diff --git a/.github/workflows/pr_comment.yml b/.github/workflows/pr_comment.yml
new file mode 100644
index 000000000..87afb5094
--- /dev/null
+++ b/.github/workflows/pr_comment.yml
@@ -0,0 +1,37 @@
+name: Comment for PR
+
+on:
+  workflow_run:
+    workflows: ["Check for Ruff Fix, Test, and Build"]
+    types:
+      - completed
+
+jobs:
+  comment:
+    runs-on: ubuntu-latest
+    steps:
+      - name: "Download Ruff Fix Outcome Artifact"
+        uses: actions/download-artifact@v2
+        with:
+          name: ruff-fix-outcome
+          path: artifacts
+
+      - name: "Read Ruff Fix Outcome"
+        id: ruff_outcome
+        run: |
+          outcome=$(cat artifacts/ruff_fix_outcome.txt)
+          echo "RUFF_FIX_OUTCOME=$outcome" >> $GITHUB_ENV
+
+      - name: "Comment on PR if Ruff Fix Failed"
+        if: env.RUFF_FIX_OUTCOME == 'true'
+        uses: actions/github-script@v5
+        with:
+          script: |
+            const pr_number = ${{ github.event.workflow_run.pull_requests[0].number }};
+            const message = 'It seems like there are issues with the formatting. Please run `ruff check . --fix-only` and commit to address these issues.';
+            github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: pr_number,
+              body: message
+            });
diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index 6b4221ade..e57c6ff0f 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -1,29 +1,50 @@
-name: Fix, Test, and Build
+name: Lint, Test, and Build
 
 on:
-  push:
-    branches:
-      - main
   pull_request:
+    types: [opened, synchronize, reopened]
 
 env:
   POETRY_VERSION: "1.6.1"
 
 jobs:
   fix:
-    name: Apply Ruff Fix
+    name: Check Ruff Fix
     runs-on: ubuntu-latest
     permissions:
       contents: write
+      pull-requests: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
-      - uses: chartboost/ruff-action@v1
+      - name: Ruff Fix Attempt
+        id: ruff_fix
+        uses: chartboost/ruff-action@v1
         with:
-          args: --fix-only
-      - uses: stefanzweifel/git-auto-commit-action@v5
+          args: --fix-only --exit-non-zero-on-fix
+        continue-on-error: true
+
+      - name: Determine Ruff Fix Outcome
+        run: |
+          if [ ${{ steps.ruff_fix.outcome }} == 'failure' ]; then
+            echo "RUFF_FAILED=true" >> $GITHUB_ENV
+            echo ${{ steps.ruff_fix.outcome }} > ruff_fix_outcome.txt
+          else
+            echo "RUFF_FAILED=false" >> $GITHUB_ENV
+            echo ${{ steps.ruff_fix.outcome }} > ruff_fix_outcome.txt
+          fi
+
+      - uses: actions/upload-artifact@v2
         with:
-          commit_message: "Automatic Style fixes"
+          name: ruff-fix-outcome
+          path: ruff_fix_outcome.txt
+
+      - name: Fail Workflow if Ruff Fix Failed
+        if: steps.ruff_fix.outcome == 'failure'
+        run: |
+          echo "Ruff fix failed, failing the workflow."
+          echo "Please run 'ruff check . --fix-only' locally and push the changes."
+          exit 1
 
