Enable analysing user documents in code sandbox and other improvements

- Run one program at a time, instead of allowing model to pass
  multiple programs to run in parallel to simplify logic for model
- Update prompt to give more example of complex, multi-line code
- Allow passing user files as input into code sandbox for analysis

- Log code execution timer at info level to evaluate execution latencies
  in production

- Type the generated code for easier processing by caller functions

src/khoj/processor/conversation/prompts.py

@@ -870,25 +870,40 @@ Khoj:
 # --
 python_code_generation_prompt = PromptTemplate.from_template(
     """
-You are Khoj, an advanced python programmer. You are tasked with constructing **up to three** python programs to best answer the user query.
+You are Khoj, an advanced python programmer. You are tasked with constructing a python program to best answer the user query.
 - The python program will run in a pyodide python sandbox with no network access.
-- You can write programs to run complex calculations, analyze data, create charts, generate documents to meticulously answer the query
-- The sandbox has access to the standard library, matplotlib, panda, numpy, scipy, bs4, sympy, brotli, cryptography, fast-parquet
+- You can write programs to run complex calculations, analyze data, create charts, generate documents to meticulously answer the query.
+- The sandbox has access to the standard library, matplotlib, panda, numpy, scipy, bs4, sympy, brotli, cryptography, fast-parquet.
+- List known file paths to required user documents in "input_files" and known links to required documents from the web in the "input_links" field.
+- The python program should be self-contained. It can only read data generated by the program itself and from provided input_files, input_links by their basename (i.e filename excluding file path).
 - Do not try display images or plots in the code directly. The code should save the image or plot to a file instead.
 - Write any document, charts etc. to be shared with the user to file. These files can be seen by the user.
 - Use as much context from the previous questions and answers as required to generate your code.
 {personality_context}
-What code will you need to write, if any, to answer the user's question?
-Provide code programs as a list of strings in a JSON object with key "codes".
+What code will you need to write to answer the user's question?
+
 Current Date: {current_date}
 User's Location: {location}
 {username}
 
-The JSON schema is of the form {{"codes": ["code1", "code2", "code3"]}}
-For example:
-{{"codes": ["print('Hello, World!')", "print('Goodbye, World!')"]}}
+The response JSON schema is of the form {{"code": "<python_code>", "input_files": ["file_path_1", "file_path_2"], "input_links": ["link_1", "link_2"]}}
+Examples:
+---
+{{
+"code": "# Input values\\nprincipal = 43235\\nrate = 5.24\\nyears = 5\\n\\n# Convert rate to decimal\\nrate_decimal = rate / 100\\n\\n# Calculate final amount\\nfinal_amount = principal * (1 + rate_decimal) ** years\\n\\n# Calculate interest earned\\ninterest_earned = final_amount - principal\\n\\n# Print results with formatting\\nprint(f"Interest Earned: ${{interest_earned:,.2f}}")\\nprint(f"Final Amount: ${{final_amount:,.2f}}")"
+}}
 
-Now it's your turn to construct python programs to answer the user's question. Provide them as a list of strings in a JSON object. Do not say anything else.
+{{
+"code": "import re\\n\\n# Read org file\\nfile_path = 'tasks.org'\\nwith open(file_path, 'r') as f:\\n    content = f.read()\\n\\n# Get today's date in YYYY-MM-DD format\\ntoday = datetime.now().strftime('%Y-%m-%d')\\npattern = r'\*+\s+.*\\n.*SCHEDULED:\s+<' + today + r'.*>'\\n\\n# Find all matches using multiline mode\\nmatches = re.findall(pattern, content, re.MULTILINE)\\ncount = len(matches)\\n\\n# Display count\\nprint(f'Count of scheduled tasks for today: {{count}}')",
+"input_files": ["/home/linux/tasks.org"]
+}}
+
+{{
+"code": "import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Load the CSV file\\ndf = pd.read_csv('world_population_by_year.csv')\\n\\n# Plot the data\\nplt.figure(figsize=(10, 6))\\nplt.plot(df['Year'], df['Population'], marker='o')\\n\\n# Add titles and labels\\nplt.title('Population by Year')\\nplt.xlabel('Year')\\nplt.ylabel('Population')\\n\\n# Save the plot to a file\\nplt.savefig('population_by_year_plot.png')",
+"input_links": ["https://population.un.org/world_population_by_year.csv"]
+}}
+
+Now it's your turn to construct a python program to answer the user's question. Provide the code, required input files and input links in a JSON object. Do not say anything else.
 Context:
 ---
 {context}

src/khoj/processor/tools/run_code.py

@@ -1,14 +1,14 @@
-import asyncio
+import base64
 import datetime
 import json
 import logging
 import os
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, List, NamedTuple, Optional
 
 import aiohttp
 
-from khoj.database.adapters import ais_user_subscribed
-from khoj.database.models import Agent, KhojUser
+from khoj.database.adapters import FileObjectAdapters
+from khoj.database.models import Agent, FileObject, KhojUser
 from khoj.processor.conversation import prompts
 from khoj.processor.conversation.utils import (
     ChatEvent,
@@ -17,7 +17,7 @@ from khoj.processor.conversation.utils import (
     construct_chat_history,
 )
 from khoj.routers.helpers import send_message_to_model_wrapper
-from khoj.utils.helpers import timer
+from khoj.utils.helpers import is_none_or_empty, timer
 from khoj.utils.rawconfig import LocationData
 
 logger = logging.getLogger(__name__)
@@ -26,6 +26,12 @@ logger = logging.getLogger(__name__)
 SANDBOX_URL = os.getenv("KHOJ_TERRARIUM_URL", "http://localhost:8080")
 
