Using Research Plans

import requests
import json
from dotenv import load_dotenv
import os

load_dotenv()
my_api_key = os.getenv('BIGDATA_API_KEY')

# No research_plan - agent determines steps dynamically
payload = {
    "template": {
        "name": "Open-ended Analysis",
        "prompt": "What are the key factors affecting {{ company_id }}'s competitive position in the AI chip market?",
        "expected_input": {
            "company_id": {"type": "rp_entity_id"}
        }
        # No research_plan provided
    },
    "input": {
        "company_id": "D8442A"  # NVIDIA
    },
    "time_range": "last_90_days"
}

headers = {
    "X-API-KEY": my_api_key,
    "Content-Type": "application/json"
}

with requests.post(
    "https://agents.bigdata.com/v1/workflow/execute",
    headers=headers,
    json=payload,
    stream=True,
    timeout=180
) as r:
    r.raise_for_status()
    for raw_line in r.iter_lines(decode_unicode=True):
        if not raw_line or not raw_line.startswith("data: "):
            continue

        event = json.loads(raw_line[6:])
        delta = event.get("delta", {})
        msg_type = delta.get("type")

        if msg_type == "THINKING":
            print(f"[Thinking] {delta.get('content', '')[:100]}...")
        elif msg_type == "ACTION":
            print(f"[Action] {delta.get('tool_name')}")
        elif msg_type == "ANSWER":
            print(delta.get("content", ""), end="", flush=True)
        elif msg_type == "COMPLETE":
            print("\n\n[Complete]")

import requests
import json
from dotenv import load_dotenv
import os

load_dotenv()
my_api_key = os.getenv('BIGDATA_API_KEY')

# With research_plan - agent follows predefined steps
payload = {
    "template": {
        "name": "Structured Credit Analysis",
        "prompt": "Perform a credit analysis of {{ company_id }} following the research plan.",
        "expected_input": {
            "company_id": {"type": "rp_entity_id"}
        },
        "research_plan": {
            "title": "Credit Analysis Framework",
            "steps": [
                {"description": "Analyze business model and market position"},
                {"description": "Review financial statements and leverage metrics"},
                {"description": "Assess liquidity and debt maturity profile"},
                {"description": "Evaluate credit ratings and outlook"},
                {"description": "Identify key risks and mitigants"},
                {"description": "Formulate credit recommendation"}
            ]
        }
    },
    "input": {
        "company_id": "4E4980"  # Apple
    },
    "time_range": "last_180_days"
}

headers = {
    "X-API-KEY": my_api_key,
    "Content-Type": "application/json"
}

current_step = None

with requests.post(
    "https://agents.bigdata.com/v1/workflow/execute",
    headers=headers,
    json=payload,
    stream=True,
    timeout=180
) as r:
    r.raise_for_status()
    for raw_line in r.iter_lines(decode_unicode=True):
        if not raw_line or not raw_line.startswith("data: "):
            continue

        event = json.loads(raw_line[6:])
        delta = event.get("delta", {})
        msg_type = delta.get("type")

        if msg_type == "PLANNING":
            plan = delta.get("plan", {})
            print(f"\n=== {plan.get('title')} ===")
            for i, step in enumerate(plan.get("steps", [])):
                status = step.get("status", "NOT_STARTED")
                icon = {"NOT_STARTED": " ", "IN_PROGRESS": ">", "COMPLETED": "x", "SKIPPED": "-", "FAILED": "!"}.get(status, " ")
                print(f"  [{icon}] {step.get('description')}")
                if status == "IN_PROGRESS" and current_step != i:
                    current_step = i
                    print(f"\n--- Working on: {step.get('description')} ---")

        elif msg_type == "ANSWER":
            print(delta.get("content", ""), end="", flush=True)

        elif msg_type == "COMPLETE":
            print("\n\n=== Analysis Complete ===")

def process_workflow_stream(response):
    """Process all message types from a workflow stream."""
    answer_text = ""
    sources = []

    for raw_line in response.iter_lines(decode_unicode=True):
        if not raw_line or not raw_line.startswith("data: "):
            continue

        try:
            event = json.loads(raw_line[6:])
        except json.JSONDecodeError:
            continue

        delta = event.get("delta", {})
        msg_type = delta.get("type")

        if msg_type == "THINKING":
            # Optional: show agent reasoning
            pass

        elif msg_type == "PLANNING":
            plan = delta.get("plan", {})
            print(f"Plan: {plan.get('title')}")
            for step in plan.get("steps", []):
                status = step.get("status", "NOT_STARTED")
                if status == "IN_PROGRESS":
                    print(f"  Working on: {step.get('description')}")

        elif msg_type == "ACTION":
            print(f"  Calling: {delta.get('tool_name')}")

        elif msg_type == "ANSWER":
            content = delta.get("content", "")
            answer_text += content
            print(content, end="", flush=True)

        elif msg_type == "GROUNDING":
            # Collect source references
            for ref in delta.get("references", []):
                if ref.get("source"):
                    sources.append(ref["source"])

        elif msg_type == "AUDIT":
            # Optional: process search traces
            for trace in delta.get("audit_traces", []):
                if trace.get("audit_type") == "SearchAuditV1":
                    query = trace.get("query", {}).get("text", "")
                    print(f"  Searched: {query}")

        elif msg_type == "COMPLETE":
            usage = delta.get("usage", [])
            print("\n\n--- Complete ---")
            for u in usage:
                print(f"  {u['type']}: {u['input_tokens']} in, {u['output_tokens']} out")

        elif msg_type == "ERROR":
            print(f"Error: {delta.get('error')}")

    return answer_text, sources

payload = {
    "template": template_id,
    "input": {"company_id": "D8442A"},
    "model_name": "pro"  # Use enhanced model
}