Sample Pipelines
We created a couple of ready-to-use, NLP pipelines for you. They're available as templates in deepest Cloud. You can take them as they are, or you can adjust them for your use case.
All these pipelines are available as YAML templates in deepset Cloud. You can choose them when creating a pipeline with YAML editor. They're listed here for your reference.
Generative Question Answering Pipeline
This pipeline uses a large language model. The model generates the answer based on the model's general knowledge of the world and the documents you feed to it. The result is a novel text. Bear in mind that in generative QA pipelines, it's often hard to tell which document the answer comes from.
Generative Question Answering with GPT-3
This template uses Open AI's GPT-3 model text-davinci-003 to generate the answer and a good vector-based Retriever. You need an API key from an active Open AI account to use this model.
# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/docs/create-a-pipeline#create-a-pipeline-using-yaml.
# This is a friendly editor that helps you create your pipelines with autosuggestions. To use them, press control + space on your keyboard.
# Whenever you need to specify a model, this editor helps you out as well. Just type your Hugging Face organization and a forward slash (/) to see available models.
# This is a Generative Question Answering pipeline for English with a good vector-based Retriever and OpenAI's GPT-3.5 model as a PromptNode
version: '1.20.0'
name: 'GenerativeQuestionAnswering_GPT-3.5'
# This section defines nodes that you want to use in your pipelines. Each node must have a name and a type. You can also set the node's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying their order in the pipeline.
# Type is the class name of the component.
components:
- name: DocumentStore
type: DeepsetCloudDocumentStore
- name: BM25Retriever # The keyword-based retriever
type: BM25Retriever
params:
document_store: DocumentStore
top_k: 10 # The number of results to return
- name: EmbeddingRetriever # Selects the most relevant documents from the document store
type: EmbeddingRetriever # Uses a Transformer model to encode the document and the query
params:
document_store: DocumentStore
embedding_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 # Model optimized for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources.
model_format: sentence_transformers
top_k: 10 # The number of results to return
- name: JoinResults # Joins the results from both retrievers
type: JoinDocuments
params:
join_mode: concatenate # Combines documents from multiple retrievers
- name: Reranker # Uses a cross-encoder model to rerank the documents returned by the two retrievers
type: SentenceTransformersRanker
params:
model_name_or_path: cross-encoder/ms-marco-MiniLM-L-6-v2 # Fast model optimized for reranking
top_k: 4 # The number of results to return
batch_size: 20 # Try to keep this number equal or larger to the sum of the top_k of the two retrievers so all docs are processed at once
- name: qa_template
type: PromptTemplate
params:
output_parser:
type: AnswerParser
prompt: "You are a technical expert. \
You answer questions truthfully based on provided documents. \
For each document check whether it is related to the question. \
Only use documents that are related to the question to answer it. \
Ignore documents that are not related to the question. \
If the answer exists in several documents, summarize them. \
Only answer based on the documents provided. Don't make things up. \
Always use references in the form [NUMBER OF DOCUMENT] when using information from a document. e.g. [3], for Document[3]. \
The reference must only refer to the number that comes in square brackets after passage. \
Otherwise, do not use brackets in your answer and reference ONLY the number of the passage without mentioning the word passage. \
If the documents can't answer the question or you are unsure say: 'The answer can't be found in the text'. \
{new_line}\
These are the documents:\
{join(documents, delimiter=new_line, pattern=new_line+'Document[$idx]:'+new_line+'$content')}\
{new_line}\
Question: {query}\
{new_line}\
Answer:\
{new_line}"
- name: PromptNode
type: PromptNode
params:
default_prompt_template: qa_template
max_length: 400 # The maximum number of tokens the generated answer can have
model_kwargs: # Specifies additional model settings
temperature: 0 # Lower temperature works best for fact-based qa
model_name_or_path: gpt-3.5-turbo
- name: FileTypeClassifier # Routes files based on their extension to appropriate converters, by default txt, pdf, md, docx, html
type: FileTypeClassifier
- name: TextConverter # Converts files into documents
type: TextConverter
