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To build a retrieval-augmented generation (RAG) system with DeepSeek-R1, pair the model with a separate document pipeline: prepare and chunk your files, embed and index those chunks, retrieve relevant passages for each question, then give the passages to R1 to answer with source citations. R1 handles generation and reasoning; it does not, by itself, search your document collection.
How does a RAG system with DeepSeek-R1 work?
A RAG application has two paths. The indexing path turns your permitted source documents into searchable chunks. The question path finds relevant chunks and supplies them, along with the user’s question, to the language model.
- Ingest: load documents and extract their text.
- Chunk and index: divide text into passages, create an embedding for each passage, and store the vectors with source metadata.
- Retrieve: embed a question and search the index for relevant passages.
- Generate: send the question and selected passages to DeepSeek-R1, then return the answer with citations that point to the original documents.
The model can only ground an answer in material the application actually retrieves and provides. RAG does not guarantee that the retrieved passages are complete or that the generated answer is faithful to them.
Choose how to run DeepSeek-R1
Decide on the inference route before building around a particular model endpoint or serving engine. The right choice depends on your data-handling requirements, infrastructure, operating capacity, and evaluation results.
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| Route | Infrastructure and effort | Key trade-off |
|---|---|---|
| Hosted API | Least model-serving work; your application calls a provider endpoint. | Check that the provider currently offers the exact R1 model you intend to use, and assess its data-handling terms, availability, latency, and usage costs. |
| Distilled checkpoint | Can be more practical for local experiments than the full checkpoint, but still requires a compatible inference setup. | Hardware needs and answer quality depend on the selected model, quantization, context length, serving engine, and workload. Evaluate it on your own questions. |
| Full R1 checkpoint | A demanding self-hosting deployment that needs substantial GPU capacity and model-serving operations. | Offers more direct control over deployment, but requires planning for hardware, concurrency, latency, and ongoing operations. |
DeepSeek-AI’s R1 repository lists the full checkpoint at 671B total parameters, 37B activated parameters, and a 128K context, and lists distilled checkpoints at 1.5B, 7B, 8B, 14B, 32B, and 70B. Those model sizes are not a guarantee that a checkpoint will fit a particular machine or meet a quality target.
For one configuration-specific example, the vLLM recipe describes an eight-H200 setup for its FP8 path and lists 805 GB VRAM minimum for its default FP8 recipe. Treat those as details of that recipe—not universal minimums for every precision, runtime, or serving configuration—and verify the current recipe before planning hardware.
Hosted API considerations
DeepSeek’s current API documentation describes compatibility with OpenAI and Anthropic formats and shows https://api.deepseek.com as the API base URL. The documentation currently demonstrates the model name deepseek-flash; that does not establish that this name is the historical open-weight R1 checkpoint or that an exact R1 variant is available through the API. Confirm the current model catalog and use the provider’s present request format and model name rather than copying older deepseek-reasoner examples without checking them. API offerings and names can change.
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Prepare and index your documents
Ingest text and preserve its source
Load only files your application is authorized to use. Extract text with care: scanned PDFs may need OCR, while tables and structured documents can lose meaning if their layout is flattened. Keep enough metadata on every passage to trace it back to its origin, such as filename, page, section, and document update time.
Access control belongs in the application, not just in the prompt. Apply permissions when selecting documents for a user and when retrieving chunks, so the model is not given passages that user is not allowed to see.
Chunk, embed, and store
Split the extracted text into coherent passages and attach the source metadata to each chunk. Generate a vector embedding for every chunk with an embedding model, then store those vectors and their metadata in a vector index. DeepSeek-R1 is the answer-generation component here; the available sources do not establish a particular R1 embedding model, so select a suitable embedding model separately.
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Chunk size and overlap are design choices to test against your corpus. OpenAI’s Retrieval API guide documents defaults of 800 tokens per chunk and 400 tokens of overlap for that service. These figures describe OpenAI’s defaults, not a universal optimum or a DeepSeek recommendation. If you change your chunking or embedding approach, plan to rebuild the affected index and track which index version the application is using.
Retrieve passages for each question
- Embed the incoming question with the same embedding model used for the document chunks.
- Search the vector index and select the passages most relevant to the question.
- Apply metadata filters for permissions or document categories before passing results to the model.
- Keep each passage’s source identifiers alongside its text so the answer can cite the original file and page or section.
Semantic search may not be enough for exact names, codes, or identifiers. You can evaluate lexical search or a hybrid of lexical and vector search alongside semantic retrieval, but no single hybrid configuration is established as best for every collection.
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Send the model the user’s question and the retrieved passages with a concise instruction to answer from that evidence, cite the supplied source identifiers, and say when the passages do not contain enough information. For example, the instruction can ask the model to:
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- Use the supplied passages as evidence rather than inventing missing details.
- Attach a source identifier to each material claim.
- State that the available passages do not answer the question when evidence is insufficient.
Make citations useful in the product: each one should resolve to the corresponding original document and, where possible, its page or section. During development, inspect whether the selected passages support the answer, whether important evidence was missed, and whether citations point to the right places.
Inference controls depend on the route. DeepSeek’s current API guide documents thinking-mode controls and says temperature has no effect in thinking mode. Separately, the DeepSeek-R1 repository recommends a temperature range of 0.5–0.7 for running the R1 series locally, with 0.6 recommended. These are recommendations for different serving contexts, not interchangeable settings; check the controls supported by your exact model and inference route.
Evaluate the RAG system before production
Build a test set from representative questions people will actually ask and verified answers or supporting sources. Run the same questions against each candidate configuration so changes to retrieval or generation can be compared fairly.
- Retrieval: Did the index return the passages that contain the needed evidence?
- Grounding: Are the answer’s claims supported by those passages?
- Citations: Do the citations resolve to the correct source and location?
- Abstention: Does the system say when the documents do not answer a question?
- Operations: What are the latency and variable costs for the workload you expect?
Compare chunking, embedding, retrieval count, and any reranking settings on this test set. There is no established universally best configuration for an unspecified corpus, and a model-maker benchmark is not a guarantee of RAG quality for your documents.
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