chore: adding better error messages for gemini

docetl/builder.py

@@ -1569,4 +1569,4 @@ def save_optimized_config(self, optimized_config_path: str):
 
 if __name__ == "__main__":
     optimizer = Optimizer("workloads/medical/map.yaml", model="gpt-4o-mini")
-    optimizer.optimize()
+    optimizer.optimize()

docetl/operations/equijoin.py

@@ -585,4 +585,4 @@ def stratified_length_sample(
         )
         sampled_pairs.extend(pairs[:group_sample_size])
 
-    return sampled_pairs
+    return sampled_pairs

docetl/operations/gather.py

@@ -150,6 +150,11 @@ def render_chunk_with_context(
         Returns:
             str: Renderted chunk with context and headers.
         """
+
+        # If there are no peripheral chunks, return the main chunk
+        if not peripheral_config:
+            return chunks[current_index][content_key]
+
         combined_parts = ["--- Previous Context ---"]
 
         combined_parts.extend(

docetl/operations/utils/api.py

@@ -14,6 +14,8 @@
 
 from rich import print as rprint
 
+BASIC_MODELS = ["gpt-4o-mini", "gpt-4o"]
+
 class APIWrapper(object):
     def __init__(self, runner):
         self.runner = runner
@@ -485,31 +487,45 @@ def _call_llm_with_cache(
 
         self.runner.rate_limiter.try_acquire("llm_call", weight=1)
         if tools is not None:
-            response = completion(
-                model=model,
-                messages=[
-                    {
-                        "role": "system",
-                        "content": system_prompt,
-                    },
-                ]
-                + messages,
-                tools=tools,
-                tool_choice=tool_choice,
-                **litellm_completion_kwargs,
-            )
+            try:
+                response = completion(
+                    model=model,
+                    messages=[
+                        {
+                            "role": "system",
+                            "content": system_prompt,
+                        },
+                    ]
+                    + messages,
+                    tools=tools,
+                    tool_choice=tool_choice,
+                    **litellm_completion_kwargs,
+                )
+            except Exception as e:
+                # Check that there's a prefix for the model name if it's not a basic model
+                if model not in BASIC_MODELS:
+                    if not "/" in model:
+                        raise ValueError(f"Note: You may also need to prefix your model name with the provider, e.g. 'openai/gpt-4o-mini' or 'gemini/gemini-1.5-flash' to conform to LiteLLM API standards. Original error: {e}")
+                raise e
         else:
-            response = completion(
-                model=model,
-                messages=[
-                    {
-                        "role": "system",
-                        "content": system_prompt,
-                    },
-                ]
-                + messages,
-                **litellm_completion_kwargs,
-            )
+            try:
+                response = completion(
+                    model=model,
+                    messages=[
+                        {
+                            "role": "system",
+                            "content": system_prompt,
+                        },
+                    ]
+                    + messages,
+                    **litellm_completion_kwargs,
+                )
+            except Exception as e:
+                # Check that there's a prefix for the model name if it's not a basic model
+                if model not in BASIC_MODELS:
+                    if not "/" in model:
+                        raise ValueError(f"Note: You may also need to prefix your model name with the provider, e.g. 'openai/gpt-4o-mini' or 'gemini/gemini-1.5-flash' to conform to LiteLLM API standards. Original error: {e}")
+                raise e
 
 
         return response

docetl/optimizers/map_optimizer/operation_creators.py

@@ -135,9 +135,7 @@ def create_split_map_gather_operations(
         if "next" in context_info:
             gather_config["peripheral_chunks"]["next"] = context_info["next"]
 
-        # Add gather to the pipeline if there are peripheral chunks
-        if gather_config["peripheral_chunks"]:
-            pipeline.append(gather_config)
+        pipeline.append(gather_config)
 
         return pipeline
 

tests/test_synth_gather.py

@@ -105,18 +105,18 @@ def test_synth_gather(config_yaml):
     config_path, long_documents_path, output_path = config_yaml
 
     # Initialize the optimizer
-    optimizer = Optimizer.from_yaml(config_path)
+    runner = DSLRunner.from_yaml(config_path)
 
     # Run the optimization
-    optimizer.optimize()
+    optimized_pipeline, cost = runner.optimize(return_pipeline=True)
 
     # Check if a gather operation was synthesized
     synthesized_gather_found = False
-    for step in optimizer.optimized_config["pipeline"]["steps"]:
+    for step in optimized_pipeline.config["pipeline"]["steps"]:
         for op in step["operations"]:
             synthesized_op = [
                 operation
-                for operation in optimizer.optimized_config["operations"]
+                for operation in optimized_pipeline.config["operations"]
                 if operation["name"] == op
             ][0]
             if synthesized_op.get("type") == "gather":
@@ -139,8 +139,7 @@ def test_synth_gather(config_yaml):
     ), "No synthesized gather operation found in the optimized config"
 
     # Run the optimized pipeline
-    runner = DSLRunner(optimizer.optimized_config_path)
-    runner.run()
+    optimized_pipeline.load_run_save()
 
     # Check if the output file was created
     assert os.path.exists(output_path), "Output file was not created"
@@ -161,7 +160,6 @@ def test_synth_gather(config_yaml):
 
     # Clean up temporary files
     os.remove(config_path)
-    os.remove(optimizer.optimized_config_path)
     os.remove(long_documents_path)
     os.remove(output_path)
 

website/public/llms.txt

@@ -1,5 +1,7 @@
 # DocETL System Description and LLM Instructions (Short)
 
+Note: use docetl.org/llms-full.txt for the full system description and LLM instructions.
+
 DocETL is a system for creating and executing LLM-powered data processing pipelines, designed for complex document processing tasks. It provides a low-code, declarative YAML interface to define complex data operations on unstructured datasets.
 
 DocETL is built and maintained by the EPIC lab at UC Berkeley. Learn more at https://www.docetl.org.
@@ -8,6 +10,7 @@ We have an integrated development environment for building and testing pipelines
 
 ## Docs
 
+- [LLM Instructions (Full)](https://www.docetl.org/llms-full.txt)
 - [Website](https://www.docetl.org)
 - [DocWrangler Playground](https://www.docetl.org/playground)
 - [Main Documentation](https://ucbepic.github.io/docetl)