collection_name="collection_name",    embedding_function=embedding_function,)print("There are", langchain_chroma._collection.count(), "in the collection")    Add of existing embedding ID: 1    Add of existing embedding ID: 2    Add of existing embedding ID: 3    Add of existing embedding ID: 1    Add of existing embedding ID: 2    Add of existing embedding ID: 3    Add of existing embedding ID: 1    Insert of existing embedding ID: 1    Add of existing embedding ID: 2    Insert of existing embedding ID: 2    Add of existing embedding ID: 3    Insert of existing embedding ID: 3    There are 3 in the collectionBasic Example (using the Docker Container)‚ÄãYou can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LangChain. Chroma has the ability to handle multiple Collections of documents, but the LangChain interface expects one, so we need to specify the collection name. The default collection name used by LangChain is "langchain".Here is how to clone, build, and run the Docker Image:git clone git@github.com:chroma-core/chroma.gitEdit the docker-compose.yml file and add ALLOW_RESET=TRUE under environment    ...    command: uvicorn chromadb.app:app --reload --workers 1 --host 0.0.0.0 --port 8000 --log-config log_config.yml    environment:      - IS_PERSISTENT=TRUE      - ALLOW_RESET=TRUE    ports:      - 8000:8000    ...Then run docker-compose up -d --build# create the chroma clientimport chromadbimport uuidfrom chromadb.config import Settingsclient = chromadb.HttpClient(settings=Settings(allow_reset=True))client.reset()  # resets the databasecollection = client.create_collection("my_collection")for doc in docs:    collection.add(        ids=[str(uuid.uuid1())], metadatas=doc.metadata, documents=doc.page_content    )# tell LangChain to use our client and collection namedb4 = Chroma(client=client, collection_name="my_collection", embedding_function=embedding_function)query = "What did the president
Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.
Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. ->: collection_name="collection_name",    embedding_function=embedding_function,)print("There are", langchain_chroma._collection.count(), "in the collection")    Add of existing embedding ID: 1    Add of existing embedding ID: 2    Add of existing embedding ID: 3    Add of existing embedding ID: 1    Add of existing embedding ID: 2    Add of existing embedding ID: 3    Add of existing embedding ID: 1    Insert of existing embedding ID: 1    Add of existing embedding ID: 2    Insert of existing embedding ID: 2    Add of existing embedding ID: 3    Insert of existing embedding ID: 3    There are 3 in the collectionBasic Example (using the Docker Container)‚ÄãYou can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LangChain. Chroma has the ability to handle multiple Collections of documents, but the LangChain interface expects one, so we need to specify the collection name. The default collection name used by LangChain is "langchain".Here is how to clone, build, and run the Docker Image:git clone git@github.com:chroma-core/chroma.gitEdit the docker-compose.yml file and add ALLOW_RESET=TRUE under environment    ...    command: uvicorn chromadb.app:app --reload --workers 1 --host 0.0.0.0 --port 8000 --log-config log_config.yml    environment:      - IS_PERSISTENT=TRUE      - ALLOW_RESET=TRUE    ports:      - 8000:8000    ...Then run docker-compose up -d --build# create the chroma clientimport chromadbimport uuidfrom chromadb.config import Settingsclient = chromadb.HttpClient(settings=Settings(allow_reset=True))client.reset()  # resets the databasecollection = client.create_collection("my_collection")for doc in docs:    collection.add(        ids=[str(uuid.uuid1())], metadatas=doc.metadata, documents=doc.page_content    )# tell LangChain to use our client and collection namedb4 = Chroma(client=client, collection_name="my_collection", embedding_function=embedding_function)query = "What did the president