Practical overview
When it fits
Use it when an LLM application needs to control which retrieved documents and conversation turns enter a prompt, particularly when history accumulates or the token budget is tight. For latency requirements below 50 ms, the README recommends keyword or TF-IDF instead of hybrid retrieval.
Who it is for
- Developers integrating context management into Python-based LLM applications.
- Teams building multi-turn systems that need to balance retrieved documents and conversation history within a prompt budget.
Documented uses
- Retrieve relevant documents using keyword, TF-IDF or hybrid search.
- Re-rank retrieved material using tag-weighted scoring.
- Compress selected content with truncation, sentence-based or extractive strategies.
- Manage conversation memory with decay, importance scoring and deduplication.
- Allocate a token budget across system prompt, history and documents, then produce a context packet.
Requirements & limits
The repository is public and identifies the project as MIT-licensed. The README lists Python 3.10 or later, NumPy as required and sentence-transformers as optional for hybrid retrieval.
- Core functionality uses the Python standard library and NumPy; NumPy is required.
- Hybrid retrieval uses sentence-transformers when installed; otherwise it falls back to random embeddings with a warning.
- The README reports hybrid retrieval at about 85 ms and recommends keyword or TF-IDF modes for sub-50 ms requirements.
- The project notes that token estimates use a simplified approach.
- The library assembles context for an LLM; it does not itself provide the model.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install context-engine and its required dependency
Clone the repository and install NumPy, which the core library requires.
Before you start
- Python 3.10 or later
- Git and pip
Steps
Clone the context-engine repository: the command below.
git clone https://github.com/Emmimal/context-engine.gitChange into the repository directory:
cd context-engine.Install the required dependency: the command below.
pip install numpyOptionally install
sentence-transformersto enable hybrid retrieval.
Expected outcome
The repository is cloned and the required NumPy dependency is installed; installing sentence-transformers additionally enables hybrid retrieval.
Guide limitations
- Without sentence-transformers, hybrid mode falls back to random embeddings with a warning; the README describes this as useful for development and testing.
Build a context packet and add conversation turns to memory
Use ContextEngine to build query context from documents, then add turns for a subsequent build.
Before you start
- Python 3.10 or later
- The context-engine repository cloned and NumPy installed
Steps
Import
ContextEngineandDocumentfromcontext_engineering, then create a document list usingDocument(id=..., content=..., tags=...).Initialize
ContextEnginewith the documents, a token budget, a retrieval mode such ashybrid, and a compression strategy such asextractive.Call
engine.build(query)and usepacket.to_prompt_string()to obtain the context prompt string.Add user and assistant turns with
engine.remember(role, text), then callengine.build()with a subsequent query;packet.diagnostics()is shown for inspecting that packet.
Expected outcome
The build call returns a context packet assembled from the configured documents and budget; remembered turns can participate in a later build.
Guide limitations
- The README notes that hybrid mode uses random embeddings with a warning if sentence-transformers is not installed.
- Token estimation uses 1 to …
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- Emmimal/context-engine · Official homepage title · retained in its source language