--- title: Pipeline id: pipeline-api description: Arranges components and integrations in flow. --- # Module async\_pipeline ## AsyncPipeline Asynchronous version of the Pipeline orchestration engine. Manages components in a pipeline allowing for concurrent processing when the pipeline's execution graph permits. This enables efficient processing of components by minimizing idle time and maximizing resource utilization. #### AsyncPipeline.run\_async\_generator ```python async def run_async_generator( data: dict[str, Any], include_outputs_from: Optional[set[str]] = None, concurrency_limit: int = 4) -> AsyncIterator[dict[str, Any]] ``` Executes the pipeline step by step asynchronously, yielding partial outputs when any component finishes. Usage: ```python from haystack import Document from haystack.components.builders import ChatPromptBuilder from haystack.dataclasses import ChatMessage from haystack.utils import Secret from haystack.document_stores.in_memory import InMemoryDocumentStore from haystack.components.retrievers.in_memory import InMemoryBM25Retriever from haystack.components.generators.chat import OpenAIChatGenerator from haystack.components.builders.prompt_builder import PromptBuilder from haystack import AsyncPipeline import asyncio # Write documents to InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents([ Document(content="My name is Jean and I live in Paris."), Document(content="My name is Mark and I live in Berlin."), Document(content="My name is Giorgio and I live in Rome.") ]) prompt_template = [ ChatMessage.from_user( ''' Given these documents, answer the question. Documents: {% for doc in documents %} {{ doc.content }} {% endfor %} Question: {{question}} Answer: ''') ] # Create and connect pipeline components retriever = InMemoryBM25Retriever(document_store=document_store) prompt_builder = ChatPromptBuilder(template=prompt_template) llm = OpenAIChatGenerator() rag_pipeline = AsyncPipeline() rag_pipeline.add_component("retriever", retriever) rag_pipeline.add_component("prompt_builder", prompt_builder) rag_pipeline.add_component("llm", llm) rag_pipeline.connect("retriever", "prompt_builder.documents") rag_pipeline.connect("prompt_builder", "llm") # Prepare input data question = "Who lives in Paris?" data = { "retriever": {"query": question}, "prompt_builder": {"question": question}, } # Process results as they become available async def process_results(): async for partial_output in rag_pipeline.run_async_generator( data=data, include_outputs_from={"retriever", "llm"} ): # Each partial_output contains the results from a completed component if "retriever" in partial_output: print("Retrieved documents:", len(partial_output["retriever"]["documents"])) if "llm" in partial_output: print("Generated answer:", partial_output["llm"]["replies"][0]) asyncio.run(process_results()) ``` **Arguments**: - `data`: Initial input data to the pipeline. - `concurrency_limit`: The maximum number of components that are allowed to run concurrently. - `include_outputs_from`: Set of component names whose individual outputs are to be included in the pipeline's output. For components that are invoked multiple times (in a loop), only the last-produced output is included. **Raises**: - `ValueError`: If invalid inputs are provided to the pipeline. - `PipelineMaxComponentRuns`: If a component exceeds the maximum number of allowed executions within the pipeline. - `PipelineRuntimeError`: If the Pipeline contains cycles with unsupported connections that would cause it to get stuck and fail running. Or if a Component fails or returns output in an unsupported type. **Returns**: An async iterator containing partial (and final) outputs. #### AsyncPipeline.run\_async ```python async def run_async(data: dict[str, Any], include_outputs_from: Optional[set[str]] = None, concurrency_limit: int = 4) -> dict[str, Any] ``` Provides an asynchronous interface to run the pipeline with provided input data. This method allows the pipeline to be integrated into an asynchronous workflow, enabling non-blocking execution of pipeline components. Usage: ```python import asyncio from haystack import Document from haystack.components.builders import ChatPromptBuilder from haystack.components.generators.chat import OpenAIChatGenerator from haystack.components.retrievers.in_memory import InMemoryBM25Retriever from haystack.core.pipeline import AsyncPipeline from haystack.dataclasses import ChatMessage from haystack.document_stores.in_memory import InMemoryDocumentStore # Write documents to InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents([ Document(content="My name is Jean and I live in Paris."), Document(content="My name is Mark and I live in Berlin."), Document(content="My name is Giorgio and I live in Rome.") ]) prompt_template = [ ChatMessage.from_user( ''' Given these documents, answer the question. Documents: {% for doc in documents %} {{ doc.content }} {% endfor %} Question: {{question}} Answer: ''') ] retriever = InMemoryBM25Retriever(document_store=document_store) prompt_builder = ChatPromptBuilder(template=prompt_template) llm = OpenAIChatGenerator() rag_pipeline = AsyncPipeline() rag_pipeline.add_component("retriever", retriever) rag_pipeline.add_component("prompt_builder", prompt_builder) rag_pipeline.add_component("llm", llm) rag_pipeline.connect("retriever", "prompt_builder.documents") rag_pipeline.connect("prompt_builder", "llm") # Ask