> ## Documentation Index > Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt > Use this file to discover all available pages before exploring further. # 并行搜索集成 > 使用 LangChain Python 与 Parallel 搜索工具集成。 > [Parallel](https://platform.parallel.ai/) 是一个专为 LLM 和 AI 应用设计的实时网络搜索和内容提取平台。 `ParallelWebSearchTool` 提供对 Parallel 搜索 API 的访问，将传统的搜索 → 抓取 → 提取流程简化为单个 API 调用，返回结构化的、针对 LLM 优化的结果。 ## 概述 ### 集成详情 | 类 | 包 | 可序列化 | JS 支持 | 包最新版本 | | :--------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------- | :--: | :---: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [`ParallelWebSearchTool`](https://reference.langchain.com/python/langchain-parallel/search_tool/ParallelWebSearchTool) | [`langchain-parallel`](https://reference.langchain.com/python/langchain-parallel/) | ❌ | ❌ | PyPI - Latest version | ### 工具功能 * **实时网络搜索**：访问当前网络信息 * **结构化结果**：返回压缩的、针对 LLM 优化的摘录 * **灵活输入**：支持自然语言目标或特定搜索查询 * **领域过滤**：通过源策略包含或排除特定领域 * **可定制输出**：控制结果数量（1-40）和摘录长度（最小 100 字符） * **丰富元数据**：可选的搜索时间、结果数量和查询信息 * **异步支持**：完整的 async/await 支持及适当的执行器处理 * **错误处理**：全面的错误处理及详细的错误消息 ## 设置 该集成位于 `langchain-parallel` 包中。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-parallel ``` ### 凭据 前往 [Parallel](https://beta.parallel.ai) 注册并生成 API 密钥。完成后，设置 `PARALLEL_API_KEY` 环境变量： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import getpass import os if not os.environ.get("PARALLEL_API_KEY"): os.environ["PARALLEL_API_KEY"] = getpass.getpass("Parallel API key:\n") ``` ## 实例化 此处展示如何实例化 `ParallelWebSearchTool` 的实例。该工具可以使用 API 密钥和基础 URL 参数进行配置： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_parallel import ParallelWebSearchTool # 基本实例化 - API 密钥来自环境变量 tool = ParallelWebSearchTool() # 使用显式 API 密钥和自定义基础 URL tool = ParallelWebSearchTool( api_key="your-api-key", base_url="https://api.parallel.ai", # 默认值 ) ``` ## 调用 ### 直接使用参数调用 您可以使用 `objective`（自然语言描述）或特定的 `search_queries` 来调用该工具。该工具支持各种配置选项，包括领域过滤和元数据收集： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 使用特定的搜索查询和高级选项 result = tool.invoke( { "search_queries": [ "AI breakthroughs 2024", "machine learning advances", "generative AI news", ], "max_results": 8, "excerpts": {"max_chars_per_result": 2000}, "mode": "one-shot", # 使用 'agentic' 以获得节省 token 的结果 "source_policy": { "include_domains": ["arxiv.org", "nature.com"], "exclude_domains": ["reddit.com", "twitter.com"], }, "fetch_policy": { "max_age_seconds": 86400, # 缓存内容 1 天 "timeout_seconds": 60, }, "include_metadata": True, "timeout": 120, # 自定义超时秒数 } ) print(result) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 使用目标（自然语言）和元数据 result = tool.invoke( { "objective": "What are the latest developments in artificial intelligence in 2024?", "max_results": 5, "include_metadata": True, # 包含搜索时间和统计信息 } ) print(result) # 示例响应结构： # { # "search_id": "search_abc123...", # "results": [ # { # "url": "https://example.com/ai-news", # "title": "Latest AI Developments 2024", # "excerpts": [ # "Recent breakthrough in transformer architectures...", # "New applications in computer vision..." # ] # } # ], # "search_metadata": { # "search_duration_seconds": 4.123, # "search_timestamp": "2024-01-15T10:30:00", # "max_results_requested": 5, # "actual_results_returned": 4, # "search_id": "search_abc123...", # "query_count": 1, # "source_policy_applied": false # } # } ``` ### 使用 `ToolCall` 调用 我们也可以使用模型生成的 `ToolCall` 来调用该工具，在这种情况下将返回 `ToolMessage`： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 这通常由模型生成，但为了演示目的，我们将直接创建工具调用。 