DSPEX MISSING COMPONENTS MASTER LIST

Documentation for DSPEX_MISSING_COMPONENTS_MASTER_LIST from the Ds ex repository.

DSPEx Missing Components: Master List

Overview

This document provides a comprehensive catalog of ALL DSPy components that are missing or incomplete in DSPEx. It serves as the definitive reference for understanding the scope of work needed to achieve feature parity with the Python DSPy library.

🚨 CRITICAL BLOCKING ISSUES (Must Fix Immediately)

1. SIMBA Teleprompter - Algorithmic Failures

❌ Program Selection Algorithm: Uses fixed scores (0.5) instead of real performance
❌ Program Pool Management: Missing top_k_plus_baseline() logic
❌ Score Calculation: Missing calc_average_score() function
❌ Main Loop Integration: Placeholder logic instead of real algorithm

Impact: SIMBA optimizer completely non-functional Priority: CRITICAL - Fix before any other work

📚 TELEPROMPTERS/OPTIMIZERS

Implemented (3/10)

✅ BootstrapFewShot (Complete)
⚠️ BEACON (Infrastructure only, missing Bayesian optimization)
⚠️ SIMBA (Broken - see critical issues above)

Missing Teleprompters (7/10)

1. MIPROv2 - Multi-prompt Instruction Proposal

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/mipro_optimizer_v2.py Features:

Multi-step instruction optimization
Automatic prompt proposal and refinement
Bayesian optimization for hyperparameters
Support for multiple signature optimization
Meta-learning components

2. COPRO - Curriculum-based Prompt Optimizer

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/copro_optimizer.py Features:

Curriculum learning approach
Progressive difficulty increase
Adaptive example selection
Multi-stage optimization

3. Ensemble

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/ensemble.py Features:

Multiple model combination
Voting mechanisms
Confidence-weighted averaging
Diversity-based selection

4. BootstrapFinetune

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/bootstrap_finetune.py Features:

Model fine-tuning on generated data
Bootstrap data generation
Training pipeline integration
Model adapter support

5. BootstrapFewShotWithRandomSearch

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/random_search.py Features:

Random search optimization
Hyperparameter exploration
Bootstrap with random sampling

6. BootstrapFewShotWithOptuna

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/teleprompt_optuna.py Features:

Optuna-based hyperparameter optimization
Advanced search strategies
Multi-objective optimization

7. BetterTogether

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/bettertogether.py Features:

Multi-agent collaboration
Program composition optimization
Joint training strategies

8. AvatarOptimizer

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/avatar_optimizer.py Features:

Avatar-based optimization
Persona-driven prompting
Character consistency

9. InferRules

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/infer_rules.py Features:

Automatic rule inference
Pattern extraction from examples
Rule-based program enhancement

10. LabeledFewShot (Vanilla)

Status: ❌ Missing entirely DSPy File: dspy/teleprompt/vanilla.py Features:

Simple labeled few-shot learning
No optimization, just demonstration
Baseline comparison method

🧠 PREDICT MODULES

Implemented (2/15)

✅ Predict (Complete)
⚠️ PredictStructured (Bypasses client system)

Missing Predict Modules (13/15)

1. ChainOfThought

Status: ❌ Missing entirely DSPy File: dspy/predict/chain_of_thought.py Features:

Step-by-step reasoning
Rationale generation
Intermediate thought tracking

2. ChainOfThoughtWithHint

Status: ❌ Missing entirely DSPy File: dspy/predict/chain_of_thought_with_hint.py Features:

CoT with external hints
Guided reasoning
Hint integration

3. ReAct (Reason + Act)

Status: ❌ Missing entirely DSPy File: dspy/predict/react.py Features:

Thought-action-observation loops
Tool integration
Multi-step reasoning
Action space definition

4. ProgramOfThought

Status: ❌ Missing entirely DSPy File: dspy/predict/program_of_thought.py Features:

Code generation capabilities
Code execution environment
Result integration
Mathematical reasoning

