--- title: Builders id: builders-api description: Extract the output of a Generator to an Answer format, and build prompts. --- # Module answer\_builder ## AnswerBuilder Converts a query and Generator replies into a `GeneratedAnswer` object. AnswerBuilder parses Generator replies using custom regular expressions. Check out the usage example below to see how it works. Optionally, it can also take documents and metadata from the Generator to add to the `GeneratedAnswer` object. AnswerBuilder works with both non-chat and chat Generators. ### Usage example ```python from haystack.components.builders import AnswerBuilder builder = AnswerBuilder(pattern="Answer: (.*)") builder.run(query="What's the answer?", replies=["This is an argument. Answer: This is the answer."]) ``` #### AnswerBuilder.\_\_init\_\_ ```python def __init__(pattern: Optional[str] = None, reference_pattern: Optional[str] = None, last_message_only: bool = False) ``` Creates an instance of the AnswerBuilder component. **Arguments**: - `pattern`: The regular expression pattern to extract the answer text from the Generator. If not specified, the entire response is used as the answer. The regular expression can have one capture group at most. If present, the capture group text is used as the answer. If no capture group is present, the whole match is used as the answer. Examples: `[^\n]+$` finds "this is an answer" in a string "this is an argument.\nthis is an answer". `Answer: (.*)` finds "this is an answer" in a string "this is an argument. Answer: this is an answer". - `reference_pattern`: The regular expression pattern used for parsing the document references. If not specified, no parsing is done, and all documents are referenced. References need to be specified as indices of the input documents and start at [1]. Example: `\[(\d+)\]` finds "1" in a string "this is an answer[1]". - `last_message_only`: If False (default value), all messages are used as the answer. If True, only the last message is used as the answer. #### AnswerBuilder.run ```python @component.output_types(answers=list[GeneratedAnswer]) def run(query: str, replies: Union[list[str], list[ChatMessage]], meta: Optional[list[dict[str, Any]]] = None, documents: Optional[list[Document]] = None, pattern: Optional[str] = None, reference_pattern: Optional[str] = None) ``` Turns the output of a Generator into `GeneratedAnswer` objects using regular expressions. **Arguments**: - `query`: The input query used as the Generator prompt. - `replies`: The output of the Generator. Can be a list of strings or a list of `ChatMessage` objects. - `meta`: The metadata returned by the Generator. If not specified, the generated answer will contain no metadata. - `documents`: The documents used as the Generator inputs. If specified, they are added to the`GeneratedAnswer` objects. If both `documents` and `reference_pattern` are specified, the documents referenced in the Generator output are extracted from the input documents and added to the `GeneratedAnswer` objects. - `pattern`: The regular expression pattern to extract the answer text from the Generator. If not specified, the entire response is used as the answer. The regular expression can have one capture group at most. If present, the capture group text is used as the answer. If no capture group is present, the whole match is used as the answer. Examples: `[^\n]+$` finds "this is an answer" in a string "this is an argument.\nthis is an answer". `Answer: (.*)` finds "this is an answer" in a string "this is an argument. Answer: this is an answer". - `reference_pattern`: The regular expression pattern used for parsing the document references. If not specified, no parsing is done, and all documents are referenced. References need to be specified as indices of the input documents and start at [1]. Example: `\[(\d+)\]` finds "1" in a string "this is an answer[1]". **Returns**: A dictionary with the following keys: - `answers`: The answers received from the output of the Generator. # Module prompt\_builder ## PromptBuilder Renders a prompt filling in any variables so that it can send it to a Generator. The prompt uses Jinja2 template syntax. The variables in the default template are used as PromptBuilder's input and are all optional. If they're not provided, they're replaced with an empty string in the rendered prompt. To try out different prompts, you can replace the prompt template at runtime by providing a template for each pipeline run invocation. ### Usage examples #### On its own This example uses PromptBuilder to render a prompt template and fill it with `target_language` and `snippet`. PromptBuilder returns a prompt with the string "Translate the following context to Spanish. Context: I can't speak Spanish.; Translation:". ```python from haystack.components.builders import PromptBuilder template = "Translate the following context to {{ target_language }}. Context: {{ snippet }}; Translation:" builder = PromptBuilder(template=template) builder.run(target_language="spanish", snippet="I can't speak spanish.") ``` #### In a Pipeline This is an example of a RAG pipeline where PromptBuilder renders a custom prompt template and fills it with the contents of the retrieved documents and a query. The rendered prompt is then sent to a Generator. ```python from haystack import Pipeline, Document from haystack.utils import Secret from haystack.components.generators import OpenAIGenerator from haystack.components.builders.prompt_builder import PromptBuilder # in a real world use case documents could come from a retriever, web, or any other source documents = [Document(content="Joe lives in Berlin"), Document(content="Joe is a software engineer")] prompt_template = """ Given these documents, answer the question. Documents: {% for doc in documents %} {{ doc.content }} {% endfor %} Question: {{query}} Answer: """ p = Pipeline() p.