diff --git a/poetry.lock b/poetry.lock
index 49e2144..7cbf1db 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1597,6 +1597,104 @@ files = [
     {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
+[[package]]
+name = "ijson"
+version = "3.2.3"
+description = "Iterative JSON parser with standard Python iterator interfaces"
+optional = false
+python-versions = "*"
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+    {file = "ijson-3.2.3.tar.gz", hash = "sha256:10294e9bf89cb713da05bc4790bdff616610432db561964827074898e174f917"},
+]
+
 [[package]]
 name = "importlib-metadata"
 version = "7.0.1"
@@ -5445,4 +5543,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<4.0"
-content-hash = "06f06416910d0f882c2e755ad286c7e6cea974948962e2afd34286d8aa1b5209"
+content-hash = "f88434bbdcaecd0c87291ea47bb9d4e22c734687c0f76b287eb0500a8dfa5999"
diff --git a/pyproject.toml b/pyproject.toml
index 99f5266..209f7ce 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]  # https://python-poetry.org/docs/pyproject/
 name = "dspygen"
-version = "2024.3.14.2"
+version = "2024.3.17"
 description = "A Ruby on Rails style framework for the DSPy (Demonstrate, Search, Predict) project for Language Models like GPT, BERT, and LLama."
 authors = ["Sean Chatman <info@chatmangpt.com>"]
 readme = "README.md"
@@ -43,6 +43,7 @@ paho-mqtt = "^2.0.0"
 psutil = "^5.9.8"
 st-pages = "^0.4.5"
 pykka = "^4.0.2"
+ijson = "^3.2.3"
 
 [tool.poetry.group.test.dependencies]  # https://python-poetry.org/docs/master/managing-dependencies/
 coverage = { extras = ["toml"], version = ">=7.2.5" }
diff --git a/src/dspygen/dsl/dsl_pipeline_executor.py b/src/dspygen/dsl/dsl_pipeline_executor.py
index db15d2b..798ac14 100644
--- a/src/dspygen/dsl/dsl_pipeline_executor.py
+++ b/src/dspygen/dsl/dsl_pipeline_executor.py
@@ -10,6 +10,7 @@
 from dspygen.dsl.utils.dsl_language_model_utils import _get_language_model_instance
 from dspygen.dsl.dsl_pydantic_models import PipelineDSLModel, LanguageModelConfig
 from dspygen.dsl.utils.dsl_module_utils import _get_module_instance
+from dspygen.dsl.utils.dsl_retrieval_model_utils import _get_retrieval_model_instance
 from dspygen.dsl.utils.dsl_signature_utils import _create_signature_from_model
 from dspygen.typetemp.functional import render
 
@@ -45,8 +46,8 @@ def _execute_step(pipeline, step):
     Execute a step in a pipeline. Creates the LM, renders the args using Jinja2,
     runs the module, and updates the context.
     """
-    if not pipeline.models:
-        pipeline.models = [LanguageModelConfig(label="default", name="OpenAI", args={})]
+    if not pipeline.lm_models:
+        pipeline.lm_models = [LanguageModelConfig(label="default", name="OpenAI", args={})]
 
     rendered_args = {arg: render(str(value), **pipeline.context) for arg, value in step.args.items()}
 
@@ -54,7 +55,9 @@ def _execute_step(pipeline, step):
 
     lm_inst = _get_language_model_instance(pipeline, step)
 
-    with dspy.context(lm=lm_inst):
+    rm_inst = _get_retrieval_model_instance(pipeline, step)
+
+    with dspy.context(lm=lm_inst, rm=rm_inst):
         module_output = module_inst.forward(**rendered_args)
 
     pipeline.context[step.module] = module_output
@@ -90,8 +93,8 @@ async def run_pipeline(request: PipelineRequest):
 
 
 def main():
-    # context = execute_pipeline('examples/blog_pipeline.yaml')
-    context = execute_pipeline('/Users/candacechatman/dev/dspygen/pipeline.yaml', {"news": "$12,500 Retainer Contract"})
+    context = execute_pipeline('/Users/candacechatman/dev/dspygen/src/dspygen/dsl/examples/example_pipeline.yaml')
+    # context = execute_pipeline('/Users/candacechatman/dev/dspygen/pipeline.yaml', {"news": "$12,500 Retainer Contract"})
     # context = execute_pipeline('examples/example_pipeline.yaml')
 
