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# Phase 6 Implementation Spec: Embeddings & Semantic Search
**Goal**: Add vector search for semantic evidence retrieval.
**Philosophy**: "Find what you mean, not just what you type."
**Prerequisite**: Phase 5 complete (Magentic working)
---
## 1. Why Embeddings?
Current limitation: **Keyword-only search misses semantically related papers.**
Example problem:
- User searches: "metformin alzheimer"
- PubMed returns: Papers with exact keywords
- MISSED: Papers about "AMPK activation neuroprotection" (same mechanism, different words)
With embeddings:
- Embed the query AND all evidence
- Find semantically similar papers even without keyword match
- Deduplicate by meaning, not just URL
---
## 2. Architecture
### Current (Phase 5)
```
Query → SearchAgent → PubMed/Web (keyword) → Evidence
```
### Phase 6
```
Query → Embed(Query) → SearchAgent
├── PubMed/Web (keyword) → Evidence
└── VectorDB (semantic) → Related Evidence
↑
Evidence → Embed → Store
```
### Shared Context Enhancement
```python
# Current
evidence_store = {"current": []}
# Phase 6
evidence_store = {
"current": [], # Raw evidence
"embeddings": {}, # URL -> embedding vector
"vector_index": None, # ChromaDB collection
}
```
---
## 3. Technology Choice
### ChromaDB (Recommended)
- **Free**, open-source, local-first
- No API keys, no cloud dependency
- Supports sentence-transformers out of the box
- Perfect for hackathon (no infra setup)
### Embedding Model
- `sentence-transformers/all-MiniLM-L6-v2` (fast, good quality)
- Or `BAAI/bge-small-en-v1.5` (better quality, still fast)
---
## 4. Implementation
### 4.1 Dependencies
Add to `pyproject.toml`:
```toml
[project.optional-dependencies]
embeddings = [
"chromadb>=0.4.0",
"sentence-transformers>=2.2.0",
]
```
### 4.2 Embedding Service (`src/services/embeddings.py`)
```python
"""Embedding service for semantic search."""
from typing import List
import chromadb
from sentence_transformers import SentenceTransformer
class EmbeddingService:
"""Handles text embedding and vector storage."""
def __init__(self, model_name: str = "all-MiniLM-L6-v2"):
self._model = SentenceTransformer(model_name)
self._client = chromadb.Client() # In-memory for hackathon
self._collection = self._client.create_collection(
name="evidence",
metadata={"hnsw:space": "cosine"}
)
def embed(self, text: str) -> List[float]:
"""Embed a single text."""
return self._model.encode(text).tolist()
def add_evidence(self, evidence_id: str, content: str, metadata: dict) -> None:
"""Add evidence to vector store."""
embedding = self.embed(content)
self._collection.add(
ids=[evidence_id],
embeddings=[embedding],
metadatas=[metadata],
documents=[content]
)
def search_similar(self, query: str, n_results: int = 5) -> List[dict]:
"""Find semantically similar evidence."""
query_embedding = self.embed(query)
results = self._collection.query(
query_embeddings=[query_embedding],
n_results=n_results
)
return [
{"id": id, "content": doc, "metadata": meta, "distance": dist}
for id, doc, meta, dist in zip(
results["ids"][0],
results["documents"][0],
results["metadatas"][0],
results["distances"][0]
)
]
def deduplicate(self, new_evidence: List, threshold: float = 0.9) -> List:
"""Remove semantically duplicate evidence."""
unique = []
for evidence in new_evidence:
similar = self.search_similar(evidence.content, n_results=1)
if not similar or similar[0]["distance"] > (1 - threshold):
unique.append(evidence)
self.add_evidence(
evidence_id=evidence.citation.url,
content=evidence.content,
metadata={"source": evidence.citation.source}
)
return unique
```
### 4.3 Enhanced SearchAgent (`src/agents/search_agent.py`)
Update SearchAgent to use embeddings:
```python
class SearchAgent(BaseAgent):
def __init__(
self,
search_handler: SearchHandlerProtocol,
evidence_store: dict,
embedding_service: EmbeddingService | None = None, # NEW
):
# ... existing init ...
self._embeddings = embedding_service
async def run(self, messages, *, thread=None, **kwargs) -> AgentRunResponse:
# ... extract query ...
# Execute keyword search
result = await self._handler.execute(query, max_results_per_tool=10)
# Semantic deduplication (NEW)
if self._embeddings:
unique_evidence = self._embeddings.deduplicate(result.evidence)
# Also search for semantically related evidence
related = self._embeddings.search_similar(query, n_results=5)
# Add related evidence not already in results
# ... merge logic ...
# ... rest of method ...
```
### 4.4 Semantic Expansion in Orchestrator
The MagenticOrchestrator can use embeddings to expand queries:
```python
# In task instruction
task = f"""Research drug repurposing opportunities for: {query}
The system has semantic search enabled. When evidence is found:
1. Related concepts will be automatically surfaced
2. Duplicates are removed by meaning, not just URL
3. Use the surfaced related concepts to refine searches
"""
```
---
## 5. Directory Structure After Phase 6
```
src/
├── services/ # NEW
│ ├── __init__.py
│ └── embeddings.py # EmbeddingService
├── agents/
│ ├── search_agent.py # Updated with embeddings
│ └── judge_agent.py
└── ...
```
---
## 6. Tests
### 6.1 Unit Tests (`tests/unit/services/test_embeddings.py`)
```python
"""Unit tests for EmbeddingService."""
import pytest
from src.services.embeddings import EmbeddingService
class TestEmbeddingService:
def test_embed_returns_vector(self):
"""Embedding should return a float vector."""
service = EmbeddingService()
embedding = service.embed("metformin diabetes")
assert isinstance(embedding, list)
assert len(embedding) > 0
assert all(isinstance(x, float) for x in embedding)
def test_similar_texts_have_close_embeddings(self):
"""Semantically similar texts should have similar embeddings."""
service = EmbeddingService()
e1 = service.embed("metformin treats diabetes")
e2 = service.embed("metformin is used for diabetes treatment")
e3 = service.embed("the weather is sunny today")
# Cosine similarity helper
from numpy import dot
from numpy.linalg import norm
cosine = lambda a, b: dot(a, b) / (norm(a) * norm(b))
# Similar texts should be closer
assert cosine(e1, e2) > cosine(e1, e3)
def test_add_and_search(self):
"""Should be able to add evidence and search for similar."""
service = EmbeddingService()
service.add_evidence(
evidence_id="test1",
content="Metformin activates AMPK pathway",
metadata={"source": "pubmed"}
)
results = service.search_similar("AMPK activation drugs", n_results=1)
assert len(results) == 1
assert "AMPK" in results[0]["content"]
```
---
## 7. Definition of Done
Phase 6 is **COMPLETE** when:
1. `EmbeddingService` implemented with ChromaDB
2. SearchAgent uses embeddings for deduplication
3. Semantic search surfaces related evidence
4. All unit tests pass
5. Integration test shows improved recall (finds related papers)
---
## 8. Value Delivered
| Before (Phase 5) | After (Phase 6) |
|------------------|-----------------|
| Keyword-only search | Semantic + keyword search |
| URL-based deduplication | Meaning-based deduplication |
| Miss related papers | Surface related concepts |
| Exact match required | Fuzzy semantic matching |
**Real example improvement:**
- Query: "metformin alzheimer"
- Before: Only papers mentioning both words
- After: Also finds "AMPK neuroprotection", "biguanide cognitive", etc.
|