streamlit-chromadb-connection 1.0.5

A simple adapter connection for any Streamlit LLM-powered app to use ChromaDB vector database.

📂 ChromaDBConnection

Connection for Chroma vector database, ChromaDBConnection, has been released which makes it easy to connect any Streamlit LLM-powered app to.

With st.connection(), connecting to a Chroma vector database becomes just a few lines of code:

import streamlit as st
from streamlit_chromadb_connection.chromadb_connection import ChromadbConnection

configuration = {
    "client": "PersistentClient",
    "path": "/tmp/.chroma"
}

collection_name = "documents_collection"

conn = st.connection("chromadb",
                     type=ChromaDBConnection,
                     **configuration)
documents_collection_df = conn.get_collection_data(collection_name)
st.dataframe(documents_collection_df)

📑 ChromaDBConnection API

_connect()

There are 2 ways to connect to a Chroma client:

1. PersistentClient: Data will be persisted to a local machine

   import streamlit as st
   from streamlit_chromadb_connection.chromadb_connection import ChromadbConnection
   
   configuration = {
       "client": "PersistentClient",
       "path": "/tmp/.chroma"
   }
   
   conn = st.connection(name="persistent_chromadb",
                        type=ChromadbConnection,
                        **configuration)
   

2. HttpClient: Data will be persisted to a cloud server where Chroma resides

   import streamlit as st
   from streamlit_chromadb_connection.chromadb_connection import ChromadbConnection
   
   configuration = {
       "client": "HttpClient",
       "host": "localhost",
       "port": 8000,
   }
   
   conn = st.connection(name="http_connection",
                        type=ChromadbConnection,
                        **configuration)
   

create_collection()

In order to create a Chroma collection, one needs to supply a collection_name and embedding_function_name, embedding_config and (optional) metadata.

There are current possible options for embedding_function_name:

• DefaultEmbeddingFunction
• SentenceTransformerEmbeddingFunction
• OpenAIEmbeddingFunction
• CohereEmbeddingFunction
• GooglePalmEmbeddingFunction
• GoogleVertexEmbeddingFunction
• HuggingFaceEmbeddingFunction
• InstructorEmbeddingFunction
• Text2VecEmbeddingFunction
• ONNXMiniLM_L6_V2

For DefaultEmbeddingFunction, the embedding_config argument can be left as an empty string. However, for other embedding functions such as OpenAIEmbeddingFunction, one needs to provide configuration such as:

embedding_config = {
    api_key: "{OPENAI_API_KEY}",
    model_name: "{OPENAI_MODEL}",
}

One can also change the distance function by changing the metadata argument, such as:

metadata = {"hnsw:space": "l2"} # Squared L2 norm
metadata = {"hnsw:space": "cosine"} # Cosine similarity
metadata = {"hnsw:space": "ip"} # Inner product

Sample code to create connection:

collection_name = "documents_collection"
embedding_function_name = "DefaultEmbeddingFunction"
conn.create_collection(collection_name=collection_name,
                       embedding_function_name=embedding_function_name,
                       embedding_config={},
                       metadata = {"hnsw:space": "cosine"})

get_collection_data()

This method returns a dataframe that consists of the embeddings and documents of a collection. The attributes argument is a list of attributes to be included in the DataFrame. The following code snippet will return all data in a collection in the form of a DataFrame, with 2 columns: documents and embeddings.

collection_name = "documents_collection"
conn.get_collection_data(collection_name=collection_name,
                         attributes= ["documents", "embeddings"])

delete_collection()

This method deletes the stated collection name.

collection_name = "documents_collection"
conn.delete_collection(collection_name=collection_name)

upload_documents()

This method uploads documents to a collection. If embeddings are not provided, the method will embed the documents using the embedding function specified in the collection.

collection_name = "documents_collection"
embedding_function_name = "DefaultEmbeddingFunction"
embedding_config = {}
conn.upload_documents(collection_name=collection_name,
                      documents=["lorem ipsum", "doc2", "doc3"],
                      metadatas=[{"chapter": "3", "verse": "16"}, {"chapter": "3", "verse": "5"}, {"chapter": "29", "verse": "11"}],
                      embeeding_function_name=embedding_function_name,
                      embedding_config=embedding_config,
                      ids=["id1", "id2", "id3"])

update_collection_data()

This method updates documents in a collection based on their ids.

embedding_function_name = "DefaultEmbeddingFunction"
embedding_config = {}
conn.upload_documents(collection_name=collection_name,
                     documents=["this is a", "this is b", "this is c"],
                     metadatas=[{"chapter": "3", "verse": "16"}, {"chapter": "3", "verse": "5"}, {"chapter": "29", "verse": "11"}],
                     embeeding_function_name=embedding_function_name,
                     embedding_config=embedding_config,
                     ids=["id1", "id2", "id3"])

conn.update_collection_data(collection_name=collection_name,
                            documents=["this is b", "this is c", "this is d"],
                            embeeding_function_name=embedding_function_name,
                            embedding_config=embedding_config,
                            ids=["id1", "id2", "id3"])

query()

This method retrieves top k relevant document based on a list of queries supplied. The result will be in a dataframe where each row will shows the top k relevant documents of each query.

collection_name = "documents_collection"
embedding_function_name = "DefaultEmbeddingFunction"
embedding_config = {}
conn.upload_documents(collection_name=collection_name,
                     documents=["lorem ipsum", "doc2", "doc3"],
                     metadatas=[{"chapter": "3", "verse": "16"}, {"chapter": "3", "verse": "5"}, {"chapter": "29", "verse": "11"}],
                     ids=["id1", "id2", "id3"],
                     embeeding_function_name=embedding_function_name,
                     embedding_config=embedding_config,
                     embeddings=None)

queried_data = conn.query(collection_name=collection_name,
                          query=["random_query1", "random_query2"],
                          num_results_limit=10,
                          attributes=["documents", "embeddings", "metadatas", "data"])

Metadata and document filters are also provided in where_metadata_filter and where_document_filter arguments respectively for more relevant search. For better understanding on the usage of where filters, please refer to: https://docs.trychroma.com/usage-guide#using-where-filters

queried_data = conn.query(collection_name=collection_name,
                          query=["this is"],
                          num_results_limit=10,
                          attributes=["documents", "embeddings", "metadatas", "data"],
                          where_metadata_filter={"chapter": "3"})

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🎉 That's it! ChromaDBConnection is ready to be used with st.connection(). 🎉

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Contribution 🔥

author={Vu Quang Minh},
github={Dev317},
year={2023}