# deep-searcher

**Repository Path**: ZTYZSY/deep-searcher

## Basic Information

- **Project Name**: deep-searcher
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-02-13
- **Last Updated**: 2025-02-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# DeepSearcher

DeepSearcher combines powerful LLMs (DeepSeek, OpenAI, etc.) and Vector Databases (Milvus, etc.) to perform search, evaluation, and reasoning based on private data, providing highly accurate answer and comprehensive report. This project is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios.

![Architecture](./assets/pic/deep-searcher-arch.png)

## 🚀 Features

- **Private Data Search**: Maximizes the utilization of enterprise internal data while ensuring data security. When necessary, it can integrate online content for more accurate answers.
- **Vector Database Management**: Supports Milvus and other vector databases, allowing data partitioning for efficient retrieval.
- **Flexible Embedding Options**: Compatible with multiple embedding models for optimal selection.
- **Multiple LLM Support**: Supports DeepSeek, OpenAI, and other large models for intelligent Q&A and content generation.
- **Document Loader**: Supports local file loading, with web crawling capabilities under development.

---

## 🎉 Demo
![demo](./assets/pic/demo.gif)


## 📖 Quick Start

### Installation
Install DeepSearcher using pip:
```bash
# Clone the repository
git clone https://github.com/zilliztech/deep-searcher.git

# Recommended: Create a Python virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
cd deep-searcher 
pip install -e .
```
Prepare your `OPENAI_API_KEY` in your environment variables. If you change the LLM in the configuration, make sure to prepare the corresponding API key.

### Quick start demo
```python
from deepsearcher.configuration import Configuration, init_config
from deepsearcher.online_query import query

config = Configuration()

# Customize your config here,
# more configuration see the Configuration Details section below.
config.set_provider_config("llm", "OpenAI", {"model": "gpt-4o-mini"})
init_config(config = config)

# Load your local data
from deepsearcher.offline_loading import load_from_local_files
load_from_local_files(paths_or_directory=your_local_path)

# (Optional) Load from web crawling (`FIRECRAWL_API_KEY` env variable required)
from deepsearcher.offline_loading import load_from_website
load_from_website(urls=website_url)

# Query
result = query("Write a report about xxx.") # Your question here
```
### Configuration Details:
#### LLM Configuration

  <pre><code>config.set_provider_config("llm", "(LLMName)", "(Arguments dict)")</code></pre>
<p>The "LLMName" can be one of the following: ["DeepSeek", "OpenAI", "SiliconFlow", "TogetherAI"]</p>
<p> The "Arguments dict" is a dictionary that contains the necessary arguments for the LLM class.</p>

<details>
  <summary>Example (OpenAI)</summary>
    <pre><code>config.set_provider_config("llm", "OpenAI", {"model": "gpt-4o"})</code></pre>
    <p> More details about OpenAI models: https://platform.openai.com/docs/models </p>
</details>

<details>
  <summary>Example (DeepSeek from official)</summary>
    <pre><code>config.set_provider_config("llm", "DeepSeek", {"model": "deepseek-chat"})</code></pre>
    <p> More details about DeepSeek: https://api-docs.deepseek.com/ </p>
</details>

<details>
  <summary>Example (DeepSeek from SiliconFlow)</summary>
    <pre><code>config.set_provider_config("llm", "SiliconFlow", {"model": "deepseek-ai/DeepSeek-V3"})</code></pre>
    <p> More details about SiliconFlow: https://docs.siliconflow.cn/quickstart </p>
</details>

<details>
  <summary>Example (DeepSeek from TogetherAI)</summary>
    <pre><code>config.set_provider_config("llm", "TogetherAI", {"model": "deepseek-ai/DeepSeek-V3"})</code></pre>
    <p> More details about TogetherAI: https://www.together.ai/ </p>
</details>

#### Embedding Model Configuration
<pre><code>config.set_embedding_config("embedding", "(EmbeddingModelName)", "(Arguments dict)")</code></pre>
<p>The "EmbeddingModelName" can be one of the following: ["MilvusEmbedding", "OpenAIEmbedding", "VoyageEmbedding"]</p>
<p> The "Arguments dict" is a dictionary that contains the necessary arguments for the embedding model class.</p>

<details>
  <summary>Example (Pymilvus built-in embedding model)</summary>
    <pre><code>config.set_embedding_config("embedding", "MilvusEmbedding", {"model": "BAAI/bge-base-en-v1.5"})</code></pre>
    <p> More details about Pymilvus: https://milvus.io/docs/embeddings.md </p>
</details>

<details>
  <summary>Example (OpenAI embedding)</summary>
    <pre><code>config.set_embedding_config("embedding", "OpenAIEmbedding", {"model": "text-embedding-3-small"})</code></pre>
    <p> More details about OpenAI models: https://platform.openai.com/docs/guides/embeddings/use-cases </p>
</details>

<details>
  <summary>Example (VoyageAI embedding)</summary>
    <pre><code>config.set_embedding_config("embedding", "VoyageEmbedding", {"model": "voyage-3"})</code></pre>
    <p> More details about VoyageAI: https://docs.voyageai.com/embeddings/ </p>
</details>








### Python CLI Mode
#### Load
```shell
deepsearcher --load "your_local_path_or_url"
```
Example loading from local file:
```shell
deepsearcher --load "/path/to/your/local/file.pdf"
```
Example loading from url (*Set `FIRECRAWL_API_KEY` in your environment variables, see [FireCrawl](https://docs.firecrawl.dev/introduction) for more details*):

```shell
deepsearcher --load "https://www.wikiwand.com/en/articles/DeepSeek"
```

#### Query
```shell
deepsearcher --query "Write a report about xxx."
```

More help information
```shell
deepsearcher --help
```

---

## 🔧 Module Support

### 🔹 Embedding Models
- [Pymilvus built-in embedding model](https://milvus.io/docs/embeddings.md)
- [OpenAI](https://platform.openai.com/docs/guides/embeddings/use-cases) (`OPENAI_API_KEY` env variable required)
- [VoyageAI](https://docs.voyageai.com/embeddings/) (`VOYAGE_API_KEY` env variable required)

### 🔹 LLM Support
- [DeepSeek](https://api-docs.deepseek.com/) (`DEEPSEEK_API_KEY` env variable required)
- [OpenAI](https://platform.openai.com/docs/models) (`OPENAI_API_KEY` env variable required)
- [SiliconFlow](https://docs.siliconflow.cn/en/userguide/introduction) (`SILICONFLOW_API_KEY` env variable required)
- [TogetherAI](https://docs.together.ai/docs/introduction) (`TOGETHER_API_KEY` env variable required)

### 🔹 Document Loader
- Local File
  - PDF(with txt/md) loader
  - [Unstructured](https://unstructured.io/) (under development) (`UNSTRUCTURED_API_KEY` and `UNSTRUCTURED_URL` env variables required)
- Web Crawler
  - [FireCrawl](https://docs.firecrawl.dev/introduction) (`FIRECRAWL_API_KEY` env variable required)
  - [Jina Reader](https://jina.ai/reader/) (`JINA_API_TOKEN` env variable required)
  - [Crawl4AI](https://docs.crawl4ai.com/) (You should run command `crawl4ai-setup` for the first time)
### 🔹 Vector Database Support
- [Milvus](https://milvus.io/) (the same as [Zilliz](https://www.zilliz.com/))

---

## 📌 Future Plans
- Enhance web crawling functionality
- Support more vector databases (e.g., FAISS...)
- Add support for additional large models
- Provide RESTful API interface

We welcome contributions! Star & Fork the project and help us build a more powerful DeepSearcher! 🎯