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Replace dpr with embeddingretriever tut11 (#2287)

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* images for tutorial 11 in .github folder for easy access

* ipynb: changed DPR to EmbeddingRetriever, incl. new graphs of pipelines

* Update Documentation & Code Style

* moved images into correct folder

* removed images path

* Update Documentation & Code Style

* fixed debugging run of p_classifier

* Update Documentation & Code Style

* Revert debug param change

* Update Documentation & Code Style

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: brandenchan <brandenchan@icloud.com>
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33
docs/_src/tutorials/tutorials/11.md
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@ -100,7 +100,7 @@ to perform Open Domain Question Answering.
from haystack import Pipeline
from haystack.utils import launch_es
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.nodes import ElasticsearchRetriever, DensePassageRetriever, FARMReader
from haystack.nodes import ElasticsearchRetriever, EmbeddingRetriever, FARMReader
# Initialize DocumentStore and index documents
@ -113,9 +113,14 @@ document_store.write_documents(got_dicts)
es_retriever = ElasticsearchRetriever(document_store=document_store)
# Initialize dense retriever
dpr_retriever = DensePassageRetriever(document_store)
document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
embedding_retriever = EmbeddingRetriever(
document_store,
model_format="sentence_transformers",
embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
)
document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)
# Initialize reader
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
```
@ -163,7 +168,7 @@ document_store.return_embedding = True
rag_generator = RAGenerator()
# Generative QA
p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)
p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)
res = p_generator.run(query="Who is the father of Arya Stark?", params={"Retriever": {"top_k": 10}})
print_answers(res, details="minimum")
@ -214,12 +219,12 @@ p_extractive.draw("pipeline_extractive.png")
```
Pipelines offer a very simple way to ensemble together different components.
In this example, we are going to combine the power of a `DensePassageRetriever`
In this example, we are going to combine the power of an `EmbeddingRetriever`
with the keyword based `ElasticsearchRetriever`.
See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why
we might want to combine a dense and sparse retriever.
![image](https://user-images.githubusercontent.com/1563902/102451782-7bd80400-4039-11eb-9046-01b002a783f8.png)
![image]()
Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together.
@ -230,16 +235,16 @@ from haystack.pipelines import JoinDocuments
# Create ensembled pipeline
p_ensemble = Pipeline()
p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
p_ensemble.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
p_ensemble.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"])
p_ensemble.add_node(
component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"]
component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "EmbeddingRetriever"]
)
p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
p_ensemble.draw("pipeline_ensemble.png")
# Run pipeline
res = p_ensemble.run(
query="Who is the father of Arya Stark?", params={"DPRRetriever": {"top_k": 5}, "ESRetriever": {"top_k": 5}}
query="Who is the father of Arya Stark?", params={"EmbeddingRetriever": {"top_k": 5}, "ESRetriever": {"top_k": 5}}
)
print_answers(res, details="minimum")
```
@ -277,10 +282,10 @@ class CustomNode(BaseComponent):
## Decision Nodes
Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.
One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to DPR + Reader.
One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader.
With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.
![image](https://user-images.githubusercontent.com/1563902/102452199-41229b80-403a-11eb-9365-7038697e7c3e.png)
![image]()
Though this looks very similar to the ensembled pipeline shown above,
the key difference is that only one of the retrievers is run for each request.
@ -304,13 +309,13 @@ class CustomQueryClassifier(BaseComponent):
p_classifier = Pipeline()
p_classifier.add_node(component=CustomQueryClassifier(), name="QueryClassifier", inputs=["Query"])
p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
p_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
p_classifier.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_2"])
p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "EmbeddingRetriever"])
p_classifier.draw("pipeline_classifier.png")
