app.py

from collections import defaultdict
import json
import os
import re

from langchain_core.documents import Document
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableParallel
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_community.callbacks import get_openai_callback
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores.utils import DistanceStrategy
from langchain_openai import ChatOpenAI
from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone
import streamlit as st


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"]

SS = st.session_state
SEED = 292764
CONGRESS_NUMBERS = [113, 114, 115, 116, 117, 118]
SPONSOR_PARTIES = ["D", "R", "L", "I"]
CONGRESS_GOV_TYPE_MAP = {
    "hconres": "house-concurrent-resolution",
    "hjres": "house-joint-resolution",
    "hr": "house-bill",
    "hres": "house-resolution",
    "s": "senate-bill",
    "sconres": "senate-concurrent-resolution",
    "sjres": "senate-joint-resolution",
    "sres": "senate-resolution",
}
OPENAI_CHAT_MODELS = [
    "gpt-3.5-turbo-0125",
    "gpt-4-0125-preview",
]


PREAMBLE = "You are an expert analyst. Use the following excerpts from US congressional legislation to respond to the user's query."
PROMPT_TEMPLATES = {
    "v1": PREAMBLE
    + """ If you don't know how to respond, just tell the user.

{context}

Question: {question}""",
    "v2": PREAMBLE
    + """ Each snippet starts with a header that includes a unique snippet number (snippet_num), a legis_id, and a title. Your response should cite particular snippets using legis_id and title. If you don't know how to respond, just tell the user.

{context}

Question: {question}""",
    "v3": PREAMBLE
    + """ Each excerpt starts with a header that includes a legis_id, and a title followed by one or more text snippets. When using text snippets in your response, you should cite the legis_id and title. If you don't know how to respond, just tell the user.

{context}

Question: {question}""",
    "v4": PREAMBLE
    + """ The excerpts are formatted as a JSON list. Each JSON object has "legis_id", "title", and "snippets" keys. If a snippet is useful in writing part of your response, then cite the "title" and "legis_id" in the response. If you don't know how to respond, just tell the user.

{context}

Query: {question}""",
}


def get_sponsor_url(bioguide_id: str) -> str:
    return f"https://bioguide.congress.gov/search/bio/{bioguide_id}"


def get_congress_gov_url(congress_num: int, legis_type: str, legis_num: int) -> str:
    lt = CONGRESS_GOV_TYPE_MAP[legis_type]
    return f"https://www.congress.gov/bill/{int(congress_num)}th-congress/{lt}/{int(legis_num)}"


def get_govtrack_url(congress_num: int, legis_type: str, legis_num: int) -> str:
    return f"https://www.govtrack.us/congress/bills/{int(congress_num)}/{legis_type}{int(legis_num)}"


def load_bge_embeddings():
    model_name = "BAAI/bge-small-en-v1.5"
    model_kwargs = {"device": "cpu"}
    encode_kwargs = {"normalize_embeddings": True}
    emb_fn = HuggingFaceBgeEmbeddings(
        model_name=model_name,
        model_kwargs=model_kwargs,
        encode_kwargs=encode_kwargs,
        query_instruction="Represent this question for searching relevant passages: ",
    )
    return emb_fn


def load_pinecone_vectorstore():
    emb_fn = load_bge_embeddings()
    vectorstore = PineconeVectorStore(
        embedding=emb_fn,
        text_key="text",
        distance_strategy=DistanceStrategy.COSINE,
        pinecone_api_key=st.secrets["pinecone_api_key"],
        index_name=st.secrets["pinecone_index_name"],
    )
    return vectorstore


def write_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"
    hf_url = "https://huggingface.co/hyperdemocracy"
    pc_url = "https://www.pinecone.io/blog/serverless"
    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})")


def group_docs(docs) -> list[tuple[str, list[Document]]]:
    doc_grps = defaultdict(list)

    # create legis_id groups
    for doc in docs:
        doc_grps[doc.metadata["legis_id"]].append(doc)

    # sort docs in each group by start index
    for legis_id in doc_grps.keys():
        doc_grps[legis_id] = sorted(
            doc_grps[legis_id],
            key=lambda x: x.metadata["start_index"],
        )

