About this Task

deepset Cloud provides the SnowflakeExecutor component that connects to your Snowflake database. It takes a SQL query as input and returns a database table that matches the query, and the generated answer.

Prerequisites

You need the following information about your Snowflake database:

Snowflake account identifier
Snowflake user login
Warehouse name
Schema name
Database name

Query Your Snowflake Database

First, connect deepset Cloud to Snowflake through the Connections page:

Click your initials in the top right corner and select Connections.
Click Connect next to the provider.
Enter your user access token and submit it.

Then, add SnowflakeExecutor to your query pipeline.

Usage Examples

This is an example of a query pipeline that uses two Generators: one to turn a natural language query into SQL and send it to SnowflakeExecutor, and another one to build an answer based on SnowflakeExecutor's output. The generated answer includes the database table in Markdown format, the query, and the answer.

components:
  bm25_retriever:
    type: >-
      haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: >-
          haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - ${OPENSEARCH_USER}
            - ${OPENSEARCH_PASSWORD}
          embedding_dim: 768
          similarity: cosine
      top_k: 20
  query_embedder:
    type: >-
      haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
    init_parameters:
      model: intfloat/e5-base-v2
  embedding_retriever:
    type: >-
      haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: >-
          haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
            - ${OPENSEARCH_HOST}
          http_auth:
            - ${OPENSEARCH_USER}
            - ${OPENSEARCH_PASSWORD}
          embedding_dim: 768
          similarity: cosine
      top_k: 20
  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate
  ranker:
    type: >-
      haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker
    init_parameters:
      model: intfloat/simlm-msmarco-reranker
      top_k: 8
      model_kwargs:
        torch_dtype: torch.float16
  sql_prompt_builder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: >-
        You are a SQL expert working with Snowflake.

        Your task is to create a Snowflake SQL query for the given question.

        Refrain from explaining your answer. Your answer must be the SQL query
        in plain text format without using Markdown.

        Here are some relevant tables, a description about it, and their
        columns:

        {% for document in documents %}

        Document[{{ loop.index }}]:

        {{ document.content }}

        {% endfor %}

        User's question: {{ question }}

        Generated SQL query:
  sql_llm:
    type: >-
      deepset_cloud_custom_nodes.generators.deepset_amazon_bedrock_generator.DeepsetAmazonBedrockGenerator
    init_parameters:
      model: anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-east-1
      max_length: 1000
      model_max_length: 200000
      temperature: 0
  snowflake_executor:
    type: >-
      deepset_cloud_custom_nodes.augmenters.snowflake_executor.DeepsetSnowflakeExecutor
    init_parameters:
      user: <SF-USER>
      account: <SF-ACCOUNT>
  replies_to_sql:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: '{{ replies[0] }}'
      output_type: str
  display_prompt_builder:
    type: haystack.components.builders.prompt_builder.PromptBuilder
    init_parameters:
      template: >-
        You are an expert data analyst.

        Your role is to answer the user's question {query} using the information
        in the table.

        You will base your response solely on the information provided in the
        table.

        Do not rely on your knowledge base; only the data that is in the table.

        Refrain from using the term "table" in your response, but instead, use
        the word "data"

        If the table is blank say:

        "The specific answer can't be found in the database. Try rephrasing your
        question."

        Additionally, you will present the table in a tabular format and provide
        the SQL query used to extract the relevant rows from the database in
        Markdown.

        If the table is larger than 10 rows, display the most important rows up
        to 10 rows. Your answer must be detailed and provide insights based on
        the question and the available data.

        SQL query:

        {{ sql_query }}

        Table:

        {{ table }}

        Answer:
  display_llm:
    type: >-
      deepset_cloud_custom_nodes.generators.deepset_amazon_bedrock_generator.DeepsetAmazonBedrockGenerator
    init_parameters:
      model: anthropic.claude-3-5-sonnet-20240620-v1:0
      aws_region_name: us-east-1
      max_length: 650
      model_max_length: 200000
      temperature: 0
  answer_builder:
    type: >-
      deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm
connections:
  - sender: bm25_retriever.documents
    receiver: document_joiner.documents
  - sender: query_embedder.embedding
    receiver: embedding_retriever.query_embedding
  - sender: embedding_retriever.documents
    receiver: document_joiner.documents
  - sender: document_joiner.documents
    receiver: ranker.documents
  - sender: ranker.documents
    receiver: sql_prompt_builder.documents
  - sender: ranker.documents
    receiver: answer_builder.documents
  - sender: sql_prompt_builder.prompt
    receiver: sql_llm.prompt
  - sender: sql_llm.replies
    receiver: replies_to_sql.replies
  - sender: replies_to_sql.output
    receiver: snowflake_executor.query
  - sender: snowflake_executor.answers
    receiver: display_prompt_builder.table
  - sender: replies_to_sql.output
    receiver: display_prompt_builder.sql_query
  - sender: display_prompt_builder.prompt
    receiver: answer_builder.prompt
  - sender: display_prompt_builder.prompt
    receiver: display_llm.prompt
  - sender: display_llm.replies
    receiver: answer_builder.replies
max_loops_allowed: 100
metadata: {}
inputs:
  query:
    - bm25_retriever.query
    - query_embedder.text
    - ranker.query
    - sql_prompt_builder.question
    - answer_builder.query
  filters:
    - bm25_retriever.filters
    - embedding_retriever.filters
outputs:
  documents: ranker.documents
  answers: answer_builder.answers