> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bigdata.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Search Quickstart Guide

> Welcome to the Bigdata.com API Quickstart guide!

Easily find the most relevant information from trusted sources and your own data. Use it to power agents that give accurate, real-time answers.

Before getting started, choose your preferred integration method 👇

<Note>
  We recommend using the API for this guide, but you can also follow along with the Python SDK.
</Note>

<Tabs>
  <Tab title="API">
    It will only take you 5 minutes and will guide you through:

    ✅ Authenticate to bigdata.com <br />
    ✅ Query bigdata.com <br />
    ✅ Examine search results<br />

    # Authenticate to bigdata.com

    Enter the X-API-KEY in the header of the request to authenticate to the API.

    `X-API-KEY: <your-api-key>`

    # Query bigdata.com

    Bigdata.com processes millions of documents detecting entities
    (companies, people, organizations, products, places, topics, etc) and
    the events those entities play a role in. You can query all that data to
    get direct insights about what you care about. You will be able to
    create workflows that will optimize your day-to-day activities.

    **Query example:** As an example, I want to look for positive news about
    the company that powered bigdata.com, RavenPack, in the month of June
    2024\.

    ```bash theme={null}
    curl -X POST 'https://api.bigdata.com/v1/search' \
      -H 'Content-Type: application/json' \
      -H 'X-API-KEY: <your-api-key>' \
      --data '{
      "search_mode": "fast",
      "query": {
        "filters": {
          "timestamp": {
            "start": "2024-05-31T22:00:00.000Z",
            "end": "2024-06-30T21:59:59.999Z"
          },
          "entity": {
            "any_of": [
              "2BE1DC"
            ]
          },
          "sentiment": {
            "values": [
              "positive"
            ]
          }
        },
        "external_search": {
          "mode": "INCLUDE",
          "values": ["web"]
        },
        "max_chunks": 2
      }
    }'
    ```

    <Tip>
      Omit **`external_search`** if you do not need web results. See [Search documents](/api-reference/search/search-documents#body-query-external-search).
    </Tip>

    Matching documents:

    ```console theme={null}
    Document headline: Asia Awards 2024: Best alternative data provider-RavenPack

    Document headline: Fixed Income Portfolio Manager Jobs
    ```

    # Examine search results

    Congratulations! 🎉 You have successfully analyzed millions of documents
    and found two with the information that you are interested in.

    Let's examine the output more closely. The results object contains the document headline, the publication date, the source of the document, the URL of the document, and the chunks that matched your query.

    ```json theme={null}
    "results": [
        {
            "id": "5D16DA066F26D8D58D375B88F8B95245",
            "headline": "Asia Awards 2024: Best alternative data provider-RavenPack",
            "timestamp": "2024-06-20T07:20:53",
            "source": {
                "id": "E85BB4",
                "name": "WatersTechnology",
                "rank": "RANK_1"
            },
            "url": "https://www.waterstechnology.com/awards-rankings/7951868/asia-awards-2024-best-alternative-data-provider-ravenpack",
            "chunks": [
                {
                    "cnum": 3,
                    "text": "The solution ;\nThe RavenPack Factor Library removes this hurdle by offering streamlined access to actionable sentiment and market-moving indicators. Derived from unstructured data including news and transcripts, they deliver daily insights ranging from market perception, negative news, and trends for over 100,000 listed companies, to business cycle and macro insights like inflation and growth nowcasts. Even quant traders can benefit from signal components they can quickly test, thanks to this simple extract, transform and load (ETL) tool.",
                    "relevance": 0.09806532640442188,
                    "sentiment": 0.24
                },
                {
                    "cnum": 2,
                    "text": "RavenPack Factors augment the information available to discretionary and quantamental investors with quantitative insights without the need for a dedicated data infrastructure and team. This new offering helps investors streamline their workflows, shorten strategies' time to market, simplify risk management and capture opportunities to improve their performance ; Peter Hafez, chief data scientist, RavenPack",
                    "relevance": 0.09728399704675467,
                    "sentiment": 0.19
                }
            ]
        }
    ]
    ```

    # Summary

    You have experienced using the Bigdata API to extract
    insights from millions of unstructured data that bigdata.com has
    processed.

    <Tip>
      We recommend exploring the following key concepts to empower your
      searches.

      * [Knowledge Graph:](./knowledge_graph) It helps
        you find Entity IDs to query.
      * [Query filters:](./search/query_filters) It describes all the
        possible filters you can use in a query.
      * [Search Results:](./search/search_results) It describes the
        Document and Chunk structure with many other parameters.
    </Tip>
  </Tab>

  <Tab title="Python SDK">
    <Warning>
      We are sunsetting our SDKs and will no longer add new features, security patches, bug fixes, or technical support for them. To access the latest capabilities and ongoing improvements, we encourage you to migrate to our [RESTful API](/api-rest/introduction).

