diff --git a/.gitignore b/.gitignore
index 7265aec..b129e14 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,3 +167,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+.streamlit/
\ No newline at end of file
diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..1298ead
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,19 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Debug Streamlit",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "streamlit",
+            "args": [
+                "run",
+                "${file}"
+            ],
+            "justMyCode": false,
+        }
+    ]
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 6996e68..3f6098d 100644
--- a/README.md
+++ b/README.md
@@ -6,24 +6,6 @@
 
 #### Generate publication highlights using AI
 
-### Setting Up OpenAI API Key
-
-To use the OpenAI API, you need to set up an API key and set it as an environment variable.
-
-1. Obtain your OpenAI API key from the [OpenAI website](https://platform.openai.com/api-keys).
-
-2. Set the API key as an environment variable:
-    - On Windows:
-        ```bash
-        set OPENAI_API_KEY=your_api_key_here
-        ```
-    - On macOS and Linux:
-        ```bash
-        export OPENAI_API_KEY=your_api_key_here
-        ```
-
-Replace `your_api_key_here` with your actual OpenAI API key.
-
 ### Installation
 
 #### Clone this repository
@@ -59,7 +41,7 @@ To install this Python package in a virtual environment, you can use either pip
 
 #### Using Anaconda
 
-1. Create a virtual environment:
+1. Create a virtual environment (**Note:** Support for models provided by Dartmouth requires Python 3.10 or higher.):
     ```bash
     conda create --name highlight_env python=3.9
     ```
@@ -69,10 +51,44 @@ To install this Python package in a virtual environment, you can use either pip
     conda activate highlight_env
     ```
 
-3. Install the package from the cloned `highlight` directory:
+3. Install the package from the cloned `highlight` directory.
     ```bash
     pip install .
     ```
+    By default, only OpenAI's models are supported. You can add support for other providers by installing one ore more of the package's optional dependencies:
+    ```bash
+    pip install ".[anthropic, dartmouth, google, mistral, ollama]"
+    ```
+
+### Configuring the LLMs
+You can configure a variety of LLMs, both remote and local, to choose from in the app. To do this, edit the file `config.toml` in the root of this repo.
+
+#### Setting Up API Keys
+If any of the configured models require an API key, you need to set them up as Streamlit secrets.
+
+First, you have to acquire the API key from the provider of your choice (e.g., [OpenAI](https://platform.openai.com/api-keys), [Google](https://ai.google.dev/gemini-api/docs/api-key), [Dartmouth](https://developer.dartmouth.edu/keys), [Mistral](https://console.mistral.ai/api-keys/), or [Anthropic](https://console.anthropic.com/settings/keys)).
+
+Then, create a folder named `.streamlit` in the root of this repo. Inside that folder, create a file named `secrets.toml`.
+
+Finally, store your API key(s) in the following format:
+
+```toml
+KEY_NAME = "your_api_key_here"
+```
+
+Replace `KEY_NAME` with the variable name corresponding to your provider:
+
+| Provider    | Variable name       |
+| --------    | -------             |
+| Anthropic   | `ANTHROPIC_API_KEY` |
+| Dartmouth   | `DARTMOUTH_API_KEY` |
+| Google      | `GOOGLE_API_KEY`    |
+| Mistral     | `MISTRAL_API_KEY`    |
+| OpenAI      | `OPENAI_API_KEY`    |
+
+
+Replace `your_api_key_here` with your actual API key.
+
 
 ### Running the App
 
diff --git a/app.py b/app.py
index d42f03f..0732410 100644
--- a/app.py
+++ b/app.py
@@ -6,21 +6,14 @@
 from pptx import Presentation
 from pptx.util import Pt
 from pptx.enum.text import PP_ALIGN
-from openai import OpenAI
+
 import streamlit as st
 
 import highlight as hlt
 
+if "config" not in st.session_state:
+    st.session_state["config"] = hlt.read_config("config.toml")
 
-if "client" not in st.session_state:
-    key = os.getenv("OPENAI_API_KEY", default=None)
-    if key is None:
-        raise KeyError((
-            "No key found for 'OPENAI_API_KEY' system variable. " + 
-            "Obtain your OpenAI API key from the OpenAI website: https://platform.openai.com/api-keys"
-        ))
-    else:
-        st.session_state.client = OpenAI(api_key=key)
 
 if "reduce_document" not in st.session_state:
     st.session_state.reduce_document = False
@@ -125,28 +118,25 @@
 # Render streamlit page
 st.title("Research Highlight Generator")
 
-st.markdown((
-    "This app uses a Large Language Model (LLM) of your choosing to generate " + 
-    " formatted research highlight content from an input file."
-))
-
-st.session_state.model = st.selectbox(
-    label="Select your model:",
-    options=("gpt-4o", "gpt-4", "gpt-3.5-turbo-16k", "gpt-3.5-turbo")
+st.markdown(
+    (
+        "This app uses a Large Language Model (LLM) of your choosing to generate "
+        + " formatted research highlight content from an input file."
+    )
 )
 
-if st.session_state.model == "gpt-4-32k":
-    st.session_state.max_allowable_tokens = 32768
-elif st.session_state.model == "gpt-4":
-    st.session_state.max_allowable_tokens = 8192
-elif st.session_state.model == "gpt-3.5-turbo-16k":
-    st.session_state.max_allowable_tokens = 16384
-elif st.session_state.model == "gpt-3.5-turbo":
-    st.session_state.max_allowable_tokens = 4096
-elif st.session_state.model == "gpt-4o":
-    st.session_state.max_allowable_tokens = 150000
-
-# set api key
+llm_id = st.selectbox(
+    label="Select your model:", options=hlt.llm.list_llms(st.session_state["config"])
+)
+st.session_state.provider, st.session_state.model = llm_id.split(": ")
+st.session_state.max_allowable_tokens = st.session_state.config["llm"][
+    st.session_state.provider
+][st.session_state.model]["max_allowable_tokens"]
+
+st.session_state.client = hlt.llm.get_llm(
+    st.session_state.provider,
+    max_context_length=st.session_state.max_allowable_tokens,
+)
 
 st.markdown("### Upload file to process:")
 uploaded_file = st.file_uploader(
@@ -165,31 +155,33 @@
 
     st.session_state.output_file = uploaded_file.name
 
-    st.code(f"""File specs:\n
+    st.code(
+        f"""File specs:\n
     - Number of pages:  {content_dict['n_pages']}
     - Number of characters:  {content_dict['n_characters']}
     - Number of words: {content_dict['n_words']}
     - Number of tokens: {content_dict['n_tokens']}
-    """)
+    """
+    )
 
