solvedesk-ai 0.3.1

Local RAG framework for building intelligent knowledge bases

SolveDesk AI [https://solvedesk-ai.netlify.app/]

Intelligent knowledge base powered by embeddings, vector search and Retrieval-Augmented Generation (RAG).

Overview

SolveDesk AI is a lightweight open-source framework for building local intelligent knowledge bases. The project provides a command-line interface for creating vector databases, importing documents, generating embeddings, performing semantic search, and integrating with local Large Language Models (LLMs).

Inspired by frameworks such as Laravel and Django, SolveDesk AI simplifies the process of building Retrieval-Augmented Generation (RAG) systems by providing ready-to-use commands and a modular architecture.

The framework can be used both as a production-ready knowledge base solution and as an educational platform for learning modern AI technologies, vector databases, embeddings, and semantic retrieval.

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Features

• Local knowledge base creation
• Semantic document search
• Retrieval-Augmented Generation (RAG)
• Vector database management
• Embedding generation
• Local LLM integration through Ollama
• Data synchronization from APIs
• CSV, JSON and XLSX import support
• Embedding quality analysis
• Document chunking
• FastAPI integration
• Command-line interface

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Architecture

Documents / API
       │
       ▼
Embedding Model
       │
       ▼
    ChromaDB
       │
       ▼
Semantic Search
       │
       ▼
      LLM
    Ollama
       │
       ▼
 Generated Response

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Technologies

Technology	Purpose
Python 3.11	Application runtime
FastAPI	REST API
ChromaDB	Vector database
silver-retriever-base-v1	Embedding model
Sentence Transformers	Embedding generation
Ollama	Local LLM integration
Matplotlib	Data visualization
Typer	Command-line interface

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Installation

Install solvedesk:

venv\Scripts\activate 
(venv) pip install solvedesk-ai

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CLI Commands

Project Configuration

(venv) C:\path\to\project> solvedesk conf init

[INFO] SolveDesk AI - Project Generator

[INPUT] Project name: Test123
[INPUT] Project description [Local RAG knowledge base]:

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[INFO] Configuration
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[DETAILS] Name        : Test123
[DETAILS] Description : Local RAG knowledge base
[DETAILS] Template    : https://github.com/studiocyfrowe/solvedesk-ai

[CONFIRM] Continue project creation? [y/N]: y

[STATUS] Downloading template...

[STATUS] Project created successfully

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[INFO] Project information
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[DETAILS] Location : C:\path\to\project\Test123
[DETAILS] Name     : Test123
[DETAILS] Description : Local RAG knowledge base

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[INFO] Next steps:

cd Test123
solvedesk db init
solvedesk llm init
solvedesk run:app

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[STATUS] Happy coding!
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Initialize project environment.

Database

Initialize vector database:

(venv) C:\path\to\project> solvedesk db init

[STATUS] Downloading embedding model...
[STATUS] Model downloaded: utils\models\silver-retriever-base-v1
[STATUS] Plik .env already exists - downloading model has been skipped
[STATUS] Created databases directory: utils\databases
[STATUS] Created vector database: utils\databases\default-db
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[SUCCESS] SolveDesk vector DB initialized
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Create vector database and download embedding model.

(venv) C:\path\to\project> solvedesk db init --chroma-dir test12345

[CONFIRM] Download embedding model (ipipan/silver-retriever-v1)? [y/N]: n
[STATUS] Plik .env already exists - downloading model has been skipped

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[STATUS] Created databases directory: utils\databases
[STATUS] Created vector database: utils\databases\test12345
[STATUS] Vector Database is ready!
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[NEXT STEP] Create new collection: solvedesk db new <collection-name>

Display available collections.

(venv) C:\path\to\project> solvedesk db list

test_col | id=235c239e-421b-4b09-95d2-8a81bbafffd3 | documents=0 | metadata={'hnsw:space': 'cosine'}
sd-collection-8780 | id=d857b0a3-27cc-4a67-8463-4d4d075b00dd | documents=0 | metadata={'hnsw:space': 'cosine'}

Create new collection by default

(venv) C:\path\to\project> solvedesk db new
[STATUS] Created new collection: sd-collection-2132

or custom name

(venv) C:\path\to\project> solvedesk db new --collection-name test-collection
[STATUS] Created new collection: test-collection

Delete single collection.

(venv) C:\path\to\project> solvedesk db delete test123
[STATUS] Collection not found: test123

(venv) C:\path\to\project> solvedesk db delete sd-collection-2132
[STATUS] Collection deleted: sd-collection-2132

Display collection details.

(venv) C:\path\to\project> solvedesk db details test-col123

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COLLECTION DETAILS
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Name: test-col123
ID: 4f9a8b8a-9ac9-48be-8f5a-9fe1e20d4551
[STATUS] Documents (count): 0
[STATUS] Metadata: {'hnsw:space': 'cosine'}

[STATUS] No documents.