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder") p.add_component(instance=OpenAIGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY")), name="llm") p.connect("prompt_builder", "llm") question = "Where does Joe live?" result = p.run({"prompt_builder": {"documents": documents, "query": question}}) print(result) ``` #### Changing the template at runtime (prompt engineering) You can change the prompt template of an existing pipeline, like in this example: ```python documents = [ Document(content="Joe lives in Berlin", meta={"name": "doc1"}), Document(content="Joe is a software engineer", meta={"name": "doc1"}), ] new_template = """ You are a helpful assistant. Given these documents, answer the question. Documents: {% for doc in documents %} Document {{ loop.index }}: Document name: {{ doc.meta['name'] }} {{ doc.content }} {% endfor %} Question: {{ query }} Answer: """ p.run({ "prompt_builder": { "documents": documents, "query": question, "template": new_template, }, }) ``` To replace the variables in the default template when testing your prompt, pass the new variables in the `variables` parameter. #### Overwriting variables at runtime To overwrite the values of variables, use `template_variables` during runtime: ```python language_template = """ You are a helpful assistant. Given these documents, answer the question. Documents: {% for doc in documents %} Document {{ loop.index }}: Document name: {{ doc.meta['name'] }} {{ doc.content }} {% endfor %} Question: {{ query }} Please provide your answer in {{ answer_language | default('English') }} Answer: """ p.run({ "prompt_builder": { "documents": documents, "query": question, "template": language_template, "template_variables": {"answer_language": "German"}, }, }) ``` Note that `language_template` introduces variable `answer_language` which is not bound to any pipeline variable. If not set otherwise, it will use its default value 'English'. This example overwrites its value to 'German'. Use `template_variables` to overwrite pipeline variables (such as documents) as well. #### PromptBuilder.\_\_init\_\_ ```python def __init__(template: str, required_variables: Optional[Union[list[str], Literal["*"]]] = None, variables: Optional[list[str]] = None) ``` Constructs a PromptBuilder component. **Arguments**: - `template`: A prompt template that uses Jinja2 syntax to add variables. For example: `"Summarize this document: {{ documents[0].content }}\nSummary:"` It's used to render the prompt. The variables in the default template are input for PromptBuilder and are all optional, unless explicitly specified. If an optional variable is not provided, it's replaced with an empty string in the rendered prompt. - `required_variables`: List variables that must be provided as input to PromptBuilder. If a variable listed as required is not provided, an exception is raised. If set to "*", all variables found in the prompt are required. Optional. - `variables`: List input variables to use in prompt templates instead of the ones inferred from the `template` parameter. For example, to use more variables during prompt engineering than the ones present in the default template, you can provide them here. #### PromptBuilder.to\_dict ```python def to_dict() -> dict[str, Any] ``` Returns a dictionary representation of the component. **Returns**: Serialized dictionary representation of the component. #### PromptBuilder.run ```python @component.output_types(prompt=str) def run(template: Optional[str] = None, template_variables: Optional[dict[str, Any]] = None, **kwargs) ``` Renders the prompt template with the provided variables. It applies the template variables to render the final prompt. You can provide variables via pipeline kwargs. In order to overwrite the default template, you can set the `template` parameter. In order to overwrite pipeline kwargs, you can set the `template_variables` parameter. **Arguments**: - `template`: An optional string template to overwrite PromptBuilder's default template. If None, the default template provided at initialization is used. - `template_variables`: An optional dictionary of template variables to overwrite the pipeline variables. - `kwargs`: Pipeline variables used for rendering the prompt. **Raises**: - `ValueError`: If any of the required template variables is not provided. **Returns**: A dictionary with the following keys: - `prompt`: The updated prompt text after rendering the prompt template. # Module chat\_prompt\_builder ## ChatPromptBuilder Renders a chat prompt from a template using Jinja2 syntax. A template can be a list of `ChatMessage` objects, or a special string, as shown in the usage examples. It constructs prompts using static or dynamic templates, which you can update for each pipeline run. Template variables in the template are optional unless specified otherwise. If an optional variable isn't provided, it defaults to an empty string. Use `variable` and `required_variables` to define input types and required variables. ### Usage examples #### Static ChatMessage prompt template ```python template = [ChatMessage.from_user("Translate to {{ target_language }}. Context: {{ snippet }}; Translation:")] builder = ChatPromptBuilder(template=template) builder.run(target_language="spanish", snippet="I can't speak spanish.") ``` #### Overriding static ChatMessage template at runtime ```python template = [ChatMessage.from_user("Translate to {{ target_language }}. Context: {{ snippet }}; Translation:")] builder = ChatPromptBuilder(template=template) builder.run(target_language="spanish", snippet="I