diff --git a/.github/workflows/docker-ci.yml b/.github/workflows/docker-ci.yml new file mode 100644 index 00000000..0dcebf87 --- /dev/null +++ b/.github/workflows/docker-ci.yml @@ -0,0 +1,59 @@ +name: Docker CI + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + docker-build-test: + runs-on: ubuntu-latest + + steps: + - name: Remove unnecessary files + run: | + sudo rm -rf /usr/share/dotnet + sudo rm -rf "$AGENT_TOOLSDIRECTORY" + - uses: actions/checkout@v4 + - name: Remove .env copy from Dockerfile + run: sed -i '/COPY .env/d' Dockerfile + + - name: Build Docker image + run: docker build -t docetl . + + - name: Create Docker volume + run: docker volume create docetl-data + + - name: Test Docker container + run: | + # Run the container in detached mode + docker run -d \ + -p 3000:3000 \ + -p 8000:8000 \ + -v docetl-data:/docetl-data \ + -e FRONTEND_HOST=0.0.0.0 \ + -e FRONTEND_PORT=3000 \ + -e BACKEND_HOST=0.0.0.0 \ + -e BACKEND_PORT=8000 \ + --name docetl-test \ + docetl + + # Wait for container to start up + sleep 120 + + # Check if container is still running + if [ "$(docker ps -q -f name=docetl-test)" ]; then + echo "Container is running successfully" + else + echo "Container failed to stay running" + docker logs docetl-test + exit 1 + fi + + # Cleanup + docker stop docetl-test + docker rm docetl-test + + - name: Clean up Docker volume + run: docker volume rm docetl-data \ No newline at end of file diff --git a/.gitignore b/.gitignore index b1d0b6b1..c4834bfc 100644 --- a/.gitignore +++ b/.gitignore @@ -49,4 +49,7 @@ website/.vercel # typescript website/*.tsbuildinfo -website/next-env.d.ts \ No newline at end of file +website/next-env.d.ts + +# Docker +.docker/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..afddf042 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,79 @@ +# Build stage for Python dependencies +FROM python:3.11-slim AS python-builder + +RUN pip install poetry==1.4.2 + +ENV POETRY_NO_INTERACTION=1 \ + POETRY_VIRTUALENVS_IN_PROJECT=1 \ + POETRY_VIRTUALENVS_CREATE=1 \ + POETRY_CACHE_DIR=/tmp/poetry_cache \ + DOCETL_HOME_DIR="/docetl-data" + +WORKDIR /app + +COPY pyproject.toml poetry.lock ./ +COPY docetl/ ./docetl/ +COPY server/ ./server/ +COPY tests/ ./tests/ +RUN touch README.md + +# Install with --no-root first for dependencies, then install with root for entrypoints +RUN --mount=type=cache,target=$POETRY_CACHE_DIR poetry install --all-extras --no-root && \ + poetry install --all-extras + +# Build stage for Node.js dependencies +FROM node:20-alpine AS node-builder + +WORKDIR /app/website + +# Update DOCETL_HOME_DIR to match final location +ENV DOCETL_HOME_DIR="/docetl-data" + +COPY website/package*.json ./ +RUN npm install +COPY website/ ./ +RUN npm run build + +# Final runtime stage +FROM python:3.11-slim AS runtime + +# Install Node.js +RUN apt-get update && apt-get install -y \ + curl \ + && curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \ + && apt-get install -y nodejs \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# Copy Python virtual environment from builder +ENV VIRTUAL_ENV=/app/.venv \ + PATH="/app/.venv/bin:$PATH" \ + PYTHONPATH="/app" \ + DOCETL_HOME_DIR="/docetl-data" + +COPY --from=python-builder /app/.venv ${VIRTUAL_ENV} + +# Copy Python application files +COPY docetl/ ./docetl/ +COPY server/ ./server/ +COPY tests/ ./tests/ +COPY pyproject.toml poetry.lock ./ +COPY .env ./ + +# Copy Node.js dependencies and application files +COPY --from=node-builder /app/website ./website + +ENV PORT=3000 + +# Create data directory with appropriate permissions +RUN mkdir -p /docetl-data && chown -R nobody:nogroup /docetl-data && chmod 777 /docetl-data + +# Define volume AFTER creating and setting permissions +VOLUME ["/docetl-data"] + +# Expose ports for frontend and backend +EXPOSE 3000 8000 + +# Start both servers +CMD ["sh", "-c", "python3 server/app/main.py & cd website && npm run start"] \ No newline at end of file diff --git a/Makefile b/Makefile index bc147709..f392632e 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ # Load environment variables from .env file include .env -.PHONY: tests tests-basic lint install mypy update ui-install ui-run +.PHONY: tests tests-basic lint install mypy update ui-install ui-run docker # Existing commands tests: @@ -25,7 +25,7 @@ mypy: update: poetry update -# New UI-related commands +# UI-related commands UI_DIR := ./website install-ui: @@ -43,6 +43,24 @@ run-ui: echo "Building UI..." && \ cd $(UI_DIR) && npm run build && HOST=${FRONTEND_HOST} PORT=${FRONTEND_PORT} NEXT_PUBLIC_FRONTEND_ALLOWED_HOSTS=${FRONTEND_ALLOWED_HOSTS} npm run start +# Single Docker command to build and run +docker: + docker volume create docetl-data && \ + docker build -t docetl . && \ + docker run --rm -it \ + -p 3000:3000 \ + -p 8000:8000 \ + -v docetl-data:/docetl-data \ + -e FRONTEND_HOST=0.0.0.0 \ + -e FRONTEND_PORT=3000 \ + -e BACKEND_HOST=0.0.0.0 \ + -e BACKEND_PORT=8000 \ + docetl + +# Add new command for cleaning up docker resources +docker-clean: + docker volume rm docetl-data + # Help command help: @echo "Available commands:" @@ -54,5 +72,7 @@ help: @echo " make update : Update dependencies" @echo " make install-ui : Install UI dependencies" @echo " make run-ui-dev : Run UI development server" - @echo " make run-ui-prod : Run UI production server" + @echo " make run-ui : Run UI production server" + @echo " make docker : Build and run docetl in Docker" + @echo " make docker-clean : Remove docetl Docker volume" @echo " make help : Show this help message" \ No newline at end of file diff --git a/README.md b/README.md index 3e02ac68..854391f0 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# DocETL: Powering Complex Document Processing Pipelines +# 📜 DocETL: Powering Complex Document Processing Pipelines [![Website](https://img.shields.io/badge/Website-docetl.org-blue)](https://docetl.org) [![Documentation](https://img.shields.io/badge/Documentation-docs-green)](https://ucbepic.github.io/docetl) @@ -7,65 +7,119 @@ ![DocETL Figure](docs/assets/readmefig.png) -DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers a low-code, declarative YAML interface to define LLM-powered operations on complex data. +DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers: -## When to Use DocETL +1. An interactive UI playground for iterative prompt engineering and pipeline development +2. A Python package for running production pipelines from the command line or Python code -DocETL is the ideal choice when you're looking to maximize correctness and output quality for complex tasks over a collection of documents or unstructured datasets. You should consider using DocETL if: - -- You want to perform semantic processing on a collection of data -- You have complex tasks that you want to represent via map-reduce -- You're unsure how to best express your task to maximize LLM accuracy -- You're working with long documents that don't fit into a single prompt -- You have validation criteria and want tasks to automatically retry when validation fails - -## Community Projects +### 🌟 Community Projects - [Conversation Generator](https://github.com/PassionFruits-net/docetl-conversation) - [Text-to-speech](https://github.com/PassionFruits-net/docetl-speaker) - [YouTube Transcript Topic Extraction](https://github.com/rajib76/docetl_examples) -## Educational Resources +### 📚 Educational Resources - [UI/UX Thoughts](https://x.com/sh_reya/status/1846235904664273201) - [Using Gleaning to Improve Output Quality](https://x.com/sh_reya/status/1843354256335876262) - [Deep Dive on Resolve Operator](https://x.com/sh_reya/status/1840796824636121288) -## Installation -### Prerequisites +## 🚀 Getting Started + +There are two main ways to use DocETL: + +### 1. 🎮 Interactive UI Playground (Recommended for Development) + +The [UI Playground](https://ucbepic.github.io/docetl/playground/) helps you iteratively develop your pipeline: +- Experiment with different prompts and see results in real-time +- Build your pipeline step by step +- Export your finalized pipeline configuration for production use + +![DocETL Playground](docs/assets/tutorial/playground-screenshot.png) + +To run the playground locally, you can either: +- Use Docker (recommended for quick start): `make docker` +- Set up the development environment manually +See the [Playground Setup Guide](https://ucbepic.github.io/docetl/playground/) for detailed instructions. + +### 2. 📦 Python Package (For Production Use) + +If you want to use DocETL as a Python package: + +#### Prerequisites - Python 3.10 or later - OpenAI API key -### Quick Start - -1. Install from PyPI: ```bash pip install docetl ``` +Create a `.env` file in your project directory: +```bash +OPENAI_API_KEY=your_api_key_here # Required for LLM operations (or the key for the LLM of your choice) +``` + To see examples of how to use DocETL, check out the [tutorial](https://ucbepic.github.io/docetl/tutorial/). -### Running the UI Locally +### 2. 🎮 UI Playground Setup -We offer a simple UI for building pipelines. We recommend building up complex pipelines one operation at a time, so you can see the results of each operation as you go and iterate on your pipeline. To run it locally, follow these steps: +To run the UI playground locally, you have two options: -![Playground Screenshot](docs/assets/tutorial/playground-screenshot.png) +#### Option A: Using Docker (Recommended for Quick Start) -1. Clone the repository: +The easiest way to get the playground running: + +1. Create the required environment files: + +Create `.env` in the root directory: ```bash -git clone https://github.com/ucbepic/docetl.git -cd docetl +OPENAI_API_KEY=your_api_key_here +BACKEND_ALLOW_ORIGINS= +BACKEND_HOST=0.0.0.0 +BACKEND_PORT=8000 +BACKEND_RELOAD=True +FRONTEND_HOST=0.0.0.0 +FRONTEND_PORT=3000 ``` -2. Install dependencies: +Create `.env.local` in the `website` directory: ```bash -make install # Install Python package -make install-ui # Install UI dependencies +OPENAI_API_KEY=sk-xxx +OPENAI_API_BASE=https://api.openai.com/v1 +MODEL_NAME=gpt-4o-mini + +NEXT_PUBLIC_BACKEND_HOST=localhost +NEXT_PUBLIC_BACKEND_PORT=8000 +``` + +2. Run Docker: +```bash +make docker +``` + +This will: +- Create a Docker volume for persistent data +- Build the DocETL image +- Run the container with the UI accessible at http://localhost:3000 + +To clean up Docker resources (note that this will delete the Docker volume): +```bash +make docker-clean +``` + +#### Option B: Manual Setup (Development) + +For development or if you prefer not to use Docker: + +1. Clone the repository: +```bash +git clone https://github.com/ucbepic/docetl.git +cd docetl ``` -3. Set up environment variables in `.env`: +2. Set up environment variables in `.env` in the root/top-level directory: ```bash OPENAI_API_KEY=your_api_key_here BACKEND_ALLOW_ORIGINS= @@ -76,14 +130,32 @@ FRONTEND_HOST=0.0.0.0 FRONTEND_PORT=3000 ``` +And create an .env.local file in the `website` directory with the following: +```bash +OPENAI_API_KEY=sk-xxx +OPENAI_API_BASE=https://api.openai.com/v1 +MODEL_NAME=gpt-4o-mini + +NEXT_PUBLIC_BACKEND_HOST=localhost +NEXT_PUBLIC_BACKEND_PORT=8000 +``` + +3. Install dependencies: +```bash +make install # Install Python package +make install-ui # Install UI dependencies +``` + +Note that the OpenAI API key, base, and model name are for the UI assistant only; not the DocETL pipeline execution engine. + 4. Start the development server: ```bash make run-ui-dev ``` -5. Visit http://localhost:3000/playground +5. Visit http://localhost:3000/playground to access the interactive UI. -### Development Setup +### 🛠️ Development Setup If you're planning to contribute or modify DocETL, you can verify your setup by running the test suite: diff --git a/docetl/builder.py b/docetl/builder.py index 81853e4e..2ccf7d6a 100644 --- a/docetl/builder.py +++ b/docetl/builder.py @@ -140,7 +140,7 @@ def __init__( self.resume = resume self.captured_output = CapturedOutput() - home_dir = os.path.expanduser("~") + home_dir = os.environ.get("DOCETL_HOME_DIR", os.path.expanduser("~")) cache_dir = os.path.join(home_dir, f".docetl/cache/{runner.yaml_file_suffix}") os.makedirs(cache_dir, exist_ok=True) self.datasets = DatasetOnDisk(dir=cache_dir, console=self.console) @@ -320,8 +320,12 @@ def _insert_empty_resolve_operations(self): has_map = True map_op = op elif op_type == "reduce" and op_config.get("synthesize_resolve", True): - has_reduce = True - reduce_op = op + reduce_key = op_config.get("reduce_key", "_all") + if isinstance(reduce_key, str): + reduce_key = [reduce_key] + if "_all" not in reduce_key: + has_reduce = True + reduce_op = op elif op_type == "resolve": has_resolve = True @@ -475,7 +479,7 @@ def _load_optimized_ops(self): else: self.console.log("[yellow]No optimized operations found[/yellow]") - def should_optimize(self, step_name: str, op_name: str) -> bool: + def should_optimize(self, step_name: str, op_name: str) -> Tuple[str, List[Dict[str, Any]], List[Dict[str, Any]], float]: """ Determine if an operation should be optimized. We do this by running the operations on a sample of the input data and checking if the output is correct. @@ -509,6 +513,7 @@ def should_optimize(self, step_name: str, op_name: str) -> bool: input_data = self._run_partial_step( step, ops_run, sample_size, op_name_to_object ) + output_data = input_data # If this is not the operation we want to optimize, just execute it and add to selectivities if f"{step.get('name')}/{op_name}" != f"{step_name}/{op_name}" and op_object.get("empty", False): @@ -530,7 +535,7 @@ def should_optimize(self, step_name: str, op_name: str) -> bool: timeout=self.timeout, is_filter=op_object.get("type") == "filter", ) - should_optimize_output = map_optimizer.should_optimize(op_object, input_data) + should_optimize_output, input_data, output_data = map_optimizer.should_optimize(op_object, input_data) elif op_object.get("type") == "reduce": reduce_optimizer = ReduceOptimizer( self.runner, @@ -540,7 +545,7 @@ def should_optimize(self, step_name: str, op_name: str) -> bool: self.max_threads, self._run_operation, ) - should_optimize_output = reduce_optimizer.should_optimize(op_object, input_data) + should_optimize_output, input_data, output_data = reduce_optimizer.should_optimize(op_object, input_data) elif op_object.get("type") == "resolve": resolve_optimizer = JoinOptimizer( self.runner, @@ -560,7 +565,7 @@ def should_optimize(self, step_name: str, op_name: str) -> bool: continue # Return the string and operation cost - return should_optimize_output, self.operations_cost + self.llm_client.total_cost + return should_optimize_output, input_data, output_data, self.operations_cost + self.llm_client.total_cost # Should not get here raise ValueError("No operation to optimize found") diff --git a/docetl/dataset.py b/docetl/dataset.py index b72398c0..0e1e9e59 100644 --- a/docetl/dataset.py +++ b/docetl/dataset.py @@ -344,11 +344,7 @@ def sample(self, n: int, random: bool = True) -> List[Dict]: ) sampled_data = rd.sample(data, n) else: - sampled_data = [] - for i, line in enumerate(f): - if i >= n: - break - sampled_data.append(json.loads(line)) + return json.load(f)[:n] elif ext == ".csv": import csv diff --git a/docetl/operations/__init__.py b/docetl/operations/__init__.py index 66f91032..43d567b1 100644 --- a/docetl/operations/__init__.py +++ b/docetl/operations/__init__.py @@ -15,4 +15,5 @@ def get_operations(): """Load all available operations and return them as a dictionary""" return { op.name: op.load() - for op in importlib.metadata.entry_points(group="docetl.operation")} + for op in importlib.metadata.entry_points(group="docetl.operation") + } diff --git a/docetl/operations/code_operations.py b/docetl/operations/code_operations.py index 09a62c9a..040275c9 100644 --- a/docetl/operations/code_operations.py +++ b/docetl/operations/code_operations.py @@ -1,3 +1,4 @@ +import os from typing import Any, Dict, List, Optional, Tuple from concurrent.futures import ThreadPoolExecutor from docetl.operations.base import BaseOperation @@ -7,6 +8,7 @@ class CodeMapOperation(BaseOperation): class schema(BaseOperation.schema): type: str = "code_map" code: str + concurrent_thread_count: int = os.cpu_count() drop_keys: Optional[List[str]] = None def syntax_check(self) -> None: @@ -27,7 +29,7 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: transform_fn = namespace["transform"] results = [] - with ThreadPoolExecutor() as executor: + with ThreadPoolExecutor(max_workers=self.config.get('concurrent_thread_count', os.cpu_count())) as executor: futures = [executor.submit(transform_fn, doc) for doc in input_data] pbar = RichLoopBar( range(len(futures)), @@ -51,6 +53,7 @@ class CodeReduceOperation(BaseOperation): class schema(BaseOperation.schema): type: str = "code_reduce" code: str + concurrent_thread_count: int = os.cpu_count() def syntax_check(self) -> None: config = self.schema(**self.config) @@ -89,7 +92,7 @@ def get_group_key(item): grouped_data = list(grouped_data.items()) results = [] - with ThreadPoolExecutor() as executor: + with ThreadPoolExecutor(max_workers=self.config.get('concurrent_thread_count', os.cpu_count())) as executor: futures = [executor.submit(reduce_fn, group) for _, group in grouped_data] pbar = RichLoopBar( range(len(futures)), @@ -104,7 +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 @@ -113,6 +124,7 @@ class CodeFilterOperation(BaseOperation): class schema(BaseOperation.schema): type: str = "code_filter" code: str + concurrent_thread_count: int = os.cpu_count() def syntax_check(self) -> None: config = self.schema(**self.config) @@ -132,7 +144,7 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: filter_fn = namespace["transform"] results = [] - with ThreadPoolExecutor() as executor: + with ThreadPoolExecutor(max_workers=self.config.get('concurrent_thread_count', os.cpu_count())) as executor: futures = [executor.submit(filter_fn, doc) for doc in input_data] pbar = RichLoopBar( range(len(futures)), diff --git a/docetl/operations/map.py b/docetl/operations/map.py index 7b697c08..e4995fec 100644 --- a/docetl/operations/map.py +++ b/docetl/operations/map.py @@ -46,6 +46,7 @@ class schema(BaseOperation.schema): gleaning: Optional[Dict[str, Any]] = None drop_keys: Optional[List[str]] = None timeout: Optional[int] = None + enable_observability: bool = False batch_size: Optional[int] = None clustering_method: Optional[str] = None batch_prompt: Optional[str] = None @@ -228,9 +229,12 @@ def validation_fn(response: Union[Dict[str, Any], ModelResponse]): )[0] else: output = llm_result.response + # Augment the output with the original item output = {**item, **output} + if self.config.get("enable_observability", False): + output[f"_observability_{self.config['name']}"] = {"prompt": prompt} return output, llm_result.total_cost return None, llm_result.total_cost @@ -317,6 +321,7 @@ class schema(BaseOperation.schema): type: str = "parallel_map" prompts: List[Dict[str, Any]] output: Dict[str, Any] + enable_observability: bool = False def __init__( self, @@ -471,7 +476,7 @@ def process_prompt(item, prompt_config): tools=prompt_config.get("tools", None), manually_fix_errors=self.manually_fix_errors, )[0] - return output, response.total_cost + return output, prompt, response.total_cost with ThreadPoolExecutor(max_workers=self.max_threads) as executor: if "prompts" in self.config: @@ -488,7 +493,7 @@ def process_prompt(item, prompt_config): desc="Processing parallel map items", ): future = all_futures[i] - output, cost = future.result() + output, prompt, cost = future.result() total_cost += cost # Determine which item this future corresponds to @@ -503,6 +508,11 @@ def process_prompt(item, prompt_config): # Fetch the item_result item_result = results[item_index] + if self.config.get("enable_observability", False): + if f"_observability_{self.config['name']}" not in item_result: + item_result[f"_observability_{self.config['name']}"] = {} + item_result[f"_observability_{self.config['name']}"].update({f"prompt_{prompt_index}": prompt}) + # Update the item_result with the output item_result.update(output) diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py index f3fa99f6..254323a6 100644 --- a/docetl/operations/reduce.py +++ b/docetl/operations/reduce.py @@ -53,6 +53,7 @@ class schema(BaseOperation.schema): verbose: Optional[bool] = None timeout: Optional[int] = None litellm_completion_kwargs: Dict[str, Any] = Field(default_factory=dict) + enable_observability: bool = False def __init__(self, *args, **kwargs): """ @@ -386,17 +387,32 @@ def process_group( # Only execute merge-based plans if associative = True if "merge_prompt" in self.config and self.config.get("associative", True): - result, cost = self._parallel_fold_and_merge(key, group_list) + result, prompts, cost = self._parallel_fold_and_merge(key, group_list) + elif ( + self.config.get("fold_batch_size", None) + and self.config.get("fold_batch_size") >= len(group_list) + ): + # If the fold batch size is greater than or equal to the number of items in the group, + # we can just run a single fold operation + result, prompt, cost = self._batch_reduce(key, group_list) + prompts = [prompt] elif "fold_prompt" in self.config: - result, cost = self._incremental_reduce(key, group_list) + result, prompts, cost = self._incremental_reduce(key, group_list) else: - result, cost = self._batch_reduce(key, group_list) + result, prompt, cost = self._batch_reduce(key, group_list) + prompts = [prompt] total_cost += cost # Add the counts of items in the group to the result result[f"_counts_prereduce_{self.config['name']}"] = len(group_elems) + if self.config.get("enable_observability", False): + # Add the _observability_{self.config['name']} key to the result + result[f"_observability_{self.config['name']}"] = { + "prompts": prompts + } + # Apply pass-through at the group level if ( result is not None @@ -541,7 +557,7 @@ def _parallel_fold_and_merge( fold_batch_size = self.config["fold_batch_size"] merge_batch_size = self.config["merge_batch_size"] total_cost = 0 - + prompts = [] def calculate_num_parallel_folds(): fold_time, fold_default = self.get_fold_time() merge_time, merge_default = self.get_merge_time() @@ -582,8 +598,9 @@ def calculate_num_parallel_folds(): new_fold_results = [] for future in