Adapt ingest dir to new ingest system
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7b27493656
commit
3ab1089c51
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@ -1,78 +1,70 @@
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#!/usr/bin/env python3
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import os, sys, hashlib, psycopg, requests, json
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import os, sys, hashlib, psycopg, requests, json, numpy as np
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from pgvector.psycopg import register_vector
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DB=os.environ.get("DB_URL","dbname=kompanion user=kompanion host=/var/run/postgresq")
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DB=os.environ.get("DB_URL","dbname=kompanion user=kompanion host=/var/run/postgresql")
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OLLAMA=os.environ.get("OLLAMA_BASE","http://127.0.0.1:11434")
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MODEL=os.environ.get("EMBED_MODEL","bge-m3:latest")
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SPACE=os.environ.get("EMBED_SPACE","dev_knowledge") # dev_knowledge | pattern_exchange | runtime_memory
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def sha256(p):
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h=hashlib.sha256()
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with open(p,"rb") as f:
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for chunk in iter(lambda: f.read(1<<20), b""): h.update(chunk)
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return h.hexdigest()
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NAMESPACE=os.environ.get("EMBED_NAMESPACE","dev_knowledge")
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def embed(text):
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r=requests.post(f"{OLLAMA}/api/embeddings", json={"model": MODEL, "prompt": text}, timeout=120)
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r.raise_for_status(); return r.json()["embedding"]
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r.raise_for_status()
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return r.json()["embedding"]
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def chunks(s, sz=1600):
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b=s.encode("utf-8");
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for i in range(0,len(b),sz): yield b[i:i+sz].decode("utf-8","ignore")
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def insert_embedding(cur, dim, kid, sid, vec):
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if dim==768:
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cur.execute("INSERT INTO komp.embedding_768(chunk_id,space_id,embedding) VALUES(%s,%s,%s) ON CONFLICT DO NOTHING",(kid,sid,vec))
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elif dim==1024:
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cur.execute("INSERT INTO komp.embedding_1024(chunk_id,space_id,embedding) VALUES(%s,%s,%s) ON CONFLICT DO NOTHING",(kid,sid,vec))
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else:
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return False
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return True
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b=s.encode("utf-8")
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for i in range(0,len(b),sz):
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yield b[i:i+sz].decode("utf-8","ignore")
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def main(root):
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with psycopg.connect(DB) as conn, conn.cursor() as cur:
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cur.execute("SELECT id,dim FROM komp.space WHERE name=%s",(SPACE,))
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row=cur.fetchone()
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if not row: raise SystemExit(f"space {SPACE} missing (init schema)")
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sid, target_dim = row
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for dirpath,_,files in os.walk(root):
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for fn in files:
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p=os.path.join(dirpath,fn)
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if os.path.getsize(p)==0: continue
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# include common text/code; PDFs via pdftotext if available
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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")):
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continue
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if fn.lower().endswith(".pdf"):
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try:
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txt=os.popen(f"pdftotext -layout -nopgbrk '{p}' - -q").read()
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except Exception:
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with psycopg.connect(DB) as conn:
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register_vector(conn)
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with conn.cursor() as cur:
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# Get the namespace id
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cur.execute("INSERT INTO namespaces (name) VALUES (%s) ON CONFLICT (name) DO UPDATE SET name = EXCLUDED.name RETURNING id", (NAMESPACE,))
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namespace_id = cur.fetchone()[0]
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for dirpath,_,files in os.walk(root):
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for fn in files:
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p=os.path.join(dirpath,fn)
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if os.path.getsize(p)==0: continue
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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")):
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continue
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else:
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try: txt=open(p,"r",encoding="utf-8",errors="ignore").read()
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except Exception: continue
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sh=sha256(p)
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cur.execute("INSERT INTO komp.source(kind,uri,meta) VALUES(%s,%s,%s) ON CONFLICT DO NOTHING RETURNING id",
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("filesystem",p,json.dumps({})))
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sid_src = cur.fetchone()[0] if cur.rowcount else None
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if not sid_src:
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cur.execute("SELECT id FROM komp.source WHERE kind='filesystem' AND uri=%s",(p,))
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sid_src=cur.fetchone()[0]
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ln=1
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for ch in chunks(txt):
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cur.execute("INSERT INTO komp.chunk(source_id,lineno,text,sha256,tokens) VALUES(%s,%s,%s,%s,%s) RETURNING id",
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(sid_src,ln,ch,sh,len(ch)//4))
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kid=cur.fetchone()[0]
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vec=embed(ch)
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if len(vec)!=target_dim:
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cur.execute("DELETE FROM komp.chunk WHERE id=%s",(kid,))
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if fn.lower().endswith(".pdf"):
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try:
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txt=os.popen(f"pdftotext -layout -nopgbrk '{p}' - -q").read()
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except Exception:
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continue
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else:
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insert_embedding(cur, target_dim, kid, sid, vec)
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ln += ch.count("\n")+1
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conn.commit()
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try:
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txt=open(p,"r",encoding="utf-8",errors="ignore").read()
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except Exception:
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continue
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# Create a memory item for the file
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cur.execute("INSERT INTO memory_items (namespace_id, key, content) VALUES (%s, %s, %s) RETURNING id", (namespace_id, p, txt))
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item_id = cur.fetchone()[0]
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seq = 0
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for ch in chunks(txt):
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# Create a memory chunk
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cur.execute("INSERT INTO memory_chunks (item_id, seq, content) VALUES (%s, %s, %s) RETURNING id", (item_id, seq, ch))
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chunk_id = cur.fetchone()[0]
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# Create an embedding for the chunk
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vec=np.array(embed(ch))
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dim = len(vec)
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cur.execute("INSERT INTO embeddings (chunk_id, model, dim, vector) VALUES (%s, %s, %s, %s)", (chunk_id, MODEL, dim, vec))
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seq += 1
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conn.commit()
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print("done")
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if __name__=='__main__':
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if len(sys.argv)<2: print("usage: ingest_dir.py <root> [space]", file=sys.stderr); sys.exit(1)
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if len(sys.argv)>=3: os.environ["EMBED_SPACE"]=sys.argv[2]
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if len(sys.argv)<2:
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print("usage: ingest_dir.py <root> [namespace]", file=sys.stderr)
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sys.exit(1)
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if len(sys.argv)>=3:
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os.environ["EMBED_NAMESPACE"]=sys.argv[2]
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main(sys.argv[1])
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