feat: allow optimizer model to be any litellm supported model

docetl/api.py

@@ -183,6 +183,7 @@ def optimize(
         model: str = "gpt-4o",
         resume: bool = False,
         timeout: int = 60,
+        litellm_kwargs: Dict[str, Any] = {},
     ) -> "Pipeline":
         """
         Optimize the pipeline using the Optimizer.
@@ -203,7 +204,13 @@ def optimize(
             yaml_file_suffix=self.name,
             max_threads=max_threads,
         )
-        optimized_config, _ = runner.optimize(return_pipeline=False)
+        optimized_config, _ = runner.optimize(
+            model=model,
+            resume=resume,
+            timeout=timeout,
+            litellm_kwargs=litellm_kwargs,
+            return_pipeline=False,
+        )
 
         updated_pipeline = Pipeline(
             name=self.name,

docetl/cli.py

@@ -29,6 +29,7 @@ def build(
 ):
     """
     Build and optimize the configuration specified in the YAML file.
+    Any arguments passed here will override the values in the YAML file.
 
     Args:
         yaml_file (Path): Path to the YAML file containing the pipeline configuration.
@@ -47,7 +48,12 @@ def build(
 
     runner = DSLRunner.from_yaml(str(yaml_file), max_threads=max_threads)
     runner.optimize(
-        save=True, return_pipeline=False, model=model, resume=resume, timeout=timeout
+        save=True,
+        return_pipeline=False,
+        model=model or runner.config.get("optimizer_model", "gpt-4o"),
+        resume=resume,
+        timeout=timeout,
+        litellm_kwargs=runner.config.get("optimizer_litellm_kwargs", {}),
     )
 
 

docetl/config_wrapper.py

@@ -109,6 +109,7 @@ def __init__(
         )
         bucket_factory = BucketCollection(**buckets)
         self.rate_limiter = pyrate_limiter.Limiter(bucket_factory, max_delay=math.inf)
+        self.is_cancelled = False
 
         self.api = APIWrapper(self)
 

docetl/operations/map.py

@@ -2,6 +2,7 @@
 The `MapOperation` and `ParallelMapOperation` classes are subclasses of `BaseOperation` that perform mapping operations on input data. They use LLM-based processing to transform input items into output items based on specified prompts and schemas, and can also perform key dropping operations.
 """
 
+import asyncio
 from concurrent.futures import ThreadPoolExecutor
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -190,6 +191,8 @@ def validation_fn(response: Union[Dict[str, Any], ModelResponse]):
                 return output, False
 
             self.runner.rate_limiter.try_acquire("call", weight=1)
+            if self.runner.is_cancelled:
+                raise asyncio.CancelledError("Operation was cancelled")
             llm_result = self.runner.api.call_llm(
                 self.config.get("model", self.default_model),
                 "map",

docetl/operations/utils/api.py

@@ -1,4 +1,5 @@
 import ast
+import asyncio
 import hashlib
 import json
 import re
@@ -71,6 +72,8 @@ def gen_embedding(self, model: str, input: List[str]) -> List[float]:
 
                 # FIXME: Should we use a different limit for embedding?
                 self.runner.rate_limiter.try_acquire("embedding_call", weight=1)
+                if self.runner.is_cancelled:
+                    raise asyncio.CancelledError("Operation was cancelled")
                 result = embedding(model=model, input=input)
                 # Cache the result
                 c.set(key, result)
@@ -582,6 +585,9 @@ def _call_llm_with_cache(
         messages = truncate_messages(messages, model)
 
         self.runner.rate_limiter.try_acquire("llm_call", weight=1)
+        if self.runner.is_cancelled:
+            raise asyncio.CancelledError("Operation was cancelled")
+
         if tools is not None:
             try:
                 response = completion(

docetl/optimizer.py

@@ -56,6 +56,7 @@ def __init__(
         self,
         runner: "DSLRunner",
         model: str = "gpt-4o",
+        litellm_kwargs: Dict[str, Any] = {},
         resume: bool = False,
         timeout: int = 60,
     ):
@@ -98,7 +99,7 @@ def __init__(
         self.status = runner.status
 
         self.optimized_config = copy.deepcopy(self.config)
-        self.llm_client = LLMClient(model)
+        self.llm_client = LLMClient(runner, model, **litellm_kwargs)
         self.timeout = timeout
         self.resume = resume
         self.captured_output = CapturedOutput()

docetl/optimizers/utils.py

@@ -14,17 +14,20 @@ class LLMClient:
     and keeps track of the total cost of API calls.
     """
 
-    def __init__(self, model: str = "gpt-4o"):
+    def __init__(self, runner, model: str = "gpt-4o", **litellm_kwargs):
         """
         Initialize the LLMClient.
 