Switch database

(venv) C:\path\to\project> solvedesk db checkout test
Switched to database: test
CHROMA_DIR=utils\databases\test

Data Synchronization

Remember to prepare data source! Example:

ticketId,ticketName,ticketBody,ticketAnswer
1,Problem z logowaniem,Użytkownik nie może zalogować się do systemu po zmianie hasła.,Należy wyczyścić cache przeglądarki i ponownie ustawić hasło.
2,Błąd płatności,System zwraca błąd 500 podczas finalizacji płatności.,Zweryfikowano integrację z operatorem płatności i zrestartowano usługę.
3,Timeout API,Zapytania do API trwają bardzo długo w godzinach szczytu.,Dodano cache Redis oraz zwiększono liczbę workerów aplikacji.
4,Nieprawidłowe dane,Raport sprzedaży pokazuje błędne wartości dla części zamówień.,Naprawiono mapowanie danych w procesie ETL.
5,Brak powiadomień email,Klienci nie otrzymują wiadomości potwierdzających rejestrację.,Skonfigurowano poprawnie serwer SMTP i kolejkę wiadomości.

(venv) C:\path\to\project> solvedesk sync file "tickets.csv" test133
[ERROR] [ERROR] Unsupported data type. Use: know_base, faq or helpdesk

Names of columns should be specified:

if type == "know-base":
    return (
        ["id", "name", "question", "answer"],
        ["name", "question", "answer"]
    )

if type == "faq":
    return (
        ["id", "question", "answer"],
        ["question", "answer"]
    )

if type == "helpdesk":
    return (
        ["id", "title", "problem", "solution"],
        ["title", "problem", "solution"]
    )

(venv) C:\path\to\project> solvedesk sync file "tickets.csv" test133 --type helpdesk

PREPROCESSING SUMMARY

[STATUS] Raw records: 5
[STATUS] Valid records: 5
[STATUS] Rejected records: 0

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CLEAN FILE PREVIEW

[
  {
    "id": 1,
    "title": "Problem z logowaniem",
    "problem": "Użytkownik nie może zalogować się do systemu po zmianie hasła.",
    "solution": "Należy wyczyścić cache przeglądarki i ponownie ustawić hasło."
  },
  {
    "id": 2,
    "title": "Błąd płatności",
    "problem": "System zwraca błąd 500 podczas finalizacji płatności.",
    "solution": "Zweryfikowano integrację z operatorem płatności i zrestartowano usługę."
  },
  {
    "id": 3,
    "title": "Timeout API",
    "problem": "Zapytania do API trwają bardzo długo w godzinach szczytu.",
    "solution": "Dodano cache Redis oraz zwiększono liczbę workerów aplikacji."
  }
]

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[STATUS] Selected data type: helpdesk
[CONFIRM] Do you want to import this cleaned data? [y/N]: y
[STATUS] Imported documents: 5

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[SUCCESS] Documents has been imported!
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(venv) C:\path\to\project> solvedesk sync api
[INPUT] Input external API URL: http://127.0.0.1:8000/data
[INPUT] Type collection name from your vector database: testapi2
[INPUT] Input token for API: secret-token
[INPUT] Specify data type [know-base]: helpdesk

[STATUS] Starting API synchronization...

[STATUS] Imported documents: 869

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[SUCCESS] API Synchronization completed successfully!
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Data Analysis

(venv) C:\path\to\project> solvedesk data revision testcol --clusters 3

Embedding quality report

Documents count: 5
Embedding size: 768
Mean similarity: 0.8784

Clusters:
Cluster 2: 2 documents
Cluster 0: 2 documents
Cluster 1: 1 documents

Token statistics:
Document 1 | Tokens: 21/512 (4.1%) | Truncated: False | Embedding size: 768
Document 2 | Tokens: 23/512 (4.49%) | Truncated: False | Embedding size: 768
Document 3 | Tokens: 26/512 (5.08%) | Truncated: False | Embedding size: 768
Document 4 | Tokens: 21/512 (4.1%) | Truncated: False | Embedding size: 768
Document 5 | Tokens: 24/512 (4.69%) | Truncated: False | Embedding size: 768

Charts created:
Clusters: reports\clusters_visualization.png
Token limit: reports\token_limit_chart.png
Cosine similarity: reports\cosine_similarity_progress.png

Generate reports containing:

• cosine similarity statistics
• cluster distribution
• token statistics
• PCA visualization

Chunking

solvedesk data chunk

Split large documents into smaller chunks suitable for RAG systems.

LLM Configuration

solvedesk llm init

Configure Ollama host and model.

Run Application

solvedesk run:app

Start FastAPI server.

---

Supported Data Structures

FAQ

{
  "question": "How to reset password?",
  "answer": "Use reset password page."
}

Knowledge Base

{
  "name": "VPN Connection",
  "question": "Cannot connect to VPN",
  "answer": "Verify credentials and VPN client configuration."
}

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Example Workflow

solvedesk conf init
solvedesk db init
solvedesk sync file
solvedesk data revision
solvedesk llm init
solvedesk run:app

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Project Goals

• Build local intelligent knowledge bases
• Simplify RAG implementation
• Support AI experimentation
• Provide full control over data
• Enable local LLM deployments
• Offer educational value for learning AI technologies

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License

Author: Dominik Hofman [https://www.linkedin.com/in/hofmandesign/] MIT License