model_generated_tool_call = { "args": { "objective": "Find recent news about climate change initiatives", "max_results": 3, "source_policy": {"include_domains": ["ipcc.ch", "unfccc.int", "nature.com"]}, "include_metadata": True, }, "id": "call_123", "name": tool.name, # "parallel_web_search" "type": "tool_call", } result = tool.invoke(model_generated_tool_call) print(result) print(f"Tool name: {tool.name}") # parallel_web_search print(f"Tool description: {tool.description}") ``` ### 异步用法 该工具支持完整的 async/await 操作，以便在异步应用中获得更好的性能： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} async def search_async(): return await tool.ainvoke( { "objective": "Latest quantum computing breakthroughs", "max_results": 5, "include_metadata": True, } ) # 运行异步搜索 result = await search_async() print(result) ``` ### 参数详情和验证 该工具执行全面的输入验证，并支持以下参数： #### 必需参数 必须提供以下至少一项： * `objective`：自然语言描述（最大 5000 个字符） * `search_queries`：搜索查询列表（最多 5 个查询，每个 200 个字符） #### 可选参数： * `max_results`：返回的结果数量（1-40，默认：10） * `excerpts`：摘录设置字典（例如，`{"max_chars_per_result": 1500}`） * `mode`：搜索模式 - 'one-shot' 用于全面结果，'agentic' 用于节省 token 的结果 * `source_policy`：域过滤，包含 `include_domains` 和/或 `exclude_domains` 列表 * `fetch_policy`：缓存控制字典（例如，`{"max_age_seconds": 86400, "timeout_seconds": 60}`） * `include_metadata`：包含搜索时间和统计信息（默认：True） * `timeout`：请求超时秒数（可选） #### 错误处理： 该工具为验证失败和 API 错误提供详细的错误消息。 ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # 综合参数使用示例 result = tool.invoke( { "objective": "Find comprehensive information about renewable energy policies in European countries", "max_results": 15, "excerpts": { "max_chars_per_result": 2500 }, # 较长的摘录以获取详细信息 "mode": "one-shot", # 全面结果 "source_policy": { "include_domains": ["europa.eu", "iea.org", "irena.org"], "exclude_domains": ["wikipedia.org", "reddit.com"], }, "fetch_policy": { "max_age_seconds": 86400, # 1 天缓存 "timeout_seconds": 90, }, "include_metadata": True, "timeout": 180, # 扩展超时以进行综合搜索 } ) # 访问结果和元数据 print(f"Found {len(result['results'])} results") if "search_metadata" in result: metadata = result["search_metadata"] print(f"Search took {metadata['search_duration_seconds']}s") print(f"Source policy applied: {metadata.get('source_policy_applied', False)}") ``` ## 链式调用 我们可以通过首先将其绑定到 [工具调用模型](/oss/python/langchain/tools/) 然后调用它来在链中使用我们的工具： 👉 阅读 [OpenAI 聊天模型集成文档](/oss/python/integrations/chat/openai/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[openai]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["OPENAI_API_KEY"] = "sk-..." model = init_chat_model("gpt-5.2") ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_openai import ChatOpenAI os.environ["OPENAI_API_KEY"] = "sk-..." model = ChatOpenAI(model="gpt-5.2") ``` 👉 阅读 [Anthropic 聊天模型集成文档](/oss/python/integrations/chat/anthropic/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[anthropic]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["ANTHROPIC_API_KEY"] = "sk-..." model = init_chat_model("claude-sonnet-4-6") ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_anthropic import ChatAnthropic os.environ["ANTHROPIC_API_KEY"] = "sk-..." model = ChatAnthropic(model="claude-sonnet-4-6") ``` 👉 阅读 [Azure 聊天模型集成文档](/oss/python/integrations/chat/azure_chat_openai/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[openai]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["AZURE_OPENAI_API_KEY"] = "..." os.environ["AZURE_OPENAI_ENDPOINT"] = "..." os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview" model = init_chat_model( "azure_openai:gpt-5.2", azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"], ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_openai import AzureChatOpenAI os.environ["AZURE_OPENAI_API_KEY"] = "..." os.environ["AZURE_OPENAI_ENDPOINT"] = "..." os.environ["OPENAI_API_VERSION"] = "2025-03-01-preview" model = AzureChatOpenAI( model="gpt-5.2", azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"] ) ``` 👉 阅读 [Google GenAI 聊天模型集成文档](/oss/python/integrations/chat/google_generative_ai/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[google-genai]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["GOOGLE_API_KEY"] = "..." model = init_chat_model("google_genai:gemini-2.5-flash-lite") ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_google_genai import ChatGoogleGenerativeAI os.environ["GOOGLE_API_KEY"] = "..." model = ChatGoogleGenerativeAI(model="gemini-2.5-flash-lite") ``` 👉 阅读 [AWS Bedrock 聊天模型集成文档](/oss/python/integrations/chat/bedrock/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[aws]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.chat_models import init_chat_model # 按照以下步骤配置您的凭据： # https://docs.aws.amazon.com/bedrock/latest/userguide/getting-started.html model = init_chat_model( "anthropic.claude-3-5-sonnet-20240620-v1:0", model_provider="bedrock_converse", ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_aws import ChatBedrock model = ChatBedrock(model="anthropic.claude-3-5-sonnet-20240620-v1:0") ``` 👉 阅读 [HuggingFace 聊天模型集成文档](/oss/python/integrations/chat/huggingface/) ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U "langchain[huggingface]" ``` ```python init_chat_model theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain.chat_models import init_chat_model os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..." model = init_chat_model( "microsoft/Phi-3-mini-4k-instruct", model_provider="huggingface", temperature=0.7, max_tokens=1024, ) ``` ```python Model Class theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import os from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_..." llm = HuggingFaceEndpoint( repo_id="microsoft/Phi-3-mini-4k-instruct", temperature=0.7, max_length=1024, ) model = ChatHuggingFace(llm=llm) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # | output: false # | echo: false # !pip install -qU langchain langchain-openai from langchain.chat_models import init_chat_model llm = init_chat_model(model="gpt-4.1", model_provider="openai") ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnableConfig, chain prompt = ChatPromptTemplate( [ ("system", "You are a helpful assistant."), ("human", "{user_input}"), ("placeholder", "{messages}"), ] ) # 指定 tool_choice 将强制模型调用此工具。 llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name) llm_chain = prompt | llm_with_tools @chain def tool_chain(user_input: str, config: RunnableConfig): input_ = {"user_input": user_input} ai_msg = llm_chain.invoke(input_, config=config) tool_msgs = tool.batch(ai_msg.tool_calls, config=config) return llm_chain.invoke({**input_, "messages": [ai_msg, *tool_msgs]}, config=config) tool_chain.invoke("What are the latest breakthrough discoveries in quantum computing?") ``` ## 最佳实践 * **使用具体目标**：更具体的目标能带来更好、更有针对性的结果 * **应用领域过滤**：使用 `source_policy` 专注于权威来源或排除不可靠的域 * **包含元数据**：设置 `include_metadata: True` 用于调试和性能优化 * **优雅处理错误**：该工具为验证和 API 失败提供详细的错误消息 * **使用异步以提高性能**：在异步应用中使用 `ainvoke()` 以获得更好的性能 ## 响应格式 该工具返回一个具有如下格式的 structured dictionary： ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} { "search_id": "search_abc123...", # 唯一搜索标识符 "results": [ # 搜索结果列表 { "url": "https://example.com/page", "title": "Page Title", "excerpts": [ # 相关文本摘录 "First relevant excerpt...", "Second relevant excerpt..." ] } ], "search_metadata": { # 可选元数据（如果 include_metadata=True） "search_duration_seconds": 4.123, "search_timestamp": "2024-01-15T10:30:00", "max_results_requested": 10, "actual_results_returned": 8, "search_id": "search_abc123...", "query_count": 3, # 使用的查询数量 "queries_used": ["query1", "query2", "query3"], # 如果提供了 search_queries "source_policy_applied": true, # 如果使用了 source_policy "included_domains": ["nature.com"], # 包含的域 "excluded_domains": ["reddit.com"] # 排除的域 } } ``` ## API 参考 有关所有功能和配置选项的详细文档，请前往 [`ParallelWebSearchTool`](https://reference.langchain.com/python/langchain-parallel/search_tool/ParallelWebSearchTool) API 参考或 [Parallel 搜索参考](https://docs.parallel.ai/api-reference/search-beta/search)。 ***
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