   test:
     name: Run Tests
@@ -31,10 +52,9 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9"]
+    if: github.event_name == 'pull_request'
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
       - name: Load cached Poetry installation
         id: cached-poetry
         uses: actions/cache@v3
@@ -64,10 +84,9 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9"]
+    if: github.event_name == 'pull_request'
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
       - name: Load cached Poetry installation
         id: cached-poetry
         uses: actions/cache@v3
@@ -97,10 +116,9 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.9"]
+    if: github.event_name == 'pull_request'
     steps:
       - uses: actions/checkout@v4
-        with:
-          ref: ${{ github.head_ref }}
       - name: Load cached Poetry installation
         id: cached-poetry
         uses: actions/cache@v3
diff --git a/docs/api/local_language_model_clients/HFModel.md b/docs/api/local_language_model_clients/HFModel.md
new file mode 100644
index 000000000..162238ae5
--- /dev/null
+++ b/docs/api/local_language_model_clients/HFModel.md
@@ -0,0 +1,7 @@
+# dspy.HFModel
+
+Initialize `HFModel` within your program with the desired model to load in. Here's an example call:
+
+```python
+llama = dspy.HFModel(model = 'meta-llama/Llama-2-7b-hf')
+```
\ No newline at end of file
diff --git a/docs/api/local_language_model_clients/MLC.md b/docs/api/local_language_model_clients/MLC.md
new file mode 100644
index 000000000..6a36f374b
--- /dev/null
+++ b/docs/api/local_language_model_clients/MLC.md
@@ -0,0 +1,41 @@
+# dspy.ChatModuleClient
+
+## Prerequisites
+
+1. Install the required packages using the following commands:
+   
+   ```shell
+   pip install --no-deps --pre --force-reinstall mlc-ai-nightly-cu118 mlc-chat-nightly-cu118 -f https://mlc.ai/wheels
+   pip install transformers
+   git lfs install
+   ```
+   
+   Adjust the pip wheels according to your OS/platform by referring to the provided commands in [MLC packages](https://mlc.ai/package/).
+
+## Running MLC Llama-2 models
+
+1. Create a directory for prebuilt models:
+
+   ```shell
+   mkdir -p dist/prebuilt
+   ```
+   
+2. Clone the necessary libraries from the repository:
+
+   ```shell
+   git clone https://github.com/mlc-ai/binary-mlc-llm-libs.git dist/prebuilt/lib
+   cd dist/prebuilt
+   ```
+   
+3. Choose a Llama-2 model from [MLC LLMs](https://huggingface.co/mlc-ai) and clone the model repository:
+
+   ```shell
+   git clone https://huggingface.co/mlc-ai/mlc-chat-Llama-2-7b-chat-hf-q4f16_1
+   ```
+
+4. Initialize the `ChatModuleClient` within your program with the desired parameters. Here's an example call:
+
+   ```python
+   llama = dspy.ChatModuleClient(model='dist/prebuilt/mlc-chat-Llama-2-7b-chat-hf-q4f16_1', model_path='dist/prebuilt/lib/Llama-2-7b-chat-hf-q4f16_1-cuda.so')
+   ```
+Please refer to the [official MLC repository](https://github.com/mlc-ai/mlc-llm) for more detailed information and [documentation](https://mlc.ai/mlc-llm/docs/get_started/try_out.html).
diff --git a/docs/api/local_language_model_clients/Ollama.md b/docs/api/local_language_model_clients/Ollama.md
new file mode 100644
index 000000000..9a063e661
--- /dev/null
+++ b/docs/api/local_language_model_clients/Ollama.md
@@ -0,0 +1,45 @@
+# dspy.OllamaLocal
+
+:::note
+Adapted from documentation provided by https://github.com/insop
+:::
+
+Ollama is a good software tool that allows you to run LLMs locally, such as Mistral, Llama2, and Phi.
+The following are the instructions to install and run Ollama.
+
+### Prerequisites
+
+Install Ollama by following the instructions from this page:
+
+- https://ollama.ai
+
+Download model: `ollama pull`
+
+Download a model by running the `ollama pull` command. You can download Mistral, Llama2, and Phi.
+
+```bash
+# download mistral
+ollama pull mistral
+```
+
+Here is the list of other models you can download:
+- https://ollama.ai/library
+
+### Running Ollama model
+
+Run model: `ollama run`
+
+You can test a model by running the model with the `ollama run` command.
+
+```bash
+# run mistral
+ollama run mistral
+```
+
+### Sending requests to the server
+
+Here is the code to load a model through Ollama:
+
+```python
+lm = dspy.OllamaLocal(model='mistral')
+```
diff --git a/docs/api/local_language_model_clients/TGI.md b/docs/api/local_language_model_clients/TGI.md
new file mode 100644
index 000000000..0b8311cc8
--- /dev/null
+++ b/docs/api/local_language_model_clients/TGI.md
@@ -0,0 +1,61 @@
+# dspy.HFClientTGI
+
+## Prerequisites
+
+- Docker must be installed on your system. If you don't have Docker installed, you can get it from [here](https://docs.docker.com/get-docker/).
+
+## Setting up the Text-Generation-Inference Server
+
+1. Clone the Text-Generation-Inference repository from GitHub by executing the following command:
+
+   ```
+   git clone https://github.com/huggingface/text-generation-inference.git
+   ```
+
+2. Change into the cloned repository directory:
+
+   ```
+   cd text-generation-inference
+   ```
+
+3. Execute the Docker command under the "Get Started" section to run the server:
+
+
+   ```
+   model=meta-llama/Llama-2-7b-hf # set to the specific Hugging Face model ID you wish to use.
+   num_shard=2 # set to the number of shards you wish to use.
+   volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
+
+   docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:0.9 --model-id $model --num-shard $num_shard
+   ```
+
+   This command will start the server and make it accessible at `http://localhost:8080`.
+
+If you want to connect to [Meta Llama 2 models](https://huggingface.co/meta-llama), make sure to use version 9.3 (or higher) of the docker image (ghcr.io/huggingface/text-generation-inference:0.9.3) and pass in your huggingface token as an environment variable.
+
+```
+   docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data -e HUGGING_FACE_HUB_TOKEN={your_token} ghcr.io/huggingface/text-generation-inference:0.9.3 --model-id $model --num-shard $num_shard
+```
+
+## Sending requests to the server
+
+After setting up the text-generation-inference server and ensuring that it displays "Connected" when it's running, you can interact with it using the `HFClientTGI`.
+
+Initialize the `HFClientTGI` within your program with the desired parameters. Here is an example call:
+
+   ```python
+   lm = dspy.HFClientTGI(model="meta-llama/Llama-2-7b-hf", port=8080, url="http://localhost")
+   ```
+
+   Customize the `model`, `port`, and `url` according to your requirements. The `model` parameter should be set to the specific Hugging Face model ID you wish to use. 
+
+
+### FAQs
+
+1. If your model doesn't require any shards, you still need to set a value for `num_shard`, but you don't need to include the parameter `--num-shard` on the command line.
+
+2. If your model runs into any "token exceeded" issues, you can set the following parameters on the command line to adjust the input length and token limit:
+   - `--max-input-length`: Set the maximum allowed input length for the text.
+   - `--max-total-tokens`: Set the maximum total tokens allowed for text generation.
+
+Please refer to the [official Text-Generation-Inference repository](https://github.com/huggingface/text-generation-inference) for more detailed information and documentation.
diff --git a/docs/api/local_language_model_clients/_category_.json b/docs/api/local_language_model_clients/_category_.json
new file mode 100644
index 000000000..8965dcf41
--- /dev/null
+++ b/docs/api/local_language_model_clients/_category_.json
@@ -0,0 +1,8 @@
+{
+    "label": "Local Language Model Clients",
+    "position": 6,
+    "link": {
+      "type": "generated-index",
+      "description": "DSPy supports various methods including `built-in wrappers`, `server integration`, and `external package integration` for model loading. This documentation provides a concise introduction on how to load in models within DSPy extending these capabilities for your specific needs."
+    }
+}
\ No newline at end of file
diff --git a/docs/api/local_language_model_clients/vLLM.md b/docs/api/local_language_model_clients/vLLM.md
new file mode 100644
index 000000000..6658addd2
--- /dev/null
+++ b/docs/api/local_language_model_clients/vLLM.md
@@ -0,0 +1,31 @@
+# dspy.HFClientVLLM
+
+### Setting up the vLLM Server
+
+Follow these steps to set up the vLLM Server:
+
+1. Build the server from source by following the instructions provided in the [Build from Source guide](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source).
+
+2. Start the server by running the following command, and specify your desired model, host, and port using the appropriate arguments. The default server address is http://localhost:8000.
+
+Example command:
+
+```bash
+   python -m vllm.entrypoints.openai.api_server --model mosaicml/mpt-7b --port 8000
+```
+
+This will launch the vLLM server.
+
+### Sending requests to the server
+
+After setting up the vLLM server and ensuring that it displays "Connected" when it's running, you can interact with it using the `HFClientVLLM`.
+
+Initialize the `HFClientVLLM` within your program with the desired parameters. Here is an example call:
+
+```python
+   lm = dspy.HFClientVLLM(model="mosaicml/mpt-7b", port=8000, url="http://localhost")
+```
+
+Customize the `model`, `port`, `url`, and `max_tokens` according to your requirements. The `model` parameter should be set to the specific Hugging Face model ID you wish to use.
+
+Please refer to the [official vLLM repository](https://github.com/vllm-project/vllm) for more detailed information and documentation.
diff --git a/docs/docs/cheatsheet.md b/docs/docs/cheatsheet.md
index c5f11e0c4..af62bd248 100644
--- a/docs/docs/cheatsheet.md
+++ b/docs/docs/cheatsheet.md
@@ -282,8 +282,13 @@ your_dspy_program_compiled = fewshot_optimizer.compile(student = your_dspy_progr
 
