297 lines
10 KiB
Python
297 lines
10 KiB
Python
import json
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import chromadb
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from chromadb.config import Settings
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import config
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# ── ChromaDB singleton ───────────────────────────────────────────────
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_store = None
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def _get_store():
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global _store
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if _store is None:
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_store = chromadb.PersistentClient(
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path=config.RAG_PERSIST_DIR,
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settings=Settings(anonymized_telemetry=False),
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)
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return _store
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def _collection(name):
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"""Get or create collection — uses ChromaDB's default ONNX embedding (all-MiniLM-L6-v2)."""
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return _get_store().get_or_create_collection(name=name)
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# ── Tool schemas ─────────────────────────────────────────────────────
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schema_store_knowledge = {
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"type": "function",
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"function": {
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"name": "store_knowledge",
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"description": (
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"Store one or more documents with arbitrary metadata into a RAG collection. "
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"Metadata is a free-form dict — choose meaningful keys for future filtering "
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"(e.g., restaurant, category, allergens, spice_level, taste_profile, price"
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", customer_id, dietary)."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"collection": {
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"type": "string",
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"description": "Target collection name (must be defined in config)"
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},
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"documents": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"id": {"type": "string", "description": "Unique document ID"},
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"text": {"type": "string", "description": "Document body text"},
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"metadata": {
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"type": "object",
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"description": "Arbitrary key-value metadata",
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"default": {}
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}
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},
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"required": ["id", "text"]
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},
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"description": "List of documents to persist"
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}
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},
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"required": ["collection", "documents"]
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}
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}
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}
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schema_search_knowledge = {
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"type": "function",
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"function": {
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"name": "search_knowledge",
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"description": (
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"Semantically search a RAG collection. Optionally narrow with a "
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"metadata filter using ChromaDB where syntax. "
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"Examples: {'category': 'main_course'}, {'spice_level': {'$lte': 2}}, "
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"{'allergens': {'$contains': 'seafood'}}."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"collection": {
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"type": "string",
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"description": "Collection name to search in"
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},
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"query": {
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"type": "string",
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"description": "Natural-language search query"
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},
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"n_results": {
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"type": "integer",
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"description": "Max results to return (default 5)",
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"default": 5
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},
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"filter": {
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"type": "object",
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"description": "Optional metadata filter dict",
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"default": None
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}
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},
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"required": ["collection", "query"]
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}
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}
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}
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schema_create_collection = {
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"type": "function",
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"function": {
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"name": "create_collection",
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"description": (
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"Create a new RAG collection for a new topic/domain. Use a short, descriptive name "
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"with underscores (e.g., 'tanaman_hias', 'customer_profiles'). Optionally provide a description."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Collection name (lowercase, underscores for spaces)"
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},
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"description": {
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"type": "string",
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"description": "What this collection stores",
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"default": ""
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}
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},
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"required": ["name"]
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}
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}
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}
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schema_delete_collection = {
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"type": "function",
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"function": {
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"name": "delete_collection",
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"description": "Permanently delete an entire RAG collection and all documents in it. Use with caution — this cannot be undone.",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Collection name to delete"
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}
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},
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"required": ["name"]
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}
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}
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}
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schema_list_collections = {
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"type": "function",
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"function": {
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"name": "list_collections",
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"description": "List all existing RAG collections with their document count and description.",
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"parameters": {"type": "object", "properties": {}}
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}
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}
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schema_inspect_collection = {
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"type": "function",
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"function": {
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"name": "inspect_collection",
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"description": (
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"Examine sample documents and metadata fields in a RAG collection. "
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"Always call this before search_knowledge to learn what metadata keys "
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"are available for filtering, then pass them in the filter parameter."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"collection": {
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"type": "string",
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"description": "Collection name to inspect"
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},
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"sample_size": {
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"type": "integer",
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"description": "Number of sample documents (default 3)",
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"default": 3
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}
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},
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"required": ["collection"]
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}
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}
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}
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# ── Tool handlers ────────────────────────────────────────────────────
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def _sanitize_meta(meta):
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"""ChromaDB metadata only allows str/int/float/bool. Convert lists to JSON string, remove empty lists."""