-    if content_dict['n_tokens'] > st.session_state.max_allowable_tokens:
+    if content_dict["n_tokens"] > st.session_state.max_allowable_tokens:
         msg = f"""
     The number of tokens in your document exceeds the maximum allowable tokens.
     This will cause your queries to fail.
     The queries account for the number of tokens in a prompt + the number of tokens in your document.
-    
+
     Maximum allowable token count: {st.session_state.max_allowable_tokens}
-    
+
     Your documents token count: {content_dict['n_tokens']}
-    
+
     Token deficit: {content_dict['n_tokens'] - st.session_state.max_allowable_tokens}
     """
         st.error(msg, icon="🚨")
 
         st.session_state.reduce_document = st.radio(
-            """Would you like me to attempt to reduce the size of 
-        your document by keeping only relevant information? 
-        If so, I will give you a file to download with the content 
+            """Would you like me to attempt to reduce the size of
+        your document by keeping only relevant information?
+        If so, I will give you a file to download with the content
         so you only have to do this once.
         If you choose to go through with this, it may take a while
         to process, usually on the order of 15 minutes for a 20K token
@@ -197,7 +189,7 @@
         Alternatively, you can copy and paste the contents that you
         know are of interest into a text file and upload that
         instead.
-    
+
         """,
             ("Yes", "No"),
         )
@@ -210,28 +202,26 @@
     title_container.markdown("##### Generate title from text content")
 
     # title criteria
-    title_container.markdown("""
+    title_container.markdown(
+        """
     The title should meet the following criteria:
     - No colons are allowed in the output.
     - Should pique the interest of the reader while still being somewhat descriptive.
     - Be understandable to a general audience.
     - Should be only once sentence.
     - Should have a maximum length of 10 words.
-    """)
+    """
+    )
 
     title_container.markdown("Set desired temperature:")
 
     # title slider
     title_temperature = title_container.slider(
-        "Title Temperature",
-        0.0,
-        1.0,
-        0.2,
-        label_visibility="collapsed"
+        "Title Temperature", 0.0, 1.0, 0.2, label_visibility="collapsed"
     )
 
     # build container content
-    if title_container.button('Generate Title'):
+    if title_container.button("Generate Title"):
 
         st.session_state.title_response = hlt.generate_content(
             client=st.session_state.client,
@@ -243,7 +233,7 @@
             temperature=title_temperature,
             box_height=50,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -253,7 +243,7 @@
                 label="Title Result:",
                 value=st.session_state.title_response,
                 label_visibility="collapsed",
-                height=50
+                height=50,
             )
 
     # subtitle section
@@ -261,29 +251,29 @@
     subtitle_container.markdown("##### Generate subtitle from text content")
 
     # subtitle criteria
-    subtitle_container.markdown("""
+    subtitle_container.markdown(
+        """
     The subtitle should meet the following criteria:
     - Be an extension of and related to, but not directly quote, the title.
     - Provide information that will make the audience want to find out more about the research.
     - Do not use more than 155 characters including spaces.
-    """)
+    """
+    )
 
     subtitle_container.markdown("Set desired temperature:")
 
     # subtitle slider
     subtitle_temperature = subtitle_container.slider(
-        "Subtitle Temperature",
-        0.0,
-        1.0,
-        0.5,
-        label_visibility="collapsed"
+        "Subtitle Temperature", 0.0, 1.0, 0.5, label_visibility="collapsed"
     )
 
     # build container content
-    if subtitle_container.button('Generate Subtitle'):
+    if subtitle_container.button("Generate Subtitle"):
 
         if st.session_state.title_response is None:
-            st.write("Please generate a Title first.  Subtitle generation considers the title response.")
+            st.write(
+                "Please generate a Title first.  Subtitle generation considers the title response."
+            )
         else:
 
             st.session_state.subtitle_response = hlt.generate_content(
@@ -299,7 +289,7 @@
                 max_word_count=100,
                 min_word_count=75,
                 max_allowable_tokens=st.session_state.max_allowable_tokens,
-                model=st.session_state.model
+                model=st.session_state.model,
             )
 
     else:
@@ -309,7 +299,7 @@
                 label="Subtitle Result:",
                 value=st.session_state.subtitle_response,
                 label_visibility="collapsed",
-                height=50
+                height=50,
             )
 
     # science section
@@ -317,9 +307,10 @@
     science_container.markdown("##### Generate science summary from text content")
 
     # science criteria
-    science_container.markdown("""
+    science_container.markdown(
+        """
     **GOAL**:  Describe the scientific results for a non-expert, non-scientist audience.
-    
+
     The description should meet the following criteria:
     - Answer what the big challenge in this field of science is that the research addresses.
     - State what the key finding is.
@@ -327,26 +318,23 @@
     - Be understandable to a high school senior or college freshman.
     - Use short sentences and succinct words.
     - Avoid technical terms if possible.  If technical terms are necessary, define them.
-    - Provide the necessary context so someone can have a very basic understanding of what you did. 
+    - Provide the necessary context so someone can have a very basic understanding of what you did.
     - Start with topics that the reader already may know and move on to more complex ideas.
     - Use present tense.
     - In general, the description should speak about the research or researchers in first person.
-    - Use a minimum of 75 words and a maximum of 100 words. 
-    """)
+    - Use a minimum of 75 words and a maximum of 100 words.
+    """
+    )
 
     science_container.markdown("Set desired temperature:")
 
     # slider
     science_temperature = science_container.slider(
-        "Science Summary Temperature",
-        0.0,
-        1.0,
-        0.3,
-        label_visibility="collapsed"
+        "Science Summary Temperature", 0.0, 1.0, 0.3, label_visibility="collapsed"
     )
 
     # build container content
-    if science_container.button('Generate Science Summary'):
+    if science_container.button("Generate Science Summary"):
         st.session_state.science_response = hlt.generate_content(
             client=st.session_state.client,
             container=science_container,
@@ -359,7 +347,7 @@
             max_word_count=100,
             min_word_count=75,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -369,44 +357,41 @@
                 label="Science Summary Result:",
                 value=st.session_state.science_response,
                 label_visibility="collapsed",
-                height=250
+                height=250,
             )
 
     # impact section
     impact_container = st.container()
     impact_container.markdown("##### Generate impact summary from text content")
 
-    impact_container.markdown("""
+    impact_container.markdown(
+        """
     **GOAL**: Describe the impact of the research to a non-expert, non-scientist audience.
-    
+
     The description should meet the following criteria:
     - Answer why the findings presented are important, i.e., what problem the research is trying to solve.
     - Answer if the finding is the first of its kind.
     - Answer what was innovative or distinct about the research.
     - Answer what the research enables other scientists in your field to do next.
-    - Include other scientific fields potentially impacted. 
-    - Be understandable to a high school senior or college freshman. 
+    - Include other scientific fields potentially impacted.
+    - Be understandable to a high school senior or college freshman.
     - Use short sentences and succinct words.
     - Avoid technical terms if possible.  If technical terms are necessary, define them.
     - Use present tense.
     - In general, the description should speak about the research or researchers in first person.
-    - Use a minimum of 75 words and a maximum of 100 words. 
-    """)
-
+    - Use a minimum of 75 words and a maximum of 100 words.
+    """
+    )
 
     impact_container.markdown("Set desired temperature:")
 
