70 lines
3.1 KiB
Python
Executable File
70 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
import os, sys, hashlib, psycopg, requests, json, numpy as np
|
|
from pgvector.psycopg import register_vector
|
|
|
|
DB=os.environ.get("DB_URL","dbname=kompanion user=kompanion host=/var/run/postgresql")
|
|
OLLAMA=os.environ.get("OLLAMA_BASE","http://127.0.0.1:11434")
|
|
MODEL=os.environ.get("EMBED_MODEL","bge-m3:latest")
|
|
NAMESPACE=os.environ.get("EMBED_NAMESPACE","dev_knowledge")
|
|
|
|
def embed(text):
|
|
r=requests.post(f"{OLLAMA}/api/embeddings", json={"model": MODEL, "prompt": text}, timeout=120)
|
|
r.raise_for_status()
|
|
return r.json()["embedding"]
|
|
|
|
def chunks(s, sz=1600):
|
|
b=s.encode("utf-8")
|
|
for i in range(0,len(b),sz):
|
|
yield b[i:i+sz].decode("utf-8","ignore")
|
|
|
|
def main(root):
|
|
with psycopg.connect(DB) as conn:
|
|
register_vector(conn)
|
|
with conn.cursor() as cur:
|
|
# Get the namespace id
|
|
cur.execute("INSERT INTO namespaces (name) VALUES (%s) ON CONFLICT (name) DO UPDATE SET name = EXCLUDED.name RETURNING id", (NAMESPACE,))
|
|
namespace_id = cur.fetchone()[0]
|
|
|
|
for dirpath,_,files in os.walk(root):
|
|
for fn in files:
|
|
p=os.path.join(dirpath,fn)
|
|
if os.path.getsize(p)==0: continue
|
|
if not any(fn.lower().endswith(ext) for ext in (".md",".txt",".json",".py",".cpp",".c",".hpp",".yaml",".yml",".toml",".pdf",".mdown",".rst",".org",".js",".ts",".sql",".sh",".ini",".conf",".cfg",".log",".mime")):
|
|
continue
|
|
|
|
if fn.lower().endswith(".pdf"):
|
|
try:
|
|
txt=os.popen(f"pdftotext -layout -nopgbrk '{p}' - -q").read()
|
|
except Exception:
|
|
continue
|
|
else:
|
|
try:
|
|
txt=open(p,"r",encoding="utf-8",errors="ignore").read()
|
|
except Exception:
|
|
continue
|
|
|
|
# Create a memory item for the file
|
|
cur.execute("INSERT INTO memory_items (namespace_id, key, content) VALUES (%s, %s, %s) RETURNING id", (namespace_id, p, txt))
|
|
item_id = cur.fetchone()[0]
|
|
|
|
seq = 0
|
|
for ch in chunks(txt):
|
|
# Create a memory chunk
|
|
cur.execute("INSERT INTO memory_chunks (item_id, seq, content) VALUES (%s, %s, %s) RETURNING id", (item_id, seq, ch))
|
|
chunk_id = cur.fetchone()[0]
|
|
|
|
# Create an embedding for the chunk
|
|
vec=np.array(embed(ch))
|
|
dim = len(vec)
|
|
cur.execute("INSERT INTO embeddings (chunk_id, model, dim, vector) VALUES (%s, %s, %s, %s)", (chunk_id, MODEL, dim, vec))
|
|
seq += 1
|
|
conn.commit()
|
|
print("done")
|
|
|
|
if __name__=='__main__':
|
|
if len(sys.argv)<2:
|
|
print("usage: ingest_dir.py <root> [namespace]", file=sys.stderr)
|
|
sys.exit(1)
|
|
if len(sys.argv)>=3:
|
|
os.environ["EMBED_NAMESPACE"]=sys.argv[2]
|
|
main(sys.argv[1]) |