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import os
import streamlit as st
os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
# +
st.set_page_config(page_title="Protected Areas Database Chat", page_icon="🦜")
st.title("Protected Areas Database Chat")
st.markdown('''
This Chatbot is designed only to answer questions based on [PAD Technical How-Tos](https://www.protectedlands.net/pad-us-technical-how-tos/). The Chatbot will refuse to answer questions outside of this context.
Example queries:
- What is the difference between Fee and Easements?
- What do the gap status codes mean?
''')
# -
# optional
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_chroma import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.llms import Ollama
from langchain_openai import ChatOpenAI
# +
llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
# Setup LLM and QA chain
models = {"chatgpt3.5": ChatOpenAI(model="gpt-3.5-turbo", temperature=0, api_key=st.secrets["OPENAI_API_KEY"], streaming=True),
# "chatgpt4": ChatOpenAI(model="gpt-4", temperature=0, api_key=st.secrets["OPENAI_API_KEY"]),
"phi3": Ollama(model="phi3", temperature=0),
"duckdb-nsql": Ollama(model="duckdb-nsql", temperature=0),
"command-r-plus": Ollama(model="command-r-plus", temperature=0),
"mistral": Ollama(model="mistral", temperature=0),
"wizardlm2": Ollama(model="wizardlm2", temperature=0),
"sqlcoder": Ollama(model="sqlcoder", temperature=0),
"zephyr": Ollama(model="zephyr", temperature=0),
"gemma": Ollama(model="gemma", temperature=0),
"llama3": Ollama(model="llama3", temperature=0),
}
with st.sidebar:
"Non-ChatGPT models assume you are running the app locally with ollama installed."
choice = st.radio("Select an LLM:", models)
llm = models[choice]
# -
# Load, chunk and index the contents of the blog.
loader = WebBaseLoader(
web_paths=(["https://www.protectedlands.net/pad-us-technical-how-tos/",
"https://www.protectedlands.net/uses-of-pad-us/",
# "https://www.protectedlands.net/pad-us-data-structure-attributes/"
]),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("main-body")
)
),
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=400)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
# Retrieve and generate using the relevant snippets of the blog.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
rag_chain.invoke("What is the difference between Fee and Easement?")
# +
from langchain_core.runnables import RunnableParallel
rag_chain_from_docs = (
RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
| prompt
| llm
| StrOutputParser()
)
rag_chain_with_source = RunnableParallel(
{"context": retriever, "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)
rag_chain_with_source.invoke("What is the difference between Fee and Easement?")
# +
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
# Setup memory for contextual conversation
msgs = StreamlitChatMessageHistory()
memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=msgs, return_messages=True)
#qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory, verbose=True)
if len(msgs.messages) == 0 or st.sidebar.button("Clear message history"):
msgs.clear()
msgs.add_ai_message("How can I help you?")
avatars = {"human": "user", "ai": "assistant"}
for msg in msgs.messages:
st.chat_message(avatars[msg.type]).write(msg.content)
if user_query := st.chat_input(placeholder="Ask me about PAD!"):
st.chat_message("user").write(user_query)
with st.chat_message("assistant"):
response = rag_chain.invoke(user_query)
response
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