feat: have global system prompt and decription

docetl/operations/code_operations.py

@@ -107,7 +107,15 @@ def get_group_key(item):
                     for k, v in group[0].items():
                         if k not in result:
                             result[k] = v
-
+
+                # Also add the reduce key
+                if reduce_keys != ["_all"]:
+                    for k in reduce_keys:
+                        if k not in result:
+                            result[k] = group[0][k]
+
+                result[f"_counts_prereduce_{self.config['name']}"] = len(group)
+
                 results.append(result)
 
         return results, 0.0

docetl/operations/utils.py

@@ -185,6 +185,7 @@ def cache_key(
     messages: List[Dict[str, str]],
     output_schema: Dict[str, str],
     scratchpad: Optional[str] = None,
+    system_prompt: Optional[Dict[str, str]] = None,
 ) -> str:
     """
     Generate a unique cache key based on function arguments.
@@ -209,6 +210,7 @@ def cache_key(
         "messages": json.dumps(messages, sort_keys=True),
         "output_schema": json.dumps(output_schema, sort_keys=True),
         "scratchpad": scratchpad,
+        "system_prompt": json.dumps(system_prompt, sort_keys=True),
     }
     return hashlib.md5(json.dumps(key_dict, sort_keys=True).encode()).hexdigest()
 
@@ -690,7 +692,7 @@ def call_llm(
         Raises:
             TimeoutError: If the call times out after retrying.
         """
-        key = cache_key(model, op_type, messages, output_schema, scratchpad)
+        key = cache_key(model, op_type, messages, output_schema, scratchpad, self.runner.config.get("system_prompt", {}))
 
         max_retries = max_retries_per_timeout
         attempt = 0
@@ -809,28 +811,41 @@ def _call_llm_with_cache(
             tools = None
             tool_choice = None
 
-        system_prompt = f"You are a helpful assistant, intelligently processing data. This is a {op_type} operation. You will perform the specified task on the provided data. The result should be a structured output that you will send back to the user."
+        persona = self.runner.config.get("system_prompt", {}).get("persona", "a helpful assistant")
+        dataset_description = self.runner.config.get("system_prompt", {}).get("dataset_description", "a collection of unstructured documents")
+        parethetical_op_instructions = "many inputs:one output" if op_type == "reduce" else "one input:one output"
+
+        system_prompt = f"You are a {persona}, intelligently transforming data. The dataset description is: {dataset_description}. You will be performing a {op_type} operation ({parethetical_op_instructions}). You will perform the specified task on the provided data, as accurately, precisely, and exhaustively as possible. The result should be a structured output that you will send back to the user."
         if scratchpad:
             system_prompt += f"""
 
-You are incrementally processing data across multiple batches. Maintain intermediate state between batches to accomplish this task effectively.
+You are incrementally processing data across multiple batches. You will see:
+1. The current batch of data to process
+2. The intermediate output so far (what you returned last time)
+3. A scratchpad for tracking additional state: {scratchpad}
+
+The intermediate output contains the partial result that directly answers the user's task, just on a subset of the data.
+The scratchpad contains supporting information needed to process future batches correctly, but isn't part of the answer itself.
 
-Current scratchpad: {scratchpad}
+Example for counting words that appear >2 times:
+- Intermediate output: {{"frequent_words": ["the", "and"]}} # Words seen 3+ times
+- Scratchpad: {{"pending": {{"cat": 2, "dog": 1}}}} # Track words seen 1-2 times
 
 As you process each batch:
-1. Update the scratchpad with crucial information for subsequent batches.
-2. This may include partial results, counters, or data that doesn't fit into {list(output_schema.keys())}.
-3. Example: For counting elements that appear more than twice, track all occurrences in the scratchpad until an item exceeds the threshold.
+1. Use both the intermediate output and scratchpad to inform your processing
+2. Update the scratchpad with any new information needed for future batches
+3. Return both your partial result (representing the answer on the current batch and the previous batches' intermediate output) and updated scratchpad
 
 Keep the scratchpad concise (~500 chars) and easily parsable. Use clear structures like:
-- Bullet points
+- Bullet points  
 - Key-value pairs
 - JSON-like format
 
 Update the 'updated_scratchpad' field in your output with the new scratchpad content.
 
 Remember: The scratchpad should contain information necessary for processing future batches, not the final result."""
 