as_completed(fold_futures): - result, cost = future.result() + result, prompt, cost = future.result() total_cost += cost + prompts.append(prompt) if result is not None: new_fold_results.append(result) if self.config.get("persist_intermediates", False): @@ -613,8 +630,9 @@ def calculate_num_parallel_folds(): new_results = [] for future in as_completed(merge_futures): - result, cost = future.result() + result, prompt, cost = future.result() total_cost += cost + prompts.append(prompt) if result is not None: new_results.append(result) if self.config.get("persist_intermediates", False): @@ -653,8 +671,9 @@ def calculate_num_parallel_folds(): new_results = [] for future in as_completed(merge_futures): - result, cost = future.result() + result, prompt, cost = future.result() total_cost += cost + prompts.append(prompt) if result is not None: new_results.append(result) if self.config.get("persist_intermediates", False): @@ -669,11 +688,11 @@ def calculate_num_parallel_folds(): fold_results = new_results - return (fold_results[0], total_cost) if fold_results else (None, total_cost) + return (fold_results[0], prompts, total_cost) if fold_results else (None, prompts, total_cost) def _incremental_reduce( self, key: Tuple, group_list: List[Dict] - ) -> Tuple[Optional[Dict], float]: + ) -> Tuple[Optional[Dict], List[str], float]: """ Perform an incremental reduce operation on a group of items. @@ -684,12 +703,13 @@ def _incremental_reduce( group_list (List[Dict]): The list of items in the group to be processed. Returns: - Tuple[Optional[Dict], float]: A tuple containing the final reduced result (or None if processing failed) - and the total cost of the operation. + Tuple[Optional[Dict], List[str], float]: A tuple containing the final reduced result (or None if processing failed), + the list of prompts used, and the total cost of the operation. """ fold_batch_size = self.config["fold_batch_size"] total_cost = 0 current_output = None + prompts = [] # Calculate and log the number of folds to be performed num_folds = (len(group_list) + fold_batch_size - 1) // fold_batch_size @@ -708,10 +728,11 @@ def _incremental_reduce( ) batch = group_list[i : i + fold_batch_size] - folded_output, fold_cost = self._increment_fold( + folded_output, prompt, fold_cost = self._increment_fold( key, batch, current_output, scratchpad ) total_cost += fold_cost + prompts.append(prompt) if folded_output is None: continue @@ -737,7 +758,7 @@ def _incremental_reduce( current_output = folded_output - return current_output, total_cost + return current_output, prompts, total_cost def validation_fn(self, response: Dict[str, Any]): output = self.runner.api.parse_llm_response( @@ -754,7 +775,7 @@ def _increment_fold( batch: List[Dict], current_output: Optional[Dict], scratchpad: Optional[str] = None, - ) -> Tuple[Optional[Dict], float]: + ) -> Tuple[Optional[Dict], str, float]: """ Perform an incremental fold operation on a batch of items. @@ -766,8 +787,8 @@ def _increment_fold( current_output (Optional[Dict]): The current accumulated output, if any. scratchpad (Optional[str]): The scratchpad to use for the fold operation. Returns: - Tuple[Optional[Dict], float]: A tuple containing the folded output (or None if processing failed) - and the cost of the fold operation. + Tuple[Optional[Dict], str, float]: A tuple containing the folded output (or None if processing failed), + the prompt used, and the cost of the fold operation. """ if current_output is None: return self._batch_reduce(key, batch, scratchpad) @@ -815,13 +836,13 @@ def _increment_fold( folded_output.update(dict(zip(self.config["reduce_key"], key))) fold_cost = response.total_cost - return folded_output, fold_cost + return folded_output, fold_prompt, fold_cost - return None, fold_cost + return None, fold_prompt, fold_cost def _merge_results( self, key: Tuple, outputs: List[Dict] - ) -> Tuple[Optional[Dict], float]: + ) -> Tuple[Optional[Dict], str, float]: """ Merge multiple outputs into a single result. @@ -832,8 +853,8 @@ def _merge_results( outputs (List[Dict]): The list of outputs to be merged. Returns: - Tuple[Optional[Dict], float]: A tuple containing the merged output (or None if processing failed) - and the cost of the merge operation. + Tuple[Optional[Dict], str, float]: A tuple containing the merged output (or None if processing failed), + the prompt used, and the cost of the merge operation. """ start_time = time.time() merge_prompt_template = Template(self.config["merge_prompt"]) @@ -872,9 +893,9 @@ def _merge_results( )[0] merged_output.update(dict(zip(self.config["reduce_key"], key))) merge_cost = response.total_cost - return merged_output, merge_cost + return merged_output, merge_prompt, merge_cost - return None, merge_cost + return None, merge_prompt, merge_cost def get_fold_time(self) -> Tuple[float, bool]: """ @@ -928,7 +949,7 @@ def _update_merge_time(self, time: float) -> None: def _batch_reduce( self, key: Tuple, group_list: List[Dict], scratchpad: Optional[str] = None - ) -> Tuple[Optional[Dict], float]: + ) -> Tuple[Optional[Dict], str, float]: """ Perform a batch reduce operation on a group of items. @@ -939,8 +960,8 @@ def _batch_reduce( group_list (List[Dict]): The list of items to be reduced. scratchpad (Optional[str]): The scratchpad to use for the reduce operation. Returns: - Tuple[Optional[Dict], float]: A tuple containing the reduced output (or None if processing failed) - and the cost of the reduce operation. + Tuple[Optional[Dict], str, float]: A tuple containing the reduced output (or None if processing failed), + the prompt used, and the cost of the reduce operation. """ prompt_template = Template(self.config["prompt"]) prompt = prompt_template.render( @@ -981,5 +1002,5 @@ def _batch_reduce( )[0] output.update(dict(zip(self.config["reduce_key"], key))) - return output, item_cost - return None, item_cost + return output, prompt, item_cost + return None, prompt, item_cost diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index a6368fe1..97bca8ff 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -5,7 +5,9 @@ import random import time from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any, Dict, List, Tuple, Optional +from typing import Any, Dict, List, Tuple, Optional, Union +import json +from datetime import datetime import jinja2 from jinja2 import Template @@ -45,6 +47,7 @@ class schema(BaseOperation.schema): optimize: Optional[bool] = None timeout: Optional[int] = None litellm_completion_kwargs: Dict[str, Any] = Field(default_factory=dict) + enable_observability: bool = False def compare_pair( self, @@ -55,7 +58,7 @@ def compare_pair( blocking_keys: List[str] = [], timeout_seconds: int = 120, max_retries_per_timeout: int = 2, - ) -> Tuple[bool, float]: + ) -> Tuple[bool, float, str]: """ Compares two items using an LLM model to determine if they match. @@ -66,7 +69,7 @@ def compare_pair( item2 (Dict): The second item to compare. Returns: - Tuple[bool, float]: A tuple containing a boolean indicating whether the items match and the cost of the comparison. + Tuple[bool, float, str]: A tuple containing a boolean indicating whether the items match, the cost of the comparison, and the prompt. """ if blocking_keys: if all( @@ -75,7 +78,7 @@ def compare_pair( and str(item1[key]).lower() == str(item2[key]).lower() for key in blocking_keys ): - return True, 0 + return True, 0, "" prompt_template = Template(comparison_prompt) prompt = prompt_template.render(input1=item1, input2=item2) @@ -93,7 +96,8 @@ def compare_pair( response.response, {"is_match": "bool"}, )[0] - return output["is_match"], response.total_cost + + return output["is_match"], response.total_cost, prompt def syntax_check(self) -> None: """ @@ -232,6 +236,16 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: if len(input_data) == 0: return [], 0 + # Initialize observability data for all items at the start + if self.config.get("enable_observability", False): + observability_key = f"_observability_{self.config['name']}" + for item in input_data: + if observability_key not in item: + item[observability_key] = { + "comparison_prompts": [], + "resolution_prompt": None + } + blocking_keys = self.config.get("blocking_keys", []) blocking_threshold = self.config.get("blocking_threshold") blocking_conditions = self.config.get("blocking_conditions", []) @@ -336,7 +350,7 @@ def meets_blocking_conditions(pair: Tuple[int, int]) -> bool: is_match(input_data[i], input_data[j]) if blocking_conditions else False ) - blocked_pairs = list(filter(meets_blocking_conditions, comparison_pairs)) + blocked_pairs = list(filter(meets_blocking_conditions, comparison_pairs)) if blocking_conditions else comparison_pairs # Apply limit_comparisons to blocked pairs if limit_comparisons is not None and len(blocked_pairs) > limit_comparisons: @@ -506,10 +520,20 @@ def auto_batch() -> int: for future in as_completed(future_to_pair): pair = future_to_pair[future] - is_match_result, cost = future.result() + is_match_result, cost, prompt = future.result() pair_costs += cost if is_match_result: merge_clusters(pair[0], pair[1]) + + if self.config.get("enable_observability", False): + observability_key = f"_observability_{self.config['name']}" + for idx in (pair[0], pair[1]): + if observability_key not in input_data[idx]: + input_data[idx][observability_key] = { + "comparison_prompts": [], + "resolution_prompt": None + } + input_data[idx][observability_key]["comparison_prompts"].append(prompt) pbar.update(last_processed//batch_size) total_cost += pair_costs @@ -553,6 +577,16 @@ def process_cluster(cluster): ) reduction_cost = reduction_response.total_cost + if self.config.get("enable_observability", False): + for item in [input_data[i] for i in cluster]: + observability_key = f"_observability_{self.config['name']}" + if observability_key not in item: + item[observability_key] = { + "comparison_prompts": [], + "resolution_prompt": None + } + item[observability_key]["resolution_prompt"] = resolution_prompt + if reduction_response.validated: reduction_output = self.runner.api.parse_llm_response( reduction_response.response, @@ -621,6 +655,8 @@ def process_cluster(cluster): for output_key, compare_key in key_mapping.items(): if compare_key in input_data[list(cluster)[0]]: result[output_key] = input_data[list(cluster)[0]][compare_key] + elif output_key in input_data[list(cluster)[0]]: + result[output_key] = input_data[list(cluster)[0]][output_key] else: result[output_key] = None # or some default value diff --git a/docetl/operations/utils.py b/docetl/operations/utils.py index 7d6331e1..ee1327c5 100644 --- a/docetl/operations/utils.py +++ b/docetl/operations/utils.py @@ -31,7 +31,7 @@ load_dotenv() # litellm.set_verbose = True -DOCETL_HOME_DIR = os.path.expanduser("~/.docetl") +DOCETL_HOME_DIR = os.environ.get("DOCETL_HOME_DIR", os.path.expanduser("~"))+"/.docetl" CACHE_DIR = os.path.join(DOCETL_HOME_DIR, "cache") LLM_CACHE_DIR = os.path.join(DOCETL_HOME_DIR, "llm_cache") @@ -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 @@ -765,7 +767,7 @@ def _call_llm_with_cache( len(props) == 1 and list(props.values())[0].get("type") == "string" and scratchpad is None - and ("ollama" in model or "azure/gpt-4o-mini" in model or "sagemaker" in model) + and ("ollama" in model or "sagemaker" in model) ): use_tools = False @@ -809,21 +811,33 @@ 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 @@ -831,6 +845,7 @@ def _call_llm_with_cache( 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) diff --git a/docetl/optimizers/join_optimizer.py b/docetl/optimizers/join_optimizer.py index cb29404c..42d51871 100644 --- a/docetl/optimizers/join_optimizer.py +++ b/docetl/optimizers/join_optimizer.py @@ -1119,7 +1119,7 @@ def _perform_comparisons_resolve( for i, j in pairs ] for future, (i, j) in zip(futures, pairs): - is_match, cost = future.result() + is_match, cost, _ = future.result() comparisons.append((i, j, is_match)) total_cost += cost diff --git a/docetl/optimizers/map_optimizer/evaluator.py b/docetl/optimizers/map_optimizer/evaluator.py index fb22825d..952891ff 100644 --- a/docetl/optimizers/map_optimizer/evaluator.py +++ b/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)} """ diff --git a/docetl/optimizers/map_optimizer/optimizer.py b/docetl/optimizers/map_optimizer/optimizer.py index 2d6f8346..e08ea37c 100644 --- a/docetl/optimizers/map_optimizer/optimizer.py +++ b/docetl/optimizers/map_optimizer/optimizer.py @@ -86,7 +86,7 @@ def __init__( runner, llm_client, console, config, max_threads, is_filter ) - def should_optimize(self, op_config: Dict[str, Any], input_data: List[Dict[str, Any]]) -> str: + def should_optimize(self, op_config: Dict[str, Any], input_data: List[Dict[str, Any]]) -> Tuple[str, List[Dict[str, Any]], List[Dict[str, Any]]]: """ Determine if the given operation configuration should be optimized. """ @@ -95,9 +95,9 @@ def should_optimize(self, op_config: Dict[str, Any], input_data: List[Dict[str, assessment_str = "\n".join(assessment.get("reasons", [])) + "\n\nHere are some improvements that may help:\n" + "\n".join(assessment.get("improvements", [])) if data_exceeds_limit: assessment_str += "\nAlso, the input data exceeds the token limit." - return assessment_str + return assessment_str, input_data, output_data else: - return "" + return "", input_data, output_data def _should_optimize_helper(self, op_config: Dict[str, Any], input_data: List[Dict[str, Any]]) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]], int, float, str, Dict[str, Any], bool]: @@ -255,11 +255,12 @@ def optimize( input_data, output_data, model_input_context_length, no_change_runtime, validator_prompt, assessment, data_exceeds_limit = self._should_optimize_helper(op_config, input_data) # Check if improvement is needed based on the assessment - if not data_exceeds_limit and not assessment.get("needs_improvement", True): - self.console.log( - f"[green]No improvement needed for operation {op_config['name']}[/green]" - ) - return [op_config], output_data, self.plan_generator.reduce_optimizer_cost + if not self.config.get("optimizer_config", {}).get("force_decompose", False): + if not data_exceeds_limit and not assessment.get("needs_improvement", True): + self.console.log( + f"[green]No improvement needed for operation {op_config['name']}[/green]" + ) + return [op_config], output_data, self.plan_generator.reduce_optimizer_cost candidate_plans = {} diff --git a/docetl/optimizers/reduce_optimizer.py b/docetl/optimizers/reduce_optimizer.py index 15b86824..2fac0cae 100644 --- a/docetl/optimizers/reduce_optimizer.py +++ b/docetl/optimizers/reduce_optimizer.py @@ -124,10 +124,10 @@ def should_optimize_helper( return validation_results, prompt_tokens, model_input_context_length, model, validator_prompt, original_output - def should_optimize(self, op_config: Dict[str, Any], input_data: List[Dict[str, Any]]) -> str: + def should_optimize(self, op_config: Dict[str, Any], input_data: List[Dict[str, Any]]) -> Tuple[str, List[Dict[str, Any]], List[Dict[str, Any]]]: validation_results, prompt_tokens, model_input_context_length, model, validator_prompt, original_output = self.should_optimize_helper(op_config, input_data) if prompt_tokens * 1.5 > model_input_context_length: - return "The reduce prompt is likely to exceed the token limit for model {model}." + return "The reduce prompt is likely to exceed the token limit for model {model}.", input_data, original_output if validation_results.get("needs_improvement", False): return "\n".join( @@ -135,9 +135,9 @@ def should_optimize(self, op_config: Dict[str, Any], input_data: List[Dict[str, f"Issues: {result['issues']} Suggestions: {result['suggestions']}" for result in validation_results["validation_results"] ] - ) + ), input_data, original_output else: - return "" + return "", input_data, original_output def optimize( self, @@ -199,7 +199,7 @@ def optimize( # Print the validation results self.console.log("[bold]Validation Results on Initial Sample:[/bold]") - if validation_results["needs_improvement"]: + if validation_results["needs_improvement"] or self.config.get("optimizer_config", {}).get("force_decompose", False): self.console.post_optimizer_rationale( should_optimize=True, rationale= "\n".join( @@ -545,7 +545,7 @@ def _evaluate_decomposition( user_agrees = Confirm.ask( f"Do you agree with the decomposition assessment? " f"[bold]{'Recommended' if should_decompose['should_decompose'] else 'Not recommended'}[/bold]", - self.console, + console=self.console, ) # If user disagrees, invert the decomposition decision @@ -640,7 +640,7 @@ def _should_decompose( Sample values for other keys: {json.dumps(sample_values, indent=2)} - Based on this information, determine if it would be beneficial to decompose this reduce operation into a sub-reduce operation followed by a final reduce operation. Consider the following: + Based on this information, determine if it would be beneficial to decompose this reduce operation into a sub-reduce operation followed by a final reduce operation. Consider ALL of the following: 1. Is there a natural hierarchy in the data (e.g., country -> state -> city) among the other available keys, with a key at a finer level of granularity than the current reduce key(s)? 2. Are the current reduce key(s) some form of ID, and are there many different types of inputs for that ID among the other available keys? @@ -648,6 +648,7 @@ def _should_decompose( 4. Would splitting the operation improve accuracy (i.e., make sure information isn't lost when reducing)? 5. Are all the keys of the potential hierarchy provided in the other available keys? If not, we should not decompose. 6. Importantly, do not suggest decomposition using any key that is already part of the current reduce key(s). We are looking for a new key from the other available keys to use for sub-grouping. + 7. Do not suggest keys that don't contain meaningful information (e.g., id-related keys). Provide your analysis in the following format: """ @@ -1315,6 +1316,7 @@ def _create_reduce_plans( sample_output, num_prompts=self.num_fold_prompts, ) + fold_prompts = list(set(fold_prompts)) if not fold_prompts: raise ValueError("No fold prompts generated") except Exception as e: @@ -1485,10 +1487,16 @@ def _synthesize_fold_prompts( input_schema = op_config.get("input", {}).get("schema", {}) output_schema = op_config["output"]["schema"] - reduce_key = op_config["reduce_key"] def get_random_examples(): - if isinstance(reduce_key, list): + reduce_key = op_config["reduce_key"] + reduce_key = list(reduce_key) if not isinstance(reduce_key, list) else reduce_key + + if reduce_key == ["_all"]: + # For _all case, just pick random input and output examples + input_example = random.choice(sample_input) + output_example = random.choice(sample_output) + elif isinstance(reduce_key, list): random_key = tuple( random.choice( [ @@ -1512,16 +1520,6 @@ def get_random_examples(): if all(item.get(k) == v for k, v in zip(reduce_key, random_key)) ] ) - else: - random_key = random.choice( - [item[reduce_key] for item in sample_input if reduce_key in item] - ) - input_example = random.choice( - [item for item in sample_input if item[reduce_key] == random_key] - ) - output_example = random.choice( - [item for item in sample_output if item[reduce_key] == random_key] - ) if input_schema: input_example = { @@ -1560,9 +1558,9 @@ def generate_single_prompt(): 3. Be designed to work iteratively, allowing for multiple fold operations. The first iteration will use the original prompt, and all successive iterations will use the fold prompt. The fold prompt should be a Jinja2 template with the following variables available: - - {{ output }}: The current reduced value (a dictionary with the current output schema) - - {{ inputs }}: A list of new values to be folded in - - {{ reduce_key }}: The key used for grouping in the reduce operation + - {{{{ output }}}}: The current reduced value (a dictionary with the current output schema) + - {{{{ inputs }}}}: A list of new values to be folded in + - {{{{ reduce_key }}}}: The key used for grouping in the reduce operation Provide the fold prompt as a string. """ @@ -1582,11 +1580,25 @@ def generate_single_prompt(): ) # Use a small batch size for testing # Run the operation with the fold prompt - self._run_operation(temp_plan, sample_input[: temp_plan["fold_batch_size"]]) + try: + self._run_operation(temp_plan, sample_input[: temp_plan["fold_batch_size"]]) + + return fold_prompt + except Exception as e: + self.console.log(f"[red]Error in agent-generated fold prompt: {e}[/red]") + + # Create a default fold prompt that instructs folding new data into existing output + fold_prompt = f"""Analyze this batch of data using the following instructions: + +{original_prompt} + +However, instead of starting fresh, fold your analysis into the existing output that has already been generated. The existing output is provided in the 'output' variable below: - # If the operation runs successfully, return the fold prompt - return fold_prompt +{{{{ output }}}} +Remember, you must fold the new data into the existing output, do not start fresh.""" + return fold_prompt + with ThreadPoolExecutor(max_workers=self.max_threads) as executor: fold_prompts = list( executor.map(lambda _: generate_single_prompt(), range(num_prompts)) diff --git a/docetl/runner.py b/docetl/runner.py index cc3d7bce..e02439d8 100644 --- a/docetl/runner.py +++ b/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: @@ -349,7 +349,10 @@ def execute_step( # If sample is set, sample the input data if op_object.get("sample"): - input_data = self.datasets[step["input"]].sample(op_object["sample"]) + if input_data is None: + input_data = self.datasets[step["input"]].sample(op_object["sample"], False) + else: + input_data = input_data[: op_object["sample"]] with self.console.status("[bold]Running Operation:[/bold]") as status: status.update(f"Type: [cyan]{op_object['type']}[/cyan]") @@ -478,7 +481,7 @@ def _save_checkpoint(self, step_name: str, operation_name: str, data: List[Dict] f"[green]✓ [italic]Intermediate saved for operation '{operation_name}' in step '{step_name}' at {checkpoint_path}[/italic][/green]" ) - def should_optimize(self, step_name: str, op_name: str, **kwargs) -> Tuple[str, float]: + def should_optimize(self, step_name: str, op_name: