🔍 Add Deep Research Agent Pipeline #227
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Pull Request Overview
This PR adds the Deep Research Agent pipeline along with extensive supporting tests, documentation, and new configuration files for various research modes. Key changes include:
- Implementation and testing of cost tracking for Exa search queries
- Detailed design documents covering pydantic migration, prompt cost visualization, and configuration of the research pipelines
- New scripts for LiteLLM testing and budget handling in pipeline steps
Reviewed Changes
Copilot reviewed 65 out of 65 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
| deep_research/design/test_exa_cost_tracking.py | New test cases for Exa cost tracking |
| deep_research/design/sample_observation.md | Sample observation document for research outputs |
| deep_research/design/pydantic_migration.md | Documentation for migrating dataclasses to Pydantic models |
| deep_research/design/prompt_cost_visualization.md | Design document for prompt-based cost visualization |
| deep_research/design/lite_test.py | Test script for validating LiteLLM responses |
| deep_research/design/exa_cost_tracking_summary.md | Summary of Exa cost tracking implementation |
| deep_research/design/exa_cost_tracking_fixes.md | Documentation of fixes applied to Exa cost tracking formatting issues |
| deep_research/design/budget_test_pipeline.py | Pipeline test for setting budget and testing LLM functionality |
| deep_research/configs/* | Multiple new configuration files for different research scenarios |
Comments suppressed due to low confidence (1)
deep_research/design/budget_test_pipeline.py:25
- Remove the leftover debugging 'breakpoint()' statement to avoid unintended interruptions in production execution.
breakpoint()
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| # Configuration | ||
| LITELLM_BASE_URL = "https://litellm-service-5ikaahlouq-uc.a.run.app" | ||
| API_KEY = "zenmllitellm" |
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Avoid hardcoding API keys in the source code; instead, load them from environment variables or secure configuration files.
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Summary
Introduces the ZenML Deep Research Agent - a production-ready MLOps pipeline for conducting deep, comprehensive research on any topic using LLMs and web search capabilities.
Key Features
Architecture Highlights
Integrations
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