         Args:
             model (str, optional): The name of the LLM model to use. Defaults to "gpt-4o".
+            **litellm_kwargs: Additional keyword arguments for the LLM model.
         """
         if model == "gpt-4o":
             model = "gpt-4o-2024-08-06"
         self.model = model
+        self.litellm_kwargs = litellm_kwargs
         self.total_cost = 0
+        self.runner = runner
 
     def generate(
         self,
@@ -59,6 +62,7 @@ def generate(
                 },
                 *messages,
             ],
+            **self.litellm_kwargs,
             response_format={
                 "type": "json_schema",
                 "json_schema": {

docetl/runner.py

@@ -622,6 +622,13 @@ def should_optimize(
         self, step_name: str, op_name: str, **kwargs
     ) -> Tuple[str, float, List[Dict[str, Any]], List[Dict[str, Any]]]:
         self.load()
+
+        # Augment the kwargs with the runner's config if not already provided
+        if "optimizer_litellm_kwargs" not in kwargs:
+            kwargs["litellm_kwargs"] = self.config.get("optimizer_litellm_kwargs", {})
+        if "optimizer_model" not in kwargs:
+            kwargs["model"] = self.config.get("optimizer_model", "gpt-4o")
+
         builder = Optimizer(self, **kwargs)
         self.optimizer = builder
         result = builder.should_optimize(step_name, op_name)
@@ -639,6 +646,12 @@ def optimize(
 
         self.load()
 
+        # Augment the kwargs with the runner's config if not already provided
+        if "optimizer_litellm_kwargs" not in kwargs:
+            kwargs["litellm_kwargs"] = self.config.get("optimizer_litellm_kwargs", {})
+        if "optimizer_model" not in kwargs:
+            kwargs["model"] = self.config.get("optimizer_model", "gpt-4o")
+
         builder = Optimizer(
             self,
             **kwargs,

server/app/routes/pipeline.py

@@ -57,7 +57,9 @@ async def run_optimization(task_id: str, yaml_config: str, step_name: str, op_na
         should_optimize, input_data, output_data, cost = await asyncio.to_thread(
             runner.should_optimize,
             step_name,
-            op_name
+            op_name,
+            model=runner.config.get("optimizer_model", "gpt-4o"),
+            litellm_kwargs=runner.config.get("optimizer_litellm_kwargs", {})
         )
 
         # Update task result
@@ -69,6 +71,7 @@ async def run_optimization(task_id: str, yaml_config: str, step_name: str, op_na
         tasks[task_id].completed_at = datetime.now()
 
     except asyncio.CancelledError:
+        runner.is_cancelled = True
         tasks[task_id].status = TaskStatus.CANCELLED
         tasks[task_id].completed_at = datetime.now()
         raise
@@ -213,6 +216,9 @@ async def run_pipeline():
 
                 if user_message == "kill":
                     runner.console.log("Stopping process...")
+                    runner.is_cancelled = True
+
+
                     await websocket.send_json({
                         "type": "error",
                         "message": "Process stopped by user request"
@@ -223,6 +229,12 @@ async def run_pipeline():
                 runner.console.post_input(user_message)
             except asyncio.TimeoutError:
                 pass  # No message received, continue with the loop
+            except asyncio.CancelledError:
+                await websocket.send_json({
+                    "type": "error",
+                    "message": "Process stopped by user request"
+                })
+                raise
 
             await asyncio.sleep(0.5)
 

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

@@ -12,6 +12,7 @@ export async function POST(request: Request) {
       sample_size,
       namespace,
       system_prompt,
+      optimizerModel,
     } = await request.json();
 
     if (!name) {
@@ -44,7 +45,8 @@ export async function POST(request: Request) {
       system_prompt,
       [],
       "",
-      false
+      false,
+      optimizerModel
     );
 
     return NextResponse.json({ pipelineConfig: yamlString });

website/src/app/api/utils.ts

@@ -50,7 +50,8 @@ export function generatePipelineConfig(
   } | null = null,
   apiKeys: APIKey[] = [],
   docetl_encryption_key: string = "",
-  enable_observability: boolean = true
+  enable_observability: boolean = true,
+  optimizerModel: string = "gpt-4o"
 ) {
   const datasets = {
     input: {
@@ -203,13 +204,18 @@ export function generatePipelineConfig(
   }
 
   // Fetch all operations up until and including the operation_id
-  const operationsToRun = operations.slice(
-    0,
-    operations.findIndex((op: Operation) => op.id === operation_id) + 1
-  );
+  const operationsToRun = operations
+    .slice(
+      0,
+      operations.findIndex((op: Operation) => op.id === operation_id) + 1
+    )
+    .filter((op) =>
+      updatedOperations.some((updatedOp) => updatedOp.name === op.name)
+    );
 
   // Fix type errors by asserting the pipeline config type
   const pipelineConfig: any = {
+    optimizer_model: optimizerModel,
     datasets,
     default_model,
     ...(enable_observability && {
@@ -297,6 +303,8 @@ export function generatePipelineConfig(
 
   const yamlString = yaml.dump(pipelineConfig);
 
+  console.log(yamlString);
+
   return {
     yamlString,
     inputPath,

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

@@ -22,6 +22,7 @@ export async function POST(request: Request) {
       system_prompt,
       namespace,
       apiKeys,
+      optimizerModel,
     } = await request.json();
 
     if (!name) {
@@ -55,7 +56,8 @@ export async function POST(request: Request) {
       system_prompt,
       apiKeys,
       docetl_encryption_key,
-      true
+      true,
+      optimizerModel
     );
 
     // Use the FastAPI endpoint to write the pipeline config

website/src/components/OperationCard.tsx

@@ -906,6 +906,7 @@ export const OperationCard: React.FC<Props> = ({ index, id }) => {
           system_prompt: systemPrompt,
           namespace: namespace,
           apiKeys: apiKeys,
+          optimizerModel: optimizerModel,
         }),
       });
 
@@ -922,7 +923,6 @@ export const OperationCard: React.FC<Props> = ({ index, id }) => {
       sendMessage({
         yaml_config: filePath,
         optimize: true,
-        optimizer_model: optimizerModel,
       });
     } catch (error) {
       console.error("Error optimizing operation:", error);