File size: 5,687 Bytes
38b4eff | 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 | #!/usr/bin/env python3
"""
NeuralAI — RAG Module
Embedding + retrieval for document Q&A.
"""
import os, hashlib
from pathlib import Path
import chromadb
from chromadb.utils.embedding_functions import DefaultEmbeddingFunction
from sentence_transformers import SentenceTransformer
import pypdf, docx
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
UPLOAD_DIR = os.path.join(BASE_DIR, "uploads")
CHROMA_DIR = os.path.join(BASE_DIR, "chroma_db")
os.makedirs(UPLOAD_DIR, exist_ok=True)
os.makedirs(CHROMA_DIR, exist_ok=True)
_embed_model = None
_chroma = None
def get_embedder():
global _embed_model
if _embed_model is None:
_embed_model = SentenceTransformer("all-MiniLM-L6-v2")
return _embed_model
def get_chroma():
global _chroma
if _chroma is None:
_chroma = chromadb.PersistentClient(path=CHROMA_DIR)
return _chroma
# ── Text Extraction ─────────────────────────────────────────────
def extract_text(filepath: str) -> str:
ext = os.path.splitext(filepath)[1].lower()
text = ""
if ext == ".pdf":
try:
reader = pypdf.PdfReader(filepath)
for page in reader.pages:
t = page.extract_text()
if t:
text += t + "\n\n"
except Exception:
return f"[PDF error: {e}]"
elif ext in (".docx", ".doc"):
try:
doc = docx.Document(filepath)
for para in doc.paragraphs:
if para.text.strip():
text += para.text + "\n"
except Exception:
return f"[DOCX error: {e}]"
elif ext == ".txt":
with open(filepath, "r", errors="ignore") as f:
text = f.read()
elif ext == ".md":
with open(filepath, "r", errors="ignore") as f:
text = f.read()
else:
return f"[Unsupported: {ext}]"
return text.strip()
# ── Chunking ──────────────────────────────────────────────────
def chunk_text(text: str, chunk_size: int = 500, overlap: int = 80) -> list[str]:
chunks = []
start = 0
text_len = len(text)
while start < text_len:
end = start + chunk_size
chunk = text[start:end].strip()
if chunk:
chunks.append(chunk)
start += chunk_size - overlap
return chunks
# ── Index Document ─────────────────────────────────────────────
def index_document(filepath: str, collection_name: str = "documents") -> dict:
filename = os.path.basename(filepath)
file_id = hashlib.sha256(filename.encode()).hexdigest()[:16]
text = extract_text(filepath)
if not text:
return {"chunks": 0, "error": "No text extracted"}
chunks = chunk_text(text)
if not chunks:
return {"chunks": 0, "error": "No chunks generated"}
embedder = get_embedder()
embeddings = embedder.encode(chunks, show_progress_bar=False).tolist()
ids = [f"{file_id}_{i}" for i in range(len(chunks))]
metadatas = [{"source": filename, "chunk_idx": i} for i in range(len(chunks))]
chroma = get_chroma()
try:
col = chroma.get_or_create_collection(
name=collection_name,
embedding_function=DefaultEmbeddingFunction()
)
except Exception:
col = chroma.get_or_create_collection(name=collection_name)
col.upsert(ids=ids, embeddings=embeddings, documents=chunks, metadatas=metadatas)
return {
"filename": filename,
"file_id": file_id,
"chunks": len(chunks),
"chars": len(text)
}
# ── Query ──────────────────────────────────────────────────────
def query_documents(query: str, collection_name: str = "documents", top_k: int = 4) -> list[dict]:
embedder = get_embedder()
chroma = get_chroma()
try:
col = chroma.get_or_create_collection(
name=collection_name,
embedding_function=DefaultEmbeddingFunction()
)
except Exception:
return []
query_emb = embedder.encode([query], show_progress_bar=False).tolist()
results = col.query(query_embeddings=query_emb, n_results=top_k)
docs = []
if results and results.get("documents"):
for i, doc in enumerate(results["documents"][0]):
meta = results["metadatas"][0][i] if results.get("metadatas") else {}
docs.append({
"content": doc,
"source": meta.get("source", "unknown"),
"chunk": meta.get("chunk_idx", 0) + 1
})
return docs
# ── Rebuild registry from disk ─────────────────────────────────
def rebuild_index_registry(collection_name: str = "documents") -> dict:
"""Scan chroma_db for orphaned files not tracked in INDEXED_FILES.json"""
chroma = get_chroma()
try:
col = chroma.get_or_create_collection(
name=collection_name,
embedding_function=DefaultEmbeddingFunction()
)
except Exception:
return {"added": 0, "sources": []}
all_data = col.get()
sources = set()
for meta in (all_data.get("metadatas") or []):
src = meta.get("source") if meta else None
if src:
sources.add(src)
return {"found": list(sources), "count": len(sources)}
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