     print(context)
diff --git a/src/dspygen/dsl/dsl_pydantic_models.py b/src/dspygen/dsl/dsl_pydantic_models.py
index 8c0a516..3c430b4 100644
--- a/src/dspygen/dsl/dsl_pydantic_models.py
+++ b/src/dspygen/dsl/dsl_pydantic_models.py
@@ -1,82 +1,110 @@
-from dspygen.utils.yaml_tools import YAMLMixin
-from pydantic import BaseModel, Extra
-from typing import List, Dict, Optional, Union
+from typing import Optional
 
 
-class InputFieldModel(BaseModel):
-    name: str
-    desc: Optional[str] = None
+from pydantic import BaseModel, Field
 
+from dspygen.utils.yaml_tools import YAMLMixin
 
-class OutputFieldModel(BaseModel):
-    name: str
-    prefix: Optional[str] = ""
-    desc: Optional[str] = ""
 
+class InputFieldModel(BaseModel):
+    """Defines an input field for a DSPy Signature."""
+    name: str = Field(
+        ...,
+        description="The key used to access and pass the input within the Signature.",
+    )
+    prefix: str = Field(
+        "",
+        description="Optional additional context or labeling for the input field.",
+    )
+    desc: str = Field(
+        ...,
+        description="Description of the input field's purpose or the nature of content it should contain.",
+    )
 
+class OutputFieldModel(BaseModel):
+    """Defines an output field for a DSPy Signature."""
+
+    name: str = Field(
+        ...,
+        description="The key used to access and pass the input within the Signature.",
+    )
+    prefix: str = Field(
+        "",
+        description="Optional additional context or labeling for the output field.",
+    )
+    desc: str = Field(
+        ...,
+        description="Description of the output field's purpose or the nature of content it should contain.",
+    )
+
+
+# Define SignatureDSLModel for capturing signature details
 class SignatureDSLModel(BaseModel):
-    name: str
-    docstring: Optional[str] = None
-    inputs: List[InputFieldModel] = []
-    outputs: List[OutputFieldModel] = []
+    name: str = Field(..., description="The unique name identifying the Signature.")
+    docstring: str = Field(..., description="Documentation of the Signature's purpose.")
+    inputs: list[InputFieldModel] = Field(default=[], description="List of input fields required by the Signature.")
+    outputs: list[OutputFieldModel] = Field(default=[], description="List of output fields produced by the Signature.")
+
 
 
 class ArgumentModel(BaseModel):
     name: str
-    value: Union[str, Dict[str, str]] = ""  # Allow for both direct values and references
+    value: str | dict = ""  # Allow for both direct values and references
 
 
+# Define ModuleDSLModel for capturing module details
 class ModuleDSLModel(BaseModel):
-    name: str = ""
-    signature: str = ""
-    assertions: List[str] = []
-    suggestions: List[str] = []
-    predictor: Optional[str] = "Predict"
-    args: List[ArgumentModel] = []
+    name: str = Field(..., description="Name of the module. Used for referencing within the pipeline.")
+    signature: str = Field(..., description="Name of the signature associated with this module.")
+    predictor: Optional[str] = Field("Predict", description="Type of predictor to be used with this module.")
+    args: list[ArgumentModel] = Field(default=[],
+                                      description="List of arguments to be passed to the module during its execution.")
 
 
+# Define PipelineStepModel for pipeline steps
 class PipelineStepModel(BaseModel):
-    module: str
-    assertions: List[str] = []
-    suggestions: List[str] = []
-    signature: str = ""
-    model: str = "default"
-    args: dict = {}  # Support nested argument structures
+    module: str = Field(..., description="Name of the module to be executed in this step of the pipeline.")
+    signature: str = Field(default="",
+                           description="Signature associated with this step. Optional if defined within the module.")
+    lm_model: str = Field(default="default", description="Identifier of the language model to be used in this step.")
+    rm_model: str = Field(default="default", description="Identifier of the retrieval model to be used in this step.")
+    args: dict = Field(default={}, description="Arguments for the module in this step.")
 
