update

app.py

@@ -6,6 +6,10 @@ import re
 
 from langchain_core.documents import Document
 from langchain_core.prompts import PromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.prompts import MessagesPlaceholder
+from langchain_core.messages import AIMessage
+from langchain_core.messages import HumanMessage
 from langchain_core.runnables import RunnableParallel
 from langchain_core.runnables import RunnablePassthrough
 from langchain_core.output_parsers import StrOutputParser
@@ -111,7 +115,7 @@ def load_pinecone_vectorstore():
     return vectorstore
 
 
-def write_outreach_links():
+def render_outreach_links():
     nomic_base_url = "https://atlas.nomic.ai/data/gabrielhyperdemocracy"
     nomic_map_name = "us-congressional-legislation-s1024o256nomic"
     nomic_url = f"{nomic_base_url}/{nomic_map_name}/map"
@@ -238,7 +242,7 @@ def get_vectorstore_filter():
     return vs_filter
 
 
-def write_doc_grp(legis_id: str, doc_grp: list[Document]):
+def render_doc_grp(legis_id: str, doc_grp: list[Document]):
     first_doc = doc_grp[0]
 
     congress_gov_url = get_congress_gov_url(
@@ -290,7 +294,7 @@ def replace_legis_ids_with_urls(text):
     return rtext
 
 
-def write_guide():
+def render_guide():
 
     st.write(
         """
@@ -311,7 +315,7 @@ Use the `Retrieval Config` to change the number of chunks retrieved from our con
     )
 
 
-def write_example_queries():
+def render_example_queries():
 
     with st.expander("Example Queries"):
         st.write(
@@ -326,23 +330,25 @@ Write a well cited 3 paragraph essay on food insecurity.
 ```
 
 ```
-Create a table summarizing the major climate change ideas with columns legis_id, title, idea.
+Create a table summarizing major climate change ideas with columns legis_id, title, idea.
 ```
 
-        """
-        )
-
-
-##################
+```
+Write an action plan to keep social security solvent.
+```
 
+```
+Suggest reforms that would benefit the Medicaid program.
+```
 
-st.title(":classical_building: LegisQA :classical_building:")
+        """
+        )
 
 
-with st.sidebar:
+def render_sidebar():
 
     with st.container(border=True):
-        write_outreach_links()
+        render_outreach_links()
 
     st.checkbox("escape markdown in answer", key="response_escape_markdown")
     st.checkbox("add legis urls in answer", value=True, key="response_add_legis_urls")
@@ -392,25 +398,9 @@ with st.sidebar:
         )
 
 
-llm = ChatOpenAI(
-    model_name=SS["model_name"],
-    temperature=SS["temperature"],
-    openai_api_key=st.secrets["openai_api_key"],
-    model_kwargs={"top_p": SS["top_p"], "seed": SEED},
-)
-vectorstore = load_pinecone_vectorstore()
-format_docs = DOC_FORMATTERS[SS["prompt_version"]]
-vs_filter = get_vectorstore_filter()
-
-query_tab, guide_tab = st.tabs(["query", "guide"])
-
-with guide_tab:
-    write_guide()
-
-
-with query_tab:
+def render_query_tab():
 
-    write_example_queries()
+    render_example_queries()
 
     with st.form("my_form"):
         st.text_area("Enter query:", key="query")
@@ -418,6 +408,7 @@ with query_tab:
 
     if query_submitted:
 
+        vs_filter = get_vectorstore_filter()
         retriever = vectorstore.as_retriever(
             search_kwargs={"k": SS["n_ret_docs"], "filter": vs_filter},
         )
@@ -457,7 +448,61 @@ with query_tab:
                 "Retrieved Chunks (note that you may need to 'right click' on links in the expanders to follow them)"
             )
             for legis_id, doc_grp in doc_grps:
-                write_doc_grp(legis_id, doc_grp)
+                render_doc_grp(legis_id, doc_grp)
 
         with st.expander("Debug"):
             st.write(SS["out"])
+
+
+def render_chat_tab():
+
+    contextualize_q_system_prompt = """Given a chat history and the latest user question \
+which might reference context in the chat history, formulate a standalone question \
+which can be understood without the chat history. Do NOT answer the question, \
+just reformulate it if needed and otherwise return it as is."""
+    contextualize_q_prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", contextualize_q_system_prompt),
+            MessagesPlaceholder(variable_name="chat_history"),
+            ("human", "{query}"),
+        ]
+    )
+    contextualize_q_chain = contextualize_q_prompt | llm | StrOutputParser()
+
+    st.write("Coming Soon")
+
+    pass
+
+
+##################
+
+
+st.title(
+    ":classical_building: LegisQA - Chat With Congressional Bills :classical_building:"
+)
+
+
+with st.sidebar:
+    render_sidebar()
+
+
+llm = ChatOpenAI(
+    model_name=SS["model_name"],
+    temperature=SS["temperature"],
+    openai_api_key=st.secrets["openai_api_key"],
+    model_kwargs={"top_p": SS["top_p"], "seed": SEED},
+)
+vectorstore = load_pinecone_vectorstore()
+format_docs = DOC_FORMATTERS[SS["prompt_version"]]
+
+
+query_tab, chat_tab, guide_tab = st.tabs(["query", "chat", "guide"])
+
+with guide_tab:
+    render_guide()
+
+with query_tab:
+    render_query_tab()
+
+with chat_tab:
+    render_chat_tab()