Querying an LLM

You can also set up LLM queries as an Action on a remote source. This is great because we can define access control for Actions as well, and any data that we query will pass through our data access control we set up earlier. In the scenario below, we want to enable our API to serve natural-language queries to our LLM model. This way, a user can ask a question such as "Who would be best in a zombie scenario?" and our API can return a generative AI response that's backed up by its training on the data in our database.

As before, if you've already set up the server, you can skip to Step 3. However, we'll go into detail below for each step.

Step 1: Write the LLM query

We'll need to write a function that takes in a GraphQL request and returns a prompt for the LLM. The code below creates a get_prompt() function that takes in a GraphQL request and returns a prompt for the LLM. The prompt is a string that contains the user's query and the content of the resumes that match the query.

def get_prompt(request):
user_query = request['input']['user_query']
# Add authenticated session variables as headers along with the admin secret
gql_headers = request['session_variables']
gql_headers['x-hasura-admin-secret'] = 'secret'
# Create a GraphQL client with the request transport
transport = RequestsHTTPTransport(
url=GRAPHQL_ENDPOINT, headers=gql_headers)
client = Client(transport=transport)
# Send the GraphQL request
gql_query = gql("""
query getItems($user_query: text!) {
Resume(where: { vector: { near_text: $user_query}}, limit: 3) {
result = client.execute(gql_query, variable_values={
'user_query': user_query})
# resumes = result['data']['Resume']
resumes = result["Resume"]
prompt = """
You are a helpful Question Answering bot.
You are provided with content from a few resumes and a question.
Answer the question based on the content of the resumes.
Provide your reasoning.
Question: {question}"""
prompt += user_query
for resume in resumes:
prompt += "Resume:"
prompt += resume["content"]
prompt += "with Application ID: "
prompt += resume["application_id"]
prompt += "\n"
return prompt

Step 2: Define the API

Then, we'll need to define the API that will handle the request. We'll use the query_llm() function below to handle the request. This function takes in a GraphQL request and returns a response from the LLM with the text-davinci-003 model.

def query_llm(request, headers):
llm = OpenAI(model="text-davinci-003",
prompt = get_prompt(request)
chain = LLMChain(llm=llm, prompt=PromptTemplate.from_template(prompt))
return str(chain.run(

Step 3: Create an Action on the Hasura Console

On the Actions page, click Create and enter the following Action Definition:

type Query {
QueryLLM(user_query: String!): String

Then, clear out the Type Configuration and provide the handler via this URL:


Note: If your're using Linux, you'll need to replace host.docker.internal with localhost.

Finally, we'll need to transform the request options as we did with our Event Trigger. Our API is expecting a POST request, so we'll set that as the Request Method before clicking Create Action at the bottom of the page.

Step 4: Execute the Action

For now, turn off our added manager and role request headers. Now you can use QueryLLM as a type in your GraphQL API 🎉

Authorized LLM call

Did you find this page helpful?
Start with GraphQL on Hasura for Free
  • ArrowBuild apps and APIs 10x faster
  • ArrowBuilt-in authorization and caching
  • Arrow8x more performant than hand-rolled APIs
footer illustration
Brand logo
© 2024 Hasura Inc. All rights reserved