---
license: mit
---
# Table of Contents
* [run](#run)
* [ChromaDBFlow](#ChromaDBFlow)
* [ChromaDBFlow](#ChromaDBFlow.ChromaDBFlow)
* [instantiate\_from\_config](#ChromaDBFlow.ChromaDBFlow.instantiate_from_config)
* [run](#ChromaDBFlow.ChromaDBFlow.run)
* [VectorStoreFlow](#VectorStoreFlow)
* [VectorStoreFlow](#VectorStoreFlow.VectorStoreFlow)
* [instantiate\_from\_config](#VectorStoreFlow.VectorStoreFlow.instantiate_from_config)
* [package\_documents](#VectorStoreFlow.VectorStoreFlow.package_documents)
* [run](#VectorStoreFlow.VectorStoreFlow.run)
* [\_\_init\_\_](#__init__)
# run
A simple script to run a Flow that can be used for development and debugging.
# ChromaDBFlow
## ChromaDBFlow Objects
```python
class ChromaDBFlow(AtomicFlow)
```
A flow that uses the ChromaDB model to write and read memories stored in a database
*Configuration Parameters*:
- `name` (str): The name of the flow. Default: "chroma_db"
- `description` (str): A description of the flow. This description is used to generate the help message of the flow.
Default: "ChromaDB is a document store that uses vector embeddings to store and retrieve documents."
- `backend` (Dict[str, Any]): The configuration of the backend which is used to fetch api keys. Default: LiteLLMBackend with the
default parameters of LiteLLMBackend (see aiflows.backends.LiteLLMBackend). Except for the following parameter whose default value is overwritten:
- `api_infos` (List[Dict[str, Any]]): The list of api infos. Default: No default value, this parameter is required.
- `model_name` (str): The name of the model. Default: "". In the current implementation, this parameter is not used.
- `similarity_search_kwargs` (Dict[str, Any]): The parameters to pass to the similarity search method of the ChromaDB. Default:
- `k` (int): The number of documents to retrieve. Default: 2
- `filter` (str): The filter to apply to the documents. Default: null
- `paths_to_data` (List[str]): The paths to the data to store in the database at instantiation. Default: []
- `chunk_size` (int): The size of the chunks to split the documents into. Default: 700
- `seperator` (str): The separator to use to split the documents. Default: "\n"
- `chunk_overlap` (int): The overlap between the chunks. Default: 0
- `persist_directory` (str): The directory to persist the database. Default: "./demo_db_dir"
- Other parameters are inherited from the default configuration of AtomicFlow (see AtomicFlow)
*Input Interface*:
- `operation` (str): The operation to perform. It can be "write" or "read".
- `content` (str or List[str]): The content to write or read. If operation is "write", it must be a string or a list of strings. If operation is "read", it must be a string.
*Output Interface*:
- `retrieved` (str or List[str]): The retrieved content. If operation is "write", it is an empty string. If operation is "read", it is a string or a list of strings.
**Arguments**:
- `backend` (`LiteLLMBackend`): The backend of the flow (used to retrieve the API key)
- `\**kwargs`: Additional arguments to pass to the flow.
#### instantiate\_from\_config
```python
@classmethod
def instantiate_from_config(cls, config)
```
This method instantiates the flow from a configuration file
**Arguments**:
- `config` (`Dict[str, Any]`): The configuration of the flow.
**Returns**:
`ChromaDBFlow`: The instantiated flow.
#### run
```python
def run(input_message: FlowMessage)
```
This method runs the flow. It runs the ChromaDBFlow. It either writes or reads memories from the database.
**Arguments**:
- `input_message` (`FlowMessage`): The input message of the flow.
# VectorStoreFlow
## VectorStoreFlow Objects
```python
class VectorStoreFlow(AtomicFlow)
```
A flow that uses the VectorStore model to write and read memories stored in a database (see VectorStoreFlow.yaml for the default configuration)
*Configuration Parameters*:
- `name` (str): The name of the flow. Default: "VecotrStoreFlow"
- `description` (str): A description of the flow. This description is used to generate the help message of the flow.
Default: "VectorStoreFlow"
- `backend` (Dict[str, Any]): The configuration of the backend which is used to fetch api keys. Default: LiteLLMBackend with the
default parameters of LiteLLMBackend (see flows.backends.LiteLLMBackend). Except for the following parameter whose default value is overwritten:
- `api_infos` (List[Dict[str, Any]]): The list of api infos. Default: No default value, this parameter is required.
- `model_name` (str): The name of the model. Default: "". In the current implementation, this parameter is not used.
- `type` (str): The type of the vector store. It can be "chroma" or "faiss". Default: "chroma"
- `embedding_size` (int): The size of the embeddings (only for faiss). Default: 1536
- `retriever_config` (Dict[str, Any]): The configuration of the retriever. Default: empty dictionary
- Other parameters are inherited from the default configuration of AtomicFlow (see AtomicFlow)
*Input Interface*:
- `operation` (str): The operation to perform. It can be "write" or "read".
- `content` (str or List[str]): The content to write or read. If operation is "write", it must be a string or a list of strings. If operation is "read", it must be a string.
*Output Interface*:
- `retrieved` (str or List[str]): The retrieved content. If operation is "write", it is an empty string. If operation is "read", it is a string or a list of strings.
**Arguments**:
- `backend` (`LiteLLMBackend`): The backend of the flow (used to retrieve the API key)
- `vector_db` (`VectorStoreRetriever`): The vector store retriever
- `type` (`str`): The type of the vector store
- `\**kwargs`: Additional arguments to pass to the flow. See :class:`aiflows.base_flows.AtomicFlow` for more details.
#### instantiate\_from\_config
```python
@classmethod
def instantiate_from_config(cls, config: Dict[str, Any])
```
This method instantiates the flow from a configuration file
**Arguments**:
- `config` (`Dict[str, Any]`): The configuration of the flow.
**Returns**:
`VectorStoreFlow`: The instantiated flow.
#### package\_documents
```python
@staticmethod
def package_documents(documents: List[str]) -> List[Document]
```
This method packages the documents in a list of Documents.
**Arguments**:
- `documents` (`List[str]`): The documents to package.
**Returns**:
`List[Document]`: The packaged documents.
#### run
```python
def run(input_message: FlowMessage)
```
This method runs the flow. It either writes or reads memories from the database.
**Arguments**:
- `input_message` (`FlowMessage`): The input data of the flow.
# \_\_init\_\_