 
 class LanguageModelConfig(BaseModel):
-    label: str
-    name: str
+    label: str = Field(default="default", description="Used for referencing in the Modules")
+    name: str = Field(default="OpenAI", description="The class name of the dspy language model to use")
     args: dict = {}
 
 
-class AssertionModel(BaseModel):
-    label: str
-    logic: str
-    message: str
-
-
-class SuggestionModel(BaseModel):
-    label: str
-    assertion: str
-    message: str
+class RetrievalModelConfig(BaseModel):
+    label: str = Field(..., description="Used for referencing in the Modules")
+    name: str = Field(..., description="The class name of the dspy retrieval model to use")
+    args: dict = {}
 
 
 class PipelineConfigModel(BaseModel):
-    global_signatures: Dict[str, SignatureDSLModel] = []
-    current_step: PipelineStepModel = None
+    global_signatures: dict = Field(default={},
+                                    description="A dictionary of global signatures available in the pipeline.")
+    current_step: Optional[PipelineStepModel] = Field(None,
+                                                      description="The current step being executed in the pipeline.")
 
     class Config:
-        extra = Extra.allow
+        extra = "allow"
 
 
 class PipelineDSLModel(BaseModel, YAMLMixin):
-    assertions: List[AssertionModel] = []
-    suggestions: List[SuggestionModel] = []
-    models: List[LanguageModelConfig] = []
-    signatures: List[SignatureDSLModel] = []
-    modules: List[ModuleDSLModel] = []
-    steps: List[PipelineStepModel] = []
-    context: dict = {}
-    config: PipelineConfigModel = PipelineConfigModel()
-
+    lm_models: list[LanguageModelConfig] = Field(default=[],
+                                                 description="list of language model configurations used in the pipeline.")
+    rm_models: list[RetrievalModelConfig] = Field(default=[],
+                                                 description="list of retrieval model configurations used in the pipeline.")
+    signatures: list[SignatureDSLModel] = Field(default=[],
+                                                description="list of signatures defined for use in the pipeline.")
+    modules: list[ModuleDSLModel] = Field(default=[],
+                                          description="list of modules defined for execution in the pipeline.")
+    steps: list[PipelineStepModel] = Field(default=[],
+                                           description="Sequential steps to be executed in the pipeline.")
+    context: dict = Field(default={},
+                          description="A context dictionary for storing global values accessible across the pipeline.")
+    config: PipelineConfigModel = Field(default_factory=PipelineConfigModel,
+                                        description="Configuration settings for the pipeline execution.")
diff --git a/src/dspygen/dsl/examples/blog_pipeline.yaml b/src/dspygen/dsl/examples/blog_pipeline.yaml
index 1249cfb..ab81fd4 100644
--- a/src/dspygen/dsl/examples/blog_pipeline.yaml
+++ b/src/dspygen/dsl/examples/blog_pipeline.yaml
@@ -1,10 +1,4 @@
-assertions:
-  - label: "AssertLen"
-    logic: "len(result) == length"
-    message: "The length of the {{ result }} should be {{ length}} but was {{ result|length }}"
-
 steps:
   - module: "dspygen.modules.blog_module.BlogModule"
-
     args:
-      subject: "Buckminster Fuller's book Critical Path off the charts!"
+      subject: "{{ subject }}"
diff --git a/src/dspygen/dsl/examples/example_pipeline.yaml b/src/dspygen/dsl/examples/example_pipeline.yaml
index 30280c8..dadd1d1 100644
--- a/src/dspygen/dsl/examples/example_pipeline.yaml
+++ b/src/dspygen/dsl/examples/example_pipeline.yaml
@@ -1,4 +1,4 @@
-models:
+lm_models:
   - label: "default"
     name: "OpenAI"
     args:
@@ -44,7 +44,7 @@ modules:
     predictor: "Predict"
     args:
       - name: "raw_data"
-        value: "{{ user_input_data }}"
+        value: "{{ user_input }}"
       - name: "data_format"
         value: "JSON"
 