 #### Compiling a compiled program - bootstrapping a bootstraped program
 
-your_dspy_program_compiledx2 = teleprompter.compile(your_dspy_program, teacher=your_dspy_program_compiled, trainset=trainset)
-
+```python
+your_dspy_program_compiledx2 = teleprompter.compile(
+    your_dspy_program,
+    teacher=your_dspy_program_compiled,
+    trainset=trainset,
+)
+```
 
 ### dspy.BootstrapFewShotWithRandomSearch
 
@@ -364,6 +369,20 @@ kwargs = dict(num_threads=NUM_THREADS, display_progress=True, display_table=0)
 compiled_program_optimized_bayesian_signature = teleprompter.compile(your_dspy_program, devset=devset[:DEV_NUM], optuna_trials_num=100, max_bootstrapped_demos=3, max_labeled_demos=5, eval_kwargs=kwargs)
 ```
 
+### Signature Optimizer with Types
+
+```python
+from dspy.teleprompt.signature_opt_typed import optimize_signature
+from dspy.evaluate.metrics import answer_exact_match
+from dspy.functional import TypedChainOfThought
+
+compiled_program = optimize_signature(
+    student=TypedChainOfThought("question -> answer"),
+    evaluator=Evaluate(devset=devset, metric=answer_exact_match, num_threads=10, display_progress=True),
+    n_iterations=50,
+).program
+```
+
 ### dspy.KNNFewShot
 
