Cognita

Why use Cognita?

Langchain/LlamaIndex provide easy to use abstractions that can be used for quick experimentation and prototyping on jupyter notebooks. But, when things move to production, there are constraints like the components should be modular, easily scalable and extendable. This is where Cognita comes in action. Cognita uses Langchain/Llamaindex under the hood and provides an organisation to your codebase, where each of the RAG component is modular, API driven and easily extendible. Cognita can be used easily in a local setup, at the same time, offers you a production ready environment along with no-code UI support. Cognita also supports incremental indexing by default.

You can try out Cognita at: https://cognita.truefoundry.com

🎉 What's new in Cognita

[September, 2024] Cognita now has AudioParser (https://github.com/fedirz/faster-whisper-server) and VideoParser (AudioParser + MultimodalParser).
[August, 2024] Cognita has now moved to using pydantic v2.
[July, 2024] Introducing model gateway a single file to manage all the models and their configurations.
[June, 2024] Cognita now supports it's own Metadatastore, powered by Prisma and Postgress. You can now use Cognita via UI completely without the need of local.metadata.yaml file. You can create collections, data sources, and index them via UI. This makes it easier to use Cognita without any code changes.
[June, 2024] Added one click local deployment of cognita. You can now run the entire cognita system using docker-compose. This makes it easier to test and develop locally.
[May, 2024] Added support for Embedding and Reranking using Infninty Server. You can now use hosted services for variatey embeddings and reranking services available on huggingface. This reduces the burden on the main cognita system and makes it more scalable.
[May, 2024] Cleaned up requirements for optional package installations for vector dbs, parsers, embedders, and rerankers.
[May, 2024] Conditional docker builds with arguments for optional package installations
[April, 2024] Support for multi-modal vision parser using GPT-4

Cognita
- Why use Cognita?
🎉 What's new in Cognita
Contents
- Introduction
  - Advantages of using Cognita are:
  - Features:
🚀 Quickstart: Running Cognita Locally
- 🐳 Using Docker compose (recommended)
- Cognita from source
⚒️ Project Architecture
💡 Writing your Query Controller (QnA):
- Steps to add your custom Query Controller:
🐳 Quickstart: Deployment with Truefoundry:
- Using the RAG UI:
💖 Open Source Contribution
🔮 Future developments
Star History

Introduction

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are:

Chunking and Embedding Job: The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be triggered via an event to keep the data updated.
Query Service: The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic.
LLM / Embedding Model Deployment: Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API.
Vector DB deployment: Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way.

Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app.

Advantages of using Cognita are:

A central reusable repository of parsers, loaders, embedders and retrievers.
Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team.
Fully API driven - which allows integration with other systems.

If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries.

Features:

Support for multiple document retrievers that use Similarity Search, Query Decompostion, Document Reranking, etc
Support for SOTA OpenSource embeddings and reranking from mixedbread-ai
Support for using LLMs using ollama
Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

🚀 Quickstart: Running Cognita Locally

🐳 Using Docker compose (recommended - version 25+)

Cognita and all of its services can be run using docker-compose. This is the recommended way to run Cognita locally. Install Docker and docker-compose for your system from: Docker Compose

Configuring Model Providers

Before starting the services, we need to configure model providers that we would need for embedding and generating answers.

To start, copy models_config.sample.yaml to models_config.yaml

cp models_config.sample.yaml models_config.yaml

By default, the config has local providers enabled that need infinity and ollama server to run embedding and LLMs locally. However, if you have a OpenAI API Key, you can uncomment the openai provider in models_config.yaml and update OPENAI_API_KEY in compose.env

Now, you can run the following command to start the services:

docker-compose --env-file compose.env up

The compose file uses compose.env file for environment variables. You can modify it as per your needs.
The compose file will start the following services:
- cognita-db - Postgres instance used to store metadata for collections and data sources.
- qdrant-server - Used to start local vector db server.
- cognita-backend - Used to start the FastAPI backend server for Cognita.
- cognita-frontend - Used to start the frontend for Cognita.
Once the services are up, you can access the qdrant server at http://localhost:6333, the backend at http://localhost:8000 and frontend at http://localhost:5001.

To start additional services such as ollama and infinity-server you can run the following command:

docker-compose --env-file compose.env --profile ollama --profile infinity up

This will start additional servers for ollama and infinity-server which can be used for LLM, Embeddings and reranking respectively. You can access the infinity-server at http://localhost:7997.
If you want to build backend / frontend image locally, for e.g when you add new requirements/packages/take a new pull from Github you can add --build flag to the command.

docker-compose --env-file compose.env up --build

docker-compose --env-file compose.env --profile ollama --profile infinity up --build

Developing in Cognita

Docker compose is a great way to run the entire Cognita system locally. Any changes that you make in the backend folder will be automatically reflected in the running backend server. You can test out different APIs and endpoints by making changes in the backend code.

