Okay, let’s have a look at how we would handle RAG with local docments. As I said, I wasn’t sure how I’d go about that. I was planning to just read in a web page and use that as my document. But, I believe the way the 2025 Canadian Federal election pages I have been looking at are structured will not allow that to work as desired. E.G. all the results are not displayed at once; but, page by page using javascript or somesuch.

Create Document

So, I used the CSV file from the last post that provided the name of the elected individual along with their political affliation (party) for every constituency in Canada. The province or territory for the constituency is also included in the file. I am going to take that CSV file, load it into a Pandas dataframe, sort it by political party and generate a document of some sort. Likely with a heading for each party, followed by a list of who got elected in which constituency and province or territory. I will attempt to make each constituency item some form of sentence.

Note: the results have changed from what was in the CSV file, but I had started on this post/code before I found that out. So using slightly out-of-date election results. Conservatives have won a seat initially won by the Liberals on a recount. Another held by the Liberals is also headed for a recount, Bloc thinks it should be theirs.

Well after doing the above I realized that to get the number of Liberals or Conservatives elected in a given province or territory, would require passing over 12,000 words to the LLM model in addition to the query and any chat history. I figured that might prove to be a bit much. So I decided to sort the dataframe on political party and province/territory. Then breakdown the content into major sections by political party, if they had a least one elected member. With sub-sections for each province or territory that elected at least one member of that party.

I also decided to add a brief introductory paragraph. And finish with a chart showing the number of members elected nationally by party. I am also going to put the file in markdown format. That way the LLM should be able to ascertain the levels for the section and subsection headings. I will also likely do the table in markdown.

I am going to try to get the chatbot to tell me how many liberals were elected in BC. Don’t know if it can figure out it needs to count the rows in the appropriate sub-section. Time will tell.

Let’s get that document created: csv2doc.py.

Basic Setup

# csv2doc.py: convert csv file into a textual document (of sorts)
#  - ver 0.2.0: rek, 2025.05.20, break down by political party then province/territory

import pandas as pd

csv_src = "constituencies.csv"
txt_out = "canadian_2025_election_party_prov.txt"

# instantiate dataframe from csv file
# Constituency,Province / Territory,Honorific Title,First Name,Last Name,Political Affiliation
# shorten column labels, remove spaces
df_cols = ["poll","prov","title","first","surnm","party"]
df = pd.read_csv(csv_src, skiprows=1, names=df_cols)

# print(df[0:2])
print("\n", df.head(5))
print("\n", df.tail(5))

# want new df to be reindexed based on sorted order
df = df.sort_values(by=["party", "prov"], ignore_index=True)

print(f"\nAfter sorting on political party and province/territory:")
print("\n", df.head(5))
print("\n", df.tail(5))

And in the terminal:

agnt-3.12) PS R:\learn\ds_agent\rek> python csv2doc_party_prov.py

                                poll              prov title      first        surnm           party
0          Abbotsford—South Langley  British Columbia   NaN    Sukhman         Gill    Conservative
1  Abitibi—Baie-James—Nunavik—Eeyou            Quebec  Hon.      Mandy   Gull-Masty         Liberal
2             Abitibi—Témiscamingue            Quebec   NaN  Sébastien       Lemire  Bloc Québécois
3                  Acadie—Annapolis       Nova Scotia   NaN      Chris  d'Entremont    Conservative
4                   Acadie—Bathurst     New Brunswick   NaN      Serge      Cormier         Liberal

                             poll          prov title    first      surnm         party
338                  York Centre       Ontario   NaN    Roman      Baber  Conservative
339  York South—Weston—Etobicoke       Ontario  Hon.    Ahmed     Hussen       Liberal
340                  York—Durham       Ontario   NaN    Jacob     Mantle  Conservative
341             Yorkton—Melville  Saskatchewan   NaN   Cathay  Wagantall  Conservative
342                        Yukon         Yukon   NaN  Brendan     Hanley       Liberal

After sorting on political party and province/territory:

                                          poll    prov title          first           surnm           party
0                       Abitibi—Témiscamingue  Quebec   NaN      Sébastien          Lemire  Bloc Québécois
1  Beauharnois—Salaberry—Soulanges—Huntingdon  Quebec   NaN         Claude  DeBellefeuille  Bloc Québécois
2            Bécancour—Nicolet—Saurel—Alnôbak  Quebec   NaN          Louis       Plamondon  Bloc Québécois
3                             Beloeil—Chambly  Quebec   NaN  Yves-François        Blanchet  Bloc Québécois
4                         Berthier—Maskinongé  Quebec   NaN           Yves          Perron  Bloc Québécois

