readme and demo

.gitignore

@@ -1 +1,443 @@
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+# End of https://www.toptal.com/developers/gitignore/api/python,java,c++,pycharm,visualstudiocode,macos,linux,windows
+
+.*
+flow_modules/

ChatWithDemonstrationsFlow.py

@@ -9,5 +9,39 @@ log = logging.get_logger(__name__)
 
 
 class ChatWithDemonstrationsFlow(SequentialFlow):
+    """ A Chat with Demonstrations Flow. It is a flow that consists of multiple sub-flows that are executed sequentially.
+    It's parent class is SequentialFlow.
+    
+        It Contains the following subflows:
+            - A Demonstration Flow: It is a flow that passes demonstrations to the ChatFlow
+            - A Chat Flow: It is a flow that uses the demonstrations to answer queries asked by the user/human.
+        
+    An illustration of the flow is as follows:
+        
+                -------> Demonstration Flow -------> Chat Flow ------->
+                
+    *Configuration Parameters*:
+        
+    - `name` (str): The name of the flow. Default: "ChatAtomic_Flow_with_Demonstrations"
+    - `description` (str): A description of the flow. This description is used to generate the help message of the flow.
+    Default:  "A sequential flow that answers questions with demonstrations"
+    - `subflows_config` (Dict[str,Any]): A dictionary of subflows configurations of the sequential Flow. Default:
+        - `Demonstration Flow`: The configuration of the Demonstration Flow. By default, it a DemonstrationsAtomicFlow.
+        Its default parmaters are defined in DemonstrationsAtomicFlow).
+        - `Chat Flow`: The configuration of the Chat Flow. By default, its a ChatAtomicFlow.
+        Its default parmaters are defined in ChatAtomicFlowModule (see Flowcard, i.e. README.md, of ChatAtomicFlowModule).
+    - `topology` (str): The topology of the flow which is "sequential".  By default, the topology is the one shown in the
+    illustration above (the topology is also described in ChatWithDemonstrationsFlow.yaml).
+                
+    *Input Interface*:
+    
+    - `query` (str): A query asked to the flow (e.g. "What is the capital of France?")
+    
+    Output Interface:
+
+    - `answer` (str): The answer of the flow to the query
+        
+    :param \**kwargs: Arguments to be passed to the parent class SequentialFlow constructor.
+    """
     def __init__(self,**kwargs):
         super().__init__(**kwargs)

ChatWithDemonstrationsFlow.yaml

@@ -6,7 +6,7 @@ subflows_config:
     _target_: aiflows.ChatWithDemonstrationsFlowModule.DemonstrationsAtomicFlow.instantiate_from_default_config
     
   chat_flow:
-    _target_: aiflows.OpenAIChatFlowModule.OpenAIChatAtomicFlow.instantiate_from_default_config
+    _target_: aiflows.ChatFlowModule.ChatAtomicFlow.instantiate_from_default_config
 
 topology:
   - goal: Get Demonstrations
@@ -24,3 +24,10 @@ topology:
       _target_: flows.interfaces.KeyInterface
       keys_to_rename:
         api_output: answer  # Rename the api_output to answer
+
+
+input_interface_initialized:
+  - "query"
+
+output_interface:
+  - "answer"

DemonstrationsAtomicFlow.py

@@ -5,12 +5,61 @@ from flows.utils import general_helpers
 from typing import Dict,Any,Optional,List
 from flows.prompt_template import JinjaPrompt
 from copy import deepcopy
-import numpy as np
 import os
 import hydra
 log = logging.get_logger(__name__)
 
