Building a RAG Pipeline with Gemini

LLMs are great at language. They're terrible at knowing what happened last week, what's in your private documents, or what your company's actual product does. The model's knowledge ends at training cutoff, and outside that envelope it will hallucinate confidently. Ask a base model about your latest product specs or a client's recent project and you'll get something that sounds right but isn't.

That's the gap RAG fills. Retrieval-Augmented Generation lets the model look things up before it answers. In this post I'll walk through what RAG is, why it matters, and how Gemini's File Search API takes most of the infrastructure pain out of building one — using a resume-parsing example because it's a clean, common use case.

The catch with bare LLMs

The static training set is the problem. The model learned from a snapshot of public text and that's all it knows. For most enterprise work, that's not enough:

Hallucinations. Without grounding, the model will produce confident-but-wrong answers. Unacceptable for anything operational.
Stale information. The model trained last year doesn't know this quarter's numbers, this week's product launch, or the regulatory change that landed last month.
No domain knowledge. Internal terminology, policies, product details — the model has never seen them.
No verifiability. When the model answers, it can't point at a source. Users can't fact-check it.

If you want to deploy an LLM against your own data, you need a way to bridge that gap. RAG is the cleanest answer most teams settle on.

What RAG actually is

RAG is an AI framework that pairs an LLM with an external, up-to-date data source. The original paper came out of Meta in 2020. The pattern: let the model "look up" relevant information from a designated knowledge base before it generates a response. Open-book exam, not closed-book.

The standard RAG flow:

User query. Someone asks a question.
Retrieval. The system consults an external knowledge base, typically indexed with vector embeddings, and pulls the most relevant documents or passages. Semantic search instead of keyword matching, so the retriever understands what the query means rather than just what tokens it contains.
Augmentation. Retrieved chunks get appended to the original query, building an "augmented prompt" with the specific context the model would never have known on its own.
Generation. The model receives the augmented prompt and answers, grounded in the supplied context.

That flow isn't abstract — this blog runs it. Below are recorded runs of a small RAG agent that answers questions about latentSource by calling the site's own search endpoint as a tool, then grounding its answer in what actually came back. Press play:

A real ReAct loop: the agent calls blog_search against the live /api/search endpoint, reads the retrieved articles (expand the tool result), reasons about them, and finishes with an answer that cites real articles by title and link. No retrieved article, no claim — that's the whole point of retrieval augmentation.

The benefits, when you do it right:

Fewer hallucinations. The model has actual sources to ground in. Studies have measured RAG-grounded responses as roughly 43% more accurate than fine-tuned-only equivalents.
Up-to-date answers. The retriever pulls from a living source. No retraining needed when data changes.
Domain coverage. Drop in your proprietary data and the model can answer about it.
Verifiability. Cite the source documents in the response and users can check the work.
Cheaper than fine-tuning. Especially when the underlying data changes often.
More control. You own the retriever and decide what the model is allowed to ground in.

Gemini File Search API

Building RAG from scratch means running an ingestion pipeline, chunking documents, generating embeddings, hosting a vector database, and tying it all to your model. That's a lot of pieces. Gemini's File Search API is Google's managed version — RAG built into the Gemini API, with most of the infrastructure abstracted away.

What it handles for you:

Storage. Files go in. The API stores them.
Chunking. It breaks documents into digestible pieces with reasonable defaults.
Embedding. Gemini's embedding models turn each chunk into a vector.
Vector search. The embeddings live in a managed vector database. No deployment, no scaling.
Format coverage. PDF, DOCX, TXT, JSON, and most common code file types.
Citations. Responses can include citations back to the source chunks.
Pricing. Storage and query-time embedding are free. You pay for the initial indexing — roughly $0.15 per million tokens at the time of writing.

The API plugs into the existing generateContent endpoint, so adopting it doesn't mean changing your generation code much.

Worked example: parsing resumes

Resumes are the classic case. Unstructured, proprietary, varied formatting. Manually extracting skills, experience, and dates is slow and error-prone.

Step 1 — ingestion. Upload resumes to a File Search Store. The API parses, chunks, embeds, and indexes.

import google.generativeai as genai
import time

# Initialize the client (ensure you have configured your API key)
# genai.configure(api_key="YOUR_API_KEY")

client = genai.Client()

# Create a File Search store
# This store will hold the embeddings of your resume documents
file_search_store = client.file_search_stores.create(
    config={'display_name': 'Resume_Data_Store'}
)
print(f"File Search Store created: {file_search_store.name}")

# Example: Uploading a resume file (replace 'resume.pdf' with your actual file)
# In a real scenario, you'd loop through multiple resume files
file_path = 'path/to/your/resume.pdf' # Placeholder
operation = client.file_search_stores.upload_to_file_search_store(
    file=file_path,
    file_search_store_name=file_search_store.name,
    config={
        'display_name': 'John_Doe_Resume',
    }
)

print(f"Uploading file '{file_path}' to store...")
while not operation.done:
    time.sleep(5) # Wait for the upload and indexing to complete
    operation = client.operations.get(operation)

print(f"File uploaded and indexed. Operation status: {operation.status}")

Step 2 — semantic search and extraction. Once the resumes are indexed, a recruiter (or an automated system) can ask things like "Find candidates with Python and Machine Learning experience who graduated after 2020" or "Summarize John Doe's work experience." Gemini, with the File Search tool enabled, runs a semantic search across the store, pulls the relevant chunks, and answers grounded in them.

# After files are uploaded and indexed, you can query
prompt = "Summarize the key skills and last two work experiences of candidates in the Resume_Data_Store."

response = client.models.generate_content(
    model="gemini-1.5-flash-latest", # Or another appropriate Gemini model
    contents=prompt,
    config=genai.types.GenerateContentConfig(
        tools=[
            genai.types.Tool(
                file_search=genai.types.FileSearch(
                    file_search_store_names=[file_search_store.name]
                )
            )
        ]
    )
)

print("\nGenerated Response:")
print(response.text)

# For better grounding, the response often includes citations
if response.candidates and response.candidates[0].grounding_metadata:
    print("\nCitations:")
    for citation in response.candidates[0].grounding_metadata.citations:
        print(f"- {citation.uri} (start_index: {citation.start_index}, end_index: {citation.end_index})")

# In a real application, you would parse the response to extract structured data.
# For example, you might instruct the LLM to output skills as a list or JSON.

Step 3 — application integration. From there, you take what the API returned and feed it into whatever system needs it: a candidate database, a recruiter UI, an internal chatbot. The hard text extraction is done. The structure-from-unstructured problem is mostly solved.

The result is a searchable, filterable representation of what was previously a stack of unparseable PDFs. Faster to triage, more accurate than manual extraction, and the index keeps working as new resumes come in.

Conclusion

RAG breaks LLMs out of the static training set. Combine retrieval with generation and the model can ground its answers in real, current, proprietary information — which is the difference between a demo and something that actually works for an enterprise.

Tools like the Gemini File Search API take most of the infrastructure work off the table. You skip running your own vector database, you skip writing chunking logic, you skip the embedding pipeline. What you get to focus on is application logic: what to ask, how to use the answer, how to cite the source. For organizations sitting on internal data they want to put in front of an LLM, this is the most direct path I've found. Worth trying on your own data before you build anything from scratch.