# Run only the dense retriever on the full sentence query
res_1 = p_classifier.run(query="Who is the father of Arya Stark?")
print("DPR Results" + "\n" + "=" * 15)
print("Embedding Retriever Results" + "\n" + "=" * 15)
print_answers(res_1)
# Run only the sparse retriever on a keyword based query

35
tutorials/Tutorial11_Pipelines.ipynb
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@ -210,7 +210,7 @@
"from haystack import Pipeline\n",
"from haystack.utils import launch_es\n",
"from haystack.document_stores import ElasticsearchDocumentStore\n",
"from haystack.nodes import ElasticsearchRetriever, DensePassageRetriever, FARMReader\n",
"from haystack.nodes import ElasticsearchRetriever, EmbeddingRetriever, FARMReader\n",
"\n",
"\n",
"# Initialize DocumentStore and index documents\n",
@ -223,9 +223,14 @@
"es_retriever = ElasticsearchRetriever(document_store=document_store)\n",
"\n",
"# Initialize dense retriever\n",
"dpr_retriever = DensePassageRetriever(document_store)\n",
"document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)\n",
"embedding_retriever = EmbeddingRetriever(\n",
" document_store,\n",
" model_format=\"sentence_transformers\",\n",
" embedding_model=\"sentence-transformers/multi-qa-mpnet-base-dot-v1\",\n",
")\n",
"document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)\n",
"\n",
"# Initialize reader\n",
"reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\")"
]
},
@ -324,7 +329,7 @@
"rag_generator = RAGenerator()\n",
"\n",
"# Generative QA\n",
"p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)\n",
"p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)\n",
"res = p_generator.run(query=\"Who is the father of Arya Stark?\", params={\"Retriever\": {\"top_k\": 10}})\n",
"print_answers(res, details=\"minimum\")\n",
"\n",
@ -428,12 +433,12 @@
},
"source": [
"Pipelines offer a very simple way to ensemble together different components.\n",
"In this example, we are going to combine the power of a `DensePassageRetriever`\n",
"In this example, we are going to combine the power of an `EmbeddingRetriever`\n",
"with the keyword based `ElasticsearchRetriever`.\n",
"See our [documentation](https://haystack.deepset.ai/docs/latest/retrievermd) to understand why\n",
"we might want to combine a dense and sparse retriever.\n",
"\n",
"![image](https://user-images.githubusercontent.com/1563902/102451782-7bd80400-4039-11eb-9046-01b002a783f8.png)\n",
"![image]()\n",
"\n",
"Here we use a `JoinDocuments` node so that the predictions from each retriever can be merged together."
]
@ -454,16 +459,16 @@
"# Create ensembled pipeline\n",
"p_ensemble = Pipeline()\n",
"p_ensemble.add_node(component=es_retriever, name=\"ESRetriever\", inputs=[\"Query\"])\n",
"p_ensemble.add_node(component=dpr_retriever, name=\"DPRRetriever\", inputs=[\"Query\"])\n",
"p_ensemble.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"Query\"])\n",
"p_ensemble.add_node(\n",
" component=JoinDocuments(join_mode=\"concatenate\"), name=\"JoinResults\", inputs=[\"ESRetriever\", \"DPRRetriever\"]\n",
" component=JoinDocuments(join_mode=\"concatenate\"), name=\"JoinResults\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"]\n",
")\n",
"p_ensemble.add_node(component=reader, name=\"Reader\", inputs=[\"JoinResults\"])\n",
"p_ensemble.draw(\"pipeline_ensemble.png\")\n",
"\n",
"# Run pipeline\n",
"res = p_ensemble.run(\n",
" query=\"Who is the father of Arya Stark?\", params={\"DPRRetriever\": {\"top_k\": 5}, \"ESRetriever\": {\"top_k\": 5}}\n",
" query=\"Who is the father of Arya Stark?\", params={\"EmbeddingRetriever\": {\"top_k\": 5}, \"ESRetriever\": {\"top_k\": 5}}\n",
")\n",
"print_answers(res, details=\"minimum\")"
]
@ -529,10 +534,10 @@
"## Decision Nodes\n",
"\n",
"Decision Nodes help you route your data so that only certain branches of your `Pipeline` are run.\n",
"One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to DPR + Reader.\n",
"One popular use case for such query classifiers is routing keyword queries to Elasticsearch and questions to EmbeddingRetriever + Reader.\n",
"With this approach you keep optimal speed and simplicity for keywords while going deep with transformers when it's most helpful.\n",
"\n",
"![image](https://user-images.githubusercontent.com/1563902/102452199-41229b80-403a-11eb-9365-7038697e7c3e.png)\n",
"![image]()\n",
"\n",
"Though this looks very similar to the ensembled pipeline shown above,\n",
"the key difference is that only one of the retrievers is run for each request.\n",
@ -566,13 +571,13 @@
"p_classifier = Pipeline()\n",
"p_classifier.add_node(component=CustomQueryClassifier(), name=\"QueryClassifier\", inputs=[\"Query\"])\n",
"p_classifier.add_node(component=es_retriever, name=\"ESRetriever\", inputs=[\"QueryClassifier.output_1\"])\n",
