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62a1756 | 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 | import os
import re
import logging
from typing import List, Tuple, Optional
import faiss
from sentence_transformers import SentenceTransformer
from PyPDF2 import PdfReader
from docx import Document
import pytesseract
from PIL import Image
import io
import openpyxl
import pandas as pd
from duckduckgo_search import DDGS
from fastapi import UploadFile
logger = logging.getLogger(__name__)
_EMBED_MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
_embedder: Optional[SentenceTransformer] = None
def _get_embedder() -> SentenceTransformer:
global _embedder
if _embedder is None:
logger.info(f"Loading embedding model: {_EMBED_MODEL_NAME}")
_embedder = SentenceTransformer(_EMBED_MODEL_NAME)
return _embedder
# Enhanced File Extraction
def extract_text(file: UploadFile) -> str:
ext = os.path.splitext(file.filename)[1].lower()
content = file.file.read()
file_bytes = io.BytesIO(content)
if ext == ".pdf":
try:
reader = PdfReader(file_bytes)
return "\n".join(page.extract_text() or "" for page in reader.pages)
except Exception as e:
logger.error(f"PDF extract failed: {e}")
return ""
elif ext == ".docx":
try:
doc = Document(file_bytes)
return "\n".join(p.text for p in doc.paragraphs if p.text)
except Exception as e:
logger.error(f"DOCX extract failed: {e}")
return ""
elif ext in [".xlsx", ".xls"]:
try:
wb = openpyxl.load_workbook(file_bytes, read_only=True, data_only=True)
text = []
for sheet in wb:
for row in sheet.iter_rows(values_only=True):
text.append(" ".join(str(cell) for cell in row if cell is not None))
return "\n".join(text)
except Exception as e:
logger.error(f"Excel extract failed: {e}")
return ""
elif ext == ".csv":
try:
df = pd.read_csv(file_bytes)
return df.to_string()
except Exception as e:
logger.error(f"CSV extract failed: {e}")
return ""
elif ext in [".jpg", ".jpeg", ".png", ".gif"]: # OCR for images
try:
img = Image.open(file_bytes)
return pytesseract.image_to_string(img)
except Exception as e:
logger.error(f"Image OCR failed: {e}")
return ""
else: # Fallback text
try:
return content.decode("utf-8", errors="ignore")
except Exception as e:
logger.error(f"Text extract failed: {e}")
return ""
def clean_text(text: str) -> str:
t = re.sub(r"[ \t]+", " ", text)
t = re.sub(r"\n{3,}", "\n\n", t)
return t.strip()
def chunk_text(text: str, max_tokens: int = 400, overlap: int = 50) -> List[str]:
text = clean_text(text)
if not text:
return []
words = text.split()
chunks, start = [], 0
while start < len(words):
end = min(len(words), start + max_tokens)
chunk = " ".join(words[start:end]).strip()
if chunk:
chunks.append(chunk)
if end == len(words):
break
start = max(0, end - overlap)
return chunks
class RagIndex:
def __init__(self, index: faiss.IndexFlatIP, dim: int, chunks: List[str]):
self.index = index
self.dim = dim
self.chunks = chunks
def build_faiss_index(chunks: List[str]) -> RagIndex:
emb = _get_embedder()
vectors = emb.encode(chunks, convert_to_numpy=True, normalize_embeddings=True)
dim = vectors.shape[1]
index = faiss.IndexFlatIP(dim)
index.add(vectors)
return RagIndex(index=index, dim=dim, chunks=chunks)
def search(index: RagIndex, query: str, top_k: int = 6) -> List[Tuple[str, float]]:
emb = _get_embedder()
q = emb.encode([query], convert_to_numpy=True, normalize_embeddings=True)
D, I = index.index.search(q, top_k)
hits = []
for score, idx in zip(D[0], I[0]):
if idx == -1:
continue
hits.append((index.chunks[idx], float(score)))
return hits
def build_context_from_files(files: List[UploadFile], prompt: str, top_k: int = 6) -> str:
all_text = []
for file in files:
txt = extract_text(file)
if txt:
all_text.append(txt)
file.file.seek(0) # Reset
big_text = "\n\n".join(all_text)
chunks = chunk_text(big_text, max_tokens=450, overlap=80)
if not chunks:
return ""
idx = build_faiss_index(chunks)
hits = search(idx, prompt, top_k=top_k)
context_sections = [f"[DOC#{i} score={score:.3f}]\n{chunk}" for i, (chunk, score) in enumerate(hits, 1)]
return "\n\n".join(context_sections)
# Web search tool
def web_search(query: str) -> str:
try:
with DDGS() as ddgs:
results = [r for r in ddgs.text(query, max_results=5)]
sections = [f"[WEB#{i}] Title: {r['title']}\nSnippet: {r['body']}\nURL: {r['href']}" for i, r in enumerate(results, 1)]
return "\n\n".join(sections) if sections else "No results found."
except Exception as e:
logger.error(f"Web search failed: {e}")
return "Web search error." |