Refactor file reading functionality by consolidating read_markdown and read_csv_preview into a new read_file function that supports multiple file types. Update relevant prompts and agent configurations to utilize the new function. Enhance notebook output to include execution tracing information for better debugging and analysis context.

Author: rajpathak-openai

examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/editor.py

@@ -1,5 +1,5 @@
 from agents import Agent, ModelSettings, function_tool, Runner, RunContextWrapper
-from tools import write_markdown, read_markdown, read_csv_preview, list_output_files
+from tools import write_markdown, read_file, list_output_files
 from utils import load_prompt, DISCLAIMER
 from pydantic import BaseModel
 import json
@@ -19,7 +19,7 @@ def build_editor_agent():
     return Agent(
         name="Memo Editor Agent",
         instructions=(editor_prompt + DISCLAIMER + tool_retry_instructions),
-        tools=[write_markdown, read_markdown, read_csv_preview, list_output_files],
+        tools=[write_markdown, read_file, list_output_files],
         model=default_model,
         model_settings=ModelSettings(temperature=0),
     )

examples/agents_sdk/multi-agent-portfolio-collaboration/investment_agents/quant.py

@@ -1,5 +1,5 @@
 from agents import Agent, ModelSettings
-from tools import run_code_interpreter, get_fred_series, read_csv_preview, list_output_files
+from tools import run_code_interpreter, get_fred_series, read_file, list_output_files
 from utils import load_prompt, DISCLAIMER
 from pathlib import Path
 
@@ -21,7 +21,7 @@ def build_quant_agent():
         name="Quantitative Analysis Agent",
         instructions=(quant_prompt + DISCLAIMER + tool_retry_instructions),
         mcp_servers=[yahoo_mcp_server],
-        tools=[run_code_interpreter, get_fred_series, read_csv_preview, list_output_files],
+        tools=[run_code_interpreter, get_fred_series, read_file, list_output_files],
         model=default_model,
         model_settings=ModelSettings(parallel_tool_calls=True, temperature=0),
     ) 

examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration.ipynb

@@ -176,7 +176,8 @@
     "    today_str = datetime.date.today().strftime(\"%B %d, %Y\")\n",
     "    question = (\n",
     "        f\"Today is {today_str}. \"\n",
-    "        \"Is walmart losing market share to instacart? Provide analysis as this will influence a trading decision.\"\n",
+    "        \"How would the planned interest rate reduction effect my holdings in GOOGL if they were to happen?\"\n",
+    "        \"Considering all the factors effecting its price right now (Macro, Technical, Fundamental, etc.), what is a realistic price target by the end of the year?\"\n",
     "    )\n",
     "    bundle = build_investment_agents()\n",
     "\n",
@@ -351,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "9fe6e452",
    "metadata": {},
    "outputs": [

examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/code_interpreter.md

@@ -11,9 +11,9 @@ You are an expert quantitative developer using OpenAI's Code Interpreter. You ar
 ## Analysis Workflow
 1. Print the schema of each input file. Understand the dataset, and make logical assumptions on analysis even if the quant doesn't explicitly provide them.
 2. Drop missing values and normalize data as needed.
-3. Run the Analysis processed data, and inspect the data to ensure it ran. 
-4. Create visualizations that best fit the analysis type.  
-5. If the analysis cannot be completed even after the data processing, do not generate outputs. Instead, return a `<reason>` tag with a clear explanation, including the available columns.
+3. Run the analysis on the processed data.
+4. **If the data is empty or contains no rows after cleaning, do not generate any outputs. Instead, return only a `<reason>` tag explaining that the data is empty or insufficient for analysis, and list the available columns.**
+5. If the data is sufficient, create visualizations and tables as appropriate for the analysis.
 
 ## Constraints
 - Do **not** fetch external data or use `yfinance`. Use only the files in `input_files`.

examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/editor_base.md

@@ -80,7 +80,7 @@ You will receive a structured dictionary with the following keys:
 4. **Format**
    - Embed files appropriately:
      - Use `list_output_files` to discover available files.
-     - Use `read_csv_preview` for `.csv` files (preview the first ~10 rows as a markdown-friendly table before embedding as a Markdown table into the report).
+     - Use `read_file` for `.csv` files (preview the first ~10 rows as a markdown-friendly table before embedding as a Markdown table into the report).
      - Use standard Markdown syntax for charts and images (only if the file exists), e.g., `![vol-chart](AVGO_NVDA_price_vol_chart.png)`.
      - You cannot read PNG files directly.
      - These must be written to the report so they render. Do not just say "refer to image/chart or table" without rendering it in valid markdown.
@@ -101,8 +101,6 @@ You will receive a structured dictionary with the following keys:
 **Example of a process (yours might be different):**
 
 1. Use `list_output_files` to get available files.
-2. Preview CSV files with `read_csv_preview` for `.csv` files.
+2. Preview CSV files with `read_file` for `.csv` files.
 3. Save the memo using `write_markdown` to generate the investment_report, add relevant charts and tables rendered in markdown.
-4. Verify the memo using the `read_markdown` tool.
-5. Return `{ "file": "investment_report.md" }` JSON to the PM Agent (not the memo, just the file).
-
+4. Return `{ "file": "investment_report.md" }` JSON to the PM Agent (not the memo, just the file).

examples/agents_sdk/multi-agent-portfolio-collaboration/prompts/quant_base.md

@@ -34,7 +34,7 @@ If you mention a file in your request but do not include it in `input_files`, th
 ---
 
 **Additional Tools Available:**
-- **read_csv_preview**: Use this tool to preview the contents of any CSV file in the outputs directory before running an analysis. This helps you understand the schema, columns, and data quality, it doesn't generate any files.
+- **read_file**: Use this tool to preview the contents of any CSV, Markdown, or text file in the outputs directory before running an analysis. For CSVs, it returns a markdown table preview. This helps you understand the schema, columns, and data quality, it doesn't generate any files.
 - **list_output_files**: Use this tool to list all available files in the outputs directory. This helps you check which files are present and avoid referencing non-existent files. If you get file not found errors use this.
 