a question question = "Who lives in Paris?" async def run_inner(data, include_outputs_from): return await rag_pipeline.run_async(data=data, include_outputs_from=include_outputs_from) data = { "retriever": {"query": question}, "prompt_builder": {"question": question}, } results = asyncio.run(run_inner(data, include_outputs_from={"retriever", "llm"})) print(results["llm"]["replies"]) # [ChatMessage(_role=, _content=[TextContent(text='Jean lives in Paris.')], # _name=None, _meta={'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'stop', 'usage': # {'completion_tokens': 6, 'prompt_tokens': 69, 'total_tokens': 75, # 'completion_tokens_details': CompletionTokensDetails(accepted_prediction_tokens=0, # audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), 'prompt_tokens_details': # PromptTokensDetails(audio_tokens=0, cached_tokens=0)}})] ``` **Arguments**: - `data`: A dictionary of inputs for the pipeline's components. Each key is a component name and its value is a dictionary of that component's input parameters: ``` data = { "comp1": {"input1": 1, "input2": 2}, } ``` For convenience, this format is also supported when input names are unique: ``` data = { "input1": 1, "input2": 2, } ``` - `include_outputs_from`: Set of component names whose individual outputs are to be included in the pipeline's output. For components that are invoked multiple times (in a loop), only the last-produced output is included. - `concurrency_limit`: The maximum number of components that should be allowed to run concurrently. **Raises**: - `ValueError`: If invalid inputs are provided to the pipeline. - `PipelineRuntimeError`: If the Pipeline contains cycles with unsupported connections that would cause it to get stuck and fail running. Or if a Component fails or returns output in an unsupported type. - `PipelineMaxComponentRuns`: If a Component reaches the maximum number of times it can be run in this Pipeline. **Returns**: A dictionary where each entry corresponds to a component name and its output. If `include_outputs_from` is `None`, this dictionary will only contain the outputs of leaf components, i.e., components without outgoing connections. #### AsyncPipeline.run ```python def run(data: dict[str, Any], include_outputs_from: Optional[set[str]] = None, concurrency_limit: int = 4) -> dict[str, Any] ``` Provides a synchronous interface to run the pipeline with given input data. Internally, the pipeline components are executed asynchronously, but the method itself will block until the entire pipeline execution is complete. In case you need asynchronous methods, consider using `run_async` or `run_async_generator`. Usage: ```python from haystack import Document from haystack.components.builders import ChatPromptBuilder from haystack.components.generators.chat import OpenAIChatGenerator from haystack.components.retrievers.in_memory import InMemoryBM25Retriever from haystack.core.pipeline import AsyncPipeline from haystack.dataclasses import ChatMessage from haystack.document_stores.in_memory import InMemoryDocumentStore # Write documents to InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents([ Document(content="My name is Jean and I live in Paris."), Document(content="My name is Mark and I live in Berlin."), Document(content="My name is Giorgio and I live in Rome.") ]) prompt_template = [ ChatMessage.from_user( ''' Given these documents, answer the question. Documents: {% for doc in documents %} {{ doc.content }} {% endfor %} Question: {{question}} Answer: ''') ] retriever = InMemoryBM25Retriever(document_store=document_store) prompt_builder = ChatPromptBuilder(template=prompt_template) llm = OpenAIChatGenerator() rag_pipeline = AsyncPipeline() rag_pipeline.add_component("retriever", retriever) rag_pipeline.add_component("prompt_builder", prompt_builder) rag_pipeline.add_component("llm", llm) rag_pipeline.connect("retriever", "prompt_builder.documents") rag_pipeline.connect("prompt_builder", "llm") # Ask a question question = "Who lives in Paris?" data = { "retriever": {"query": question}, "prompt_builder": {"question": question}, } results = rag_pipeline.run(data) print(results["llm"]["replies"]) # [ChatMessage(_role=, _content=[TextContent(text='Jean lives in Paris.')], # _name=None, _meta={'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'stop', 'usage': # {'completion_tokens': 6, 'prompt_tokens': 69, 'total_tokens': 75, 'completion_tokens_details': # CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, # rejected_prediction_tokens=0), 'prompt_tokens_details': PromptTokensDetails(audio_tokens=0, # cached_tokens=0)}})] ``` **Arguments**: - `data`: A dictionary of inputs for the pipeline's components. Each key is a component name and its value is a dictionary of that component's input parameters: ``` data = { "comp1": {"input1": 1, "input2": 2}, } ``` For convenience, this format is also supported when input names are unique: ``` data = { "input1": 1, "input2": 2, } ``` - `include_outputs_from`: Set of component names whose individual outputs are to be included in the pipeline's output. For components that are invoked multiple times (in a loop), only the last-produced output is included. - `concurrency_limit`: The maximum number of components that should be allowed to run concurrently. **Raises**: - `ValueError`: If invalid inputs are provided to the pipeline. - `PipelineRuntimeError`: If the Pipeline contains cycles with unsupported connections that would cause it to get stuck and fail running. Or if a Component fails or returns output in an unsupported type. - `PipelineMaxComponentRuns`: If a Component reaches the maximum number of times it can be run in this Pipeline. - `RuntimeError`: If called from within an async context. Use `run_async` instead. **Returns**: A dictionary where each entry corresponds to a component name and its output. If `include_outputs_from` is `None`, this dictionary will only contain the outputs of leaf components, i.e., components without outgoing connections. #### AsyncPipeline.