About this Task

deepset Cloud provides the DeepsetSnowflakeRetriever component that connects to your Snowflake database. To query your Snowflake data, you add this component to your query pipeline. 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 DeepsetSnowflakeRetriever to your query pipeline or use one of our ready-made Text-to-SQL pipeline templates.

Usage Example

This is an example of a query pipeline with DeepsetSnowflakeRetriever and two Generators: one to translate a natural language query into SQL and send it to DeepsetSnowflakeRetriever and another to construct an answer.

# If you need help with the YAML format, have a look at https://docs.cloud.deepset.ai/v2.0/docs/create-a-pipeline#create-a-pipeline-using-pipeline-editor.
# This section defines components that you want to use in your pipelines. Each component must have a name and a type. You can also set the component's parameters here.
# The name is up to you, you can give your component a friendly name. You then use components' names when specifying the connections in the pipeline.
# Type is the class path of the component. You can check the type on the component's documentation page.
components:
  bm25_retriever: # Selects the most similar documents from the document store
    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: 1024
          similarity: cosine
      top_k: 20 # The number of results to return

  query_embedder:
    type: haystack.components.embedders.sentence_transformers_text_embedder.SentenceTransformersTextEmbedder
    init_parameters:
      model: "BAAI/bge-m3"
      tokenizer_kwargs:
        model_max_length: 1024

  embedding_retriever: # Selects the most similar documents from the document store
    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: 1024
          similarity: cosine
      top_k: 20 # The number of results to return

  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: "BAAI/bge-reranker-v2-m3"
      top_k: 5
      model_kwargs:
        torch_dtype: "torch.float16"
      tokenizer_kwargs:
        model_max_length: 1024

  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 them, and their
          columns:
  
          {% for document in documents %}
  
          Document[{{ loop.index }}]:
  
          {{ document.content }}
  
          {% endfor %}
  
          User's question: {{ question }}
  
          Generated SQL query:

  sql_llm:
    type: haystack.components.generators.openai.OpenAIGenerator
    init_parameters:
      api_key: {"type": "env_var", "env_vars": ["OPENAI_API_KEY"], "strict": False}
      model: "gpt-4o"
      generation_kwargs:
        max_tokens: 650
        temperature: 0.0
        seed: 0

  snowflake_retriever:
    type: deepset_cloud_custom_nodes.retrievers.snowflake_retriever.DeepsetSnowflakeRetriever
    init_parameters:
      user: "<snowflake-user-identifier>"
      account: "<snowflake-account-identifier>"

  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 {{ question }} using the information
        in the table.

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

        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: haystack.components.generators.openai.OpenAIGenerator
    init_parameters:
      api_key: {"type": "env_var", "env_vars": ["OPENAI_API_KEY"], "strict": False}
      model: "gpt-4o"
      generation_kwargs:
        max_tokens: 2000
        temperature: 0.0
        seed: 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_retriever.query
  - sender: snowflake_retriever.table
    receiver: display_prompt_builder.table
  - sender: replies_to_sql.output
    receiver: display_prompt_builder.sql_query
  - sender: display_prompt_builder.prompt
    receiver: display_llm.prompt
  - sender: display_prompt_builder.prompt
    receiver: answer_builder.prompt
  - sender: display_llm.replies
    receiver: answer_builder.replies

inputs:  # Define the inputs for your pipeline
  query:  # These components will receive the query as input
  - "bm25_retriever.query"
  - "query_embedder.text"
  - "ranker.query"
  - "sql_prompt_builder.question"
  - "display_prompt_builder.question"
  - "answer_builder.query"

  filters:  # These components will receive a potential query filter as input
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:  # Defines the output of your pipeline
  documents: "ranker.documents"  # The output of the pipeline is the retrieved documents
  answers: "answer_builder.answers"  # The output of the pipeline is the generated answers