@@ -57,13 +57,13 @@ modules:
 
 steps:
   - module: "DataProcessorModule"
-    model: "default"
+    lm_model: "default"
     args:
       raw_data: "id, name, age\n1, John, 25\n2, Jane, 30"
       data_format: "YAML"
 
   - module: "ReportGeneratorModule"
-    model: "fast"
+    lm_model: "fast"
     args:
       processed_data: "{{ processed_data }}"
       report_template: "templates/standard_report.html"
diff --git a/src/dspygen/dsl/utils/dsl_language_model_utils.py b/src/dspygen/dsl/utils/dsl_language_model_utils.py
index 701810c..069093f 100644
--- a/src/dspygen/dsl/utils/dsl_language_model_utils.py
+++ b/src/dspygen/dsl/utils/dsl_language_model_utils.py
@@ -1,13 +1,15 @@
 import dspy
 
+from dspygen.dsl.dsl_pydantic_models import PipelineDSLModel
 
-def _get_language_model_instance(pipeline, step):
+
+def _get_language_model_instance(pipeline: PipelineDSLModel, step):
     """
     Get the language model instance for a given step from the top level definition.
     """
-    lm_label = step.model
+    lm_label = step.lm_model
     # Find the lm class within the dspy module. Need to import the class dynamically from the dspy module
-    lm_config = next((m for m in pipeline.models if m.label == lm_label), None)
+    lm_config = next((m for m in pipeline.lm_models if m.label == lm_label), None)
     lm_class = getattr(dspy, lm_config.name)
     lm_inst = lm_class(**lm_config.args)
     return lm_inst
\ No newline at end of file
diff --git a/src/dspygen/dsl/utils/dsl_retrieval_model_utils.py b/src/dspygen/dsl/utils/dsl_retrieval_model_utils.py
new file mode 100644
index 0000000..b53717b
--- /dev/null
+++ b/src/dspygen/dsl/utils/dsl_retrieval_model_utils.py
@@ -0,0 +1,17 @@
+import dspy
+
+from dspygen.dsl.dsl_pydantic_models import PipelineStepModel, PipelineDSLModel
+
+
+def _get_retrieval_model_instance(pipeline: PipelineDSLModel, step: PipelineStepModel):
+    """
+    Get the retrieval model instance for a given step from the top level definition.
+    """
+    rm_label = step.rm_model
+    # Find the rm class within the dspy module. Need to import the class dynamically from the dspy module
+    rm_config = next((m for m in pipeline.rm_models if m.label == rm_label), None)
+
+    if rm_config and hasattr(dspy, rm_config.name):
+        rm_class = getattr(dspy, rm_config.name)
+        rm_inst = rm_class(**rm_config.args)
+        return rm_inst
diff --git a/src/dspygen/experiments/chatgpt_conversation_parser.py b/src/dspygen/experiments/chatgpt_conversation_parser.py
new file mode 100644
index 0000000..0a3c2a1
--- /dev/null
+++ b/src/dspygen/experiments/chatgpt_conversation_parser.py
@@ -0,0 +1,89 @@
+import ijson
+
+
+from typing import Optional
+
+from pydantic import BaseModel, ValidationError
+
+
+class Author(BaseModel):
+    role: str
+    name: Optional[str] = None
+    metadata: dict
+
+
+class ContentPart(BaseModel):
+    content_type: str
+    parts: list[str] | None
+
+
+class Message(BaseModel):
+    id: str
+    author: Author
+    content: ContentPart
+    status: str
+    metadata: dict
+
+
+class Data(BaseModel):
+    id: str
+    message: Message | None
+    parent: str | None
+    children: list[str]
+
+
+class Conversation(BaseModel):
+    title: str
+    mapping: dict
+
+
+# Function to process each conversation chunk
+def process_conversations_chunk(chunk):
+    # Define Pydantic models as before
+
+    # Process each conversation in the chunk
+    for chunked in chunk:
+        try:
+            conversation = Conversation(**chunked)
+            # Do whatever processing you need with the conversation
+            # print(f"Title: {conversation.title}")
+            for key in conversation.mapping:
+                data = Data(**conversation.mapping[key])
+                if data.message and data.message.author.role == "assistant":
+                    for part in data.message.content.parts:
+                        if "ContractSign" in part:
+                            print(part)
+                        # encoding = tiktoken.encoding_for_model("text-embedding-ada-002")
+                        # print(len(encoding.encode(part)))