     # slider
     impact_temperature = impact_container.slider(
-        "Impact Summary Temperature",
-        0.0,
-        1.0,
-        0.0,
-        label_visibility="collapsed"
+        "Impact Summary Temperature", 0.0, 1.0, 0.0, label_visibility="collapsed"
     )
 
     # build container content
-    if impact_container.button('Generate Impact Summary'):
+    if impact_container.button("Generate Impact Summary"):
         st.session_state.impact_response = hlt.generate_content(
             client=st.session_state.client,
             container=impact_container,
@@ -419,7 +404,7 @@
             max_word_count=100,
             min_word_count=75,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -429,40 +414,38 @@
                 label="Impact Summary Result:",
                 value=st.session_state.impact_response,
                 label_visibility="collapsed",
-                height=250
+                height=250,
             )
 
     # general summary section
     summary_container = st.container()
     summary_container.markdown("##### Generate general summary from text content")
 
-    summary_container.markdown("""
+    summary_container.markdown(
+        """
     **GOAL**: Generate a general summary of the current research.
-    
+
     The summary should meet the following criteria:
     - Should relay key findings and value.
-    - The summary should be still accessible to the non-specialist but may be more technical if necessary. 
-    - Do not mention the names of institutions. 
-    - If there is a United States Department of Energy Office of Science user facility involved, such as NERSC, you can mention the user facility. 
+    - The summary should be still accessible to the non-specialist but may be more technical if necessary.
+    - Do not mention the names of institutions.
+    - If there is a United States Department of Energy Office of Science user facility involved, such as NERSC, you can mention the user facility.
     - Should be 1 or 2 paragraphs detailing the research.
     - Use present tense.
     - In general, the description should speak about the research or researchers in first person.
     - Use no more than 200 words.
-    """)
+    """
+    )
 
     summary_container.markdown("Set desired temperature:")
 
     # slider
     summary_temperature = summary_container.slider(
-        "General Summary Temperature",
-        0.0,
-        1.0,
-        0.3,
-        label_visibility="collapsed"
+        "General Summary Temperature", 0.0, 1.0, 0.3, label_visibility="collapsed"
     )
 
     # build container content
-    if summary_container.button('Generate General Summary'):
+    if summary_container.button("Generate General Summary"):
         st.session_state.summary_response = hlt.generate_content(
             client=st.session_state.client,
             container=summary_container,
@@ -475,7 +458,7 @@
             max_word_count=200,
             min_word_count=100,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -485,12 +468,14 @@
                 label="General Summary Result:",
                 value=st.session_state.summary_response,
                 label_visibility="collapsed",
-                height=400
+                height=400,
             )
 
     # figure recommendations section
     figure_container = st.container()
-    figure_container.markdown("##### Generate figure search string recommendations from the general summary")
+    figure_container.markdown(
+        "##### Generate figure search string recommendations from the general summary"
+    )
     figure_container.markdown("Set desired temperature:")
 
     # slider
@@ -499,11 +484,11 @@
         0.0,
         1.0,
         0.9,
-        label_visibility="collapsed"
+        label_visibility="collapsed",
     )
 
     # build container content
-    if figure_container.button('Generate Figure Recommendations'):
+    if figure_container.button("Generate Figure Recommendations"):
 
         if st.session_state.summary_response is None:
             st.write("Please generate a general summary first.")
@@ -518,7 +503,7 @@
                 temperature=figure_temperature,
                 box_height=200,
                 max_allowable_tokens=st.session_state.max_allowable_tokens,
-                model=st.session_state.model
+                model=st.session_state.model,
             )
 
     else:
@@ -529,10 +514,9 @@
                 label="Figure Recommendations Result:",
                 value=st.session_state.figure_response,
                 label_visibility="collapsed",
-                height=200
+                height=200,
             )
 
-
     figure_summary_container = st.container()
     figure_summary_container.markdown(
         "##### Generate a figure caption that summarizes the work generally to use with the artistic photo above"
@@ -541,15 +525,11 @@
     # slider
     figure_summary_container.markdown("Set desired temperature:")
     figure_summary_temperature = figure_summary_container.slider(
-        "Figure Caption Temperature",
-        0.0,
-        1.0,
-        0.1,
-        label_visibility="collapsed"
+        "Figure Caption Temperature", 0.0, 1.0, 0.1, label_visibility="collapsed"
     )
 
     # build container content
-    if figure_summary_container.button('Generate Figure Caption'):
+    if figure_summary_container.button("Generate Figure Caption"):
 
         if st.session_state.summary_response is None:
             st.write("Please generate a general summary first.")
@@ -564,7 +544,7 @@
                 temperature=figure_temperature,
                 box_height=200,
                 max_allowable_tokens=st.session_state.max_allowable_tokens,
-                model=st.session_state.model
+                model=st.session_state.model,
             ).replace('"', "")
 
     else:
@@ -574,14 +554,14 @@
                 label="Figure Caption Result:",
                 value=st.session_state.figure_caption,
                 label_visibility="collapsed",
-                height=200
+                height=200,
             )
 
     # citation recommendations section
     citation_container = st.container()
     citation_container.markdown("##### Citation for the paper in Chicago style")
-    
-    if citation_container.button('Generate Citation'):
+
+    if citation_container.button("Generate Citation"):
         st.session_state.citation = hlt.generate_content(
             client=st.session_state.client,
             container=citation_container,
@@ -592,7 +572,7 @@
             temperature=0.0,
             box_height=200,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         ).replace('"', "")
 
     else:
@@ -601,14 +581,14 @@
                 label="Citation",
                 value=st.session_state.citation,
                 label_visibility="collapsed",
-                height=200
+                height=200,
             )
 
     # funding recommendations section
     funding_container = st.container()
     funding_container.markdown("##### Funding statement from the paper")
-    
-    if funding_container.button('Generate funding statement'):
+
+    if funding_container.button("Generate funding statement"):
         st.session_state.funding = hlt.generate_content(
             client=st.session_state.client,
             container=funding_container,
@@ -619,7 +599,7 @@
             temperature=0.0,
             box_height=200,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         ).replace('"', "")
 
     else:
@@ -628,7 +608,7 @@
                 label="Funding statement",
                 value=st.session_state.funding,
                 label_visibility="collapsed",
-                height=200
+                height=200,
             )
 
     # point of contact box
@@ -637,20 +617,19 @@
 
     # select the POC information from the dropdown
     st.session_state.point_of_contact = st.session_state.project_dict[
-            poc_container.selectbox(
+        poc_container.selectbox(
             label="Select the project who funded the work:",
             options=[
-                "COMPASS-GLM", 
-                "GCIMS", 
+                "COMPASS-GLM",
+                "GCIMS",
                 "ICoM",
                 "IM3",
                 "Puget Sound",
                 "Other",
-            ]
+            ],
         )
     ]
 
-    
     poc_container.write("What will be written to the document as the point of contact:")
     poc_parts = st.session_state.point_of_contact.split("\n")
     poc_container.success(
@@ -666,23 +645,25 @@
 