 
+class GeneratedCode(NamedTuple):
+    code: str
+    input_files: List[str]
+    input_links: List[str]
+
+
 async def run_code(
     query: str,
     conversation_history: dict,
@@ -41,11 +47,11 @@ async def run_code(
 ):
     # Generate Code
     if send_status_func:
-        async for event in send_status_func(f"**Generate code snippets** for {query}"):
+        async for event in send_status_func(f"**Generate code snippet** for {query}"):
             yield {ChatEvent.STATUS: event}
     try:
         with timer("Chat actor: Generate programs to execute", logger):
-            codes = await generate_python_code(
+            generated_code = await generate_python_code(
                 query,
                 conversation_history,
                 context,
@@ -59,15 +65,26 @@ async def run_code(
     except Exception as e:
         raise ValueError(f"Failed to generate code for {query} with error: {e}")
 
+    # Prepare Input Data
+    input_data = []
+    user_input_files: List[FileObject] = []
+    for input_file in generated_code.input_files:
+        user_input_files += await FileObjectAdapters.aget_file_objects_by_name(user, input_file)
+    for f in user_input_files:
+        input_data.append(
+            {
+                "filename": os.path.basename(f.file_name),
+                "b64_data": base64.b64encode(f.raw_text.encode("utf-8")).decode("utf-8"),
+            }
+        )
+
     # Run Code
     if send_status_func:
-        async for event in send_status_func(f"**Running {len(codes)} code snippets**"):
+        async for event in send_status_func(f"**Running code snippet**"):
             yield {ChatEvent.STATUS: event}
     try:
-        tasks = [execute_sandboxed_python(code, sandbox_url) for code in codes]
-        with timer("Chat actor: Execute generated programs", logger):
-            results = await asyncio.gather(*tasks)
-        for result in results:
+        with timer("Chat actor: Execute generated program", logger, log_level=logging.INFO):
+            result = await execute_sandboxed_python(generated_code.code, input_data, sandbox_url)
             code = result.pop("code")
             logger.info(f"Executed Code:\n--@@--\n{code}\n--@@--Result:\n--@@--\n{result}\n--@@--")
             yield {query: {"code": code, "results": result}}
@@ -81,14 +98,13 @@ async def generate_python_code(
     context: str,
     location_data: LocationData,
     user: KhojUser,
-    query_images: List[str] = None,
+    query_images: list[str] = None,
     agent: Agent = None,
     tracer: dict = {},
     query_files: str = None,
-) -> List[str]:
+) -> GeneratedCode:
     location = f"{location_data}" if location_data else "Unknown"
     username = prompts.user_name.format(name=user.get_full_name()) if user.get_full_name() else ""
-    subscribed = await ais_user_subscribed(user)
     chat_history = construct_chat_history(conversation_history)
 
     utc_date = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d")
@@ -118,21 +134,26 @@ async def generate_python_code(
     # Validate that the response is a non-empty, JSON-serializable list
     response = clean_json(response)
     response = json.loads(response)
-    codes = [code.strip() for code in response["codes"] if code.strip()]
+    code = response.get("code", "").strip()
+    input_files = response.get("input_files", [])
+    input_links = response.get("input_links", [])
 
-    if not isinstance(codes, list) or not codes or len(codes) == 0:
+    if not isinstance(code, str) or is_none_or_empty(code):
         raise ValueError
-    return codes
+    return GeneratedCode(code, input_files, input_links)
 
 
-async def execute_sandboxed_python(code: str, sandbox_url: str = SANDBOX_URL) -> dict[str, Any]:
+async def execute_sandboxed_python(code: str, input_data: list[dict], sandbox_url: str = SANDBOX_URL) -> dict[str, Any]:
     """
     Takes code to run as a string and calls the terrarium API to execute it.
     Returns the result of the code execution as a dictionary.
+
+    Reference data i/o format based on Terrarium example client code at:
+    https://github.com/cohere-ai/cohere-terrarium/blob/main/example-clients/python/terrarium_client.py
     """
     headers = {"Content-Type": "application/json"}
     cleaned_code = clean_code_python(code)
-    data = {"code": cleaned_code}
+    data = {"code": cleaned_code, "files": input_data}
 
     async with aiohttp.ClientSession() as session:
         async with session.post(sandbox_url, json=data, headers=headers) as response: