renamed flows to aiflows

ChatAtomicFlow.py

@@ -5,14 +5,14 @@ import time
 
 from typing import Dict, Optional, Any
 
-from flows.base_flows import AtomicFlow
+from aiflows.base_flows import AtomicFlow
 
-from flows.utils import logging
-from flows.messages.flow_message import UpdateMessage_ChatMessage
+from aiflows.utils import logging
+from aiflows.messages.flow_message import UpdateMessage_ChatMessage
 
-from flows.prompt_template import JinjaPrompt
+from aiflows.prompt_template import JinjaPrompt
 
-from flows.backends.llm_lite import LiteLLMBackend
+from aiflows.backends.llm_lite import LiteLLMBackend
 
 log = logging.get_logger(__name__)
 
@@ -34,7 +34,7 @@ class ChatAtomicFlow(AtomicFlow):
     - `assistant_name` (str): The name of the assistant (roles of LLM). Default: "assistant"
     - `backend` Dict[str,Any]: The backend of the flow. Used to call models via an API.
     See litellm's supported models and APIs here: https://docs.litellm.ai/docs/providers.
-    The default parameters of the backend are all defined at flows.backends.llm_lite.LiteLLMBackend
+    The default parameters of the backend are all defined at aiflows.backends.llm_lite.LiteLLMBackend
     (also see the defaul parameters of litellm's completion parameters: https://docs.litellm.ai/docs/completion/input#input-params-1).
     Except for the following parameters who are overwritten by the ChatAtomicFlow in ChatAtomicFlow.yaml:
         - `model_name` (Union[Dict[str,str],str]): The name of the model to use. 
@@ -50,15 +50,15 @@ class ChatAtomicFlow(AtomicFlow):
         - `presence_penalty` (number): It is used to penalize new tokens based on their existence in the text so far. Default: 0.0
         - `stream` (bool): Whether to stream the response or not. Default: True
     - `system_message_prompt_template` (Dict[str,Any]): The template of the system message. It is used to generate the system message. 
-    By default its of type flows.prompt_template.JinjaPrompt.
+    By default its of type aiflows.prompt_template.JinjaPrompt.
     None of the parameters of the prompt are defined by default and therefore need to be defined if one wants to use the system prompt. 
-    Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
+    Default parameters are defined in aiflows.prompt_template.jinja2_prompts.JinjaPrompt.
     - `init_human_message_prompt_template` (Dict[str,Any]): The prompt template of the human/user message used to initialize the conversation
     (first time in). It is used to generate the human message. It's passed as the user message to the LLM.
-    By default its of type flows.prompt_template.JinjaPrompt. None of the parameters of the prompt are defined by default and therefore need to be defined if one
-    wants to use the init_human_message_prompt_template. Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
+    By default its of type aiflows.prompt_template.JinjaPrompt. None of the parameters of the prompt are defined by default and therefore need to be defined if one
+    wants to use the init_human_message_prompt_template. Default parameters are defined in aiflows.prompt_template.jinja2_prompts.JinjaPrompt.
     - `human_message_prompt_template` (Dict[str,Any]): The prompt template of the human/user message (message used everytime the except the first time in).
-    It's passed as the user message to the LLM. By default its of type flows.prompt_template.JinjaPrompt and has the following parameters:
+    It's passed as the user message to the LLM. By default its of type aiflows.prompt_template.JinjaPrompt and has the following parameters:
         - `template` (str): The template of the human message. Default: see ChatAtomicFlow.yaml for the default value.
         - `input_variables` (List[str]): The input variables of the human message prompt template. Default: ["query"]
     - `previous_messages` (Dict[str,Any]): Defines which previous messages to include in the input of the LLM. Note that if `first_k`and `last_k` are both none,
@@ -87,7 +87,7 @@ class ChatAtomicFlow(AtomicFlow):
     :type init_human_message_prompt_template: Optional[JinjaPrompt]
     :param backend: The backend of the flow. It is a LLM that is queried via an API. See litellm's supported models and APIs here: https://docs.litellm.ai/docs/providers
     :type backend: LiteLLMBackend
-    :param \**kwargs: Additional arguments to pass to the flow. See :class:`flows.base_flows.AtomicFlow` for more details.
+    :param \**kwargs: Additional arguments to pass to the flow. See :class:`aiflows.base_flows.AtomicFlow` for more details.
     """
     REQUIRED_KEYS_CONFIG = ["backend"]
 