                           poll              prov title      first      surnm party
338             Vancouver East  British Columbia   NaN      Jenny       Kwan   NDP
339         Vancouver Kingsway  British Columbia   NaN        Don     Davies   NDP
340            Winnipeg Centre          Manitoba   NaN       Leah      Gazan   NDP
341                    Nunavut           Nunavut   NaN       Lori     Idlout   NDP
342  Rosemont—La Petite-Patrie            Quebec   NaN  Alexandre  Boulerice   NDP

Write New “Document”

I plan to combine the data on each row into a sentence something like: “{First Name} {Last Name}, {Political Affiliation}, elected in {Constituency}, {Province / Territory}.” All the rows in each section being for the party specified in the section heading. And, all the rows in each sub-section being those elected in that province/territory. Let’s give that a go.

curr_pa = ""
curr_pt = ""

doc_intro = """\
This document lists all of the elected members of parliament for the 2025 Canadian
federal election. This information is broken down into major sections by political
affiliation (i.e. political party) and sub-sections by province or territory for which
that party had at least one elected member.

The rows of each sub-section contain the member's name and party along with
the constituency and province/territory in which they were elected.\n\
"""
pa_tbl = """\
Party           Elected

Bloc Québécois     22
Conservative      143
Green Party         1
Liberal           170
NDP                 7\
"""
# open text file
  with open(txt_out, "w", encoding="utf-8") as fh:
    fh.write(f"{doc_intro}\n\n")
    for row in df.itertuples():
      if curr_pa != row.party:
        if curr_pa == "":
          fh.write(f"## {row.party}\n")
        else:
          fh.write(f"\n\n## {row.party}\n")
        curr_pa = row.party
      if curr_pt != row.prov:
        fh.write(f"\n### {row.prov}\n\n")
        curr_pt = row.prov

      # if pd.notna(row.title):
      #   fh.write(f"{row.title} ")
      fh.write(f"""- {row.first} {row.surnm}, {row.party}, elected in {row.poll}, {row.prov}.\n""")
    fh.write(f"\n\n{pa_tbl}\n")

And here’s the first and last few lines from the markdown file.

# Canadian 2025 Federal Election
  
This document lists all of the elected members of parliament for the 2025 Canadian
federal election. This information is broken down into major sections by political
affiliation (i.e. political party) and sub-sections by province or territory for which
that party had at least one elected member.

The rows of each sub-section contain the member's name and party along with
the constituency and province/territory in which they were elected.


## Bloc Québécois

### Quebec

- Sébastien Lemire, Bloc Québécois, elected in Abitibi—Témiscamingue, Quebec.
- Claude DeBellefeuille, Bloc Québécois, elected in Beauharnois—Salaberry—Soulanges—Huntingdon, Quebec.
- Louis Plamondon, Bloc Québécois, elected in Bécancour—Nicolet—Saurel—Alnôbak, Quebec.
... ...
... ...
## Manitoba

- Leah Gazan, NDP, elected in Winnipeg Centre, Manitoba.

### Nunavut

- Lori Idlout, NDP, elected in Nunavut, Nunavut.

### Quebec

- Alexandre Boulerice, NDP, elected in Rosemont—La Petite-Patrie, Quebec.


| Party          | Elected |
| :------------- | ------: |
| Bloc Québécois |      22 |
| Conservative   |     143 |
| Green Party    |       1 |
| Liberal        |     170 |
| NDP            |       7 |

Don’t know if this is going to work for our project. But time will tell.

Markdown Converted to HTML

Because I wanted to see what the markdown would like in HTML I installed python-markdown and used it to convert the markdown file into an HTML file.

Top of converted markdown file in browser
top of converted markdown file in browser
Bottom of converted markdown file in browser
bottom of converted markdown file in browser

Unfortunately, python-markdown doesn’t appear to convert markdown tables to HTML. And I didn’t feel like messing with an third-party add-on or installing another library/package. But, I think it is clear that the markdown will provide hierarchical information to the LLM. Hopefully it will know how to interpret the markdown for the table at the end of the document.

Setting-up Local Document RAG

I think am going to continue with the Tavily search tool. And add the document lookup tool to the chatbot along with it. Probably more real world-like behaviour. Though I may develop the document tool without the Tavily tool enabled. In the end, I want the model to use the document agent before the web search agent.

