feat: Example of Neo4jDocumentStore used in a question answering pipe…

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examples/better_retrieval_via_embedding_retrieval.py

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+import logging
+
+from haystack.nodes import EmbeddingRetriever, FARMReader
+from haystack.pipelines import ExtractiveQAPipeline
+from haystack.utils import (
+    clean_wiki_text,
+    convert_files_to_docs,
+    fetch_archive_from_http,
+    print_answers,
+)
+
+from neo4j_haystack import Neo4jDocumentStore
+
+logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
+logging.getLogger("haystack").setLevel(logging.INFO)
+
+# Make sure you have a running Neo4j database, e.g. with Docker:
+# docker run \
+#     --restart always \
+#     --publish=7474:7474 --publish=7687:7687 \
+#     --env NEO4J_AUTH=neo4j/passw0rd \
+#     neo4j:5.15.0
+
+document_store = Neo4jDocumentStore(
+    url="bolt://localhost:7687",
+    username="neo4j",
+    password="passw0rd",
+    database="neo4j",
+    embedding_dim=768,
+    similarity="cosine",
+    recreate_index=True,
+)
+
+# Let's first get some files that we want to use
+doc_dir = "data/docs"
+s3_url = "https://s3.eu-central-1.amazonaws.com/deepset.ai-farm-qa/datasets/documents/wiki_gameofthrones_txt6.zip"
+fetch_archive_from_http(url=s3_url, output_dir=doc_dir)
+
+# Convert files to dicts
+docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
+
+# Now, let's write the dicts containing documents to our DB.
+document_store.write_documents(docs)
+
+retriever = EmbeddingRetriever(
+    document_store=document_store, embedding_model="sentence-transformers/multi-qa-mpnet-base-cos-v1"
+)
+
+# Important:
+# Now that we initialized the Retriever, we need to call update_embeddings() to iterate over all
+# previously indexed documents and update their embedding representation.
+# While this can be a time consuming operation (depending on the corpus size), it only needs to be done once.
+# At query time, we only need to embed the query and compare it to the existing document embeddings, which is very fast.
+document_store.update_embeddings(retriever)
+
+reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
+
+pipe = ExtractiveQAPipeline(reader, retriever)
+
+prediction = pipe.run(
+    query="Who created the Dothraki vocabulary?", params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}
+)
+
+print_answers(prediction, details="minimum")