    # sort groups by number of docs
    doc_grps = sorted(
        tuple(doc_grps.items()),
        key=lambda x: -len(x[1]),
    )

    return doc_grps


def format_docs_v1(docs):
    """Simple double new line join"""
    return "\n\n".join([doc.page_content for doc in docs])


def format_docs_v2(docs):
    """Format with snippet_num, legis_id, and title"""

    def format_doc(idoc, doc):
        return "snippet_num: {}\nlegis_id: {}\ntitle: {}\n... {} ...\n".format(
            idoc,
            doc.metadata["legis_id"],
            doc.metadata["title"],
            doc.page_content,
        )

    snips = []
    for idoc, doc in enumerate(docs):
        txt = format_doc(idoc, doc)
        snips.append(txt)

    return "\n===\n".join(snips)


def format_docs_v3(docs):

    def format_header(doc):
        return "legis_id: {}\ntitle: {}".format(
            doc.metadata["legis_id"],
            doc.metadata["title"],
        )

    def format_content(doc):
        return "... {} ...\n".format(
            doc.page_content,
        )

    snips = []
    doc_grps = group_docs(docs)
    for legis_id, doc_grp in doc_grps:
        first_doc = doc_grp[0]
        head = format_header(first_doc)
        contents = []
        for idoc, doc in enumerate(doc_grp):
            txt = format_content(doc)
            contents.append(txt)
        snips.append("{}\n\n{}".format(head, "\n".join(contents)))

    return "\n===\n".join(snips)


def format_docs_v4(docs):
    """JSON grouped"""

    doc_grps = group_docs(docs)
    out = []
    for legis_id, doc_grp in doc_grps:
        dd = {
            "legis_id": doc_grp[0].metadata["legis_id"],
            "title": doc_grp[0].metadata["title"],
            "snippets": [doc.page_content for doc in doc_grp],
        }
        out.append(dd)
    return json.dumps(out, indent=4)


DOC_FORMATTERS = {
    "v1": format_docs_v1,
    "v2": format_docs_v2,
    "v3": format_docs_v3,
    "v4": format_docs_v4,
}


def escape_markdown(text):
    MD_SPECIAL_CHARS = r"\`*_{}[]()#+-.!$"
    for char in MD_SPECIAL_CHARS:
        text = text.replace(char, "\\" + char)
    return text


def get_vectorstore_filter():
    vs_filter = {}
    if SS["filter_legis_id"] != "":
        vs_filter["legis_id"] = SS["filter_legis_id"]
    if SS["filter_bioguide_id"] != "":
        vs_filter["sponsor_bioguide_id"] = SS["filter_bioguide_id"]
    vs_filter = {**vs_filter, "congress_num": {"$in": SS["filter_congress_nums"]}}
    vs_filter = {**vs_filter, "sponsor_party": {"$in": SS["filter_sponsor_parties"]}}
    return vs_filter


def write_doc_grp(legis_id: str, doc_grp: list[Document]):
    first_doc = doc_grp[0]

    congress_gov_url = get_congress_gov_url(
        first_doc.metadata["congress_num"],
        first_doc.metadata["legis_type"],
        first_doc.metadata["legis_num"],
    )
    congress_gov_link = f"[congress.gov]({congress_gov_url})"

    gov_track_url = get_govtrack_url(
        first_doc.metadata["congress_num"],
        first_doc.metadata["legis_type"],
        first_doc.metadata["legis_num"],
    )
    gov_track_link = f"[govtrack.us]({gov_track_url})"

    ref = "{} chunks from {}\n\n{}\n\n{}\n\n[{} ({}) ]({})".format(
        len(doc_grp),
        first_doc.metadata["legis_id"],
        first_doc.metadata["title"],
        congress_gov_link,
        first_doc.metadata["sponsor_full_name"],
        first_doc.metadata["sponsor_bioguide_id"],
        get_sponsor_url(first_doc.metadata["sponsor_bioguide_id"]),
    )
    doc_contents = [
        "[start_index={}] ".format(int(doc.metadata["start_index"])) + doc.page_content
        for doc in doc_grp
    ]
    with st.expander(ref):
        st.write(escape_markdown("\n\n...\n\n".join(doc_contents)))


def legis_id_to_link(legis_id: str) -> str:
    congress_num, legis_type, legis_num = legis_id.split("-")
    return get_congress_gov_url(congress_num, legis_type, legis_num)


def legis_id_match_to_link(matchobj):
    mstring = matchobj.string[matchobj.start() : matchobj.end()]
    url = legis_id_to_link(mstring)
    link = f"[{mstring}]({url})"
    return link


def replace_legis_ids_with_urls(text):
    pattern = "11[345678]-[a-z]+-\d{1,5}"
    rtext = re.sub(pattern, legis_id_match_to_link, text)
    return rtext