+                        # print(part)
+        except ValidationError:
+            # print(e)
+            pass
+
+
+def main():
+    # Define the path to your large JSON file
+    json_file_path = "/Users/candacechatman/dev/dspygen/data/conversations.json"
+
+    # Open the JSON file for streaming
+    with open(json_file_path, "rb") as json_file:
+        conversations_generator = ijson.items(
+            json_file, "item"
+        )  # Assumes each conversation is a separate JSON object
+
+        # Process the conversations in chunks (adjust the chunk size as needed)
+        chunk_size = 10  # Define your desired chunk size
+        chunk = []
+        for conversation in conversations_generator:
+            chunk.append(conversation)
+            if len(chunk) >= chunk_size:
+                process_conversations_chunk(chunk)
+                chunk = []
+
+        # Process any remaining conversations in the last chunk
+        if chunk:
+            process_conversations_chunk(chunk)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/dspygen/experiments/wizard.py b/src/dspygen/experiments/wizard.py
new file mode 100644
index 0000000..8136457
--- /dev/null
+++ b/src/dspygen/experiments/wizard.py
@@ -0,0 +1,69 @@
+import typer
+from typing import Optional
+from pydantic import BaseModel, ValidationError
+import yaml
+import os
+import json
+
+from dspygen.dsl.dsl_pydantic_models import PipelineConfigModel
+
+app = typer.Typer()
+
+
+def load_user_history() -> dict:
+    """
+    Simulate loading user's history to predict configurations.
+    """
+    # Placeholder for loading and analyzing user's history
+    return {}
+
+
+def generate_optimized_configuration(user_history: dict) -> PipelineConfigModel:
+    """
+    Generate an optimized pipeline configuration based on the user's history.
+    """
+    # Placeholder for AI logic to generate optimized configs
+    return PipelineConfigModel()
+
+
+def save_configuration(config: PipelineConfigModel, filename: str):
+    """
+    Save the generated pipeline configuration to a YAML file.
+    """
+    with open(filename, 'w') as file:
+        yaml.dump(json.loads(config.json()), file)
+
+
+@app.command()
+def create_pipeline(filename: Optional[str] = typer.Option(None, "--filename", "-f",
+                                                           help="Filename to save the pipeline configuration.")):
+    """
+    Main command to initiate the pipeline configuration generation process.
+    """
+    user_history = load_user_history()
+    optimized_config = generate_optimized_configuration(user_history)
+
+    # Proactively offer the optimized configuration with an option for details or modifications
+    typer.echo("Based on your history, an optimized pipeline configuration has been generated.")
+    action = typer.prompt("Do you want to proceed with the optimized configuration? (Y/n/details)", default="Y")
+
+    if action.lower() == 'n':
+        typer.echo("Exiting... You can start over and customize your configuration.")
+        raise typer.Exit()
+    elif action.lower() == 'details':
+        typer.echo(optimized_config.json(indent=2))
+        modify = typer.prompt("Do you want to modify this configuration? (Y/n)", default="n")
+        if modify.lower() == 'y':
+            # Placeholder for modification logic
+            typer.echo("Modifying configurations... (This part is yet to be implemented)")
+
+    # Confirm and save the configuration
+    if not filename:
+        filename = typer.prompt("Enter a filename to save your configuration (default: optimized_pipeline.yaml): ",
+                                default="optimized_pipeline.yaml")
+    save_configuration(optimized_config, filename)
+    typer.echo(f"Configuration saved to {filename}. Your optimized pipeline is ready!")
+
+
+if __name__ == "__main__":
+    app()
diff --git a/test_pipeline.http b/test_pipeline.http
index 57e7e83..7dbc86b 100644
--- a/test_pipeline.http
+++ b/test_pipeline.http
@@ -1,4 +1,4 @@
-POST http://127.0.0.1:8888/execute_pipeline/
+POST http://127.0.0.1:8000/execute_pipeline/
 Content-Type: application/json
 
 {