Chroma

ChromaReader

Bases: BaseReader

Chroma reader.

Retrieve documents from existing persisted Chroma collections.

Parameters:

Name	Type	Description	Default
collection_name	str	Name of the persisted collection.	required
persist_directory	Optional[str]	Directory where the collection is persisted.	None

Source code in llama-index-integrations/readers/llama-index-readers-chroma/llama_index/readers/chroma/base.py

class ChromaReader(BaseReader):
    """Chroma reader.

    Retrieve documents from existing persisted Chroma collections.

    Args:
        collection_name: Name of the persisted collection.
        persist_directory: Directory where the collection is persisted.

    """

    def __init__(
        self,
        collection_name: str,
        persist_directory: Optional[str] = None,
        chroma_api_impl: str = "rest",
        chroma_db_impl: Optional[str] = None,
        host: str = "localhost",
        port: int = 8000,
    ) -> None:
        """Initialize with parameters."""
        import_err_msg = (
            "`chromadb` package not found, please run `pip install chromadb`"
        )
        try:
            import chromadb
        except ImportError:
            raise ImportError(import_err_msg)

        if collection_name is None:
            raise ValueError("Please provide a collection name.")
        # from chromadb.config import Settings

        if persist_directory is not None:
            self._client = chromadb.PersistentClient(
                path=persist_directory if persist_directory else "./chroma",
            )
        elif (host is not None) or (port is not None):
            self._client = chromadb.HttpClient(
                host=host,
                port=port,
            )

        self._collection = self._client.get_collection(collection_name)

    def create_documents(self, results: Any) -> List[Document]:
        """Create documents from the results.

        Args:
            results: Results from the query.

        Returns:
            List of documents.
        """
        documents = []
        for result in zip(
            results["ids"][0],
            results["documents"][0],
            results["embeddings"][0],
            results["metadatas"][0],
        ):
            document = Document(
                id_=result[0],
                text=result[1],
                embedding=result[2],
                metadata=result[3],
            )
            documents.append(document)

        return documents

    def load_data(
        self,
        query_embedding: Optional[List[float]] = None,
        limit: int = 10,
        where: Optional[dict] = None,
        where_document: Optional[dict] = None,
        query: Optional[Union[str, List[str]]] = None,
    ) -> Any:
        """Load data from the collection.

        Args:
            limit: Number of results to return.
            where: Filter results by metadata. {"metadata_field": "is_equal_to_this"}
            where_document: Filter results by document. {"$contains":"search_string"}

        Returns:
            List of documents.
        """
        where = where or {}
        where_document = where_document or {}
        if query_embedding is not None:
            results = self._collection.search(
                query_embedding=query_embedding,
                n_results=limit,
                where=where,
                where_document=where_document,
                include=["metadatas", "documents", "distances", "embeddings"],
            )
            return self.create_documents(results)
        elif query is not None:
            query = query if isinstance(query, list) else [query]
            results = self._collection.query(
                query_texts=query,
                n_results=limit,
                where=where,
                where_document=where_document,
                include=["metadatas", "documents", "distances", "embeddings"],
            )
            return self.create_documents(results)
        else:
            raise ValueError("Please provide either query embedding or query.")

create_documents

create_documents(results: Any) -> List[Document]

Create documents from the results.

Parameters:

Name	Type	Description	Default
results	Any	Results from the query.	required

Returns:

Type	Description
List[Document]	List of documents.

Source code in llama-index-integrations/readers/llama-index-readers-chroma/llama_index/readers/chroma/base.py

def create_documents(self, results: Any) -> List[Document]:
    """Create documents from the results.

    Args:
        results: Results from the query.

    Returns:
        List of documents.
    """
    documents = []
    for result in zip(
        results["ids"][0],
        results["documents"][0],
        results["embeddings"][0],
        results["metadatas"][0],
    ):
        document = Document(
            id_=result[0],
            text=result[1],
            embedding=result[2],
            metadata=result[3],
        )
        documents.append(document)

    return documents

load_data

load_data(query_embedding: Optional[List[float]] = None, limit: int = 10, where: Optional[dict] = None, where_document: Optional[dict] = None, query: Optional[Union[str, List[str]]] = None) -> Any

Load data from the collection.

Parameters:

Name	Type	Description	Default
limit	int	Number of results to return.	10
where	Optional[dict]	Filter results by metadata. {"metadata_field": "is_equal_to_this"}	None
where_document	Optional[dict]	Filter results by document. {"$contains":"search_string"}	None

Returns:

Type	Description
Any	List of documents.

Source code in llama-index-integrations/readers/llama-index-readers-chroma/llama_index/readers/chroma/base.py

def load_data(
    self,
    query_embedding: Optional[List[float]] = None,
    limit: int = 10,
    where: Optional[dict] = None,
    where_document: Optional[dict] = None,
    query: Optional[Union[str, List[str]]] = None,
) -> Any:
    """Load data from the collection.

    Args:
        limit: Number of results to return.
        where: Filter results by metadata. {"metadata_field": "is_equal_to_this"}
        where_document: Filter results by document. {"$contains":"search_string"}

    Returns:
        List of documents.
    """
    where = where or {}
    where_document = where_document or {}
    if query_embedding is not None:
        results = self._collection.search(
            query_embedding=query_embedding,
            n_results=limit,
            where=where,
            where_document=where_document,
            include=["metadatas", "documents", "distances", "embeddings"],
        )
        return self.create_documents(results)
    elif query is not None:
        query = query if isinstance(query, list) else [query]
        results = self._collection.query(
            query_texts=query,
            n_results=limit,
            where=where,
            where_document=where_document,
            include=["metadatas", "documents", "distances", "embeddings"],
        )
        return self.create_documents(results)
    else:
        raise ValueError("Please provide either query embedding or query.")