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Log as Inferences

If you are already storing your LLM application data in tables or files, Arize supports an alternative way to log LLM application data as inferences.

The following code example provides a brief overview of uploading prompts, embeddings, and other model parameters. Run the above Colab for a more detailed view LLM ingestion.

Example Row

prompt_textprompt_vectorresponse_textopenai_relevance_0retrieval_text_0text_similarity_0text_similarity_1user_feedbackprediction_ts

How often does Arize query the table for table import jobs?

[ 0.00393428 -0.00417591 -0.00854287...

Arize will regularly sync new data from your data source with Arize...

irrelevant

Arize will attempt a dry run to validate your job for any access...

0.86539755255...

0.8653975525...

NaN

2023-04-05 20:33:22.006650000

# Declare prompt and response columns
prompt_columns=EmbeddingColumnNames(
    vector_column_name="prompt_vector",
    data_column_name="prompt_text"
)

response_columns="response_text"

# feature & tag columns can be optionally defined with typing:
tag_columns = TypedColumns(
    to_float=["text_similarity_0", "text_similarity_1"],
    inferred=["openai_relevance_0", "retrieval_text_1"],
)

# Define the Schema, including embedding information
schema = Schema(
    timestamp_column_name="prediction_ts",
    actual_label_column_name="user_feedback",
    tag_column_names=tag_columns,
    prompt_column_names=prompt_columns,
    response_column_names=response_columns
)

# Log the dataframe with the schema mapping 
response = arize_client.log(
    model_id="search-and-retrieval-with-corpus-dataset",
    model_version= "v1",
    model_type=ModelTypes.GENERATIVE_LLM,
    environment=Environments.PRODUCTION,
    dataframe=test_dataframe,
    schema=schema,
)

Case-Specific LLM Ingestion

Prompt & Response

Upload LLM prompt and responses via the prompt_column_names and response_column_names fields.

Upload Without Embeddings

Upload With Embeddings

Prompt & Response without Embeddings

Upload prompts and responses without embeddings vectors using the relevant column name for your prompt and/or response text.

The following examples include both prompt and response information. However, you can send either a prompt or a response if you do not have both.

# Declare prompt & response text columns
prompt_columns="document"
response_columns="summary"
# Define the Schema
schema = Schema(
    ...
    prompt_column_names=prompt_columns,
    response_column_names=response_columns,
)

Prompt & Response with Embeddings

Upload prompt and responses with embedding vectors using the EmbeddingColumnNames object to define the prompt_column_names and response_column_names in your model schema.

  • The vector_column_name should match the column name representing your embedding vectors.

⚠️ Note: The embedding vector is the dense vector representation of the unstructured input. Embedding features are not sparse vectors.

  • The data_column_name should match the column name representing the raw text associated with the vector stored.

The data_column_name is typically used for NLP use cases. The column can contain both strings (full sentences) or a list of strings (token arrays). 

# Declare prompt & response embedding columns
prompt_columns=EmbeddingColumnNames(
    vector_column_name="prompt_vector", #optional
    data_column_name="response"
),
response_columns=EmbeddingColumnNames(
    vector_column_name="response_vector", #optional
    data_column_name="response"
)
# Define the Schema
schema = Schema(
    ...
    prompt_column_names=prompt_columns,
    response_column_names=response_columns,
)

Prompt Playground

Upload prompt versions and the prompt templates using the PromptTemplateColumnNames object.

  • PromptTemplateColumnNames: The field that groups prompt templates with their versions

  • template_column_name: The field that contains the prompt template in string format

  • template_version_column_name: The field that defines the template version

Example fields:

The template_column_name variables are represented via the double key braces {{variable_name}}.

Given the context of '{{retrieval_text_0}} + {{retrieval_text_1}}', and based on the frequently asked questions from our users, answer the user query as follows: '{{user_query}}'. Follow the instructions here exactly: '{{instruction}}'.

The template_version_column_name field enables you to filter by version in Arize.

# Declare prompt template columns
prompt_template_columns = PromptTemplateColumnNames(
        template_column_name="prompt_template",
        template_version_column_name="prompt_template_name"
)
# Define the Schema
schema = Schema(
    ...
    prompt_template_column_names=prompt_template_columns,
)

Learn more about prompt engineering here.

LLM Configuration Parameters

Track and monitor original and modified LLMs with the LLMConfigColumnNames object.

  • LLMConfigColumnNames: This field groups the LLM with its hyperparameters

  • model_column_name: This field contains the LLM names used to produce responses (i.e. gpt-3.5turbo or gpt-4).

  • params_column_name: This field contains the hyperparameters used to configure the LLM. The contents of the column must be well-formatted JSON string (i.e. {'max_tokens': 500, 'presence_penalty': 0.66, 'temperature': 0.28}).

# Declare LLM config columns
llm_config_columns = LLMConfigColumnNames(
        model_column_name="llm_config_model_name",
        params_column_name="llm_params",
)
# Define the Schema
schema = Schema(
    ...
    llm_config_column_names=llm_config_columns,
)

Track Token Usage

Track token usage and response latency from the LLM run inference with the LLMRunMetadataColumnNames field.

  • LLMRunMetadataColumnNames: This field groups together the run metadata

llm_run_metadata = LLMRunMetadataColumnNames(
    total_token_count_column_name="total_tokens_used",
    prompt_token_count_column_name="prompt_tokens_used",
    response_token_count_column_name="response_tokens_used",
    response_latency_ms_column_name="response_latency",
)
# Define the Schema
schema = Schema(
    ...
    llm_run_metadata_column_names=llm_run_metadata,
)

Learn more about metadata tracking here.

Retrieval Debugging with Knowledge Base (Corpus) Data

Upload the dataset of documents, such as the Knowledge Base (or Corpus), of the deployed application with the CorpusSchema object. 

# Logging the Corpus dataset
response = arize_client.log(  
        dataframe=corpus_df, # Refers to the above dataframe with the example row 
        model_id="search-and-retrieval-with-corpus-dataset",
        model_type=ModelTypes.GENERATIVE_LLM,
        environment=Environments.CORPUS,
        schema=CorpusSchema(
            document_id_column_name='document_id',
            document_text_embedding_column_names=EmbeddingColumnNames(
                vector_column_name='text_vector',
                data_column_name='text'
            ),
            document_version_column_name='document_version'
        ),
)

Learn more about how Corpus datasets are used here.

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