app.py

"""
"""

from collections import defaultdict
import json
import os
import re

from langchain_core.documents import Document
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableParallel
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_together import ChatTogether
from langchain_google_genai import ChatGoogleGenerativeAI
import streamlit as st

import utils_mod
import doc_format_mod
import guide_mod
import sidebar_mod
import usage_mod
import vectorstore_mod


st.set_page_config(layout="wide", page_title="LegisQA")
os.environ["LANGCHAIN_API_KEY"] = st.secrets["langchain_api_key"]
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"] = st.secrets["langchain_project"]
os.environ["TOKENIZERS_PARALLELISM"] = "false"


SS = st.session_state
SEED = 292764
CONGRESS_NUMBERS = [113, 114, 115, 116, 117, 118]
SPONSOR_PARTIES = ["D", "R", "L", "I"]

OPENAI_CHAT_MODELS = {
    "gpt-4o-mini": {"cost": {"pmi": 0.15, "pmo": 0.60}},
    "gpt-4o": {"cost": {"pmi": 5.00, "pmo": 15.0}},
}
ANTHROPIC_CHAT_MODELS = {
    "claude-3-haiku-20240307": {"cost": {"pmi": 0.25, "pmo": 1.25}},
    "claude-3-5-sonnet-20240620": {"cost": {"pmi": 3.00, "pmo": 15.0}},
    "claude-3-opus-20240229": {"cost": {"pmi": 15.0, "pmo": 75.0}},
}
TOGETHER_CHAT_MODELS = {
    "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo": {"cost": {"pmi": 0.18, "pmo": 0.18}},
    "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": {
        "cost": {"pmi": 0.88, "pmo": 0.88}
    },
    "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo": {
        "cost": {"pmi": 5.00, "pmo": 5.00}
    },
}
GOOGLE_CHAT_MODELS = {
    "gemini-1.5-flash": {"cost": {"pmi": 0.0, "pmo": 0.0}},
    "gemini-1.5-pro": {"cost": {"pmi": 0.0, "pmo": 0.0}},
    "gemini-1.5-pro-exp-0801": {"cost": {"pmi": 0.0, "pmo": 0.0}},
}


PROVIDER_MODELS = {
    "OpenAI": OPENAI_CHAT_MODELS,
    "Anthropic": ANTHROPIC_CHAT_MODELS,
    "Together": TOGETHER_CHAT_MODELS,
    "Google": GOOGLE_CHAT_MODELS,
}


def render_example_queries():

    with st.expander("Example Queries"):
        st.write(
            """

```
What are the themes around artificial intelligence?
```

```
Write a well cited 3 paragraph essay on food insecurity.
```

```
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.
```

        """
        )


def get_generative_config(key_prefix: str) -> dict:
    output = {}

    key = "provider"
    output[key] = st.selectbox(
        label=key, options=PROVIDER_MODELS.keys(), key=f"{key_prefix}|{key}"
    )

    key = "model_name"
    output[key] = st.selectbox(
        label=key,
        options=PROVIDER_MODELS[output["provider"]],
        key=f"{key_prefix}|{key}",
    )

    key = "temperature"
    output[key] = st.slider(
        key,
        min_value=0.0,
        max_value=2.0,
        value=0.0,
        key=f"{key_prefix}|{key}",
    )

    key = "max_output_tokens"
    output[key] = st.slider(
        key,
        min_value=1024,
        max_value=2048,
        key=f"{key_prefix}|{key}",
    )

    key = "top_p"
    output[key] = st.slider(
        key, min_value=0.0, max_value=1.0, value=0.9, key=f"{key_prefix}|{key}"
    )

    key = "should_escape_markdown"
    output[key] = st.checkbox(
        key,
        value=False,
        key=f"{key_prefix}|{key}",
    )

    key = "should_add_legis_urls"
    output[key] = st.checkbox(
        key,
        value=True,
        key=f"{key_prefix}|{key}",
    )

    return output


def get_retrieval_config(key_prefix: str) -> dict:
    output = {}

    key = "n_ret_docs"
    output[key] = st.slider(
        "Number of chunks to retrieve",
        min_value=1,
        max_value=32,
        value=8,
        key=f"{key_prefix}|{key}",
    )

