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from typing import List, Dict, Any, Tuple, Optional
from pathlib import Path
from flashrank import Ranker, RerankRequest
from config import (
TOP_K_INITIAL,
TOP_K_RERANKED,
RERANKER_MODEL_NAME,
ENABLE_QUERY_REWRITING,
DUAL_QUERY_MERGE_STRATEGY
)
from vector_store import get_vector_store
from query_rewriter import get_query_rewriter
FLASHRANK_CACHE = str(Path(__file__).resolve().parent / "flashrank_cache")
logger = logging.getLogger(__name__)
class RetrievalEngine:
def __init__(self, vector_store=None):
self.vector_store = vector_store or get_vector_store()
self._reranker = None
@property
def reranker(self):
if self._reranker is None:
self._reranker = Ranker(
model_name=RERANKER_MODEL_NAME,
cache_dir=FLASHRANK_CACHE
)
return self._reranker
def _merge_and_deduplicate(
self,
results_a: List[Dict[str, Any]],
results_b: List[Dict[str, Any]],
strategy: str = "score"
) -> List[Dict[str, Any]]:
seen_ids = set()
merged = []
if strategy == "interleave":
max_len = max(len(results_a), len(results_b))
for i in range(max_len):
if i < len(results_a):
chunk_id = (results_a[i].get("source", ""), results_a[i].get("chunk_index", 0))
if chunk_id not in seen_ids:
seen_ids.add(chunk_id)
merged.append(results_a[i])
if i < len(results_b):
chunk_id = (results_b[i].get("source", ""), results_b[i].get("chunk_index", 0))
if chunk_id not in seen_ids:
seen_ids.add(chunk_id)
merged.append(results_b[i])
else:
combined = results_a + results_b
combined.sort(key=lambda x: x.get("score", 0), reverse=True)
for result in combined:
chunk_id = (result.get("source", ""), result.get("chunk_index", 0))
if chunk_id not in seen_ids:
seen_ids.add(chunk_id)
merged.append(result)
return merged
def _single_query_search(
self,
query: str,
use_reranking: bool,
top_k_initial: int,
top_k_final: int
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
initial_results = self.vector_store.search(query=query, top_k=top_k_initial)
stage_debug = {
"stage1_count": len(initial_results),
"stage1_top_score": initial_results[0]["score"] if initial_results else 0
}
if not use_reranking or len(initial_results) == 0:
return initial_results[:top_k_final], stage_debug
passages = [
{"id": i, "text": r["text"], "meta": r}
for i, r in enumerate(initial_results)
]
rerank_request = RerankRequest(query=query, passages=passages)
reranked = self.reranker.rerank(rerank_request)
reranked_results = []
for item in reranked:
original = item["meta"]
reranked_results.append({
"id": original["id"],
"score": item["score"],
"original_score": original["score"],
"text": item["text"],
"source": original["source"],
"chunk_index": original["chunk_index"],
"page_number": original.get("page_number", -1),
"metadata": original["metadata"]
})
reranked_results.sort(key=lambda x: x["score"], reverse=True)
stage_debug["stage2_top_score"] = reranked_results[0]["score"] if reranked_results else 0
for rank, item in enumerate(reranked_results[:top_k_final], 1):
logger.debug("reranker rank=%d score=%.4f source=%s chunk=%d", rank, item["score"], item.get("source", "?"), item.get("chunk_index", -1))
return reranked_results[:top_k_final], stage_debug
def retrieve(
self,
query: str,
chat_history: Optional[List[Dict[str, str]]] = None,
use_reranking: bool = True,
top_k_initial: int = TOP_K_INITIAL,
top_k_final: int = TOP_K_RERANKED,
skip_rewrite: bool = False,
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
chat_history = chat_history or []
search_query = query
rewrite_debug = None
use_dual_query = False
if not skip_rewrite and ENABLE_QUERY_REWRITING and chat_history:
rewriter = get_query_rewriter()
search_query, rewrite_debug = rewriter.rewrite(query, chat_history)
use_dual_query = ENABLE_QUERY_REWRITING and search_query != query
debug_info = {
"query": query,
"search_query": search_query,
"query_rewritten": search_query != query,
"rewrite_info": rewrite_debug,
"use_reranking": use_reranking,
"use_dual_query": use_dual_query,
"top_k_initial": top_k_initial,
"top_k_final": top_k_final
}
if use_dual_query:
original_results, original_debug = self._single_query_search(
query, use_reranking, top_k_initial, top_k_final * 2
)
rewritten_results, rewritten_debug = self._single_query_search(
search_query, use_reranking, top_k_initial, top_k_final * 2
)
merged_results = self._merge_and_deduplicate(
rewritten_results, original_results, DUAL_QUERY_MERGE_STRATEGY
)
final_results = merged_results[:top_k_final]
debug_info["dual_query_stats"] = {
"original_count": len(original_results),
"rewritten_count": len(rewritten_results),
"merged_count": len(merged_results),
"merge_strategy": DUAL_QUERY_MERGE_STRATEGY
}
debug_info["stage1_count"] = original_debug.get("stage1_count", 0)
debug_info["stage1_top_score"] = max(
original_debug.get("stage1_top_score", 0),
rewritten_debug.get("stage1_top_score", 0)
)
debug_info["final_method"] = "dual_query_reranked" if use_reranking else "dual_query_vector"
if use_reranking:
debug_info["stage2_top_score"] = final_results[0]["score"] if final_results else 0
return final_results, debug_info
final_results, stage_debug = self._single_query_search(
search_query, use_reranking, top_k_initial, top_k_final
)
debug_info.update(stage_debug)
debug_info["final_method"] = "reranked" if use_reranking else "vector_only"
return final_results, debug_info
def build_context(self, results: List[Dict[str, Any]]) -> str:
if not results:
return "Bağlam bulunamadı."
parts = []
for i, r in enumerate(results, 1):
source = r.get("source", "Bilinmeyen")
page = r.get("page_number", -1)
text = r.get("text", "")
if page > 0:
parts.append(f"[Kaynak {i}: {source}, Sayfa {page}]\n{text}")
else:
parts.append(f"[Kaynak {i}: {source}]\n{text}")
return "\n\n---\n\n".join(parts)
def format_sources(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
sources = []
for i, r in enumerate(results, 1):
sources.append({
"index": i,
"source": r.get("source", "Bilinmeyen"),
"page_number": r.get("page_number", -1),
"text": r.get("text", ""),
"score": r.get("score", 0),
"original_score": r.get("original_score", r.get("score", 0))
})
return sources
_retrieval_engine_instance = None
def get_retrieval_engine() -> RetrievalEngine:
global _retrieval_engine_instance
if _retrieval_engine_instance is None:
_retrieval_engine_instance = RetrievalEngine()
return _retrieval_engine_instance
def reset_retrieval_engine():
global _retrieval_engine_instance
_retrieval_engine_instance = None
if __name__ == "__main__":
from vector_store import VectorStore
from embeddings import get_embedder
store = VectorStore(use_memory=True)
store.add_documents(
["Atlas ERP muhasebe modülü.", "Finans raporlama özellikleri."],
[{"source": "test.pdf", "chunk_index": 0}, {"source": "test.pdf", "chunk_index": 1}]
)
engine = RetrievalEngine(vector_store=store)
results, debug = engine.retrieve("muhasebe", use_reranking=True, top_k_final=2)
print(f"Debug: {debug}")
print(f"Context:\n{engine.build_context(results)}")
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