Mastering Agentic RAG Flows with LangGraph: Building Intelligent Retrieval Systems Across Multiple Data Sources

Why build agentic flows in LangGraph

A simple RAG implementation might work for isolated use cases, but in real-world scenarios, users often ask about various data sources in different formats. For example, they may ask for information stored in PDFs, internal databases, or web services. A well-architected RAG flow allows for:

1. Handling Multiple Intents: Users could ask for general information, query metadata, or require specific data from a SQL database. A single approach won’t work for all intents.

2. Multi-format Data Access: Organizations often store their data in several formats (PDFs, PowerPoints, intranet files like SharePoint, etc.). A robust RAG flow allows seamless access to all these sources.

3. Minimizing Hallucinations: Large language models (LLMs) tend to generate “hallucinations” or false information. A ReAct agent model integrated with RAG allows the system to retrieve relevant data, perform actions, and reason, minimizing the chances of incorrect information.

How to Build a RAG Flow

1. Define Intent Detection

First, the system needs to understand the user’s intent, which defines what type of operation should be triggered. Some examples include:

• Greetings: No search is needed, just a basic LLM response.

• Metadata Query: A search over metadata (e.g., how many documents a person created in the last year).

• Specific Question: A direct query that requires retrieving and analyzing the actual content.

2. Split Complex Queries

Users often combine multiple questions in a single query. The RAG flow must be capable of splitting these into individual questions and treating them as separate tasks. For example, if a user asks about the weather and about sales data, these two questions are handled differently — one calls a weather API, and the other runs a SQL query.

3. Retrieve Data from Multiple Sources

A key part of the RAG model is the retrieval phase, where relevant documents or data are fetched from different sources. For example:

• Perform a vector search in a document store (like PDFs, Word documents).

• Retrieve structured data from a SQL database.

• Search online or within an organization’s internal wiki (e.g., Confluence or SharePoint).

4. Context Processing and Reranking

Once the relevant documents are retrieved, they are often reranked based on their relevance to the query. This step ensures that the most relevant pieces of data are passed to the language model to generate an accurate response.

5. Generate and Deliver the Answer

After gathering the relevant context, the system generates an answer using an LLM. For simple questions, it can directly respond. For more complex queries requiring external data, the LLM uses the provided context to form a well-supported answer.

6. Citing Sources

Transparency is key in any AI-driven system. After the answer is generated, the system should provide citations for the sources used to generate the response. This not only builds trust but also allows users to verify the information.

7. Handle SQL Queries and APIs

When questions require data from structured sources like SQL databases or APIs, the system calls the relevant agents. For example, a user might ask about specific company metrics, which would trigger a SQL query to the database, retrieving the data and integrating it into the LLM response.

Example Use Case

Imagine a user asking, “How many clients ordered a product yesterday, and what’s the weather like in San Francisco today?” This would trigger two different paths in the RAG flow:

1. Weather Query: The system calls a weather API.

2. Order Query: It runs an SQL query to retrieve the sales data.

Once both answers are collected, they are combined and presented to the user.

Tools You Can Use

• LangGraph: This open-source framework allows you to visually define the flow of your RAG system using flowcharts, making it easier to manage complex interactions.

• LangGraph Studio: A tool for stepping through the flow and debugging by inspecting the data as it passes through each node.

Final Thoughts

By creating a well-structured RAG flow with intelligent agents and the ability to handle various data sources, you can build a robust system that provides accurate answers to complex questions. This type of system is especially useful in enterprise settings where data is stored in multiple formats and users need reliable answers from various data points.

If you want more details, check out the full guide on how I implemented this on our Medium page!