Knowledge DB and Chatbot and Helpdesk self-service
Intro¶
If you wanna manage knowledge in our company, so that
- the Role Role: Customer
in a App: Helpdesk
can- use Capability:search and find content at anytime
- use a Capability:chatbot and ask questions
- the Role Role: Support employee
in a App: knowledgeDB
can- add content as Template: runbook and Template: FAQ to Capability: KnowledgeDB, as part of
"ITILs Closure: Once the incident is resolved and the service is restored, the incident is closed. "
- add content as Template: runbook and Template: FAQ to Capability: KnowledgeDB, as part of
- the Role Role: Developer
in an App: knowledgeDB
can- add documentation in Template:service_markdown
Option1: Handcrafted solution. use Confluence as knowledgeDB¶
One can manage the knowledge data in Confluence.
But the extraction of the unnecessarily complex.
- Thats the path only for the Capability:chatbot
- The problem is - with the unstructured content on confluence.
https://www.youtube.com/watch?v=zjdFOwBxBRo
Option2: Handcrafted solution. Use Git as Knoledge DB¶
One can manage the knowledge data in Git, in Markdown.
This is how to works
Configure¶
- choose text-embedding-ada-002 is a text embedding model developed by OpenAI. It's designed to convert text into numerical representations (vectors) that capture the semantic meaning of the text. These vectors can then be used for various natural language processing tasks, such as search and chat - choose a vector DB "Weaviate" to store the data, ready for queries
Load and Split documents and generate embeddings and store¶
- use Unstructured library implemented in LangChain.
LangChain facilitating interaction between LLMs and the environment
- the result is stored in vector DB Weaviate
#### Query the LLM usingthe vector DB - use AzureOpenAI to query
GUI¶
- use "Azure AI Bot Service" https://learn.microsoft.com/en-us/azure/bot-service/bot-service-design-pattern-embed-web-site?view=azure-bot-service-4.0 - Embed bot into the documentation page https://github.com/squidfunk/mkdocs-material/issues/1804 - Connect a bot to Web Chat https://learn.microsoft.com/de-de/azure/bot-service/bot-service-channel-connect-webchat?view=azure-bot-service-4.0#get-your-bot-secret-key
Advantage: the linting of the markdown gives one the quality assurance
# Runbook Title
## Overview
Provide a brief description of the runbook's purpose and scope.
## Prerequisites
List any prerequisites or requirements needed before executing the runbook.
## Steps
1. **Step 1: Description**
- Detailed instructions for Step 1.
- Include any necessary commands or code snippets.
2. **Step 2: Description**
- Detailed instructions for Step 2.
- Include any necessary commands or code snippets.
3. **Step 3: Description**
- Detailed instructions for Step 3.
- Include any necessary commands or code snippets.
## Troubleshooting
Provide common issues and their solutions.
## Contact Information
List the contact information for support or escalation.
## Version Control
- **Version:** 1.0
- **Last Updated:** YYYY-MM-DD
- **Updated By:** Name
## References
Include any references or links to additional resources.
Cost: Free to low, for AI queries. Like 15$ for 1 Million tokens https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
Option3: Some Buy solution like zendesk.de¶
- Messaging and live chat
- AI and automation
- Data privacy and protection
- Help center
- Agent workspace
- VoiceNew
- Workforce engagement
Disadvantage: data lives outside the company
Cost: 55EUR per 2nd level per Month. like 3000 EUR yearly https://www.zendesk.de/pricing/
Option4: Use the "Gemini kill RAG" alternative¶
https://www.linkedin.com/feed/update/urn:li:activity:7295488197940166656/But teh DB is still needed
``` “what’s the lifespan of the context cache”? As far as I can tell, it’s only a few hours at best. ````
But there is a solution, which actually replaces the load, split, embed part
- Use Gemini 2.0 Flash to convert PDF pages directly into chunked text.
- Store chunks in KDB.AI for vector search.
- Tie it all together in a RAG workflow.