ChatAtomicFlow.yaml

@@ -11,7 +11,7 @@ user_name: user
 assistant_name: assistant
 
 backend:
-  _target_: flows.backends.llm_lite.LiteLLMBackend
+  _target_: aiflows.backends.llm_lite.LiteLLMBackend
   api_infos: ???
   model_name: "gpt-3.5-turbo"
   n: 1
@@ -26,14 +26,14 @@ backend:
 
 
 system_message_prompt_template:
-  _target_: flows.prompt_template.JinjaPrompt
+  _target_: aiflows.prompt_template.JinjaPrompt
 
 
 init_human_message_prompt_template:
-  _target_: flows.prompt_template.JinjaPrompt
+  _target_: aiflows.prompt_template.JinjaPrompt
 
 human_message_prompt_template:
-  _target_: flows.prompt_template.JinjaPrompt
+  _target_: aiflows.prompt_template.JinjaPrompt
   template: "{{query}}"
   input_variables:
     - "query"
@@ -41,7 +41,7 @@ input_interface_initialized:
   - "query"
 
 query_message_prompt_template:
-  _target_: flows.prompt_template.JinjaPrompt
+  _target_: aiflows.prompt_template.JinjaPrompt
 
 
 previous_messages:

README.md

@@ -40,7 +40,7 @@ Default: "Flow which uses as tool an LLM though an API"
 - `assistant_name` (str): The name of the assistant (roles of LLM). Default: "assistant"
 - `backend` Dict[str,Any]: The backend of the flow. Used to call models via an API.
 See litellm's supported models and APIs here: https://docs.litellm.ai/docs/providers.
-The default parameters of the backend are all defined at flows.backends.llm_lite.LiteLLMBackend
+The default parameters of the backend are all defined at aiflows.backends.llm_lite.LiteLLMBackend
 (also see the defaul parameters of litellm's completion parameters: https://docs.litellm.ai/docs/completion/input#input-params-1).
 Except for the following parameters who are overwritten by the ChatAtomicFlow in ChatAtomicFlow.yaml:
     - `model_name` (Union[Dict[str,str],str]): The name of the model to use. 
@@ -56,15 +56,15 @@ Except for the following parameters who are overwritten by the ChatAtomicFlow in
     - `presence_penalty` (number): It is used to penalize new tokens based on their existence in the text so far. Default: 0.0
     - `stream` (bool): Whether to stream the response or not. Default: True
 - `system_message_prompt_template` (Dict[str,Any]): The template of the system message. It is used to generate the system message. 
-By default its of type flows.prompt_template.JinjaPrompt.
+By default its of type aiflows.prompt_template.JinjaPrompt.
 None of the parameters of the prompt are defined by default and therefore need to be defined if one wants to use the system prompt. 
-Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
+Default parameters are defined in aiflows.prompt_template.jinja2_prompts.JinjaPrompt.
 - `init_human_message_prompt_template` (Dict[str,Any]): The prompt template of the human/user message used to initialize the conversation
 (first time in). It is used to generate the human message. It's passed as the user message to the LLM.
-By default its of type flows.prompt_template.JinjaPrompt. None of the parameters of the prompt are defined by default and therefore need to be defined if one
-wants to use the init_human_message_prompt_template. Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
+By default its of type aiflows.prompt_template.JinjaPrompt. None of the parameters of the prompt are defined by default and therefore need to be defined if one
+wants to use the init_human_message_prompt_template. Default parameters are defined in aiflows.prompt_template.jinja2_prompts.JinjaPrompt.
 - `human_message_prompt_template` (Dict[str,Any]): The prompt template of the human/user message (message used everytime the except the first time in).
-It's passed as the user message to the LLM. By default its of type flows.prompt_template.JinjaPrompt and has the following parameters:
+It's passed as the user message to the LLM. By default its of type aiflows.prompt_template.JinjaPrompt and has the following parameters:
     - `template` (str): The template of the human message. Default: see ChatAtomicFlow.yaml for the default value.
     - `input_variables` (List[str]): The input variables of the human message prompt template. Default: ["query"]
 - `previous_messages` (Dict[str,Any]): Defines which previous messages to include in the input of the LLM. Note that if `first_k`and `last_k` are both none,
@@ -91,7 +91,7 @@ all the messages of the flows's history are added to the input of the LLM. Defau
 - `human_message_prompt_template` (`JinjaPrompt`): The template of the human message. It is used to generate the human message.
 - `init_human_message_prompt_template` (`Optional[JinjaPrompt]`): The template of the human message that is used to initialize the conversation (first time in). It is used to generate the human message.
 - `backend` (`LiteLLMBackend`): The backend of the flow. It is a LLM that is queried via an API. See litellm's supported models and APIs here: https://docs.litellm.ai/docs/providers
-- `\**kwargs`: Additional arguments to pass to the flow. See :class:`flows.base_flows.AtomicFlow` for more details.
+- `\**kwargs`: Additional arguments to pass to the flow. See :class:`aiflows.base_flows.AtomicFlow` for more details.
 
