onecontext 0.0.8

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OneContext

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Table of Contents

• LLM Context as a Service
• Quick Start
• License

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LLM Context as a Service

OneContext makes it easy to augment your LLM application with your own data in a few API calls. Simply upload your data to a Knowledge Base and directly query with natural languge to retrieve relevant context for your LLM application.

We manage the full document processing and retrieval pipeline so that you don't have to:

• document ingestion, chunking and cleaning
• effcient vector embeddings at scale using state of the art open source models
• low latency multi stage query pipeline to provide the most relevant context for your LLM application

We keep up with the latest research to provide an accurate and fast retrieval pipeline based on model evalution and best practice heuristics.

Multi stage query pipeline out of the box:

• Fast base-model retrieves a large pool of documents
• Cross-encoder reranks the retrieved documents to provide the precise results relevant to the query.

Use Cases:

• Question Answering over a large knowledge base
• Long term memorry for chatbots
• Runtime context for instruction following agents
• Prevent and detect hallucinations based on custom data

Quick Start

pip install onecontext

Configuration

export ONECONTEXT_API_KEY="YOUR_API_KEY"

You can get the api key for free at OneContext

Usage

Create a Knowledge Base:

from onecontext import KnowledgeBase

my_knowledge_base = KnowledgeBase("my_knowledge_base")

my_knowledge_base.create()

List Knowledge Bases

from onecontext import list_knowledge_bases

print(list_knowledge_bases())

Upload files to the Knowledge Base:

my_knowledge_base = KnowledgeBase("my_knowledge_base")

directory = "/path/to/local_folder"

my_knowledge_base.upload_from_directory(directory)

Once the files have been uploaded they will be processed, chunked and embedded by OneContext.

Check sync status:

print(my_knowledge_base.is_synced)

Query the Knowledge Base

from onecontext import Retriever

retriever = Retriever(knowledge_bases=[my_knowledge_base])

documents = retriever.query("what is onecontext?", output_k=20)

By default the query pipeline is composed of two steps:

• Retrieval: fetch the larger pool of documents (rerank_pool_size)
• Re-ranking: re-rank the results with a downstream model to get most relevant documents

To improve recall you can increase the rerank_pool_size:

documents = retriever.query("what is onecontext?", output_k=10, rerank_pool_size=80)

You can also skip the re-ranking step entirely:

documents = retriever.query_no_rerank("what is onecontext?")

License

onecontext is distributed under the terms of the MIT license.