Use self deployed Vector DB for langflow

[Preet1234-ux]

I have deployed a vector DB and langflow on Kubernetes platform . I want to use my deployed vector DB in my flow . How to achieve this ?

[dosubot[bot]]

To use your self-deployed vector database with Langflow on Kubernetes, follow these steps:

1. Deploy OpenAI Embeddings Component: This component generates embeddings and requires an OpenAI API key. Configure it with the necessary parameters, such as model and openai_api_key.

2. Configure VectaraComponent: Essential for integrating Vectara with Langflow. Set it up with your Vectara details (vectara_customer_id, vectara_corpus_id, vectara_api_key). You can also configure documents for direct corpus upserts or files_url for creating Vectara objects from file URLs.

3. Kubernetes Deployment:

   • Containerize Langflow: Ensure Langflow is containerized. Create a Docker image with all dependencies and configurations.
   • Create Kubernetes Deployment: Write a deployment YAML file for your Langflow Docker image, including Vectara configuration as environment variables.
   • Expose Langflow: Use a Kubernetes service to make your Langflow application accessible.

4. Run and Test: Deploy with kubectl apply -f your_deployment.yaml and test the setup to ensure Langflow can interact with OpenAI and Vectara as expected.

Secure your API keys using Kubernetes secrets and adjust Kubernetes resource limits according to your workload for optimal performance. For more details on VectaraComponent configuration, refer to the VectorStore-RAG-Flows.json and Vectara.py files.

Details

Sources

• src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
• src/backend/base/langflow/components/vectorstores/AstraDB.py
• src/backend/langflow/components/vectorstores/Vectara.py
• render.yaml
• src/backend/base/langflow/components/vectorstores/init.py
• src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py
• src/backend/base/langflow/services/settings/constants.py
• src/backend/base/langflow/services/settings/base.py
• .env.example
• deploy/.env.example

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[Preet1234-ux]

@dosu

I think you didn't understand my question . My query was :
I have deployed Vector DB Milvus on kubernetes . I have deployed langflow also .
Now I want to use my deployed vector DB milvus in my langflow pipeline . I have attached my flow as well . Currently I am using langflow provided fiass DB which I want to replace with my deployed vector DB Milvus . How to achieve this ?

[dosubot[bot]]

To integrate your Milvus vector database with Langflow deployed on Kubernetes, replacing the default Fiass DB, follow these steps:

1. Ensure Milvus Accessibility: Your Milvus instance must be accessible from within your Kubernetes cluster. This involves setting up internal networking or ensuring Milvus is reachable over the network with proper security and access controls.

2. Adjust Configuration:

   • Locate Langflow's configuration that specifies the vector database connection. This could be in a config.yaml, .env, or directly in the application's source code.
   • Update this configuration to point to your Milvus instance, including host, port, and authentication details.

3. Modify Langflow for Milvus:

   • Langflow's codebase may need adjustments to interact with Milvus' API or client library, especially if it's tightly coupled with Fiass. Modify the parts of Langflow that handle vector database operations to use Milvus' client methods.
   • Ensure Milvus' data schema or indexing specifics are accommodated in these modifications.

4. Update Dependencies:

   • Add Milvus' client library as a dependency in Langflow's project. This typically involves modifying a requirements.txt file for Python projects or a similar dependency management file.

5. Testing:

   • Thoroughly test the integration in a development or staging environment to ensure Langflow can successfully connect to Milvus and perform vector operations as expected.

6. Deployment:

   • Deploy the updated Langflow application to your Kubernetes cluster. This may involve rebuilding Docker images and updating Kubernetes manifests.

7. Monitor and Optimize:

   • After deployment, monitor the application's performance and stability, optimizing the configuration as needed based on operational load and usage patterns.

This approach requires familiarity with Langflow's architecture and possibly modifying its source code for seamless integration with Milvus.

Details

Sources

• render.yaml
• src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
• src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
• src/backend/base/langflow/services/settings/base.py
• docker_example/docker-compose.yml
• .env.example
• src/backend/base/langflow/components/vectorstores/init.py
• src/backend/base/langflow/components/vectorstores/AstraDB.py
• src/backend/langflow/components/vectorstores/FAISS.py
• src/backend/base/langflow/components/vectorstores/FAISS.py
• src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py
• src/frontend/package.json

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[Preet1234-ux]

@dosu

Thanks for the details . Can you please provide me the code and the changes that I need to make to use milvus DB in langflow ?

[dosubot[bot]]

To integrate Milvus DB with Langflow, follow these steps:

1. Dependency Installation: Add pymilvus (compatible with your Milvus version) to your project's dependencies. For example, for version 2.0.0rc7, use:

   pip install pymilvus==2.0.0rc7

2. Configuration Update: Replace the existing vector database connection details in Langflow's configuration with those of your Milvus instance, including host, port, and collection name.

3. Codebase Adjustments: Modify Langflow's source code to use Milvus' API for vector operations. This involves changing the methods for connecting to, inserting into, and querying from Milvus collections, utilizing the pymilvus client.

4. Data Schema and Indexing: Adjust your data schema and indexing in Langflow to align with Milvus' requirements, including defining vector fields and dimensions, and configuring the index type and parameters.

5. Data Ingestion and Querying Modifications: Update the logic for data ingestion and querying in Langflow to interact with Milvus instead of the default database, ensuring that the RAG flow can retrieve and generate responses based on vectors stored in Milvus.

6. Environment Variables Update: Update all references and environment variables in Langflow from the previous database configuration to Milvus.

7. Testing: Thoroughly test the updated Langflow application to ensure it can successfully interact with Milvus for vector storage and retrieval operations.

For specific code changes, you'll need to modify components like AstraDBVectorStoreComponent to connect to Milvus and adapt data ingestion and querying logic accordingly. The exact modifications will depend on your Langflow and Milvus setup. Always refer to the latest Milvus documentation for accurate and up-to-date information on using the pymilvus client.

Details

Sources

• src/backend/base/langflow/components/vectorstores/AstraDB.py
• src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
• src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.