     # template parameters
     word_parameters = {
-        'title': st.session_state.title_response,
-        'subtitle': st.session_state.subtitle_response,
-        'photo': st.session_state.photo,
-        'photo_link': st.session_state.photo_link,
-        'photo_site_name': st.session_state.photo_site_name,
-        'image_caption': st.session_state.figure_caption,
-        'science': st.session_state.science_response,
-        'impact': st.session_state.impact_response,
-        'summary': st.session_state.summary_response,
-        'funding': st.session_state.funding,
-        'citation': st.session_state.citation,
-        'related_links': st.session_state.related_links,
-        'point_of_contact': st.session_state.point_of_contact,
+        "title": st.session_state.title_response,
+        "subtitle": st.session_state.subtitle_response,
+        "photo": st.session_state.photo,
+        "photo_link": st.session_state.photo_link,
+        "photo_site_name": st.session_state.photo_site_name,
+        "image_caption": st.session_state.figure_caption,
+        "science": st.session_state.science_response,
+        "impact": st.session_state.impact_response,
+        "summary": st.session_state.summary_response,
+        "funding": st.session_state.funding,
+        "citation": st.session_state.citation,
+        "related_links": st.session_state.related_links,
+        "point_of_contact": st.session_state.point_of_contact,
     }
 
     # template word document
-    word_template_file = importlib.resources.files('highlight.data').joinpath('highlight_template.docx')
+    word_template_file = importlib.resources.files("highlight.data").joinpath(
+        "highlight_template.docx"
+    )
     template = DocxTemplate(word_template_file)
 
     template.render(word_parameters)
@@ -693,7 +674,7 @@
             label="Export Word Document",
             data=bio.getvalue(),
             file_name="modified_template.docx",
-            mime="docx"
+            mime="docx",
         )
 
     # power point slide content
@@ -703,28 +684,26 @@
     objective_container = st.container()
     objective_container.markdown("##### Generate objective summary from text content")
 
-    objective_container.markdown("""
+    objective_container.markdown(
+        """
     **GOAL**:  Generate one sentence stating the core purpose of the study.
-    
+
     The sentence should meet the following criteria:
     - Use active verbs for the start of each point.
     - Use present tense.
     - Do not include methodology related to statistical, technological, and theory based
-    """)
+    """
+    )
 
     objective_container.markdown("Set desired temperature:")
 
     # slider
     objective_temperature = objective_container.slider(
-        "Objective Temperature",
-        0.0,
-        1.0,
-        0.3,
-        label_visibility="collapsed"
+        "Objective Temperature", 0.0, 1.0, 0.3, label_visibility="collapsed"
     )
 
     # build container content
-    if objective_container.button('Generate Objective'):
+    if objective_container.button("Generate Objective"):
         st.session_state.objective_response = hlt.generate_content(
             client=st.session_state.client,
             container=objective_container,
@@ -735,7 +714,7 @@
             temperature=objective_temperature,
             box_height=250,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -745,35 +724,33 @@
                 label="Objective Result:",
                 value=st.session_state.objective_response,
                 label_visibility="collapsed",
-                height=250
+                height=250,
             )
 
     # approach section
     approach_container = st.container()
     approach_container.markdown("##### Generate approach summary from text content")
 
-    approach_container.markdown("""
+    approach_container.markdown(
+        """
     **GOAL**:  Clearly and concisely state in 2-3 short points how this work accomplished the stated objective from a methodolgocial perspecive.
-    - Based off of the objective summary 
-    - Only include methodology including but not limited to: statistical, technological, and theory based approaches. 
+    - Based off of the objective summary
+    - Only include methodology including but not limited to: statistical, technological, and theory based approaches.
     - Use a different action verb to start sentences than what is used to begin the objective statement.
-    - Use active verbs for the start of each point.  
+    - Use active verbs for the start of each point.
     - Use present tense.
-    """)
+    """
+    )
 
     approach_container.markdown("Set desired temperature:")
 
     # slider
     approach_temperature = approach_container.slider(
-        "Approach Temperature",
-        0.0,
-        1.0,
-        0.1,
-        label_visibility="collapsed"
+        "Approach Temperature", 0.0, 1.0, 0.1, label_visibility="collapsed"
     )
 
     # build container content
-    if approach_container.button('Generate Approach'):
+    if approach_container.button("Generate Approach"):
         st.session_state.approach_response = hlt.generate_content(
             client=st.session_state.client,
             container=approach_container,
@@ -785,7 +762,7 @@
             box_height=250,
             additional_content=st.session_state.objective_response,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -795,15 +772,16 @@
                 label="Approach Result:",
                 value=st.session_state.approach_response,
                 label_visibility="collapsed",
-                height=250
+                height=250,
             )
 
     # power point impact section
     ppt_impact_container = st.container()
     ppt_impact_container.markdown("##### Generate impact points from text content")
 
-    ppt_impact_container.markdown("""
-    **GOAL**:  Clearly and concisely state in 3 points the key results and outcomes from this research. 
+    ppt_impact_container.markdown(
+        """
+    **GOAL**:  Clearly and concisely state in 3 points the key results and outcomes from this research.
     - State what the results indicate.
     - Include results that may be considered profound or surprising.
     - Each point should be 1 concise sentence.
@@ -815,15 +793,11 @@
 
     # slider
     ppt_impact_temperature = ppt_impact_container.slider(
-        "Impact Points Temperature",
-        0.0,
-        1.0,
-        0.1,
-        label_visibility="collapsed"
+        "Impact Points Temperature", 0.0, 1.0, 0.1, label_visibility="collapsed"
     )
 
     # build container content
-    if ppt_impact_container.button('Generate Impact Points'):
+    if ppt_impact_container.button("Generate Impact Points"):
         st.session_state.ppt_impact_response = hlt.generate_content(
             client=st.session_state.client,
             container=ppt_impact_container,
@@ -834,7 +808,7 @@
             temperature=ppt_impact_temperature,
             box_height=250,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -844,35 +818,33 @@
                 label="Impact Points Result:",
                 value=st.session_state.ppt_impact_response,
                 label_visibility="collapsed",
-                height=250
+                height=250,
             )
 
     # power point figure selection section
     ppt_figure_selection = st.container()
     ppt_figure_selection.markdown("##### Select a representative figure from the paper")
 
-    ppt_figure_selection.markdown("""
+    ppt_figure_selection.markdown(
+        """
     **GOAL**:  What figure best represents the high impact content that can be easily understood by a non-technical, non-scientifc audience.
-    
+
     Limit the response to:
     1. The figure name as it is written in the text,
     2. An explanation of why it was chosen,
     3. And what the figure is about in less than 50 words.
-    """)
+    """
+    )
 
     ppt_figure_selection.markdown("Set desired temperature:")
 
     # slider
     ppt_figure_selection_temperature = ppt_figure_selection.slider(
-        "Figure recommendation Temperature",
-        0.0,
-        1.0,
-        0.2,
-        label_visibility="collapsed"
+        "Figure recommendation Temperature", 0.0, 1.0, 0.2, label_visibility="collapsed"
     )
 
