feat: ✨ Working PoC of the Dungeon Masters Vault

delete_me.py

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+# class PrecomputedEmbeddings(Embeddings):
+#     def __init__(self, embeddings: List[List[float]]):
+#         self.embeddings = embeddings  # Store all precomputed vectors
+
+#     def embed_documents(self, texts: List[str]) -> List[List[float]]:
+#         return self.embeddings  # Return the precomputed ones (order must match!)
+    
+#     def embed_query(self, text):
+#         return self.embeddings[0]
+
+# def embedder(texts: List[str]) -> List[List[float]]:
+#     embeddings = []
+#     base_url = "http://192.168.0.49:1234"  # ✅ Add 'http://'
+#     embed_url = f"{base_url}/v1/embeddings"
+#     headers = {"Content-Type": "application/json"}
+
+#     for text in texts:
+#         payload = {
+#             "model": "text-embedding-qwen3-embedding-8b",
+#             "input": text
+#         }
+
+#         try:
+#             response = requests.post(embed_url, json=payload, headers=headers)  # ✅ POST not GET
+#             if response.status_code == 200:
+#                 data = response.json()  # ✅ Parse JSON!
+#                 embedding = data["data"][0]["embedding"]  # ✅ Extract the actual vector
+#                 embeddings.append(embedding)
+#             else:
+#                 print(f"❌ Embedding failed for '{text[:30]}...': {response.status_code} - {response.text}")
+#                 # Optionally: insert placeholder zeros if you need to continue
+#                 # embeddings.append([0.0] * 768)  # ← adjust dimension as needed!
+#         except Exception as e:
+#             print(f"⚠️ Exception embedding '{text[:30]}...': {e}")
+#             # embeddings.append([0.0] * 768)  # fallback
+
+#     return embeddings
+
+# def store_chunks_with_embeddings_locally(chunks, db_path="./local_faiss_db"):
+#     """
+#     Stores pre-computed chunks and their embeddings into a local FAISS database.
+    
+#     Args:
+#         chunks: list of LangChain Document objects (with page_content and metadata)
+#         embeddings: list of embedding vectors (list of lists of floats) — must match length of chunks
+#         db_path: where to save the FAISS index files locally
+#     """
+
+#     texts = [chunk.page_content for chunk in chunks]
+#     embeddings = embedder(texts)
+#     if len(chunks) != len(embeddings):
+#         raise ValueError(f"Mismatch! Got {len(chunks)} chunks but {len(embeddings)} embeddings.")
+
+#     # Create LangChain Document list (we already have this)
+#     documents = chunks  # assuming they're already Document objects
+
+#     # Build FAISS vectorstore using precomputed embeddings
+#     # FAISS.from_embeddings() lets us pass our own embeddings + texts
+#     vectorstore = FAISS.from_embeddings(
+#         text_embeddings=list(zip([doc.page_content for doc in documents], embeddings)),
+#         embedding=PrecomputedEmbeddings(embeddings[0])  # We’ll define this next
+#     )
+
+#     # Save to disk
+#     vectorstore.save_local(db_path)
+#     print(f"✅ Successfully stored {len(chunks)} chunks + embeddings into local FAISS DB at '{db_path}'")