initial (#6355)

examples/preview/retrievers/in_memory_bm25_documentsearch.py

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+from haystack.preview import Document
+from haystack.preview.components.retrievers import InMemoryBM25Retriever
+from haystack.preview.document_stores import InMemoryDocumentStore
+from haystack.preview.pipeline import Pipeline
+
+# Create components and a query pipeline
+document_store = InMemoryDocumentStore()
+retriever = InMemoryBM25Retriever(document_store=document_store)
+
+pipeline = Pipeline()
+pipeline.add_component(instance=retriever, name="retriever")
+
+# Add Documents
+documents = [
+    Document(content="There are over 7,000 languages spoken around the world today."),
+    Document(
+        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors."
+    ),
+    Document(
+        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves."
+    ),
+]
+document_store.write_documents(documents)
+
+# Run the pipeline
+result = pipeline.run(data={"retriever": {"query": "How many languages are there?"}})
+
+print(result["retriever"]["documents"][0])

examples/preview/retrievers/in_memory_bm25_rag.py

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+import os
+
+from haystack.preview import Document
+from haystack.preview import Pipeline
+from haystack.preview.components.builders.answer_builder import AnswerBuilder
+from haystack.preview.components.builders.prompt_builder import PromptBuilder
+from haystack.preview.components.generators import GPTGenerator
+from haystack.preview.components.retrievers import InMemoryBM25Retriever
+from haystack.preview.document_stores import InMemoryDocumentStore
+
+# Create a RAG query pipeline
+prompt_template = """
+    Given these documents, answer the question.\nDocuments:
+    {% for doc in documents %}
+        {{ doc.content }}
+    {% endfor %}
+
+    \nQuestion: {{question}}
+    \nAnswer:
+    """
+
+rag_pipeline = Pipeline()
+rag_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=InMemoryDocumentStore()), name="retriever")
+rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
+rag_pipeline.add_component(instance=GPTGenerator(api_key=os.environ.get("OPENAI_API_KEY")), name="llm")
+rag_pipeline.add_component(instance=AnswerBuilder(), name="answer_builder")
+rag_pipeline.connect("retriever", "prompt_builder.documents")
+rag_pipeline.connect("prompt_builder", "llm")
+rag_pipeline.connect("llm.replies", "answer_builder.replies")
+rag_pipeline.connect("llm.metadata", "answer_builder.metadata")
+rag_pipeline.connect("retriever", "answer_builder.documents")
+
+# Draw the pipeline
+rag_pipeline.draw("./rag_pipeline.png")
+
+# Add Documents
+documents = [
+    Document(content="There are over 7,000 languages spoken around the world today."),
+    Document(
+        content="Elephants have been observed to behave in a way that indicates a high level of self-awareness, such as recognizing themselves in mirrors."
+    ),
+    Document(
+        content="In certain parts of the world, like the Maldives, Puerto Rico, and San Diego, you can witness the phenomenon of bioluminescent waves."
+    ),
+]
+rag_pipeline.get_component("retriever").document_store.write_documents(documents)
+
+# Run the pipeline
+question = "How many languages are there?"
+result = rag_pipeline.run(
+    {"retriever": {"query": question}, "prompt_builder": {"question": question}, "answer_builder": {"query": question}}
+)
+print(result["answer_builder"]["answers"][0])