Integrate lookup + neural inference path
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start by reading the proposed HybridGenerator interface and the inference flow in this issue; no implementation files or tests are identified. Define the single generation entry point, routing and context handoff behavior, configurable strategy, routing logs, metadata, and graceful neural-model fallback, then verify every listed success criterion.
Written by the indexing model from the issue text.
Description
Summary
Combine the n-gram lookup and neural fallback into a unified inference pipeline.
Success Criteria
- Single entry point for generation
- Automatic routing based on confidence gate
- Seamless context handoff from lookup to neural
- Configurable routing strategy
- Logging of routing decisions for analysis
- Graceful degradation if neural model unavailable
Inference Flow
```
Input → Tokenize → Check n-gram confidence
↓ high confidence ↓ low confidence
Lookup Generate Neural Generate
↓ ↓
└────── Merge ────────┘
↓
Detokenize → Output
```
Interface
```python
class HybridGenerator:
def init(self, ngram_tables, neural_model, threshold=0.8):
...
def generate(self, input_context: str) -> GenerationResult:
"""Returns generated text and metadata (which path used, confidence, etc.)"""
...
```
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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