Use prompt templates with LangChain RAG agent.

chat_prompt_template_langchain_rag_agent.py

import os

# if not in your .env file
os.environ['OPENAI_API_KEY'] = ''

from langchain_community.document_loaders import TextLoader
from langchain.tools.retriever import create_retriever_tool
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain_core.prompts import (
    ChatPromptTemplate,
)

loader = TextLoader("data.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()

db = FAISS.from_documents(texts, embeddings)
retriever = db.as_retriever()

tool = create_retriever_tool(
  retriever,
  "name",
  "description",
)
tools = [tool]

# base prompt template - you can use any other ones
prompt = hub.pull("hwchase17/openai-tools-agent")

# build the prompt template using the base
# THEN add in any custom system messages
# add format for the prompt
final_prompt = ChatPromptTemplate.from_messages(
  [
    prompt,
    ("system", "Use only the first person. Don't include any extra text. Don't repeat yourself."),
    ("human", "Find excerpts that demonstrate: {input}"),
  ]
)

# create the agent, pass in the model, tools (DB/retriever), and the prompt template
llm = ChatOpenAI(temperature=0)
agent = create_openai_tools_agent(llm, tools, final_prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
resp = agent_executor.invoke({"input": "Knowledge of butterflies."})
print( resp['input'] + '\n' + resp['output'] )