 class DemonstrationsAtomicFlow(AtomicFlow):
+    """ This class implements a Demonstrations Atomic Flow. It is a flow which is usually used to pass demonstrations (of user assistant interactions)
+    to the ChatAtomicFlow.
+    
+    *Configuration Parameters*:
+    
+    - `name` (str): The name of the flow. Default: "DemonstrationsAtomicFlow"
+    - `description` (str): A description of the flow. This description is used to generate the help message of the flow.
+    Default: "A flow that passes demonstrations to the ChatFlow"
+    - `data` (List[Dict[str, Any]]): The data of the demonstrations.
+    If data is None, the data is loaded from the file specified in the params["data_dir"].
+    Default: No default value this field must be set.
+    - `params` (Dict[str, Any]): The parameters specific to the dataset of the demonstrations. Its default parameters are:
+        - `data_dir` (str): The directory where the demonstrations are stored. If the data is not directly passed to the flow through `data` then
+        the data is loaded from this directory. Default: No default value this field must be set.
+        - `demonstrations_id` (str): The id of the demonstrations (name of the data file). If the data is not directly passed to the flow through `data` then
+        the data is loaded from this file. Default: No default value this field must be set.
+        - `demonstrations_k` (int): The number of demonstrations to pass to the ChatFlow.
+        If None, all the demonstrations are passed to the ChatFlow. Default: None
+    - `query_prompt_template` (Dict[str, Any]): The prompt template used to generate the query of the demonstrations. 
+    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 query_prompt_template. Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
+    - `response_prompt_template` (Dict[str, Any]): The prompt template used to generate the response of the demonstrations. 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 response_prompt_template. Default parameters are defined in flows.prompt_template.jinja2_prompts.JinjaPrompt.
+
+    *Input Interface*:
+    
+    - The input interface expected by its successor flow (e.g. typically ChatAtomicFlow so the input interface is the one expected by ChatAtomicFlow)
+        
+    *Output Interface*:
+
+    - The input interface expected by its successor flow (e.g. typically ChatAtomicFlow so the input interface expected by ChatAtomicFlow))
+    - `demonstrations` (List[Dict[str, Any]]): A list of demonstrations. Each demonstration is a dictionary with the following keys:
+                                                    - idx (int): The index of the demonstration
+                                                    - query (str): The query of the demonstration
+                                                    - response (str): The response of the demonstration
+        
+    :param params: The parameters specific to the dataset of the demonstrations. It must sould contain the following keys:
+                        - 'data_dir' (str): The directory where the demonstrations are stored. This field is used if the data is not directly passed to the flow through the 'data' field.
+                        - 'demonstrations_id' (str): The id of the demonstrations (name of the data file). This field is used if the data is not directly passed to the flow through the 'data' field.
+                        - 'demonstrations_k' (int): The number of demonstrations to pass to the ChatFlow. If None, all the demonstrations are passed to the ChatFlow.
+                        - 'ids_to_keep' (Optional[Union[str, List[str]]]): The ids of the demonstrations to keep. If None, all the demonstrations are kept.
+    :type params: Dict[str, Any]
+    :param query_prompt_template: The prompt template used to generate the query of the demonstrations.
+    :type query_prompt_template: JinjaPrompt
+    :param response_prompt_template: The prompt template used to generate the response of the demonstrations.
+    :type response_prompt_template: JinjaPrompt
+    :param data: The data of the demonstrations. If None, the data is loaded from the file specified in the params.
+    :type data: Optional[List[Dict[str, Any]]]
+    """
     demonstrations_k: Optional[int] = None
     query_prompt_template: JinjaPrompt
     response_prompt_template: JinjaPrompt
@@ -31,6 +80,13 @@ class DemonstrationsAtomicFlow(AtomicFlow):
             
     @classmethod
     def _set_up_prompts(cls, config):
+        """ This method instantiates the prompt templates of the flow (used when instantiating the flow from a config file)
+        
+            :param config: The configuration of the flow.
+            :type config: Dict[str, Any]
+            :return: A dictionary of keyword arguments to pass to the constructor of the flow.
+            :rtype: Dict[str, Any]
+        """
         kwargs = {}
         kwargs["query_prompt_template"] = \
             hydra.utils.instantiate(config['query_prompt_template'], _convert_="partial")
@@ -40,6 +96,13 @@ class DemonstrationsAtomicFlow(AtomicFlow):
         
