add example (#334)

integrations/pgvector/examples/example.py

@@ -0,0 +1,58 @@
+# 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.preprocessors import DocumentSplitter
+from haystack.components.writers import DocumentWriter
+from haystack_integrations.components.retrievers.pgvector import PgvectorEmbeddingRetriever
+from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
+
+# Initialize PgvectorDocumentStore
+document_store = PgvectorDocumentStore(
+    connection_string="postgresql://postgres:postgres@localhost:5432/postgres",
+    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("embedder", SentenceTransformersDocumentEmbedder())
+indexing.add_component("writer", DocumentWriter(document_store))
+indexing.connect("converter", "splitter")
+indexing.connect("splitter", "embedder")
+indexing.connect("embedder", "writer")
+
+indexing.run({"converter": {"sources": file_paths}})
+
+# Create the querying Pipeline and try a query
+querying = Pipeline()
+querying.add_component("embedder", SentenceTransformersTextEmbedder())
+querying.add_component("retriever", PgvectorEmbeddingRetriever(document_store=document_store, top_k=3))
+querying.connect("embedder", "retriever")
+
+results = querying.run({"embedder": {"text": "What is a cross-encoder?"}})
+
+for doc in results["retriever"]["documents"]:
+    print(doc)
+    print("-" * 10)

integrations/pgvector/pyproject.toml

@@ -153,6 +153,8 @@ ban-relative-imports = "parents"
 [tool.ruff.per-file-ignores]
 # Tests can use magic values, assertions, and relative imports
 "tests/**/*" = ["PLR2004", "S101", "TID252"]
+# examples can contain "print" commands
+"examples/**/*" = ["T201"]
 
 [tool.coverage.run]
 source_pkgs = ["src", "tests"]