     # build container content
-    if ppt_figure_selection.button('Generate Figure Recommendation'):
+    if ppt_figure_selection.button("Generate Figure Recommendation"):
         st.session_state.figure_recommendation = hlt.generate_content(
             client=st.session_state.client,
             container=ppt_figure_selection,
@@ -883,7 +855,7 @@
             temperature=ppt_figure_selection_temperature,
             box_height=250,
             max_allowable_tokens=st.session_state.max_allowable_tokens,
-            model=st.session_state.model
+            model=st.session_state.model,
         )
 
     else:
@@ -893,23 +865,29 @@
                 label="Figure Recommendation Result:",
                 value=st.session_state.figure_recommendation,
                 label_visibility="collapsed",
-                height=250
+                height=250,
             )
 
     # Add PowerPoint export container at the end
     export_ppt_container = st.container()
-    export_ppt_container.markdown("##### Export PowerPoint Presentation with New Content")
+    export_ppt_container.markdown(
+        "##### Export PowerPoint Presentation with New Content"
+    )
 
-    if ("title_response" in st.session_state and
-        "objective_response" in st.session_state and
-        "ppt_impact_response" in st.session_state and
-        "approach_response" in st.session_state):
+    if (
+        "title_response" in st.session_state
+        and "objective_response" in st.session_state
+        and "ppt_impact_response" in st.session_state
+        and "approach_response" in st.session_state
+    ):
 
-        if export_ppt_container.button('Export PowerPoint'):
+        if export_ppt_container.button("Export PowerPoint"):
 
             try:
                 # Load the PowerPoint template
-                ppt_template_file = importlib.resources.files('highlight.data').joinpath('highlight_template.pptx')
+                ppt_template_file = importlib.resources.files(
+                    "highlight.data"
+                ).joinpath("highlight_template.pptx")
                 prs = Presentation(ppt_template_file)
 
                 # Split the impact and approach responses into bullet points (assuming they are separated by newlines)
@@ -927,7 +905,9 @@
                                 # Ensure font size and bold settings are maintained for each paragraph
                                 for paragraph in shape.text_frame.paragraphs:
                                     for run in paragraph.runs:
-                                        run.font.size = Pt(24)  # Example size, adjust as needed
+                                        run.font.size = Pt(
+                                            24
+                                        )  # Example size, adjust as needed
                                         run.font.bold = True  # Maintain bold
                                         run.alignment = PP_ALIGN.LEFT  # Align title
 
@@ -938,13 +918,19 @@
                                 # Ensure font size and bold settings are maintained for each paragraph
                                 for paragraph in shape.text_frame.paragraphs:
                                     for run in paragraph.runs:
-                                        run.font.size = Pt(11)  # Example size for citation, adjust as needed
-                                        run.font.bold = False  # Citation typically isn't bold
+                                        run.font.size = Pt(
+                                            11
+                                        )  # Example size for citation, adjust as needed
+                                        run.font.bold = (
+                                            False  # Citation typically isn't bold
+                                        )
                                         run.alignment = PP_ALIGN.LEFT  # Align citation
 
                             if shape.text_frame.text == "objective_0":
                                 # Set the text of the text box to the objective response
-                                shape.text_frame.text = st.session_state.objective_response
+                                shape.text_frame.text = (
+                                    st.session_state.objective_response
+                                )
 
                                 # Optional: Adjust font size and alignment for the objective
                                 for paragraph in shape.text_frame.paragraphs:
@@ -957,11 +943,15 @@
                                 shape.text_frame.clear()
 
                                 # Add bullet points for approach
-                                for i, approach_point in enumerate(approach_points[:3]):  # Only take the first 3 approach points
+                                for i, approach_point in enumerate(
+                                    approach_points[:3]
+                                ):  # Only take the first 3 approach points
                                     p = shape.text_frame.add_paragraph()
                                     p.text = approach_point
                                     p.level = 0  # This sets it as a bullet point
-                                    p.font.size = Pt(13)  # Adjust bullet point font size
+                                    p.font.size = Pt(
+                                        13
+                                    )  # Adjust bullet point font size
                                     p.alignment = PP_ALIGN.LEFT  # Align bullet points
 
                             # Handle the impact bullet points in the same text box
@@ -970,11 +960,15 @@
                                 shape.text_frame.clear()
 
                                 # Add bullet points for impact
-                                for i, impact_point in enumerate(impact_points[:3]):  # Only take the first 3 impact points
+                                for i, impact_point in enumerate(
+                                    impact_points[:3]
+                                ):  # Only take the first 3 impact points
                                     p = shape.text_frame.add_paragraph()
                                     p.text = impact_point
                                     p.level = 0  # This sets it as a bullet point
-                                    p.font.size = Pt(13)  # Adjust bullet point font size
+                                    p.font.size = Pt(
+                                        13
+                                    )  # Adjust bullet point font size
                                     p.alignment = PP_ALIGN.LEFT  # Align bullet points
 
                 # Save the modified presentation to a BytesIO object
@@ -987,13 +981,20 @@
                     label="Export PowerPoint Presentation",
                     data=ppt_io,
                     file_name="modified_highlight_template.pptx",
-                    mime="application/vnd.openxmlformats-officedocument.presentationml.presentation"
+                    mime="application/vnd.openxmlformats-officedocument.presentationml.presentation",
                 )
 
-                export_ppt_container.success("PowerPoint presentation generated successfully!", icon="✅")
+                export_ppt_container.success(
+                    "PowerPoint presentation generated successfully!", icon="✅"
+                )
 
             except Exception as e:
-                export_ppt_container.error(f"An error occurred while generating the PowerPoint: {e}", icon="🚨")
+                export_ppt_container.error(
+                    f"An error occurred while generating the PowerPoint: {e}", icon="🚨"
+                )
 