\_\_init\_\_ ```python def __init__(metadata: Optional[dict[str, Any]] = None, max_runs_per_component: int = 100, connection_type_validation: bool = True) ``` Creates the Pipeline. **Arguments**: - `metadata`: Arbitrary dictionary to store metadata about this `Pipeline`. Make sure all the values contained in this dictionary can be serialized and deserialized if you wish to save this `Pipeline` to file. - `max_runs_per_component`: How many times the `Pipeline` can run the same Component. If this limit is reached a `PipelineMaxComponentRuns` exception is raised. If not set defaults to 100 runs per Component. - `connection_type_validation`: Whether the pipeline will validate the types of the connections. Defaults to True. #### AsyncPipeline.\_\_eq\_\_ ```python def __eq__(other: object) -> bool ``` Pipeline equality is defined by their type and the equality of their serialized form. Pipelines of the same type share every metadata, node and edge, but they're not required to use the same node instances: this allows pipeline saved and then loaded back to be equal to themselves. #### AsyncPipeline.\_\_repr\_\_ ```python def __repr__() -> str ``` Returns a text representation of the Pipeline. #### AsyncPipeline.to\_dict ```python def to_dict() -> dict[str, Any] ``` Serializes the pipeline to a dictionary. This is meant to be an intermediate representation but it can be also used to save a pipeline to file. **Returns**: Dictionary with serialized data. #### AsyncPipeline.from\_dict ```python @classmethod def from_dict(cls: type[T], data: dict[str, Any], callbacks: Optional[DeserializationCallbacks] = None, **kwargs: Any) -> T ``` Deserializes the pipeline from a dictionary. **Arguments**: - `data`: Dictionary to deserialize from. - `callbacks`: Callbacks to invoke during deserialization. - `kwargs`: `components`: a dictionary of `{name: instance}` to reuse instances of components instead of creating new ones. **Returns**: Deserialized component. #### AsyncPipeline.dumps ```python def dumps(marshaller: Marshaller = DEFAULT_MARSHALLER) -> str ``` Returns the string representation of this pipeline according to the format dictated by the `Marshaller` in use. **Arguments**: - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. **Returns**: A string representing the pipeline. #### AsyncPipeline.dump ```python def dump(fp: TextIO, marshaller: Marshaller = DEFAULT_MARSHALLER) -> None ``` Writes the string representation of this pipeline to the file-like object passed in the `fp` argument. **Arguments**: - `fp`: A file-like object ready to be written to. - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. #### AsyncPipeline.loads ```python @classmethod def loads(cls: type[T], data: Union[str, bytes, bytearray], marshaller: Marshaller = DEFAULT_MARSHALLER, callbacks: Optional[DeserializationCallbacks] = None) -> T ``` Creates a `Pipeline` object from the string representation passed in the `data` argument. **Arguments**: - `data`: The string representation of the pipeline, can be `str`, `bytes` or `bytearray`. - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. - `callbacks`: Callbacks to invoke during deserialization. **Raises**: - `DeserializationError`: If an error occurs during deserialization. **Returns**: A `Pipeline` object. #### AsyncPipeline.load ```python @classmethod def load(cls: type[T], fp: TextIO, marshaller: Marshaller = DEFAULT_MARSHALLER, callbacks: Optional[DeserializationCallbacks] = None) -> T ``` Creates a `Pipeline` object a string representation. The string representation is read from the file-like object passed in the `fp` argument. **Arguments**: - `fp`: A file-like object ready to be read from. - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. - `callbacks`: Callbacks to invoke during deserialization. **Raises**: - `DeserializationError`: If an error occurs during deserialization. **Returns**: A `Pipeline` object. #### AsyncPipeline.add\_component ```python def add_component(name: str, instance: Component) -> None ``` Add the given component to the pipeline. Components are not connected to anything by default: use `Pipeline.connect()` to connect components together. Component names must be unique, but component instances can be reused if needed. **Arguments**: - `name`: The name of the component to add. - `instance`: The component instance to add. **Raises**: - `ValueError`: If a component with the same name already exists. - `PipelineValidationError`: If the given instance is not a component. #### AsyncPipeline.remove\_component ```python def remove_component(name: str) -> Component ``` Remove and returns component