1aa60dada2
Implements three-phase AI assistant feature: - Phase 0: RAG infrastructure with pgvector embeddings, Voyage AI integration, tree chunking service, and semantic search over team's flow library - Phase 1: In-session copilot panel during flow navigation with contextual AI help, current step awareness, and suggested related flows - Phase 2: Standalone AI chat page with persistent conversation history, pin/delete, and configurable retention policies (account-level) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
"""Embedding provider abstraction for RAG.
|
|
|
|
Uses Voyage AI (voyage-3.5, 1024 dims) as the embedding provider.
|
|
Supports document and query input types for asymmetric search.
|
|
"""
|
|
import logging
|
|
from typing import Optional
|
|
|
|
from app.core.config import settings
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
async def get_embedding(
|
|
text: str,
|
|
input_type: str = "document",
|
|
) -> Optional[list[float]]:
|
|
"""Get embedding vector for text using Voyage AI.
|
|
|
|
Args:
|
|
text: The text to embed.
|
|
input_type: "document" for indexing, "query" for search queries.
|
|
|
|
Returns:
|
|
List of floats (1024 dims) or None if embedding service unavailable.
|
|
"""
|
|
if not settings.VOYAGE_API_KEY:
|
|
logger.warning("VOYAGE_API_KEY not set — embedding service unavailable")
|
|
return None
|
|
|
|
try:
|
|
import voyageai
|
|
|
|
client = voyageai.AsyncClient(api_key=settings.VOYAGE_API_KEY)
|
|
result = await client.embed(
|
|
texts=[text],
|
|
model=settings.EMBEDDING_MODEL,
|
|
input_type=input_type,
|
|
)
|
|
return result.embeddings[0]
|
|
except Exception as e:
|
|
logger.error("Embedding failed: %s", e)
|
|
return None
|
|
|
|
|
|
async def get_embeddings_batch(
|
|
texts: list[str],
|
|
input_type: str = "document",
|
|
) -> Optional[list[list[float]]]:
|
|
"""Get embedding vectors for multiple texts in a single API call.
|
|
|
|
Args:
|
|
texts: List of texts to embed.
|
|
input_type: "document" for indexing, "query" for search queries.
|
|
|
|
Returns:
|
|
List of embedding vectors or None if service unavailable.
|
|
"""
|
|
if not texts:
|
|
return []
|
|
|
|
if not settings.VOYAGE_API_KEY:
|
|
logger.warning("VOYAGE_API_KEY not set — embedding service unavailable")
|
|
return None
|
|
|
|
try:
|
|
import voyageai
|
|
|
|
client = voyageai.AsyncClient(api_key=settings.VOYAGE_API_KEY)
|
|
result = await client.embed(
|
|
texts=texts,
|
|
model=settings.EMBEDDING_MODEL,
|
|
input_type=input_type,
|
|
)
|
|
return result.embeddings
|
|
except Exception as e:
|
|
logger.error("Batch embedding failed: %s", e)
|
|
return None
|