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Merge pull request #1846 from oracle-devrel/oci_langflow01
updated README
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ai/generative-ai-service/oci_langflow/README.md

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@@ -7,6 +7,66 @@ This repository contains the code for a prototype of the integration of OCI Gene
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Reviewed: 25.06.2025
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## Components for Retrieval-Augmented Generation (RAG)
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In this release (jan 2025), we have included the essential components required to build a robust Retrieval-Augmented Generation (RAG) solution:
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1. OCI Embeddings Model
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Based on Cohere, this component generates embeddings for your data, enabling effective search and retrieval.
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2. OCI Chat Model
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Powered by Meta or Cohere models, this component handles conversational AI tasks for natural and engaging user interactions.
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3. OCI Vector Store
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Built on Oracle Database 23AI, this component provides a highly efficient and scalable vector store for managing and querying embeddings.
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## Langflow setup
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To setup an environment with Langflow this is the recommended sequence of steps:
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1. Create and activate a "conda environment" using Python 3.11
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```
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conda create -n oci_langflow python==3.11
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conda activate oci_langflow
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```
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2. Install the tool [uv](https://docs.astral.sh/uv/getting-started/) in this environment, using pip
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3. Install Langflow using uv. Follow the instructions [here](https://docs.langflow.org/get-started-installation)
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```
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uv pip install langflow
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```
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4. Install oci Python sdk and update langchain-community
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```
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pip install oci -U
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```
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## Setup your environment for OCI
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* Clone the github repository
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```
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git clone https://github.com/luigisaetta/oci_langflow.git
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```
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* modify the `set_env.sh` file. Change the env variable `LANGFLOW_COMPONENTS_PATH` to point your local **oci_custom** directory
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* execute the following command to set the environment variables:
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```
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source ./set_env.sh
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```
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* start langflow using the command:
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```
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uv run langflow run
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```
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## Notes
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### Setup: do not install Langflow using directly pip, use uv.
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There are many components and pip really struggle to manage them correctly.
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As a result the installation is really slow.
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The preferred way to do (as suggested also in the official documentation) is to use
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uv.
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### The **Philosophy**
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The main idea is to provide the code as an easy way that can be used from people.
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Then, if they want, they can add all the details they really need.
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Therefore, for example, wrappers expose only the mandatory parameters that needs to be customized.
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For the remaining:
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1. I have followed Langchain defaults.
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2. The code is simple and easy to be customized.
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## Security
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Please consult the [security guide](./SECURITY.md) for our responsible security
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vulnerability disclosure process.

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