from haystack import Pipeline, Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.writers import DocumentWriter
from haystack.components.embedders import SentenceTransformersDocumentEmbedder

# Sample documents
documents = [
    Document(content="Coffee shop opens at 9am and closes at 5pm."),
    Document(content="Gym room opens at 6am and closes at 10pm.")
]

# Create the document store
document_store = InMemoryDocumentStore()

# Create a pipeline to turn the texts into embeddings and store them in the document store
indexing_pipeline = Pipeline()
indexing_pipeline.add_component(
    "doc_embedder", SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
)
indexing_pipeline.add_component("doc_writer", DocumentWriter(document_store=document_store))

indexing_pipeline.connect("doc_embedder.documents", "doc_writer.documents")

indexing_pipeline.run({"doc_embedder": {"documents": documents}})