 ```python
diff --git a/dsp/modules/azure_openai.py b/dsp/modules/azure_openai.py
index c90f634e4..d930bec6b 100644
--- a/dsp/modules/azure_openai.py
+++ b/dsp/modules/azure_openai.py
@@ -107,9 +107,6 @@ def __init__(
             kwargs["model"] = model
 
         self.kwargs = {
-            "api_base": api_base,
-            "api_version": api_version,
-            "api_key": api_key,
             "temperature": 0.0,
             "max_tokens": 150,
             "top_p": 1,
diff --git a/dspy/evaluate/evaluate.py b/dspy/evaluate/evaluate.py
index f38bcff38..4d1fd62f4 100644
--- a/dspy/evaluate/evaluate.py
+++ b/dspy/evaluate/evaluate.py
@@ -11,7 +11,11 @@
     from IPython.display import display as ipython_display
 except ImportError:
     ipython_display = print
-    HTML = lambda x: x
+
+    def HTML(x):
+        return x
+
+
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from dsp.evaluation.utils import *
@@ -23,8 +27,18 @@
 
 
 class Evaluate:
-    def __init__(self, *, devset, metric=None, num_threads=1, display_progress=False,
-                 display_table=False, display=True, max_errors=5, return_outputs=False):
+    def __init__(
+        self,
+        *,
+        devset,
+        metric=None,
+        num_threads=1,
+        display_progress=False,
+        display_table=False,
+        display=True,
+        max_errors=5,
+        return_outputs=False,
+    ):
         self.devset = devset
         self.metric = metric
         self.num_threads = num_threads
@@ -40,8 +54,9 @@ def _execute_single_thread(self, wrapped_program, devset, display_progress):
         ncorrect = 0
         ntotal = 0
         reordered_devset = []
-        
-        pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True, disable=not display_progress)
+
+        pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True,
+                         disable=not display_progress)
         for idx, arg in devset:
             example_idx, example, prediction, score = wrapped_program(idx, arg)
             reordered_devset.append((example_idx, example, prediction, score))
@@ -49,21 +64,24 @@ def _execute_single_thread(self, wrapped_program, devset, display_progress):
             ntotal += 1
             self._update_progress(pbar, ncorrect, ntotal)
         pbar.close()
-        
+
         return reordered_devset, ncorrect, ntotal
 
     def _execute_multi_thread(self, wrapped_program, devset, num_threads, display_progress):
         ncorrect = 0
         ntotal = 0
         reordered_devset = []
-        
+
         with ThreadPoolExecutor(max_workers=num_threads) as executor:
-            futures = {executor.submit(wrapped_program, idx, arg) for idx, arg in devset}
-            pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True, disable=not display_progress)
+            futures = {executor.submit(wrapped_program, idx, arg)
+                       for idx, arg in devset}
+            pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True,
+                             disable=not display_progress)
 
             for future in as_completed(futures):
                 example_idx, example, prediction, score = future.result()
-                reordered_devset.append((example_idx, example, prediction, score))
+                reordered_devset.append(
+                    (example_idx, example, prediction, score))
                 ncorrect += score
                 ntotal += 1
                 self._update_progress(pbar, ncorrect, ntotal)
@@ -72,12 +90,22 @@ def _execute_multi_thread(self, wrapped_program, devset, num_threads, display_pr
         return reordered_devset, ncorrect, ntotal
 
     def _update_progress(self, pbar, ncorrect, ntotal):
-        pbar.set_description(f"Average Metric: {ncorrect} / {ntotal}  ({round(100 * ncorrect / ntotal, 1)})")
+        pbar.set_description(
+            f"Average Metric: {ncorrect} / {ntotal}  ({round(100 * ncorrect / ntotal, 1)})")
         pbar.update()
 
-    def __call__(self, program, metric=None, devset=None, num_threads=None,
-                 display_progress=None, display_table=None, display=None,
-                 return_all_scores=False, return_outputs=False):
+    def __call__(
+        self,
+        program,
+        metric=None,
+        devset=None,
+        num_threads=None,
+        display_progress=None,
+        display_table=None,
+        display=None,
+        return_all_scores=False,
+        return_outputs=False,
+    ):
         metric = metric if metric is not None else self.metric
         devset = devset if devset is not None else self.devset
         num_threads = num_threads if num_threads is not None else self.num_threads
@@ -89,26 +117,30 @@ def __call__(self, program, metric=None, devset=None, num_threads=None,
         display_table = display_table if display else False
         return_outputs = return_outputs if return_outputs is not False else self.return_outputs
         results = []
-        
+
         def wrapped_program(example_idx, example):
             # NOTE: TODO: Won't work if threads create threads!
             creating_new_thread = threading.get_ident() not in dsp.settings.stack_by_thread
             if creating_new_thread:
-                dsp.settings.stack_by_thread[threading.get_ident()] = list(dsp.settings.main_stack)
+                dsp.settings.stack_by_thread[threading.get_ident()] = list(
+                    dsp.settings.main_stack)
                 # print(threading.get_ident(), dsp.settings.stack_by_thread[threading.get_ident()])
 
