgres ankane/pgvector
For more information on installing pgvector, visit the pgvector GitHub repository.
To use pgvector with Haystack, install the pgvector-haystack
integration:
pip install pgvector-haystack
Usage
On its own
This Retriever needs the PgvectorDocumentStore
and indexed Documents to run.
import os
from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import PgvectorEmbeddingRetriever
os.environ["PG_CONN_STR"] = "postgresql://postgres:postgres@localhost:5432/postgres"
document_store = PgvectorDocumentStore()
retriever = PgvectorEmbeddingRetriever(document_store=document_store)
# using a fake vector to keep the example simple
retriever.run(query_embedding=[0.1]*768)
In a Pipeline
import os
from haystack.document_stores import DuplicatePolicy
from haystack import Document, Pipeline
from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder
from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import PgvectorEmbeddingRetriever
os.environ["PG_CONN_STR"] = "postgresql://postgres:postgres@localhost:5432/postgres"
document_store = PgvectorDocumentStore(
embedding_dimension=768,
vector_function="cosine_similarity",
recreate_table=True,
)
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()
document_embedder.warm_up()
documents_with_embeddings = document_embedder.run(documents)
document_store.write_documents(documents_with_embeddings.get("documents"), policy=DuplicatePolicy.OVERWRITE)
query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component("retriever", PgvectorEmbeddingRetriever(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])
Updated 2 months ago
Check out the API reference in the GitHub repo or in our docs.