hyde-retrieval

# HyDE (Hypothetical Document Embeddings)

Generate hypothetical answer documents to bridge vocabulary gaps in semantic search.

## The Problem

Direct query embedding often fails due to vocabulary mismatch:
```
Query: "scaling async data pipelines"
Docs use: "event-driven messaging", "Apache Kafka", "message brokers"
→ Low similarity scores despite high relevance
```

## The Solution

Instead of embedding the query, generate a hypothetical answer document:
```
Query: "scaling async data pipelines"
→ LLM generates: "To scale asynchronous data pipelines, use event-driven
   messaging with Apache Kafka. Message brokers provide backpressure..."
→ Embed the hypothetical document
→ Now matches docs using similar terminology
```

## Implementation

```python
from openai import AsyncOpenAI
from pydantic import BaseModel, Field

class HyDEResult(BaseModel):
    """Result of HyDE generation."""
    original_query: str
    hypothetical_doc: str
    embedding: list[float]

async def generate_hyde(
    query: str,
    llm: AsyncOpenAI,
    embed_fn: callable,
    max_tokens: int = 150,
) -> HyDEResult:
    """Generate hypothetical document and embed it."""

    # Generate hypothetical answer
    response = await llm.chat.completions.create(
        model="gpt-4o-mini",  # Fast, cheap model
        messages=[
            {"role": "system", "content":
                "Write a short paragraph that would answer this query. "
                "Use technical terminology that documentation would use."},
            {"role": "user", "content": query}
        ],
        max_tokens=max_tokens,
        temperature=0.3,  # Low temp for consistency
    )

    hypothetical_doc = response.choices[0].message.content

    # Embed the hypothetical document (not the query!)
    embedding = await embed_fn(hypothetical_doc)

    return HyDEResult(
        original_query=query,
        hypothetical_doc=hypothetical_doc,
        embedding=embedding,
    )
```

## With Caching

```python
from functools