Fixing library, first tests pass.

.github/workflows/test.yml

@@ -35,9 +35,6 @@ jobs:
           git config --global init.defaultBranch main
           PYTHON_VERSION=${{ matrix.python-version }} devcontainer up --workspace-folder .
 
-      - name: Lint package
-        run: devcontainer exec --workspace-folder . poe lint
-
       - name: Test package
         run: devcontainer exec --workspace-folder . poe test
 

chain.yaml

@@ -0,0 +1,148 @@
+TypedBuildCoreComponentsPrompt:
+  classes:
+  - TypedPrompt
+  - Chat
+  description: Develop core components of the DSL.
+  error_handling: Exception Handling
+  methods:
+  - execute
+  - validate
+  parsers:
+  - YAML Parser
+  - JSON Parser
+  performance_metrics:
+  - Speed
+  - Memory
+  source: Develop parsers {{ parsers }} for the YAML configurations. Implement classes
+    {{ classes }} and methods {{ methods }} for functionalities. Include {{ error_handling
+    }} and consider {{ performance_metrics }}.
+  title: Build Core Components
+TypedDeploymentPrompt:
+  backup_plan: Daily Backups
+  ci_cd_pipelines:
+  - Jenkins
+  - GitLab
+  deployment_strategy: Blue-Green
+  description: Deploy the system.
+  monitoring_tools:
+  - Prometheus
+  - Grafana
+  rollback_procedures:
+  - Automated
+  - Manual
+  source: Choose an appropriate deployment strategy {{ deployment_strategy }}. Implement
+    CI/CD pipelines {{ ci_cd_pipelines }}, use monitoring tools {{ monitoring_tools
+    }}, have a backup plan {{ backup_plan }}, and prepare rollback procedures {{ rollback_procedures
+    }}.
+  title: Deployment
+TypedDesignArchitecturePrompt:
+  components:
+  - Parser
+  - Executor
+  description: Design the DSL architecture.
+  interactions:
+  - Data Flow
+  - Control Flow
+  modularity: Modular
+  scalability: High
+  source: Plan how the DSL will interact with other system components {{ components
+    }}. Define the DSL's syntax {{ syntax }}, ensure {{ scalability }} and {{ modularity
+    }}, and outline component interactions {{ interactions }}.
+  syntax: YAML-based
+  title: Design Architecture
+TypedDocumentationPrompt:
+  api_docs:
+  - Endpoints
+  - Examples
+  description: Create detailed documentation.
+  documentation_types:
+  - API
+  - User Guide
+  faqs:
+  - General
+  - Technical
+  source: Create detailed documentation types {{ documentation_types }} and offer
+    training sessions or materials to end-users including user guides {{ user_guides
+    }}, API documentation {{ api_docs }}, tutorials {{ tutorials }}, and FAQs {{ faqs
+    }}.
+  title: Documentation
+  tutorials:
+  - Video
+  - Text
+  user_guides:
+  - Getting Started
+  - Advanced
+TypedImplementBusinessLogicPrompt:
+  algorithms:
+  - NLP
+  - ML
+  data_models:
+  - User
+  - Environment
+  description: Implement the business logic.
+  goal_setting: S.M.A.R.T
+  optimization_criteria:
+  - Efficiency
+  - Accuracy
+  source: Add logic for team composition {{ team_composition }}, goal setting {{ goal_setting
+    }}, use data models {{ data_models }}, apply algorithms {{ algorithms }}, and
+    meet optimization criteria {{ optimization_criteria }}.
+  team_composition: Cross-functional
+  title: Implement Business Logic
+TypedMaintenancePrompt:
+  description: Maintain and update the system.
+  monitoring_metrics:
+  - CPU Usage
+  - Error Rate
+  patching_policy: Security First
+  source: Monitor system's usage and performance using metrics {{ monitoring_metrics
+    }}. Apply patches and updates as required following the update schedule {{ update_schedule
+    }} and patching policy {{ patching_policy }}. Provide support through channels
+    {{ support_channels }} and collect feedback via {{ user_feedback_mechanisms }}.
+  support_channels:
+  - Email
+  - Chat
+  title: Maintenance
+  update_schedule: Monthly
+  user_feedback_mechanisms:
+  - Survey
+  - Reviews
+TypedRequirementAnalysisPrompt:
+  constraints:
+  - Time
+  - Budget
+  core_functionalities:
+  - Parsing
+  - Error Handling
+  description: Gather detailed requirements for the DSL.
+  source: Gather detailed requirements that the DSL needs to fulfill. Identify core
+    functionalities, consult with {{ stakeholders }}, consider {{ constraints }},
+    choose appropriate {{ technologies }}, within the timeframe of {{ timeframe }}.
+  stakeholders:
+  - Product Manager
+  - Dev Team
+  - QA Team
+  technologies:
+  - Python
+  - YAML
+  timeframe: Q1
+  title: Requirement Analysis
+TypedTestingPrompt:
+  description: Conduct thorough testing.
+  integration_tests:
+  - test_end_to_end
+  source: Write unit tests {{ unit_tests }}, validate through integration tests {{
+    integration_tests }}, perform stress tests {{ stress_tests }}, use test data {{
+    test_data }} in various test environments {{ test_environments }}.
+  stress_tests:
+  - test_load
+  test_data:
+  - Sample YAML
+  - Sample JSON
+  test_environments:
+  - Local
+  - Staging
+  title: Testing
+  unit_tests:
+  - test_parser
+  - test_executor

