adjustments to rag engine
This commit is contained in:
+226
-217
@@ -8,248 +8,257 @@ from chromadb.config import Settings
|
||||
from src import models
|
||||
from src import mcp_client
|
||||
|
||||
try:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
except ImportError:
|
||||
SentenceTransformer = None
|
||||
_SENTENCE_TRANSFORMERS = None
|
||||
_GOOGLE_GENAI = None
|
||||
|
||||
from google import genai
|
||||
from google.genai import types
|
||||
from src import ai_client
|
||||
def _get_sentence_transformers():
|
||||
global _SENTENCE_TRANSFORMERS
|
||||
if _SENTENCE_TRANSFORMERS is None:
|
||||
from sentence_transformers import SentenceTransformer
|
||||
_SENTENCE_TRANSFORMERS = SentenceTransformer
|
||||
return _SENTENCE_TRANSFORMERS
|
||||
|
||||
def _get_google_genai():
|
||||
global _GOOGLE_GENAI
|
||||
if _GOOGLE_GENAI is None:
|
||||
from google import genai
|
||||
from google.genai import types
|
||||
_GOOGLE_GENAI = (genai, types)
|
||||
return _GOOGLE_GENAI
|
||||
|
||||
class BaseEmbeddingProvider:
|
||||
def embed(self, texts: List[str]) -> List[List[float]]:
|
||||
raise NotImplementedError()
|
||||
def embed(self, texts: List[str]) -> List[List[float]]:
|
||||
raise NotImplementedError()
|
||||
|
||||
class LocalEmbeddingProvider(BaseEmbeddingProvider):
|
||||
def __init__(self, model_name: str = 'all-MiniLM-L6-v2'):
|
||||
if SentenceTransformer is None:
|
||||
raise ImportError("sentence-transformers is not installed")
|
||||
self.model = SentenceTransformer(model_name)
|
||||
def __init__(self, model_name: str = 'all-MiniLM-L6-v2'):
|
||||
ST = _get_sentence_transformers()
|
||||
if ST is None:
|
||||
raise ImportError("sentence-transformers is not installed")
|
||||
self.model = ST(model_name)
|
||||
|
||||
def embed(self, texts: List[str]) -> List[List[float]]:
|
||||
embeddings = self.model.encode(texts)
|
||||
return embeddings.tolist()
|
||||
def embed(self, texts: List[str]) -> List[List[float]]:
|
||||
embeddings = self.model.encode(texts)
|
||||
return embeddings.tolist()
|
||||
|
||||
class GeminiEmbeddingProvider(BaseEmbeddingProvider):
|
||||
def __init__(self, model_name: str = 'text-embedding-004'):
|
||||
self.model_name = model_name
|
||||
def __init__(self, model_name: str = 'text-embedding-004'):
|
||||
self.model_name = model_name
|
||||
|
||||
def embed(self, texts: List[str]) -> List[List[float]]:
|
||||
ai_client._ensure_gemini_client()
|
||||
client = ai_client._gemini_client
|
||||
if not client:
|
||||
raise ValueError("Gemini client not initialized")
|
||||
|
||||
# For text-embedding-004, we can embed a batch
|
||||
res = client.models.embed_content(
|
||||
model=self.model_name,
|
||||
contents=texts,
|
||||
config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")
|
||||
)
|
||||
return [e.values for e in res.embeddings]
|
||||
def embed(self, texts: List[str]) -> List[List[float]]:
|
||||
google_module = _get_google_genai()
|
||||
if google_module is None:
|
||||
raise ImportError("google-genai is not installed")
|
||||
genai_pkg, types = google_module
|
||||
from src import ai_client
|
||||
ai_client._ensure_gemini_client()
|
||||
client = ai_client._gemini_client
|
||||
if not client:
|
||||
raise ValueError("Gemini client not initialized")
|
||||
res = client.models.embed_content(
|
||||
model=self.model_name,
|
||||
contents=texts,
|
||||
config=types.EmbedContentConfig(task_type="RETRIEVAL_DOCUMENT")
|
||||
)
|
||||
return [e.values for e in res.embeddings]
|
||||
|
||||
class RAGEngine:
|
||||
def __init__(self, config: models.RAGConfig, base_dir: str = "."):
|
||||
self.config = config
|
||||
self.base_dir = base_dir
|
||||
self.client = None
|
||||
self.collection = None
|
||||
self.embedding_provider = None
|
||||
|
||||
if not self.config.enabled:
|
||||
return
|
||||
|
||||
self._init_embedding_provider()
|
||||
self._init_vector_store()
|
||||
def __init__(self, config: models.RAGConfig, base_dir: str = "."):
|
||||
self.config = config
|
||||
self.base_dir = base_dir
|
||||
self.client = None
|
||||
self.collection = None
|
||||
self.embedding_provider = None
|
||||
|
||||
def _init_embedding_provider(self):
|
||||
if self.config.embedding_provider == 'gemini':
|
||||
self.embedding_provider = GeminiEmbeddingProvider()
|
||||
elif self.config.embedding_provider == 'local':
|
||||
self.embedding_provider = LocalEmbeddingProvider()
|
||||
else:
|
||||
raise ValueError(f"Unknown embedding provider: {self.config.embedding_provider}")
|
||||
if not self.config.enabled:
|
||||
return
|
||||
|
||||
def _init_vector_store(self):
|
||||
vs_config = self.config.vector_store
|
||||
if vs_config.provider == 'chroma':
|
||||
db_path = os.path.join(self.base_dir, ".slop_cache", "chroma_db")
|
||||
os.makedirs(db_path, exist_ok=True)
|
||||
self.client = chromadb.PersistentClient(path=db_path)
|
||||
self.collection = self.client.get_or_create_collection(name=vs_config.collection_name)
|
||||
elif vs_config.provider == 'mock':
|
||||
self.client = "mock"
|
||||
self.collection = "mock"
|
||||
else:
|
||||
raise ValueError(f"Unknown vector store provider: {vs_config.provider}")
|
||||
self._init_embedding_provider()
|
||||
self._init_vector_store()
|
||||
|
||||
def is_empty(self) -> bool:
|
||||
if not self.config.enabled:
|
||||
return True
|
||||
if self.config.vector_store.provider == 'mock' or self.collection == "mock":
|
||||
return True
|
||||
if self.collection is None:
|
||||
return True
|
||||
return self.collection.count() == 0
|
||||
def _init_embedding_provider(self):
|
||||
if self.config.embedding_provider == 'gemini':
|
||||
self.embedding_provider = GeminiEmbeddingProvider()
|
||||
elif self.config.embedding_provider == 'local':
|
||||
self.embedding_provider = LocalEmbeddingProvider()
|
||||
else:
|
||||
raise ValueError(f"Unknown embedding provider: {self.config.embedding_provider}")
|
||||
|
||||
def add_documents(self, ids: List[str], texts: List[str], metadatas: Optional[List[Dict[str, Any]]] = None):
|
||||
"""
|
||||
[C: tests/test_rag_engine.py:test_rag_engine_chroma]
|
||||
"""
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
embeddings = self.embedding_provider.embed(texts)
|
||||
self.collection.upsert(
|
||||
ids=ids,
|
||||
embeddings=embeddings,
|
||||
documents=texts,
|
||||
metadatas=metadatas
|
||||
)
|
||||
def _init_vector_store(self):
|
||||
vs_config = self.config.vector_store
|
||||
if vs_config.provider == 'chroma':
|
||||
db_path = os.path.join(self.base_dir, ".slop_cache", "chroma_db")
|
||||
os.makedirs(db_path, exist_ok=True)
|
||||
self.client = chromadb.PersistentClient(path=db_path)
|
||||
self.collection = self.client.get_or_create_collection(name=vs_config.collection_name)
|
||||
elif vs_config.provider == 'mock':
|
||||
self.client = "mock"
|
||||
self.collection = "mock"
|
||||
else:
|
||||
raise ValueError(f"Unknown vector store provider: {vs_config.provider}")
|
||||
|
||||
def _chunk_text(self, content: str) -> List[str]:
|
||||
"""Character-based chunking with overlap."""
