# Ollama doesn't support tool calls in the same way, so we return the content

from mem0 import Memory

config = {
    "graph_store": {
        "provider": "neo4j",
        "config": {"url": "bolt://localhost:7687", "username": "neo4j", "password": "test"},
    },
    "llm": {
        "provider": "ollama",
        "config": {"model": "ministral-3:3b", "temperature": 0.1},
    },
    "embedder": {
        "provider": "ollama",
        "config": {"model": "nomic-embed-text"},
    },
    "vector_store": {
        "provider": "qdrant",
        "config": {"collection_name": "test", "path": "/tmp/test_qdrant", "embedding_model_dims": 768},
    },
}

m = Memory.from_config(config_dict=config)
result = m.add("Alice teaches CSE 151A at UCSD. DRSN sells on Amazon.", user_id="test", infer=True)
print(result["relations"])
# {'deleted_entities': [], 'added_entities': []}   <-- always empty

# Current code (line ~95):
response = self.client.chat(**params)
# `tools` is a parameter but never added to `params`

# Current code (line ~50):
if tools:
    processed_response = {
        "content": content,
        "tool_calls": [],  # <-- always empty
    }
    # Ollama doesn't support tool calls in the same way, so we return the content
    return processed_response

# 1. In generate_response(), before client.chat():
if tools:
    params["tools"] = tools
response = self.client.chat(**params)

# 2. In _parse_response(), parse actual tool_calls:
if tools:
    processed_response = {"content": content, "tool_calls": []}
    if isinstance(response, dict):
        raw_calls = response.get("message", {}).get("tool_calls", [])
    else:
        raw_calls = getattr(response.message, "tool_calls", None) or []
    for tc in raw_calls:
        if isinstance(tc, dict):
            fn = tc.get("function", {})
            name = fn.get("name", "")
            args = fn.get("arguments", {})
        else:
            fn = getattr(tc, "function", None)
            name = getattr(fn, "name", "") if fn else ""
            args = getattr(fn, "arguments", {}) if fn else {}
        processed_response["tool_calls"].append({"name": name, "arguments": args})
    return processed_response