%pip install--upgrade--quiet  mlx-lm transformers huggingface_hub

from langchain_community.llms.mlx_pipelineimport MLXPipeline

llm= MLXPipeline.from_model_id(
"mlx-community/quantized-gemma-2b-it",
    pipeline_kwargs={"max_tokens":10,"temp":0.1},
)

from langchain_community.chat_models.mlximport ChatMLX
from langchain_core.messagesimport HumanMessage

messages=[
    HumanMessage(
        content="What happens when an unstoppable force meets an immovable object?"
),
]

chat_model= ChatMLX(llm=llm)

chat_model._to_chat_prompt(messages)

res= chat_model.invoke(messages)
print(res.content)

from langchainimport hub
from langchain.agentsimport AgentExecutor, load_tools
from langchain.agents.format_scratchpadimport format_log_to_str
from langchain.agents.output_parsersimport(
    ReActJsonSingleInputOutputParser,
)
from langchain.tools.renderimport render_text_description
from langchain_community.utilitiesimport SerpAPIWrapper

# setup tools
tools= load_tools(["serpapi","llm-math"], llm=llm)

# setup ReAct style prompt
# Based on 'hwchase17/react' prompt modification, cause mlx does not support the `System` role
human_prompt="""
Answer the following questions as best you can. You have access to the following tools:

{tools}

The way you use the tools is by specifying a json blob.
Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).

The only values that should be in the "action" field are: {tool_names}

The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:

\`\`\`
{{
  "action": $TOOL_NAME,
  "action_input": $INPUT
}}
\`\`\`

ALWAYS use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action:
\`\`\`
$JSON_BLOB
\`\`\`
Observation: the result of the action
... (this Thought/Action/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin! Reminder to always use the exact characters `Final Answer` when responding.

{input}

{agent_scratchpad}

"""

prompt= human_prompt.partial(
    tools=render_text_description(tools),
    tool_names=", ".join([t.namefor tin tools]),
)

# define the agent
chat_model_with_stop= chat_model.bind(stop=["\nObservation"])
agent=(
{
"input":lambda x: x["input"],
"agent_scratchpad":lambda x: format_log_to_str(x["intermediate_steps"]),
}
| prompt
| chat_model_with_stop
| ReActJsonSingleInputOutputParser()
)

# instantiate AgentExecutor
agent_executor= AgentExecutor(agent=agent, tools=tools, verbose=True)

agent_executor.invoke(
{
"input":"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?"
}
)