Templates (#12294)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>

templates/xml-agent/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

templates/xml-agent/README.md

@@ -0,0 +1,17 @@
+# XML Agent
+
+This template creates an agent that uses XML syntax to communicate its decisions of what actions to take.
+For this example, we use Anthropic since Anthropic's Claude models are particularly good at writing XML syntax.
+This example creates an agent that can optionally look up things on the internet using You.com's retriever.
+
+##  LLM
+
+This template will use `Anthropic` by default. 
+
+Be sure that `ANTHROPIC_API_KEY` is set in your environment.
+
+##  Tools
+
+This template will use `You.com` by default. 
+
+Be sure that `YDC_API_KEY` is set in your environment.

templates/xml-agent/main.py

@@ -0,0 +1,5 @@
+from xml_agent.agent import agent_executor
+
+if __name__ == "__main__":
+    question = "who won the womens world cup in 2023?"
+    print(agent_executor.invoke({"question": question, "chat_history": []}))

templates/xml-agent/pyproject.toml

@@ -0,0 +1,20 @@
+[tool.poetry]
+name = "xml-agent"
+version = "0.1.0"
+description = ""
+authors = ["Lance Martin <lance@langchain.dev>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.322"
+anthropic = ">=0.5.0"
+langchainhub = ">=0.1.13"
+
+[tool.langserve]
+export_module = "xml_agent"
+export_attr = "agent_executor"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/xml-agent/xml_agent/__init__.py

@@ -0,0 +1,3 @@
+from xml_agent.agent import agent_executor
+
+__all__ = ["agent_executor"]

templates/xml-agent/xml_agent/agent.py

@@ -0,0 +1,48 @@
+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
+
+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
+
+
+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.")
+
+tools = [retriever_tool]
+
+prompt = conversational_prompt.partial(
+    tools=render_text_description(tools),
+    tool_names=", ".join([t.name for t in tools]),
+)
+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
+
+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 = agent_executor | (lambda x: x["output"])

templates/xml-agent/xml_agent/prompts.py

@@ -0,0 +1,50 @@
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.schema import AgentAction, AgentFinish
+
+template = """You are a helpful assistant. Help the user answer any questions.
+
+You have access to the following tools:
+
+{tools}
+
+In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
+For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
+
+<tool>search</tool><tool_input>weather in SF</tool_input>
+<observation>64 degrees</observation>
+
+When you are done, you can respond as normal to the user.
+
+Example 1:
+
+Human: Hi!
+
+Assistant: Hi! How are you?
+
+Human: What is the weather in SF?
+Assistant: <tool>search</tool><tool_input>weather in SF</tool_input>
+<observation>64 degrees</observation>
+It is 64 degress in SF
+
+
+Begin!"""
+
+conversational_prompt = ChatPromptTemplate.from_messages([
+    ("system", template),
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("user", "{question}"),
+    ("ai", "{agent_scratchpad}")
+])
+
+
+def parse_output(message):
+    text = message.content
+    if "</tool>" in text:
+        tool, tool_input = text.split("</tool>")
+        _tool = tool.split("<tool>")[1]
+        _tool_input = tool_input.split("<tool_input>")[1]
+        if "</tool_input>" in _tool_input:
+            _tool_input = _tool_input.split("</tool_input>")[0]
+        return AgentAction(tool=_tool, tool_input=_tool_input, log=text)
+    else:
+        return AgentFinish(return_values={"output": text}, log=text)