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fix: RAG services improvements (#1565)

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* fix: rag nix runner

* feat: improve rag default models

* fix: change log levels to avoid huge log files in rag service
This commit is contained in:
Omar Crespo
2025-03-20 00:28:04 -05:00
committed by GitHub
parent a9ec013f1c
commit 968d5fbd52
4 changed files with 26 additions and 23 deletions
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6
py/rag-service/run.sh
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@@ -1,9 +1,9 @@
#!/bin/bash
#!/usr/bin/env bash
# Set the target directory (use the first argument or default to a temporary directory)
# Set the target directory (use the first argument or default to a local state directory)
TARGET_DIR=$1
if [ -z "$TARGET_DIR" ]; then
TARGET_DIR="/tmp/avante-rag-service"
TARGET_DIR="$HOME/.local/state/avante-rag-service"
fi
# Create the target directory if it doesn't exist
mkdir -p "$TARGET_DIR"

1
py/rag-service/shell.nix
View File

@@ -12,6 +12,7 @@ in pkgs.mkShell {
PYTHONUNBUFFERED = 1;
PYTHONDONTWRITEBYTECODE = 1;
LD_LIBRARY_PATH = "${pkgs.stdenv.cc.cc.lib}/lib:$LD_LIBRARY_PATH";
PORT = 20250;
};
shellHook = ''

36
py/rag-service/src/main.py
View File

@@ -47,7 +47,7 @@ from llama_index.core import (
from llama_index.core.node_parser import CodeSplitter
from llama_index.core.schema import Document
from llama_index.embeddings.ollama import OllamaEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding, OpenAIEmbeddingModelType
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.ollama import Ollama
from llama_index.llms.openai import OpenAI
from llama_index.vector_stores.chroma import ChromaVectorStore
@@ -133,7 +133,7 @@ async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]: # noqa: ARG001
# Start indexing
await index_remote_resource_async(resource)
logger.info("Successfully synced resource: %s", resource.uri)
logger.debug("Successfully synced resource: %s", resource.uri)
except (OSError, ValueError, RuntimeError) as e:
error_msg = f"Failed to sync resource {resource.uri}: {e}"
@@ -359,9 +359,9 @@ else:
if base_url == "":
base_url = "https://api.openai.com/v1"
if rag_embed_model == "":
rag_embed_model = OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002
rag_embed_model = "text-embedding-3-small"
if rag_llm_model == "":
rag_llm_model = "gpt-3.5-turbo"
rag_llm_model = "gpt-4o-mini"
embed_model = OpenAIEmbedding(model=rag_embed_model, api_base=base_url)
llm_model = OpenAI(model=rag_llm_model, api_base=base_url)
@@ -477,10 +477,10 @@ def process_document_batch(documents: list[Document]) -> bool: # noqa: PLR0915,
# Check if document with same hash has already been successfully processed
status_records = indexing_history_service.get_indexing_status(doc=doc)
if status_records and status_records[0].status == "completed":
logger.info("Document with same hash already processed, skipping: %s", doc.doc_id)
logger.debug("Document with same hash already processed, skipping: %s", doc.doc_id)
continue
logger.info("Processing document: %s", doc.doc_id)
logger.debug("Processing document: %s", doc.doc_id)
try:
content = doc.get_content()
@@ -636,7 +636,7 @@ def get_gitcrypt_files(directory: Path) -> list[str]:
# Log if git-crypt patterns were found
if git_crypt_patterns:
logger.info("Excluding git-crypt encrypted files: %s", git_crypt_patterns)
logger.debug("Excluding git-crypt encrypted files: %s", git_crypt_patterns)
except (subprocess.SubprocessError, OSError) as e:
logger.warning("Error getting git-crypt files: %s", str(e))
@@ -752,11 +752,11 @@ def scan_directory(directory: Path) -> list[str]:
for file in file_paths:
file_ext = Path(file).suffix.lower()
if file_ext in binary_extensions:
logger.info("Skipping binary file: %s", file)
logger.debug("Skipping binary file: %s", file)
continue
if spec and spec.match_file(os.path.relpath(file, directory)):
logger.info("Ignoring file: %s", file)
logger.debug("Ignoring file: %s", file)
else:
matched_files.append(file)
@@ -765,13 +765,13 @@ def scan_directory(directory: Path) -> list[str]:
def update_index_for_file(directory: Path, abs_file_path: Path) -> None:
"""Update the index for a single file."""
logger.info("Starting to index file: %s", abs_file_path)
logger.debug("Starting to index file: %s", abs_file_path)
rel_file_path = abs_file_path.relative_to(directory)
spec = get_pathspec(directory)
if spec and spec.match_file(rel_file_path):
logger.info("File is ignored, skipping: %s", abs_file_path)
logger.debug("File is ignored, skipping: %s", abs_file_path)
return
resource = resource_service.get_resource(path_to_uri(directory))
@@ -787,13 +787,13 @@ def update_index_for_file(directory: Path, abs_file_path: Path) -> None:
required_exts=required_exts,
).load_data()
logger.info("Updating index: %s", abs_file_path)
logger.debug("Updating index: %s", abs_file_path)
processed_documents = split_documents(documents)
success = process_document_batch(processed_documents)
if success:
resource_service.update_resource_indexing_status(resource.uri, "indexed", "")
logger.info("File indexing completed: %s", abs_file_path)
logger.debug("File indexing completed: %s", abs_file_path)
else:
resource_service.update_resource_indexing_status(resource.uri, "failed", "unknown error")
logger.error("File indexing failed: %s", abs_file_path)
@@ -858,7 +858,7 @@ async def index_remote_resource_async(resource: Resource) -> None:
resource_service.update_resource_indexing_status(resource.uri, "indexing", "")
url = resource.uri
try:
logger.info("Loading resource content: %s", url)
logger.debug("Loading resource content: %s", url)
# Fetch markdown content
markdown = fetch_markdown(url)
@@ -868,7 +868,7 @@ async def index_remote_resource_async(resource: Resource) -> None:
# Extract links from markdown
links = markdown_to_links(url, markdown)
logger.info("Found %d sub links", len(links))
logger.debug("Found %d sub links", len(links))
logger.debug("Link list: %s", links)
# Use thread pool for parallel batch processing
@@ -885,13 +885,13 @@ async def index_remote_resource_async(resource: Resource) -> None:
# Create documents from links
documents = [Document(text=markdown, doc_id=link) for link, markdown in link_md_pairs]
logger.info("Found %d documents", len(documents))
logger.debug("Found %d documents", len(documents))
logger.debug("Document list: %s", [doc.doc_id for doc in documents])
# Process documents in batches
total_documents = len(documents)
batches = [documents[i : i + BATCH_SIZE] for i in range(0, total_documents, BATCH_SIZE)]
logger.info("Splitting documents into %d batches for processing", len(batches))
logger.debug("Splitting documents into %d batches for processing", len(batches))
with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
results = await loop.run_in_executor(
@@ -901,7 +901,7 @@ async def index_remote_resource_async(resource: Resource) -> None:
# Check processing results
if all(results):
logger.info("Resource %s indexing completed", url)
logger.debug("Resource %s indexing completed", url)
resource_service.update_resource_indexing_status(resource.uri, "indexed", "")
else:
failed_batches = len([r for r in results if not r])