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out = {}
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for k, v in meta.items():
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if isinstance(v, list):
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if len(v) == 0:
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continue
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out[k] = json.dumps(v, ensure_ascii=False)
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elif isinstance(v, (str, int, float, bool)):
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out[k] = v
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else:
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out[k] = str(v)
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return out
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def store_knowledge(collection, documents):
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try:
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col = _collection(collection)
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ids, texts, metas = [], [], []
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for doc in documents:
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ids.append(doc["id"])
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texts.append(doc["text"])
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metas.append(_sanitize_meta(doc.get("metadata", {})))
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col.add(ids=ids, documents=texts, metadatas=metas)
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return f"Stored {len(documents)} document(s) in '{collection}'."
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except Exception as e:
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return f"Error: {e}"
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def search_knowledge(collection, query, n_results=5, filter=None):
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try:
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col = _collection(collection)
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kw = {"query_texts": [query], "n_results": n_results}
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if filter:
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kw["where"] = filter
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r = col.query(**kw)
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if not r["ids"] or not r["ids"][0]:
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return "No results found."
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out = []
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for i in range(len(r["ids"][0])):
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did = r["ids"][0][i]
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txt = r["documents"][0][i]
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if len(txt) > 500:
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txt = txt[:500] + "..."
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meta = json.dumps(r["metadatas"][0][i], ensure_ascii=False) if r.get("metadatas") else "{}"
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dist = ""
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if r.get("distances"):
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dist = f" (score: {r['distances'][0][i]:.4f})"
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out.append(f"[{did}]{dist}\n text: {txt}\n metadata: {meta}")
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return "\n---\n".join(out)
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except Exception as e:
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return f"Error: {e}"
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def create_collection(name, description=""):
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try:
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col = _get_store().get_or_create_collection(name=name)
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col.modify(metadata={"description": description})
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return f"Collection '{name}' is ready."
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except Exception as e:
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return f"Error: {e}"
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def delete_collection(name):
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try:
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_get_store().delete_collection(name)
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return f"Deleted collection '{name}'."
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except Exception as e:
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return f"Error: {e}"
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def list_collections():
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try:
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cols = _get_store().list_collections()
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if not cols:
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return "No collections exist yet."
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out = ["Available collections:"]
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for col in cols:
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meta = col.metadata or {}
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desc = meta.get("description", "")
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cnt = col.count()
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tag = f" ({desc})" if desc else ""
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out.append(f"- {col.name}{tag} [{cnt} docs]")
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return "\n".join(out)
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except Exception as e:
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return f"Error: {e}"
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def inspect_collection(collection, sample_size=3):
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try:
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col = _collection(collection)
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cnt = col.count()
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if cnt == 0:
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return f"Collection '{collection}' is empty."
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n = min(sample_size, cnt)
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r = col.get(limit=n, include=["documents", "metadatas"])
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out = [f"Collection: {collection} | Total documents: {cnt}", f"Sample ({n}):"]
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for i in range(len(r["ids"])):
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txt = r["documents"][i]
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if len(txt) > 200:
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txt = txt[:200] + "..."
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meta = json.dumps(r["metadatas"][i], ensure_ascii=False) if r.get("metadatas") and r["metadatas"][i] else "(none)"
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out.append(f"\n [{r['ids'][i]}] text: {txt} metadata: {meta}")
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keys = set()
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for m in r["metadatas"]:
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if m:
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keys.update(m.keys())
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if keys:
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out.append(f"\nMetadata keys: {', '.join(sorted(keys))}")
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return "\n".join(out)
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except Exception as e:
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return f"Error: {e}"
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