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adding ollama embeddings and expanding to metadata search

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Aljaz Ceru
2025-10-23 06:58:34 +02:00
parent 5d964dfd54
commit 8b6d241921
11 changed files with 289 additions and 35 deletions
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30
backend/modules/rag/main.py
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@@ -148,8 +148,8 @@ class RAGModule(BaseModule):
if config:
self.config.update(config)
# Ensure embedding model configured (defaults to local BGE small)
default_embedding_model = getattr(settings, 'RAG_EMBEDDING_MODEL', 'BAAI/bge-small-en')
# Ensure embedding model configured (defaults to local BGE-M3)
default_embedding_model = getattr(settings, 'RAG_EMBEDDING_MODEL', 'bge-m3')
self.config.setdefault("embedding_model", default_embedding_model)
self.default_embedding_model = default_embedding_model
@@ -431,20 +431,20 @@ class RAGModule(BaseModule):
async def _initialize_embedding_model(self):
"""Initialize embedding model"""
from app.services.embedding_service import embedding_service
from app.services.ollama_embedding_service import ollama_embedding_service
model_name = self.config.get("embedding_model", self.default_embedding_model)
embedding_service.model_name = model_name
ollama_embedding_service.model_name = model_name
# Initialize the embedding service
success = await embedding_service.initialize()
success = await ollama_embedding_service.initialize()
if success:
self.embedding_service = embedding_service
self.embedding_service = ollama_embedding_service
logger.info(f"Successfully initialized embedding service with {model_name}")
return {
"model_name": model_name,
"dimension": embedding_service.dimension or 384
"dimension": ollama_embedding_service.dimension or 1024
}
else:
# Fallback to mock implementation
@@ -452,7 +452,7 @@ class RAGModule(BaseModule):
self.embedding_service = None
return {
"model_name": model_name,
"dimension": 384 # Default dimension matching local bge-small embeddings
"dimension": 1024 # Default dimension matching BGE-M3 embeddings
}
async def _initialize_content_processing(self):
@@ -596,7 +596,7 @@ class RAGModule(BaseModule):
# Create collection with the current embedding dimension
vector_dimension = self.embedding_model.get(
"dimension",
getattr(self.embedding_service, "dimension", 384) or 384
getattr(self.embedding_service, "dimension", 1024) or 1024
)
self.qdrant_client.create_collection(
@@ -664,7 +664,7 @@ class RAGModule(BaseModule):
else:
# Fallback to deterministic random embedding for consistency
np.random.seed(hash(text) % 2**32)
fallback_dim = self.embedding_model.get("dimension", getattr(self.embedding_service, "dimension", 384) or 384)
fallback_dim = self.embedding_model.get("dimension", getattr(self.embedding_service, "dimension", 1024) or 1024)
return np.random.random(fallback_dim).tolist()
async def _generate_embeddings(self, texts: List[str], is_document: bool = True) -> List[List[float]]:
@@ -1617,11 +1617,11 @@ class RAGModule(BaseModule):
# Special handling for collections with different vector dimensions
SPECIAL_COLLECTIONS = {
"bitbox02_faq_local": {
"dimension": 384,
"dimension": 1024,
"model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
},
"bitbox_local_rag": {
"dimension": 384,
"dimension": 1024,
"model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
}
}