⚒️ Project Architecture

Overall the architecture of Cognita is composed of several entities

Cognita Components:

Data Sources - These are the places that contain your documents to be indexed. Usually these are S3 buckets, databases, TrueFoundry Artifacts or even local disk
Metadata Store - This store contains metadata about the collection themselves. A collection refers to a set of documents from one or more data sources combined. For each collection, the collection metadata stores
- Name of the collection
- Name of the associated Vector DB collection
- Linked Data Sources
- Parsing Configuration for each data source
- Embedding Model and Configuration to be used
LLM Gateway - This is a central proxy that allows proxying requests to various Embedding and LLM models across many providers with a unified API format. This can be OpenAIChat, OllamaChat, or even TruefoundryChat that uses TF LLM Gateway.
Vector DB - This stores the embeddings and metadata for parsed files for the collection. It can be queried to get similar chunks or exact matches based on filters. We are currently supporting Qdrant and SingleStore as our choice of vector database.
Indexing Job - This is an asynchronous Job responsible for orchestrating the indexing flow. Indexing can be started manually or run regularly on a cron schedule. It will
- Scan the Data Sources to get list of documents
- Check the Vector DB state to filter out unchanged documents
- Downloads and parses files to create smaller chunks with associated metadata
- Embeds those chunks using the AI Gateway and puts them into Vector DB
  
  The source code for this is in the backend/indexer/
API Server - This component processes the user query to generate answers with references synchronously. Each application has full control over the retrieval and answer process. Broadly speaking, when a user sends a request
- The corresponsing Query Controller bootstraps retrievers or multi-step agents according to configuration.
- User's question is processed and embedded using the AI Gateway.
- One or more retrievers interact with the Vector DB to fetch relevant chunks and metadata.
- A final answer is formed by using a LLM via the AI Gateway.
- Metadata for relevant documents fetched during the process can be optionally enriched. E.g. adding presigned URLs.
  
  The code for this component is in backend/server/

Data Indexing:

A Cron on some schedule will trigger the Indexing Job
The data source associated with the collection are scanned for all data points (files)
The job compares the VectorDB state with data source state to figure out newly added files, updated files and deleted files. The new and updated files are downloaded
The newly added files and updated files are parsed and chunked into smaller pieces each with their own metadata
The chunks are embedded using embedding models like text-ada-002 from openai or mxbai-embed-large-v1 from mixedbread-ai
The embedded chunks are put into VectorDB with auto generated and provided metadata

❓ Question-Answering using API Server:

Users sends a request with their query
It is routed to one of the app's query controller
One or more retrievers are constructed on top of the Vector DB
Then a Question Answering chain / agent is constructed. It embeds the user query and fetches similar chunks.
A single shot Question Answering chain just generates an answer given similar chunks. An agent can do multi step reasoning and use many tools before arriving at an answer. In both cases, the API server uses LLM models (like GPT 3.5, GPT 4, etc)
Before returning the answer, the metadata for relevant chunks can be updated with things like presigned urls, surrounding slides, external data source links.
The answer and relevant document chunks are returned in response.

Note: In case of agents the intermediate steps can also be streamed. It is up to the specific app to decide.

Customizing the Code for your usecase

Cognita goes by the tagline -

Everything is available and Everything is customizable.

Cognita makes it really easy to switch between parsers, loaders, models and retrievers.

Customizing Dataloaders:

You can write your own data loader by inherting the BaseDataLoader class from backend/modules/dataloaders/loader.py
Finally, register the loader in backend/modules/dataloaders/__init__.py

Testing a dataloader on localdir, in root dir, copy the following code as test.py and execute it. We show how to test an existing LocalDirLoader here:

from backend.modules.dataloaders import LocalDirLoader
from backend.types import DataSource

data_source = DataSource(
type="local",
uri="sample-data/creditcards",
)

loader = LocalDirLoader()


loaded_data_pts = loader.load_full_data(
    data_source=data_source,
    dest_dir="test/creditcards",
)


for data_pt in loaded_data_pts:
    print(data_pt)

Customizing Embedder:

The codebase currently uses OpenAIEmbeddings you can registered as default.
You can register your custom embeddings in backend/modules/embedder/__init__.py
You can also add your own embedder an example of which is given under backend/modules/embedder/mixbread_embedder.py. It inherits langchain embedding class.

Customizing Parsers:

You can write your own parser by inherting the BaseParser class from backend/modules/parsers/parser.py
Finally, register the parser in backend/modules/parsers/__init__.py

Testing a Parser on a local file, in root dir, copy the following code as test.py and execute it. Here we show how we can test existing MarkdownParser:

import asyncio
from backend.modules.parsers import MarkdownParser

parser = MarkdownParser()
chunks =  asyncio.run(
    parser.get_chunks(
        filepath="sample-data/creditcards/diners-club-black.md",
    )
)
print(chunks)

Adding Custom VectorDB:

To add your own interface for a VectorDB you can inhertit BaseVectorDB from backend/modules/vector_db/base.py
Register the vectordb under backend/modules/vector_db/__init__.py

💡 Writing your Query Controller (QnA):

Code responsible for implementing the Query interface of RAG application. The methods defined in these query controllers are added routes to your FastAPI server.

