Hey everyone! 👋 I've been experimenting with connecting DeepSeek Chat to our internal company wiki to help our team generate RFP responses faster, and wanted to share the workflow since it's been a game-changer for our pre-sales engineers.
The basic idea is to use DeepSeek's API alongside a simple Python script that fetches relevant documentation from our Confluence instance. We use semantic search to pull the most relevant wiki pages based on the RFP question, then feed that context into DeepSeek for drafting. Here's the core part of our integration script:
```python
import requests
import json
def query_wiki(search_term):
# Your wiki's search API endpoint
params = {'query': search_term, 'limit': 3}
response = requests.get(WIKI_API_URL, params=params)
return extract_content(response.json())
def generate_rfp_response(question, wiki_context):
prompt = f"""
Based on the following company documentation:
{wiki_context}
Draft a professional response to this RFP question:
{question}
Focus on accuracy and reference specific features mentioned in the docs.
"""
# DeepSeek API call
messages = [{"role": "user", "content": prompt}]
response = requests.post(
'https://api.deepseek.com/chat/completions',
headers={'Authorization': f'Bearer {API_KEY}'},
json={'model': 'deepseek-chat', 'messages': messages}
)
return response.json()['choices'][0]['message']['content']
```
Key benefits we've observed:
- **Consistency**: All responses reference the same source material, avoiding contradictory statements
- **Speed**: Cuts initial draft time from hours to minutes
- **Accuracy**: Reduces "made up" features that engineers later have to correct
- **Traceability**: We log which wiki pages were referenced for each answer
The biggest challenge was tuning the search to fetch *relevant* context—too broad and the response gets generic, too narrow and it misses important details. We ended up using a hybrid approach:
1. Keyword extraction from the RFP question
2. Semantic search via our wiki's built-in search API
3. A fallback to manual topic tagging for critical sections
Has anyone else tried similar integrations with their internal knowledge bases? I'm curious about:
- How you handle hallucination risks with technical specifications
- Whether you've built review workflows into the process
- If you're using other LLMs for comparison
For teams heavy on RFPs, this connection to live documentation feels like having a super-organized colleague who *actually* updates the wiki! 🦉