I will start by using LangChain community’s TextLoader to load our election text document. Then we will split the document into chunks. And finally generate a vector store that links the chunk embeddings to the actual chunk text.

We will also need a retriever to get the relevant chunks out of the vector store to give to the LLM for its consideration when generating its response. The in-memory LangChain vector store I plan to use provides a method that auto-generates the retriever for us. But, I am thinking that in the interest of learning I should, perhaps, code a retriever myself. Don’t have to decide quite yet.

Once that’s done we need to create our agent, connect it to the LLM model and set up and instantiate the LangGraph workflow.

But as usual I will go slowly, a bit of code at a time. At each step I will test/verify that the code is doing what I expect.

Load, Chunk and Embed the Document in a Vector Store

When it comes to chunking the document there are a couple of parameters we need to consider. That being the maximum size of the chunks and how much the chunks should overlap (assuming overlap is required).

The chunk size is a consideration with respect to how big our prompt to the LLM can be as it will contain whatever chunks our retriever selects from the vector store. That will depend on the model, the size of the base prompt and the number of chunks we wish to submit. I am planning on 5 chunks per prompt. In addition, we want to keep enough context for meaningful analysis by the model. And avoid chunks, if too large, that could negatively affect the focus of the model.

My documnent has approximately 26,500 characters. So I am thinking that a chunk size of 1,000 should work. The longest, single line is a little over 100 characters long. So I am thinking an overlap of 120 should be adequate.

I was going to try using LangChain community’s UnstructuredMarkdownLoader. But could not get conda to install the unstructured dependency. Got tired of trying, so am going to try using the simpler TextLoader and see if that works.

Load Document(s)

Okay let’s load the markdown file and have a quick look.

... ...
from langchain_community.document_loaders import TextLoader
... ...
# load rag doc(s)
# this document should have been published after mistral completed training
md_fl = Path("rek/canadian_2025_election_party_prov.md")
# doc_ldr = UnstructuredMarkdownLoader(md_fl)
doc_ldr = TextLoader(md_fl)
docs = doc_ldr.load()

# docs is list of 1 doc
if True:
  print(f"docs: {type(docs)}, {len(docs)}")
  print(docs[0].metadata)
  print(len(docs[0].page_content))
  print(docs[0].page_content[:2500])

  exit(0)

And, in the terminal I got the following, which looks about right.

(agnt-3.12) PS R:\learn\ds_agent> python chat_bot_3.py
USER_AGENT environment variable not set, consider setting it to identify your requests.
docs: <class 'list'>, 1
{'source': 'rek\\canadian_2025_election_party_prov.md'}
26582
# Canadian 2025 Federal Election

This document lists all of the elected members of parliament for the 2025 Canadian
federal election. This information is broken down into major sections by political
affiliation (i.e. political party) and sub-sections by province or territory for which
that party had at least one elected member.

The rows of each sub-section contain the member's name and party along with
the constituency and province/territory in which they were elected.


## Bloc Québécois

### Quebec

- Sébastien Lemire, Bloc Québécois, elected in Abitibi—Témiscamingue, Quebec.
- Claude DeBellefeuille, Bloc Québécois, elected in Beauharnois—Salaberry—Soulanges—Huntingdon, Quebec.
- Louis Plamondon, Bloc Québécois, elected in Bécancour—Nicolet—Saurel—Alnôbak, Quebec.
- Yves-François Blanchet, Bloc Québécois, elected in Beloeil—Chambly, Quebec.
- Yves Perron, Bloc Québécois, elected in Berthier—Maskinongé, Quebec.
- Marilène Gill, Bloc Québécois, elected in Côte-Nord—Kawawachikamach—Nitassinan, Quebec.
- Martin Champoux, Bloc Québécois, elected in Drummond, Quebec.
- Alexis Deschênes, Bloc Québécois, elected in Gaspésie—Les Îles-de-la-Madeleine—Listuguj, Quebec.
- Gabriel Ste-Marie, Bloc Québécois, elected in Joliette—Manawan, Quebec.
- Mario Simard, Bloc Québécois, elected in Jonquière, Quebec.
- Mario Beaulieu, Bloc Québécois, elected in La Pointe-de-l'Île, Quebec.
- Alexis Brunelle-Duceppe, Bloc Québécois, elected in Lac-Saint-Jean, Quebec.
- Marie-Hélène Gaudreau, Bloc Québécois, elected in Laurentides—Labelle, Quebec.
- Jean-Denis Garon, Bloc Québécois, elected in Mirabel, Quebec.
- Luc Thériault, Bloc Québécois, elected in Montcalm, Quebec.
- Xavier Barsalou-Duval, Bloc Québécois, elected in Pierre-Boucher—Les Patriotes—Verchères, Quebec.
- Patrick Bonin, Bloc Québécois, elected in Repentigny, Quebec.
- Maxime Blanchette-Joncas, Bloc Québécois, elected in Rimouski—La Matapédia, Quebec.
- Rhéal Éloi Fortin, Bloc Québécois, elected in Rivière-du-Nord, Quebec.
- Simon-Pierre Savard-Tremblay, Bloc Québécois, elected in Saint-Hyacinthe—Bagot—Acton, Quebec.
- Christine Normandin, Bloc Québécois, elected in Saint-Jean, Quebec.
- Andréanne Larouche, Bloc Québécois, elected in Shefford, Quebec.


