Создание агента RAG с двумя источниками знаний с помощью Exa и Milvus

В этом руководстве показано, как создать агента, который ищет информацию как в публичном интернете (с помощью Exa), так и в частной базе знаний (с помощью Milvus), а затем синтезирует единый ответ. Агент использует вызов функций OpenAI для автоматического принятия решения о том, к какому источнику следует обратиться, исходя из вопроса пользователя.

Exa - это поисковый API, разработанный для приложений искусственного интеллекта, который работает на базе Zilliz Cloud (полностью управляемый Milvus). В отличие от традиционных поисковых систем, основанных на ключевых словах, Exa поддерживает семантический (нейронный) поиск - вы описываете то, что вам нужно, на естественном языке, и он понимает ваше намерение. Он также обеспечивает извлечение контента, выделение и фильтрацию на основе категорий. Milvus - это векторная база данных с открытым исходным кодом, созданная для масштабируемого поиска по сходству. Объединив их с агентом LLM, вы можете создать систему, которая будет извлекать как внутренние собственные данные, так и актуальную веб-информацию в рамках одного рабочего процесса.

Предварительные условия

Прежде чем запускать этот блокнот, убедитесь, что у вас установлены следующие зависимости:

$ pip install exa_py pymilvus openai

Вам понадобятся API-ключи от Exa и OpenAI. Задайте их в качестве переменных окружения:

import os

os.environ["EXA_API_KEY"] = "***********"
os.environ["OPENAI_API_KEY"] = "sk-***********"

Инициализация клиентов

Настройте клиенты Exa, OpenAI и Milvus. Мы используем модель OpenAI text-embedding-3-small для генерации векторных вкраплений, а Milvus Lite - для локального хранения векторов с нулевой настройкой инфраструктуры.

import json
from openai import OpenAI
from pymilvus import MilvusClient, DataType
from exa_py import Exa

llm = OpenAI()
exa = Exa(api_key=os.environ["EXA_API_KEY"])
milvus = MilvusClient(uri="./milvus_exa_demo.db")

EMBED_MODEL = "text-embedding-3-small"
EMBED_DIM = 1536
COLLECTION = "private_kb"

Определите вспомогательную функцию для генерации вкраплений. Мы будем использовать ее во всем блокноте для индексации и запросов:

def embed_text(text: str | list[str]) -> list:
    """Generate embedding vector(s) using OpenAI."""
    resp = llm.embeddings.create(
        input=text if isinstance(text, list) else [text],
        model=EMBED_MODEL,
    )
    if isinstance(text, list):
        return [item.embedding for item in resp.data]
    return resp.data[0].embedding

Создание частной базы знаний (Milvus)

Мы смоделируем набор внутренних документов компании - спецификации продуктов, политики, отчеты о доходах и документы по API, - которые не будут появляться в открытом доступе в Интернете. В реальном сценарии они могут быть взяты из ваших внутренних вики, баз данных или систем управления документами.

private_docs = [
    {
        "id": 1,
        "text": (
            "Acme Widget Pro supports up to 10,000 concurrent connections. "
            "It uses a proprietary compression algorithm (AcmeZip v3) that "
            "reduces payload size by 72% compared to gzip."
        ),
        "source": "product-spec.pdf",
    },
    {
        "id": 2,
        "text": (
            "Our return policy allows customers to return any product within "
            "30 days of purchase for a full refund. After 30 days, only store "
            "credit is offered. Damaged items must be reported within 48 hours."
        ),
        "source": "return-policy.md",
    },
    {
        "id": 3,
        "text": (
            "Q3 2025 revenue was $4.2M, up 18% from Q2. The growth was "
            "primarily driven by enterprise customers adopting Widget Pro. "
            "Churn rate dropped to 3.1%."
        ),
        "source": "q3-earnings.pdf",
    },
    {
        "id": 4,
        "text": (
            "Internal API rate limits: free tier 100 req/min, pro tier "
            "5,000 req/min, enterprise tier 50,000 req/min. Rate limit "
            "headers are X-RateLimit-Remaining and X-RateLimit-Reset."
        ),
        "source": "api-docs.md",
    },
    {
        "id": 5,
        "text": (
            "Employee onboarding checklist: 1) Sign NDA, 2) Set up VPN access, "
            "3) Enroll in mandatory security training, 4) Request Jira and "
            "Confluence access from IT, 5) Schedule 1:1 with manager."
        ),
        "source": "onboarding-guide.md",
    },
]

