File size: 10,751 Bytes
d197c9d 5dccc28 90b7bb0 d197c9d 5dccc28 90b7bb0 d197c9d 5dccc28 d197c9d 90b7bb0 d197c9d 5dccc28 d197c9d 5dccc28 d197c9d 5dccc28 d197c9d 5dccc28 d197c9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 | import json
from typing import Any
import requests
from langchain_groq import ChatGroq
from langchain_text_splitters import RecursiveCharacterTextSplitter
from sqlalchemy import delete, func, select
from sqlalchemy.orm import Session
from app.config import get_settings
from app.models import DocumentChunk
class JinaEmbeddings:
def __init__(self, *, api_key: str, base_url: str, model: str, dimensions: int) -> None:
self.api_key = api_key
self.base_url = base_url
self.model = model
self.dimensions = dimensions
def embed_documents(self, texts: list[str]) -> list[list[float]]:
return self._embed(texts=texts, task="retrieval.passage")
def embed_query(self, text: str) -> list[float]:
vectors = self._embed(texts=[text], task="retrieval.query")
return vectors[0] if vectors else [0.0] * self.dimensions
def _embed(self, *, texts: list[str], task: str) -> list[list[float]]:
if not texts:
return []
response = requests.post(
self.base_url,
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {self.api_key}",
},
json={
"model": self.model,
"task": task,
"embedding_type": "float",
"normalized": True,
"input": texts,
},
timeout=60,
)
response.raise_for_status()
data = response.json().get("data", [])
vectors = [row.get("embedding", []) for row in data]
validated: list[list[float]] = []
for vector in vectors:
if len(vector) != self.dimensions:
raise ValueError(
f"Jina embedding dimension mismatch: got {len(vector)}, expected {self.dimensions}. "
"Adjust EMBEDDING_DIMENSIONS or switch embedding model."
)
validated.append(vector)
return validated
class JinaReranker:
def __init__(self, *, api_key: str, base_url: str, model: str) -> None:
self.api_key = api_key
self.base_url = base_url
self.model = model
def rerank(self, *, query: str, documents: list[str], top_n: int) -> list[dict[str, Any]]:
if not documents:
return []
response = requests.post(
self.base_url,
headers={
"Content-Type": "application/json",
"Authorization": f"Bearer {self.api_key}",
},
json={
"model": self.model,
"query": query,
"top_n": top_n,
"documents": documents,
"return_documents": False,
},
timeout=60,
)
response.raise_for_status()
return response.json().get("results", [])
class VectorStoreService:
def __init__(self) -> None:
self.settings = get_settings()
if not self.settings.jina_api_key:
raise RuntimeError("JINA_API_KEY is required for document embedding and retrieval.")
self.splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=150,
separators=[
"\n\n",
"\n",
". ",
"? ",
"! ",
"; ",
", ",
" ",
"",
],
keep_separator=True,
)
self.embeddings = JinaEmbeddings(
api_key=self.settings.jina_api_key,
base_url=self.settings.jina_api_base,
model=self.settings.jina_embedding_model,
dimensions=self.settings.embedding_dimensions,
)
self.retrieval_router = (
ChatGroq(
api_key=self.settings.groq_api_key,
model=self.settings.model_name,
temperature=0,
)
if self.settings.groq_api_key
else None
)
self.reranker = JinaReranker(
api_key=self.settings.jina_api_key,
base_url=self.settings.jina_reranker_api_base,
model=self.settings.jina_reranker_model,
)
def _get_embeddings(self) -> Any:
return self.embeddings
def _choose_retrieval_sizes(
self,
*,
db: Session,
query: str,
file_hashes: list[str],
requested_k: int,
) -> tuple[int, int]:
available_chunks = db.scalar(
select(func.count())
.select_from(DocumentChunk)
.where(DocumentChunk.file_hash.in_(file_hashes))
) or 0
if available_chunks <= 0:
return 0, 0
if self.retrieval_router is None:
raise RuntimeError("GROQ_API_KEY is required for LLM-based retrieval size selection.")