    key = "filter_legis_id"
    output[key] = st.text_input("Bill ID (e.g. 118-s-2293)", key=f"{key_prefix}|{key}")

    key = "filter_bioguide_id"
    output[key] = st.text_input("Bioguide ID (e.g. R000595)", key=f"{key_prefix}|{key}")

    key = "filter_congress_nums"
    output[key] = st.multiselect(
        "Congress Numbers",
        CONGRESS_NUMBERS,
        default=CONGRESS_NUMBERS,
        key=f"{key_prefix}|{key}",
    )

    key = "filter_sponsor_parties"
    output[key] = st.multiselect(
        "Sponsor Party",
        SPONSOR_PARTIES,
        default=SPONSOR_PARTIES,
        key=f"{key_prefix}|{key}",
    )

    return output


def get_llm(gen_config: dict):

    match gen_config["provider"]:

        case "OpenAI":
            llm = ChatOpenAI(
                model=gen_config["model_name"],
                temperature=gen_config["temperature"],
                api_key=st.secrets["openai_api_key"],
                top_p=gen_config["top_p"],
                seed=SEED,
                max_tokens=gen_config["max_output_tokens"],
            )

        case "Anthropic":
            llm = ChatAnthropic(
                model_name=gen_config["model_name"],
                temperature=gen_config["temperature"],
                api_key=st.secrets["anthropic_api_key"],
                top_p=gen_config["top_p"],
                max_tokens_to_sample=gen_config["max_output_tokens"],
            )

        case "Together":
            llm = ChatTogether(
                model=gen_config["model_name"],
                temperature=gen_config["temperature"],
                max_tokens=gen_config["max_output_tokens"],
                top_p=gen_config["top_p"],
                seed=SEED,
                api_key=st.secrets["together_api_key"],
            )

        case "Google":
            llm = ChatGoogleGenerativeAI(
                model=gen_config["model_name"],
                temperature=gen_config["temperature"],
                api_key=st.secrets["google_api_key"],
                max_output_tokens=gen_config["max_output_tokens"],
                top_p=gen_config["top_p"],
            )

        case _:
            raise ValueError()

    return llm


def create_rag_chain(llm, retriever):
    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. When citing legis_id, use the same format as the excerpts (e.g. "116-hr-125"). If you don't know how to respond, just tell the user.

---

Congressional Legislation Excerpts:

{context}

---

Query: {query}"""

    prompt = ChatPromptTemplate.from_messages(
        [
            ("human", QUERY_RAG_TEMPLATE),
        ]
    )

    rag_chain = (
        RunnableParallel(
            {
                "docs": retriever,
                "query": RunnablePassthrough(),
            }
        )
        .assign(context=lambda x: doc_format_mod.format_docs(x["docs"]))
        .assign(aimessage=prompt | llm)
    )

    return rag_chain


def process_query(gen_config: dict, ret_config: dict, query: str):
    vectorstore = vectorstore_mod.load_pinecone_vectorstore()
    llm = get_llm(gen_config)
    vs_filter = vectorstore_mod.get_vectorstore_filter(ret_config)
    retriever = vectorstore.as_retriever(
        search_kwargs={"k": ret_config["n_ret_docs"], "filter": vs_filter},
    )
    rag_chain = create_rag_chain(llm, retriever)
    response = rag_chain.invoke(query)
    return response


def render_response(
    response: dict,
    model_info: dict,
    provider: str,
    should_escape_markdown: bool,
    should_add_legis_urls: bool,
    tag: str | None = None,
):
    response_text = response["aimessage"].content
    if should_escape_markdown:
        response_text = utils_mod.escape_markdown(response_text)
    if should_add_legis_urls:
        response_text = utils_mod.replace_legis_ids_with_urls(response_text)

    with st.container(border=True):
        if tag is None:
            st.write("Response")
        else:
            st.write(f"Response ({tag})")
        st.info(response_text)

    usage_mod.display_api_usage(response["aimessage"], model_info, provider, tag=tag)
    doc_format_mod.render_retrieved_chunks(response["docs"], tag=tag)