Создайте коллекцию Milvus с явной схемой, вложите документы и вставьте их:

if milvus.has_collection(COLLECTION):
    milvus.drop_collection(COLLECTION)

schema = milvus.create_schema(auto_id=False, enable_dynamic_field=True)
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="vector", datatype=DataType.FLOAT_VECTOR, dim=EMBED_DIM)
schema.add_field(field_name="text", datatype=DataType.VARCHAR, max_length=65535)
schema.add_field(field_name="source", datatype=DataType.VARCHAR, max_length=512)

index_params = milvus.prepare_index_params()
index_params.add_index(
    field_name="vector", index_type="AUTOINDEX", metric_type="COSINE"
)

milvus.create_collection(
    collection_name=COLLECTION,
    schema=schema,
    index_params=index_params,
    # consistency_level="Strong",
)

# Embed all documents in one batch call
embeddings = embed_text([doc["text"] for doc in private_docs])

milvus.insert(
    collection_name=COLLECTION,
    data=[
        {
            "id": doc["id"],
            "vector": emb,
            "text": doc["text"],
            "source": doc["source"],
        }
        for doc, emb in zip(private_docs, embeddings)
    ],
)

print(f"Inserted {len(private_docs)} documents into Milvus.")

Inserted 5 documents into Milvus.

Давайте проверим, как работает поиск, с помощью быстрого тестового запроса:

query = "What is the return policy?"
results = milvus.search(
    collection_name=COLLECTION,
    data=[embed_text(query)],
    limit=2,
    output_fields=["text", "source"],
)

for hit in results[0]:
    print(f"[score={hit['distance']:.3f}] ({hit['entity']['source']})")
    print(f"  {hit['entity']['text'][:120]}...")
    print()

[score=0.665] (return-policy.md)
  Our return policy allows customers to return any product within 30 days of purchase for a full refund. After 30 days, on...

[score=0.119] (q3-earnings.pdf)
  Q3 2025 revenue was $4.2M, up 18% from Q2. The growth was primarily driven by enterprise customers adopting Widget Pro. ...

Изучение возможностей поиска Exa

Прежде чем создавать агента, давайте изучим возможности поиска Exa. Exa поддерживает несколько режимов поиска, которые полезны для различных сценариев.

Семантический поиск с извлечением контента - Exa может возвращать не только ссылки, но и текст статьи, ключевые моменты и резюме, созданные искусственным интеллектом, в одном запросе:

web_results = exa.search_and_contents(
    query="latest trends in AI agents 2026",
    type="auto",
    num_results=3,
    text={"max_characters": 3000},
    highlights={"num_sentences": 3},
)

for r in web_results.results:
    print(f"[{r.title}]")
    print(f"  URL: {r.url}")
    if r.highlights:
        print(f"  Highlight: {r.highlights[0][:150]}...")
    print()

[The AI Trends Shaping 2026. A month into the new year is as good a… | by ODSC - Open Data Science | Mar, 2026 | Medium]
  URL: https://odsc.medium.com/the-ai-trends-shaping-2026-34078dad4d49
  Highlight:  ahead. January brought Claude CoWork, Anthropic’s “AI coworker” that turns agents into desktop collaborators; OpenClaw (formerly Moltbot, formerly Cl...

[AI agent trends 2026 report]
  URL: https://cloud.google.com/resources/content/ai-agent-trends-2026
  Highlight: >. The era of simple prompts is over. We're witnessing the agent leap—where AI orchestrates complex, end-to-end workflows semi-autonomously. For enter...

[The Rise of Agentic AI: Why 2026 is the Year AI Started 'Doing']
  URL: https://www.marketdrafts.com/2026/02/rise-of-agentic-ai-2026-trends.html?m=1
  Highlight:  The era of "Generative AI" (which creates content) is being superseded by "Agentic AI" (which executes actions). We are witnessing a fundamental arch...

Фильтрация на основе категорий - вы можете ограничить результаты определенными типами контента, такими как "research paper", "news", "company" или "tweet". Это полезно, когда вам нужны высококачественные источники и вы хотите избежать шума:

filtered_results = exa.search_and_contents(
    query="retrieval augmented generation real world applications",
    category="research paper",
    num_results=3,
    highlights={"num_sentences": 2},
)

for r in filtered_results.results:
    print(f"- {r.title}")
    print(f"  {r.url}\n")

- 10 RAG examples and use cases from real companies
  https://www.evidentlyai.com/blog/rag-examples

- Implementing Retrieval-Augmented Generation (RAG) with Real-World Constraints
  https://dev.to/dextralabs/implementing-retrieval-augmented-generation-rag-with-real-world-constraints-3ajm

