
Name	PgvectorKeywordRetriever
Source	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pgvector/
Most common position in a pipeline	1. Before a `PromptBuilder` in a RAG pipeline 2. The last component in the semantic search pipeline 3. Before an `ExtractiveReader` in an extractive QA pipeline
Mandatory input variables	“query”: A string
Output variables	“document”: A list of documents (matching the query)

Overview

The PgvectorKeywordRetriever is a keyword-based Retriever compatible with the PgvectorDocumentStore.

The component uses the ts_rank_cd function of PostgreSQL to rank the documents.
It considers how often the query terms appear in the document, how close together the terms are in the document, and how important is the part of the document where they occur.
For more details, see Postgres documentation.

Keep in mind that, unlike similar components such as ElasticsearchBM25Retriever, this Retriever does not apply fuzzy search out of the box, so it’s necessary to carefully formulate the query in order to avoid getting zero results.

In addition to the query, the PgvectorKeywordRetriever accepts other optional parameters, including top_k (the maximum number of documents to retrieve) and filters to narrow the search space.

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 how to install pgvector, visit the pgvector GitHub repository.

Install the pgvector-haystack integration:

pip install pgvector-haystack

Usage

On its own

This Retriever needs the PgvectorDocumentStore and indexed documents to run.

Set an environment variable PG_CONN_STR with the connection string to your PostgreSQL database.

from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import PgvectorKeywordRetriever

document_store = PgvectorDocumentStore()
retriever = PgvectorKeywordRetriever(document_store=document_store)

retriever.run(query="my nice query")

In a RAG pipeline

The prerequisites necessary for running this code are:

Set an environment variable OPENAI_API_KEY with your OpenAI API key.
Set an environment variable PG_CONN_STR with the connection string to your PostgreSQL database.

from haystack import Document
from haystack import Pipeline
from haystack.components.builders.answer_builder import AnswerBuilder
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.document_stores.types import DuplicatePolicy

from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import (
    PgvectorKeywordRetriever,
)

# Create a RAG query pipeline
prompt_template = """
    Given these documents, answer the question.\nDocuments:
    {% for doc in documents %}
        {{ doc.content }}
    {% endfor %}

    \nQuestion: {{question}}
    \nAnswer:
    """

document_store = PgvectorDocumentStore(
    language="english",  # this parameter influences text parsing for keyword retrieval
    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."
    ),
]

# DuplicatePolicy.SKIP param is optional, but useful to run the script multiple times without throwing errors
document_store.write_documents(documents=documents, policy=DuplicatePolicy.SKIP)

retriever = PgvectorKeywordRetriever(document_store=document_store)
rag_pipeline = Pipeline()
rag_pipeline.add_component(name="retriever", instance=retriever)
rag_pipeline.add_component(
    instance=PromptBuilder(template=prompt_template), name="prompt_builder"
)
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")
rag_pipeline.connect("llm.replies", "answer_builder.replies")
rag_pipeline.connect("llm.meta", "answer_builder.meta")
rag_pipeline.connect("retriever", "answer_builder.documents")

question = "languages spoken around the world today"
result = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
        "answer_builder": {"query": question},
    }
)
print(result["answer_builder"])