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Onboard your Broker research through an investment_research connector as private documents. This guide follows the same end-to-end workflow as the Investment Research Connector tutorial on Google Colab: authenticate, create the connector, audit ingestion, discover broker tags, then run Search and Research Agent with filters scoped to that corpus.
Private & Secure: No LLM training on your data
Prefer a notebook? Open the Colab tutorial and run the cells in order. Every REST call uses your API key in the X-API-KEY header. See Authentication for key handling. Open in Colab

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End-to-end workflow

After the connector syncs, Bigdata enriches each report (extraction, structure, and annotation) and indexes it for the Search Service and Research Agent. The steps below mirror the notebook.
1

Set up authentication

Send your API key on every request (repeat the two headers on each call below, or set a shell variable you expand into -H flags):
export API_KEY="YOUR_API_KEY"
Broker credentials are used only when creating the connector (see Create connector); they are not returned or updatable via the update-connector API.
2

Create an investment research connector

POST /contents/v1/connectors with type: "investment_research" and broker credentials in config. Set share_with_org if colleagues should see ingested documents.
curl -s -X POST 'https://api.bigdata.com/contents/v1/connectors' \
  -H "X-API-KEY: ${API_KEY}" \
  -H 'Content-Type: application/json' \
  -d '{
    "type": "investment_research",
    "label": "Broker Research Reports",
    "description": "Collects broker research reports from the broker feed",
    "share_with_org": false,
    "config": {
      "user_id": "YOUR_BROKER_USER_ID",
      "user_password": "YOUR_BROKER_PASSWORD"
    }
  }'
The response includes connector_id. Save it for listing documents and for dashboards; ingestion runs asynchronously after creation.
3

Audit synced documents and processing status

GET List documents with connector=<connector_id> (and optionally ownership=all) and paginate with page / page_size. Sort by created_at to see the newest items first.
# Replace CONNECTOR_UUID with your connector_id
curl -s -G 'https://api.bigdata.com/contents/v1/documents' \
  -H "X-API-KEY: ${API_KEY}" \
  --data-urlencode "ownership=all" \
  --data-urlencode "connector=CONNECTOR_UUID" \
  --data-urlencode "page=1" \
  --data-urlencode "page_size=50" \
  --data-urlencode "sort_by=created_at" \
  --data-urlencode "sort_order=desc"
Each document has status: processing while enrichment runs, then completed when it is ready for Search and Research Agent. You can also use Get document metadata on a single id for detail.You can confirm broker-ingested items with origin=investment_research when listing across connectors; each item still includes request_origin, connector_id, and tags.
4

List broker tags and pick a filter

Ingested reports get automatic tags such as broker:<broker_name>. GET /contents/v1/tags with prefix=broker: to list broker tags and their file_count.
curl -s -G 'https://api.bigdata.com/contents/v1/tags' \
  -H "X-API-KEY: ${API_KEY}" \
  --data-urlencode "prefix=broker:"
The Colab notebook picks the tag with the highest file_count as a convenience filter for the next steps; you can pass any broker tag (or several) into Search and Research Agent filters.
5

Query with the Search API

POST /v1/search with query.text and query.filters so results stay on your private investment research archive. Typical combinations (see Query filters):
  • Category: Category filter with mode: "INCLUDE" and values: ["my_files"] limits hits to your private content (same as My Files in the Developer Platform).
  • Source: Source filter, include the Investment Research source id for your workspace (the Colab example uses E5A77C; confirm the id you need in your environment).
  • Document taxonomy: Investment research document types, e.g. INVESTMENT-RESEARCH with subtype COMPANY_REPORT.
  • Broker tag: File tag filter, tag.any_of with your broker:... name.
curl -s -X POST 'https://api.bigdata.com/v1/search' \
  -H "X-API-KEY: ${API_KEY}" \
  -H 'Content-Type: application/json' \
  -d '{
    "search_mode": "fast",
    "query": {
      "ranking_params": {
        "content_diversification": { "enabled": true }
      },
      "filters": {
        "category": {
          "mode": "INCLUDE",
          "values": ["my_files"]
        },
        "source": {
          "mode": "INCLUDE",
          "values": ["E5A77C"]
        },
        "tag": {
          "any_of": ["broker:Your Broker Name"]
        },
        "document_type": {
          "mode": "INCLUDE",
          "values": [
            {
              "type": "INVESTMENT-RESEARCH",
              "subtypes": ["COMPANY_REPORT"]
            }
          ]
        }
      }
    }
  }'
The notebook renders hits in a small table and builds links to open a hit in the app, for example https://app.bigdata.com/documents/{id}?private=true&cnum={chunk} using the Search hit id and first chunk number.
6

Query with the Research Agent API

For summarization and question answering over the same corpus, POST to https://agents.bigdata.com/v1/research-agent with a user message and tools_configs.search.query_filters aligned with the Search filters above: tags (broker tag) and content scoped to the Investment Research source id.The Research Agent returns a stream of events (data: ... lines). The Colab notebook buffers ANSWER events until COMPLETE, then optionally parses AUDIT traces to show which chunks grounded the reply. Same buffering pattern as Non-streaming response.For parameter details, see the Research Agent API. Try the Research Agent playground with My Files / your private sources selected in the UI for an interactive equivalent.

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Search and Research Agent in the Developer Platform

In the Developer Platform, open Search or Research Agent, use the source selector, and limit results to My Files category or Investment Research source within this category to interact with this content.

Search Service Playground

Search across private investment research and other sources; use the source selector and filters to match the API workflow above.

Research Agent Playground

Ground answers in broker research by scoping tools to private content and the same tags or sources you use in the API.

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Claude and Bigdata MCP

You can work with your broker research the same way in Claude once the Bigdata MCP integration for Claude is enabled for your workspace. Claude can call the bigdata_search tool (see bigdata_search) to run semantic search over your corpus and pass the returned chunks into the conversation as grounding context. Ask in natural language, for example:
Summarize the latest research documents from my files. Include source attribution.
Claude will typically issue a search with filters such as category set to my_files so results stay in your private content (uploads, connector-backed research, and other ingested files visible to your account). It merges the retrieved excerpts with your instructions and can cite titles, publishers, and links in the reply, similar to scoping My Files in the API or Developer Platform. Claude using Bigdata search over My Files to summarize private research with source attribution
For richer report-style workflows on top of MCP tools, consider the Financial Research Analyst skill.