- 
  https://www.arxiv.org/pdf/2502.14930

Поиск похожих статей - задав URL, Exa может найти другие статьи с похожим содержанием. Это полезно для расширения исследования с хорошей отправной точки:

if web_results.results:
    source_url = web_results.results[0].url
    similar = exa.find_similar_and_contents(
        url=source_url,
        num_results=3,
        highlights={"num_sentences": 2},
    )
    print(f"Articles similar to: {source_url}\n")
    for r in similar.results:
        print(f"- {r.title}")
        print(f"  {r.url}\n")

Articles similar to: https://odsc.medium.com/the-ai-trends-shaping-2026-34078dad4d49

- AI Trends 2026: From Agent Demos to Production Reality
  https://opendatascience.com/the-ai-trends-shaping-2026/

- The Most Important AI Trends to Watch in 2026
  https://medium.com/the-ai-studio/the-most-important-ai-trends-to-watch-in-2026-54af64d45021

Определение инструментов агента

Теперь мы определим две функции инструментов, которые будет использовать агент. Частный инструмент KB ищет в Milvus, используя векторное сходство, а веб-инструмент ищет в публичном интернете через Exa:

def search_private_kb(query: str) -> str:
    """Search the internal knowledge base using Milvus vector search."""
    results = milvus.search(
        collection_name=COLLECTION,
        data=[embed_text(query)],
        limit=3,
        output_fields=["text", "source"],
    )
    chunks = []
    for hit in results[0]:
        chunks.append(f"[{hit['entity']['source']}] {hit['entity']['text']}")
    return "\n\n".join(chunks) if chunks else "No relevant internal documents found."


def search_web(query: str) -> str:
    """Search the public web using Exa for up-to-date information."""
    results = exa.search_and_contents(
        query=query,
        type="auto",
        num_results=3,
        highlights={"num_sentences": 3},
    )
    items = []
    for r in results.results:
        highlight = r.highlights[0] if r.highlights else "No snippet available."
        items.append(f"[{r.title}]({r.url})\n{highlight}")
    return "\n\n".join(items) if items else "No web results found."


TOOL_FNS = {
    "search_private_kb": search_private_kb,
    "search_web": search_web,
}

Создание агента

Агент использует вызов функций OpenAI, чтобы решить, какой инструмент(ы) вызвать. Он работает по простому циклу: LLM получает запрос пользователя, решает, какие инструменты вызвать (если таковые имеются), выполняет их, а затем синтезирует окончательный ответ из полученного контекста.

TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "search_private_kb",
            "description": (
                "Search the company's internal knowledge base (product docs, "
                "policies, earnings, API docs, HR guides). Use this for any "
                "question about internal/proprietary information."
            ),
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "The search query"}
                },
                "required": ["query"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": (
                "Search the public web for up-to-date external information - "
                "news, trends, competitor analysis, open-source projects, etc. "
                "Use this when the question is about the outside world."
            ),
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "The search query"}
                },
                "required": ["query"],
            },
        },
    },
]

SYSTEM_PROMPT = """You are a helpful assistant with access to two search tools:

1. **search_private_kb** - searches the company's internal knowledge base.
2. **search_web** - searches the public internet via Exa.

Routing rules:
- Questions about internal products, policies, metrics, or processes: use search_private_kb.
- Questions about external trends, news, competitors, or general knowledge: use search_web.
- Questions that need both internal and external context: call BOTH tools, then synthesize.

Always cite your sources. For internal docs, mention the filename. For web results, include the URL."""


def run_agent(user_query: str) -> str:
    """Run the agent loop: LLM -> tool calls -> LLM -> final answer."""
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_query},
    ]

    print(f"User: {user_query}\n")

    # First LLM call - may request tool calls
    response = llm.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=TOOLS,
    )
    msg = response.choices[0].message
    messages.append(msg)

    # If no tool calls, return directly
    if not msg.tool_calls:
        print(f"Agent (no tools used): {msg.content}")
        return msg.content

    # Execute each tool call
    for tc in msg.tool_calls:
        fn_name = tc.function.name
        fn_args = json.loads(tc.function.arguments)
        print(f"  -> Calling {fn_name}(query={fn_args['query']!r})")

        result = TOOL_FNS[fn_name](**fn_args)
        messages.append(
            {
                "role": "tool",
                "tool_call_id": tc.id,
                "content": result,
            }
        )

    # Second LLM call - synthesize final answer
    response = llm.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=TOOLS,
    )
    answer = response.choices[0].message.content
    print(f"\nAgent:\n{answer}")
    return answer

Демонстрация

Теперь давайте протестируем агента на трех сценариях, демонстрирующих различное поведение маршрутизации.

