| Name | llm-hippocampus JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | None |
| Summary | LLM Hippocampus — a Context Engineering playground |
| upload_time | 2025-09-15 10:34:55 |
| maintainer | None |
| docs_url | None |
| author | joelz |
| requires_python | >=3.11 |
| license | None |
| keywords |
python
redis
langchain
openai
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<h1>🚀 LLM Hippocampus</h1>
[](https://opensource.org/licenses/MIT)
🎯 Build up and manage the LLM 's memory
</div>
🔥 **LLM Hippocampus** helping your project for building and experimenting with **Context Engineering** applications. harness the full power of Redis for **lightning-fast vector search**, **intelligent semantic caching**, **persistent LLM memory**, and **smart context engineering **.
✨ **What makes this special?**
- 🚀 **One-command setup** - pip install llm-hippocampus
- ⚡ **LLM support** - OpenAI
- 🎯 **Redis-powered** - Vector search, caching, and memory management
- 🐳 **Docker ready** - Building...
- 🔧 **Developer-first** - Support to Hot load by installing llm-hippocampus
---
## Table of Contents
- [Quick Start](#quick-start)
- [Prerequisites](#prerequisites)
- [Getting Started](#getting-started)
- [Available Commands](#available-commands)
- [Development Workflows](#development-workflows)
- [Environment Configuration](#environment-configuration)
- [Using Google VertexAI](#using-google-vertexai)
- [Project Structure](#project-structure)
- [Connecting to Redis Cloud](#connecting-to-redis-cloud)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [License](#license)
- [Learn More](#learn-more)
## Quick Start
**Get up and install in your project:**
```bash
pip install llm-hippocampus
or
uv add llm-hippocampus
```
Welcome to LLM Hippocampus! 🎉
---
## Prerequisites
1. Make sure you have the following tools available:
- [python](https://www.docker.com/products/docker-desktop/) 3.11+
- [uv](https://docs.astral.sh/uv/)
- [Redis Stack](https://redis.io/)
2. Setup one or more of the following:
- [OpenAI API](https://platform.openai.com/)
- You will need an API Key
## Getting Started
```python
from dotenv import load_dotenv
from llm_hippocampus import env
from llm_hippocampus.core.utils import list2np_array
from llm_hippocampus.core.redis import create_search_index, client, load_data2search_index, vector_query
from llm_hippocampus.session import Session
load_dotenv()
# Load the model
session = Session()
embeddings = session.get_embedding_model()
schema = {
"index": {
"name": "data_agent_chain",
"prefix": "data_agent_chain",
},
"fields": [
{"name": "query", "type": "text"},
{"name": "scope", "type": "text"},
{"name": "intent", "type": "text"},
{
"name": "query_embedding",
"type": "vector",
"attrs": {
"dims": 768,
"distance_metric": "cosine",
"algorithm": "flat",
"datatype": "float32"
}
}
]
}
data = [
{
'query': 'SAAJ91的管理费率和托管费率是多少？',
'scope': "产品基本信息",
'intent': '管理费率、托管费率',
'query_embedding': list2np_array(embeddings.encode(
"SAAJ91的管理费率和托管费率是多少？",
precision=schema["fields"][3]["attrs"]["datatype"],
truncate_dim=schema["fields"][3]["attrs"]["dims"])).tobytes()
},
{
'query': 'SATP77在于2025年06月01日至2025年06月30日的股票持仓明细',
'scope': "股票持仓信息",
'intent': '股票持仓信息',
'query_embedding': list2np_array(embeddings.encode(
"SATP77在于2025年06月01日至2025年06月30日的股票持仓明细",
precision=schema["fields"][3]["attrs"]["datatype"],
truncate_dim=schema["fields"][3]["attrs"]["dims"])).tobytes()
