DeepsetChatHistoryParser

Parse a string containing chat history and a current question into a list of ChatMessage objects.

Basic Information

Type: deepset_cloud_custom_nodes.parsers.chat_history_parser.DeepsetChatHistoryParser
Components it can connect with:
- Agent: DeepsetChatHistoryParser can send chat history and the current query to the Agent.
- Query: DeepsetChatHistoryParser receives the query and converts it together with chat history into a list of ChatMessages.

Inputs

Parameter	Type	Default	Description
history_and_query	str		Chat history and the current question to convert into a list of `ChatMessage` objects.

Outputs

Parameter	Type	Default	Description
messages	List[ChatMessage]		A list of ChatMessage objects. Each message has a role (either user or assistant) and content.

Overview

DeepsetChatHistoryParser is primarily used with an Agent to supply both the chat history and the current query. It expects input in the following format:
"Chat History: [{...array of messages...}] Current Question: {question text}". Each message in the chat history should be a dictionary containing two keys: role and content. If the input doesn't match this structure, DeepsetChatHistoryParser returns it as a single message from the user.

Usage Example

Initializing the Component

components:
  DeepsetChatHistoryParser:
    type: parsers.chat_history_parser.DeepsetChatHistoryParser
    init_parameters:

Using the Component in a Pipeline

This is an example of an agentic pipeline with DeepsetChatHistoryParser used to provide the current query and chat history to the Agent:

components:
  adapter:
    init_parameters:
      custom_filters: {}
      output_type: typing.List[str]
      template: '{{ [(messages|last).text] }}'
      unsafe: false
    type: haystack.components.converters.output_adapter.OutputAdapter
  agent:
    init_parameters:
      chat_generator:
        init_parameters:
          api_base_url:
          api_key:
            env_vars:
            - OPENAI_API_KEY
            strict: false
            type: env_var
          generation_kwargs: {}
          max_retries:
          model: gpt-4o
          organization:
          streaming_callback:
          timeout:
          tools:
          tools_strict: false
        type: haystack.components.generators.chat.openai.OpenAIChatGenerator
      exit_conditions:
      - text
      max_agent_steps: 100
      raise_on_tool_invocation_failure: false
      state_schema: {}
      streaming_callback:
      system_prompt: |-
        You are a deep research assistant.
        You create comprehensive research reports to answer the user's questions.
        You use the 'search'-tool to answer any questions.
        You perform multiple searches until you have the information you need to answer the question.
        Make sure you research different aspects of the question.
        Use markdown to format your response.
        When you use information from the websearch results, cite your sources using markdown links.
        It is important that you cite accurately.
      tools:
      - data:
          component:
            init_parameters:
              input_mapping:
                query:
                - search.query
              output_mapping:
                builder.prompt: result
              pipeline:
                components:
                  builder:
                    init_parameters:
                      required_variables:
                      template: |-
                        {% for doc in docs %}
                          {% if doc.content and doc.meta.url|length > 0 %}
                          <search-result url="{{ doc.meta.url }}">
                              {{ doc.content|truncate(25000) }}
                          </search-result>
                          {% endif %}
                        {% endfor %}
                      variables:
                    type: haystack.components.builders.prompt_builder.PromptBuilder
                  converter:
                    init_parameters:
                      extraction_kwargs: {}
                      store_full_path: false
                    type: haystack.components.converters.html.HTMLToDocument
                  fetcher:
                    init_parameters:
                      raise_on_failure: false
                      retry_attempts: 2
                      timeout: 3
                      user_agents:
                      - haystack/LinkContentFetcher/2.11.1
                    type: haystack.components.fetchers.link_content.LinkContentFetcher
                  search:
                    init_parameters:
                      api_key:
                        env_vars:
                        - SERPERDEV_API_KEY
                        strict: false
                        type: env_var
                      search_params: {}
                      top_k: 10
                    type: haystack.components.websearch.serper_dev.SerperDevWebSearch
                connection_type_validation: true
                connections:
                - receiver: fetcher.urls
                  sender: search.links
                - receiver: converter.sources
                  sender: fetcher.streams
                - receiver: builder.docs
                  sender: converter.documents
                max_runs_per_component: 100
                metadata: {}
            type: haystack.core.super_component.super_component.SuperComponent
          description: Use this tool to search for information on the internet.
          inputs_from_state:
          name: search
          parameters:
        type: haystack.tools.component_tool.ComponentTool
    type: haystack.components.agents.agent.Agent
  answer_builder:
    init_parameters:
      pattern:
      reference_pattern:
    type: haystack.components.builders.answer_builder.AnswerBuilder
  history_parser:
    init_parameters: {}
    type: deepset_cloud_custom_nodes.parsers.chat_history_parser.DeepsetChatHistoryParser


connections:
- receiver: agent.messages
  sender: history_parser.messages
- receiver: adapter.messages
  sender: agent.messages
- receiver: answer_builder.replies
  sender: adapter.output

inputs:
  query:
  - answer_builder.query
  - history_parser.history_and_query

outputs:
  answers: answer_builder.answers

pipeline_output_type: chat

max_runs_per_component: 100

metadata: {}

Parameters

Init Parameters

This component doesn't take any initialization parameters.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
history_and_query	str		Chat history and the current query to be parsed into ChatMessages.

Updated 2 months ago