How to Intermediate · 3 min read

How to use DeepSeek for RAG

Q: How to use DeepSeek for RAG

Use DeepSeek's deepseek-chat model with a vector database to implement Retrieval-Augmented Generation (RAG). Query your vector store for relevant documents, then pass those as context in the messages parameter to client.chat.completions.create for informed responses.

Quick answer

Use DeepSeek's deepseek-chat model with a vector database to implement Retrieval-Augmented Generation (RAG). Query your vector store for relevant documents, then pass those as context in the messages parameter to client.chat.completions.create for informed responses.

PREREQUISITES

Python 3.8+
DeepSeek API key
pip install openai>=1.0
A vector store like FAISS or Chroma installed and populated

Setup

Install the OpenAI-compatible openai Python package and set your DeepSeek API key as an environment variable.

Install the package: pip install openai
Set environment variable in your shell: export DEEPSEEK_API_KEY='your_api_key'

bash

pip install openai

Step by step

This example shows how to query a vector store for relevant documents, then use deepseek-chat to generate an answer augmented by those documents.

python

import os
from openai import OpenAI

# Initialize DeepSeek client with base_url
client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"], base_url="https://api.deepseek.com")

# Example: retrieved documents from your vector store (mocked here)
docs = [
    "DeepSeek is a Chinese AI company providing large language models.",
    "RAG combines retrieval with generation for better context-aware answers."
]

# Construct context prompt with retrieved docs
context = "\n\n".join(docs)

# User question
user_question = "What is DeepSeek and how does RAG work?"

# Prepare messages with context
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {user_question}"}
]

# Call DeepSeek chat completion
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages,
    max_tokens=512
)

print(response.choices[0].message.content)

output

DeepSeek is a Chinese AI company that provides large language models. Retrieval-Augmented Generation (RAG) combines document retrieval with language generation to produce context-aware and accurate answers by leveraging relevant external information.

Common variations

You can adapt this pattern by:

Using different vector stores like FAISS or Chroma for document retrieval.
Changing the max_tokens or model to deepseek-chat variants if available.
Implementing async calls with asyncio and openai if supported.

Troubleshooting

If you get authentication errors, verify your DEEPSEEK_API_KEY environment variable is set correctly.
If responses are incomplete, increase max_tokens.
For connection issues, check your network and DeepSeek API status.

✅

Key Takeaways

Use DeepSeek's deepseek-chat model with a vector store to implement RAG effectively.
Pass retrieved documents as context in the messages parameter for informed generation.
Always secure your API key via environment variables and handle errors gracefully.

Verified 2026-04 · deepseek-chat

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