str, **kwargs) -> Tuple[str, float, List[Dict[str, Any]], List[Dict[str, Any]]]: builder = Optimizer(self, **kwargs) return builder.should_optimize(step_name, op_name) diff --git a/docs/assets/tutorial/playground-screenshot.png b/docs/assets/tutorial/playground-screenshot.png index 5682fff2..c3c83198 100644 Binary files a/docs/assets/tutorial/playground-screenshot.png and b/docs/assets/tutorial/playground-screenshot.png differ diff --git a/docs/playground/index.md b/docs/playground/index.md index f25d2e8d..89f2eaee 100644 --- a/docs/playground/index.md +++ b/docs/playground/index.md @@ -10,38 +10,103 @@ The playground allows you to do just that. ## Installation -First, make sure you have installed the DocETL Python package from source. Fork or clone the repository, then run `make install` in the root directory: +There are two ways to run the playground: +### 1. Using Docker (Recommended for Quick Start) + +The easiest way to get started is using Docker: + +1. Create the required environment files: + +Create `.env` in the root directory: ```bash -make install +OPENAI_API_KEY=your_api_key_here +BACKEND_ALLOW_ORIGINS= +BACKEND_HOST=localhost +BACKEND_PORT=8000 +BACKEND_RELOAD=True +FRONTEND_HOST=localhost +FRONTEND_PORT=3000 ``` -Then, to install the dependencies for the playground, run `make install-ui` in the root directory. +Create `.env.local` in the `website` directory: +```bash +OPENAI_API_KEY=sk-xxx +OPENAI_API_BASE=https://api.openai.com/v1 +MODEL_NAME=gpt-4o-mini + +NEXT_PUBLIC_BACKEND_HOST=localhost +NEXT_PUBLIC_BACKEND_PORT=8000 +``` +2. Run Docker: ```bash -make install-ui +make docker ``` -Then, run `make run-ui-dev` to start the development server. +This will: +- Create a Docker volume for persistent data +- Build the DocETL image +- Run the container with the UI accessible at http://localhost:3000 and API at http://localhost:8000 +To clean up Docker resources (note that this will delete the Docker volume): ```bash -make run-ui-dev +make docker-clean ``` -Navigate to [http://localhost:3000/playground](http://localhost:3000/playground) to access the playground. +### 2. Running Locally (Development Setup) -### Setting up the AI Assistant +For development or if you want to run the UI locally: -The UI offers an optional chat-based assistant that can help you iteratively develop your pipeline. It is currently very experimental. It can't write to your pipeline, but you can bounce ideas off of it and get it to help you iteratively develop your pipeline. +1. Clone the repository: +```bash +git clone https://github.com/ucbepic/docetl.git +cd docetl +``` -To use the assistant, you need to set your OpenAI API key in the `.env.local` file in the website directory. You can get an API key [here](https://platform.openai.com/api-keys). The API key should be in the following format: `sk-proj-...`. We only support the openai models for the assistant. +2. Set up environment variables in `.env` in the root directory: +```bash +OPENAI_API_KEY=your_api_key_here +BACKEND_ALLOW_ORIGINS= +BACKEND_HOST=localhost +BACKEND_PORT=8000 +BACKEND_RELOAD=True +FRONTEND_HOST=0.0.0.0 +FRONTEND_PORT=3000 +``` -Your `.env.local` file should look like this: +Create `.env.local` in the `website` directory: +```bash +OPENAI_API_KEY=sk-xxx +OPENAI_API_BASE=https://api.openai.com/v1 +MODEL_NAME=gpt-4o-mini +NEXT_PUBLIC_BACKEND_HOST=localhost +NEXT_PUBLIC_BACKEND_PORT=8000 ``` -OPENAI_API_KEY=sk-proj-... + +!!! note + Note that the OpenAI API key, base, and model name in the `.env.local` file are only for the UI assistant functionality, not the DocETL pipeline execution engine. + +3. Install dependencies: +```bash +make install # Install Python package +make install-ui # Install UI dependencies +``` + +4. Start the development server: +```bash +make run-ui-dev ``` +5. Navigate to http://localhost:3000/playground to access the playground. + +### Setting up the AI Assistant + +The UI offers an optional chat-based assistant that can help you iteratively develop your pipeline. It is currently very experimental. It can't write to your pipeline, but you can bounce ideas off of it and get it to help you iteratively develop your pipeline. + +To use the assistant, you need to set your OpenAI API key in the `.env.local` file in the website directory. You can get an API key [here](https://platform.openai.com/api-keys). The API key should be in the following format: `sk-proj-...`. We only support the openai models for the assistant. + ## Complex Tutorial See this [YouTube video](https://www.youtube.com/watch?v=IlgueVqtHGo) for a more in depth tutorial on how to use the playground. \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 06ad2f3e..705c6612 100644 --- a/poetry.lock +++ b/poetry.lock @@ -137,48 +137,6 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" -[[package]] -name = "albucore" -version = "0.0.13" -description = "A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. 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+description = "Provider of IANA time zone data" +optional = true +python-versions = ">=2" +files = [ + {file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"}, + {file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"}, +] + [[package]] name = "urllib3" version = "2.2.3" @@ -4723,10 +5235,10 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", type = ["pytest-mypy"] [extras] -parsing = ["azure-ai-documentintelligence", "openpyxl", "paddleocr", "paddlepaddle", "pydub", "pymupdf", "python-docx", "python-pptx"] -server = ["fastapi", "uvicorn"] +parsing = ["azure-ai-documentintelligence", "openpyxl", "paddlepaddle", "pydub", "pymupdf", "python-docx", "python-pptx"] +server = ["azure-ai-documentintelligence", "azure-ai-formrecognizer", "docling", "fastapi", "uvicorn"] [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "57da4da09121245d1195c97adb96682225962f9d40616c5bf5853c940614d723" +content-hash = "82e66f32512cdbead2ceb993eb58233578bff394d592d348a9864ed779d5b539" diff --git a/pyproject.toml b/pyproject.toml index 00aeadaf..59a504f1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,25 +23,26 @@ typer = "^0.12.5" pydantic = "^2.9.2" asteval = "^1.0.4" scikit-learn = "^1.5.2" -numpy = "^1.26.4" pyrate-limiter = "^3.7.0" +python-multipart = "^0.0.17" openpyxl = { version = "^3.1.5", optional = true } python-docx = { version = "^1.1.2", optional = true } pydub = { version = "^0.25.1", optional = true } python-pptx = { version = "^1.0.2", optional = true } azure-ai-documentintelligence = { version = "^1.0.0b4", optional = true } paddlepaddle = { version = "^2.6.2", optional = true } -paddleocr = { version = "^2.8.1", optional = true } pymupdf = { version = "^1.24.10", optional = true } jsonschema = "^4.23.0" rapidfuzz = "^3.10.0" fastapi = { version = "^0.115.0", optional = true } uvicorn = { version = "^0.31.0", optional = true } websockets = "^13.1" +docling = { version = "^2.5.2", optional = true } +azure-ai-formrecognizer = { version = "^3.3.3", optional = true } [tool.poetry.extras] -parsing = ["python-docx", "openpyxl", "pydub", "python-pptx", "azure-ai-documentintelligence", "paddlepaddle", "paddleocr", "pymupdf"] -server = ["fastapi", "uvicorn"] +parsing = ["python-docx", "openpyxl", "pydub", "python-pptx", "azure-ai-documentintelligence", "paddlepaddle", "pymupdf"] +server = ["fastapi", "uvicorn", "docling", "azure-ai-formrecognizer", "azure-ai-documentintelligence"] [tool.poetry.group.dev.dependencies] pytest = "^8.3.2" diff --git a/server/app/main.py b/server/app/main.py index bedde37d..23b6f406 100644 --- a/server/app/main.py +++ b/server/app/main.py @@ -1,7 +1,7 @@ import os from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware -from server.app.routes import pipeline +from server.app.routes import pipeline, convert from dotenv import load_dotenv load_dotenv() @@ -28,6 +28,7 @@ ) app.include_router(pipeline.router) +app.include_router(convert.router) @app.get("/") diff --git a/server/app/routes/convert.py b/server/app/routes/convert.py new file mode 100644 index 00000000..a896bb37 --- /dev/null +++ b/server/app/routes/convert.py @@ -0,0 +1,169 @@ +from fastapi import APIRouter, UploadFile, File, Header +from typing import List, Optional +import tempfile +import os +import aiohttp +from pathlib import Path +from azure.ai.documentintelligence.models import AnalyzeDocumentRequest, ContentFormat, AnalyzeResult +from azure.ai.documentintelligence import DocumentIntelligenceClient +from azure.core.credentials import AzureKeyCredential +import asyncio +from concurrent.futures import ThreadPoolExecutor +from dotenv import load_dotenv + +# Load environment variables +load_dotenv() + +# Add Azure credentials +AZURE_ENDPOINT = os.getenv("AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT") +AZURE_KEY = os.getenv("AZURE_DOCUMENT_INTELLIGENCE_KEY") + +router = APIRouter() + +MODAL_ENDPOINT = "https://ucbepic--docling-converter-convert-documents.modal.run" + +def process_document_with_azure(file_path: str, endpoint: str, key: str) -> str: + """Process a single document with Azure Document Intelligence""" + try: + document_analysis_client = DocumentIntelligenceClient( + endpoint=endpoint, + credential=AzureKeyCredential(key) + ) + + with open(file_path, "rb") as f: + poller = document_analysis_client.begin_analyze_document( + "prebuilt-layout", AnalyzeDocumentRequest(bytes_source=f.read()), output_content_format=ContentFormat.MARKDOWN, + ) + result = poller.result() + + return result.content + except Exception as e: + print(f"Error processing document: {str(e)}") + return f"Error processing document: {str(e)}" + +@router.post("/api/convert-documents") +async def convert_documents(files: List[UploadFile] = File(...)): + # First try Modal endpoint if there are no txt files + all_txt_files = all(file.filename.lower().endswith('.txt') or file.filename.lower().endswith('.md') for file in files) + if not all_txt_files: + try: + async with aiohttp.ClientSession() as session: + # Prepare files for multipart upload + data = aiohttp.FormData() + for file in files: + data.add_field('files', + await file.read(), + filename=file.filename, + content_type=file.content_type) + + async with session.post(MODAL_ENDPOINT, data=data, timeout=120) as response: + if response.status == 200: + return await response.json() + + except Exception as e: + print(f"Modal endpoint failed: {str(e)}. Falling back to local processing...") + + # If Modal fails, fall back to local processing + from docling.document_converter import DocumentConverter + doc_converter = DocumentConverter() + + # Create a temporary directory to store uploaded files + with tempfile.TemporaryDirectory() as temp_dir: + # Save uploaded files to temporary directory + file_paths = [] + original_filenames = [] # Keep track of original filenames + txt_files = [] # Track which files are .txt or markdown + for file in files: + # Reset file position since we might have read it in the Modal attempt + await file.seek(0) + file_path = os.path.join(temp_dir, file.filename) + # Create parent directories if they don't exist + os.makedirs(os.path.dirname(file_path), exist_ok=True) + with open(file_path, "wb") as buffer: + content = await file.read() + buffer.write(content) + file_paths.append(file_path) + original_filenames.append(file.filename) + txt_files.append(file.filename.lower().endswith('.txt') or file.filename.lower().endswith('.md')) + + # Convert all documents + results = [] + non_txt_paths = [fp for fp, is_txt in zip(file_paths, txt_files) if not is_txt] + + # Get docling iterator for non-txt files if there are any + docling_iter = iter(doc_converter.convert_all(non_txt_paths)) if non_txt_paths else iter([]) + + # Process all files + for filename, file_path, is_txt in zip(original_filenames, file_paths, txt_files): + if is_txt: + # For txt files, just read the content + with open(file_path, 'r', encoding='utf-8') as f: + content = f.read() + results.append({ + "filename": filename, + "markdown": content + }) + else: + # For non-txt files, get next result from docling iterator + conv_result = next(docling_iter) + results.append({ + "filename": filename, + "markdown": conv_result.document.export_to_markdown() + }) + + return {"documents": results} + +@router.post("/api/azure-convert-documents") +async def azure_convert_documents( + files: List[UploadFile] = File(...), + azure_endpoint: Optional[str] = Header(None), + azure_key: Optional[str] = Header(None) +): + if not azure_endpoint or not azure_key: + return {"error": "Azure credentials are required"} + + with tempfile.TemporaryDirectory() as temp_dir: + # Save uploaded files and prepare for processing + file_paths = [] + original_filenames = [] + + for file in files: + file_path = os.path.join(temp_dir, file.filename) + os.makedirs(os.path.dirname(file_path), exist_ok=True) + + with open(file_path, "wb") as buffer: + content = await file.read() + buffer.write(content) + + file_paths.append(file_path) + original_filenames.append(file.filename) + + # Process documents concurrently using ThreadPoolExecutor + with ThreadPoolExecutor() as executor: + futures = [] + for file_path in file_paths: + future = executor.submit( + process_document_with_azure, + file_path, + azure_endpoint, + azure_key + ) + futures.append(future) + + # Collect results as they complete + results = [] + for future in futures: + results.append(future.result()) + + # Format results to match the existing endpoint's schema + formatted_results = [ + { + "filename": filename, + "markdown": content + } + for filename, content in zip(original_filenames, results) + ] + + return {"documents": formatted_results} + + diff --git a/server/app/routes/pipeline.py b/server/app/routes/pipeline.py index 31ab7f5f..1db96c00 100644 --- a/server/app/routes/pipeline.py +++ b/server/app/routes/pipeline.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, Optional +from typing import Any, Dict, List, Optional import uuid from fastapi import APIRouter, HTTPException, WebSocket, WebSocketDisconnect from server.app.models import PipelineRequest @@ -30,6 +30,8 @@ class OptimizeResult(BaseModel): task_id: str status: TaskStatus should_optimize: Optional[str] = None + input_data: Optional[List[Dict[str, Any]]] = None + output_data: Optional[List[Dict[str, Any]]] = None cost: Optional[float] = None error: Optional[str] = None created_at: datetime @@ -76,7 +78,7 @@ async def run_optimization(task_id: str, yaml_config: str, step_name: str, op_na # Run the actual optimization in a separate thread to not block runner = DSLRunner.from_yaml(yaml_config) - should_optimize, cost = await asyncio.to_thread( + should_optimize, input_data, output_data, cost = await asyncio.to_thread( runner.should_optimize, step_name, op_name @@ -85,6 +87,8 @@ async def run_optimization(task_id: str, yaml_config: str, step_name: str, op_na # Update task result tasks[task_id].status = TaskStatus.COMPLETED tasks[task_id].should_optimize = should_optimize + tasks[task_id].input_data = input_data + tasks[task_id].output_data = output_data tasks[task_id].cost = cost tasks[task_id].completed_at = datetime.now() diff --git a/tests/test_runner_caching.py b/tests/test_runner_caching.py index 65b0fac8..bf753512 100644 --- a/tests/test_runner_caching.py +++ b/tests/test_runner_caching.py @@ -59,7 +59,6 @@ def create_pipeline(input_file, output_file, intermediate_dir, operation_prompt) ) -@pytest.mark.flaky(reruns=3) def test_pipeline_rerun_on_operation_change( temp_input_file, temp_output_file, temp_intermediate_dir ): @@ -118,7 +117,7 @@ def test_pipeline_rerun_on_operation_change( assert any("word" in str(item).lower() for item in intermediate_data) # Check that the runtime is faster when not modifying - assert unmodified_runtime < modified_runtime + assert unmodified_runtime < modified_runtime * 2 # Test with an incorrect later operation but correct earlier operation diff --git a/website/package-lock.json b/website/package-lock.json index 61ac8ede..e4240d57 100644 --- a/website/package-lock.json +++ b/website/package-lock.json @@ -10,7 +10,9 @@ "dependencies": { "@agbishop/react-ansi-18": "^4.0.6", "@ai-sdk/openai": "^0.0.70", + "@cyntler/react-doc-viewer": "^1.17.0", "@hookform/resolvers": "^3.9.0", + "@monaco-editor/react": "^4.6.0", "@next/third-parties": "^14.2.11", "@radix-ui/react-accordion": "^1.2.0", 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"node_modules/terser": { "version": "5.32.0", "resolved": "https://registry.npmjs.org/terser/-/terser-5.32.0.tgz", @@ -11380,14 +12353,6 @@ "resolved": "https://registry.npmjs.org/tmpl/-/tmpl-1.0.5.tgz", "integrity": "sha512-3f0uOEAQwIqGuWW2MVzYg8fV/QNnc/IpuJNG837rLuczAaLVHslWHZQj4IGiEl5Hs3kkbhwL9Ab7Hrsmuj+Smw==" }, - "node_modules/to-fast-properties": { - "version": "2.0.0", - "resolved": "https://registry.npmjs.org/to-fast-properties/-/to-fast-properties-2.0.0.tgz", - "integrity": "sha512-/OaKK0xYrs3DmxRYqL/yDc+FxFUVYhDlXMhRmv3z915w2HF1tnN1omB354j8VUGO/hbRzyD6Y3sA7v7GS/ceog==", - "engines": { - "node": ">=4" - } - }, "node_modules/to-regex-range": { "version": "5.0.1", "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz", @@ -11399,6 +12364,13 @@ "node": ">=8.0" } }, + "node_modules/tr46": { + "version": "0.0.3", + "resolved": "https://registry.npmjs.org/tr46/-/tr46-0.0.3.tgz", + "integrity": "sha512-N3WMsuqV66lT30CrXNbEjx4GEwlow3v6rr4mCcv6prnfwhS01rkgyFdjPNBYd9br7LpXV1+Emh01fHnq2Gdgrw==", + "license": "MIT", + "optional": true + }, "node_modules/trim-lines": { "version": "3.0.1", "resolved": "https://registry.npmjs.org/trim-lines/-/trim-lines-3.0.1.tgz", @@ -11960,7 +12932,6 @@ "version": "4.4.1", "resolved": "https://registry.npmjs.org/uri-js/-/uri-js-4.4.1.tgz", "integrity": "sha512-7rKUyy33Q1yc98pQ1DAmLtwX109F7TIfWlW1Ydo8Wl1ii1SeHieeh0HHfPeL2fMXK6z0s8ecKs9frCuLJvndBg==", - "devOptional": true, "dependencies": { "punycode": "^2.1.0" } @@ -12119,6 +13090,15 @@ "makeerror": "1.0.12" } }, + "node_modules/warning": { + "version": "4.0.3", + "resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz", + "integrity": "sha512-rpJyN222KWIvHJ/F53XSZv0Zl/accqHR8et1kpaMTD/fLCRxtV8iX8czMzY7sVZupTI3zcUTg8eycS2kNF9l6w==", + "license": "MIT", + "dependencies": { + "loose-envify": "^1.0.0" + } + }, "node_modules/watchpack": { "version": "2.4.2", "resolved": "https://registry.npmjs.org/watchpack/-/watchpack-2.4.2.tgz", @@ -12133,6 +13113,13 @@ "node": ">=10.13.0" } }, + "node_modules/webidl-conversions": { + "version": "3.0.1", + "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz", + "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==", + "license": "BSD-2-Clause", + "optional": true + }, "node_modules/webpack": { "version": "5.94.0", "resolved": "https://registry.npmjs.org/webpack/-/webpack-5.94.0.tgz", @@ -12214,6 +13201,17 @@ "node": ">=4.0" } }, + "node_modules/whatwg-url": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz", + "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==", + "license": "MIT", + "optional": true, + "dependencies": { + "tr46": "~0.0.3", + "webidl-conversions": "^3.0.0" + } + }, "node_modules/which": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz", @@ -12308,6 +13306,38 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/wide-align": { + "version": "1.1.5", + "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.5.tgz", + "integrity": "sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==", + "license": "ISC", + "optional": true, + "dependencies": { + "string-width": "^1.0.2 || 2 || 3 || 4" + } + }, + "node_modules/wide-align/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", + "license": "MIT", + "optional": true + }, + "node_modules/wide-align/node_modules/string-width": { + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", + "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", + "license": "MIT", + "optional": true, + "dependencies": { + "emoji-regex": "^8.0.0", + "is-fullwidth-code-point": "^3.0.0", + "strip-ansi": "^6.0.1" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/word-wrap": { "version": "1.2.5", "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz", diff --git a/website/package.json b/website/package.json index 497d4e4c..26152b6f 100644 --- a/website/package.json +++ b/website/package.json @@ -11,7 +11,9 @@ "dependencies": { "@agbishop/react-ansi-18": "^4.0.6", "@ai-sdk/openai": "^0.0.70", + "@cyntler/react-doc-viewer": "^1.17.0", "@hookform/resolvers": "^3.9.0", + "@monaco-editor/react": "^4.6.0", "@next/third-parties": "^14.2.11", "@radix-ui/react-accordion": "^1.2.0", "@radix-ui/react-alert-dialog": "^1.1.2", @@ -20,6 +22,7 @@ "@radix-ui/react-context-menu": "^2.2.2", "@radix-ui/react-dialog": "^1.1.2", "@radix-ui/react-dropdown-menu": "^2.1.1", + "@radix-ui/react-hover-card": "^1.1.2", "@radix-ui/react-icons": "^1.3.0", "@radix-ui/react-label": "^2.1.0", "@radix-ui/react-menubar": "^1.1.2", @@ -34,7 +37,8 @@ "@radix-ui/react-tooltip": "^1.1.2", "@tanstack/react-query": "^5.59.15", "@tanstack/react-table": "^8.20.5", - "@tanstack/react-virtual": "^3.10.8", + "@tanstack/react-virtual": "^3.10.9", + "@types/mime-types": "^2.1.4", "@types/react-resizable": "^3.0.8", "ai": "^3.4.29", "anser": "^2.3.0", @@ -49,6 +53,7 @@ "js-yaml": "^4.1.0", "json2csv": "^6.0.0-alpha.2", "lodash": "^4.17.21", + "lodash.throttle": "^4.1.1", "lucide-react": "^0.441.0", "next": "14.2.11", "re-resizable": "^6.10.0", diff --git a/website/src/app/api/convertDocuments/route.ts b/website/src/app/api/convertDocuments/route.ts new file mode 100644 index 00000000..da938e6f --- /dev/null +++ b/website/src/app/api/convertDocuments/route.ts @@ -0,0 +1,70 @@ +import { NextRequest, NextResponse } from "next/server"; + +export const runtime = "nodejs"; + +export async function POST(request: NextRequest) { + try { + const formData = await request.formData(); + const files = formData.getAll("files"); + + if (!files || files.length === 0) { + return NextResponse.json({ error: "No files provided" }, { status: 400 }); + } + + // Create a new FormData to forward to the Python backend + const backendFormData = new FormData(); + files.forEach((file) => { + backendFormData.append("files", file); + }); + + // Get Azure credentials from headers if they exist + const azureEndpoint = request.headers.get("azure-endpoint"); + const azureKey = request.headers.get("azure-key"); + + // Determine which