A data agent is an LLM-based agent that can reason about enterprise data. The data agent and the agent configs that define them are at the heart of oxy.

Typically, to prompt engineer a data agent (an agent that can reason around data), you need a heavily manual workflow involving database schema ingestion, LLM prompting and step chaining/iteration, and SQL query retrieval and injection. Our .agent.yml files abstract away much of this complexity, allowing you to focus more on the logic of the prompted LLM rather than the details of the code execution.

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Agent components

Specifically, in our agent yml files, you need to specify the following:

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Context

The context object allows for deterministic injection of files into system_instructions. Primarily this is an organizational and code re-use utility so rather than prompting your agents with long text blobs, you can instead divide these prompts into logical sections, broken into distinct files. Each context entry in the list requires three fields:

You can reference file-type context objects in your system_instructions using the following syntax: {{ context.name }} For objects that are of type semantic_model, you can access their properties using: {{ context.name.property }} For example, if you have a semantic model context named “acme”, you can access its entities using: {{ context.acme.entities }}

For smaller projects, we encourage saving any pertinent SQL files and injecting these in as context objects, rather than opting for leveraging retrieval to pull in relevant sources. We find that this tends to provide much more deterministic outputs.

This can be accomplished by adding the following section to an .agent.yml file:

context:
  - name: queries
    type: file
    src:
      - "*.sql"

which can then be referenced within system_instructions as follows:

prompt: |
  Here are some queries you can use:
  {{ context.queries }}

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Tools

To enable the LLM to flexibly accomplish a wider range of tasks, our internal chain logic is as follows:

1. We render your system_instructions using all retrieved queries and context.

2. We feed the rendered system_instructions and prompt into the LLM tool calling API.

3. We repeat steps 1 and 2 with the results of step 2 until the request specified in system_instructions is fulfilled.

To improve the capacity of the LLM to accomplish specific tasks, you can add a tools section with specific tools that can be used by the agent. The following tools are available:

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type: execute_sql / validate_sql

There are two kinds of sql-writing tools — execute_sql, which will execute the sql and return the results to the LLM, and validate_sql, which will attempt to write queries until the query successful executes, sending only the query itself back to the LLM (not the result set).

Both can be configured by specifying a database to connect to, as shown below:

  - name: execute_sql  # any name you'd like
    type: execute_sql
    database: primary_database

  - name: validate_sql  # any name you'd like 
    type: validate_sql
    database: primary_database

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type: retrieval

While we generally recommend avoid using retrieval (and instead using context) to avoid injecting additional nondeterminism into the system, we do support in-process retrieval against embeddings. The retrieval tool spins up an in-process vector db and can be configured as follows:

  - name: retrieval
    type: retrieval
    src:
      - "data/*"

where src specifies the directory that will be searched through and embedded.

To use this tool, embeddings must be built before any oxy run command. To learn about how to build embeddings, see Embedding management. In short, you’ll need to log into the huggingface CLI, then build embeddings using the following command:

oxy build

Additional parameters can be supplied as follows:

  - name: retrieval
    type: retrieval
    src:
      - "data/*"
    # embed_model: text-embedding-3-small
    # api_url: https://api.openai.com/v1
    # key_var: OPENAI_API_KEY
    # api_key: secret
    # n_dims: 512
    # top_k: 4
    # factor: 5

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Database

Database information can be accessed within system_instructions by using the databases namespace, then referencing by name, as follows:

**Dataset: {{ databases.primary_database.name }}**
**Dialect: {{ databases.primary_database.dialect }}**
**Tables:**
{% for table in databases.primary_database.tables %}
{{ table }}
{% endfor %}

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Sample config

model: "openai-4o-mini"
context:
  - name: anon_youtube
    type: semantic_model
    src: data/acme.sem.yml
system_instructions: |
  ## Instructions
  You are a Data Analyst expert. Your role is to assist the user in generating and executing SQL queries to answer their questions.
  ### Adhere to these rules:
  - **Carefully analyze the user's question and the database schema, word by word**, to ensure the query accurately addresses the request.
  - **Use table aliases** to avoid ambiguity in queries. For example: `SELECT t1.col1, t2.col1 FROM table1 AS t1 JOIN table2 AS t2 ON t1.id = t2.id`.
  - **Automatically execute the SQL query** without seeking additional confirmation.
  - If the SQL query results in an error, **attempt to debug, fix, and re-execute it** without requesting confirmation.
  - Ensure the SQL query adheres to the **specific dialect of the database database** being used.
  - **Return the SQL query as plain text** (not in JSON format).

  **Schema information:**
  Entities:
  {{ context.acme.entities }}
  Dimensions:
  {{ context.acme.dimensions }}
  Measures:
  {{ context.acme.measures }}

  **Dataset: {{ databases.primary_database.name }}**
  **Dialect: {{ databases.primary_database.dialect }}**
  **Tables:**
  {% for table in databases.primary_database.tables %}
  {{ table }}
  {% endfor %}

  You can use the following queries as hints:
  {{ tools.retrieval }}

tools:
  - name: retrieval
    type: retrieval
    src:
      - "data/*"
  - name: execute_sql
    type: execute_sql
    database: local