 <a id="ChatAtomicFlow.ChatAtomicFlow.set_up_flow_state"></a>
 

demo.yaml

@@ -1,15 +1,15 @@
 input_interface:  # Connector between the "input data" and the Flow
-  _target_: flows.interfaces.KeyInterface
+  _target_: aiflows.interfaces.KeyInterface
   additional_transformations:
-    - _target_: flows.data_transformations.KeyMatchInput  # Pass the input parameters specified by the flow
+    - _target_: aiflows.data_transformations.KeyMatchInput  # Pass the input parameters specified by the flow
 
 output_interface:  # Connector between the Flow's output and the caller
-  _target_: flows.interfaces.KeyInterface
+  _target_: aiflows.interfaces.KeyInterface
   keys_to_rename:
     api_output: answer  # Rename the api_output to answer
 
 flow:  # Overrides the ChatAtomicFlow config
-  _target_: aiflows.ChatFlowModule.ChatAtomicFlow.instantiate_from_default_config
+  _target_: flow_modules.aiflows.ChatFlowModule.ChatAtomicFlow.instantiate_from_default_config
 
   name: "SimpleQA_Flow"
   description: "A flow that answers questions."
@@ -20,7 +20,7 @@ flow:  # Overrides the ChatAtomicFlow config
 
   # ~~~ backend model parameters ~~
   backend:
-    _target_: flows.backends.llm_lite.LiteLLMBackend
+    _target_: aiflows.backends.llm_lite.LiteLLMBackend
     api_infos: ???
     model_name: 
       openai: "gpt-3.5-turbo"
@@ -40,7 +40,7 @@ flow:  # Overrides the ChatAtomicFlow config
 
   # ~~~ Prompt specification ~~~
   system_message_prompt_template:
-    _target_: flows.prompt_template.JinjaPrompt
+    _target_: aiflows.prompt_template.JinjaPrompt
     template: |2-
       You are a helpful chatbot that truthfully answers questions.
     input_variables: []
@@ -48,7 +48,7 @@ flow:  # Overrides the ChatAtomicFlow config
  
 
   init_human_message_prompt_template:
-    _target_: flows.prompt_template.JinjaPrompt
+    _target_: aiflows.prompt_template.JinjaPrompt
     template: |2-
       Answer the following question: {{question}}
     input_variables: ["question"]

run.py

@@ -2,13 +2,13 @@ import os
 
 import hydra
 
-import flows
-from flows.flow_launchers import FlowLauncher
-from flows.backends.api_info import ApiInfo
-from flows.utils.general_helpers import read_yaml_file
+import aiflows
+from aiflows.flow_launchers import FlowLauncher
+from aiflows.backends.api_info import ApiInfo
+from aiflows.utils.general_helpers import read_yaml_file
 
-from flows import logging
-from flows.flow_cache import CACHING_PARAMETERS, clear_cache
+from aiflows import logging
+from aiflows.flow_cache import CACHING_PARAMETERS, clear_cache
 
 CACHING_PARAMETERS.do_caching = False  # Set to True in order to disable caching
 # clear_cache() # Uncomment this line to clear the cache
@@ -18,7 +18,7 @@ logging.set_verbosity_debug()
 dependencies = [
     {"url": "aiflows/ChatFlowModule", "revision": os.getcwd()},
 ]
-from flows import flow_verse
+from aiflows import flow_verse
 flow_verse.sync_dependencies(dependencies)
 
 if __name__ == "__main__":