5. MultiChainComparison

Status: ❌ Missing entirely DSPy File: dspy/predict/multi_chain_comparison.py Features:

Multiple reasoning chains
Comparison mechanisms
Best chain selection
Confidence scoring

6. BestOfN

Status: ❌ Missing entirely DSPy File: dspy/predict/best_of_n.py Features:

Multiple generation sampling
Best response selection
Quality-based filtering

7. Refine

Status: ❌ Missing entirely DSPy File: dspy/predict/refine.py Features:

Iterative refinement
Draft-and-revise pattern
Quality improvement loops

8. Retry

Status: ❌ Missing entirely DSPy File: dspy/predict/retry.py Features:

Automatic retry on failure
Backoff strategies
Error-specific retry logic
Max attempt limits

9. Parallel

Status: ⚠️ Infrastructure exists but missing DSPy interface DSPy File: dspy/predict/parallel.py Features:

Standardized parallel execution
Result aggregation patterns
Error handling across tasks

10. Aggregation

Status: ❌ Missing entirely DSPy File: dspy/predict/aggregation.py Features:

Response aggregation strategies
Majority voting
Weighted combinations

11. CodeAct

Status: ❌ Missing entirely DSPy File: dspy/predict/code_act.py Features:

Code-based action execution
Programming environment integration
Interactive code generation

12. KNN (K-Nearest Neighbors)

Status: ❌ Missing entirely DSPy File: dspy/predict/knn.py Features:

K-nearest neighbor retrieval
Example-based prediction
Similarity-based reasoning

13. Parameter

Status: ❌ Missing entirely DSPy File: dspy/predict/parameter.py Features:

Parameter management
Learnable parameters
Optimization state tracking

🔍 RETRIEVAL SYSTEM

Implemented (0/25)

Status: ❌ COMPLETELY MISSING - This is the largest functional gap

Missing Retrieval Components (25/25)

Core Retrieval Framework

❌ DSPEx.Retrieve - Base retrieval behavior
❌ DSPEx.Retrieve.Embeddings - Basic embeddings retrieval

Vector Database Integrations

❌ ChromaDB (dspy/retrieve/chromadb_rm.py)
❌ Pinecone (dspy/retrieve/pinecone_rm.py)
❌ Weaviate (dspy/retrieve/weaviate_rm.py)
❌ Qdrant (dspy/retrieve/qdrant_rm.py)
❌ Milvus (dspy/retrieve/milvus_rm.py)
❌ FAISS (dspy/retrieve/faiss_rm.py)
❌ LanceDB (dspy/retrieve/lancedb_rm.py)
❌ Deeplake (dspy/retrieve/deeplake_rm.py)
❌ Epsilla (dspy/retrieve/epsilla_rm.py)
❌ MyScale (dspy/retrieve/my_scale_rm.py)
❌ MongoDB Atlas (dspy/retrieve/mongodb_atlas_rm.py)
❌ PGVector (dspy/retrieve/pgvector_rm.py)
❌ Neo4j (dspy/retrieve/neo4j_rm.py)
❌ FalkorDB (dspy/retrieve/falkordb_rm.py)

Specialized Retrievers

❌ ColBERTv2 (dspy/dsp/colbertv2.py) - Dense retrieval with late interaction
❌ Azure AI Search (dspy/retrieve/azureaisearch_rm.py)
❌ Databricks (dspy/retrieve/databricks_rm.py)
❌ Clarifai (dspy/retrieve/clarifai_rm.py)
❌ Marqo (dspy/retrieve/marqo_rm.py)
❌ Snowflake (dspy/retrieve/snowflake_rm.py)
❌ Vectara (dspy/retrieve/vectara_rm.py)
❌ Watson Discovery (dspy/retrieve/watson_discovery_rm.py)
❌ You.com (dspy/retrieve/you_rm.py)

Additional Retrieval Features

❌ LlamaIndex Integration (dspy/retrieve/llama_index_rm.py)
❌ RAGatouille (dspy/retrieve/ragatouille_rm.py)
❌ Hybrid Search capabilities
❌ Reranking mechanisms