     else:
-        export_ppt_container.error("Please generate the objective and impact responses before exporting.", icon="⚠️")
+        export_ppt_container.error(
+            "Please generate the objective and impact responses before exporting.",
+            icon="⚠️",
+        )
diff --git a/config.toml b/config.toml
new file mode 100644
index 0000000..bbc8e9a
--- /dev/null
+++ b/config.toml
@@ -0,0 +1,58 @@
+[llm]
+
+[llm.anthropic]
+models = ["claude-3-5-sonnet-20240620", "claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"]
+
+[llm.anthropic.claude-3-5-sonnet-20240620]
+max_allowable_tokens = 200000
+
+[llm.anthropic.claude-3-opus-20240229]
+max_allowable_tokens = 200000
+
+[llm.anthropic.claude-3-sonnet-20240229]
+max_allowable_tokens = 200000
+
+[llm.anthropic.claude-3-haiku-20240307]
+max_allowable_tokens = 200000
+
+[llm.google]
+models = ["gemini-1.5-flash-8b"]
+
+[llm.google."gemini-1.5-flash-8b"]
+max_allowable_tokens = 1_000_000
+
+[llm.mistral]
+models = ["mistral-small-latest"]
+
+[llm.mistral.mistral-small-latest]
+max_allowable_tokens = 32000
+
+[llm.openai]
+models = ["gpt-4o", "gpt-4", "gpt-3.5-turbo-16k", "gpt-3.5-turbo"]
+
+[llm.openai.gpt-4o]
+max_allowable_tokens = 150000
+
+[llm.openai.gpt-4]
+max_allowable_tokens = 8192
+
+[llm.openai."gpt-3.5-turbo"]
+max_allowable_tokens = 4096
+
+[llm.openai."gpt-3.5-turbo-16k"]
+max_allowable_tokens = 16384
+
+[llm.ollama]
+models = ["llama3.2:1b", "llama3.1:latest"]
+
+[llm.ollama."llama3.2:1b"]
+max_allowable_tokens = 128000
+
+[llm.ollama."llama3.1:latest"]
+max_allowable_tokens = 128000
+
+[llm.dartmouth]
+models = ["llama-3-1-8b-instruct"]
+
+[llm.dartmouth."llama-3-1-8b-instruct"]
+max_allowable_tokens = 95000
\ No newline at end of file
diff --git a/highlight/__init__.py b/highlight/__init__.py
index d83aa03..11b5dd2 100644
--- a/highlight/__init__.py
+++ b/highlight/__init__.py
@@ -1,5 +1,6 @@
 from highlight.prompts import prompt_queue
 from highlight.utils import *
+import highlight.llm as llm
 
 
 __version__ = "0.1.0"
diff --git a/highlight/llm.py b/highlight/llm.py
new file mode 100644
index 0000000..37a674b
--- /dev/null
+++ b/highlight/llm.py
@@ -0,0 +1,73 @@
+from typing import Literal
+from langchain_core.runnables import Runnable, ConfigurableField
+
+
+def get_llm(
+    provider: Literal["openai", "ollama", "dartmouth"], max_context_length: int
+) -> Runnable:
+    """Returns a LangChain Runnable interfacing with the specified provider"""
+    if provider == "anthropic":
+        from langchain_anthropic import ChatAnthropic
+
+        return ChatAnthropic(model_name="not-specified").configurable_fields(
+            model=ConfigurableField(id="model"),
+            max_tokens=ConfigurableField(id="max_tokens"),
+            temperature=ConfigurableField(id="temperature"),
+        )
+    elif provider == "google":
+        from langchain_google_genai import ChatGoogleGenerativeAI
+
+        return ChatGoogleGenerativeAI(
+            model="not-specified", max_tokens=1
+        ).configurable_fields(
+            model=ConfigurableField(id="model"),
+            max_output_tokens=ConfigurableField(id="max_tokens"),
+            temperature=ConfigurableField(id="temperature"),
+        )
+
+    elif provider == "mistral":
+        from langchain_mistralai import ChatMistralAI
+
+        return ChatMistralAI().configurable_fields(
+            model=ConfigurableField(id="model"),
+            max_tokens=ConfigurableField(id="max_tokens"),
+            temperature=ConfigurableField(id="temperature"),
+        )
+
+    elif provider == "openai":
+        from langchain_openai import ChatOpenAI
+
+        return ChatOpenAI().configurable_fields(
+            model_name=ConfigurableField(id="model"),
+            max_tokens=ConfigurableField(id="max_tokens"),
+            temperature=ConfigurableField(id="temperature"),
+        )
+    elif provider == "ollama":
+        from langchain_ollama import ChatOllama
+
+        return ChatOllama(
+            model="llama3", num_ctx=max_context_length
+        ).configurable_fields(
+            model=ConfigurableField(id="model"),
+            num_predict=ConfigurableField(id="max_tokens"),
+            temperature=ConfigurableField(id="temperature"),
+        )
+    elif provider == "dartmouth":
+        from langchain_dartmouth.llms import ChatDartmouth
+
+        return ChatDartmouth().configurable_fields(
+            model_name=ConfigurableField(id="model"),
+            max_tokens=ConfigurableField(id="max_tokens"),
+            temperature=ConfigurableField(id="temperature"),
+        )
+    else:
+        raise ValueError("Invalid provider")
+
+
+def list_llms(config: dict):
+    """List all Large Language Models available in the configuration"""
+    llms = []
+    for provider, spec in config["llm"].items():
+        for model in spec["models"]:
+            llms.append(f"{provider}: {model}")
+    return llms
diff --git a/highlight/prompts.py b/highlight/prompts.py
index 778f578..72d7561 100644
--- a/highlight/prompts.py
+++ b/highlight/prompts.py
@@ -5,10 +5,9 @@
 
 prompt_queue = {
     "system": """You are a technical science editor.  You are constructing high impact highlight content from recent publications.""",
-
     "title": """
-    Generate a title for the text delimited by triple backticks. 
-    
+    Generate a title for the text delimited by triple backticks.
+
     The title should meet the following criteria:
     - No colons are allowed in the output.
     - Should pique the interest of the reader while still being somewhat descriptive.
@@ -18,7 +17,7 @@
     - Should have a maximum length of 10 words.
     - Return only the title.
     - Do not use words like "revolutionizing" or "unraveling"
-    
+
     The following is an example to use for formatting only.  Do not use it in the response.  \
     The example is delimited by three pound signs.
     ###
@@ -26,12 +25,12 @@
     ###
 
     The following is the input text delimited by triple backticks.  Do not use colons in the response.
+    Do not use markdown.
     ```{0}```
     """,
-
     "subtitle": """
     Generate a subtitle for the text delimited by triple backticks.
-    
+
     The subtitle should meet the following criteria:
     - Strictly do not allow colons in the response text.
     - Be an extension of and related to, but not directly quote, this title delimited by single backticks `{1}`
@@ -40,7 +39,7 @@
     - Return only the subtitle.
     - Only capitalize the first letter of the starting word in the sentence unless the word is a proper noun.
     - Add a period at the end of the response.
-    
+
     The following is an example to use for formatting only.  Do not use it in the response.  \
     The example is delimited by three pound signs.
     ###
@@ -48,12 +47,12 @@
     ###
 