     @classmethod
     def instantiate_from_config(cls, config):
+        """ This method instantiates the flow from a config file. 
+        
+        :param config: The configuration of the flow.
+        :type config: Dict[str, Any]
+        :return: The instantiated flow.
+        :rtype: Flow
+        """
         flow_config = deepcopy(config)
 
         kwargs = {"flow_config": flow_config}
@@ -52,14 +115,35 @@ class DemonstrationsAtomicFlow(AtomicFlow):
         return cls(**kwargs)
             
     def _get_query_message_content(self, sample_data: Dict):
+        """ This method returns the query message content of a demonstration given the sample data (by rendering the query prompt template).
+        
+        :param sample_data: The sample data of the demonstration.
+        :type sample_data: Dict[str, Any]
+        :return: The query message content of the demonstration.
+        :rtype: str
+        """
         input_variables = self.query_prompt_template.input_variables
         return self.query_prompt_template.format(**{k: sample_data[k] for k in input_variables})
 
     def _get_response_message_content(self, sample_data: Dict):
+        """ This method returns the response message content of a demonstration given the sample data (by rendering the response prompt template).
+        
+        :param sample_data: The sample data of the demonstration.
+        :type sample_data: Dict[str, Any]
+        :return: The response message content of the demonstration.
+        :rtype: str
+        """
         input_variables = self.response_prompt_template.input_variables
         return self.response_prompt_template.format(**{k: sample_data[k] for k in input_variables})
         
     def _get_io_pair(self, idx):
+        """ This method, given the index of a demonstration, returns an query-response pair from the demonstrations data.
+        
+        :param idx: The index of the demonstration.
+        :type idx: int
+        :return: The query-response pair at idx from the demonstrations data.
+        :rtype: Dict[str, Any]
+        """
         dp = self.data[idx]
         
         query_data = dp["query_data"]
@@ -71,11 +155,19 @@ class DemonstrationsAtomicFlow(AtomicFlow):
         return {"idx": idx, "query": query,"response": response}
             
     def _get_io_pairs(self,input_data: Dict[str, Any]) -> List[Any]:
+        """ This method returns the demonstrations that are passed to the destination flow (typically ChatAtomicFlow).
+        
+        :param input_data: The input data of the flow.
+        :type input_data: Dict[str, Any]
+        :return: The demonstrations that are passed to the destination flow.
+        :rtype: List[Any]
+        """
         demonstrations_k = self.demonstrations_k if self.demonstrations_k is not None else len(self.data)
         io_pairs = [self._get_io_pair(idx) for idx in range(demonstrations_k)]
         return io_pairs
         
     def _load_data(self):
+        """ This method loads the demonstrations from the file specified in the params. It also filters the demonstrations if the ids_to_keep parameter is specified."""
         demonstrations_file = os.path.join(self.params["data_dir"], f"{self.params['demonstrations_id']}.jsonl")
         self.data = general_helpers.read_jsonlines(demonstrations_file)
         
@@ -91,4 +183,11 @@ class DemonstrationsAtomicFlow(AtomicFlow):
 
     def run(self,
             input_data: Dict[str, Any]) -> Dict[str, Any]:
+        """ This method runs the flow. It returns the input data of the flow with the demonstrations added to it.
+        
+        :param input_data: The input data of the flow.
+        :type input_data: Dict[str, Any]
+        :return: The input data of the flow with the demonstrations added to it.
+        :rtype: Dict[str, Any]
+        """
         return {**input_data,**{"demonstrations": self._get_io_pairs(input_data=input_data)}}

README.md

@@ -1,3 +0,0 @@
----
-license: mit
----

__init__.py

@@ -1,6 +1,6 @@
 # ~~~ Specify the dependencies ~~
 dependencies = [
-    {"url": "aiflows/OpenAIChatFlowModule", "revision": "d69ba2125de99d2edb631dd51d22225ed9e3446c"},
+    {"url": "aiflows/ChatFlowModule", "revision": "a749ad10ed39776ba6721c37d0dc22af49ca0f17"},
 ]
 from flows import flow_verse
 flow_verse.sync_dependencies(dependencies)

