# Trace with Langfuse Monitor your pipelines with Langfuse. *** ## About This Task Langfuse is a powerful tool for observability and tracing in complex AI workflows. It captures detailed traces of your pipeline operations, making it easier to debug, monitor, and improve your applications. The integrates with Langfuse using the `LangfuseConnector`. To get started, connect the platform to Langfuse with your API key. Then, simply add the `LangfuseConnector` to your pipelines to start collecting and sending traces to Langfuse. ## Prerequisites - You need the Langfuse public and secret API keys. You can get them from your Langfuse project settings. You must create new keys as the secret key can only be viewed and copied once, during creation. - Create two secrets: - One for the Langfuse secret key. Call this secret `LANGFUSE_SECRET_KEY`. - One for the Langfuse public key. Call this secret `LANGFUSE_PUBLIC_KEY`. For more information on secrets, see [Add Secrets](/docs/how-to-guides/managing-access/add-secrets.mdx) and [Secrets and Integrations](/docs/concepts/secrets-and-integrations.mdx). ## Use Langfuse 1. Connect to Langfuse by creating two Langfuse secrets: 1. In , click your profile icon in the top right corner and choose *Settings*. 2. Depending on the scope of the secrets, go to *Workspace* or *Organization* secrets. 2. Click **Create Secret**. 3. Copy the secret key from your Langfuse project and paste it into the *Value* field. 4. Type `LANGFUSE_SECRET_KEY` as the secret key and save the secret. 5. Click **Create Secret**. 6. Copy the public key from your Langfuse project and paste it into the *Value* field. 7. Type `LANGFUSE_PUBLIC_KEY` as the secret key and save the secret. 2. Add the `LangfuseConnector` component to the pipeline you want to trace but do not connect it to any other component. 3. Set the `name` parameter in `LangfuseConnector` to your pipeline name and save the pipeline. When you run a query with this pipeline, its traces will appear in your Langfuse project under _Traces_. ## Example This is an example of a RAG pipeline with Langfuse tracing enabled. `LangfuseConnector` is in the pipeline but it's not connected to any other component:
YAML configuration ```yaml # haystack-pipeline components: Agent: type: haystack.components.agents.agent.Agent init_parameters: chat_generator: init_parameters: model: gpt-5.5 type: haystack.components.generators.chat.openai_responses.OpenAIResponsesChatGenerator tools: - type: haystack.tools.pipeline_tool.PipelineTool data: name: search_internal_it description: >- Search Acme Corp's internal IT documentation. Use this tool for questions about internal IT policies, procedures, and processes—such as requesting software licenses, getting VPN access, IT support escalation, new employee IT setup, or any company-specific IT how-tos. input_mapping: filters: - retriever.filters_bm25 - retriever.filters_embedding files: - multi_file_converter.sources query: - chat_summary_llm.question output_mapping: chat_summary_llm.last_message: updated_query meta_field_grouping_ranker.documents: documents qa_llm.messages: messages pipeline: pipeline_output_type: chat components: retriever: 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: internal-it-docs max_chunk_bytes: 104857600 return_embedding: false method: mappings: settings: create_index: true http_auth: use_ssl: verify_certs: timeout: top_k: 20 embedder: type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder init_parameters: normalize_embeddings: true model: intfloat/e5-base-v2 fuzziness: 0 ranker: type: deepset_cloud_custom_nodes.rankers.nvidia.ranker.DeepsetNvidiaRanker init_parameters: model: tomaarsen/Qwen3-Reranker-0.6B-seq-cls 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 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 chat_summary_llm: type: haystack.components.generators.chat.llm.LLM init_parameters: chat_generator: init_parameters: model: gpt-5.4 type: haystack.components.generators.chat.openai_responses.OpenAIResponsesChatGenerator system_prompt: user_prompt: >- {% message role="user" %} You are part of a chatbot. You receive a question (Current Question) and a chat history. Use the context from the chat history and reformulate the question so that it is suitable for retrieval augmented generation. If X is followed by Y, only ask for Y and do not repeat X again. If the question does not require any context from the chat history, output it unedited. Don't make questions too long, but short and precise. Stay as close as possible to the current question. Only output the new question, nothing else! {{ question }} New question: {% endmessage %} required_variables: "*" streaming_callback: qa_llm: type: haystack.components.generators.chat.llm.LLM init_parameters: chat_generator: init_parameters: model: gpt-5.4 type: haystack.components.generators.chat.openai_responses.OpenAIResponsesChatGenerator system_prompt: "" 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.text }} Answer: {% endmessage %} required_variables: "*" streaming_callback: connections: - sender: retriever.documents receiver: ranker.documents - sender: multi_file_converter.documents receiver: meta_field_grouping_ranker.documents - sender: ranker.documents receiver: meta_field_grouping_ranker.documents - sender: chat_summary_llm.last_message receiver: retriever.query - sender: chat_summary_llm.last_message receiver: ranker.query - sender: chat_summary_llm.last_message receiver: qa_llm.question - sender: meta_field_grouping_ranker.documents receiver: qa_llm.documents max_runs_per_component: 100 metadata: {} is_pipeline_async: false inputs_from_state: {} outputs_to_string: {} outputs_to_state: {} _meta: name: search_internal_it description: >- Search Acme Corp's internal IT documentation. Use this tool for questions about internal IT policies, procedures, and processes—such as requesting software licenses, getting VPN access, IT support escalation, new employee IT setup, or any company-specific IT how-tos. tool_id: pipeline_version_id: 5916d707-f550-4b0a-a4d1-6be06a471f98 - type: haystack.tools.pipeline_tool.PipelineTool data: name: search_vendor_docs description: >- Search vendor software documentation. Use this tool for questions about how to use specific applications and products—such as Zoom features, Microsoft 365, setting up email on mobile, or any other software how-to questions. input_mapping: filters: - retriever.filters_bm25 - retriever.filters_embedding files: - multi_file_converter.sources query: - chat_summary_llm.question output_mapping: chat_summary_llm.last_message: updated_query meta_field_grouping_ranker.documents: documents qa_llm.messages: messages pipeline: pipeline_output_type: chat components: retriever: 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: vendor-documents max_chunk_bytes: 104857600 return_embedding: false method: mappings: settings: create_index: true http_auth: use_ssl: verify_certs: timeout: top_k: 20 embedder: type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder init_parameters: normalize_embeddings: true model: intfloat/e5-base-v2 fuzziness: 0 ranker: type: deepset_cloud_custom_nodes.rankers.nvidia.ranker.DeepsetNvidiaRanker init_parameters: model: tomaarsen/Qwen3-Reranker-0.6B-seq-cls 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 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 chat_summary_llm: type: haystack.components.generators.chat.llm.LLM init_parameters: chat_generator: init_parameters: model: gpt-5.4 type: haystack.components.generators.chat.openai_responses.OpenAIResponsesChatGenerator system_prompt: user_prompt: >- {% message role="user" %} You are part of a chatbot. You receive a question (Current Question) and a chat history. Use the context from the chat history and reformulate the question so that it is suitable for retrieval augmented generation. If X is followed by Y, only ask for Y and do not repeat X again. If the question does not require any context from the chat history, output it unedited. Don't make questions too long, but short and precise. Stay as close as possible to the current question. Only output the new question, nothing else! {{ question }} New question: {% endmessage %} required_variables: "*" streaming_callback: qa_llm: type: haystack.components.generators.chat.llm.LLM init_parameters: chat_generator: init_parameters: model: gpt-5.4 type: haystack.components.generators.chat.openai_responses.OpenAIResponsesChatGenerator system_prompt: "" 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.text }} Answer: {% endmessage %} required_variables: "*" streaming_callback: connections: - sender: retriever.documents receiver: ranker.documents - sender: multi_file_converter.documents receiver: meta_field_grouping_ranker.documents - sender: ranker.documents receiver: meta_field_grouping_ranker.documents - sender: chat_summary_llm.last_message receiver: retriever.query - sender: chat_summary_llm.last_message receiver: ranker.query - sender: chat_summary_llm.last_message receiver: qa_llm.question - sender: meta_field_grouping_ranker.documents receiver: qa_llm.documents max_runs_per_component: 100 metadata: {} is_pipeline_async: false inputs_from_state: {} outputs_to_string: {} outputs_to_state: {} _meta: name: search_vendor_docs description: >- Search vendor software documentation. Use this tool for questions about how to use specific applications and products—such as Zoom features, Microsoft 365, setting up email on mobile, or any other software how-to questions. tool_id: pipeline_version_id: c164dc6d-3b28-406e-8766-a47b19cbee65 system_prompt: >- {% message role="system" %} You are an IT helpdesk assistant for Corp. You help employees solve IT problems and answer questions about IT processes and software. You have two tools: - search_internal_it: Use this for questions about Acme Corp's internal IT policies and procedures—requesting software, getting VPN access, IT support processes, internal how-tos. - search_vendor_docs: Use this for questions about how to use software products—Zoom, Microsoft 365, and other applications. When a question involves both internal processes and software (for example, "I can't connect to Teams because my VPN is blocking it"), use both tools before answering. Base your answer only on the information you find. Cite which documents you used. If you can't find the answer in either knowledge base, say so clearly and tell the employee to contact the IT helpdesk directly at helpdesk@acmecorp.internal. {% endmessage %} user_prompt: required_variables: exit_conditions: state_schema: {} max_agent_steps: 100 streaming_callback: raise_on_tool_invocation_failure: false LangfuseConnector: type: haystack_integrations.components.connectors.langfuse.langfuse_connector.LangfuseConnector init_parameters: name: helpdesk-agent public: false public_key: type: env_var env_vars: - LANGFUSE_PUBLIC_KEY strict: false secret_key: type: env_var env_vars: - LANGFUSE_SECRET_KEY strict: false httpx_client: span_handler: host: langfuse_client_kwargs: max_runs_per_component: 100 metadata: {} inputs: messages: - Agent.messages filters: [] files: [] outputs: answers: documents: messages: Agent.last_message ```