             # print(type(example), example)
 
             try:
                 prediction = program(**example.inputs())
-                score = metric(example, prediction)  # FIXME: TODO: What's the right order? Maybe force name-based kwargs!
-                
+                score = metric(
+                    example,
+                    prediction,
+                )  # FIXME: TODO: What's the right order? Maybe force name-based kwargs!
+
                 # increment assert and suggest failures to program's attributes
-                if hasattr(program, '_assert_failures'):
+                if hasattr(program, "_assert_failures"):
                     program._assert_failures += dsp.settings.assert_failures
-                if hasattr(program, '_suggest_failures'):
+                if hasattr(program, "_suggest_failures"):
                     program._suggest_failures += dsp.settings.suggest_failures
-                
+
                 return example_idx, example, prediction, score
             except Exception as e:
                 with self.error_lock:
@@ -125,19 +157,29 @@ def wrapped_program(example_idx, example):
         devset = list(enumerate(devset))
 
         if num_threads == 1:
-            reordered_devset, ncorrect, ntotal = self._execute_single_thread(wrapped_program, devset, display_progress)
+            reordered_devset, ncorrect, ntotal = self._execute_single_thread(
+                wrapped_program, devset, display_progress)
         else:
-            reordered_devset, ncorrect, ntotal = self._execute_multi_thread(wrapped_program, devset, num_threads, display_progress)
+            reordered_devset, ncorrect, ntotal = self._execute_multi_thread(
+                wrapped_program,
+                devset,
+                num_threads,
+                display_progress,
+            )
         if return_outputs:  # Handle the return_outputs logic
-            results = [(example, prediction, score) for _, example, prediction, score in reordered_devset]
+            results = [(example, prediction, score)
+                       for _, example, prediction, score in reordered_devset]
 
         if display:
-            print(f"Average Metric: {ncorrect} / {ntotal}  ({round(100 * ncorrect / ntotal, 1)}%)")
+            print(
+                f"Average Metric: {ncorrect} / {ntotal}  ({round(100 * ncorrect / ntotal, 1)}%)")
 
         predicted_devset = sorted(reordered_devset)
 
         # data = [{**example, **prediction, 'correct': score} for example, prediction, score in zip(reordered_devset, preds, scores)]
-        data = [merge_dicts(example, prediction) | {'correct': score} for _, example, prediction, score in predicted_devset]
+        data = [
+            merge_dicts(example, prediction) | {"correct": score} for _, example, prediction, score in predicted_devset
+        ]
 
         df = pd.DataFrame(data)
 
@@ -145,9 +187,10 @@ def wrapped_program(example_idx, example):
         df = df.applymap(truncate_cell)
 
         # Rename the 'correct' column to the name of the metric object
-        assert(callable(metric))
-        metric_name = metric.__name__ if isinstance(metric, types.FunctionType) else metric.__class__.__name__
-        df.rename(columns={'correct': metric_name}, inplace=True)
+        assert callable(metric)
+        metric_name = metric.__name__ if isinstance(
+            metric, types.FunctionType) else metric.__class__.__name__
+        df.rename(columns={"correct": metric_name}, inplace=True)
 
         if display_table:
             if isinstance(display_table, int):
@@ -158,23 +201,23 @@ def wrapped_program(example_idx, example):
                 truncated_rows = 0
 
             styled_df = configure_dataframe_display(df_to_display, metric_name)
-            
+
             ipython_display(styled_df)
 
             if truncated_rows > 0:
                 # Simplified message about the truncated rows
                 message = f"""
                 <div style='
-                    text-align: center; 
-                    font-size: 16px; 
-                    font-weight: bold; 
-                    color: #555; 
+                    text-align: center;
+                    font-size: 16px;
+                    font-weight: bold;
+                    color: #555;
                     margin: 10px 0;'>
                     ... {truncated_rows} more rows not displayed ...
                 </div>
                 """
                 ipython_display(HTML(message))
-                
+
         if return_all_scores and return_outputs:
             return round(100 * ncorrect / ntotal, 2), results
         elif return_all_scores:
@@ -206,28 +249,35 @@ def truncate_cell(content):
     """Truncate content of a cell to 25 words."""
     words = str(content).split()
     if len(words) > 25:
-        return ' '.join(words[:25]) + '...'
+        return " ".join(words[:25]) + "..."
     return content
 