Steps to add your custom Query Controller:

Add your Query controller class in backend/modules/query_controllers/
Add query_controller decorator to your class and pass the name of your custom controller as argument

from backend.server.decorator import query_controller

@query_controller("/my-controller")
class MyCustomController():
    ...

Add methods to this controller as per your needs and use our http decorators like post, get, delete to make your methods an API

from backend.server.decorator import post

@query_controller("/my-controller")
class MyCustomController():
    ...

    @post("/answer")
    def answer(query: str):
        # Write code to express your logic for answer
        # This API will be exposed as POST /my-controller/answer
        ...

Import your custom controller class at backend/modules/query_controllers/__init__.py

...
from backend.modules.query_controllers.sample_controller.controller import MyCustomController

As an example, we have implemented sample controller in backend/modules/query_controllers/example. Please refer for better understanding

🐳 Quickstart: Deployment with Truefoundry:

To be able to Query on your own documents, follow the steps below:

Register at TrueFoundry, follow here
- Fill up the form and register as an organization (let's say <org_name>)
- On Submit, you will be redirected to your dashboard endpoint ie https://<org_name>.truefoundry.cloud
- Complete your email verification
- Login to the platform at your dashboard endpoint ie. https://<org_name>.truefoundry.cloud
Note: Keep your dashboard endpoint handy, we will refer it as "TFY_HOST" and it should have structure like "https://<org_name>.truefoundry.cloud"
Setup a cluster, use TrueFoundry managed for quick setup
- Give a unique name to your Cluster and click on Launch Cluster
- It will take few minutes to provision a cluster for you
- On Configure Host Domain section, click Register for the pre-filled IP
- Next, Add a Docker Registry to push your docker images to.
- Next, Deploy a Model, you can choose to Skip this step
Add a Storage Integration
Create a ML Repo
- Navigate to ML Repo tab
- Click on + New ML Repo button on top-right
- Give a unique name to your ML Repo (say 'docs-qa-llm')
- Select Storage Integration
- On Submit, your ML Repo will be created
  
  For more details: link
Create a Workspace
- Navigate to Workspace tab
- Click on + New Workspace button on top-right
- Select your Cluster
- Give a name to your Workspace (say 'docs-qa-llm')
- Enable ML Repo Access and Add ML Repo Access
- Select your ML Repo and role as Project Admin
- On Submit, a new Workspace will be created. You can copy the Workspace FQN by clicking on FQN.
For more details: link
Deploy RAG Application
- Navigate to Deployments tab
- Click on + New Deployment buttton on top-right
- Select Application Catalogue
- Select your workspace
- Select RAG Application
- Fill up the deployment template
  - Give your deployment a Name
  - Add ML Repo
  - You can either add an existing Qdrant DB or create a new one
  - By default, main branch is used for deployment (You will find this option in Show Advance fields). You can change the branch name and git repository if required.
    
    Make sure to re-select the main branch, as the SHA commit, does not get updated automatically.
  - Click on Submit your application will be deployed.

Using the RAG UI:

The following steps will showcase how to use the cognita UI to query documents:

Create Data Source
- Click on Data Sources tab
- Click + New Datasource
- Data source type can be either files from local directory, web url, github url or providing Truefoundry artifact FQN.
  - E.g: If Localdir is selected upload files from your machine and click Submit.
- Created Data sources list will be available in the Data Sources tab.
Create Collection
- Click on Collections tab
- Click + New Collection
- Enter Collection Name
- Select Embedding Model
- Add earlier created data source and the necessary configuration
- Click Process to create the collection and index the data.
As soon as you create the collection, data ingestion begins, you can view it's status by selecting your collection in collections tab. You can also add additional data sources later on and index them in the collection.
Response generation
- Select the collection
- Select the LLM and it's configuration
- Select the document retriever
- Write the prompt or use the default prompt
- Ask the query

💖 Open Source Contribution

Your contributions are always welcome! Feel free to contribute ideas, feedback, or create issues and bug reports if you find any! Before contributing, please read the Contribution Guide.

🔮 Future developments

Contributions are welcomed for the following upcoming developments:

Support for other vector databases like Chroma, Weaviate, etc
Support for Scalar + Binary Quantization embeddings.
Support for RAG Evalutaion of different retrievers.
Support for RAG Visualization.
Support for conversational chatbot with context
Support for RAG optimized LLMs like stable-lm-3b, dragon-yi-6b, etc
Support for GraphDB

d-vignesh/cognita