Building Context-Aware Systems: A Software Engineer's Guide to WeKnora (Go, RAG, Multi-Tenant)

Tencent/WeKnora

This framework is essentially a robust, Go-based platform designed to make it much easier to build sophisticated, production-ready applications powered by Large Language Models (LLMs), focusing specifically on deep document understanding and providing context-aware answers.

Here is a breakdown of what it is, why it's useful to an engineer, and how you might get started with it.

As a software engineer, WeKnora solves several major pain points when building LLM-based applications

High Concurrency
Go's lightweight goroutines make it easy to handle many simultaneous user queries efficiently. This is critical for scaling an API that relies on external LLM calls or complex search operations.

Structured API
The framework provides a structured backend ready for deployment, handling API routing, database interactions, and system monitoring—things you would otherwise have to build from scratch.

Grounding/Factuality
It ensures the LLM's responses are derived directly from the provided source documents. Your users won't get generic or incorrect answers; they will get answers specific to your knowledge base.

Data Separation (Multi-Tenancy)
If you are building a product for multiple clients, WeKnora's design immediately handles the complexity of keeping Client A's documents and answers separate from Client B's.

It handles the entire RAG pipeline

Document Chunking
Breaking large files into searchable pieces.

Embedding Generation
Converting text pieces into numerical vectors (embeddings) that capture their semantic meaning.

Semantic Retrieval
Using a vector database (like Chroma or Milvus) to find document pieces that are semantically similar (not just keyword similar) to the user's query.

Since WeKnora is a framework, your main tasks involve setting up the environment and integrating your data.

Prerequisites
You'll need Go installed and likely a Vector Database (e.g., Milvus, which is common in the Go ecosystem) and an LLM API Key (e.g., OpenAI, or an accessible local model).

Clone the Repository

git clone https://github.com/Tencent/WeKnora.git
cd WeKnora

Configure
You would modify the configuration files (usually .yaml or environment variables) to point the application to your

LLM API endpoint/key.

Vector database connection details.

Tenant/user setup (if using multi-tenancy).

This is where you load your specific documents into the system so the RAG pipeline can work.

Concept
The framework provides an API endpoint (or a CLI tool) to upload documents. When you upload a PDF, DOCX, or text file, the framework automatically executes the following steps

Parsing
Extracts text from the document.

Chunking
Divides the text into manageable chunks.

Embedding
Generates a vector embedding for each chunk.

Storage
Saves the text chunks and their corresponding embeddings into the Vector Database, linked to a specific tenant/namespace.

Sample Go Code Snippet (Conceptual Ingestion Service) (Note: This is an illustrative example of what your service might do; the actual WeKnora API/SDK calls would be more specific.)

package main

import (
	"log"
	"weknora/document" // Hypothetical WeKnora document package
)

func main() {
	// 1. Define the document and the tenant it belongs to
	filePath := "./company_policy_manual.pdf"
	tenantID := "FinanceTeam"
	
	log.Printf("Starting ingestion for Tenant: %s", tenantID)

	// 2. Call the framework's ingestion function
	err := document.Ingest(filePath, tenantID)

	if err != nil {
		log.Fatalf("Error during document ingestion: %v", err)
	}

	log.Println("Document successfully processed, chunked, and embedded.")
}

Once the data is ingested, users can send a natural language query. The RAG pipeline in WeKnora does the heavy lifting

Query Embedding
The user's question is converted into an embedding.

Retrieval
The framework searches the Vector Database for the top k most semantically similar chunks to the query.

Context Construction
These retrieved chunks are bundled together into a "context."

Generation
The LLM is given a specific prompt like
"Answer the following question based ONLY on the provided context
[Retrieved Context]. Question
[User's Query]."

Result
The LLM generates the final, context-aware answer.

Sample Go Code Snippet (Conceptual Query Service)

package main

import (
	"fmt"
	"weknora/query" // Hypothetical WeKnora query package
)

func main() {
	userQuery := "What is the policy for remote work flexibility in Q4?"
	tenantID := "FinanceTeam"
	
	fmt.Printf("User Query: %s\n", userQuery)

	// 1. Call the framework's RAG/Query function
	answer, sourceDocuments, err := query.RAG(userQuery, tenantID)

	if err != nil {
		fmt.Printf("Query error: %v\n", err)
		return
	}

	// 2. Output the result
	fmt.Println("\n--- AI Answer ---")
	fmt.Println(answer)
	
	// 3. Critically, output the source documents for verifiability
	fmt.Println("\n--- Source Documents (Evidence) ---")
	for _, source := range sourceDocuments {
		fmt.Printf("- Chunk from: %s (Score: %.2f)\n", source.DocumentName, source.Score)
	}
}

This framework allows you to focus on the user experience and business logic (like multi-tenancy rules and data sources) while relying on WeKnora to handle the complex, high-performance RAG and LLM plumbing in Go.