This content originally appeared on DEV Community and was authored by Akmal Chaudhri
Abstract
In a previous article, we saw how to use Ollama with SingleStore. In this article, we’ll modify the previous example and replace the existing LLM with DeepSeek-R1 instead.
The notebook file used in this article is available on GitHub.
Introduction
We’ll follow the setup instructions from the previous article.
Fill out the notebook
We’ll configure the code to use the smallest DeepSeek-R1 model, as follows:
llm = "deepseek-r1:1.5b"
ollama.pull(llm)
We’ll use LangChain to store the vector embeddings and documents, as follows:
docsearch = SingleStoreDB.from_documents(
docs,
embeddings,
table_name = "langchain_docs",
distance_strategy = DistanceStrategy.EUCLIDEAN_DISTANCE,
use_vector_index = True,
vector_size = dimensions
)
Next, we’ll use the following prompt:
prompt = "What animals are llamas related to?"
docs = docsearch.similarity_search(prompt)
data = docs[0].page_content
print(data)
Example output:
Llamas are members of the camelid family meaning they're pretty closely related to vicuñas and camels
We’ll then use the prompt and response as input to DeepSeek-R1, as follows:
output = ollama.generate(
model = llm,
prompt = f"Using this data: {data}. Respond to this prompt: {prompt}."
)
content = output["response"]
remove_think_tags = True
if remove_think_tags:
content = re.sub(r"<think>.*?</think>", "", content, flags = re.DOTALL)
print(content)
We’ll disable <think> and </think> using a flag so that we can control the output of its reasoning process.
Example output:
LLAMAS ARE RELATED TO CAMELS (THROUGH SIMILAR HOVES) AND VICUNVAS (THROUGH THEIR SIMILAR SKIN TEXTURE). They may also be indirectly related to other animals that use products with rubbery or bumpy skin, but their primary connections are through these shared characteristics.
The answer contains a mixture of correct and vague statements. For example, llamas and camels are related, but not because of hooves.
Summary
Using the local Ollama installation gives us great flexibility and choice when it comes to which LLM to use. In this article, we’ve been able to replace one LLM quite easily with another.
This content originally appeared on DEV Community and was authored by Akmal Chaudhri