      SDK support will officially end on **December 31, 2026**. On this date, the underlying endpoints used by the SDKs and related documentation will be decommissioned.

      To avoid any disruption to your services, please ensure your migration is complete by that date.

      For migration assistance, please contact us at [support@bigdata.com](mailto:support@bigdata.com).
    </Warning>

    It will only take you 5 minutes and will guide you through:

    ✅ Install `bigdata-client` package<br />
    ✅ Authenticate to bigdata.com <br />
    ✅ Query bigdata.com <br />
    ✅ Examine search results<br />

    **Ready to get started? Let's dive in!**

    <a href="https://colab.research.google.com/drive/1vsXbmhUOs3O0L1wXSTf_XgteN4zFu3x6" target="_blank">
      <img alt="Open in Colab" noZoom src="https://colab.research.google.com/assets/colab-badge.svg" />
    </a>

    # Install `bigdata-client` package

    First of all, let's install `bigdata-client` package in a python
    virtual environment.

    Open the terminal and create a virtual environment with the following
    command:

    ```console theme={null}
    $ python3 -m venv bigdata_venv
    ```

    Activate the virtual environment, every time you want to use it:

    ```console theme={null}
    $ source ./bigdata_venv/bin/activate
    ```

    And install the `bigdata-client` within the environment `bigdata_venv`.

    ```console theme={null}
    (bigdata_venv) $ pip install bigdata-client
    ```

    # Authenticate to bigdata.com

    Enter the python interpreter with the following command

    ```console theme={null}
    (bigdata_venv) $ python3
    ```

    Now you can import the Bigdata object from the bigdata\_client package,

    ```python theme={null}
    >>> from bigdata_client import Bigdata
    ```

    And initiate it with your bigdata.com personal credentials.

    ```python theme={null}
    >>> bigdata = Bigdata("YOUR_USERNAME", "YOUR_PASSWORD")
    ```

    # Query bigdata.com

    Bigdata.com processes millions of documents detecting entities
    (companies, people, organizations, products, places, topics, etc) and
    the events those entities play a role in. You can query all that data to
    get direct insights about what you care about. You will be able to
    create workflows that will optimize your day-to-day activities.

    **Query example:** As an example, I want to look for positive news about
    the company that powered bigdata.com, RavenPack, in the month of June
    2024\.

    ```python theme={null}
    from bigdata_client.query import Entity, SentimentRange
    from bigdata_client.daterange import AbsoluteDateRange

    # RavenPack ID: bigdata.com is powered by RavenPack. In another tutorial
    # we will see how to look for entities IDs (Companies, People, etc).
    RAVENPACK_ENTIY_ID="2BE1DC"

    # Query positive news about RavenPack
    query = Entity(RAVENPACK_ENTIY_ID) & SentimentRange([0, 1])

    # Define a date range for the month of June 2024
    in_june = AbsoluteDateRange("2024-06-01T08:00:00", "2024-07-01T00:00:00")

    # Create a bigdata.com search
    search = bigdata.search.new(query, date_range=in_june)

    # Run the search and retrieve the two top documents
    documents = search.run(2)
    for doc in documents:
        print(f"\nDocument headline: {doc.headline}")
    ```

    Output:

    ```console theme={null}
    Document headline: Asia Awards 2024: Best alternative data provider-RavenPack

    Document headline: Fixed Income Portfolio Manager Jobs
    ```

    # Examine search results

    Congratulations! 🎉 You have successfully analyzed millions of documents
    and found two with the information that you are interested in.

    Let's examine the results more closely. The following command will
    print the document headline, number of chunks that matched your query,
    and chunk details.

    ```python theme={null}
    # Read all retrieved documents and print some details
    for doc in documents:
        print(f"\nDocument headline: {doc.headline}")
        print(f"Number of chunks: {len(doc.chunks)}")
        for chunk in doc.chunks:
            # Print the sentiment detected in the text and the text itself
            print(f"  Chunk sentiment [-1, 1]: {chunk.sentiment}")
            print(f"  Chunk text: {chunk.text}")
    ```

    # Summary

    You have experienced using the `bigdata-client` package to extract
    insights from millions of unstructured data that bigdata.com has
    processed.

    <Tip>
      We recommend exploring the following key concepts to empower your
      searches.

      * [Knowledge Graph:](./knowledge_graph) It helps
        you find Entity IDs to query.
      * [Query filters:](./search/query_filters) It describes all the
        possible filters you can use in a query.
      * [Search Results:](./search/search_results) It describes the
        Document and Chunk structure with many other parameters.
      * [Upload your own content:](./upload_content) You can also upload
        your private data. Bigdata.com will process them and consult them to
        answer only your queries.
    </Tip>
  </Tab>
</Tabs>