+
 def configure_dataframe_display(df, metric_name):
     """Set various pandas display options for DataFrame."""
     pd.options.display.max_colwidth = None
-    pd.set_option('display.max_colwidth', 20)  # Adjust the number as needed
-    pd.set_option('display.width', 400)  # Adjust
+    pd.set_option("display.max_colwidth", 20)  # Adjust the number as needed
+    pd.set_option("display.width", 400)  # Adjust
 
     # df[metric_name] = df[metric_name].apply(lambda x: f'✔️ [{x}]' if x is True else f'❌ [{x}]')
-    df.loc[:, metric_name] = df[metric_name].apply(lambda x: f'✔️ [{x}]' if x is True else f'{x}')
+    df.loc[:, metric_name] = df[metric_name].apply(
+        lambda x: f"✔️ [{x}]" if x is True else f"{x}")
 
     # Return styled DataFrame
-    return df.style.set_table_styles([
-        {'selector': 'th', 'props': [('text-align', 'left')]},
-        {'selector': 'td', 'props': [('text-align', 'left')]},
-    ]).set_properties(**{
-        'text-align': 'left',
-        'white-space': 'pre-wrap',
-        'word-wrap': 'break-word',
-        'max-width': '400px',
-    })
+    return df.style.set_table_styles(
+        [
+            {"selector": "th", "props": [("text-align", "left")]},
+            {"selector": "td", "props": [("text-align", "left")]},
+        ],
+    ).set_properties(
+        **{
+            "text-align": "left",
+            "white-space": "pre-wrap",
+            "word-wrap": "break-word",
+            "max-width": "400px",
+        },
+    )
+
 
 # FIXME: TODO: The merge_dicts stuff above is way too quick and dirty.
 # TODO: the display_table can't handle False but can handle 0! Not sure how it works with True exactly, probably fails too.
diff --git a/dspy/teleprompt/random_search.py b/dspy/teleprompt/random_search.py
index bc0e5ef61..b95bd6e46 100644
--- a/dspy/teleprompt/random_search.py
+++ b/dspy/teleprompt/random_search.py
@@ -24,13 +24,14 @@
 
 
 class BootstrapFewShotWithRandomSearch(Teleprompter):
-    def __init__(self, metric, teacher_settings={}, max_bootstrapped_demos=4, max_labeled_demos=16, max_rounds=1, num_candidate_programs=16, num_threads=6, max_errors=10, stop_at_score=None):
+    def __init__(self, metric, teacher_settings={}, max_bootstrapped_demos=4, max_labeled_demos=16, max_rounds=1, num_candidate_programs=16, num_threads=6, max_errors=10, stop_at_score=None, metric_threshold=None):
         self.metric = metric
         self.teacher_settings = teacher_settings
         self.max_rounds = max_rounds
 
         self.num_threads = num_threads
         self.stop_at_score = stop_at_score
+        self.metric_threshold = metric_threshold
         self.min_num_samples = 1
         self.max_num_samples = max_bootstrapped_demos
         self.max_errors = max_errors
@@ -71,7 +72,7 @@ def compile(self, student, *, teacher=None, trainset, valset=None, restrict=None
             
             elif seed == -1:
                 # unshuffled few-shot
-                program = BootstrapFewShot(metric=self.metric, max_bootstrapped_demos=self.max_num_samples,
+                program = BootstrapFewShot(metric=self.metric, metric_threshold=self.metric_threshold, max_bootstrapped_demos=self.max_num_samples,
                                            max_labeled_demos=self.max_labeled_demos,
                                            teacher_settings=self.teacher_settings, max_rounds=self.max_rounds)
                 program2 = program.compile(student, teacher=teacher, trainset=trainset2)
@@ -82,7 +83,7 @@ def compile(self, student, *, teacher=None, trainset, valset=None, restrict=None
                 random.Random(seed).shuffle(trainset2)
                 size = random.Random(seed).randint(self.min_num_samples, self.max_num_samples)
 
-                teleprompter = BootstrapFewShot(metric=self.metric, max_bootstrapped_demos=size,
+                teleprompter = BootstrapFewShot(metric=self.metric, metric_threshold=self.metric_threshold, max_bootstrapped_demos=size,
                                                 max_labeled_demos=self.max_labeled_demos,
                                                 teacher_settings=self.teacher_settings,
                                                 max_rounds=self.max_rounds)
diff --git a/poetry.lock b/poetry.lock
index 30ec76981..136e7473b 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -366,13 +366,13 @@ lxml = ["lxml"]
 
 [[package]]
 name = "cachetools"
-version = "5.3.2"
+version = "5.3.3"
 description = "Extensible memoizing collections and decorators"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
-    {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
 ]
 