can't speak spanish.") msg = "Translate to {{ target_language }} and summarize. Context: {{ snippet }}; Summary:" summary_template = [ChatMessage.from_user(msg)] builder.run(target_language="spanish", snippet="I can't speak spanish.", template=summary_template) ``` #### Dynamic ChatMessage prompt template ```python from haystack.components.builders import ChatPromptBuilder from haystack.components.generators.chat import OpenAIChatGenerator from haystack.dataclasses import ChatMessage from haystack import Pipeline from haystack.utils import Secret # no parameter init, we don't use any runtime template variables prompt_builder = ChatPromptBuilder() llm = OpenAIChatGenerator(api_key=Secret.from_token(""), model="gpt-4o-mini") pipe = Pipeline() pipe.add_component("prompt_builder", prompt_builder) pipe.add_component("llm", llm) pipe.connect("prompt_builder.prompt", "llm.messages") location = "Berlin" language = "English" system_message = ChatMessage.from_system("You are an assistant giving information to tourists in {{language}}") messages = [system_message, ChatMessage.from_user("Tell me about {{location}}")] res = pipe.run(data={"prompt_builder": {"template_variables": {"location": location, "language": language}, "template": messages}}) print(res) >> {'llm': {'replies': [ChatMessage(_role=, _content=[TextContent(text= "Berlin is the capital city of Germany and one of the most vibrant and diverse cities in Europe. Here are some key things to know...Enjoy your time exploring the vibrant and dynamic capital of Germany!")], _name=None, _meta={'model': 'gpt-4o-mini', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 27, 'completion_tokens': 681, 'total_tokens': 708}})]}} messages = [system_message, ChatMessage.from_user("What's the weather forecast for {{location}} in the next {{day_count}} days?")] res = pipe.run(data={"prompt_builder": {"template_variables": {"location": location, "day_count": "5"}, "template": messages}}) print(res) >> {'llm': {'replies': [ChatMessage(_role=, _content=[TextContent(text= "Here is the weather forecast for Berlin in the next 5 days:\n\nDay 1: Mostly cloudy with a high of 22°C (72°F) and...so it's always a good idea to check for updates closer to your visit.")], _name=None, _meta={'model': 'gpt-4o-mini', 'index': 0, 'finish_reason': 'stop', 'usage': {'prompt_tokens': 37, 'completion_tokens': 201, 'total_tokens': 238}})]}} ``` #### String prompt template ```python from haystack.components.builders import ChatPromptBuilder from haystack.dataclasses.image_content import ImageContent template = """ {% message role="system" %} You are a helpful assistant. {% endmessage %} {% message role="user" %} Hello! I am {{user_name}}. What's the difference between the following images? {% for image in images %} {{ image | templatize_part }} {% endfor %} {% endmessage %} """ images = [ImageContent.from_file_path("apple.jpg"), ImageContent.from_file_path("orange.jpg")] builder = ChatPromptBuilder(template=template) builder.run(user_name="John", images=images) ``` #### ChatPromptBuilder.\_\_init\_\_ ```python def __init__(template: Optional[Union[list[ChatMessage], str]] = None, required_variables: Optional[Union[list[str], Literal["*"]]] = None, variables: Optional[list[str]] = None) ``` Constructs a ChatPromptBuilder component. **Arguments**: - `template`: A list of `ChatMessage` objects or a string template. The component looks for Jinja2 template syntax and renders the prompt with the provided variables. Provide the template in either the `init` method` or the `run` method. - `required_variables`: List variables that must be provided as input to ChatPromptBuilder. If a variable listed as required is not provided, an exception is raised. If set to "*", all variables found in the prompt are required. Optional. - `variables`: List input variables to use in prompt templates instead of the ones inferred from the `template` parameter. For example, to use more variables during prompt engineering than the ones present in the default template, you can provide them here. #### ChatPromptBuilder.run ```python @component.output_types(prompt=list[ChatMessage]) def run(template: Optional[Union[list[ChatMessage], str]] = None, template_variables: Optional[dict[str, Any]] = None, **kwargs) ``` Renders the prompt template with the provided variables. It applies the template variables to render the final prompt. You can provide variables with pipeline kwargs. To overwrite the default template, you can set the `template` parameter. To overwrite pipeline kwargs, you can set the `template_variables` parameter. **Arguments**: - `template`: An optional list of `ChatMessage` objects or string template to overwrite ChatPromptBuilder's default template. If `None`, the default template provided at initialization is used. - `template_variables`: An optional dictionary of template variables to overwrite the pipeline variables. - `kwargs`: Pipeline variables used for rendering the prompt. **Raises**: - `ValueError`: If `chat_messages` is empty or contains elements that are not instances of `ChatMessage`. **Returns**: A dictionary with the following keys: - `prompt`: The updated list of `ChatMessage` objects after rendering the templates. #### ChatPromptBuilder.to\_dict ```python def to_dict() -> dict[str, Any] ``` Returns a dictionary representation of the component. **Returns**: Serialized dictionary representation of the component. #### ChatPromptBuilder.from\_dict ```python @classmethod def from_dict(cls, data: dict[str, Any]) -> "ChatPromptBuilder" ``` Deserialize this component from a dictionary. **Arguments**: - `data`: The dictionary to deserialize and create the component. **Returns**: The deserialized component.