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import os
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.format_scratchpad import format_log_to_messages
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.render import render_text_description_and_args
from langchain_community.chat_models import ChatOllama
from langchain_core.messages import AIMessage, HumanMessage
from neo4j_semantic_ollama.information_tool import InformationTool
from neo4j_semantic_ollama.memory_tool import MemoryTool
from neo4j_semantic_ollama.recommendation_tool import RecommenderTool
from neo4j_semantic_ollama.smalltalk_tool import SmalltalkTool
llm = ChatOllama(
model="mixtral",
temperature=0,
base_url=os.environ["OLLAMA_BASE_URL"],
streaming=True,
)
chat_model_with_stop = llm.bind(stop=["\nObservation"])
tools = [InformationTool(), RecommenderTool(), MemoryTool(), SmalltalkTool()]
# Inspiration taken from hub.pull("hwchase17/react-json")
system_message = f"""Answer the following questions as best you can.
You can answer directly if the user is greeting you or similar.
Otherise, you have access to the following tools:
{render_text_description_and_args(tools).replace('{', '{{').replace('}', '}}')}
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: {[t.name for t in tools]}
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
}}}}
```
The $JSON_BLOB must always be enclosed with triple backticks!
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.'
"""
prompt = ChatPromptTemplate.from_messages(
[
(
"user",
system_message,
),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
def _format_chat_history(chat_history: List[Tuple[str, str]]):
buffer = []
for human, ai in chat_history:
buffer.append(HumanMessage(content=human))
buffer.append(AIMessage(content=ai))
return buffer
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]),
"chat_history": lambda x: (
_format_chat_history(x["chat_history"]) if x.get("chat_history") else []
),
}
| prompt
| chat_model_with_stop
| ReActJsonSingleInputOutputParser()
)
# Add typing for input
class AgentInput(BaseModel):
input: str
chat_history: List[Tuple[str, str]] = Field(
..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
)
agent_executor = AgentExecutor(agent=agent, tools=tools).with_types(
input_type=AgentInput
)
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