|
||||
chunks = []
|
||||
if not content:
|
||||
return chunks
|
||||
chunk_size = self.config.chunk_size
|
||||
overlap = self.config.chunk_overlap
|
||||
start = 0
|
||||
while start < len(content):
|
||||
end = start + chunk_size
|
||||
chunks.append(content[start:end])
|
||||
if end >= len(content):
|
||||
break
|
||||
start += (chunk_size - overlap)
|
||||
return chunks
|
||||
def is_empty(self) -> bool:
|
||||
if not self.config.enabled:
|
||||
return True
|
||||
if self.config.vector_store.provider == 'mock' or self.collection == "mock":
|
||||
return True
|
||||
if self.collection is None:
|
||||
return True
|
||||
return self.collection.count() == 0
|
||||
|
||||
def _chunk_code(self, content: str, file_path: str) -> List[str]:
|
||||
"""AST-aware chunking for Python code."""
|
||||
try:
|
||||
from src.file_cache import ASTParser
|
||||
parser = ASTParser("python")
|
||||
tree = parser.parse(content)
|
||||
chunks = []
|
||||
|
||||
# Capture classes and top-level functions
|
||||
for node in tree.root_node.children:
|
||||
if node.type in ("function_definition", "class_definition"):
|
||||
chunks.append(content[node.start_byte:node.end_byte])
|
||||
|
||||
# Fallback if no structural chunks found or if file is small
|
||||
if not chunks or len(content) < self.config.chunk_size:
|
||||
return self._chunk_text(content)
|
||||
return chunks
|
||||
except Exception:
|
||||
return self._chunk_text(content)
|
||||
def add_documents(self, ids: List[str], texts: List[str], metadatas: Optional[List[Dict[str, Any]]] = None):
|
||||
"""
|
||||
[C: tests/test_rag_engine.py:test_rag_engine_chroma]
|
||||
"""
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
embeddings = self.embedding_provider.embed(texts)
|
||||
self.collection.upsert(
|
||||
ids=ids,
|
||||
embeddings=embeddings,
|
||||
documents=texts,
|
||||
metadatas=metadatas
|
||||
)
|
||||
|
||||
def index_file(self, file_path: str):
|
||||
"""Reads, chunks, and indexes a file into the vector store."""
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
|
||||
full_path = os.path.join(self.base_dir, file_path)
|
||||
if not os.path.exists(full_path):
|
||||
return
|
||||
|
||||
try:
|
||||
mtime = os.path.getmtime(full_path)
|
||||
except Exception:
|
||||
return
|
||||
def _chunk_text(self, content: str) -> List[str]:
|
||||
"""Character-based chunking with overlap."""
|
||||
chunks = []
|
||||
if not content:
|
||||
return chunks
|
||||
chunk_size = self.config.chunk_size
|
||||
overlap = self.config.chunk_overlap
|
||||
start = 0
|
||||
while start < len(content):
|
||||
end = start + chunk_size
|
||||
chunks.append(content[start:end])
|
||||
if end >= len(content):
|
||||
break
|
||||
start += (chunk_size - overlap)
|
||||
return chunks
|
||||
|
||||
# Incremental check: see if we already have this file with the same mtime
|
||||
try:
|
||||
res = self.collection.get(where={"path": file_path}, limit=1, include=["metadatas"])
|
||||
if res and res["metadatas"] and res["metadatas"][0]:
|
||||
if res["metadatas"][0].get("mtime") == mtime:
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
def _chunk_code(self, content: str, file_path: str) -> List[str]:
|
||||
"""AST-aware chunking for Python code."""
