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85 lines
2.7 KiB
Plaintext
85 lines
2.7 KiB
Plaintext
---
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title: "SnowflakeTableRetriever"
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id: snowflaketableretriever
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slug: "/snowflaketableretriever"
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description: "Connects to a Snowflake database to execute an SQL query."
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---
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# SnowflakeTableRetriever
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Connects to a Snowflake database to execute an SQL query.
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<div className="key-value-table">
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| --- | --- |
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| **Most common position in a pipeline** | Before a [`PromptBuilder`](../builders/promptbuilder.mdx) |
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| **Mandatory init variables** | `user`: User's login <br /> <br />`account`: Snowflake account identifier <br /> <br />`api_key`: Snowflake account password. Can be set with `SNOWFLAKE_API_KEY` env var |
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| **Mandatory run variables** | `query`: An SQL query to execute |
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| **Output variables** | `dataframe`: The resulting Pandas dataframe version of the table |
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| **API reference** | [Snowflake](/reference/integrations-snowflake) |
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| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/snowflake |
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</div>
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## Overview
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The `SnowflakeTableRetriever` connects to a Snowflake database and retrieves data using an SQL query. It then returns a Pandas dataframe and a Markdown version of the table:
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To start using the integration, install it with:
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```bash
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pip install snowflake-haystack
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```
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## Usage
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### On its own
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```python
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from haystack_integrations.components.retrievers.snowflake import SnowflakeTableRetriever
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snowflake = SnowflakeRetriever(
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user="<ACCOUNT-USER>",
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account="<ACCOUNT-IDENTIFIER>",
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api_key=Secret.from_env_var("SNOWFLAKE_API_KEY"),
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warehouse="<WAREHOUSE-NAME>",
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)
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snowflake.run(query="""select * from table limit 10;"""")
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```
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### In a pipeline
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In the following pipeline example, the `PromptBuilder` is using the table received from the `SnowflakeTableRetriever` to create a prompt template and pass it on to an LLM:
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```python
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from haystack import Pipeline
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from haystack.utils import Secret
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from haystack.components.builders import PromptBuilder
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from haystack.components.generators import OpenAIGenerator
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from haystack_integrations.components.retrievers.snowflake import (
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SnowflakeTableRetriever,
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)
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executor = SnowflakeTableRetriever(
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user="<ACCOUNT-USER>",
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account="<ACCOUNT-IDENTIFIER>",
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api_key=Secret.from_env_var("SNOWFLAKE_API_KEY"),
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warehouse="<WAREHOUSE-NAME>",
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)
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pipeline = Pipeline()
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pipeline.add_component(
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"builder",
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PromptBuilder(template="Describe this table: {{ table }}"),
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)
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pipeline.add_component("snowflake", executor)
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pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o"))
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pipeline.connect("snowflake.table", "builder.table")
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pipeline.connect("builder", "llm")
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pipeline.run(data={"query": "select employee, salary from table limit 10;"})
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```
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