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refactor: update model field types and improve logging in ConfigService

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Sosokker 2025-06-27 21:51:04 +07:00
parent a37e457a01
commit fc0d1a3a16
4 changed files with 60 additions and 314 deletions
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11
app/schemas/models.py
View File

@ -1,5 +1,4 @@
from pydantic import BaseModel, Field
from typing import Optional
class QueryRequest(BaseModel):
@ -17,13 +16,13 @@ class IngestResponse(BaseModel):
class ConfigUpdateRequest(BaseModel):
embedding_model: Optional[str] = Field(
embedding_model: str | None = Field(
None, description="Name of the embedding model to use"
)
reranker_model: Optional[str] = Field(
reranker_model: str | None = Field(
None, description="Name of the reranker model to use"
)
llm_model: Optional[str] = Field(None, description="Name of the LLM model to use")
llm_provider: Optional[str] = Field(
llm_model: str | None = Field(None, description="Name of the LLM model to use")
llm_provider: str | None = Field(
None, description="Name of the LLM provider to use"
)
)

32
app/services/config_service.py
View File

@ -18,13 +18,11 @@ class ConfigService:
"reranker_model": False,
}
# Register available models
self._register_models()
def _register_models(self):
# Register embedding models
"""Register all default models"""
embedding_model_registry.register("MiniLMEmbeddingModel", MiniLMEmbeddingModel)
# Register reranker models
reranker_registry.register("MiniLMReranker", MiniLMReranker)
async def initialize_models(self):
@ -33,7 +31,7 @@ class ConfigService:
default_embedding_model_name = settings.EMBEDDING_MODEL
await self.set_embedding_model(default_embedding_model_name)
logger.info(
f"Default embedding model initialized: {default_embedding_model_name}"
"Default embedding model initialized: %s", default_embedding_model_name
)
# Initialize default reranker model (if any)
@ -42,7 +40,7 @@ class ConfigService:
default_reranker_model_name = "MiniLMReranker" # Or from settings
await self.set_reranker_model(default_reranker_model_name)
logger.info(
f"Default reranker model initialized: {default_reranker_model_name}"
"Default reranker model initialized: %s", default_reranker_model_name
)
async def set_embedding_model(self, model_name: str) -> str:
@ -57,21 +55,22 @@ class ConfigService:
try:
self._loading_status["embedding_model"] = True
logger.info(f"Attempting to load embedding model: {model_name}")
logger.info("Attempting to load embedding model: %s", model_name)
model_constructor = embedding_model_registry.get(model_name)
self._current_embedding_model = model_constructor()
settings.EMBEDDING_MODEL = model_name # Update settings
logger.info(f"Successfully loaded embedding model: {model_name}")
return f"Embedding model set to '{model_name}' successfully."
except KeyError:
logger.warning(f"Embedding model '{model_name}' not found in registry.")
logger.warning("Embedding model '%s' not found in registry.", model_name)
return (
f"Embedding model '{model_name}' not available. "
f"Current model remains '{self._current_embedding_model.__class__.__name__ if self._current_embedding_model else 'None'}'."
)
except Exception as e:
logger.exception(f"Error loading embedding model {model_name}: {e}")
logger.exception("Error loading embedding model %s: %s", model_name, e)
return f"Failed to load embedding model '{model_name}': {e}"
else:
logger.info("Successfully loaded embedding model: %s", model_name)
return f"Embedding model set to '{model_name}' successfully."
finally:
self._loading_status["embedding_model"] = False
@ -87,21 +86,22 @@ class ConfigService:
try:
self._loading_status["reranker_model"] = True
logger.info(f"Attempting to load reranker model: {model_name}")
logger.info("Attempting to load reranker model: %s", model_name)
model_constructor = reranker_registry.get(model_name)
self._current_reranker_model = model_constructor()
# settings.RERANKER_MODEL = model_name # Add this to settings if you want to persist
logger.info(f"Successfully loaded reranker model: {model_name}")
return f"Reranker model set to '{model_name}' successfully."
# settings.RERANKER_MODEL = model_name
except KeyError:
logger.warning(f"Reranker model '{model_name}' not found in registry.")
logger.warning("Reranker model '%s' not found in registry.", model_name)
return (
f"Reranker model '{model_name}' not available. "
f"Current model remains '{self._current_reranker_model.__class__.__name__ if self._current_reranker_model else 'None'}'."
)
except Exception as e:
logger.exception(f"Error loading reranker model {model_name}: {e}")
logger.exception("Error loading reranker model %s: %s", model_name, e)
return f"Failed to load reranker model '{model_name}': {e}"
else:
logger.info("Successfully loaded reranker model: %s", model_name)
return f"Reranker model set to '{model_name}' successfully."
finally:
self._loading_status["reranker_model"] = False

