[deepset-ai/haystack-core-integrations#727]

integrations/pgvector/examples/hybrid_retrieval.py

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+# Before running this example, ensure you have PostgreSQL installed with the pgvector extension.
+# For a quick setup using Docker:
+# docker run -d -p 5432:5432 -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres
+# -e POSTGRES_DB=postgres ankane/pgvector
+
+# Install required packages for this example, including pgvector-haystack and other libraries needed
+# for Markdown conversion and embeddings generation. Use the following command:
+# pip install pgvector-haystack markdown-it-py mdit_plain "sentence-transformers>=2.2.0"
+
+# Download some Markdown files to index.
+# git clone https://github.com/anakin87/neural-search-pills
+
+import glob
+
+from haystack import Pipeline
+from haystack.components.converters import MarkdownToDocument
+from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
+from haystack.components.joiners import DocumentJoiner
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack_integrations.components.retrievers.pgvector import PgvectorEmbeddingRetriever, PgvectorKeywordRetriever
+from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
+
+# Set an environment variable `PG_CONN_STR` with the connection string to your PostgreSQL database.
+# e.g., "postgresql://USER:PASSWORD@HOST:PORT/DB_NAME"
+
+# Initialize PgvectorDocumentStore
+document_store = PgvectorDocumentStore(
+    table_name="haystack_test",
+    embedding_dimension=768,
+    vector_function="cosine_similarity",
+    recreate_table=True,
+    search_strategy="hnsw",
+)
+
+# Create the indexing Pipeline and index some documents
+file_paths = glob.glob("neural-search-pills/pills/*.md")
+
+
+indexing = Pipeline()
+indexing.add_component("converter", MarkdownToDocument())
+indexing.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2))
+indexing.add_component("document_embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "splitter")
+indexing.connect("splitter", "document_embedder")
+indexing.connect("document_embedder", "writer")
+
+indexing.run({"converter": {"sources": file_paths}})
+
+# Create the querying Pipeline and try a query
+querying = Pipeline()
+querying.add_component("text_embedder", SentenceTransformersTextEmbedder())
+querying.add_component("retriever", PgvectorEmbeddingRetriever(document_store=document_store, top_k=3))
+querying.add_component("keyword_retriever", PgvectorKeywordRetriever(document_store=document_store, top_k=3))
+querying.add_component(
+    "joiner",
+    DocumentJoiner(join_mode="reciprocal_rank_fusion", top_k=3),
+)
+querying.connect("text_embedder", "retriever")
+querying.connect("keyword_retriever", "joiner")
+querying.connect("retriever", "joiner")
+
+query = "cross-encoder"
+results = querying.run({"text_embedder": {"text": query}, "keyword_retriever": {"query": query}})
+
+for doc in results["joiner"]["documents"]:
+    print(doc)
+    print("-" * 10)