- name: PDFConverter # Converts PDFs into documents
type: PDFToTextConverter
- name: Preprocessor # Splits documents into smaller ones and cleans them up
type: PreProcessor
params:
# With a vector-based retriever, it's good to split your documents into smaller ones
split_by: word # The unit by which you want to split the documents
split_length: 250 # The max number of words in a document
split_overlap: 20 # Enables the sliding window approach
language: en
split_respect_sentence_boundary: True # Retains complete sentences in split documents
# Here you define how the nodes are organized in the pipelines
# For each node, specify its input
pipelines:
- name: query
nodes:
- name: BM25Retriever
inputs: [Query]
- name: EmbeddingRetriever
inputs: [Query]
- name: JoinResults
inputs: [BM25Retriever, EmbeddingRetriever]
- name: Reranker
inputs: [JoinResults]
- name: PromptNode
inputs: [Reranker]
- name: indexing
nodes:
# Depending on the file type, we use a Text or PDF converter
- name: FileTypeClassifier
inputs: [File]
- name: TextConverter
inputs: [FileTypeClassifier.output_1] # Ensures that this converter receives txt files
- name: PDFConverter
inputs: [FileTypeClassifier.output_2] # Ensures that this converter receives PDFs
- name: Preprocessor
inputs: [TextConverter, PDFConverter]
- name: EmbeddingRetriever
inputs: [Preprocessor]
- name: DocumentStore
inputs: [EmbeddingRetriever]
Document Search Pipelines
Document search pipelines, also called document retrieval pipelines, return whole documents as answers.
Semantic Document Search Pipeline
This is a sample pipeline that returns documents as answers. It searches for documents based on semantic similarity. It uses a vector-based search.
# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/docs/create-a-pipeline#create-a-pipeline-using-yaml.
# This is a friendly editor that helps you create your pipelines with autosuggestions. To use them, press Control + Space on your keyboard.
# Whenever you need to specify a model, this editor helps you out as well. Just type your Hugging Face organization and a forward slash (/) to see available models.
# This is a document search pipeline that searches for documents based on semantic similarity. It uses a vector-based search.
version: '1.20.0'
name: 'SemanticDocumentSearch'
# This section defines nodes that you want to use in your pipelines. Each node must have a name and a type. You can also set the node's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying their order in the pipeline.
# Type is the class name of the component.
components:
- name: DocumentStore
type: DeepsetCloudDocumentStore # The only supported document store in deepset Cloud
- name: Retriever # Selects the most relevant documents from the document store
type: EmbeddingRetriever # Uses one Transformer model to encode the document and the query
params:
document_store: DocumentStore
embedding_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 # Model optimized for semantic search
model_format: sentence_transformers
top_k: 20 # The number of results to return
- name: FileTypeClassifier # Routes files based on their extension to appropriate converters
type: FileTypeClassifier
- name: TextConverter # Converts TXT files into documents
type: TextConverter
- name: PDFConverter # Converts PDFs into documents
type: PDFToTextConverter
- name: Preprocessor # Splits documents into smaller ones and cleans them up
type: PreProcessor
params:
# With a vector-based retriever, it's good to split your documents into smaller ones
split_by: word # The unit by which you want to split the documents
split_length: 250 # The max number of words in a document
split_overlap: 30 # Enables the sliding window approach
split_respect_sentence_boundary: True # Retains complete sentences in split documents
language: en # Used by NLTK to best detect the sentence boundaries for that language
# Here you define how the nodes are organized in the pipelines
# For each node, specify its input
pipelines:
- name: query
nodes:
- name: Retriever
inputs: [Query]
- name: indexing
nodes:
# Depending on the file type we use a Text or PDF converter
- name: FileTypeClassifier
inputs: [File]
- name: TextConverter
inputs: [FileTypeClassifier.output_1] # Ensures this converter receives TXT files
- name: PDFConverter
inputs: [FileTypeClassifier.output_2] # Ensures this converter receives PDFs
- name: Preprocessor
inputs: [TextConverter, PDFConverter]
- name: Retriever
inputs: [Preprocessor]
- name: DocumentStore
inputs: [Retriever]
Keyword Document Search Pipeline
This pipeline is a good starting point for a document search pipeline. It returns documents as answers, based on keyword matches with your query. It uses the BM25 algorithm to prioritize the keywords.
# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/docs/create-a-pipeline#create-a-pipeline-using-yaml.
# This is a friendly editor that helps you create your pipelines with autosuggestions. To use them, press Control + Space on your keyboard.
# Whenever you need to specify a model, this editor helps you out as well. Just type your Hugging Face organization and a forward slash (/) to see available models.
# A pipeline for document search that uses a traditional, keyword-based retriever (using Elasticsearch's BM25 algorithm).
# It relies on matching keywords between query and document and is often a solid baseline to start with.
version: '1.20.0'
name: 'KeywordDocumentSearch'
# This section defines nodes that you want to use in your pipelines. Each node must have a name and a type. You can also set the node's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying their order in the pipeline.
# Type is the class name of the component.
components:
- name: DocumentStore
type: DeepsetCloudDocumentStore # This is the only supported document store in deepset Cloud
- name: Retriever # Selects the most relevant documents from the document store
type: BM25Retriever # The keyword-based retriever
params:
document_store: DocumentStore
top_k: 20 # The number of results to return
- name: FileTypeClassifier # Routes files based on their extension to appropriate converters, by default txt, pdf, md, docx, html
type: FileTypeClassifier
- name: TextConverter # Converts files into documents
type: TextConverter
- name: PDFConverter # Converts PDFs into documents
type: PDFToTextConverter
- name: Preprocessor # Splits files into smaller documents and cleans them up
type: PreProcessor
params:
# With a keyword-based retriever, you can keep slightly longer documents
split_by: word # The unit by which you want to split the documents
split_length: 500 # The max number of words in a document
split_overlap: 30 # Enables the sliding window approach
split_respect_sentence_boundary: True # Retains complete sentences in split documents
language: en # Used by NLTK to best detect the sentence boundaries for that language
# Here you define how the nodes are organized in the pipelines
# For each node, specify its input
pipelines:
- name: query
nodes:
- name: Retriever
inputs: [Query]
- name: indexing
nodes:
- name: FileTypeClassifier
inputs: [File]
- name: TextConverter
inputs: [FileTypeClassifier.output_1] # Ensures this converter gets TXT files
- name: PDFConverter
inputs: [FileTypeClassifier.output_2] # Ensures this converter gets PDF files
- name: Preprocessor
inputs: [TextConverter, PDFConverter]
- name: Retriever
inputs: [Preprocessor]
- name: DocumentStore
inputs: [Retriever]
Hybrid Document Search Pipeline
This pipeline combines the advantages of keyword-based and vector-based searches. Such a combination usually yields the best results without any training.
# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/docs/create-a-pipeline#create-a-pipeline-using-yaml.
# This is a friendly editor that helps you create your pipelines with autosuggestions. To use them, press Control + Space on your keyboard.
# Whenever you need to specify a model, this editor helps you out as well. Just type your Hugging Face organization and a forward slash (/) to see available models.
# This is a document search pipeline that combines vector-based and keyword-based searches. Such combination usually yields the best results without any training.
version: '1.20.0'
name: 'HybridDocumentSearch'
# This section defines nodes that you want to use in your pipelines. Each node must have a name and a type. You can also set the node's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying their order in the pipeline.
# Type is the class name of the component.
components:
- name: DocumentStore
type: DeepsetCloudDocumentStore # The only supported document store in deepset Cloud
- name: BM25Retriever # The keyword-based retriever
type: BM25Retriever
params:
document_store: DocumentStore
top_k: 20 # The number of results to return
- name: EmbeddingRetriever # Selects the most relevant documents from the document store
type: EmbeddingRetriever # Uses a Transformer model to encode the document and the query
params:
document_store: DocumentStore
embedding_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 # Model optimized for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources.