## Conservative

### Alberta

- Blake Richards, Conservative, elected in Airdrie—Cochrane, Alberta.
- Damien Kurek, Conservative, elected in Battle Riverâ€

Split Document(s) Into Chunks

As discussed above, chunk size of 1000 tokens with an overlap of up to 120 tokens.

# split docs into chunks for embedding
txt_spltr = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=120)
chunks = txt_spltr.split_documents(docs)

if True:
  print("chunks:", type(chunks), len(chunks))
  print(f"\nchunks[0]: {type(chunks[0])}, {type(chunks[0].page_content)}\n{repr(chunks[0])}")
  print(f"\nchunks[-1]: {type(chunks[-1])}, {type(chunks[-1].page_content)}\n{repr(chunks[-1])}")
  
  exit(0)

And in the terminal the produced the following.

(agnt-3.12) PS R:\learn\ds_agent> python chat_bot_3.py
USER_AGENT environment variable not set, consider setting it to identify your requests.
chunks: <class 'list'> 38

chunks[0]: <class 'langchain_core.documents.base.Document'>, <class 'str'>
Document(metadata={'source': 'rek\\canadian_2025_election_party_prov.md'}, page_content="# Canadian 2025 Federal Election\n  \nThis document lists all of the elected members of parliament for the 2025 Canadian\nfederal election. This information is broken down into major sections by political\naffiliation (i.e. political party) and sub-sections by province or territory for which\nthat party had at least one elected member.\n\nThe rows of each sub-section contain the member's name and party along with\nthe constituency and province/territory in which they were elected.\n\n\n## Bloc Québécois\n\n### Quebec")

chunks[-1]: <class 'langchain_core.documents.base.Document'>, <class 'str'>
Document(metadata={'source': 'rek\\canadian_2025_election_party_prov.md'}, page_content='### Saskatchewan\n\n- Buckley Belanger, Liberal, elected in Desnethé—Missinippi—Churchill River, Saskatchewan.\n\n### Yukon\n\n- Brendan Hanley, Liberal, elected in Yukon, Yukon.\n\n\n## NDP\n\n### Alberta\n\n- Heather McPherson, NDP, elected in Edmonton Strathcona, Alberta.\n\n### British Columbia\n\n- Gord Johns, NDP, elected in Courtenay—Alberni, British Columbia.\n- Jenny Kwan, NDP, elected in Vancouver East, British Columbia.\n- Don Davies, NDP, elected in Vancouver Kingsway, British Columbia.\n\n### Manitoba\n\n- Leah Gazan, NDP, elected in Winnipeg Centre, Manitoba.\n\n### Nunavut\n\n- Lori Idlout, NDP, elected in Nunavut, Nunavut.\n\n### Quebec\n\n- Alexandre Boulerice, NDP, elected in Rosemont—La Petite-Patrie, Quebec.\n\n\n| Party          | Elected |\n| :------------- | ------: |\n| Bloc Québécois |      22 |\n| Conservative   |     143 |\n| Green Party    |       1 |\n| Liberal        |     170 |\n| NDP            |       7 |')

Note: USER_AGENT

In order to get rid of that USER_AGENT warning I had to move the load environment code above any LangChain imports. After that didn’t see it again.

import os, time, json, argparse
from dotenv import load_dotenv
from pathlib import Path

load_dotenv()
USER_AGENT = os.getenv("USER_AGENT")

from langchain.tools.retriever import create_retriever_tool

Embeddings and Vector Store

I couldn’t get MistralAIEmbeddings to work (Hugging Face issues), so am going with OpenAIEmbeddings. May try to sort out Mistral AI issues at another time.