|
||||
try:
|
||||
from src.file_cache import ASTParser
|
||||
parser = ASTParser("python")
|
||||
tree = parser.parse(content)
|
||||
chunks = []
|
||||
|
||||
try:
|
||||
with open(full_path, "r", encoding="utf-8", errors="ignore") as f:
|
||||
content = f.read()
|
||||
except Exception:
|
||||
return
|
||||
for node in tree.root_node.children:
|
||||
if node.type in ("function_definition", "class_definition"):
|
||||
chunks.append(content[node.start_byte:node.end_byte])
|
||||
|
||||
# Remove old entries for this file
|
||||
self.collection.delete(where={"path": file_path})
|
||||
|
||||
if file_path.lower().endswith(".py"):
|
||||
chunks = self._chunk_code(content, file_path)
|
||||
else:
|
||||
chunks = self._chunk_text(content)
|
||||
|
||||
if not chunks:
|
||||
return
|
||||
if not chunks or len(content) < self.config.chunk_size:
|
||||
return self._chunk_text(content)
|
||||
return chunks
|
||||
except Exception:
|
||||
return self._chunk_text(content)
|
||||
|
||||
ids = [f"{file_path}_{i}" for i in range(len(chunks))]
|
||||
metadatas = [{"path": file_path, "chunk": i, "mtime": mtime} for i in range(len(chunks))]
|
||||
self.add_documents(ids, chunks, metadatas)
|
||||
def index_file(self, file_path: str):
|
||||
"""Reads, chunks, and indexes a file into the vector store."""
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
|
||||
def _search_mcp(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
|
||||
async def _async_search_mcp():
|
||||
tool_name = self.config.vector_store.mcp_tool or "rag_search"
|
||||
args = {"query": query, "top_k": top_k}
|
||||
res_str = await mcp_client.async_dispatch(tool_name, args)
|
||||
try:
|
||||
data = json.loads(res_str)
|
||||
if isinstance(data, list):
|
||||
return data
|
||||
elif isinstance(data, dict) and "results" in data:
|
||||
return data["results"]
|
||||
return []
|
||||
except:
|
||||
return []
|
||||
full_path = os.path.join(self.base_dir, file_path)
|
||||
if not os.path.exists(full_path):
|
||||
return
|
||||
|
||||
return asyncio.run(_async_search_mcp())
|
||||
try:
|
||||
mtime = os.path.getmtime(full_path)
|
||||
except Exception:
|
||||
return
|
||||
|
||||
def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
[C: tests/mock_concurrent_mma.py:main, tests/test_rag_engine.py:test_rag_engine_chroma]
|
||||
"""
|
||||
if not self.config.enabled:
|
||||
return []
|
||||
if self.config.vector_store.provider == 'mcp':
|
||||
return self._search_mcp(query, top_k)
|
||||
if self.collection == "mock":
|
||||
return []
|
||||
|
||||
query_embedding = self.embedding_provider.embed([query])[0]
|
||||
results = self.collection.query(
|
||||
query_embeddings=[query_embedding],
|
||||
n_results=top_k
|
||||
)
|
||||
|
||||
ret = []
|
||||
if results and results["ids"] and results["ids"][0]:
|
||||
for i in range(len(results["ids"][0])):
|
||||
ret.append({
|
||||
"id": results["ids"][0][i],
|
||||
"document": results["documents"][0][i],
|
||||
"metadata": results["metadatas"][0][i] if results["metadatas"] else {},
|
||||
"distance": results["distances"][0][i] if "distances" in results and results["distances"] else 0.0
|
||||
})
|
||||
return ret
|
||||
try:
|
||||
res = self.collection.get(where={"path": file_path}, limit=1, include=["metadatas"])
|
||||
if res and res["metadatas"] and res["metadatas"][0]:
|
||||
if res["metadatas"][0].get("mtime") == mtime:
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def delete_documents(self, ids: List[str]):
|
||||
"""
|
||||
[C: tests/test_rag_engine.py:test_rag_engine_chroma]
|
||||
"""
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