"p_classifier.add_node(component=dpr_retriever, name=\"DPRRetriever\", inputs=[\"QueryClassifier.output_2\"])\n",
"p_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"ESRetriever\", \"DPRRetriever\"])\n",
"p_classifier.add_node(component=embedding_retriever, name=\"EmbeddingRetriever\", inputs=[\"QueryClassifier.output_2\"])\n",
"p_classifier.add_node(component=reader, name=\"QAReader\", inputs=[\"ESRetriever\", \"EmbeddingRetriever\"])\n",
"p_classifier.draw(\"pipeline_classifier.png\")\n",
"\n",
"# Run only the dense retriever on the full sentence query\n",
"res_1 = p_classifier.run(query=\"Who is the father of Arya Stark?\")\n",
"print(\"DPR Results\" + \"\\n\" + \"=\" * 15)\n",
"print(\"Embedding Retriever Results\" + \"\\n\" + \"=\" * 15)\n",
"print_answers(res_1)\n",
"\n",
"# Run only the sparse retriever on a keyword based query\n",
@ -772,4 +777,4 @@
},
"nbformat": 4,
"nbformat_minor": 2
}
}

29
tutorials/Tutorial11_Pipelines.py
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@ -11,7 +11,7 @@ from haystack import Pipeline
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.nodes import (
ElasticsearchRetriever,
DensePassageRetriever,
EmbeddingRetriever,
FARMReader,
RAGenerator,
BaseComponent,
@ -39,8 +39,12 @@ def tutorial11_pipelines():
es_retriever = ElasticsearchRetriever(document_store=document_store)
# Initialize dense retriever
dpr_retriever = DensePassageRetriever(document_store)
document_store.update_embeddings(dpr_retriever, update_existing_embeddings=False)
embedding_retriever = EmbeddingRetriever(
document_store,
model_format="sentence_transformers",
embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
)
document_store.update_embeddings(embedding_retriever, update_existing_embeddings=False)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
@ -83,7 +87,7 @@ def tutorial11_pipelines():
# Generative QA
query = "Who is the father of Arya Stark?"
p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=dpr_retriever)
p_generator = GenerativeQAPipeline(generator=rag_generator, retriever=embedding_retriever)
res = p_generator.run(query=query, params={"Retriever": {"top_k": 10}})
print()
print_answers(res, details="minimum")
@ -129,9 +133,11 @@ def tutorial11_pipelines():
# Create ensembled pipeline
p_ensemble = Pipeline()
p_ensemble.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
p_ensemble.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["Query"])
p_ensemble.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["Query"])
p_ensemble.add_node(
component=JoinDocuments(join_mode="concatenate"), name="JoinResults", inputs=["ESRetriever", "DPRRetriever"]
component=JoinDocuments(join_mode="concatenate"),
name="JoinResults",
inputs=["ESRetriever", "EmbeddingRetriever"],
)
p_ensemble.add_node(component=reader, name="Reader", inputs=["JoinResults"])
p_ensemble.draw("pipeline_ensemble.png")
@ -139,7 +145,8 @@ def tutorial11_pipelines():
# Run pipeline
query = "Who is the father of Arya Stark?"
res = p_ensemble.run(
query="Who is the father of Arya Stark?", params={"ESRetriever": {"top_k": 5}, "DPRRetriever": {"top_k": 5}}
query="Who is the father of Arya Stark?",
params={"ESRetriever": {"top_k": 5}, "EmbeddingRetriever": {"top_k": 5}},
)
print("\nQuery: ", query)
print("Answers:")
@ -167,8 +174,8 @@ def tutorial11_pipelines():
p_classifier = Pipeline()
p_classifier.add_node(component=CustomQueryClassifier(), name="QueryClassifier", inputs=["Query"])
p_classifier.add_node(component=es_retriever, name="ESRetriever", inputs=["QueryClassifier.output_1"])
p_classifier.add_node(component=dpr_retriever, name="DPRRetriever", inputs=["QueryClassifier.output_2"])
p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "DPRRetriever"])
p_classifier.add_node(component=embedding_retriever, name="EmbeddingRetriever", inputs=["QueryClassifier.output_2"])
p_classifier.add_node(component=reader, name="QAReader", inputs=["ESRetriever", "EmbeddingRetriever"])
p_classifier.draw("pipeline_classifier.png")
# Run only the dense retriever on the full sentence query
@ -176,7 +183,7 @@ def tutorial11_pipelines():
res_1 = p_classifier.run(query=query)
print()
print("\nQuery: ", query)
print(" * DPR Answers:")
print(" * Embedding Retriever Answers:")
print_answers(res_1, details="minimum")
# Run only the sparse retriever on a keyword based query
@ -198,7 +205,7 @@ def tutorial11_pipelines():
# 2) You can provide `debug` as a parameter when running your pipeline
result = p_classifier.run(query="Who is the father of Arya Stark?", params={"ESRetriever": {"debug": True}})
# 3) You can provide the `debug` paramter to all nodes in your pipeline
# 3) You can provide the `debug` parameter to all nodes in your pipeline
result = p_classifier.run(query="Who is the father of Arya Stark?", params={"debug": True})
pprint(result["_debug"])