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Code Issues Packages Projects Releases Wiki Activity

Format Templates (#12396)

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This commit is contained in:
Erick Friis
2023-10-26 19:44:30 -07:00
committed by GitHub
parent 25c98dbba9
commit 4b16601d33
59 changed files with 800 additions and 441 deletions
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50
templates/xml-agent/xml_agent/agent.py
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@@ -1,14 +1,19 @@
from langchain.chat_models import ChatAnthropic
from langchain.tools.render import render_text_description
from langchain.agents.format_scratchpad import format_xml
from langchain.agents import AgentExecutor
from langchain.retrievers.you import YouRetriever
from langchain.agents.agent_toolkits.conversational_retrieval.tool import create_retriever_tool
from langchain.pydantic_v1 import BaseModel
from xml_agent.prompts import conversational_prompt, parse_output
from langchain.schema import AIMessage, HumanMessage
from typing import List, Tuple
from langchain.agents import AgentExecutor
from langchain.agents.agent_toolkits.conversational_retrieval.tool import (
create_retriever_tool,
)
from langchain.agents.format_scratchpad import format_xml
from langchain.chat_models import ChatAnthropic
from langchain.pydantic_v1 import BaseModel
from langchain.retrievers.you import YouRetriever
from langchain.schema import AIMessage, HumanMessage
from langchain.tools.render import render_text_description
from xml_agent.prompts import conversational_prompt, parse_output
def _format_chat_history(chat_history: List[Tuple[str, str]]):
buffer = []
for human, ai in chat_history:
@@ -21,7 +26,9 @@ model = ChatAnthropic(model="claude-2")
# Fake Tool
retriever = YouRetriever(k=5)
retriever_tool = create_retriever_tool(retriever, "search", "Use this to search for current events.")
retriever_tool = create_retriever_tool(
retriever, "search", "Use this to search for current events."
)
tools = [retriever_tool]
@@ -31,18 +38,25 @@ prompt = conversational_prompt.partial(
)
llm_with_stop = model.bind(stop=["</tool_input>"])
agent = {
"question": lambda x: x["question"],
"agent_scratchpad": lambda x: format_xml(x['intermediate_steps']),
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
} | prompt | llm_with_stop | parse_output
agent = (
{
"question": lambda x: x["question"],
"agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]),
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
}
| prompt
| llm_with_stop
| parse_output
)
class AgentInput(BaseModel):
question: str
chat_history: List[Tuple[str, str]]
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, handle_parsing_errors=True).with_types(
input_type=AgentInput
)
agent_executor = AgentExecutor(
agent=agent, tools=tools, verbose=True, handle_parsing_errors=True
).with_types(input_type=AgentInput)
agent_executor = agent_executor | (lambda x: x["output"])