Сценарий A: Внутренний вопрос (маршрутизация в Milvus)

Спросите о внутренней политике - агент должен позвонить на search_private_kb и получить ответ из наших личных документов:

run_agent("What is the return policy for Acme products?")

User: What is the return policy for Acme products?



  -> Calling search_private_kb(query='return policy Acme products')



Agent:
The Acme products return policy allows customers to return any product within 30 days of purchase for a full refund. After 30 days, only store credit is offered. It's important to note that damaged items must be reported within 48 hours of receipt ([source: return-policy.md]).





"The Acme products return policy allows customers to return any product within 30 days of purchase for a full refund. After 30 days, only store credit is offered. It's important to note that damaged items must be reported within 48 hours of receipt ([source: return-policy.md])."

Сценарий B: Внешний вопрос (маршруты к Exa)

Спросите о внешних тенденциях - агент должен позвонить на search_web и получить актуальную информацию из публичного интернета:

run_agent("What are the latest AI agent frameworks trending in 2026?")

User: What are the latest AI agent frameworks trending in 2026?



  -> Calling search_web(query='latest AI agent frameworks 2026')



Agent:
In 2026, several AI agent frameworks are trending, each offering unique features and capabilities that cater to various needs. Here are some of the most prominent ones:

1. **LangChain and LangGraph**: These frameworks remain highly popular for building large language model (LLM)-powered applications. LangGraph, in particular, models agents as state graphs, which is useful for action-oriented workflows. LangChain continues to dominate due to its comprehensive feature set for production-grade control and orchestration.

2. **LangSmith Agent Builder**: Released into general availability in 2026, this tool allows teams to create AI agents using natural language, simplifying the process of agent development.

3. **Semantic Kernel and AutoGen**: These have been integrated into Azure AI Foundry, creating a unified framework. Semantic Kernel uses a plugin-based middleware pattern, enhancing existing applications with AI capabilities efficiently.

4. **OpenClaw**: An open-source framework that operates locally, OpenClaw transforms your computer into an autonomous agent host, differing from cloud-based solutions by keeping data and operations localized. This framework supports a large community and includes extensive skills for customization.

These frameworks cater to various requirements, whether it's production-grade solutions, open-source options, or frameworks focused on local deployment. Each framework has its strengths, depending on the use case and the existing ecosystem it fits into.

Sources:
- [Agentic AI Frameworks: The Complete Guide (2026)](https://aiagentskit.com/blog/agentic-ai-frameworks/)
- [OpenClaw: The Open-Source AI Agent Framework That Runs Your Life Locally](https://www.clawbot.blog/blog/openclaw-the-open-source-ai-agent-framework-that-runs-your-life-locally)
- [The Best AI Agent Frameworks for 2026](https://medium.com/data-science-collective/the-best-ai-agent-frameworks-for-2026-tier-list-b3a4362fac0d)





"In 2026, several AI agent frameworks are trending, each offering unique features and capabilities that cater to various needs. Here are some of the most prominent ones:\n\n1. **LangChain and LangGraph**: These frameworks remain highly popular for building large language model (LLM)-powered applications. LangGraph, in particular, models agents as state graphs, which is useful for action-oriented workflows. LangChain continues to dominate due to its comprehensive feature set for production-grade control and orchestration.\n\n2. **LangSmith Agent Builder**: Released into general availability in 2026, this tool allows teams to create AI agents using natural language, simplifying the process of agent development.\n\n3. **Semantic Kernel and AutoGen**: These have been integrated into Azure AI Foundry, creating a unified framework. Semantic Kernel uses a plugin-based middleware pattern, enhancing existing applications with AI capabilities efficiently.\n\n4. **OpenClaw**: An open-source framework that operates locally, OpenClaw transforms your computer into an autonomous agent host, differing from cloud-based solutions by keeping data and operations localized. This framework supports a large community and includes extensive skills for customization.\n\nThese frameworks cater to various requirements, whether it's production-grade solutions, open-source options, or frameworks focused on local deployment. Each framework has its strengths, depending on the use case and the existing ecosystem it fits into.\n\nSources:\n- [Agentic AI Frameworks: The Complete Guide (2026)](https://aiagentskit.com/blog/agentic-ai-frameworks/)\n- [OpenClaw: The Open-Source AI Agent Framework That Runs Your Life Locally](https://www.clawbot.blog/blog/openclaw-the-open-source-ai-agent-framework-that-runs-your-life-locally)\n- [The Best AI Agent Frameworks for 2026](https://medium.com/data-science-collective/the-best-ai-agent-frameworks-for-2026-tier-list-b3a4362fac0d)"

Сценарий C: Гибридный вопрос (маршруты к обоим)

Задайте вопрос, требующий как внутренних спецификаций, так и внешних бенчмарков - агент должен вызвать оба инструмента и синтезировать сравнение:

run_agent(
    "How does our Widget Pro's throughput compare to "
    "open-source alternatives on the market?"
)

User: How does our Widget Pro's throughput compare to open-source alternatives on the market?



