Initial clean commit - unified Lyra stack

rag/rag_api.py

@@ -0,0 +1,56 @@
+# rag_api.py
+from fastapi import FastAPI, Body
+from pydantic import BaseModel
+import os, chromadb
+from openai import OpenAI
+from dotenv import load_dotenv
+load_dotenv()
+
+# ---- setup ----
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+chroma = chromadb.PersistentClient(path="./chromadb")
+collection = chroma.get_or_create_collection("lyra_chats")
+
+app = FastAPI(title="Lyra RAG API")
+
+class Query(BaseModel):
+    query: str
+    n_results: int = 5
+
+@app.post("/rag/search")
+def rag_search(q: Query = Body(...)):
+    # embed query
+    q_emb = client.embeddings.create(
+        model="text-embedding-3-small",
+        input=q.query
+    ).data[0].embedding
+
+    # retrieve matches
+    results = collection.query(query_embeddings=[q_emb], n_results=q.n_results)
+
+    docs = results["documents"][0]
+    metas = results["metadatas"][0]
+    context = "\n\n".join(docs)
+
+    # synthesize short answer
+    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: {q.query}"}
+        ]
+    ).choices[0].message.content
+
+    return {
+        "query": q.query,
+        "answer": answer,
+        "results": [
+            {"source": m.get("source"), "title": m.get("title"),
+             "role": m.get("role"), "excerpt": d[:300]}
+            for d, m in zip(docs, metas)
+        ]
+    }
+
+@app.get("/health")
+def health():
+    return {"status": "ok", "collection_count": collection.count()}