from the pipeline. Remove an existing component from the pipeline by providing its name. All edges that connect to the component will also be deleted. **Arguments**: - `name`: The name of the component to remove. **Raises**: - `ValueError`: If there is no component with that name already in the Pipeline. **Returns**: The removed Component instance. #### AsyncPipeline.connect ```python def connect(sender: str, receiver: str) -> "PipelineBase" ``` Connects two components together. All components to connect must exist in the pipeline. If connecting to a component that has several output connections, specify the inputs and output names as 'component_name.connections_name'. **Arguments**: - `sender`: The component that delivers the value. This can be either just a component name or can be in the format `component_name.connection_name` if the component has multiple outputs. - `receiver`: The component that receives the value. This can be either just a component name or can be in the format `component_name.connection_name` if the component has multiple inputs. **Raises**: - `PipelineConnectError`: If the two components cannot be connected (for example if one of the components is not present in the pipeline, or the connections don't match by type, and so on). **Returns**: The Pipeline instance. #### AsyncPipeline.get\_component ```python def get_component(name: str) -> Component ``` Get the component with the specified name from the pipeline. **Arguments**: - `name`: The name of the component. **Raises**: - `ValueError`: If a component with that name is not present in the pipeline. **Returns**: The instance of that component. #### AsyncPipeline.get\_component\_name ```python def get_component_name(instance: Component) -> str ``` Returns the name of the Component instance if it has been added to this Pipeline or an empty string otherwise. **Arguments**: - `instance`: The Component instance to look for. **Returns**: The name of the Component instance. #### AsyncPipeline.inputs ```python def inputs( include_components_with_connected_inputs: bool = False ) -> dict[str, dict[str, Any]] ``` Returns a dictionary containing the inputs of a pipeline. Each key in the dictionary corresponds to a component name, and its value is another dictionary that describes the input sockets of that component, including their types and whether they are optional. **Arguments**: - `include_components_with_connected_inputs`: If `False`, only components that have disconnected input edges are included in the output. **Returns**: A dictionary where each key is a pipeline component name and each value is a dictionary of inputs sockets of that component. #### AsyncPipeline.outputs ```python def outputs( include_components_with_connected_outputs: bool = False ) -> dict[str, dict[str, Any]] ``` Returns a dictionary containing the outputs of a pipeline. Each key in the dictionary corresponds to a component name, and its value is another dictionary that describes the output sockets of that component. **Arguments**: - `include_components_with_connected_outputs`: If `False`, only components that have disconnected output edges are included in the output. **Returns**: A dictionary where each key is a pipeline component name and each value is a dictionary of output sockets of that component. #### AsyncPipeline.show ```python def show(*, server_url: str = "https://mermaid.ink", params: Optional[dict] = None, timeout: int = 30, super_component_expansion: bool = False) -> None ``` Display an image representing this `Pipeline` in a Jupyter notebook. This function generates a diagram of the `Pipeline` using a Mermaid server and displays it directly in the notebook. **Arguments**: - `server_url`: The base URL of the Mermaid server used for rendering (default: 'https://mermaid.ink'). See https://github.com/jihchi/mermaid.ink and https://github.com/mermaid-js/mermaid-live-editor for more info on how to set up your own Mermaid server. - `params`: Dictionary of customization parameters to modify the output. Refer to Mermaid documentation for more details Supported keys: - format: Output format ('img', 'svg', or 'pdf'). Default: 'img'. - type: Image type for /img endpoint ('jpeg', 'png', 'webp'). Default: 'png'. - theme: Mermaid theme ('default', 'neutral', 'dark', 'forest'). Default: 'neutral'. - bgColor: Background color in hexadecimal (e.g., 'FFFFFF') or named format (e.g., '!white'). - width: Width of the output image (integer). - height: Height of the output image (integer). - scale: Scaling factor (1–3). Only applicable if 'width' or 'height' is specified. - fit: Whether to fit the diagram size to the page (PDF only, boolean). - paper: Paper size for PDFs (e.g., 'a4', 'a3'). Ignored if 'fit' is true. - landscape: Landscape orientation for PDFs (boolean). Ignored if 'fit' is true. - `timeout`: Timeout in seconds for the request to the Mermaid server. - `super_component_expansion`: If set to True and the pipeline contains SuperComponents the diagram will show the internal structure of super-components as if they were components part of the pipeline instead of a "black-box". Otherwise, only the super-component itself will be displayed. **Raises**: - `PipelineDrawingError`: If the function is called outside of a Jupyter notebook or if there is an issue with rendering. #### AsyncPipeline.draw ```python def draw(*, path: Path, server_url: str = "https://mermaid.ink", params: Optional[dict] = None, timeout: int = 30, super_component_expansion: bool = False) -> None ``` Save an image representing this `Pipeline` to the specified file path. This function generates a diagram of the `Pipeline` using the Mermaid server and saves it to the provided path. **Arguments**: - `path`: The file path where the generated image will be saved. - `server_url`: The base URL of the Mermaid server used for rendering (default: 'https://mermaid.ink'). See https://github.com/jihchi/mermaid.ink and https://github.com/mermaid-js/mermaid-live-editor for more info on how to set up your own Mermaid server. - `params`: Dictionary of customization parameters to modify the output. Refer to Mermaid documentation for more details Supported keys: - format: Output format ('img', 'svg', or 'pdf'). Default: 'img'. - type: Image type for /img endpoint ('jpeg', 'png', 'webp'). Default: 'png'. - theme: Mermaid theme ('default', 'neutral', 'dark', 'forest'). Default: 'neutral'. - bgColor: Background color in hexadecimal (e.g., 'FFFFFF') or named format (e.g., '!white'). - width: Width of the output image (integer). - height: Height of the output image (integer). - scale: Scaling factor (1–3). Only applicable if 'width' or 'height' is specified. - fit: Whether to fit the diagram size to the page (PDF only, boolean). - paper: Paper size for PDFs (e.g., 'a4', 'a3'). Ignored if 'fit' is true. - landscape: Landscape orientation for PDFs (boolean). Ignored if 'fit' is true. - `timeout`: Timeout in seconds for the request to the Mermaid server. - `super_component_expansion`: If set to True and the pipeline contains SuperComponents the diagram will show the internal structure of super-components as if they were components part of the pipeline instead of a "black-box". Otherwise, only the super-component itself will be displayed. **Raises**: - `PipelineDrawingError`: If there is an issue with rendering or saving the image. #### AsyncPipeline.walk ```python def walk() -> Iterator[tuple[str, Component]] ``` Visits each component in the pipeline exactly once and yields its name and instance. No guarantees are provided on the visiting order. **Returns**: An iterator of tuples of component name and component instance. #### AsyncPipeline.warm\_up ```python def warm_up() -> None ``` Make sure all nodes are warm. It's the node's responsibility to make sure this method can be called at every `Pipeline.run()` without re-initializing everything. #### AsyncPipeline.validate\_input ```python def validate_input(data: dict[str, Any]) -> None ``` Validates pipeline input data. Validates that data: * Each Component name actually exists in the Pipeline * Each Component is not missing any input * Each Component has only one input per input socket, if not variadic * Each Component doesn't receive inputs that are already sent by another Component **Arguments**: - `data`: A dictionary of inputs for the pipeline's components. Each key is a component name. **Raises**: - `ValueError`: If inputs are invalid according to the above. #### AsyncPipeline.from\_template ```python @classmethod def from_template( cls, predefined_pipeline: PredefinedPipeline, template_params: Optional[dict[str, Any]] = None) -> "PipelineBase" ``` Create a Pipeline from a predefined template. See `PredefinedPipeline` for available options. **Arguments**: - `predefined_pipeline`: The predefined pipeline to use. - `template_params`: An optional dictionary of parameters to use when rendering the pipeline template. **Returns**: An instance of `Pipeline`. #### AsyncPipeline.validate\_pipeline ```python @staticmethod def validate_pipeline(priority_queue: FIFOPriorityQueue) -> None ``` Validate the pipeline to check if it is blocked or has no valid entry point. **Arguments**: - `priority_queue`: Priority queue of component names. **Raises**: - `PipelineRuntimeError`: If the pipeline is blocked or has no valid entry point. # Module pipeline ## Pipeline Synchronous version of the orchestration engine. Orchestrates component execution according to the execution graph, one after the other. #### Pipeline.run ```python def run(data: dict[str, Any], include_outputs_from: Optional[set[str]] = None, *, break_point: Optional[Union[Breakpoint, AgentBreakpoint]] = None, pipeline_snapshot: Optional[PipelineSnapshot] = None ) -> dict[str, Any] ``` Runs the Pipeline with given input data. Usage: ```python from haystack import Pipeline, Document from haystack.utils import Secret from haystack.document_stores.in_memory import InMemoryDocumentStore from haystack.components.retrievers.in_memory import InMemoryBM25Retriever from haystack.components.generators import OpenAIGenerator from haystack.components.builders.answer_builder import AnswerBuilder from haystack.components.builders.prompt_builder import PromptBuilder # Write documents to InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents([ Document(content="My name is Jean and I live in Paris."), Document(content="My name is Mark and I live in Berlin."), Document(content="My name is Giorgio and I live in Rome.") ]) prompt_template = """ Given these documents, answer the question. Documents: {% for doc in documents %} {{ doc.content }} {% endfor %} Question: {{question}} Answer: """ retriever = InMemoryBM25Retriever(document_store=document_store) prompt_builder = PromptBuilder(template=prompt_template) llm = OpenAIGenerator(api_key=Secret.from_token(api_key)) rag_pipeline = Pipeline() rag_pipeline.add_component("retriever", retriever) rag_pipeline.add_component("prompt_builder", prompt_builder) rag_pipeline.add_component("llm", llm) rag_pipeline.connect("retriever", "prompt_builder.documents") rag_pipeline.connect("prompt_builder", "llm") # Ask a question question = "Who lives in Paris?" results = rag_pipeline.run( { "retriever": {"query": question}, "prompt_builder": {"question": question}, } ) print(results["llm"]["replies"]) # Jean lives in Paris ``` **Arguments**: - `data`: A dictionary of inputs for the pipeline's components. Each key is a component name and its value is a dictionary of that component's input parameters: ``` data = { "comp1": {"input1": 1, "input2": 2}, } ``` For convenience, this format is also supported when input names are unique: ``` data = { "input1": 1, "input2": 2, } ``` - `include_outputs_from`: Set of component names whose individual outputs are to be included in the pipeline's output. For components that are invoked multiple times (in a loop), only the last-produced output is included. - `break_point`: A set of breakpoints that can be used to debug the pipeline execution. - `pipeline_snapshot`: A dictionary containing a snapshot of a previously saved pipeline execution. **Raises**: - `ValueError`: If invalid inputs are provided to the pipeline. - `PipelineRuntimeError`: If the Pipeline contains cycles with unsupported connections that would cause it to get stuck and fail running. Or if a Component fails or returns output in an unsupported type. - `PipelineMaxComponentRuns`: If a Component reaches the maximum number of times it can be run in this Pipeline. - `PipelineBreakpointException`: When a pipeline_breakpoint is triggered. Contains the component name, state, and partial results. **Returns**: A dictionary where each entry corresponds to a component name and its output. If `include_outputs_from` is `None`, this dictionary will only contain the outputs of leaf components, i.e., components without outgoing connections. #### Pipeline.\_\_init\_\_ ```python def __init__(metadata: Optional[dict[str, Any]] = None, max_runs_per_component: int = 100, connection_type_validation: bool = True) ``` Creates the Pipeline. **Arguments**: - `metadata`: Arbitrary dictionary to store metadata about this `Pipeline`. Make sure all the values contained in this dictionary can be serialized and deserialized if you wish to save this `Pipeline` to file. - `max_runs_per_component`: How many times the `Pipeline` can run the same Component. If this limit is reached a `PipelineMaxComponentRuns` exception is raised. If not set defaults to 100 runs per Component. - `connection_type_validation`: Whether the pipeline will validate the types of the connections. Defaults to True. #### Pipeline.\_\_eq\_\_ ```python def __eq__(other: object) -> bool ``` Pipeline equality is defined by their type and the equality of their serialized form. Pipelines of the same type share every metadata, node and edge, but they're not required to use the same node instances: this allows pipeline saved and then loaded back to be equal to themselves. #### Pipeline.\_\_repr\_\_ ```python def __repr__() -> str ``` Returns a text representation of the Pipeline. #### Pipeline.to\_dict ```python def to_dict() -> dict[str, Any] ``` Serializes the pipeline to a dictionary. This is meant to be an intermediate representation but it can be also used to save a pipeline to file. **Returns**: Dictionary with serialized data. #### Pipeline.from\_dict ```python @classmethod def from_dict(cls: type[T], data: dict[str, Any], callbacks: Optional[DeserializationCallbacks] = None, **kwargs: Any) -> T ``` Deserializes the pipeline from a dictionary. **Arguments**: - `data`: Dictionary to deserialize from. - `callbacks`: Callbacks to invoke during deserialization. - `kwargs`: `components`: a dictionary of `{name: instance}` to reuse instances of components instead of creating new ones. **Returns**: Deserialized component. #### Pipeline.dumps ```python def dumps(marshaller: Marshaller = DEFAULT_MARSHALLER) -> str ``` Returns the string representation of this pipeline according to the format dictated by the `Marshaller` in use. **Arguments**: - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. **Returns**: A string representing the pipeline. #### Pipeline.dump ```python def dump(fp: TextIO, marshaller: Marshaller = DEFAULT_MARSHALLER) -> None ``` Writes the string representation of this pipeline to the file-like object passed in the `fp` argument. **Arguments**: - `fp`: A file-like object ready to be written to. - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. #### Pipeline.loads ```python @classmethod def loads(cls: type[T], data: Union[str, bytes, bytearray], marshaller: Marshaller = DEFAULT_MARSHALLER, callbacks: Optional[DeserializationCallbacks] = None) -> T ``` Creates a `Pipeline` object from the string representation passed in the `data` argument. **Arguments**: - `data`: The string representation of the pipeline, can be `str`, `bytes` or `bytearray`. - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. - `callbacks`: Callbacks to invoke during deserialization. **Raises**: - `DeserializationError`: If an error