📊 EVALUATION SYSTEM

Implemented (1/10)

⚠️ Basic evaluation framework (limited)

Missing Evaluation Components (9/10)

1. Advanced Metrics

DSPy File: dspy/evaluate/metrics.py

❌ Answer Exact Match improvements
❌ Answer Passage Match
❌ Semantic F1 Score
❌ BLEU Score
❌ ROUGE Score
❌ BERTScore

2. Evaluation Framework Enhancements

DSPy File: dspy/evaluate/evaluate.py

❌ Multi-threaded evaluation
❌ Progress display
❌ Result tables
❌ Statistical analysis
❌ Error breakdown

3. Auto-Evaluation

DSPy File: dspy/evaluate/auto_evaluation.py

❌ LLM-based evaluation
❌ Reference-free metrics
❌ Quality assessment

4. Specialized Evaluators

❌ CompleteAndGrounded
❌ Faithfulness metrics
❌ Relevance scoring
❌ Hallucination detection

🛡️ ASSERTIONS AND CONSTRAINTS

Implemented (0/5)

Status: ❌ COMPLETELY MISSING - Core DSPy feature

Missing Assertion Components (5/5)

1. Assertion Framework

DSPy File: dspy/primitives/assertions.py

❌ dspy.Assert() - Hard constraints with retry
❌ dspy.Suggest() - Soft hints for improvement
❌ Context management - Assertion integration
❌ Backtracking - Retry with constraints
❌ Constraint satisfaction - Runtime validation

🎛️ ADAPTERS AND TYPES

Implemented (2/8)

✅ ChatAdapter (Basic)
✅ JSONAdapter (Basic)

Missing Adapter Components (6/8)

1. Advanced Adapters

❌ TwoStepAdapter (dspy/adapters/two_step_adapter.py)
❌ Adapter utilities (dspy/adapters/utils.py)

2. Type System

❌ Image types (dspy/adapters/types/image.py)
❌ Audio types (dspy/adapters/types/audio.py)
❌ Tool types (dspy/adapters/types/tool.py)
❌ History types (dspy/adapters/types/history.py)

🏗️ PRIMITIVES AND CORE COMPONENTS

Implemented (4/8)

✅ Example (Complete)
✅ Module (Complete)
✅ Program (Complete)
✅ Prediction (Complete)

Missing Primitive Components (4/8)

1. Python Interpreter

DSPy File: dspy/primitives/python_interpreter.py

❌ Safe code execution
❌ Sandbox environment
❌ Result handling

2. Enhanced Module Features

❌ Module composition patterns
❌ Parameter management
❌ State serialization
❌ Module introspection

🌐 CLIENT AND MODEL SYSTEM

Implemented (3/20)

✅ Basic Client (Limited providers)
✅ ClientManager (Good)
✅ OpenAI integration (Basic)

Missing Client Components (17/20)

1. Model Providers (Missing 15/20)

DSPy integrates with 100+ models via LiteLLM

❌ Anthropic Claude
❌ Google Gemini (partial)
❌ Cohere
❌ Hugging Face
❌ Azure OpenAI
❌ AWS Bedrock
❌ Google Vertex AI
❌ Local model support
❌ Ollama integration
❌ vLLM integration
❌ Together AI
❌ Anyscale
❌ Groq
❌ Fireworks AI
❌ And 50+ more providers

2. Embedding Models (Missing 5/5)

❌ OpenAI embeddings
❌ Cohere embeddings
❌ Hugging Face embeddings
❌ Local embedding models
❌ Embedding caching

3. Advanced Client Features (Missing 5/5)

❌ Rate limiting (has stub)
❌ Circuit breakers (noted as bypassed)
❌ Advanced caching
❌ Request retries
❌ Model fallbacks

🔧 TOOLS AND UTILITIES

Implemented (2/15)

✅ Basic caching
✅ Basic logging

Missing Utility Components (13/15)