},
{
'query': '截至于2025年01月01日至2025年12月31日，001120的户均定投金额？',
'scope': "客户定投情况",
'intent': '客户定投情况',
'query_embedding': list2np_array(embeddings.encode(
"截至于2025年01月01日至2025年12月31日，001120的户均定投金额",
precision=schema["fields"][3]["attrs"]["datatype"],
truncate_dim=schema["fields"][3]["attrs"]["dims"])).tobytes()
}
]
redis_client = client(env.REDIS_URL)
index = create_search_index(redis_client, schema)
keys = load_data2search_index(index, data)
query = "400001的管理费率"
args = {
"distance_threshold": session.distance_threshold,
"top_k": session.top_k,
"vector_field_name": "query_embedding",
"precision": schema["fields"][3]["attrs"]["datatype"],
"truncate_dim": schema["fields"][3]["attrs"]["dims"],
"return_fileds": ["query", "scope", "intent"],
}
results = vector_query(query, index, embeddings, schema, **args)
```
### Development Workflows
- Building
## Project Structure
## Contributing
🤝 Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Troubleshooting
## Learn More
Raw data
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"description": "<div align=\"center\">\r\n<h1>\ud83d\ude80 LLM Hippocampus</h1>\r\n\r\n[](https://opensource.org/licenses/MIT)\r\n\r\n\r\n\r\n\r\n\ud83c\udfaf Build up and manage the LLM 's memory\r\n\r\n</div>\r\n\r\n\ud83d\udd25 **LLM Hippocampus** helping your project for building and experimenting with **Context Engineering** applications. harness the full power of Redis for **lightning-fast vector search**, **intelligent semantic caching**, **persistent LLM memory**, and **smart context engineering **.\r\n\r\n\u2728 **What makes this special?**\r\n- \ud83d\ude80 **One-command setup** - pip install llm-hippocampus\r\n- \u26a1 **LLM support** - OpenAI\r\n- \ud83c\udfaf **Redis-powered** - Vector search, caching, and memory management\r\n- \ud83d\udc33 **Docker ready** - Building... \r\n- \ud83d\udd27 **Developer-first** - Support to Hot load by installing llm-hippocampus\r\n\r\n---\r\n\r\n## Table of Contents\r\n\r\n- [Quick Start](#quick-start)\r\n- [Prerequisites](#prerequisites)\r\n- [Getting Started](#getting-started)\r\n - [Available Commands](#available-commands)\r\n - [Development Workflows](#development-workflows)\r\n - [Environment Configuration](#environment-configuration)\r\n- [Using Google VertexAI](#using-google-vertexai)\r\n- [Project Structure](#project-structure)\r\n- [Connecting to Redis Cloud](#connecting-to-redis-cloud)\r\n- [Troubleshooting](#troubleshooting)\r\n- [Contributing](#contributing)\r\n- [License](#license)\r\n- [Learn More](#learn-more)\r\n\r\n\r\n## Quick Start\r\n\r\n**Get up and install in your project:**\r\n\r\n```bash\r\npip install llm-hippocampus\r\nor\r\nuv add llm-hippocampus\r\n```\r\n\r\nWelcome to LLM Hippocampus! \ud83c\udf89\r\n\r\n---\r\n\r\n## Prerequisites\r\n\r\n1. Make sure you have the following tools available:\r\n - [python](https://www.docker.com/products/docker-desktop/) 3.11+\r\n - [uv](https://docs.astral.sh/uv/)\r\n - [Redis Stack](https://redis.io/)\r\n2. Setup one or more of the following:\r\n - [OpenAI API](https://platform.openai.com/)\r\n - You will need an API Key\r\n\r\n## Getting Started\r\n```python\r\nfrom dotenv import load_dotenv\r\nfrom llm_hippocampus import env\r\nfrom llm_hippocampus.core.utils import list2np_array\r\nfrom llm_hippocampus.core.redis import create_search_index, client, load_data2search_index, vector_query\r\nfrom