demo.yaml

@@ -0,0 +1,76 @@
+flow:  # Overrides the OpenAIChatAtomicFlow config
+  _target_: aiflows.ChatWithDemonstrationsFlowModule.ChatWithDemonstrationsFlow.instantiate_from_default_config
+  name: "SimpleQA_Flow_with_Demonstrations"
+  description: "A sequential flow that answers questions with demonstrations"
+  
+  input_interface:  # Connector between the "input data" and the Flow
+    - "questions"
+  output_interface:  # Connector between the Flow's output and the caller
+    - "answer"
+  
+  subflows_config:
+    demonstration_flow:
+      data: 
+        - query_data:
+            query: "What is the capital of Turkey?"
+          response_data:
+            response: "Istambul, my sir."
+        - query_data:
+            query: "what is the capital of Germany?"
+          response_data:
+            response: "Berlin, my sir."
+      params:
+        data_dir: null
+        demonstrations_id: my_sir_demo
+      query_prompt_template:
+        template: |2-
+          Answer the following question: {{query}}
+        input_variables:
+          - "query"
+      response_prompt_template:
+        template: |2-
+          {{response}}
+        input_variables:
+          - response
+
+    chat_flow:
+      name: "SimpleQA_Flow"
+      # ~~~ Input interface specification ~~~
+      input_interface_non_initialized:
+        - "question"
+
+      # ~~~ backend model parameters ~~
+      backend:
+        _target_: flows.backends.llm_lite.LiteLLMBackend
+        api_infos: ???
+        model_name: 
+          openai: "gpt-3.5-turbo"
+          azure: "azure/gpt-4"
+    
+        # ~~~ generation_parameters ~~
+        n: 1
+        max_tokens: 3000
+        temperature: 0.3
+
+        top_p: 0.2
+        frequency_penalty: 0
+        presence_penalty: 0
+
+      n_api_retries: 6
+      wait_time_between_retries: 20
+
+      # ~~~ Prompt specification ~~~
+      system_message_prompt_template:
+        _target_: flows.prompt_template.JinjaPrompt
+        template: |2-
+          You are a helpful chatbot that truthfully answers questions. Answer in a similar way to your previous replies.
+        input_variables: []
+        partial_variables: {}
+ 
+
+      init_human_message_prompt_template:
+        _target_: flows.prompt_template.JinjaPrompt
+        template: |2-
+          Answer the following question: {{question}}
+        input_variables: ["question"]
+        partial_variables: {}

run.py

@@ -0,0 +1,76 @@
+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
+
+from flows import logging
+from flows.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
+
+logging.set_verbosity_debug()  # Uncomment this line to see verbose logs
+
+from flows import flow_verse
+
+
+dependencies = [
+    {"url": "aiflows/ChatWithDemonstrationsFlowModule", "revision": os.getcwd()},
+]
+
+flow_verse.sync_dependencies(dependencies)
+
+if __name__ == "__main__":
+    # ~~~ Set the API information ~~~
+    # OpenAI backend
+
+    api_information = [ApiInfo(backend_used="openai",
+                              api_key = os.getenv("OPENAI_API_KEY"))]
+    
+    
+    # # Azure backend
+    # api_information = ApiInfo(backend_used = "azure",
+    #                           api_base = os.getenv("AZURE_API_BASE"),
+    #                           api_key = os.getenv("AZURE_OPENAI_KEY"),
+    #                           api_version =  os.getenv("AZURE_API_VERSION") )
+
+    root_dir = "."
+    cfg_path = os.path.join(root_dir, "demo.yaml")
+    cfg = read_yaml_file(cfg_path)
+
+    cfg["flow"]["subflows_config"]["chat_flow"]["backend"]["api_infos"] = api_information
+    
+    # ~~~ Instantiate the Flow ~~~
+    flow_with_interfaces = {
+        "flow": hydra.utils.instantiate(cfg['flow'], _recursive_=False, _convert_="partial"),
+        "input_interface": (
+            None
+            if cfg.get( "input_interface", None) is None
+            else hydra.utils.instantiate(cfg['input_interface'], _recursive_=False)
+        ),
+        "output_interface": (
+            None
+            if cfg.get( "output_interface", None) is None
+            else hydra.utils.instantiate(cfg['output_interface'], _recursive_=False)
+        ),
+    }
+    # ~~~ Get the data ~~~
+    data = {"id": 0, "question": "What's the capital of France?"}  # This can be a list of samples
+    # data = {"id": 0, "question": "Who was the NBA champion in 2023?"}  # This can be a list of samples
+    # ~~~ Run inference ~~~
+    path_to_output_file = None
+    # path_to_output_file = "output.jsonl"  # Uncomment this line to save the output to disk
+
+    _, outputs = FlowLauncher.launch(
+        flow_with_interfaces=flow_with_interfaces,
+        data=data,
+        path_to_output_file=path_to_output_file
+    )
+
+    # ~~~ Print the output ~~~
+    flow_output_data = outputs[0]
+    print(flow_output_data)