def render_query_rag_tab():
    key_prefix = "query_rag"
    render_example_queries()

    with st.form(f"{key_prefix}|query_form"):
        query = st.text_area(
            "Enter a query that can be answered with congressional legislation:"
        )
        cols = st.columns(2)
        with cols[0]:
            query_submitted = st.form_submit_button("Submit")
        with cols[1]:
            status_placeholder = st.empty()

    col1, col2 = st.columns(2)
    with col1:
        with st.expander("Generative Config"):
            gen_config = get_generative_config(key_prefix)
    with col2:
        with st.expander("Retrieval Config"):
            ret_config = get_retrieval_config(key_prefix)

    rkey = f"{key_prefix}|response"
    if query_submitted:
        with status_placeholder:
            with st.spinner("generating response"):
                SS[rkey] = process_query(gen_config, ret_config, query)

    if response := SS.get(rkey):
        model_info = PROVIDER_MODELS[gen_config["provider"]][gen_config["model_name"]]
        render_response(
            response,
            model_info,
            gen_config["provider"],
            gen_config["should_escape_markdown"],
            gen_config["should_add_legis_urls"],
        )

        with st.expander("Debug"):
            st.write(response)


def render_query_rag_sbs_tab():
    base_key_prefix = "query_rag_sbs"

    with st.form(f"{base_key_prefix}|query_form"):
        query = st.text_area(
            "Enter a query that can be answered with congressional legislation:"
        )
        cols = st.columns(2)
        with cols[0]:
            query_submitted = st.form_submit_button("Submit")
        with cols[1]:
            status_placeholder = st.empty()

    grp1a, grp2a = st.columns(2)

    gen_configs = {}
    ret_configs = {}
    with grp1a:
        st.header("Group 1")
        key_prefix = f"{base_key_prefix}|grp1"
        with st.expander("Generative Config"):
            gen_configs["grp1"] = get_generative_config(key_prefix)
        with st.expander("Retrieval Config"):
            ret_configs["grp1"] = get_retrieval_config(key_prefix)

    with grp2a:
        st.header("Group 2")
        key_prefix = f"{base_key_prefix}|grp2"
        with st.expander("Generative Config"):
            gen_configs["grp2"] = get_generative_config(key_prefix)
        with st.expander("Retrieval Config"):
            ret_configs["grp2"] = get_retrieval_config(key_prefix)

    grp1b, grp2b = st.columns(2)
    sbs_cols = {"grp1": grp1b, "grp2": grp2b}
    grp_names = {"grp1": "Group 1", "grp2": "Group 2"}

    for post_key_prefix in ["grp1", "grp2"]:
        with sbs_cols[post_key_prefix]:
            key_prefix = f"{base_key_prefix}|{post_key_prefix}"
            rkey = f"{key_prefix}|response"
            if query_submitted:
                with status_placeholder:
                    with st.spinner(
                        "generating response for {}".format(grp_names[post_key_prefix])
                    ):
                        SS[rkey] = process_query(
                            gen_configs[post_key_prefix],
                            ret_configs[post_key_prefix],
                            query,
                        )

            if response := SS.get(rkey):
                model_info = PROVIDER_MODELS[gen_configs[post_key_prefix]["provider"]][
                    gen_configs[post_key_prefix]["model_name"]
                ]
                render_response(
                    response,
                    model_info,
                    gen_configs[post_key_prefix]["provider"],
                    gen_configs[post_key_prefix]["should_escape_markdown"],
                    gen_configs[post_key_prefix]["should_add_legis_urls"],
                    tag=grp_names[post_key_prefix],
                )


def main():

    st.title(":classical_building: LegisQA :classical_building:")
    st.header("Query Congressional Bills")

    with st.sidebar:
        sidebar_mod.render_sidebar()

    query_rag_tab, query_rag_sbs_tab, guide_tab = st.tabs(
        [
            "RAG",
            "RAG (side-by-side)",
            "Guide",
        ]
    )

    with query_rag_tab:
        render_query_rag_tab()

    with query_rag_sbs_tab:
        render_query_rag_sbs_tab()

    with guide_tab:
        guide_mod.render_guide()


if __name__ == "__main__":
    main()