48
app/services/rag_service.py
View File

@ -4,6 +4,8 @@ from pathlib import Path
from typing import TypedDict
import litellm
from PyPDF2 import PdfReader
from PyPDF2.errors import PyPdfError
from structlog import get_logger
from app.core.interfaces import EmbeddingModel, Reranker, VectorDB
@ -67,8 +69,29 @@ Answer:"""
self.vector_db.upsert_documents(documents_to_upsert)
def ingest_document(self, file_path: Path, source_name: str):
with Path(file_path).open("r", encoding="utf-8") as f:
text = f.read()
path = Path(file_path)
ext = path.suffix
text = ""
if ext == ".pdf":
try:
reader = PdfReader(str(file_path))
text = "\n".join(page.extract_text() or "" for page in reader.pages)
except PyPdfError as e:
logger.exception("PDF processing error for %s", file_path)
raise ValueError(
f"Failed to extract text from PDF due to a PDF processing error: {e}"
) from e
except Exception as e:
logger.exception(
"An unexpected error occurred during PDF processing for %s",
file_path,
)
raise RuntimeError(
f"An unexpected error occurred during PDF processing: {e}"
) from e
else:
with Path(file_path).open("r", encoding="utf-8") as f:
text = f.read()
text_chunks = self._split_text(text)
self._ingest_document(text_chunks, source_name)
@ -102,8 +125,14 @@ Answer:"""
max_tokens=500,
)
answer_text = response.choices[0].message.content.strip()
answer_text = None
choices = getattr(response, "choices", None)
if choices and len(choices) > 0:
first_choice = choices[0]
message = getattr(first_choice, "message", None)
content = getattr(message, "content", None)
if content:
answer_text = content.strip()
if not answer_text:
answer_text = "No answer generated"
sources = ["No sources"]
@ -146,12 +175,13 @@ Answer:"""
stream=True,
)
# Yield each chunk of the response as it's generated
for chunk in response:
if chunk.choices:
delta = chunk.choices[0].delta
if hasattr(delta, "content") and delta.content:
yield f'data: {{"token": "{json.dumps(delta.content)}"}}\n\n'
choices = getattr(chunk, "choices", None)
if choices and len(choices) > 0:
delta = getattr(choices[0], "delta", None)
content = getattr(delta, "content", None)
if content:
yield f'data: {{"token": {json.dumps(content)}}}\n\n'
# Yield sources at the end
yield f'data: {{"sources": {json.dumps(sources)}}}\n\n'