  -> Calling search_private_kb(query='Widget Pro throughput comparison')


  -> Calling search_web(query='open-source widget throughput comparison')



Agent:
The throughput of our Widget Pro is quite competitive when compared to open-source alternatives on the market. Here's a detailed comparison:

### Widget Pro

- **Concurrent Connections**: Supports up to 10,000 concurrent connections.
- **Compression**: Utilizes AcmeZip v3, a proprietary compression algorithm that reduces payload size by 72% compared to gzip (source: [product-spec.pdf]).
- **API Rate Limits**: Offers different tiers:
  - Free tier: 100 requests/minute.
  - Pro tier: 5,000 requests/minute.
  - Enterprise tier: 50,000 requests/minute (source: [api-docs.md]).

### Open-Source Alternatives

From the available resources, open-source widget solutions such as Chatwoot and Tiledesk are popular in handling customer engagement with a flexible and customizable approach (source: [ChatMaxima article](https://chatmaxima.com/blog/15-open-source-free-live-chat-widget-solutions-to-boost-your-customer-engagement-in-2024/)). However, specific throughput metrics such as maximum concurrent connections or API limits are generally not highlighted in open-source product descriptions unless directly benchmarked.

These alternatives often emphasize customization, control, and integration with AI-driven capabilities but do not always specify throughput in terms comparable with Widget Pro. They might be more suited for organizations looking to tailor solutions to specific needs rather than focusing solely on throughput efficiency.

In conclusion, Widget Pro appears to offer high throughput suitable for enterprises with robust API support, while open-source options offer flexibility and customization with varying degrees of performance metrics.





"The throughput of our Widget Pro is quite competitive when compared to open-source alternatives on the market. Here's a detailed comparison:\n\n### Widget Pro\n\n- **Concurrent Connections**: Supports up to 10,000 concurrent connections.\n- **Compression**: Utilizes AcmeZip v3, a proprietary compression algorithm that reduces payload size by 72% compared to gzip (source: [product-spec.pdf]).\n- **API Rate Limits**: Offers different tiers:\n  - Free tier: 100 requests/minute.\n  - Pro tier: 5,000 requests/minute.\n  - Enterprise tier: 50,000 requests/minute (source: [api-docs.md]).\n\n### Open-Source Alternatives\n\nFrom the available resources, open-source widget solutions such as Chatwoot and Tiledesk are popular in handling customer engagement with a flexible and customizable approach (source: [ChatMaxima article](https://chatmaxima.com/blog/15-open-source-free-live-chat-widget-solutions-to-boost-your-customer-engagement-in-2024/)). However, specific throughput metrics such as maximum concurrent connections or API limits are generally not highlighted in open-source product descriptions unless directly benchmarked.\n\nThese alternatives often emphasize customization, control, and integration with AI-driven capabilities but do not always specify throughput in terms comparable with Widget Pro. They might be more suited for organizations looking to tailor solutions to specific needs rather than focusing solely on throughput efficiency.\n\nIn conclusion, Widget Pro appears to offer high throughput suitable for enterprises with robust API support, while open-source options offer flexibility and customization with varying degrees of performance metrics."

Очистка

Когда вы закончите, сбросьте коллекцию, чтобы освободить ресурсы.

milvus.drop_collection(COLLECTION)

Заключение

В этом руководстве мы создали RAG-агент с двумя источниками, который сочетает в себе Milvus для поиска частных знаний и Exa для публичного веб-поиска. Ключевыми компонентами являются:

• Milvus хранит и извлекает внутренние документы с помощью поиска по векторному сходству, обеспечивая конфиденциальность и возможность поиска по собственным данным.
• Exa обеспечивает семантический веб-поиск с такими функциями, как фильтрация категорий, извлечение контента и обнаружение похожих статей.
• Вызов функций OpenAI позволяет LLM автоматически направлять запросы в нужный источник - или в оба источника - в зависимости от цели вопроса.

Этот паттерн применим в корпоративных сценариях, когда ИИ-помощнику необходим доступ как к конфиденциальным внутренним документам, так и к внешней информации в режиме реального времени.