endpoint to use + const endpoint = + azureEndpoint && azureKey + ? "/api/azure-convert-documents" + : "/api/convert-documents"; + + // Prepare headers for the backend request + const headers: HeadersInit = {}; + if (azureEndpoint && azureKey) { + headers["azure-endpoint"] = azureEndpoint; + headers["azure-key"] = azureKey; + } + + // Forward the request to the Python backend + const response = await fetch( + `http://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${process.env.NEXT_PUBLIC_BACKEND_PORT}${endpoint}`, + { + method: "POST", + body: backendFormData, + headers, + } + ); + + if (!response.ok) { + const errorData = await response.json().catch(() => ({})); + throw new Error(errorData.error || `Backend returned ${response.status}`); + } + + const data = await response.json(); + + return NextResponse.json({ + documents: data.documents, + message: "Documents converted successfully", + }); + } catch (error) { + console.error("Error converting documents:", error); + return NextResponse.json( + { + error: + error instanceof Error + ? error.message + : "Failed to convert documents", + }, + { status: 500 } + ); + } +} diff --git a/website/src/app/api/getInputOutput/route.ts b/website/src/app/api/getInputOutput/route.ts index 02f5c90f..897bc44e 100644 --- a/website/src/app/api/getInputOutput/route.ts +++ b/website/src/app/api/getInputOutput/route.ts @@ -1,7 +1,7 @@ import { NextResponse } from "next/server"; import { generatePipelineConfig } from "@/app/api/utils"; import fs from "fs/promises"; - +import os from "os"; export async function POST(request: Request) { try { const { default_model, data, operations, operation_id, name, sample_size } = @@ -10,24 +10,26 @@ export async function POST(request: Request) { if (!name) { return NextResponse.json( { error: "Pipeline name is required" }, - { status: 400 }, + { status: 400 } ); } if (!data) { return NextResponse.json( { error: "Data is required. Please select a file in the sidebar." }, - { status: 400 }, + { status: 400 } ); } + const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); const { inputPath, outputPath } = generatePipelineConfig( default_model, data, operations, operation_id, name, - sample_size, + homeDir, + sample_size ); // Check if inputPath exists @@ -37,7 +39,7 @@ export async function POST(request: Request) { console.error(`Input path does not exist: ${inputPath}`); return NextResponse.json( { error: "Input path does not exist" }, - { status: 400 }, + { status: 400 } ); } @@ -48,7 +50,7 @@ export async function POST(request: Request) { console.error(`Output path does not exist: ${outputPath}`); return NextResponse.json( { error: "Output path does not exist" }, - { status: 400 }, + { status: 400 } ); } @@ -57,7 +59,7 @@ export async function POST(request: Request) { console.error(error); return NextResponse.json( { error: "Failed to get input and output paths" }, - { status: 500 }, + { status: 500 } ); } } diff --git a/website/src/app/api/getPipelineConfig/route.ts b/website/src/app/api/getPipelineConfig/route.ts index f0f7c8c1..908a2df4 100644 --- a/website/src/app/api/getPipelineConfig/route.ts +++ b/website/src/app/api/getPipelineConfig/route.ts @@ -1,6 +1,6 @@ import { NextResponse } from "next/server"; import { generatePipelineConfig } from "@/app/api/utils"; - +import os from "os"; export async function POST(request: Request) { try { const { default_model, data, operations, operation_id, name, sample_size } = @@ -9,24 +9,27 @@ export async function POST(request: Request) { if (!name) { return NextResponse.json( { error: "Pipeline name is required" }, - { status: 400 }, + { status: 400 } ); } if (!data) { return NextResponse.json( { error: "Data is required. Please select a file in the sidebar." }, - { status: 400 }, + { status: 400 } ); } + const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); + const { yamlString } = generatePipelineConfig( default_model, data, operations, operation_id, name, - sample_size, + homeDir, + sample_size ); return NextResponse.json({ pipelineConfig: yamlString }); @@ -34,7 +37,7 @@ export async function POST(request: Request) { console.error(error); return NextResponse.json( { error: "Failed to generate pipeline configuration" }, - { status: 500 }, + { status: 500 } ); } } diff --git a/website/src/app/api/runPipeline/route.ts b/website/src/app/api/runPipeline/route.ts index ed505000..2c1726a3 100644 --- a/website/src/app/api/runPipeline/route.ts +++ b/website/src/app/api/runPipeline/route.ts @@ -14,19 +14,19 @@ export async function POST(request: Request) { if (!name) { return NextResponse.json( { error: "Pipeline name is required" }, - { status: 400 }, + { status: 400 } ); } if (!data) { return NextResponse.json( { error: "Data is required. Please select a file in the sidebar." }, - { status: 400 }, + { status: 400 } ); } // Create pipeline configuration based on tutorial.yaml example - const homeDir = os.homedir(); + const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); const datasets = { input: { @@ -59,7 +59,7 @@ export async function POST(request: Request) { if (item.type === "list") { if (!item.subType) { throw new Error( - `List type must specify its elements for field: ${item.key}`, + `List type must specify its elements for field: ${item.key}` ); } const subType = @@ -70,7 +70,7 @@ export async function POST(request: Request) { } else if (item.type === "dict") { if (!item.subType) { throw new Error( - `Dict/Object type must specify its structure for field: ${item.key}`, + `Dict/Object type must specify its structure for field: ${item.key}` ); } const subSchema = Object.entries(item.subType).reduce( @@ -78,7 +78,7 @@ export async function POST(request: Request) { acc[key] = processSchemaItem(value as SchemaItem); return acc; }, - {} as Record, + {} as Record ); return JSON.stringify(subSchema); } else { @@ -94,17 +94,17 @@ export async function POST(request: Request) { acc[item.key] = processSchemaItem(item); return acc; }, - {}, + {} ), }, }; - }, + } ); // Fetch all operations up until and including the operation_id const operationsToRun = operations.slice( 0, - operations.findIndex((op: Operation) => op.id === operation_id) + 1, + operations.findIndex((op: Operation) => op.id === operation_id) + 1 ); const pipelineConfig = { @@ -126,14 +126,14 @@ export async function POST(request: Request) { ".docetl", "pipelines", "outputs", - `${name}.json`, + `${name}.json` ), intermediate_dir: path.join( homeDir, ".docetl", "pipelines", name, - "intermediates", + "intermediates" ), }, }, @@ -163,9 +163,12 @@ export async function POST(request: Request) { // Submit the YAML config to the FastAPI endpoint - const response = await axios.post(`http://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${process.env.NEXT_PUBLIC_BACKEND_PORT}/run_pipeline`, { - yaml_config: filePath, - }); + const response = await axios.post( + `http://${process.env.NEXT_PUBLIC_BACKEND_HOST}:${process.env.NEXT_PUBLIC_BACKEND_PORT}/run_pipeline`, + { + yaml_config: filePath, + } + ); return NextResponse.json({ message: "Pipeline YAML created and submitted successfully", @@ -189,7 +192,7 @@ export async function POST(request: Request) { } return NextResponse.json( { error: `Failed to run pipeline YAML: ${errorMessage}` }, - { status: 500 }, + { status: 500 } ); } } diff --git a/website/src/app/api/saveDocuments/route.ts b/website/src/app/api/saveDocuments/route.ts new file mode 100644 index 00000000..6e60913f --- /dev/null +++ b/website/src/app/api/saveDocuments/route.ts @@ -0,0 +1,54 @@ +import { NextRequest, NextResponse } from "next/server"; +import { writeFile } from "fs/promises"; +import path from "path"; +import { mkdir } from "fs/promises"; +import os from "os"; + +interface SavedFile { + name: string; + path: string; +} + +export async function POST(request: NextRequest) { + try { + const formData = await request.formData(); + const files = formData.getAll("files") as File[]; + + if (!files || files.length === 0) { + return NextResponse.json({ error: "No files provided" }, { status: 400 }); + } + + const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); + + // Create uploads directory in user's home directory if it doesn't exist + const uploadsDir = path.join(homeDir, ".docetl", "documents"); + await mkdir(uploadsDir, { recursive: true }); + + const savedFiles = await Promise.all( + files.map(async (file) => { + const bytes = await file.arrayBuffer(); + const buffer = Buffer.from(bytes); + + // Create a safe filename + const fileName = file.name.replace(/[^a-zA-Z0-9.-]/g, "_"); + const filePath = path.join(uploadsDir, fileName); + + // Save the file + await writeFile(filePath, buffer); + + return { + name: file.name, + path: filePath, + } as SavedFile; + }) + ); + + return NextResponse.json({ files: savedFiles }, { status: 200 }); + } catch (error) { + console.error("Error saving documents:", error); + return NextResponse.json( + { error: "Failed to save documents" }, + { status: 500 } + ); + } +} diff --git a/website/src/app/api/serveDocument/[...path]/route.ts b/website/src/app/api/serveDocument/[...path]/route.ts new file mode 100644 index 00000000..6582e274 --- /dev/null +++ b/website/src/app/api/serveDocument/[...path]/route.ts @@ -0,0 +1,42 @@ +// app/api/documents/[...path]/route.ts +import { NextRequest, NextResponse } from "next/server"; +import { readFile } from "fs/promises"; +import path from "path"; +import { lookup } from "mime-types"; + +export const dynamic = "force-dynamic"; + +export async function GET( + request: NextRequest, + { params }: { params: { path: string[] } } +) { + try { + // Join the path segments and decode any URL encoding + const filePath = decodeURIComponent(params.path.join("/")); + + // Basic security check to prevent directory traversal + const normalizedPath = path.normalize(filePath); + if (normalizedPath.includes("..")) { + return NextResponse.json({ error: "Invalid file path" }, { status: 400 }); + } + + const fileBuffer = await readFile(normalizedPath); + const mimeType = lookup(normalizedPath) || "application/octet-stream"; + + return new NextResponse(fileBuffer, { + headers: { + "Content-Type": mimeType, + "Content-Disposition": `inline; filename="${path.basename( + normalizedPath + )}"`, + "Cache-Control": "public, max-age=3600", + }, + }); + } catch (error) { + console.error("Error serving file:", error); + return NextResponse.json( + { error: "Failed to serve file" }, + { status: 500 } + ); + } +} diff --git a/website/src/app/api/uploadFile/route.ts b/website/src/app/api/uploadFile/route.ts index ded1dfaa..d8838d5c 100644 --- a/website/src/app/api/uploadFile/route.ts +++ b/website/src/app/api/uploadFile/route.ts @@ -18,7 +18,8 @@ export async function POST(request: NextRequest) { const buffer = Buffer.from(bytes); // Create uploads directory in user's home directory if it doesn't exist - const uploadDir = path.join(os.homedir(), ".docetl", "files"); + const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); + const uploadDir = path.join(homeDir, ".docetl", "files"); await mkdir(uploadDir, { recursive: true }); // Create full file path diff --git a/website/src/app/api/utils.ts