# How to Build a Multi-Agent RAG System Using LangChain and Oracle AI Database

> Build a multi-agent RAG system with LangChain and Oracle AI Database. Use OraDBVectorStore, OracleSession, and RagEnsemble for powerful, ACID-compliant retrieval, memory, and agent coordination.

- Repository: [Oracle Developers/oracle-ai-developer-hub](https://github.com/oracle-devrel/oracle-ai-developer-hub)
- Tags: how-to-guide
- Published: 2026-05-10

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**You can build a production-ready multi-agent RAG system using LangChain and Oracle AI Database by leveraging `OraDBVectorStore` for ACID-compliant vector storage, `OracleSession` for persistent conversation memory, and `RagEnsemble` to coordinate specialized agents that retrieve, reason, and synthesize responses.**

The `oracle-devrel/oracle-ai-developer-hub` repository provides a complete reference implementation demonstrating how to build a multi-agent RAG system using LangChain and Oracle AI Database. This architecture unifies vector embeddings, relational metadata, and conversational state within a single Oracle database instance, eliminating the complexity of managing separate vector databases and ephemeral memory stores.

## Core Architecture Components

### OraDBVectorStore for Vector Storage

The `OraDBVectorStore` class in [`apps/agentic_rag/src/OraDBVectorStore.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/OraDBVectorStore.py) serves as the primary interface between LangChain agents and the Oracle AI Database. It manages four distinct collections—`PDFCOLLECTION`, `WEBCOLLECTION`, `REPOCOLLECTION`, and `GENERALCOLLECTION`—each optimized for specific document types. Unlike external vector services, this implementation stores embeddings directly in Oracle tables and executes Euclidean similarity search via the native `OracleVS` engine, ensuring ACID consistency between your vector index and relational metadata.

### OracleSession for Persistent Memory

Located in [`apps/agentic_rag/src/oracle_session.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/oracle_session.py) (documented in [`workshops/from_rag_to_agents_workshop/docs/part-8-session-memory.md`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/workshops/from_rag_to_agents_workshop/docs/part-8-session-memory.md)), the `OracleSession` adapter implements LangChain's async session-memory interface using JSON CLOBs stored in a `chat_history` table. This design survives container restarts and allows SQL-based analytics on conversation logs. Key methods include `get_items()` for retrieving history, `add_items()` for persisting turns, `pop_item()` for token-budget trimming, and `clear_session()` for full resets without affecting the vector index.

### LocalRagAgent as the LLM Interface

The `LocalRagAgent` class in [`apps/agentic_rag/src/local_rag_agent.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/local_rag_agent.py) provides a `ChatOpenAI`-compatible wrapper that binds together the vector store, session memory, and language model. This abstraction allows you to swap LLM providers (OpenAI, Azure, Ollama) without modifying downstream agent logic, as the agent exposes a standard `run()` method that internally handles retrieval-augmented generation.

### RagEnsemble for Multi-Agent Orchestration

The `RagEnsemble` class in [`apps/agentic_rag/src/reasoning/rag_ensemble.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/reasoning/rag_ensemble.py) enables sophisticated multi-agent pipelines by orchestrating specialized agents—such as a "retriever" agent for vector search, a "reasoner" agent for synthesis, and a "memory" agent for context management. It merges outputs from parallel agent executions into a single coherent response, supporting patterns like "search → verify → synthesize."

## Step-by-Step Implementation Guide

### Initialize the Vector Store and Session

Begin by instantiating the vector store and persistent session. The vector store automatically loads database credentials from [`config.yaml`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/config.yaml) via [`db_utils.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/db_utils.py).

```python
from apps.agentic_rag.src.OraDBVectorStore import OraDBVectorStore
from apps.agentic_rag.src.local_rag_agent import LocalRagAgent
from apps.agentic_rag.src.oracle_session import OracleSession

# Vector store with automatic configuration loading

vector_store = OraDBVectorStore()

# Persistent session for conversation state

session = OracleSession(
    session_id="user123", 
    connection=vector_store.connection
)

# LLM wrapper compatible with LangChain interfaces

rag_agent = LocalRagAgent(
    vector_store=vector_store,
    session=session,
    llm_name="gpt-4o-mini"
)

```

### Ingest Documents into Oracle AI Database

Use the `Chunker` utility and collection-specific methods to embed and store documents. The system supports PDFs, web pages, and source code repositories.

```python
from apps.agentic_rag.src.research.chunker import Chunker
from apps.agentic_rag.src.research.pdf_loader import PDFLoader

# Load and chunk a research paper

pdf_path = "data/research_paper.pdf"
chunks = Chunker.from_loader(PDFLoader(pdf_path)).to_dicts()

# Store in PDFCOLLECTION with metadata

vector_store.add_pdf_chunks(chunks, document_id="paper001")