     The following is the input text delimited by triple backticks.  Do not use colons in the response.
+    Do not use markdown.
     ```{0}```
     """,
-
     "science": """
     Describe the scientific results for a non-expert, non-scientist audience for the text delimited by triple backticks.
-    
+
     The description should meet the following criteria:
     - Answer what the big challenge in this field of science is that the research addresses.
     - State what the key finding is.
@@ -61,78 +60,77 @@
     - Be understandable to a high school senior or college freshman.
     - Use short sentences and succinct words.
     - Avoid technical terms if possible.  If technical terms are necessary, define them.
-    - Provide the necessary context so someone can have a very basic understanding of what you did. 
+    - Provide the necessary context so someone can have a very basic understanding of what you did.
     - Start with topics that the reader already may know and move on to more complex ideas.
     - Use present tense.
     - In general, the description should speak about the research or researchers in first person.
-    - Use a minimum of 75 words and a maximum of 100 words. 
+    - Use a minimum of 75 words and a maximum of 100 words.
     - Produce only one paragraph.
     - Return only the description.
-    
+    - Do not use markdown.
+    - Do not use triple backticks to delimit your response.
+
     Finally, do not exceed 100 words in the response.
-    
+
     ```{0}```
     """,
-
     "impact": """
     Describe the impact of the research to a non-expert, non-scientist audience for the text delimited by triple backticks.
-    
+
     The description should meet the following criteria:
     - Answer why the findings presented are important, i.e., what problem the research is trying to solve.
     - Answer if the finding is the first of its kind.
     - Answer what was innovative or distinct about the research.
     - Answer what the research enables other scientists in your field to do next.
-    - Include other scientific fields potentially impacted. 
-    - Be understandable to a high school senior or college freshman. 
+    - Include other scientific fields potentially impacted.
+    - Be understandable to a high school senior or college freshman.
     - Use short sentences and succinct words.
     - Avoid technical terms if possible.  If technical terms are necessary, define them.
     - Use present tense.
     - In general, the description should speak about the research or researchers in first person.
-    - Use a minimum of 75 words and a maximum of 100 words. 
-    
+    - Use a minimum of 75 words and a maximum of 100 words.
+
+    Do not use markdown.
     Finally, do not exceed 100 words in the response.
 
 
     ```{0}```
     """,
-
     "summary": """
     Generate a summary of the current research represented in the text delimited by triple backticks.
-    
+
     The summary should meet the following criteria:
     - Should relay key findings and value.
-    - The summary should be still accessible to the non-specialist but may be more technical if necessary. 
-    - Do not mention the names of institutions. 
-    - If there is a United States Department of Energy Office of Science user facility involved, such as NERSC, you can mention the user facility. 
+    - The summary should be still accessible to the non-specialist but may be more technical if necessary.
+    - Do not mention the names of institutions.
+    - If there is a United States Department of Energy Office of Science user facility involved, such as NERSC, you can mention the user facility.
     - Should be 1 or 2 paragraphs detailing the research.
     - Use present tense.
     - In general, the description should speak about the research or researchers in first person.
     - Use no more than 200 words.
     - Return only the summary.
-    
+
+    Do not use markdown.
     Finally, do not exceed 200 words in the response.
 
-    
+
     ```{0}```
     """,
-
     "figure": """
     Generate a list of 5 search strings for use in the website that hosts free stock photos  \
     (e.g., https://www.pexels.com/) that would be representative of an aspect of the following research statement \
-    delimited by triple backticks.  
+    delimited by triple backticks.
 
     ```{0}```
     """,
-
     "caption": """
     Generate a caption for a photograph describing an aspect of the research for the text delimited by triple backticks.
-    
+
     The caption should meet the following criteria:
     - The caption should be greater than 200 characters and less than 255 character long.
 
     ```{0}```
     """,
-
     "objective": """
     Generate one sentence stating the core purpose of the study.
 