occurs during deserialization. **Returns**: A `Pipeline` object. #### Pipeline.load ```python @classmethod def load(cls: type[T], fp: TextIO, marshaller: Marshaller = DEFAULT_MARSHALLER, callbacks: Optional[DeserializationCallbacks] = None) -> T ``` Creates a `Pipeline` object a string representation. The string representation is read from the file-like object passed in the `fp` argument. **Arguments**: - `fp`: A file-like object ready to be read from. - `marshaller`: The Marshaller used to create the string representation. Defaults to `YamlMarshaller`. - `callbacks`: Callbacks to invoke during deserialization. **Raises**: - `DeserializationError`: If an error occurs during deserialization. **Returns**: A `Pipeline` object. #### Pipeline.add\_component ```python def add_component(name: str, instance: Component) -> None ``` Add the given component to the pipeline. Components are not connected to anything by default: use `Pipeline.connect()` to connect components together. Component names must be unique, but component instances can be reused if needed. **Arguments**: - `name`: The name of the component to add. - `instance`: The component instance to add. **Raises**: - `ValueError`: If a component with the same name already exists. - `PipelineValidationError`: If the given instance is not a component. #### Pipeline.remove\_component ```python def remove_component(name: str) -> Component ``` Remove and returns component from the pipeline. Remove an existing component from the pipeline by providing its name. All edges that connect to the component will also be deleted. **Arguments**: - `name`: The name of the component to remove. **Raises**: - `ValueError`: If there is no component with that name already in the Pipeline. **Returns**: The removed Component instance. #### Pipeline.connect ```python def connect(sender: str, receiver: str) -> "PipelineBase" ``` Connects two components together. All components to connect must exist in the pipeline. If connecting to a component that has several output connections, specify the inputs and output names as 'component_name.connections_name'. **Arguments**: - `sender`: The component that delivers the value. This can be either just a component name or can be in the format `component_name.connection_name` if the component has multiple outputs. - `receiver`: The component that receives the value. This can be either just a component name or can be in the format `component_name.connection_name` if the component has multiple inputs. **Raises**: - `PipelineConnectError`: If the two components cannot be connected (for example if one of the components is not present in the pipeline, or the connections don't match by type, and so on). **Returns**: The Pipeline instance. #### Pipeline.get\_component ```python def get_component(name: str) -> Component ``` Get the component with the specified name from the pipeline. **Arguments**: - `name`: The name of the component. **Raises**: - `ValueError`: If a component with that name is not present in the pipeline. **Returns**: The instance of that component. #### Pipeline.get\_component\_name ```python def get_component_name(instance: Component) -> str ``` Returns the name of the Component instance if it has been added to this Pipeline or an empty string otherwise. **Arguments**: - `instance`: The Component instance to look for. **Returns**: The name of the Component instance. #### Pipeline.inputs ```python def inputs( include_components_with_connected_inputs: bool = False ) -> dict[str, dict[str, Any]] ``` Returns a dictionary containing the inputs of a pipeline. Each key in the dictionary corresponds to a component name, and its value is another dictionary that describes the input sockets of that component, including their types and whether they are optional. **Arguments**: - `include_components_with_connected_inputs`: If `False`, only components that have disconnected input edges are included in the output. **Returns**: A dictionary where each key is a pipeline component name and each value is a dictionary of inputs sockets of that component. #### Pipeline.outputs ```python def outputs( include_components_with_connected_outputs: bool = False ) -> dict[str, dict[str, Any]] ``` Returns a dictionary containing the outputs of a pipeline. Each key in the dictionary corresponds to a component name, and its value is another dictionary that describes the output sockets of that component. **Arguments**: - `include_components_with_connected_outputs`: If `False`, only components that have disconnected output edges are included in the output. **Returns**: A dictionary where each key is a pipeline component name and each value is a dictionary of output sockets of that component. #### Pipeline.show ```python def show(*, server_url: str = "https://mermaid.ink", params: Optional[dict] = None, timeout: int = 30, super_component_expansion: bool = False) -> None ``` Display an image representing this `Pipeline` in a Jupyter notebook. This function generates a diagram of the `Pipeline` using a Mermaid server and displays it directly in the notebook. **Arguments**: - `server_url`: The base URL of the Mermaid server used for rendering (default: 'https://mermaid.ink'). See https://github.com/jihchi/mermaid.ink and https://github.com/mermaid-js/mermaid-live-editor for more info on how to set up your own Mermaid