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* fix: ensure config is not flattened, add tests * chore: refactor inits to model_validator * chore: refactor rag tool config parsing * chore: add initial docs * chore: add additional validation aliases for provider env vars * chore: add solid docs * chore: move imports to top * fix: revert circular import * fix: lazy import qdrant-client * fix: allow collection name config * chore: narrow model names for google * chore: update additional docs * chore: add backward compat on model name aliases * chore: add tests for config changes
83 lines
3.4 KiB
Plaintext
83 lines
3.4 KiB
Plaintext
---
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title: PG RAG Search
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description: The `PGSearchTool` is designed to search PostgreSQL databases and return the most relevant results.
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icon: elephant
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mode: "wide"
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---
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## Overview
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<Note>
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The PGSearchTool is currently under development. This document outlines the intended functionality and interface.
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As development progresses, please be aware that some features may not be available or could change.
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</Note>
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## Description
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The PGSearchTool is envisioned as a powerful tool for facilitating semantic searches within PostgreSQL database tables. By leveraging advanced Retrieve and Generate (RAG) technology,
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it aims to provide an efficient means for querying database table content, specifically tailored for PostgreSQL databases.
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The tool's goal is to simplify the process of finding relevant data through semantic search queries, offering a valuable resource for users needing to conduct advanced queries on
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extensive datasets within a PostgreSQL environment.
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## Installation
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The `crewai_tools` package, which will include the PGSearchTool upon its release, can be installed using the following command:
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```shell
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pip install 'crewai[tools]'
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```
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<Note>
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The PGSearchTool is not yet available in the current version of the `crewai_tools` package. This installation command will be updated once the tool is released.
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</Note>
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## Example Usage
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Below is a proposed example showcasing how to use the PGSearchTool for conducting a semantic search on a table within a PostgreSQL database:
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```python Code
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from crewai_tools import PGSearchTool
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# Initialize the tool with the database URI and the target table name
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tool = PGSearchTool(
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db_uri='postgresql://user:password@localhost:5432/mydatabase',
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table_name='employees'
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)
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```
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## Arguments
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The PGSearchTool is designed to require the following arguments for its operation:
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| Argument | Type | Description |
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|:---------------|:---------|:-------------------------------------------------------------------------------------------------------------------------------------|
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| **db_uri** | `string` | **Mandatory**. A string representing the URI of the PostgreSQL database to be queried. This argument will be mandatory and must include the necessary authentication details and the location of the database. |
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| **table_name** | `string` | **Mandatory**. A string specifying the name of the table within the database on which the semantic search will be performed. This argument will also be mandatory. |
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## Custom Model and Embeddings
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The tool intends to use OpenAI for both embeddings and summarization by default. Users will have the option to customize the model using a config dictionary as follows:
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```python Code
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tool = PGSearchTool(
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config=dict(
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llm=dict(
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provider="ollama", # or google, openai, anthropic, llama2, ...
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config=dict(
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model="llama2",
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# temperature=0.5,
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# top_p=1,
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# stream=true,
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),
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),
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embedder=dict(
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provider="google-generativeai", # or openai, ollama, ...
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config=dict(
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model_name="gemini-embedding-001",
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task_type="RETRIEVAL_DOCUMENT",
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# title="Embeddings",
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),
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),
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)
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)
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``` |