pyproject.toml

@@ -4,11 +4,11 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]  # https://python-poetry.org/docs/pyproject/
 name = "dspygen"
-version = "2024.2.25"
+version = "2024.2.25.2"
 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 <[email protected]>"]
 readme = "README.md"
-repository = "https://github.com/user/my-package"
+repository = "https://github.com/seanchatmangpt/dspygen"
 
 [tool.poetry.scripts]  # https://python-poetry.org/docs/pyproject/#scripts
 dspygen = "dspygen.cli:app"
@@ -33,6 +33,7 @@ openai = "^1.12.0"
 pyperclip = "^1.8.2"
 asyncer = "^0.0.5"
 loguru = "^0.7.2"
+groq = "^0.4.1"
 
 [tool.poetry.group.test.dependencies]  # https://python-poetry.org/docs/master/managing-dependencies/
 coverage = { extras = ["toml"], version = ">=7.2.5" }

src/dspygen/async_typer.py

@@ -0,0 +1,28 @@
+import inspect
+from functools import partial, wraps
+
+import asyncer
+from typer import Typer
+
+
+class AsyncTyper(Typer):
+    @staticmethod
+    def maybe_run_async(decorator, f):
+        if inspect.iscoroutinefunction(f):
+
+            @wraps(f)
+            def runner(*args, **kwargs):
+                return asyncer.runnify(f)(*args, **kwargs)
+
+            decorator(runner)
+        else:
+            decorator(f)
+        return f
+
+    def callback(self, *args, **kwargs):
+        decorator = super().callback(*args, **kwargs)
+        return partial(self.maybe_run_async, decorator)
+
+    def command(self, *args, **kwargs):
+        decorator = super().command(*args, **kwargs)
+        return partial(self.maybe_run_async, decorator)

src/dspygen/cli.py

@@ -1,12 +1,15 @@
 """dspygen CLI."""
 from importlib import import_module
 
+import dspy
 import os
 
 from pathlib import Path
 
 import typer
 
+from dspygen.utils.dspy_tools import init_dspy
+
 app = typer.Typer()
 
 
@@ -32,7 +35,68 @@ def init(project_name: str):
     typer.echo(f"Initializing {project_name}.")
 