 [[package]]
@@ -755,20 +755,20 @@ test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "datasets"
-version = "2.17.1"
+version = "2.18.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.17.1-py3-none-any.whl", hash = "sha256:346974daf2fe9c14ddb35646896b2308b95e7dc27709d1a6e25273573b140cf8"},
-    {file = "datasets-2.17.1.tar.gz", hash = "sha256:66ec24077807f374f379b62ab0256c4dcb7c38a57ff1529a22993e8d95f2f9f1"},
+    {file = "datasets-2.18.0-py3-none-any.whl", hash = "sha256:f1bbf0e2896917a914de01cbd37075b14deea3837af87ad0d9f697388ccaeb50"},
+    {file = "datasets-2.18.0.tar.gz", hash = "sha256:cdf8b8c6abf7316377ba4f49f9589a4c74556d6b481afd0abd2284f3d69185cb"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.9"
 filelock = "*"
-fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
+fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]}
 huggingface-hub = ">=0.19.4"
 multiprocess = "*"
 numpy = ">=1.17"
@@ -785,11 +785,11 @@ xxhash = "*"
 apache-beam = ["apache-beam (>=2.26.0)"]
 audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
-dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.1.5)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-quality = ["ruff (>=0.1.5)"]
+quality = ["ruff (>=0.3.0)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
 tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
@@ -1096,18 +1096,17 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.10.0"
+version = "2024.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
-    {file = "fsspec-2023.10.0.tar.gz", hash = "sha256:330c66757591df346ad3091a53bd907e15348c2ba17d63fd54f5c39c4457d2a5"},
+    {file = "fsspec-2024.2.0-py3-none-any.whl", hash = "sha256:817f969556fa5916bc682e02ca2045f96ff7f586d45110fcb76022063ad2c7d8"},
+    {file = "fsspec-2024.2.0.tar.gz", hash = "sha256:b6ad1a679f760dda52b1168c859d01b7b80648ea6f7f7c7f5a8a91dc3f3ecb84"},
 ]
 
 [package.dependencies]
 aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
 
 [package.extras]
 abfs = ["adlfs"]
@@ -1124,7 +1123,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -1280,13 +1279,13 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "griffe"
-version = "0.41.0"
+version = "0.41.3"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "griffe-0.41.0-py3-none-any.whl", hash = "sha256:8aa7fc6eb00cb80af9c0198178c6b7110cb59fa2c5187bb13ea25eebbe4dd928"},
-    {file = "griffe-0.41.0.tar.gz", hash = "sha256:850128c3198c18713eaf0a6cc8572e590a16b1965f72a4e871e66cf84740903f"},
+    {file = "griffe-0.41.3-py3-none-any.whl", hash = "sha256:27b4610f1ba6e5d039e9f0a2c97232e13463df75e53cb1833e0679f3377b9de2"},
+    {file = "griffe-0.41.3.tar.gz", hash = "sha256:9edcfa9f57f4d9c5fcc6d5ce067c67a685b7101a21a7d11848ce0437368e474c"},
 ]
 
 [package.dependencies]
@@ -1559,13 +1558,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.20.3"
+version = "0.21.3"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.20.3-py3-none-any.whl", hash = "sha256:d988ae4f00d3e307b0c80c6a05ca6dbb7edba8bba3079f74cda7d9c2e562a7b6"},
-    {file = "huggingface_hub-0.20.3.tar.gz", hash = "sha256:94e7f8e074475fbc67d6a71957b678e1b4a74ff1b64a644fd6cbb83da962d05d"},
+    {file = "huggingface_hub-0.21.3-py3-none-any.whl", hash = "sha256:b183144336fdf2810a8c109822e0bb6ef1fd61c65da6fb60e8c3f658b7144016"},
+    {file = "huggingface_hub-0.21.3.tar.gz", hash = "sha256:26a15b604e4fc7bad37c467b76456543ec849386cbca9cd7e1e135f53e500423"},
 ]
 
 [package.dependencies]
@@ -1582,11 +1581,12 @@ all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi",
 cli = ["InquirerPy (==0.3.4)"]
 dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
 quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
@@ -1686,13 +1686,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.29.2"
+version = "6.29.3"
 description = "IPython Kernel for Jupyter"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.2-py3-none-any.whl", hash = "sha256:50384f5c577a260a1d53f1f59a828c7266d321c9b7d00d345693783f66616055"},
-    {file = "ipykernel-6.29.2.tar.gz", hash = "sha256:3bade28004e3ff624ed57974948116670604ac5f676d12339693f3142176d3f0"},
+    {file = "ipykernel-6.29.3-py3-none-any.whl", hash = "sha256:5aa086a4175b0229d4eca211e181fb473ea78ffd9869af36ba7694c947302a21"},
+    {file = "ipykernel-6.29.3.tar.gz", hash = "sha256:e14c250d1f9ea3989490225cc1a542781b095a18a19447fcf2b5eaf7d0ac5bd2"},
 ]
 
 [package.dependencies]
@@ -1715,7 +1715,7 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.4)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
@@ -2249,17 +2249,18 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp
 