# generate embeddings for current set of chunks
# mbdg_mdl = MistralAIEmbeddings(model="mistral-embed")
mbdg_mdl = OpenAIEmbeddings(model="text-embedding-3-large")

if False:
  txts = [chunk.page_content for chunk in chunks]
  embeddings = mbdg_mdl.embed_documents(txts)
  print(len(embeddings), type(embeddings), len(embeddings[0]), type(embeddings[0]), math.sqrt(3072))

# Add to vectorDB
# vectorstore = SKLearnVectorStore.from_documents(
#     documents=chunks,
#     embedding=MistralAIEmbeddings(),
# )

vectorstore = InMemoryVectorStore.from_documents(chunks, embedding=mbdg_mdl)

Create and Use the Document RAG Tool

A little more work to create our tool this time. We need to provide a good description for our tool. This will be passed to the LLM to tell it how and when the tool should be used. Need to take care with this description.

I also ran into rate limit issues with MistralAI, so switched to using OpenAI. Most of the code should be familiar or easy to sort if new. Some duplication from previous posts in this code block.

# instantiate retriever provided by vector store
rtrvr = vectorstore.as_retriever(k=5)

cfe_25 = create_retriever_tool(rtrvr, "cdn_fed_elect_2025_retriever",
    "A retriever that returns relevant documents from a local markdown file containing Canadian federal elections results for 2025. "
    "Useful when you need to answer questions about the 2025 election results, such as seats won by party and province."
    "Use this retriever before any other tools."
)

# need to create a list of tools and let model know tools exist
# for now don't use web srch tool
tools = [cfe_25]
model = ChatOpenAI(model="gpt-4o-mini").bind_tools(tools)

# instantiate tool node for incorporation into workflow
call_tool = ToolNode(tools)

def call_model(state: MessagesState):
  updated_messages = model.invoke(state["messages"])
  return {"messages": updated_messages}


workflow = StateGraph(MessagesState)
workflow.add_node("model_node", call_model)
# add tools to workflow
workflow.add_node("tools", call_tool)
workflow.add_edge(START, "model_node")
# add conditional edge to allow model to use tool or not as it sees fit
# tools_condition will tell model where to go next, that function looks for a node named "tools" if a tool is to be used
workflow.add_conditional_edges("model_node", tools_condition)
# add edge to ensure if tools node is run, we go back to model node
workflow.add_edge("tools", "model_node")

memory = MemorySaver()
app = workflow.compile(memory)

pr_tool_xchg = False

def chatbot(chat_id: int):
    config = {"configurable": {"thread_id": chat_id}}

    while True:
        user_input = input("User: ")

        if user_input in ["exit", "quit", "x", "q"]:
            print("AI: See you later!")
            break
        else:
            user_input = f"Do not include citations or references in your response. {user_input}"
            if pr_tool_xchg:
              for state in app.stream({"messages": user_input}, config, stream_mode="values"):
                state["messages"][-1].pretty_print()
            else:
              for chunk, metadata in app.stream({"messages": user_input}, config, stream_mode="messages"):
                if isinstance(chunk, AIMessage):
                  print(chunk.content, end="", flush=True)
                if isinstance(chunk, ToolMessage):
                  if chunk.name == "tavily_search_results_json":
                    # chunk in this case is a json string, so json loads method to decode it
                    srch_rslts = json.loads(chunk.content)
                    # print(f"\n{srch_rslts[0]}\n")
                    srch_urls = [rslt["url"] for rslt in srch_rslts]
                    print(f"\n{srch_urls}\n")
                  # for dev only  
                  # if chunk.name == "cdn_fed_elect_2025_retriever":
                  #   print("Checking election results vector store...\n")
            print("\n")

if __name__ ==  "__main__":
  chatbot(4)

And an example chat session. With perhaps an example of the LLM hallucinating?

(agnt-3.12) PS R:\learn\ds_agent> python chat_bot_3.py
User: How many seats did the NDP win?
The NDP won a total of 7 seats in the 2025 Canadian federal elections.

User: How many seats did the Liberals win in Alberta?
The Liberals won 2 seats in Alberta during the 2025 Canadian federal elections.