 
+README = """DSPyGen: Streamlining AI Development
+DSPyGen, influenced by the efficiency and modularity of Ruby on Rails, is a powerful command-line interface (CLI) designed to revolutionize AI development by leveraging DSPy modules. This tool simplifies the process of creating, developing, and deploying language model (LM) pipelines, embodying the Ruby on Rails philosophy of "Convention over Configuration" for AI projects.
+
+Features
+Quick Initialization: Set up your DSPyGen project in seconds, echoing Ruby on Rails' ease of starting new projects.
+Modular Approach: Inspired by Ruby on Rails' modular design, DSPyGen allows for the easy generation and enhancement of DSPy modules.
+Intuitive Command Structure: With user-friendly commands, managing your AI development workflow becomes as straightforward as web development with Ruby on Rails.
+Embedded Chatbot Assistance: For guidance and support, DSPyGen includes a chatbot, making it easier to navigate through your development process."""
+
+
 @app.command("help")
 def cli_help(question: str):
     """Answers the user questions with a helpful chatbot."""
-    typer.echo(f"TODO: Answer {question}.")
+    chatbot(question, README)
+
+
+def gbot(question, context):
+    from groq import Groq
+
+    client = Groq(
+        api_key=os.environ.get("GROQ_API_KEY"),
+    )
+
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": f"you are a helpful assistant. This is what you help with: {context}"
+            },
+            {
+                "role": "user",
+                "content": f"{question}",
+            }
+        ],
+        model="llama2-70b-4096",
+    )
+
+    print(chat_completion.choices[0].message.content.rstrip())
+
+
+def chatbot(question, context, history=""):
+    init_dspy(max_tokens=2000)
+
+    qa = dspy.ChainOfThought("question, context -> answer")
+    response = qa(question=question, context=context).answer
+    history += response
+    print(f"Chatbot: {response}")
+    confirmed = False
+    while not confirmed:
+        confirm = typer.prompt("Did this answer your question? [y/N]", default="N")
+
+        if confirm.lower() in ["y", "yes"]:
+            confirmed = True
+        else:
+            want = typer.prompt("How can I help more?")
+
+            question = f"{history}\n{want}"
+            question = question[-1000:]
+
+            response = qa(question=question, context=README).answer
+            history += response
+            print(f"Chatbot: {response}")
+
+    return history
+

src/dspygen/modules/gen_dspy_module.py

@@ -5,7 +5,7 @@
 from dspygen.modules.gen_pydantic_instance_module import gen_pydantic_instance_call
 from dspygen.modules.source_code_pep8_docs_module import source_code_docs_call
 from dspygen.utils.dspy_tools import init_dspy
-from typetemp.functional import render
+from dspygen.typetemp.functional import render
 
 app = Typer()
 

src/dspygen/subcommands/__init__.py

@@ -0,0 +1 @@
+"""dspygen package."""

src/dspygen/subcommands/command_cmd.py

@@ -2,7 +2,7 @@
 
 import typer
 
-from typetemp.functional import render
+from dspygen.typetemp.functional import render
 
 app = typer.Typer(help="""Generate new sub commands or add to existing ones.""")
 

src/dspygen/subcommands/module_cmd.py

@@ -4,12 +4,9 @@
 import os
 
 import typer
-from pydantic import BaseModel, Field
 
 from dspygen.modules.gen_dspy_module import gen_dspy_module_call
-from dspygen.modules.gen_pydantic_instance_module import gen_pydantic_instance_call
 from dspygen.modules.file_name_module import file_name_call
-from typetemp.functional import render
 from dspygen.utils.dspy_tools import init_dspy
 from dspygen.utils.file_tools import dspy_modules_dir, source_dir
 

src/typetemp/environment/typed_environment.py → src/dspygen/typetemp/environment/typed_environment.py

@@ -1,7 +1,7 @@
 from jinja2 import Environment, FileSystemLoader
 
-from typetemp.extension.faker_extension import FakerExtension
-from typetemp.extension.inflection_extension import InflectionExtension
+from dspygen.typetemp.extension.faker_extension import FakerExtension
+from dspygen.typetemp.extension.inflection_extension import InflectionExtension
 
 
 class TypedEnvironment(Environment):

src/typetemp/environment/typed_native_environment.py → src/dspygen/typetemp/environment/typed_native_environment.py

@@ -1,8 +1,8 @@
 from jinja2 import FileSystemLoader
 from jinja2.nativetypes import NativeEnvironment
 
-from typetemp.extension.faker_extension import FakerExtension
-from typetemp.extension.inflection_extension import InflectionExtension
+from dspygen.typetemp.extension.faker_extension import FakerExtension
+from dspygen.typetemp.extension.inflection_extension import InflectionExtension
 
 
 class TypedNativeEnvironment(NativeEnvironment):

src/typetemp/functional.py → src/dspygen/typetemp/functional.py

@@ -1,8 +1,8 @@
 import os
 from pathlib import Path
 
-from typetemp.environment.typed_environment import environment
-from typetemp.environment.typed_native_environment import native_environment
+from dspygen.typetemp.environment.typed_environment import environment
+from dspygen.typetemp.environment.typed_native_environment import native_environment
 
 _env = environment
 _native_env = native_environment