RAG Pipeline with Attachments

This is an example of a retrieval-augmented generation (RAG) pipeline that runs on your files in Haystack Platform. Use it when users should be able to attach files to a question and get a cited answer that draws on both the attached files and your indexed documents. It retrieves and ranks documents from your files, converts the attachments at query time, and passes everything to the LLM.

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How the Pipeline Works

Pipelines contain recipes for how to execute a query. This pipeline does the following:

• The retriever (an OpenSearchHybridRetriever) selects the most similar documents from the document store, using its embedder.
• The ranker reranks the documents, and the meta_field_grouping_ranker groups them by file.
• The multi_file_converter converts files attached at query time into documents, and the attachments_joiner combines them with the ranked documents.
• The llm answers the question based on the documents and cites them by number.

The pipeline takes these inputs: query (the question), filters (metadata filters for retrieval), and files (the files to attach at query time). It returns documents, the documents passed to the LLM, and messages, which contain the generated answer.

Pipeline YAML

To use this pipeline, copy the YAML and follow the steps in Pipeline Examples.

Example of a pipeline

# haystack-pipeline
components:
  retriever:
    # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.open_search_hybrid_retriever.OpenSearchHybridRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          embedding_dim: 768
          hosts:
          index: Standard-Index-English-EU-Demo-Schengen
          max_chunk_bytes: 104857600
          return_embedding: false
          method:
          mappings:
          settings:
            index.knn: true
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20 # The number of results to return
      fuzziness: 0
      embedder:
        type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder
        init_parameters:
          normalize_embeddings: true
          model: intfloat/e5-base-v2

  ranker:
    type: deepset_cloud_custom_nodes.rankers.nvidia.ranker.DeepsetNvidiaRanker
    init_parameters:
      model: intfloat/simlm-msmarco-reranker
      top_k: 8

  meta_field_grouping_ranker:
    type: haystack.components.rankers.meta_field_grouping_ranker.MetaFieldGroupingRanker
    init_parameters:
      group_by: file_id
      subgroup_by:
      sort_docs_by: split_id

  llm:
    type: haystack.components.generators.chat.llm.LLM
    init_parameters:
      # You can swap this for any other model. Switch to the Builder view and choose another model from the list on the component card.
      chat_generator:
        type: haystack.components.generators.chat.openai_responses.OpenAIResponsesChatGenerator
        init_parameters:
          model: "gpt-5.4"
      user_prompt: >-
        {% message role="user" %}

        You are a technical expert.

        You answer questions truthfully based on provided documents.

        Ignore typing errors in the question.

        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.

        Just output the structured, informative and precise answer and nothing
        else.

        If the documents can't answer the question, say so.

        Always use references in the form [NUMBER OF DOCUMENT] when using
        information from a document, e.g. [3] for Document [3] .

        Never name the documents, only enter a number in square brackets as a
        reference.

        The reference must only refer to the number that comes in square
        brackets after the document.

        Otherwise, do not use brackets in your answer and reference ONLY the
        number of the document without mentioning the word document.

        These are the documents:

        {%- if documents|length > 0 %}

        {% for document in documents %}

        Document [{{ loop.index }}] :

        Name of Source File: {{ document.meta.file_name }}

        {{ document.content }}

        {% endfor %}

        {%- else %}

        No relevant documents found.

        Respond with "Sorry, no matching documents were found, please adjust the
        filters or try a different question."

        {% endif %}

        Question: {{ question }}

        Answer:

        {% endmessage %}
      required_variables: "*"
      system_prompt: ""

  attachments_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate
      weights:
      top_k:
      sort_by_score: false

  multi_file_converter:
    type: haystack.core.super_component.super_component.SuperComponent
    init_parameters:
      input_mapping:
        sources:
        - file_classifier.sources
      is_pipeline_async: false
      output_mapping:
        tabular_joiner.documents: documents
      pipeline:
        components:
          file_classifier:
            type: haystack.components.routers.file_type_router.FileTypeRouter
            init_parameters:
              mime_types:
              - text/plain
              - application/pdf
              - text/markdown
              - text/html
              - application/vnd.openxmlformats-officedocument.wordprocessingml.document
              - application/vnd.openxmlformats-officedocument.presentationml.presentation
              - application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
              - text/csv

          text_converter:
            type: haystack.components.converters.txt.TextFileToDocument
            init_parameters:
              encoding: utf-8

          pdf_converter:
            type: haystack.components.converters.pdfminer.PDFMinerToDocument
            init_parameters:
              line_overlap: 0.5
              char_margin: 2
              line_margin: 0.5
              word_margin: 0.1
              boxes_flow: 0.5
              detect_vertical: true
              all_texts: false
              store_full_path: false

          markdown_converter:
            type: haystack.components.converters.txt.TextFileToDocument
            init_parameters:
              encoding: utf-8

          html_converter:
            type: haystack.components.converters.html.HTMLToDocument
            init_parameters:
              extraction_kwargs:
                output_format: markdown
                target_language:
                include_tables: true
                include_links: true

          docx_converter:
            type: haystack.components.converters.docx.DOCXToDocument
            init_parameters:
              link_format: markdown

          pptx_converter:
            type: haystack.components.converters.pptx.PPTXToDocument
            init_parameters: {}

          xlsx_converter:
            type: haystack.components.converters.xlsx.XLSXToDocument
            init_parameters: {}

          csv_converter:
            type: haystack.components.converters.csv.CSVToDocument
            init_parameters:
              encoding: utf-8

          splitter:
            type: haystack.components.preprocessors.document_splitter.DocumentSplitter
            init_parameters:
              split_by: word
              split_length: 250
              split_overlap: 30
              respect_sentence_boundary: true
              language: en

          tabular_joiner:
            type: haystack.components.joiners.document_joiner.DocumentJoiner
            init_parameters:
              join_mode: concatenate
              sort_by_score: false
        connections:
        - sender: file_classifier.text/plain
          receiver: text_converter.sources
        - sender: file_classifier.application/pdf
          receiver: pdf_converter.sources
        - sender: file_classifier.text/markdown
          receiver: markdown_converter.sources
        - sender: file_classifier.text/html
          receiver: html_converter.sources
        - sender: file_classifier.application/vnd.openxmlformats-officedocument.wordprocessingml.document
          receiver: docx_converter.sources
        - sender: file_classifier.application/vnd.openxmlformats-officedocument.presentationml.presentation
          receiver: pptx_converter.sources
        - sender: file_classifier.application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
          receiver: xlsx_converter.sources
        - sender: file_classifier.text/csv
          receiver: csv_converter.sources
        - sender: text_converter.documents
          receiver: splitter.documents
        - sender: pdf_converter.documents
          receiver: splitter.documents
        - sender: markdown_converter.documents
          receiver: splitter.documents
        - sender: html_converter.documents
          receiver: splitter.documents
        - sender: pptx_converter.documents
          receiver: splitter.documents
        - sender: docx_converter.documents
          receiver: splitter.documents
        - sender: xlsx_converter.documents
          receiver: tabular_joiner.documents
        - sender: csv_converter.documents
          receiver: tabular_joiner.documents
        - sender: splitter.documents
          receiver: tabular_joiner.documents

connections:
- sender: retriever.documents
  receiver: ranker.documents
- sender: ranker.documents
  receiver: meta_field_grouping_ranker.documents
- sender: multi_file_converter.documents
  receiver: attachments_joiner.documents
- sender: meta_field_grouping_ranker.documents
  receiver: attachments_joiner.documents
- sender: attachments_joiner.documents
  receiver: llm.documents

inputs:
  query:
  - retriever.query
  - ranker.query
  - llm.question
  filters:
  - retriever.filters_bm25
  - retriever.filters_embedding
  files:
  - multi_file_converter.sources

outputs:
  documents: attachments_joiner.documents
  messages: llm.messages

max_runs_per_component: 100

metadata: {}

Related Information

• Pipelines
• Pipeline YAML Format
• Pipeline Examples