docs(haystack): add rag example from haystack (#812)

python/instrumentation/openinference-instrumentation-haystack/examples/haystack_rag_pipeline.py

@@ -0,0 +1,72 @@
+from datasets import load_dataset
+from haystack import Document, Pipeline
+from haystack.components.builders import PromptBuilder
+from haystack.components.embedders import (
+    SentenceTransformersDocumentEmbedder,
+    SentenceTransformersTextEmbedder,
+)
+from haystack.components.generators import OpenAIGenerator
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from openinference.instrumentation.haystack import HaystackInstrumentor
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk import trace as trace_sdk
+from opentelemetry.sdk.trace.export import SimpleSpanProcessor
+
+endpoint = "http://127.0.0.1:6006/v1/traces"
+tracer_provider = trace_sdk.TracerProvider()
+tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
+
+HaystackInstrumentor().instrument(tracer_provider=tracer_provider)
+
+document_store = InMemoryDocumentStore()
+
+dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
+docs = [Document(content=doc["content"], meta=doc["meta"]) for doc in dataset]
+
+
+doc_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+doc_embedder.warm_up()
+
+docs_with_embeddings = doc_embedder.run(docs)
+document_store.write_documents(docs_with_embeddings["documents"])
+
+text_embedder = SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
+
+retriever = InMemoryEmbeddingRetriever(document_store)
+
+template = """
+Given the following information, answer the question.
+
+Context:
+{% for document in documents %}
+    {{ document.content }}
+{% endfor %}
+
+Question: {{question}}
+Answer:
+"""
+
+prompt_builder = PromptBuilder(template=template)
+
+generator = OpenAIGenerator(model="gpt-3.5-turbo")
+
+basic_rag_pipeline = Pipeline()
+# Add components to your pipeline
+basic_rag_pipeline.add_component("text_embedder", text_embedder)
+basic_rag_pipeline.add_component("retriever", retriever)
+basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
+basic_rag_pipeline.add_component("llm", generator)
+
+# Now, connect the components to each other
+basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
+basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
+basic_rag_pipeline.connect("prompt_builder", "llm")
+
+question = "What does Rhodes Statue look like?"
+
+response = basic_rag_pipeline.run(
+    {"text_embedder": {"text": question}, "prompt_builder": {"question": question}}
+)
+
+print(response["llm"]["replies"][0])