rag chat

pages/3_🕮_Docs_Demo.py

@@ -0,0 +1,127 @@
+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:
+    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.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
+

requirements.txt

@@ -4,6 +4,9 @@ altair
 langchain
 langchain-community
 langchain-openai
+langchainhub
+langchain-chroma 
+bs4
 SQLAlchemy==1.4.52
 streamlit
 geopandas