mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
ed789be8f4
- chat models, messages - documents - agentaction/finish - baseretriever,document - stroutputparser - more messages - basemessage - format_document - baseoutputparser --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
import os
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from typing import List, Tuple
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from langchain.agents import AgentExecutor
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from langchain.agents.format_scratchpad import format_log_to_messages
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from langchain.agents.output_parsers import (
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ReActJsonSingleInputOutputParser,
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)
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.pydantic_v1 import BaseModel, Field
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from langchain.tools.render import render_text_description_and_args
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from langchain_community.chat_models import ChatOllama
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from langchain_core.messages import AIMessage, HumanMessage
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from neo4j_semantic_ollama.information_tool import InformationTool
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from neo4j_semantic_ollama.memory_tool import MemoryTool
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from neo4j_semantic_ollama.recommendation_tool import RecommenderTool
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from neo4j_semantic_ollama.smalltalk_tool import SmalltalkTool
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llm = ChatOllama(
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model="mixtral",
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temperature=0,
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base_url=os.environ["OLLAMA_BASE_URL"],
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streaming=True,
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)
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chat_model_with_stop = llm.bind(stop=["\nObservation"])
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tools = [InformationTool(), RecommenderTool(), MemoryTool(), SmalltalkTool()]
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# Inspiration taken from hub.pull("hwchase17/react-json")
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system_message = f"""Answer the following questions as best you can.
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You can answer directly if the user is greeting you or similar.
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Otherise, you have access to the following tools:
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{render_text_description_and_args(tools).replace('{', '{{').replace('}', '}}')}
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The way you use the tools is by specifying a json blob.
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Specifically, this json should have a `action` key (with the name of the tool to use)
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and a `action_input` key (with the input to the tool going here).
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The only values that should be in the "action" field are: {[t.name for t in tools]}
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The $JSON_BLOB should only contain a SINGLE action,
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do NOT return a list of multiple actions.
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Here is an example of a valid $JSON_BLOB:
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```
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{{{{
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"action": $TOOL_NAME,
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"action_input": $INPUT
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}}}}
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```
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The $JSON_BLOB must always be enclosed with triple backticks!
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ALWAYS use the following format:
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Question: the input question you must answer
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Thought: you should always think about what to do
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Action:```
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$JSON_BLOB
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```
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Observation: the result of the action...
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(this Thought/Action/Observation can repeat N times)
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Thought: I now know the final answer
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Final Answer: the final answer to the original input question
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Begin! Reminder to always use the exact characters `Final Answer` when responding.'
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"""
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"user",
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system_message,
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),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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def _format_chat_history(chat_history: List[Tuple[str, str]]):
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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agent = (
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{
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"input": lambda x: x["input"],
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"agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]),
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"chat_history": lambda x: (
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_format_chat_history(x["chat_history"]) if x.get("chat_history") else []
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),
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}
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| prompt
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| chat_model_with_stop
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| ReActJsonSingleInputOutputParser()
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)
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# Add typing for input
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class AgentInput(BaseModel):
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input: str
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chat_history: List[Tuple[str, str]] = Field(
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..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
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)
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agent_executor = AgentExecutor(agent=agent, tools=tools).with_types(
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input_type=AgentInput
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)
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