Merge branch 'main' into adopt-secret-amazon_chat

integrations/fastembed/example/example.py

@@ -0,0 +1,33 @@
+from haystack import Document, Pipeline
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack_integrations.components.embedders.fastembed import FastembedDocumentEmbedder, FastembedTextEmbedder
+
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+
+documents = [
+    Document(content="My name is Wolfgang and I live in Berlin"),
+    Document(content="I saw a black horse running"),
+    Document(content="Germany has many big cities"),
+    Document(content="fastembed is supported by and maintained by Qdrant."),
+]
+
+document_embedder = FastembedDocumentEmbedder()
+document_embedder.warm_up()
+documents_with_embeddings = document_embedder.run(documents)["documents"]
+document_store.write_documents(documents_with_embeddings)
+
+query_pipeline = Pipeline()
+query_pipeline.add_component("text_embedder", FastembedTextEmbedder())
+query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
+query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+
+query = "Who supports fastembed?"
+
+result = query_pipeline.run({"text_embedder": {"text": query}})
+
+print(result["retriever"]["documents"][0])  # noqa: T201
+
+# Document(id=...,
+#  content: 'fastembed is supported by and maintained by Qdrant.',
+#  score: 0.758..)