215 lines
7.4 KiB
Python
215 lines
7.4 KiB
Python
"""RAG service for embedding, retrieval, and response generation."""
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import uuid
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from typing import TYPE_CHECKING, List, Tuple, Dict, Any, Optional
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import logging
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from app.core.config import Settings
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from app.core.database import get_chroma_client
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if TYPE_CHECKING:
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from app.services.prompt_service import PromptService
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logger = logging.getLogger(__name__)
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class RAGService:
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"""Service for document ingestion, retrieval, and response generation."""
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def __init__(
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self,
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chroma_client=None,
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llm_client=None,
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settings: Optional[Settings] = None,
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prompt_service: "PromptService | None" = None,
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):
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self.chroma_client = chroma_client or get_chroma_client()
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self.llm_client = llm_client
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self.settings = settings
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self._prompt_service = prompt_service
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self._collection = None
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@property
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def collection(self):
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if self._collection is None:
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from app.core.database import get_or_create_collection, get_embedding_function_settings
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embedding_fn = None
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if self.settings is not None:
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embedding_fn = get_embedding_function_settings(self.settings)
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self._collection = get_or_create_collection(
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self.chroma_client, "documents", embedding_function=embedding_fn
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)
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return self._collection
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def ingest_document(
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self,
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file_path: str,
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chunks: List[str],
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metadata_list: List[Dict[str, Any]],
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document_id: Optional[str] = None,
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) -> str:
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if not chunks:
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return ""
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document_id = document_id or str(uuid.uuid4())
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ids = [f"{document_id}_{i}" for i in range(len(chunks))]
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self.collection.add(
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documents=chunks,
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metadatas=metadata_list,
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ids=ids,
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)
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return document_id
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def retrieve(
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self,
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query_keywords: List[str],
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n_results: int = 10,
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) -> List[Tuple[str, Dict[str, Any], float]]:
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query_text = " ".join(query_keywords)
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results = self.collection.query(
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query_texts=[query_text],
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n_results=n_results,
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)
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chunks = []
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if results["documents"] and results["documents"][0]:
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for i, doc in enumerate(results["documents"][0]):
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metadata = results["metadatas"][0][i] if results["metadatas"][0] else {}
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distance = results["distances"][0][i] if results["distances"][0] else 0.0
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chunks.append((doc, metadata, distance))
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return chunks
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async def generate_response(
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self,
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question: str,
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chunks: List[str],
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metadata_list: List[Dict[str, Any]],
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) -> Tuple[str, str]:
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"""Generate a RAG response and return it alongside the prompt used.
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Args:
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question: The user's question.
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chunks: Retrieved chunk texts.
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metadata_list: Metadata dicts corresponding to each chunk.
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Returns:
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A tuple of (answer, prompt). answer is the LLM-generated response
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(or a fallback message). prompt is the rendered prompt string, or
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``""`` when no prompt was built.
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"""
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if not chunks:
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return "I could not find any relevant information to answer your question.", ""
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if self.llm_client is None:
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return "LLM client not configured.", ""
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context_parts = []
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for i, (chunk, meta) in enumerate(zip(chunks, metadata_list)):
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source = meta.get("filename", "unknown")
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summary = meta.get("content_summary", "")
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page_num = meta.get("page_number")
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citation_label = f"{source}, page {page_num}" if page_num else source
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context_parts.append(
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f"[{citation_label}] Source: {source}\n"
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f"Summary: {summary}\n"
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f"Content: {chunk}\n"
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)
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context = "\n".join(context_parts)
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if self._prompt_service is not None:
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template = self._prompt_service.get_prompt_template("generate")
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else:
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template = (
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f"Question: {'{question}'}\n\n"
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f"Answer the question using ONLY these document chunks. "
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f"Do not use any external knowledge. "
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f"Format your answer as bullet points. "
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f"Cite your sources inline using the exact bracket labels provided, "
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f"e.g. [filename, page N]. Place the citation at the end of each relevant point.\n\n"
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f"Document chunks:\n{'{context}'}\n\n"
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f"Answer:"
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)
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# str.replace is safe even with stray curly braces in user text.
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prompt = template.replace("{question}", question).replace("{context}", context)
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result = await self.llm_client.complete(prompt=prompt, temperature=0.3, step_name="ResponseGeneration")
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return result, prompt
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def list_documents(self) -> Tuple[List[Dict[str, Any]], int, int]:
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from collections import defaultdict
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all_data = self.collection.get(include=["metadatas"])
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if not all_data["metadatas"]:
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return [], 0, 0
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docs = defaultdict(lambda: {"filename": "", "chunk_count": 0, "upload_date": ""})
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for chunk_id, meta in zip(all_data["ids"], all_data["metadatas"]):
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parts = chunk_id.rsplit("_", 1)
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doc_id = parts[0] if len(parts) == 2 else chunk_id
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docs[doc_id]["filename"] = meta.get("filename", "unknown")
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docs[doc_id]["chunk_count"] += 1
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docs[doc_id]["upload_date"] = meta.get("upload_date", "")
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total_chunks = sum(d["chunk_count"] for d in docs.values())
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doc_list = [
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{
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"document_id": doc_id,
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"filename": info["filename"],
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"chunk_count": info["chunk_count"],
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"upload_date": info["upload_date"],
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}
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for doc_id, info in docs.items()
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]
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return doc_list, len(doc_list), total_chunks
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def list_chunks(self, document_id: str) -> List[Dict[str, Any]]:
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all_data = self.collection.get(include=["metadatas"])
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chunks = []
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for chunk_id, meta in zip(all_data["ids"], all_data["metadatas"]):
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if chunk_id.startswith(f"{document_id}_"):
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chunks.append({
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"chunk_id": chunk_id,
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"chunk_index": meta.get("chunk_index", 0),
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"content_summary": meta.get("content_summary", ""),
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"page_number": meta.get("page_number"),
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"chunk_file_path": meta.get("chunk_file_path"),
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})
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chunks.sort(key=lambda x: x["chunk_index"])
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return chunks
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def delete_document(self, document_id: str) -> Tuple[bool, int]:
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all_data = self.collection.get(include=["metadatas"])
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ids_to_delete = [
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chunk_id for chunk_id in all_data["ids"]
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if chunk_id.startswith(f"{document_id}_")
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]
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if not ids_to_delete:
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return False, 0
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self.collection.delete(ids=ids_to_delete)
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return True, len(ids_to_delete)
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def delete_chunk(self, chunk_id: str) -> bool:
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all_data = self.collection.get(include=["metadatas"])
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if chunk_id not in all_data["ids"]:
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return False
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self.collection.delete(ids=[chunk_id])
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return True
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