283
app/services/rag_service_v1.py
View File

@ -1,283 +0,0 @@
import os
from collections.abc import Generator
from pathlib import Path
from typing import TypedDict
import litellm
import numpy as np
import psycopg2
from dotenv import load_dotenv
from psycopg2 import extras
from psycopg2.extensions import AsIs, register_adapter
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
from structlog import get_logger
from app.core.config import settings
from app.core.exception import DocumentExtractionError, DocumentInsertionError
from app.core.utils import RecursiveCharacterTextSplitter
register_adapter(np.ndarray, AsIs) # for psycopg2 adapt
register_adapter(np.float32, AsIs) # for psycopg2 adapt
logger = get_logger()
# pyright: reportArgumentType=false
# Load environment variables
load_dotenv()
# Initialize the embedding model globally to load it only once
EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
EMBEDDING_DIM = 384 # Dimension of the all-MiniLM-L6-v2 model
os.environ["GEMINI_API_KEY"] = settings.GEMINI_API_KEY
class AnswerResult(TypedDict):
answer: str
sources: list[str]
class RAGService:
def __init__(self):
logger.info("Initializing RAGService...")
# Load the embedding model ONCE
self.embedding_model = SentenceTransformer(
"all-MiniLM-L6-v2", device="cpu"
) # Use 'cuda' if GPU is available
self.db_conn = psycopg2.connect(
host=settings.POSTGRES_SERVER,
port=settings.POSTGRES_PORT,
user=settings.POSTGRES_USER,
password=settings.POSTGRES_PASSWORD,
dbname=settings.POSTGRES_DB,
)
logger.info("RAGService initialized.")
self.prompt = """Answer the question based on the following context.
If you don't know the answer, say you don't know. Don't make up an answer.
Context:
{context}
Question: {question}
Answer:"""
def _split_text(
self, text: str, chunk_size: int = 500, chunk_overlap: int = 100
) -> list[str]:
"""
Split text into chunks with specified size and overlap.
Args:
text: Input text to split
chunk_size: Maximum size of each chunk in characters
chunk_overlap: Number of characters to overlap between chunks
Returns:
List of text chunks
"""
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
)
return text_splitter.split_text(text)
def _get_embedding(self, text: str, show_progress_bar: bool = False) -> np.ndarray:
"""
Generate embedding for a text chunk.
Args:
text: Input text to embed
show_progress_bar: Whether to show a progress bar
Returns:
Numpy array containing the embedding vector
"""
return EMBEDDING_MODEL.encode(
text, convert_to_numpy=True, show_progress_bar=show_progress_bar
)
def _store_document(
self, contents: list[str], embeddings: list[np.ndarray], source: str
) -> int:
"""
Store a document chunk in the database.
Args:
contents: List of text content of the chunk
embeddings: List of embedding vectors of the chunk
source: Source file path
Returns:
ID of the inserted document
"""
data_to_insert = [
(chunk, f"[{', '.join(map(str, embedding))}]", source)
for chunk, embedding in zip(contents, embeddings, strict=True)
]
query = """
INSERT INTO documents (content, embedding, source)
VALUES %s
RETURNING id
"""
with self.db_conn.cursor() as cursor:
extras.execute_values(
cursor,
query,
data_to_insert,
template="(%s, %s::vector, %s)",
page_size=100,
)
inserted_ids = [row[0] for row in cursor.fetchall()]
self.db_conn.commit()
if not inserted_ids:
raise DocumentInsertionError("No documents were inserted.")
logger.info("Successfully bulk-ingested %d documents", len(inserted_ids))
logger.info("Inserted document IDs: %s", inserted_ids)
return inserted_ids[0]
def _extract_text_from_pdf(self, pdf_path: str) -> str:
"""
Extract text from a PDF file.
Args:
pdf_path: Path to the PDF file
Returns:
Extracted text as a single string
"""
try:
reader = PdfReader(pdf_path)
text = ""
for page in reader.pages:
text += page.extract_text() + "\n"
return text.strip()
except Exception as e:
raise DocumentExtractionError(
"Error extracting text from PDF: " + str(e)
) from e
def _get_relevant_context(self, question: str, top_k: int) -> list[tuple[str, str]]:
"""Get the most relevant document chunks for a given question"""
question_embedding = self.embedding_model.encode(
question, convert_to_numpy=True
)
try:
with self.db_conn.cursor() as cursor:
cursor.execute(
"""
SELECT content, source
FROM documents
ORDER BY embedding <-> %s::vector
LIMIT %s
""",
(question_embedding.tolist(), top_k),
)
results = cursor.fetchall()
return results
except Exception as e:
logger.exception("Error retrieving context: %s", e)
return []
def ingest_document(self, file_path: str, filename: str):
logger.info("Ingesting %s...", filename)
if not Path(file_path).exists():
err = f"File not found: {filename}"
raise FileNotFoundError(err)
logger.info("Processing PDF: %s : %s", filename, file_path)
text = self._extract_text_from_pdf(file_path)
if not text.strip():
err = "No text could be extracted from the PDF"
raise ValueError(err)
chunks = self._split_text(text)
logger.info("Split PDF into %d chunks", len(chunks))
embeddings = self._get_embedding(chunks, show_progress_bar=True)
self._store_document(chunks, embeddings, filename)
logger.info("Successfully processed %d chunks from %s", len(chunks), filename)
def answer_query(self, question: str) -> AnswerResult:
relevant_context = self._get_relevant_context(question, 5)
context_str = "\n\n".join([chunk[0] for chunk in relevant_context])
sources = list({chunk[1] for chunk in relevant_context if chunk[1]})
try:
response = litellm.completion(
model="gemini/gemini-2.0-flash",
messages=[
{
"role": "system",
"content": "You are a helpful assistant that answers questions based on the provided context.",
},
{
"role": "user",
"content": self.prompt.format(
context=context_str, question=question
),
},
],
temperature=0.1,
max_tokens=500,
)
answer_text = response.choices[0].message.content.strip()
if not answer_text:
answer_text = "No answer generated"
sources = ["No sources"]
return AnswerResult(answer=answer_text, sources=sources)
except Exception:
logger.exception("Error generating response")
return AnswerResult(
answer="Error generating response", sources=["No sources"]
)
def answer_query_stream(self, question: str) -> Generator[str, None, None]:
"""Answer a query using streaming."""
relevant_context = self._get_relevant_context(question, 5)
context_str = "\n\n".join([chunk[0] for chunk in relevant_context])
sources = list({chunk[1] for chunk in relevant_context if chunk[1]})
prompt = self.prompt.format(context=context_str, question=question)
try:
response = litellm.completion(
model="gemini/gemini-2.0-flash",
messages=[{"role": "user", "content": prompt}],
stream=True,
)
# First, yield the sources so the UI can display them immediately
import json
sources_json = json.dumps(sources)
yield f'data: {{"sources": {sources_json}}}\n\n'
# Then, stream the answer tokens
for chunk in response:
token = chunk.choices[0].delta.content
if token: # Ensure there's content to send
# SSE format: data: {"token": "..."}\n\n
yield f'data: {{"token": "{json.dumps(token)}"}}\n\n'
# Signal the end of the stream with a special message
yield 'data: {"end_of_stream": true}\n\n'
except Exception:
logger.exception("Error generating response")
yield 'data: {"error": "Error generating response"}\n\n'