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README.md

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+# Persana Lead Gen Agent
+
+Uses a process of human in the loop iterative refinement search to find leads given a company description and target customer description.
+
+## Usage
+
+### With uv (preferred)
+
+1. Create a new folder
+2. [Install uv](https://docs.astral.sh/uv/getting-started/installation/)
+3. Init workspace
+
+```bash
+uv init
+```
+
+4. Install dependencies
+
+```bash
+uv add textual rich modaic dspy python-dotenv
+```
+
+5. Run the file
+
+```bash
+uv run main.py
+```
+
+### With pip
+
+1. Copy `main.py` to a new workspace folder.
+2. Create a `.env` file with your API keys using the `.example.env` file.
+3. Install dependencies
+
+```bash
+pip install textual rich modaic dspy python-dotenv
+```
+
+3. Run the file with `python main.py`.
+
+Follow the prompts to create a new `dataset.jsonl` file.
+
+## Run Prompt Optimization
+
+Once you have a `dataset.jsonl` file, you can optimize the agent with dspy's built in prompt optimization.
+
+1. Create a file called `compile.py` with the following code. Replace `<your-username>` with your modaic username.
+
+```python
+from dotenv import load_dotenv
+import os
+import dspy
+import json
+from dspy import Prediction, Example
+from modaic import AutoAgent
+
+searcher = AutoAgent.from_precompiled("swagginty/persana-lead-gen", api_key=os.getenv("PERSANA_KEY"))
+feedback_creator = searcher.feedback_creator
+
+
+class SearchExample(Example):
+    company_description: str
+    target_customer: str
+    selected_profiles: list[dict]
+
+
+class SearchPrediction(Prediction):
+    profiles: list[dict]
+    search_parameters: dict
+
+
+def evaluate_results(
+    target: SearchExample,
+    predictied: SearchPrediction,
+    trace=None,
+    pred_name=None,
+    pred_trace=None,
+) -> Prediction:
+    """
+    Evaluates the search results target results were retrieved
+    """
+    # How many of the target profiles were retrieved
+    pred_ids = {result["profile_id"] for result in predictied.profiles}
+    count = 0
+    for t_result in target.selected_profiles:
+        if t_result["profile_id"] in pred_ids:
+            count += 1
+
+    score = count / len(target.selected_profiles)
+
+    target_ids = {result["profile_id"] for result in target.selected_profiles}
+    # Which retrieved profiles were target profiles
+    selected_preds = [
+        result for result in predictied.profiles if result["profile_id"] in target_ids
+    ]
+    # Which retrieved profiles were not target profiles
+    unselected_preds = [
+        result
+        for result in predictied.profiles
+        if result["profile_id"] not in target_ids
+    ]
+    # Resuse feedback creator to get feedback for prompt creation
+    feedback = feedback_creator(
+        search_parameters=predictied.search_parameters,
+        selected_profiles=selected_preds,
+        unselected_profiles=unselected_preds,
+        user_feedback=None,
+    ).feedback
+    return Prediction(
+        score=score,
+        feedback=feedback,
+    )
+
+
+if __name__ == "__main__":
+    load_dotenv()
+
+    data = [json.loads(line) for line in open("dataset.jsonl", "r")]
+    trainset = [
+        dspy.Example(
+            company_description=e["company_description"],
+            target_customer=e["target_customer"],
+            selected_profiles=e["selected_profiles"],
+        ).with_inputs("company_description", "target_customer")
+        for e in data
+    ]
+    compiler = dspy.GEPA(
+        metric=evaluate_results,
+        auto="light",
+        reflection_lm=dspy.LM("openai/gpt-5", temperature=1.0, max_tokens=32000),
+    )
+    compiled_searcher = compiler.compile(searcher, trainset=trainset)
+    compiled_searcher.save("compiled_searcher.json")
+    compiled_searcher.push_to_hub("<your-username>/persana-lead-gen") # Replace <your-username> with your username
+```
+
+2. Run the file
+   With uv:
+
+```bash
+uv run compile.py
+```
+
+With python:
+
+```bash
+python compile.py
+```