chroma db setup

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research how to set up chroma db and connect with Python

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🔬 chroma db setup

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research how to set up chroma db and connect with Python

- [x] **🔬 Research Chroma DB**

Research what Chroma DB is, its features, and its use cases. Understand the requirements for setting up Chroma DB, including any necessary software, hardware, or other prerequisites. This research should provide a clear understanding of what Chroma DB is and what is needed to set it up.

💬 Conclusion
The task of researching Chroma DB has been successfully completed. We now have a clear understanding of what Chroma DB is, its main features, common use cases, and the requirements for setting it up. This information will be useful for the next steps of setting up Chroma DB and connecting it with Python.

- [x] **🔬 Research Chroma DB Setup Process**

Research the detailed process of setting up Chroma DB. This should include step-by-step instructions, any necessary configuration, and potential issues or challenges that may arise during the setup process. The result of this research should be a clear understanding of how to set up Chroma DB, including any potential pitfalls and how to avoid them.

💬 Conclusion
The task of researching the Chroma DB setup process has been completed successfully. The response provides a detailed guide on how to set up Chroma DB, including step-by-step instructions, necessary configuration, and potential issues or challenges that may arise during the setup process. This information should provide a clear understanding of how to set up Chroma DB and how to avoid potential pitfalls.

💬 Conclusion
We have successfully researched how to set up Chroma DB and connect it with Python. The process involves setting up Chroma DB on AWS, installing the Chroma Vector Store API for Python, and using the API to interact with the database. This information should provide a clear understanding of how to set up Chroma DB and connect it with Python.

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Chroma DB Overview

Chroma DB is an open-source vector database developed by Chroma. It is designed for efficient storage and retrieval of vector embeddings, making it ideal for generative AI applications. Chroma DB can function as both an in-memory database and a backend, with the capability to persist data between sessions. It provides a core API with four functions for easy prototyping and supports integrations with LangChain, LlamaIndex, and more.

Setting up Chroma DB

To set up Chroma DB, you will need an AWS account and credentials. The AWS CLI is used to configure and run Chroma DB. The deployment process involves running a CloudFormation template provided by Chroma. The template launches an EC2 instance on AWS and installs Chroma DB on it.

Here are the steps to set up Chroma DB:

1. Get an AWS Account: You will need an AWS account to deploy Chroma DB.
2. Get credentials: Configure the AWS CLI with your AWS access key and secret access key.
3. Run CloudFormation: Use the AWS CLI to launch the CloudFormation template provided by Chroma.
4. Customize the Stack (optional): The CloudFormation template allows you to pass specific key/value pairs to override aspects of the stack, such as the instance type and EC2 KeyPair.
5. Configure the Chroma Library: Once the Chroma server is running, configure the Chroma client library to use the server's IP address and port 8000.
6. Clean Up (optional): To remove all AWS resources and destroy the Chroma stack, use the AWS CLI delete-stack command.

Connecting Chroma DB with Python

To connect Chroma DB with Python, you need to install the Chroma Vector Store API for Python using pip:

pip install chromadb

Then, you can create a persistent in-memory database by configuring the client with the following parameters:

import chromadb
import chromadb.config
from chromadb.server.fastapi import FastAPI

settings = chromadb.config.Settings(
    chroma_db_impl="duckdb+parquet", 
    persist_directory='chroma_data'
)
server = FastAPI(settings)
app = server.app

After that, you can create a collection in the Vector Store, add documents to the collection, and query the vector store to retrieve relevant documents.

collection = client.create_collection("my_information")

collection.add(
    documents=["This is a document containing car information",
               "This is a document containing information about dogs",
               "This document contains four wheeler catalogue"],
    metadatas=[{"source": "Car Book"},
               {"source": "Dog Book"},
               {"source": "Vehicle Info"}],
    ids=["id1", "id2", "id3"]
)

results = collection.query(
    query_texts=["Car"],
    n_results=2
)

Please note that this is a simplified summary of the setup process for Chroma DB. For a more comprehensive guide, including potential issues and workarounds, refer to the official documentation.