Using langchain To Run Queries Against GPT4All in the Context of a Single Documentary Knowledge Source

In the previous post, Running GPT4All On a Mac Using Python langchain in a Jupyter Notebook, I posted a simple walkthough of getting GPT4All running locally on a mid-2015 16GB Macbook Pro using langchain. In this post, I’ll provide a simple recipe showing how we can run a query that is augmented with context retrieved from single document based knowledg source.

I’ve updated the previously shared notebook here to include the following…

Example Query Supported by a Document Based Knowledge Source

Example document query using the example from the langchain docs.

The idea is to run the query against a document source to retrieve some relevant context, and use that as part of the prompt context.


template = """

Question: {question}


prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)

A naive prompt gives an irrelevant answer:

query = "What did the president say about Ketanji Brown Jackson"
CPU times: user 58.3 s, sys: 3.59 s, total: 1min 1s
Wall time: 9.75 s
'\nAnswer: The Pittsburgh Steelers'

Now let’s try with a source document.

from langchain.document_loaders import TextLoader

# Ideally....
loader = TextLoader('./state_of_the_union.txt')

However, creating the embeddings is qute slow so I’m going to use a fragment of the text:

#ish via chatgpt...
def search_context(src, phrase, buffer=100):
  with open(src, 'r') as f:
    words = txt.split()
    index = words.index(phrase)
    start_index = max(0, index - buffer)
    end_index = min(len(words), index + buffer+1)
    return ' '.join(words[start_index:end_index])

fragment = './fragment.txt'
with open(fragment, 'w') as fo:
    _txt = search_context('./state_of_the_union.txt', "Ketanji")
!cat $fragment

Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. We can do both. At our border, we’ve installed new technology like cutting-edge
loader = TextLoader('./fragment.txt')

Generate an index from the knowledge source text:

#%pip install chromadb
from langchain.indexes import VectorstoreIndexCreator
# Time: ~0.5s per token
# NOTE: "You must specify a persist_directory oncreation to persist the collection."
# TO DO: How do we load in an already generated and persisted index?
index = VectorstoreIndexCreator(embedding=llama_embeddings,
                                vectorstore_kwargs={"persist_directory": "db"}
Using embedded DuckDB with persistence: data will be stored in: db

CPU times: user 2 µs, sys: 2 µs, total: 4 µs
Wall time: 7.87 µs​

# The following errors...
#index.query(query, llm=llm)
# With the full SOTU text, I got:
# Error: llama_tokenize: too many tokens;
# Also occasionally getting:
# ValueError: Requested tokens exceed context window of 512

# If we do get passed that,
# NotEnoughElementsException

# For the latter, somehow need to set something like search_kwargs={"k": 1}

It seems the retriever is expecting, by default, 4 results documents. I can’t see how to pass in a lower limit (a single response document is acceptable in this case), so we nd to roll our own chain…​


# Roll our own....

from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA

documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# Again, we should persist the db and figure out how to reuse it
docsearch = Chroma.from_documents(texts, llama_embeddings)
Using embedded DuckDB without persistence: data will be transient

CPU times: user 5min 59s, sys: 1.62 s, total: 6min 1s
Wall time: 49.2 s

# Just getting a single result document from the knowledge lookup is fine...

qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff",
                                 retriever=docsearch.as_retriever(search_kwargs={"k": MIN_DOCS}))
CPU times: user 861 µs, sys: 2.97 ms, total: 3.83 ms
Wall time: 7.09 ms

How about running our query now in the context of the knowledge source?


What did the president say about Ketanji Brown Jackson
CPU times: user 7min 39s, sys: 2.59 s, total: 7min 42s
Wall time: 1min 6s

' The president honored Justice Stephen Breyer and acknowledged his service to this country before introducing Justice Ketanji Brown Jackson, who will be serving as the newest judge on the United States Court of Appeals for the District of Columbia Circuit.'

How about a more precise query?

query = "Identify three things the president said about Ketanji Brown Jackson"
CPU times: user 10min 20s, sys: 4.2 s, total: 10min 24s
Wall time: 1min 35s

' The president said that she was nominated by Barack Obama to become the first African American woman to sit on the United States Court of Appeals for the District of Columbia Circuit. He also mentioned that she was an Army veteran, a Constitutional scholar, and is retiring Justice of the United States Supreme Court.'

Hmm… are we in a conversation and picking up on previous outputs? In previous attempts I did appear to be getting quite relevant answers… Are we perhaps getting more than a couple of results docs and picking the less good one? Or is the model hit and miss on what it retrieves? Can we view the sample results docs from the knoweldge lookup to help get a feel for what’s going on?

Let’s see if we can format the response…


query = """
Identify three things the president said about Ketanji Brown Jackson. Provide the answer in the form: 

- ITEM 1
- ITEM 2
- ITEM 3
CPU times: user 12min 31s, sys: 4.24 s, total: 12min 35s
Wall time: 1min 45s

"\n\nITEM 1: President Trump honored Justice Breyer for his service to this country, but did not specifically mention Ketanji Brown Jackson.\n\nITEM 2: The president did not identify any specific characteristics about Justice Breyer that would be useful in identifying her.\n\nITEM 3: The president did not make any reference to Justice Breyer's current or past judicial rulings or cases during his speech."

Author: Tony Hirst

I'm a Senior Lecturer at The Open University, with an interest in #opendata policy and practice, as well as general web tinkering...

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