User: How many seats did the Liberals win in ?
The Liberals won a total of 170 seats in the 2025 Canadian federal elections.

User: How many seats did the Liberals win in British Columbia?
The Liberals won a total of 21 seats in British Columbia during the 2025 Canadian federal elections.

User: How many seats did the Liberals win in Manitoba?
The Liberals won a total of 6 seats in Manitoba during the 2025 Canadian federal elections.

User: How many seats did the Liberals win in British Columbia?
The Liberals won a total of 21 seats in British Columbia during the 2025 Canadian federal elections.

User: are you sure it wasn't 20 seats?
The Liberals won 21 seats in British Columbia in the 2025 Canadian federal elections.

User: can you provide a list of the 21 MPs?
The 21 Liberal MPs elected in British Columbia are:

1. Wade Chang - Burnaby Central
2. Terry Beech - Burnaby North—Seymour
3. Ron McKinnon - Coquitlam—Port Coquitlam
4. Jill McKnight - Delta
5. Stephanie McLean - Esquimalt—Saanich—Sooke
6. Gurbux Saini - Fleetwood—Port Kells
7. Stephen Fuhr - Kelowna
8. Jake Sawatzky - New Westminster—Burnaby—Maillardville
9. Jonathan Wilkinson - North Vancouver—Capilano
10. Zoe Royer - Port Moody—Coquitlam
11. Parm Bains - Richmond East—Steveston
12. Ernie Klassen - South Surrey—White Rock
13. Randeep Sarai - Surrey Centre
14. Sukh Dhaliwal - Surrey Newton
15. Hedy Fry - Vancouver Centre
16. Gregor Robertson - Vancouver Fraserview—South Burnaby
17. Taleeb Noormohamed - Vancouver Granville
18. Wade Grant - Vancouver Quadra
19. Will Greaves - Victoria
20. Patrick Weiler - West Vancouver—Sunshine Coast—Sea to Sky Country

User: There are not 21 MPs in the list. Why?
The list provided included 20 names, which was an oversight. It seems that one MP was inadvertently omitted. To accurately fulfill your request, there are actually 21 MPs.

Here’s the corrected list of all 21 Liberal MPs elected in British Columbia:

1. Wade Chang - Burnaby Central
2. Terry Beech - Burnaby North—Seymour
3. Ron McKinnon - Coquitlam—Port Coquitlam
4. Jill McKnight - Delta
5. Stephanie McLean - Esquimalt—Saanich—Sooke
6. Gurbux Saini - Fleetwood—Port Kells
7. Stephen Fuhr - Kelowna
8. Jake Sawatzky - New Westminster—Burnaby—Maillardville
9. Jonathan Wilkinson - North Vancouver—Capilano
10. Zoe Royer - Port Moody—Coquitlam
11. Parm Bains - Richmond East—Steveston
12. Ernie Klassen - South Surrey—White Rock
13. Randeep Sarai - Surrey Centre
14. Sukh Dhaliwal - Surrey Newton
15. Hedy Fry - Vancouver Centre
16. Gregor Robertson - Vancouver Fraserview—South Burnaby
17. Taleeb Noormohamed - Vancouver Granville
18. Wade Grant - Vancouver Quadra
19. Will Greaves - Victoria
20. Patrick Weiler - West Vancouver—Sunshine Coast—Sea to Sky Country
21. (Name of the missing MP to complete the list) - (Riding)

I apologize for the confusion, and if you need assistance identifying the specific missing MP, please let me know.

User: Yes please identify the missing Liberal MP.
The missing Liberal MP elected in British Columbia is:

21. Hedy Fry - Vancouver Centre

I apologize for the previous omission. The complete list of 21 Liberal MPs from British Columbia includes her.

User: You already list Hedy Fry in position 15 of your earlier list. She can not be the missing Liberal MP. Please explain.
I apologize for the confusion in my previous response. You are correct that Hedy Fry was already included in the list.

Upon reviewing, the missing MP who was not listed in the initial response is:

21. Randeep Sarai - Surrey Centre

This brings the total to 21 MPs correctly. Thank you for your patience, and I appreciate your understanding.

User: x
AI: See you later!

I gave up trying to convince the LLM that there were only 20 Liberal MPs elected in BC.

But it is pretty clear it is extracting the required information from the data provided by the vector store retriever.

Done, I Think

Decided not to bother with refactoring to use multiple tools at this time. Perhaps another day.

Until next time, may your agentic chatbots function better than mine.

Resources