 _You may use these tools to inspect available data and plan your analysis more effectively before calling run_code_interpreter._

examples/agents_sdk/multi-agent-portfolio-collaboration/tools.py

@@ -80,7 +80,7 @@ def run_code_interpreter(request: str, input_files: list[str]) -> str:
             raise ValueError(
                 f"File not found: {file_path}. "
                 "Use the list_output_files tool to see which files exist, "
-                "and the read_csv_preview tool to see the contents of CSV files."
+                "and the read_file tool to see the contents of CSV files."
             )
         with abs_path.open("rb") as f:
             uploaded = client.files.create(file=f, purpose="user_data")
@@ -156,34 +156,43 @@ def write_markdown(filename: str, content: str) -> str:
     return json.dumps({"file": filename})
 
 @function_tool
-def read_markdown(filename: str) -> str:
-    """Read a Markdown file from the outputs directory.
-
-    If the caller provides a filename with **no** extension, we assume they meant a Markdown file
-    and append ``.md`` automatically. However, if the caller explicitly provides an extension that
-    is *not* ``.md``, we return an error explaining that only Markdown files are supported.
+def read_file(filename: str, n_rows: int = 10) -> str:
     """
+    Read and preview the contents of a file from the outputs directory.
 
-    # Determine if an explicit, non-Markdown extension was provided
-    suffix = Path(filename).suffix
-    if suffix and suffix.lower() != ".md":
-        return json.dumps({
-            "error": f"Wrong extension. cannot read '{suffix}' files; only .md files are supported",
-            "file": filename,
-        })
+    Supports reading CSV, Markdown (.md), and plain text (.txt) files. For CSV files, returns a preview of the last `n_rows` as a Markdown table. For Markdown and text files, returns the full text content. For unsupported file types, returns an error message.
 
-    # If no extension or already .md, ensure filename ends with .md
-    if not filename.endswith(".md"):
-        filename += ".md"
+    Args:
+        filename: The name of the file to read, relative to the outputs directory. Supported extensions: .csv, .md, .txt.
+        n_rows: The number of rows to preview for CSV files (default: 10).
 
+    Returns:
+        str: A JSON string containing either:
+            - For CSV: {"file": filename, "preview_markdown": "<markdown table>"}
+            - For Markdown/Text: {"file": filename, "content": "<text content>"}
+            - For errors: {"error": "<error message>", "file": filename}
+    """
     path = output_file(filename, make_parents=False)
     if not path.exists():
         return json.dumps({"error": "file not found", "file": filename})
 
-    with open(path, "r", encoding="utf-8") as f:
-        content = f.read()
-
-    return json.dumps({"file": filename, "content": content})
+    suffix = Path(filename).suffix.lower()
+    if suffix == ".csv":
+        try:
+            df = pd.read_csv(path).tail(n_rows)
+            table_md = df.to_markdown(index=False)
+            return json.dumps({"file": filename, "preview_markdown": table_md})
+        except Exception as e:
+            return json.dumps({"error": str(e), "file": filename})
+    elif suffix == ".md" or suffix == ".txt":
+        try:
+            with open(path, "r", encoding="utf-8") as f:
+                content = f.read()
+            return json.dumps({"file": filename, "content": content})
+        except Exception as e:
+            return json.dumps({"error": str(e), "file": filename})
+    else:
+        return json.dumps({"error": f"Unsupported file type: {suffix}", "file": filename})
 
 @function_tool
 def get_fred_series(series_id: str, start_date: str, end_date: str, download_csv: bool = False) -> str:
@@ -253,24 +262,6 @@ def get_fred_series(series_id: str, start_date: str, end_date: str, download_csv
     except Exception as e:
         return json.dumps({"error": str(e), "series_id": series_id})
 
-@function_tool
-def read_csv_preview(filename: str, n_rows: int = 10) -> str:
-    """Return last `n_rows` of a CSV in outputs/ as Markdown table JSON."""
-    if not filename.endswith(".csv"):
-        filename += ".csv"
-
-    path = output_file(filename, make_parents=False)
-    if not path.exists():
-        return json.dumps({"error": "file not found", "file": filename})
-
-    try:
-        df = pd.read_csv(path).tail(n_rows)
-    except Exception as e:
-        return json.dumps({"error": str(e), "file": filename})
-
-    table_md = df.to_markdown(index=False)
-    return json.dumps({"file": filename, "preview_markdown": table_md})
-
 @function_tool
 def list_output_files(extension: str = None) -> str:
     """
@@ -289,8 +280,7 @@ def list_output_files(extension: str = None) -> str:
 __all__ = [
     "run_code_interpreter",
     "write_markdown",
-    "read_markdown",
     "get_fred_series",
-    "read_csv_preview",
     "list_output_files",
+    "read_file",
 ]