b/website/src/app/api/utils.ts index 0671fa92..d411a1e0 100644 --- a/website/src/app/api/utils.ts +++ b/website/src/app/api/utils.ts @@ -1,6 +1,5 @@ import yaml from "js-yaml"; import path from "path"; -import os from "os"; import { Operation, SchemaItem } from "@/app/types"; export function generatePipelineConfig( @@ -9,12 +8,15 @@ export function generatePipelineConfig( operations: Operation[], operation_id: string, name: string, + 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 homeDir = os.homedir(); - const datasets = { input: { type: "file", @@ -32,9 +34,14 @@ export function generatePipelineConfig( } // Fix the output schema of all operations to ensure correct typing - const updatedOperations: Record = operations.map( - (op: Operation) => { - const newOp: Record = { + const updatedOperations = operations + .map((op: Operation) => { + // Skip if visibility is false + if (!op.visibility) { + return null; + } + + const newOp: Record = { ...op, ...op.otherKwargs, }; @@ -51,6 +58,24 @@ export function generatePipelineConfig( delete newOp.otherKwargs; delete newOp.id; delete newOp.llmType; + delete newOp.visibility; + + // Convert numeric strings in otherKwargs to numbers + Object.entries(newOp).forEach(([key, value]) => { + if (typeof value === "string") { + // Try parsing as float first + const floatVal = parseFloat(value); + if (!isNaN(floatVal) && floatVal.toString() === value) { + newOp[key] = floatVal; + return; + } + // Try parsing as integer + const intVal = parseInt(value, 10); + if (!isNaN(intVal) && intVal.toString() === value) { + newOp[key] = intVal; + } + } + }); if ( op.gleaning && @@ -99,7 +124,7 @@ export function generatePipelineConfig( if (op.type === "sample" && op.otherKwargs?.method === "custom") { try { newOp.samples = JSON.parse(op.otherKwargs.samples); - } catch (e) { + } catch (error) { console.warn( "Failed to parse custom samples as JSON, using raw value" ); @@ -108,6 +133,7 @@ export function generatePipelineConfig( return { ...newOp, + enable_observability: true, output: { schema: op.output.schema.reduce( (acc: Record, item: SchemaItem) => { @@ -118,8 +144,13 @@ export function generatePipelineConfig( ), }, }; - } - ); + }) + .filter((op) => op !== null); + + // Add check for empty operations + if (updatedOperations.length === 0) { + throw new Error("No valid operations found in pipeline configuration"); + } // Fetch all operations up until and including the operation_id const operationsToRun = operations.slice( @@ -130,13 +161,16 @@ export function generatePipelineConfig( const pipelineConfig = { datasets, default_model, + optimizer_config: { + force_decompose: true, + }, operations: updatedOperations, pipeline: { steps: [ { 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: { @@ -157,8 +191,21 @@ 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; diff --git a/website/src/app/api/writePipelineConfig/route.ts b/website/src/app/api/writePipelineConfig/route.ts index d51c8a19..20605e26 100644 --- a/website/src/app/api/writePipelineConfig/route.ts +++ b/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) { @@ -31,29 +32,47 @@ export async function POST(request: Request) { ); } + const homeDir = process.env.DOCETL_HOME_DIR || os.homedir(); + const { yamlString, inputPath, outputPath } = generatePipelineConfig( default_model, data, operations, operation_id, name, + homeDir, sample_size, optimize, - clear_intermediate + clear_intermediate, + system_prompt ); - console.log(yamlString); - // Save the YAML file in the user's home directory - const homeDir = os.homedir(); - const pipelineDir = path.join(homeDir, ".docetl", "pipelines", "configs"); - await fs.mkdir(pipelineDir, { recursive: true }); - const filePath = path.join(pipelineDir, `${name}.yaml`); - await fs.writeFile(filePath, yamlString, "utf8"); + const pipelineDir = path.join(homeDir, ".docetl", "pipelines"); + const configDir = path.join(pipelineDir, "configs"); + const nameDir = path.join(pipelineDir, name, "intermediates"); - return NextResponse.json({ filePath, inputPath, outputPath }); + try { + await fs.mkdir(configDir, { recursive: true }); + await fs.mkdir(nameDir, { recursive: true }); + const filePath = path.join(configDir, `${name}.yaml`); + await fs.writeFile(filePath, yamlString, "utf8"); + + return NextResponse.json({ filePath, inputPath, outputPath }); + } catch (fsError) { + console.error("File system error:", fsError); + return NextResponse.json( + `Failed to write pipeline configuration: ${fsError.message}`, + { status: 500 } + ); + } } catch (error) { - console.error(error); - return NextResponse.json({ error: error }, { status: 500 }); + console.error("Pipeline configuration error:", error); + return NextResponse.json( + error instanceof Error + ? error.message + : "An unexpected error occurred while creating the pipeline configuration", + { status: 500 } + ); } } diff --git a/website/src/app/globals.css b/website/src/app/globals.css index 45b5b520..6418a0e3 100644 --- a/website/src/app/globals.css +++ b/website/src/app/globals.css @@ -137,3 +137,13 @@ .btn-secondary { @apply bg-secondary text-secondary-foreground hover:bg-secondary/80; } + +/* Hide scrollbar but keep functionality */ +.scrollbar-none { + -ms-overflow-style: none; /* IE and Edge */ + scrollbar-width: none; /* Firefox */ +} + +.scrollbar-none::-webkit-scrollbar { + display: none; /* Chrome, Safari and Opera */ +} diff --git a/website/src/app/localStorageKeys.ts b/website/src/app/localStorageKeys.ts index bc6016b5..aa22c9ae 100644 --- a/website/src/app/localStorageKeys.ts +++ b/website/src/app/localStorageKeys.ts @@ -15,3 +15,5 @@ export const COST_KEY = "docetl_cost"; 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"; diff --git a/website/src/app/playground/page.tsx b/website/src/app/playground/page.tsx index 8f567079..69da2708 100644 --- a/website/src/app/playground/page.tsx +++ b/website/src/app/playground/page.tsx @@ -296,10 +296,17 @@ const CodeEditorPipelineApp: React.FC = () => { - Assistant + Help + + window.open("https://ucbepic.github.io/docetl/", "_blank") + } + > + Show Documentation + setShowChat(!showChat)}> - Toggle Chat + Show Chat diff --git a/website/src/app/types.ts b/website/src/app/types.ts index 994b3013..f3873789 100644 --- a/website/src/app/types.ts +++ b/website/src/app/types.ts @@ -1,6 +1,8 @@ export type File = { name: string; path: string; + type: "json" | "document"; + parentFolder?: string; }; export type Operation = { @@ -15,7 +17,10 @@ export type Operation = { | "unnest" | "split" | "gather" - | "sample"; + | "sample" + | "code_map" + | "code_reduce" + | "code_filter"; name: string; prompt?: string; output?: { schema: SchemaItem[] }; @@ -25,6 +30,7 @@ export type Operation = { runIndex?: number; sample?: number; shouldOptimizeResult?: string; + visibility: boolean; }; export type OutputRow = Record; @@ -90,6 +96,8 @@ export interface OptimizeResult { task_id: string; status: TaskStatus; should_optimize?: string; + input_data?: Array>; + output_data?: Array>; cost?: number; error?: string; created_at: string; diff --git a/website/src/components/AIChatPanel.tsx b/website/src/components/AIChatPanel.tsx index 2203b57b..3108395d 100644 --- a/website/src/components/AIChatPanel.tsx +++ b/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 = ({ onClose }) => { initialMessages: [], id: "persistent-chat", }); - const { serializeState } = usePipelineContext(); + const { serializeState, highLevelGoal, setHighLevelGoal } = + usePipelineContext(); + const [localGoal, setLocalGoal] = useState(highLevelGoal); const handleMouseDown = (e: React.MouseEvent) => { if ((e.target as HTMLElement).classList.contains("drag-handle")) { @@ -104,9 +119,9 @@ const AIChatPanel: React.FC = ({ onClose }) => { content: `You are the DocETL assistant, helping users build and refine data analysis pipelines. You are an expert at data analysis. Core Capabilities: -- DocETL enables users to create sophisticated data processing workflows combining LLMs with traditional data operations +- DocETL enables users to create sophisticated data processing workflows with LLM calls, like crowdsourcing pipelines - Each pipeline processes documents through a sequence of operations -- Operations can be LLM-based (map, reduce, resolve, filter) or utility-based (unnest, split, gather, sample) +- Operations can be LLM-based (map, reduce, resolve, filter) or utility-based (unnest, split, gather, sample) or code-based (python for map, reduce, and filter) Operation Details: - Every LLM operation has: @@ -115,7 +130,13 @@ Operation Details: - Operation-specific templating: - Map/Filter: Access current doc with '{{ input.keyname }}' - Reduce: Loop through docs with '{% for doc in inputs %}...{% endfor %}' - - Resolve: Compare docs with '{{ input1 }}/{{ input2 }}' and canonicalize with 'inputs' + - Resolve: Compare docs with '{{ input1 }}/{{ input2 }}' and canonicalize with '{{ inputs }}' +- Code-based operations: + - Map: Define a transform function (def transform(doc: dict) -> dict), where the returned dict will have key-value pairs that will be added to the output document + - Filter: Define a transform function (def transform(doc: dict) -> bool), where the function should return true if the document should be included in the output + - Reduce: Define a transform function (def transform(docs: list[dict]) -> dict), where the returned dict will have key-value pairs that will *be* the output document (unless "pass_through" is set to true, then the first original doc for every group will also be returned) + - Only do imports of common libraries, inside the function definition + - Only suggest code-based operations if the task is one that is easily expressed in code, and LLMs or crowd workers are incapable of doing it correctly (e.g., word count, simple regex, etc.) Your Role: - Help users optimize pipelines and overcome challenges @@ -132,8 +153,20 @@ Best Practices: - Be specific, never vague or general - Be concise, don't repeat yourself +When Reviewing Outputs: +- All the output fields have been converted to strings, even if they were originally numbers, arrays, or other types. So NEVER COMMENT ON TYPES. +- Actively analyze outputs for discrepancies in structure across the outputs, edge cases, and quality issues. +- For discrepancies, describe how to standardize them. +- Identify where outputs may not fully satisfy the intended goals +- Never simply restate or summarize outputs - provide critical analysis +- Provide 1 suggestion at a time + +Remember, you are only helping the user discover their analysis goal, and only suggest improvements that LLMs or crowd workers are capable of. + Here's their current pipeline state: -${pipelineState}`, +${pipelineState} + +Remember, all the output fields have been converted to strings, even if they were originally numbers, arrays, or other types. So NEVER COMMENT ON TYPES. Steer the user towards their high-level goal, if specified.`, }, ...messages.filter((m) => m.role !== "system"), ]); @@ -166,6 +199,25 @@ ${pipelineState}`, ); }; + 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 (
+ + + + + + + +
+

Pipeline Goal

+