+
         # Truncate messages if they exceed the model's context length
         messages = truncate_messages(messages, model)
 

docetl/optimizers/map_optimizer/evaluator.py

@@ -307,14 +307,14 @@ def _assess_operation(
             self.llm_client.model,
         )
 
-        prompt = f"""Task: Assess the performance of a data processing operation based on sample input-output pairs and a custom validator prompt.
+        prompt = f"""Task: Assess the performance of a data processing operation based on sample rows and a custom validator prompt. You will see the output of the operation for each row.
 
         Operation Name: {op_config['name']}
         Operation Type: {op_config['type']}
         Current Task Prompt: {op_config.get('prompt', 'N/A')}
 
-        Sample Input-Output Pairs:
-        ---Pair 1---
+        Sample Rows:
+        ---Row 1---
         {json.dumps({"input": input_1, "output": output_1}, indent=2)}
         """
 
@@ -332,7 +332,7 @@ def _assess_operation(
                 self.llm_client.model,
             )
             prompt += f"""
-        ---Pair 2---
+        ---Row 2---
         {json.dumps({"input": input_2, "output": output_2}, indent=2)}
         """
 
@@ -350,7 +350,7 @@ def _assess_operation(
                 self.llm_client.model,
             )
             prompt += f"""
-        ---Pair 3---
+        ---Row 3---
         {json.dumps({"input": input_3, "output": output_3}, indent=2)}
         """
 

docetl/runner.py

@@ -102,7 +102,7 @@ def __init__(self, config: Dict, max_threads: int = None, **kwargs):
 
                 all_ops_until_and_including_current = [
                     op_map[prev_op] for prev_op in step["operations"][:idx]
-                ] + [op_map[op_name]]
+                ] + [op_map[op_name]] + [self.config.get("system_prompt", {})]
                 # If there's no model in the op, add the default model
                 for op in all_ops_until_and_including_current:
                     if "model" not in op:

website/src/app/api/utils.ts

@@ -12,7 +12,11 @@ export function generatePipelineConfig(
   homeDir: string,
   sample_size: number | null,
   optimize: boolean = false,
-  clear_intermediate: boolean = false
+  clear_intermediate: boolean = false,
+  system_prompt: {
+    datasetDescription: string | null;
+    persona: string | null;
+  } | null = null
 ) {
   const datasets = {
     input: {
@@ -156,7 +160,7 @@ export function generatePipelineConfig(
         {
           name: "data_processing",
           input: Object.keys(datasets)[0], // Assuming the first dataset is the input
-          operations: operationsToRun.map((op: any) => op.name),
+          operations: operationsToRun.map((op) => op.name),
         },
       ],
       output: {
@@ -177,8 +181,21 @@ export function generatePipelineConfig(
         ),
       },
     },
+    system_prompt: {},
   };
 
+  if (system_prompt) {
+    if (system_prompt.datasetDescription) {
+      // @ts-ignore
+      pipelineConfig.system_prompt!.dataset_description =
+        system_prompt.datasetDescription;
+    }
+    if (system_prompt.persona) {
+      // @ts-ignore
+      pipelineConfig.system_prompt!.persona = system_prompt.persona;
+    }
+  }
+
   // Get the inputPath from the intermediate_dir
   let inputPath;
   let outputPath;

website/src/app/api/writePipelineConfig/route.ts

@@ -15,6 +15,7 @@ export async function POST(request: Request) {
       sample_size,
       optimize = false,
       clear_intermediate = false,
+      system_prompt,
     } = await request.json();
 
     if (!name) {
@@ -42,7 +43,8 @@ export async function POST(request: Request) {
       homeDir,
       sample_size,
       optimize,
-      clear_intermediate
+      clear_intermediate,
+      system_prompt
     );
 
     // Save the YAML file in the user's home directory

website/src/app/localStorageKeys.ts

@@ -16,3 +16,4 @@ export const DEFAULT_MODEL_KEY = "docetl_defaultModel";
 export const OPTIMIZER_MODEL_KEY = "docetl_optimizerModel";
 export const AUTO_OPTIMIZE_CHECK_KEY = "docetl_autoOptimizeCheck";
 export const HIGH_LEVEL_GOAL_KEY = "docetl_highLevelGoal";
+export const SYSTEM_PROMPT_KEY = "docetl_systemPrompt";

website/src/app/playground/page.tsx

@@ -296,10 +296,17 @@ const CodeEditorPipelineApp: React.FC = () => {
                 </MenubarContent>
               </MenubarMenu>
               <MenubarMenu>
-                <MenubarTrigger>Assistant</MenubarTrigger>
+                <MenubarTrigger>Help</MenubarTrigger>
                 <MenubarContent>
+                  <MenubarItem
+                    onSelect={() =>
+                      window.open("https://ucbepic.github.io/docetl/", "_blank")
+                    }
+                  >
+                    Show Documentation
+                  </MenubarItem>
                   <MenubarItem onSelect={() => setShowChat(!showChat)}>
-                    Toggle Chat
+                    Show Chat
                   </MenubarItem>
                 </MenubarContent>
               </MenubarMenu>

website/src/components/AIChatPanel.tsx

@@ -1,8 +1,14 @@
 "use client";
 