def write_guide():

    st.write(
        """
When you send a query to LegisQA, it will attempt to retrieve relevant content from the past six congresses ([113th-118th](https://en.wikipedia.org/wiki/List_of_United_States_Congresses)) covering 2013 to the present, pass it to a [large language model (LLM)](https://en.wikipedia.org/wiki/Large_language_model), and generate a response. This technique is known as Retrieval Augmented Generation (RAG). You can read [an academic paper](https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html) or [a high level summary](https://research.ibm.com/blog/retrieval-augmented-generation-RAG) to get more details. Once the response is generated, the retrieved content will be available for inspection with links to the bills and sponsors.


## Disclaimer

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

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.

    """
    )


def write_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 the major climate change ideas with columns legis_id, title, idea.
```

        """
        )


##################


st.title(":classical_building: LegisQA :classical_building:")


with st.sidebar:

    with st.container(border=True):
        write_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")

    with st.expander("Generative Config"):
        st.selectbox(label="model name", options=OPENAI_CHAT_MODELS, key="model_name")
        st.slider(
            "temperature", min_value=0.0, max_value=2.0, value=0.0, key="temperature"
        )
        st.slider("top_p", min_value=0.0, max_value=1.0, value=1.0, key="top_p")

    with st.expander("Retrieval Config"):
        st.slider(
            "Number of chunks to retrieve",
            min_value=1,
            max_value=32,
            value=8,
            key="n_ret_docs",
        )
        st.text_input("Bill ID (e.g. 118-s-2293)", key="filter_legis_id")
        st.text_input("Bioguide ID (e.g. R000595)", key="filter_bioguide_id")
        st.multiselect(
            "Congress Numbers",
            CONGRESS_NUMBERS,
            default=CONGRESS_NUMBERS,
            key="filter_congress_nums",
        )
        st.multiselect(
            "Sponsor Party",
            SPONSOR_PARTIES,
            default=SPONSOR_PARTIES,
            key="filter_sponsor_parties",
        )

    with st.expander("Prompt Config"):
        st.selectbox(
            label="prompt version",
            options=PROMPT_TEMPLATES.keys(),
            index=3,
            key="prompt_version",
        )
        st.text_area(
            "prompt template",
            PROMPT_TEMPLATES[SS["prompt_version"]],
            height=300,
            key="prompt_template",
        )


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:

    write_example_queries()

    with st.form("my_form"):
        st.text_area("Enter query:", key="query")
        query_submitted = st.form_submit_button("Submit")

    if query_submitted:

        retriever = vectorstore.as_retriever(
            search_kwargs={"k": SS["n_ret_docs"], "filter": vs_filter},
        )
        prompt = PromptTemplate.from_template(SS["prompt_template"])

        # # takes in a dict. adds context key with formatted docs
        # rag_chain_from_docs = (
        #     RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
        #     | prompt
        #     | llm
        #     | StrOutputParser()
        # )

        # # takes in a query string.
        # # passes to retriever and passthru
        # # assign answer 
        # rag_chain_with_source = RunnableParallel(
        #     {"context": retriever, "question": RunnablePassthrough()}
        # ).assign(answer=rag_chain_from_docs)


        # takes in a dict. adds context key with formatted docs
        rag_chain_from_docs = (
            RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
            | prompt
            | llm
            | StrOutputParser()
        )

        # takes in a query string.
        # passes to retriever and passthru
        # assign answer 
        rag_chain_with_source = RunnableParallel(
            {"context": retriever, "question": RunnablePassthrough()}
        ).assign(answer=rag_chain_from_docs)



        print(rag_chain_with_source)

        with get_openai_callback() as cb:
            SS["out"] = rag_chain_with_source.invoke(SS["query"])
            SS["cb"] = cb

    if "out" in SS:

        out_display = SS["out"]["answer"]
        if SS["response_escape_markdown"]:
            out_display = escape_markdown(out_display)
        if SS["response_add_legis_urls"]:
            out_display = replace_legis_ids_with_urls(out_display)
        with st.container(border=True):
            st.write("Response")
            st.info(out_display)
        with st.container(border=True):
            st.write("API Usage")
            st.warning(SS["cb"])

        with st.container(border=True):
            doc_grps = group_docs(SS["out"]["context"])
            st.write(
                "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)

# with st.expander("Debug doc format"):
#    st.text_area("formatted docs", value=format_docs(SS["out"]["context"]), height=600)