llm_hippocampus.session import Session\r\nload_dotenv()\r\n# Load the model\r\nsession = Session()\r\nembeddings = session.get_embedding_model()\r\nschema = {\r\n \"index\": {\r\n \"name\": \"data_agent_chain\",\r\n \"prefix\": \"data_agent_chain\",\r\n },\r\n \"fields\": [\r\n {\"name\": \"query\", \"type\": \"text\"},\r\n {\"name\": \"scope\", \"type\": \"text\"},\r\n {\"name\": \"intent\", \"type\": \"text\"},\r\n {\r\n \"name\": \"query_embedding\",\r\n \"type\": \"vector\",\r\n \"attrs\": {\r\n \"dims\": 768,\r\n \"distance_metric\": \"cosine\",\r\n \"algorithm\": \"flat\",\r\n \"datatype\": \"float32\"\r\n }\r\n }\r\n ]\r\n}\r\n\r\ndata = [\r\n {\r\n 'query': 'SAAJ91\u7684\u7ba1\u7406\u8d39\u7387\u548c\u6258\u7ba1\u8d39\u7387\u662f\u591a\u5c11\uff1f',\r\n 'scope': \"\u4ea7\u54c1\u57fa\u672c\u4fe1\u606f\",\r\n 'intent': '\u7ba1\u7406\u8d39\u7387\u3001\u6258\u7ba1\u8d39\u7387',\r\n 'query_embedding': list2np_array(embeddings.encode(\r\n \"SAAJ91\u7684\u7ba1\u7406\u8d39\u7387\u548c\u6258\u7ba1\u8d39\u7387\u662f\u591a\u5c11\uff1f\",\r\n precision=schema[\"fields\"][3][\"attrs\"][\"datatype\"],\r\n truncate_dim=schema[\"fields\"][3][\"attrs\"][\"dims\"])).tobytes()\r\n },\r\n {\r\n 'query': 'SATP77\u5728\u4e8e2025\u5e7406\u670801\u65e5\u81f32025\u5e7406\u670830\u65e5\u7684\u80a1\u7968\u6301\u4ed3\u660e\u7ec6',\r\n 'scope': \"\u80a1\u7968\u6301\u4ed3\u4fe1\u606f\",\r\n 'intent': '\u80a1\u7968\u6301\u4ed3\u4fe1\u606f',\r\n 'query_embedding': list2np_array(embeddings.encode(\r\n \"SATP77\u5728\u4e8e2025\u5e7406\u670801\u65e5\u81f32025\u5e7406\u670830\u65e5\u7684\u80a1\u7968\u6301\u4ed3\u660e\u7ec6\",\r\n precision=schema[\"fields\"][3][\"attrs\"][\"datatype\"],\r\n truncate_dim=schema[\"fields\"][3][\"attrs\"][\"dims\"])).tobytes()\r\n },\r\n {\r\n 'query': '\u622a\u81f3\u4e8e2025\u5e7401\u670801\u65e5\u81f32025\u5e7412\u670831\u65e5\uff0c001120\u7684\u6237\u5747\u5b9a\u6295\u91d1\u989d\uff1f',\r\n 'scope': \"\u5ba2\u6237\u5b9a\u6295\u60c5\u51b5\",\r\n 'intent': '\u5ba2\u6237\u5b9a\u6295\u60c5\u51b5',\r\n 'query_embedding': list2np_array(embeddings.encode(\r\n \"\u622a\u81f3\u4e8e2025\u5e7401\u670801\u65e5\u81f32025\u5e7412\u670831\u65e5\uff0c001120\u7684\u6237\u5747\u5b9a\u6295\u91d1\u989d\",\r\n precision=schema[\"fields\"][3][\"attrs\"][\"datatype\"],\r\n truncate_dim=schema[\"fields\"][3][\"attrs\"][\"dims\"])).tobytes()\r\n }\r\n]\r\n\r\nredis_client = client(env.REDIS_URL)\r\nindex = create_search_index(redis_client, schema)\r\nkeys = load_data2search_index(index, data)\r\n\r\nquery = \"400001\u7684\u7ba1\u7406\u8d39\u7387\"\r\nargs = {\r\n \"distance_threshold\": session.distance_threshold,\r\n \"top_k\": session.top_k,\r\n \"vector_field_name\": \"query_embedding\",\r\n \"precision\": schema[\"fields\"][3][\"attrs\"][\"datatype\"],\r\n \"truncate_dim\": schema[\"fields\"][3][\"attrs\"][\"dims\"],\r\n \"return_fileds\": [\"query\", \"scope\", \"intent\"],\r\n}\r\n\r\nresults = vector_query(query, index, embeddings, schema, **args)\r\n```\r\n\r\n### Development Workflows\r\n- Building\r\n\r\n\r\n## Project Structure\r\n\r\n\r\n## Contributing\r\n\r\n\ud83e\udd1d Contributions are welcome! Please feel free to submit a Pull Request.\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License - see the LICENSE file for details.\r\n\r\n## Troubleshooting\r\n\r\n## Learn More\r\n",
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