```

### Execute Single-Turn Queries with Memory

The `run()` method automatically retrieves relevant chunks from the appropriate collection and injects conversation history from `OracleSession` into the LLM prompt.

```python
user_question = "What are the main challenges of RAG systems?"

# Internal flow:

# 1. session.get_items() → retrieves prior context

# 2. vector_store.query_pdf_collection(user_question) → fetches top-k chunks

# 3. LLM generates answer using retrieved chunks + history

answer = rag_agent.run(user_question)
print(answer)

```

### Deploy Multi-Agent Ensembles

For complex reasoning tasks, use `RagEnsemble` to coordinate multiple specialized agents that operate in parallel over the same vector store and session.

```python
from apps.agentic_rag.src.reasoning.rag_ensemble import RagEnsemble

ensemble = RagEnsemble(
    vector_store=vector_store,
    session=session,
    agents=[
        {"name": "retriever", "type": "retrieval"},
        {"name": "reasoner", "type": "reasoning"},
        {"name": "memory", "type": "memory"}
    ]
)

final_response = ensemble.run("Explain how vector search works in Oracle AI DB")
print(final_response)

```

### Manage Session Memory Programmatically

Control conversation context using the session adapter's trimming and reset capabilities.

```python

# Remove the most recent turn to manage token budgets

removed_item = await session.pop_item(limit=1)
print("Forgot:", removed_item)

# Reset the entire conversation thread

await session.clear_session()

```

## Why Use Oracle AI Database for Multi-Agent RAG?

**Single Source of Truth**: Both vector embeddings and session state reside in the same relational engine, enabling complex SQL queries that join conversation logs with document metadata.

**Native Vector Search**: The `OracleVS` implementation performs Euclidean similarity search directly inside the database, eliminating network latency and synchronization issues common with external vector services.

**Durable Memory**: Unlike in-memory LangChain buffers, the CLOB-based `chat_history` table persists across application restarts and supports horizontal scaling across multiple agent instances.

**Fine-Grained Control**: Methods like `pop_item()` and `clear_session()` allow precise token-budget management and session lifecycle control without requiring vector index rebuilds.

## Summary

- **Unified Storage**: The `OraDBVectorStore` class stores document chunks and embeddings in Oracle AI Database tables, providing ACID-compliant vector search across four specialized collections.
- **Persistent Memory**: `OracleSession` stores conversation history as JSON CLOBs, enabling durable agent memory that survives restarts and supports SQL analytics.
- **Modular Agents**: `LocalRagAgent` provides a LangChain-compatible interface for LLM interaction, while `RagEnsemble` coordinates multiple specialized agents for complex reasoning pipelines.
- **Production Ready**: The architecture supports custom LLM providers, hybrid retrieval patterns, and dynamic session management suitable for enterprise multi-agent deployments.

## Frequently Asked Questions

### What is the advantage of using Oracle AI Database over separate vector databases?

Using Oracle AI Database eliminates the need to synchronize data between a relational database and an external vector store. According to the `oracle-devrel/oracle-ai-developer-hub` source code, storing both embeddings and session memory in Oracle allows you to execute SQL queries that join vector search results with relational metadata, while maintaining ACID consistency across your entire RAG pipeline.

### How does session memory persistence work in this architecture?

The `OracleSession` adapter stores conversation items as JSON CLOBs in a dedicated `chat_history` table, as implemented in [`apps/agentic_rag/src/oracle_session.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/oracle_session.py). Unlike standard LangChain memory buffers that store data in RAM, this approach persists conversation state to disk, allowing agents to resume threads after application restarts and enabling administrators to audit interactions via standard SQL queries.

### Can I integrate custom LLM providers other than OpenAI?

Yes. The `LocalRagAgent` class in [`apps/agentic_rag/src/local_rag_agent.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/local_rag_agent.py) abstracts the LLM behind a LangChain-compatible interface. You can replace the default `ChatOpenAI` configuration with any LangChain-supported provider—such as `AzureOpenAI`, `Ollama`, or custom endpoints—by modifying the `llm_name` parameter or extending the agent's initialization logic.

### How do I extend the system to handle new document types?

To add support for new document collections, modify [`apps/agentic_rag/src/OraDBVectorStore.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/apps/agentic_rag/src/OraDBVectorStore.py) to define a new entry in the `collections` dictionary and implement a corresponding `add_<name>_chunks` method. The existing architecture in [`rag_ensemble.py`](https://github.com/oracle-devrel/oracle-ai-developer-hub/blob/main/rag_ensemble.py) will automatically recognize new collections, allowing agents to query them via methods like `query_custom_collection()` following the same pattern used for `query_pdf_collection()`.