server. - `params`: Dictionary of customization parameters to modify the output. Refer to Mermaid documentation for more details Supported keys: - format: Output format ('img', 'svg', or 'pdf'). Default: 'img'. - type: Image type for /img endpoint ('jpeg', 'png', 'webp'). Default: 'png'. - theme: Mermaid theme ('default', 'neutral', 'dark', 'forest'). Default: 'neutral'. - bgColor: Background color in hexadecimal (e.g., 'FFFFFF') or named format (e.g., '!white'). - width: Width of the output image (integer). - height: Height of the output image (integer). - scale: Scaling factor (1–3). Only applicable if 'width' or 'height' is specified. - fit: Whether to fit the diagram size to the page (PDF only, boolean). - paper: Paper size for PDFs (e.g., 'a4', 'a3'). Ignored if 'fit' is true. - landscape: Landscape orientation for PDFs (boolean). Ignored if 'fit' is true. - `timeout`: Timeout in seconds for the request to the Mermaid server. - `super_component_expansion`: If set to True and the pipeline contains SuperComponents the diagram will show the internal structure of super-components as if they were components part of the pipeline instead of a "black-box". Otherwise, only the super-component itself will be displayed. **Raises**: - `PipelineDrawingError`: If the function is called outside of a Jupyter notebook or if there is an issue with rendering. #### Pipeline.draw ```python def draw(*, path: Path, server_url: str = "https://mermaid.ink", params: Optional[dict] = None, timeout: int = 30, super_component_expansion: bool = False) -> None ``` Save an image representing this `Pipeline` to the specified file path. This function generates a diagram of the `Pipeline` using the Mermaid server and saves it to the provided path. **Arguments**: - `path`: The file path where the generated image will be saved. - `server_url`: The base URL of the Mermaid server used for rendering (default: 'https://mermaid.ink'). See https://github.com/jihchi/mermaid.ink and https://github.com/mermaid-js/mermaid-live-editor for more info on how to set up your own Mermaid server. - `params`: Dictionary of customization parameters to modify the output. Refer to Mermaid documentation for more details Supported keys: - format: Output format ('img', 'svg', or 'pdf'). Default: 'img'. - type: Image type for /img endpoint ('jpeg', 'png', 'webp'). Default: 'png'. - theme: Mermaid theme ('default', 'neutral', 'dark', 'forest'). Default: 'neutral'. - bgColor: Background color in hexadecimal (e.g., 'FFFFFF') or named format (e.g., '!white'). - width: Width of the output image (integer). - height: Height of the output image (integer). - scale: Scaling factor (1–3). Only applicable if 'width' or 'height' is specified. - fit: Whether to fit the diagram size to the page (PDF only, boolean). - paper: Paper size for PDFs (e.g., 'a4', 'a3'). Ignored if 'fit' is true. - landscape: Landscape orientation for PDFs (boolean). Ignored if 'fit' is true. - `timeout`: Timeout in seconds for the request to the Mermaid server. - `super_component_expansion`: If set to True and the pipeline contains SuperComponents the diagram will show the internal structure of super-components as if they were components part of the pipeline instead of a "black-box". Otherwise, only the super-component itself will be displayed. **Raises**: - `PipelineDrawingError`: If there is an issue with rendering or saving the image. #### Pipeline.walk ```python def walk() -> Iterator[tuple[str, Component]] ``` Visits each component in the pipeline exactly once and yields its name and instance. No guarantees are provided on the visiting order. **Returns**: An iterator of tuples of component name and component instance. #### Pipeline.warm\_up ```python def warm_up() -> None ``` Make sure all nodes are warm. It's the node's responsibility to make sure this method can be called at every `Pipeline.run()` without re-initializing everything. #### Pipeline.validate\_input ```python def validate_input(data: dict[str, Any]) -> None ``` Validates pipeline input data. Validates that data: * Each Component name actually exists in the Pipeline * Each Component is not missing any input * Each Component has only one input per input socket, if not variadic * Each Component doesn't receive inputs that are already sent by another Component **Arguments**: - `data`: A dictionary of inputs for the pipeline's components. Each key is a component name. **Raises**: - `ValueError`: If inputs are invalid according to the above. #### Pipeline.from\_template ```python @classmethod def from_template( cls, predefined_pipeline: PredefinedPipeline, template_params: Optional[dict[str, Any]] = None) -> "PipelineBase" ``` Create a Pipeline from a predefined template. See `PredefinedPipeline` for available options. **Arguments**: - `predefined_pipeline`: The predefined pipeline to use. - `template_params`: An optional dictionary of parameters to use when rendering the pipeline template. **Returns**: An instance of `Pipeline`. #### Pipeline.validate\_pipeline ```python @staticmethod def validate_pipeline(priority_queue: FIFOPriorityQueue) -> None ``` Validate the pipeline to check if it is blocked or has no valid entry point. **Arguments**: - `priority_queue`: Priority queue of component names. **Raises**: - `PipelineRuntimeError`: If the pipeline is blocked or has no valid entry point.