Haystack Agents是Haystack NLP框架的创新功能，专为处理需要多步骤推理、与外部工具交互以及动态适应用户查询的场景而设计。与LangChain等通用框架不同，Haystack Agents深度集成在Haystack生态系统中，擅长文档检索、自定义工具集成和多步骤推理。它采用工具驱动架构，工具作为独立模块执行特定任务，如文档搜索、计算或API交互。通过动态决定使用哪些工具、使用顺序以及如何组合输出，Haystack Agents能生成连贯的响应，适用于构建复杂的NLP应用，如客户支持机器人、教育工具和商业智能解决方案。

🛠️ Haystack Agents采用工具驱动架构，将工具定义为执行特定任务的独立模块，例如文档搜索、数学计算或API交互。这种模块化设计使得代理能够灵活地处理各种复杂的NLP任务。

🔍 Haystack Agents擅长利用先进的检索器搜索大型数据集，并通过集成API来扩展功能，例如进行计算或数据库查询。此外，它还能解决需要逻辑推理的复杂查询，超越简单的问答。

💡 Haystack Agents与流行的ML库和基础设施（如Elasticsearch、Hugging Face模型和预训练的transformers）无缝集成，具有开源和模块化的特点。这使得开发人员能够轻松地利用现有资源构建自定义的NLP应用。

🧮 Haystack Agents 可以通过定义不同的工具来实现不同的功能，例如，使用搜索工具从文档存储中检索信息，使用计算器工具执行数学运算。这些工具可以动态组合，以响应复杂的查询。

Modern NLP applications often demand multi-step reasoning, interaction with external tools, and the ability to adapt dynamically to user queries. Haystack Agents, an innovative feature of the Haystack NLP framework by deepset, exemplifies this new wave of advanced NLP capabilities.

Haystack Agents are built to handle scenarios requiring:

Complex multi-step reasoning.Integration of external tools or APIs.Retrieval-augmented workflows that go beyond simple question answering.

This article delves deep into the Haystack Agents framework, exploring its features, architecture, and real-world applications. To provide practical insights, we’ll build a QA Agent that uses tools like a search engine and a calculator.

Why Choose Haystack Agents?

Unlike general-purpose frameworks such as LangChain, Haystack Agents are deeply integrated within the Haystack ecosystem, making them highly effective for specialized tasks like document retrieval, custom tool integration, and multi-step reasoning. These agents excel in searching through large datasets using advanced retrievers, extending functionality by incorporating APIs for tasks such as calculations or database queries, and addressing complex queries requiring logical deductions. Being open-source and modular, Haystack Agents seamlessly integrate with popular ML libraries and infrastructures like Elasticsearch, Hugging Face models, and pre-trained transformers.

Architecture of Haystack Agents

Haystack Agents are structured using a tool-driven architecture. Here, tools function as individual modules designed for specific tasks, such as document search, calculations, or API interactions. The agent dynamically determines which tools to use, the sequence of their use, and how to combine their outputs to generate a coherent response. The architecture includes key components like tools, which execute specific action prompts that guide the agent’s decision-making process. These retrievers facilitate document search within large datasets, and nodes and pipelines manage data processing and workflow orchestration in Haystack.

Use Case: Building a QA Agent with Search and Calculator Tools

For this tutorial, our QA Agent will perform the following:

Retrieve answers to factual questions from a document store.Perform mathematical calculations using a calculator tool.Dynamically combine results when required.

Step 1: Install Prerequisites

Before diving into the implementation, ensure your environment is set up:

1. Install Python 3.8 or higher.

2. Install Haystack with all dependencies:

# bashpip install farm-haystack[all]

3. Launch Elasticsearch, the backbone of our document store:

# bashdocker run -d -p 9200:9200 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.17.1

Step 2: Initialize the Document Store and Retriever

The document store is the central repository for storing and querying documents, while the retriever finds relevant documents for a given query.

# pythonfrom haystack.utils import launch_esfrom haystack.nodes import EmbeddingRetrieverfrom haystack.pipelines import DocumentSearchPipelinefrom haystack.document_stores import ElasticsearchDocumentStore# Launch Elasticsearchlaunch_es()# Initialize Document Storedocument_store = ElasticsearchDocumentStore()# Add documents to the storedocs = [    {"content": "Albert Einstein was a theoretical physicist who developed the theory of relativity."},    {"content": "The capital of France is Paris."},    {"content": "The square root of 16 is 4."}]document_store.write_documents(docs)# Initialize Retrieverretriever = EmbeddingRetriever(    document_store=document_store,    embedding_model="sentence-transformers/all-MiniLM-L6-v2",    use_gpu=True)# Update embeddingsdocument_store.update_embeddings(retriever)

Step 3: Define Tools

Tools are the building blocks of Haystack Agents. Each tool serves a specific purpose, like searching for documents or performing calculations.

# pythonfrom haystack.agents.base import Tool# Search Toolsearch_pipeline = DocumentSearchPipeline(retriever)search_tool = Tool(    name="Search",    pipeline_or_node=search_pipeline,    description="Use this tool for answering factual questions using a document store.")# Calculator Tooldef calculate(expression: str) -> str:    try:        result = eval(expression)        return str(result)    except Exception as e:        return f"Error in calculation: {e}"calculator_tool = Tool(    name="Calculator",    pipeline_or_node=calculate,    description="Use this tool to perform mathematical calculations.")

Step 4: Initialize the Agent

Agents in Haystack are configured with tools and a prompt template that defines how they interact with the tools.

# pythonfrom haystack.agents import Agent# Initialize Agentagent = Agent(    tools=[search_tool, calculator_tool],    prompt_template="Answer questions using the provided tools. Combine results if needed.")

Step 5: Query the Agent

Interact with the agent by posing natural language queries.

# python# Factual Questionresponse = agent.run("Who developed the theory of relativity?")print("Agent Response:", response)# Mathematical Calculationresponse = agent.run("What is the result of 8 * (2 + 3)?")print("Agent Response:", response)# Combined Queryresponse = agent.run("What is the square root of 16, and who developed it?")print("Agent Response:", response)

Advanced Features of Haystack Agents

Custom Tools: Integrate APIs or domain-specific tools to extend functionality (e.g., weather APIs, stock market data).Fine-Tuned Models: Replace the default embedding model with a fine-tuned one for specialized tasks.Chained Pipelines: Use multiple pipelines to process complex queries involving multiple data sources.

In conclusion, Haystack Agents offer a powerful, flexible, and modular framework for building advanced NLP applications that require dynamic multi-step reasoning and tool usage. With their seamless integration into the Haystack ecosystem, these agents excel in tasks like document retrieval, custom API integration, and logical processing, making them ideal for solving complex real-world problems. They are particularly well-suited for applications such as customer support bots, which combine document search with external APIs for real-time ticket resolution, educational tools that retrieve information and perform calculations to answer user queries, and business intelligence solutions that aggregate data from multiple sources and generate insights.

Sources

https://github.com/deepset-ai/haystack https://docs.haystack.deepset.ai/docs/intro https://hub.docker.com/_/elasticsearch https://github.com/UKPLab/sentence-transformers

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