-import React, { useRef, useState, useEffect } from "react";
+import React, {
+  useRef,
+  useState,
+  useEffect,
+  useMemo,
+  useCallback,
+} from "react";
 import { ResizableBox } from "react-resizable";
-import { Eraser, RefreshCw, X, Copy } from "lucide-react";
+import { RefreshCw, X, Copy } from "lucide-react";
 import { Button } from "@/components/ui/button";
 import { Input } from "@/components/ui/input";
 import { ScrollArea } from "@/components/ui/scroll-area";
@@ -13,6 +19,13 @@ import "react-resizable/css/styles.css";
 import { LLMContextPopover } from "@/components/LLMContextPopover";
 import { usePipelineContext } from "@/contexts/PipelineContext";
 import ReactMarkdown from "react-markdown";
+import {
+  Popover,
+  PopoverContent,
+  PopoverTrigger,
+} from "@/components/ui/popover";
+import { Textarea } from "@/components/ui/textarea";
+import { debounce } from "lodash";
 
 interface AIChatPanelProps {
   onClose: () => void;
@@ -45,7 +58,9 @@ const AIChatPanel: React.FC<AIChatPanelProps> = ({ onClose }) => {
     initialMessages: [],
     id: "persistent-chat",
   });
-  const { serializeState } = usePipelineContext();
+  const { serializeState, highLevelGoal, setHighLevelGoal } =
+    usePipelineContext();
+  const [localGoal, setLocalGoal] = useState(highLevelGoal);
 
   const handleMouseDown = (e: React.MouseEvent<HTMLDivElement>) => {
     if ((e.target as HTMLElement).classList.contains("drag-handle")) {
@@ -184,6 +199,25 @@ Remember, all the output fields have been converted to strings, even if they wer
     );
   };
 
+  const debouncedSetHighLevelGoal = useMemo(
+    () => debounce((value: string) => setHighLevelGoal(value), 1000),
+    [setHighLevelGoal]
+  );
+
+  useEffect(() => {
+    return () => {
+      debouncedSetHighLevelGoal.cancel();
+    };
+  }, [debouncedSetHighLevelGoal]);
+
+  const handleGoalUpdate = useCallback(
+    (newGoal: string) => {
+      setLocalGoal(newGoal);
+      debouncedSetHighLevelGoal(newGoal);
+    },
+    [debouncedSetHighLevelGoal]
+  );
+
   return (
     <div
       style={{
@@ -210,6 +244,33 @@ Remember, all the output fields have been converted to strings, even if they wer
             <LLMContextPopover />
           </span>
           <div className="flex items-center gap-1">
+            <Popover>
+              <PopoverTrigger asChild>
+                <span className="text-s text-primary font-medium flex items-center gap-2 cursor-pointer">
+                  <Button
+                    variant="ghost"
+                    size="sm"
+                    className="h-4 px-2 text-xs"
+                  >
+                    {highLevelGoal ? "Edit Analysis Goal" : "Set Analysis Goal"}
+                  </Button>
+                </span>
+              </PopoverTrigger>
+              <PopoverContent className="w-80 z-[10000]" side="top" align="end">
+                <div className="space-y-2">
+                  <h4 className="font-medium text-sm">Pipeline Goal</h4>
+                  <Textarea
+                    placeholder="Describe the high-level goal of your pipeline..."
+                    className="min-h-[100px]"
+                    value={localGoal}
+                    onChange={(e) => handleGoalUpdate(e.target.value)}
+                  />
+                  <p className="text-xs text-muted-foreground">
+                    This helps the assistant provide more relevant suggestions.
+                  </p>
+                </div>
+              </PopoverContent>
+            </Popover>
             <Button
               variant="ghost"
               size="sm"
@@ -242,8 +303,10 @@ Remember, all the output fields have been converted to strings, even if they wer
                     onClick={() => {
                       handleInputChange({
                         target: { value: suggestion },
-                      } as any);
-                      handleMessageSubmit({ preventDefault: () => {} } as any);
+                      } as React.ChangeEvent<HTMLInputElement>);
+                      handleMessageSubmit({
+                        preventDefault: () => {},
+                      } as React.FormEvent);
                     }}
                   >
                     {suggestion}