new layout

app.py

@@ -120,10 +120,12 @@ def render_outreach_links():
     nomic_url = f"{nomic_base_url}/{nomic_map_name}/map"
     hf_url = "https://huggingface.co/hyperdemocracy"
     pc_url = "https://www.pinecone.io/blog/serverless"
+    together_url = "https://www.together.ai/"
     st.subheader(":brain: About [hyperdemocracy](https://hyperdemocracy.us)")
     st.subheader(f":world_map: Visualize [nomic atlas]({nomic_url})")
     st.subheader(f":hugging_face: Raw [huggingface datasets]({hf_url})")
     st.subheader(f":evergreen_tree: Index [pinecone serverless]({pc_url})")
+    st.subheader(f":pancakes: Inference [together.ai]({together_url})")
 
 
 def group_docs(docs) -> list[tuple[str, list[Document]]]:
@@ -247,7 +249,7 @@ When you send a query to LegisQA, it will attempt to retrieve relevant content f
 This is a research project. The RAG technique helps to ground the LLM response by providing context from a trusted source, but it does not guarantee a high quality response. We encourage you to play around, find questions that work and find questions that fail. There is a small monthly budget dedicated to the OpenAI endpoints. Once that is used up each month, queries will no longer work.
 
 
-## Sidebar Config
+## Config
 
 Use the `Generative Config` to change LLM parameters.
 Use the `Retrieval Config` to change the number of chunks retrieved from our congress corpus and to apply various filters to the content before it is retrieved (e.g. filter to a specific set of congresses). Use the `Prompt Config` to try out different document formatting and prompting strategies.
@@ -443,17 +445,6 @@ def render_sidebar():
 
 def render_query_rag_tab():
 
-    key_prefix = "query_rag"
-    render_example_queries()
-
-    col1, col2 = st.columns(2)
-    with col1:
-        with st.expander("Generative Config"):
-            render_generative_config(key_prefix)
-    with col2:
-        with st.expander("Retrieval Config"):
-            render_retrieval_config(key_prefix)
-
     QUERY_RAG_TEMPLATE = """You are an expert legislative analyst. Use the following excerpts from US congressional legislation to respond to the user's query. The excerpts are formatted as a JSON list. Each JSON object has "legis_id", "title", "introduced_date", "sponsor", and "snippets" keys. If a snippet is useful in writing part of your response, then cite the "legis_id", "title", "introduced_date", and "sponsor" in the response. If you don't know how to respond, just tell the user.
 
 ---
@@ -472,11 +463,22 @@ Query: {query}"""
         ]
     )
 
+    key_prefix = "query_rag"
+    render_example_queries()
+
     with st.form(f"{key_prefix}|query_form"):
         st.text_area(
             "Enter a query that can be answered with congressional legislation:",
             key=f"{key_prefix}|query",
         )
+        col1, col2 = st.columns(2)
+        with col1:
+            with st.expander("Generative Config"):
+                render_generative_config(key_prefix)
+        with col2:
+            with st.expander("Retrieval Config"):
+                render_retrieval_config(key_prefix)
+
         query_submitted = st.form_submit_button("Submit")
 
     if query_submitted:
@@ -567,23 +569,23 @@ Query: {query}"""
         )
         query_submitted = st.form_submit_button("Submit")
 
-    grp1a, grp2a = st.columns(2)
+        grp1a, grp2a = st.columns(2)
 
-    with grp1a:
-        st.header("Group 1")
-        key_prefix = f"{base_key_prefix}|grp1"
-        with st.expander("Generative Config"):
-            render_generative_config(key_prefix)
-        with st.expander("Retrieval Config"):
-            render_retrieval_config(key_prefix)
+        with grp1a:
+            st.header("Group 1")
+            key_prefix = f"{base_key_prefix}|grp1"
+            with st.expander("Generative Config"):
+                render_generative_config(key_prefix)
+            with st.expander("Retrieval Config"):
+                render_retrieval_config(key_prefix)
 
-    with grp2a:
-        st.header("Group 2")
-        key_prefix = f"{base_key_prefix}|grp2"
-        with st.expander("Generative Config"):
-            render_generative_config(key_prefix)
-        with st.expander("Retrieval Config"):
-            render_retrieval_config(key_prefix)
+        with grp2a:
+            st.header("Group 2")
+            key_prefix = f"{base_key_prefix}|grp2"
+            with st.expander("Generative Config"):
+                render_generative_config(key_prefix)
+            with st.expander("Retrieval Config"):
+                render_retrieval_config(key_prefix)
 
     grp1b, grp2b = st.columns(2)
     sbs_cols = {"grp1": grp1b, "grp2": grp2b}
@@ -663,9 +665,9 @@ vectorstore = load_pinecone_vectorstore()
 
 query_rag_tab, query_rag_sbs_tab, guide_tab = st.tabs(
     [
-        "query_rag",
-        "query_rag_sbs",
-        "guide",
+        "RAG",
+        "RAG (side-by-side)",
+        "Guide",
     ]
 )