prompt = (
"You are a retrieval planner for a RAG system.\n"
"Choose how many chunks to keep after reranking and how many vector candidates to send to the reranker.\n"
"Return only valid JSON with this exact schema:\n"
'{"final_k": 4, "candidate_k": 12}\n\n'
"Rules:\n"
f"- final_k must be between 1 and {min(8, available_chunks)}\n"
f"- candidate_k must be between final_k and {min(30, available_chunks)}\n"
"- candidate_k should usually be around 2x to 4x final_k\n"
"- Use larger values for broad, comparative, or synthesis-heavy queries\n"
"- Use smaller values for narrow fact lookup queries\n\n"
f"Query: {query}\n"
f"Selected documents: {len(file_hashes)}\n"
f"Available chunks: {available_chunks}\n"
f"Requested final_k hint: {requested_k}\n"
f"Configured minimum final_k: {self.settings.retrieval_k}\n"
f"Configured minimum candidate_k: {self.settings.rerank_candidate_k}\n"
)
response = self.retrieval_router.invoke(prompt)
content = response.content if isinstance(response.content, str) else str(response.content)
if "```json" in content:
content = content.split("```json", 1)[1].split("```", 1)[0].strip()
elif "```" in content:
content = content.split("```", 1)[1].split("```", 1)[0].strip()
data = json.loads(content)
final_k = int(data["final_k"])
candidate_k = int(data["candidate_k"])
final_k = max(1, min(final_k, available_chunks, 8))
candidate_floor = max(final_k, self.settings.rerank_candidate_k)
candidate_k = max(final_k, candidate_k)
candidate_k = min(max(candidate_floor, candidate_k), available_chunks, 30)
return final_k, candidate_k
def _rerank_matches(self, *, query: str, matches: list[dict[str, Any]], top_n: int) -> list[dict[str, Any]]:
if self.reranker is None or not matches:
return matches[:top_n]
try:
results = self.reranker.rerank(
query=query,
documents=[match["content"] for match in matches],
top_n=min(top_n, len(matches)),
)
except requests.RequestException:
return matches[:top_n]
reranked: list[dict[str, Any]] = []
for item in results:
index = item.get("index")
if not isinstance(index, int) or index < 0 or index >= len(matches):
continue
match = dict(matches[index])
score = item.get("relevance_score")
if isinstance(score, (int, float)):
match["rerank_score"] = float(score)
reranked.append(match)
return reranked or matches[:top_n]
def add_document(self, *, db: Session, document_id: int, file_hash: str, filename: str, pages: list[tuple[int, str]]) -> None:
chunk_rows: list[tuple[int | None, str]] = []
for page_number, page_text in pages:
if not page_text.strip():
continue
page_chunks = self.splitter.split_text(page_text)
chunk_rows.extend((page_number, chunk) for chunk in page_chunks if chunk.strip())
chunks = [chunk for _, chunk in chunk_rows]
if not chunks:
return
embeddings_client = self._get_embeddings()
embeddings = embeddings_client.embed_documents(chunks)
db.execute(delete(DocumentChunk).where(DocumentChunk.document_id == document_id))
rows = [
DocumentChunk(
document_id=document_id,
file_hash=file_hash,
filename=filename,
chunk_index=index,
page_number=page_number,
content=chunk,
embedding=embedding,
)
for index, ((page_number, chunk), embedding) in enumerate(zip(chunk_rows, embeddings, strict=False))
]
db.add_all(rows)
db.flush()
def similarity_search(self, *, db: Session, query: str, file_hashes: list[str], k: int = 4) -> list[dict[str, Any]]:
if not file_hashes:
return []
final_k, candidate_k = self._choose_retrieval_sizes(
db=db,
query=query,
file_hashes=file_hashes,
requested_k=k,
)
if final_k == 0:
return []
query_embedding = self._get_embeddings().embed_query(query)
stmt = (
select(
DocumentChunk.document_id,
DocumentChunk.content,
DocumentChunk.filename,
DocumentChunk.file_hash,
DocumentChunk.chunk_index,
DocumentChunk.page_number,
DocumentChunk.embedding.cosine_distance(query_embedding).label("distance"),
)
.where(DocumentChunk.file_hash.in_(file_hashes))
.order_by(DocumentChunk.embedding.cosine_distance(query_embedding))
.limit(candidate_k)
)
results = db.execute(stmt).all()
matches: list[dict[str, Any]] = []
for row in results:
matches.append(
{
"content": row.content,
"metadata": {
"document_id": row.document_id,
"filename": row.filename,
"file_hash": row.file_hash,
"chunk_index": row.chunk_index,
"page_number": row.page_number,
},
"distance": row.distance,
}
)
return self._rerank_matches(query=query, matches=matches, top_n=final_k)
|