Initial clean commit - unified Lyra stack

rag/rag_query.py

@@ -0,0 +1,37 @@
+# rag_query.py
+import os, sys, chromadb
+from openai import OpenAI
+from dotenv import load_dotenv
+load_dotenv()
+
+query = " ".join(sys.argv[1:]) or input("Ask Lyra-Archive: ")
+
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+chroma = chromadb.PersistentClient(path="./chromadb")
+collection = chroma.get_or_create_collection("lyra_chats")
+
+# embed the question
+q_emb = client.embeddings.create(
+    model="text-embedding-3-small",
+    input=query
+).data[0].embedding
+
+# search the collection
+results = collection.query(query_embeddings=[q_emb], n_results=5)
+
+print("\n🔍 Top related excerpts:\n")
+for doc, meta in zip(results["documents"][0], results["metadatas"][0]):
+    print(f"📄 {meta['source']} ({meta['role']}) — {meta['title']}")
+    print(doc[:300].strip(), "\n---")
+
+# synthesize an answer
+context = "\n\n".join(results["documents"][0])
+answer = client.chat.completions.create(
+    model="gpt-4o-mini",
+    messages=[
+        {"role": "system", "content": "Answer based only on the context below. Be concise and practical."},
+        {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
+    ]
+).choices[0].message.content
+
+print("\n💡 Lyra-Archive Answer:\n", answer)