self.collection.delete(ids=ids)
|
||||
try:
|
||||
with open(full_path, "r", encoding="utf-8", errors="ignore") as f:
|
||||
content = f.read()
|
||||
except Exception:
|
||||
return
|
||||
|
||||
def get_all_indexed_paths(self) -> List[str]:
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return []
|
||||
res = self.collection.get(include=["metadatas"])
|
||||
if not res or not res["metadatas"]:
|
||||
return []
|
||||
return list(set(m.get("path") for m in res["metadatas"] if m.get("path")))
|
||||
self.collection.delete(where={"path": file_path})
|
||||
|
||||
def delete_documents_by_path(self, file_paths: List[str]):
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
for path in file_paths:
|
||||
self.collection.delete(where={"path": path})
|
||||
if file_path.lower().endswith(".py"):
|
||||
chunks = self._chunk_code(content, file_path)
|
||||
else:
|
||||
chunks = self._chunk_text(content)
|
||||
|
||||
if not chunks:
|
||||
return
|
||||
|
||||
ids = [f"{file_path}_{i}" for i in range(len(chunks))]
|
||||
metadatas = [{"path": file_path, "chunk": i, "mtime": mtime} for i in range(len(chunks))]
|
||||
self.add_documents(ids, chunks, metadatas)
|
||||
|
||||
def _search_mcp(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
|
||||
async def _async_search_mcp():
|
||||
tool_name = self.config.vector_store.mcp_tool or "rag_search"
|
||||
args = {"query": query, "top_k": top_k}
|
||||
res_str = await mcp_client.async_dispatch(tool_name, args)
|
||||
try:
|
||||
data = json.loads(res_str)
|
||||
if isinstance(data, list):
|
||||
return data
|
||||
elif isinstance(data, dict) and "results" in data:
|
||||
return data["results"]
|
||||
return []
|
||||
except:
|
||||
return []
|
||||
|
||||
return asyncio.run(_async_search_mcp())
|
||||
|
||||
def search(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
[C: tests/mock_concurrent_mma.py:main, tests/test_rag_engine.py:test_rag_engine_chroma]
|
||||
"""
|
||||
if not self.config.enabled:
|
||||
return []
|
||||
if self.config.vector_store.provider == 'mcp':
|
||||
return self._search_mcp(query, top_k)
|
||||
if self.collection == "mock":
|
||||
return []
|
||||
|
||||
query_embedding = self.embedding_provider.embed([query])[0]
|
||||
results = self.collection.query(
|
||||
query_embeddings=[query_embedding],
|
||||
n_results=top_k
|
||||
)
|
||||
|
||||
ret = []
|
||||
if results and results["ids"] and results["ids"][0]:
|
||||
for i in range(len(results["ids"][0])):
|
||||
ret.append({
|
||||
"id": results["ids"][0][i],
|
||||
"document": results["documents"][0][i],
|
||||
"metadata": results["metadatas"][0][i] if results["metadatas"] else {},
|
||||
"distance": results["distances"][0][i] if "distances" in results and results["distances"] else 0.0
|
||||
})
|
||||
return ret
|
||||
|
||||
def delete_documents(self, ids: List[str]):
|
||||
"""
|
||||
[C: tests/test_rag_engine.py:test_rag_engine_chroma]
|
||||
"""
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
self.collection.delete(ids=ids)
|
||||
|
||||
def get_all_indexed_paths(self) -> List[str]:
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return []
|
||||
res = self.collection.get(include=["metadatas"])
|
||||
if not res or not res["metadatas"]:
|
||||
return []
|
||||
return list(set(m.get("path") for m in res["metadatas"] if m.get("path")))
|
||||
|
||||
def delete_documents_by_path(self, file_paths: List[str]):
|
||||
if not self.config.enabled or self.collection == "mock":
|
||||
return
|
||||
for path in file_paths:
|
||||
self.collection.delete(where={"path": path})
|
||||
Reference in New Issue
Block a user