 [[package]]
 name = "mkdocs-autorefs"
-version = "0.5.0"
+version = "1.0.1"
 description = "Automatically link across pages in MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_autorefs-0.5.0-py3-none-any.whl", hash = "sha256:7930fcb8ac1249f10e683967aeaddc0af49d90702af111a5e390e8b20b3d97ff"},
-    {file = "mkdocs_autorefs-0.5.0.tar.gz", hash = "sha256:9a5054a94c08d28855cfab967ada10ed5be76e2bfad642302a610b252c3274c0"},
+    {file = "mkdocs_autorefs-1.0.1-py3-none-any.whl", hash = "sha256:aacdfae1ab197780fb7a2dac92ad8a3d8f7ca8049a9cbe56a4218cd52e8da570"},
+    {file = "mkdocs_autorefs-1.0.1.tar.gz", hash = "sha256:f684edf847eced40b570b57846b15f0bf57fb93ac2c510450775dcf16accb971"},
 ]
 
 [package.dependencies]
 Markdown = ">=3.3"
+markupsafe = ">=2.0.1"
 mkdocs = ">=1.1"
 
 [[package]]
@@ -2278,13 +2279,13 @@ mkdocs = ">=1.0.3"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.11"
+version = "9.5.12"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.11-py3-none-any.whl", hash = "sha256:788ee0f3e036dca2dc20298d65e480297d348a44c9d7b2ee05c5262983e66072"},
-    {file = "mkdocs_material-9.5.11.tar.gz", hash = "sha256:7af7f8af0dea16175558f3fb9245d26c83a17199baa5f157755e63d7437bf971"},
+    {file = "mkdocs_material-9.5.12-py3-none-any.whl", hash = "sha256:d6f0c269f015e48c76291cdc79efb70f7b33bbbf42d649cfe475522ebee61b1f"},
+    {file = "mkdocs_material-9.5.12.tar.gz", hash = "sha256:5f69cef6a8aaa4050b812f72b1094fda3d079b9a51cf27a247244c03ec455e97"},
 ]
 
 [package.dependencies]
@@ -2318,13 +2319,13 @@ files = [
 
 [[package]]
 name = "mkdocstrings"
-version = "0.24.0"
+version = "0.24.1"
 description = "Automatic documentation from sources, for MkDocs."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocstrings-0.24.0-py3-none-any.whl", hash = "sha256:f4908560c10f587326d8f5165d1908817b2e280bbf707607f601c996366a2264"},
-    {file = "mkdocstrings-0.24.0.tar.gz", hash = "sha256:222b1165be41257b494a9d29b14135d2b7ca43f38161d5b10caae03b87bd4f7e"},
+    {file = "mkdocstrings-0.24.1-py3-none-any.whl", hash = "sha256:b4206f9a2ca8a648e222d5a0ca1d36ba7dee53c88732818de183b536f9042b5d"},
+    {file = "mkdocstrings-0.24.1.tar.gz", hash = "sha256:cc83f9a1c8724fc1be3c2fa071dd73d91ce902ef6a79710249ec8d0ee1064401"},
 ]
 
 [package.dependencies]
@@ -3112,13 +3113,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
 
 [[package]]
 name = "posthog"
-version = "3.4.2"
+version = "3.5.0"
 description = "Integrate PostHog into any python application."
 optional = true
 python-versions = "*"
 files = [
-    {file = "posthog-3.4.2-py2.py3-none-any.whl", hash = "sha256:c7e79b2e585d16e93749874bcbcdad78d857037398ce0d8d6c474a04d0bd3bbe"},
-    {file = "posthog-3.4.2.tar.gz", hash = "sha256:f0eafa663fbc4a942b49b6168a62a890635407044bbc7593051dcb9cc1208873"},
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+version = "10.7.1"
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 ]
 
 [package.dependencies]
@@ -5568,4 +5595,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.12"
-content-hash = "8b4cc583653becb3be9f5bc4c34cdf5ced146ba32157a5bb4bdc7885291c0403"
+content-hash = "233e4dfd38ce1f291ed8e966663cce04ffedfa6b0363f3c92447b23467b983e6"
diff --git a/pyproject.toml b/pyproject.toml
index e2c719eaf..7d4c7b563 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,7 +52,7 @@ docs = [
     "sphinx-reredirects>=0.1.2",
     "sphinx-automodapi==0.16.0",
 ]
-test = ["pytest>=6.2.5"]
+dev = ["pytest>=6.2.5"]
 
 [project.urls]
 homepage = "https://github.com/stanfordnlp/dspy"
@@ -103,8 +103,9 @@ sphinx-reredirects = { version = "^0.1.2", optional = true }
 sphinx-automodapi = { version = "0.16.0", optional = true }
 
 
-[tool.poetry.group.test.dependencies]
+[tool.poetry.group.dev.dependencies]
 pytest = "^6.2.5"
+ruff = "^0.3.0"
 
 
 [tool.poetry.extras]