model_format: sentence_transformers
top_k: 20 # The number of results to return
- name: JoinResults # Joins the results from both retrievers
type: JoinDocuments
params:
join_mode: concatenate # Combines documents from multiple retrievers
- name: Reranker # Uses a cross-encoder model to rerank the documents returned by the two retrievers
type: SentenceTransformersRanker
params:
model_name_or_path: cross-encoder/ms-marco-MiniLM-L-6-v2 # Fast model optimized for reranking
top_k: 20 # The number of results to return
batch_size: 30 # Try to keep this number equal to or greater than the sum of the top_k of the two retrievers so all docs are processed at once
- name: FileTypeClassifier # Routes files based on their extension to appropriate converters, useful if you have different file types
type: FileTypeClassifier
- name: TextConverter # Converts files into documents
type: TextConverter
- name: PDFConverter # Converts PDFs into documents
type: PDFToTextConverter
- name: Preprocessor # Splits documents into smaller ones and cleans them up
type: PreProcessor
params:
# With a vector-based retriever, it's good to split your documents into smaller ones
split_by: word # The unit by which you want to split the documents
split_length: 250 # The max number of words in a document
split_overlap: 30 # Enables the sliding window approach
split_respect_sentence_boundary: True # Retains complete sentences in split documents
language: en # Used by NLTK to best detect the sentence boundaries for that language
# Here you define how the nodes are organized in the pipelines
# For each node, specify its input
pipelines:
- name: query
nodes:
- name: BM25Retriever
inputs: [Query]
- name: EmbeddingRetriever
inputs: [Query]
- name: JoinResults
inputs: [BM25Retriever, EmbeddingRetriever]
- name: Reranker
inputs: [JoinResults]
- name: indexing
nodes:
# Depending on the file type, we use a Text or PDF converter
- name: FileTypeClassifier
inputs: [File]
- name: TextConverter
inputs: [FileTypeClassifier.output_1] # Ensures this converter gets TXT files
- name: PDFConverter
inputs: [FileTypeClassifier.output_2] # Ensures this converter gets PDF files
- name: Preprocessor
inputs: [TextConverter, PDFConverter]
- name: EmbeddingRetriever
inputs: [Preprocessor]
- name: DocumentStore
inputs: [EmbeddingRetriever]
Extractive Question Answering Pipelines
These pipelines return highlighted text passages as answers. They're good if you need to extract the answer from your documents and know the exact place where the answer is.
English Question Answering Pipeline
This is a good starting point for a question answering system. It uses a vector-based search and a reader node that highlights the answers in text passages.
# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/docs/create-a-pipeline#create-a-pipeline-using-yaml.
# This is a friendly editor that helps you create your pipelines with autosuggestions. To use them, press Control + Space on your keyboard.
# Whenever you need to specify a model, this editor helps you out as well. Just type your Hugging Face organization and a forward slash (/) to see available models.
# This is baseline Question Answering pipeline for English. It has a good, vector-based Retriever and a small, fast Reader.
version: '1.20.0'
name: 'QuestionAnswering_en'
# This section defines nodes that you want to use in your pipelines. Each node must have a name and a type. You can also set the node's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying their order in the pipeline.
# Type is the class name of the component.
components:
- name: DocumentStore
type: DeepsetCloudDocumentStore # The only supported document store in deepset Cloud
- name: Retriever # Selects the most relevant documents from the document store and passes them on to the Reader
type: EmbeddingRetriever # Uses a Transformer model to encode the document and the query
params:
document_store: DocumentStore
embedding_model: sentence-transformers/multi-qa-mpnet-base-dot-v1 # Model optimized for semantic search
model_format: sentence_transformers
top_k: 20 # The number of results to return
- name: Reader # The component that actually fetches answers from among the 20 documents returned by retriever
type: FARMReader # Transformer-based reader, specializes in extractive QA
params:
model_name_or_path: deepset/deberta-v3-large-squad2 # An optimized variant of BERT, a strong all-round model
context_window_size: 700 # The size of the window around the answer span
- name: FileTypeClassifier # Routes files based on their extension to appropriate converters, by default txt, pdf, md, docx, html
type: FileTypeClassifier
- name: TextConverter # Converts files into documents
type: TextConverter
- name: PDFConverter # Converts PDFs into documents
type: PDFToTextConverter
- name: Preprocessor # Splits documents into smaller ones and cleans them up
type: PreProcessor
params:
# With a vector-based retriever, it's good to split your documents into smaller ones
split_by: word # The unit by which you want to split the documents
split_length: 250 # The max number of words in a document
split_overlap: 30 # Enables the sliding window approach
split_respect_sentence_boundary: True # Retains complete sentences in split documents
language: en # Used by NLTK to best detect the sentence boundaries for that language
# Here you define how the nodes are organized in the pipelines
# For each node, specify its input
pipelines:
- name: query
nodes:
- name: Retriever
inputs: [Query]
- name: Reader
inputs: [Retriever]
- name: indexing
nodes:
# Depending on the file type, we use a Text or PDF converter
- name: FileTypeClassifier
inputs: [File]
- name: TextConverter
inputs: [FileTypeClassifier.output_1] # Ensures this converter receives TXT files
- name: PDFConverter
inputs: [FileTypeClassifier.output_2] # Ensures this converter receives PDFs
- name: Preprocessor
inputs: [TextConverter, PDFConverter]
- name: Retriever
inputs: [Preprocessor]
- name: DocumentStore
inputs: [Retriever]
German Question Answering Pipeline
This pipeline is a good starting point. It uses a vector-based search and a German question answering model. It highlights the answers within text passages thanks to the use of a Reader node.
# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/docs/create-a-pipeline#create-a-pipeline-using-yaml.
# This is a friendly editor that helps you create your pipelines with autosuggestions. To use them, press Control + Space on your keyboard.
# Whenever you need to specify a model, this editor helps you out as well. Just type your Hugging Face organization and a forward slash (/) to see available models.
# This is a baseline Question Answering pipeline for German. It uses a vector-based search and a German QA model.
version: '1.20.0'
name: 'QuestionAnswering_de'
# This section defines nodes that you want to use in your pipelines. Each node must have a name and a type. You can also set the node's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying their order in the pipeline.
# Type is the class name of the component.
components:
- name: DocumentStore
type: DeepsetCloudDocumentStore # The only supported document store in deepset Cloud
params:
similarity: cosine # Recommended similarity for paraphrase-multilingual-mpnet-base-v2
- name: Retriever # Selects the most relevant documents from the document store and then passes them on to the Reader
type: EmbeddingRetriever # Uses a Transformer model to encode the document and the query
params:
document_store: DocumentStore
embedding_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model_format: sentence_transformers
scale_score: false
top_k: 20 # The number of results to return
# A "Reader" model that goes through those 20 candidate documents and identifies the exact answer
- name: Reader # The component that actually fetches answers from the 20 documents returned by retriever
type: FARMReader # Transformer-based reader, specializes in extractive QA
params:
model_name_or_path: deepset/gelectra-large-germanquad
context_window_size: 700 # The size of the window around the answer span
- name: FileTypeClassifier # Routes files based on their extension to appropriate converters, by default txt, pdf, md, docx, html
type: FileTypeClassifier
- name: TextConverter # Converts files into documents
type: TextConverter
- name: PDFConverter # Converts PDFs into documents
type: PDFToTextConverter
- name: Preprocessor # Splits documents into smaller ones and cleans them up
type: PreProcessor
params:
# With a vector-based (dense) retriever, it's good to split your documents into smaller ones
split_by: word # The unit by which you want to split the documents
split_length: 250 # The max number of words in a document
split_overlap: 30 # Enables the sliding window approach
split_respect_sentence_boundary: True # Retains complete sentences in split documents
language: de # Used by NLTK to best detect the sentence boundaries for that language
# Here you define how the nodes are organized in the pipelines
# For each node, specify its input
pipelines:
- name: query
nodes:
- name: Retriever
inputs: [Query]
- name: Reader
inputs: [Retriever]
- name: indexing
nodes:
- name: FileTypeClassifier
inputs: [File]
- name: TextConverter
inputs: [FileTypeClassifier.output_1] # Ensures this converter receives TXT files
- name: PDFConverter
inputs: [FileTypeClassifier.output_2] # Ensures this converter receives PDFs
- name: Preprocessor
inputs: [TextConverter, PDFConverter]
- name: Retriever
inputs: [Preprocessor]
- name: DocumentStore
inputs: [Retriever]
Updated about 1 year ago