1. Advanced Utilities

❌ Streaming support (dspy/streaming/)
❌ Asyncify utilities (dspy/utils/asyncify.py)
❌ Usage tracking (dspy/utils/usage_tracker.py)
❌ Saving/loading (dspy/utils/saving.py)
❌ History inspection (dspy/utils/inspect_history.py)
❌ Parallelizer (dspy/utils/parallelizer.py)
❌ Unbatchify (dspy/utils/unbatchify.py)
❌ Exception handling (dspy/utils/exceptions.py)

2. Tool Integration

❌ LangChain tools (dspy/utils/langchain_tool.py)
❌ MCP (Model Context Protocol) (dspy/utils/mcp.py)
❌ Python interpreter (dspy/primitives/python_interpreter.py)

3. Observability

❌ Advanced telemetry
❌ Distributed tracing
❌ Performance monitoring
❌ Error analytics

🗂️ DATASETS AND DATA HANDLING

Implemented (0/10)

Status: ❌ No dataset utilities

Missing Dataset Components (10/10)

1. Built-in Datasets

DSPy File: dspy/datasets/

❌ GSM8K (dspy/datasets/gsm8k.py)
❌ HotpotQA (dspy/datasets/hotpotqa.py)
❌ Math datasets (dspy/datasets/math.py)
❌ Colors dataset (dspy/datasets/colors.py)

2. Data Loading

❌ DataLoader (dspy/datasets/dataloader.py)
❌ Dataset utilities (dspy/datasets/dataset.py)
❌ Example loading patterns

3. Specialized Datasets

❌ ALFWorld (dspy/datasets/alfworld/)
❌ Custom dataset loaders

📈 EXPERIMENTAL FEATURES

Implemented (0/5)

Status: ❌ No experimental features

Missing Experimental Components (5/5)

1. Advanced Features

DSPy File: dspy/experimental/

❌ Module graph analysis (module_graph.py)
❌ Synthetic data generation (synthetic_data.py)
❌ Synthesizer framework (synthesizer/)

📊 COMPREHENSIVE GAP SUMMARY

Category	Total Components	Implemented	Missing	Completion %
Teleprompters	10	1 (partial)	9	10%
Predict Modules	15	2	13	13%
Retrieval System	25	0	25	0%
Evaluation	10	1 (partial)	9	10%
Assertions	5	0	5	0%
Adapters/Types	8	2	6	25%
Primitives	8	4	4	50%
Client/Models	20	3	17	15%
Tools/Utilities	15	2	13	13%
Datasets	10	0	10	0%
Experimental	5	0	5	0%
TOTAL	131	15	116	11%

🎯 CRITICAL PATH FOR FUNCTIONALITY

Phase 1: Fix SIMBA (BLOCKING)

✅ Program selection algorithm
✅ Program pool management
✅ Score calculation logic
✅ Main loop integration

Phase 2: Core Functionality (HIGH IMPACT)

✅ ChainOfThought module
✅ Basic retrieval system
✅ Assertions framework
✅ ReAct module

Phase 3: Ecosystem Expansion (MEDIUM IMPACT)

✅ Vector database integrations (ChromaDB, Pinecone)
✅ Advanced evaluation metrics
✅ Additional teleprompters (MIPROv2)
✅ More predict modules

Phase 4: Production Features (POLISH)

✅ Advanced caching and observability
✅ Streaming support
✅ Additional model providers
✅ Dataset utilities

🏆 SUCCESS METRICS

SIMBA Works: Fix blocking algorithmic issues
RAG Capability: End-to-end retrieval-augmented generation
Advanced Reasoning: ChainOfThought, ReAct, MultiChain
Production Ready: Robust error handling, monitoring, caching
Ecosystem Parity: 80%+ component coverage compared to DSPy

Current Status: 11% component parity Target Status: 80%+ component parity

This master list shows DSPEx has a solid foundation (excellent infrastructure) but needs substantial work to match DSPy’s comprehensive ecosystem. The most critical path is fixing SIMBA’s algorithmic issues, then building core reasoning modules and the retrieval system.