@@ -155,28 +153,26 @@
     Here is the input text delimited by triple backticks:
     ```{2}```
     """,
-
-    "approach": """Clearly and concisely state in 2-3 short points how this work accomplished the stated objective from a methodolgocial perspecive. 
+    "approach": """Clearly and concisely state in 2-3 short points how this work accomplished the stated objective from a methodolgocial perspecive.
     - Do not restate the objective or include results.
-    - Only include methodology including but not limited to: statistical, technological, and theory based approaches. 
-    - Here is the objective statement: {1} 
+    - Only include methodology including but not limited to: statistical, technological, and theory based approaches.
+    - Here is the objective statement: {1}
     - Use a different action verb to start sentences than what is used to begin the objective statement.
-    - Use active verbs for the start of each point.  
+    - Use active verbs for the start of each point.
     - Use present tense.
     - Format the results as a hyphen-separated list.
     - The response must be in a hyphen-separated list.
-  
+
     # Example response for the purposes of formatting
     RESPONSE:
     - Evaluate contemporary and hypothesized Western U.S. infrastructures with variable renewable generation shares for sensitivity to drought and Southern California heat wave scenarios on generation and load.
     - Use a stochastic temperature simulation combined with spatially resolved historical drought as a toolset to incorporate other grid stressors in high-resolution power system models, leading to improved sensitivity analyses not limited by the current ability of climate models to capture extreme conditions.
-    
+
     # Input to process
     TEXT: {0}
     RESPONSE:
     """,
-
-    "ppt_impact": """Clearly and concisely state in 3 bullet points the key results and outcomes from this research. 
+    "ppt_impact": """Clearly and concisely state in 3 bullet points the key results and outcomes from this research.
     - State what the results indicate.
     - Include results that may be considered profound or surprising.
     - Each point should be 1 concise sentence.
@@ -195,20 +191,17 @@
     TEXT: {0}
     RESPONSE:
     """,
-
     "reduce_wordcount": """Reduce the current text to be greater than {0} words and less than or equal to {1} words.  \
     The following is the text delimited by triple backquotes:
-     
+
      ```{2}```
     """,
-
     "figure_caption": """Summarize the key findings of the paper as a figure caption.
     - Limit the response to 25 words.
-    
+
     TEXT: {0}
     RESPONSE:
     """,
-
     "figure_choice": """What figure from the results of the paper best represents the high impact content that can be easily understood by a non-technical, non-scientifc audience.
     Limit the response to:
     1. The figure name as it is written in the text,
@@ -218,18 +211,16 @@
     TEXT: {0}
     RESPONSE:
     """,
+    "citation": """Generate the citation for this publication in Chicago style.
+    Do not use the example directly.
 
-    "citation": """Generate the citation for this publication in Chicago style. 
-    Do not use the example directly.  
-    
     # Example:
     Hadjimichael, A., J. Yoon, P. Reed, N. Voisin, W. Xu. 2023. “Exploring the Consistency of Water Scarcity Inferences between Large-Scale Hydrologic and Node-Based Water System Model Representations of the Upper Colorado River Basin,” J. Water Resour. Plann. Manage., 149(2): 04022081. DOI: 10.1061/JWRMD5.WRENG-5522
 
     TEXT: {0}
     RESPONSE:
     """,
-
-    "funding": """Extract the funding statement from the content provided. 
+    "funding": """Extract the funding statement from the content provided.
     Only provide the funding statment.  Do not provide header content like **Funding Statement:**.
 
     TEXT: {0}
diff --git a/highlight/utils.py b/highlight/utils.py
index 452df90..c5a049c 100644
--- a/highlight/utils.py
+++ b/highlight/utils.py
@@ -5,6 +5,14 @@
 
 import highlight.prompts as prompts
 
+from pathlib import Path
+from typing import Union
+
+try:
+    import tomllib
+except ImportError:
+    import tomli as tomllib
+
 
 def get_token_count(text, model="gpt-4o"):
     """
@@ -25,6 +33,21 @@ def get_token_count(text, model="gpt-4o"):
     return n_text_tokens
 
 
+def read_config(config_file: Union[str, Path]) -> dict:
+    """
+    Read the configuration file and return its contents as a dictionary.
+
+    Args:
+        config_file (str | Path): The path to the configuration file.
+
+    Returns:
+        dict: The contents of the configuration file as a dictionary.
+    """
+    with open(config_file, "rb") as cf:
+        config = tomllib.load(cf)
+    return config
+
+
 def read_pdf(file_object: object, reference_indicator: str = "References\n") -> dict:
     """
     Extract text content from a PDF file until a specified reference indicator is encountered.
@@ -68,7 +91,7 @@ def read_pdf(file_object: object, reference_indicator: str = "References\n") ->
         "n_pages": n_pages,
         "n_characters": len(content),
         "n_words": len(content.split(" ")),
-        "n_tokens": get_token_count(content)
+        "n_tokens": get_token_count(content),
     }
 
 
@@ -87,23 +110,18 @@ def read_text(file_object: object) -> dict:
             - n_words (int): The number of words in the extracted content.
             - n_tokens (int): The number of tokens in the extracted content.
     """
-    content = bytes.decode(file_object.read(), 'utf-8')
+    content = bytes.decode(file_object.read(), "utf-8")
 
     return {
         "content": content,
         "n_pages": 1,
         "n_characters": len(content),
         "n_words": len(content.replace("\n", " ").split()),
-        "n_tokens": get_token_count(content)
+        "n_tokens": get_token_count(content),
     }
 
 
-def content_reduction(
-    client,
-    document_list,
-    system_scope,
-    model
-):
+def content_reduction(client, document_list, system_scope, model):
     """
     Reduce the input text by removing irrelevant content.
 
@@ -125,18 +143,15 @@ def content_reduction(
         page_tokens = get_token_count(page_content)
 
         messages = [
-            {"role": "system", "content": system_scope},
-            {"role": "user", "content": prompt.format(text=page_content)}
+            ("system", system_scope),
+            ("user", prompt.format(text=page_content)),
         ]
 
-        response = client.chat.completions.create(
-            model=model,
-            max_tokens=page_tokens,
-            temperature=0.0,
-            messages=messages
-        )
+        response = client.with_config(
+            configurable={"model": model, "max_tokens": page_tokens, "temperature": 0.0}
+        ).invoke(messages)
 
-        content += response.choices[0].message.content
+        content += response.content
 
     return content
 
@@ -148,7 +163,7 @@ def generate_prompt_content(
     max_tokens=50,
     temperature=0.0,
     max_allowable_tokens=8192,
-    model="gpt-4o"
+    model="gpt-4o",
 ):
     """
     Generate content using the OpenAI API based on the provided prompt and parameters.
@@ -172,24 +187,27 @@ def generate_prompt_content(
     n_prompt_tokens = get_token_count(prompt) + max_tokens
 
     if n_prompt_tokens > max_allowable_tokens:
-        raise RuntimeError((
-            f"ERROR:  input text tokens needs to be reduced due to exceeding the maximum ",
-            " allowable tokens per prompt by {n_prompt_tokens - max_allowable_tokens} tokens."
-        ))
+        raise RuntimeError(
+            (
+                f"ERROR:  input text tokens needs to be reduced due to exceeding the maximum ",
+                " allowable tokens per prompt by {n_prompt_tokens - max_allowable_tokens} tokens.",
+            )
+        )
 
     messages = [
-        {"role": "system", "content": system_scope},
-        {"role": "user", "content": prompt}
+        ("system", system_scope),
+        ("user", prompt),
     ]
 
-    response = client.chat.completions.create(
-        model=model,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        messages=messages
-    )
+    response = client.with_config(
+        configurable={
+            "model": model,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+        }
+    ).invoke(messages)
 
-    content = response.choices[0].message.content
+    content = response.content
 
     return content
 
@@ -207,7 +225,7 @@ def generate_content(
     max_word_count=100,
     min_word_count=75,
     max_allowable_tokens: int = 150000,
-    model="gpt-4o"
+    model="gpt-4o",
 ):
     """
     Generate content using the OpenAI API based on the provided parameters and display it in a Streamlit container.
@@ -238,7 +256,7 @@ def generate_content(
         max_tokens=max_tokens,
         max_allowable_tokens=max_allowable_tokens,
         additional_content=additional_content,
-        model=model
+        model=model,
     )
 
     container.markdown(result_title)
@@ -248,27 +266,30 @@ def generate_content(
     if word_count > max_word_count:
 
         # construct word count reduction prompt
-        reduction_prompt = prompts.prompt_queue["reduce_wordcount"].format(min_word_count, max_word_count, response)
+        reduction_prompt = prompts.prompt_queue["reduce_wordcount"].format(
+            min_word_count, max_word_count, response
+        )
 
         messages = [
-            {"role": "system", "content": prompts.prompt_queue["system"]},
-            {"role": "user", "content": reduction_prompt}
+            ("system", prompts.prompt_queue["system"]),
+            ("user", reduction_prompt),
         ]
 
-        reduced_response = client.chat.completions.create(
-            model=model,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            messages=messages
-        )
+        reduced_response = client.with_config(
+            configurable={
+                "model": model,
+                "max_tokens": max_tokens,
+                "temperature": temperature,
+            }
+        ).invoke(messages)
 
-        response = reduced_response.choices[0].message.content
+        response = reduced_response.content
 
     container.text_area(
         label=result_title,
         value=response,
         label_visibility="collapsed",
-        height=box_height
+        height=box_height,
     )
 
     st.write(f"Word count:  {len(response.split())}")
@@ -284,7 +305,7 @@ def generate_prompt(
     max_allowable_tokens: int = 150000,
     temperature: float = 0.0,
     additional_content: str = None,
-    model: str = "gpt-4"
+    model: str = "gpt-4",
 ) -> str:
     """
     Generate a prompt using the provided parameters and the prompt queue.
@@ -305,39 +326,31 @@ def generate_prompt(
 
     if prompt_name in ("objective",):
         prompt = prompts.prompt_queue[prompt_name].format(
-            prompts.EXAMPLE_TEXT_ONE, 
-            prompts.EXAMPLE_TEXT_TWO, 
-            content
+            prompts.EXAMPLE_TEXT_ONE, prompts.EXAMPLE_TEXT_TWO, content
         )
 
     elif prompt_name in ("approach",):
         if additional_content is None:
             additional_content = content
-        prompt = prompts.prompt_queue[prompt_name].format(
-            content, 
-            additional_content
-        )
+        prompt = prompts.prompt_queue[prompt_name].format(content, additional_content)
 
     elif prompt_name in ("subtitle",):
         if additional_content is None:
             additional_content = content
-        prompt = prompts.prompt_queue[prompt_name].format(
-            content, 
-            additional_content
-        )
+        prompt = prompts.prompt_queue[prompt_name].format(content, additional_content)
 
     elif prompt_name in (
-        "figure", 
-        "caption", 
-        "impact", 
-        "summary", 
-        "title", 
-        "science", 
+        "figure",
+        "caption",
+        "impact",
+        "summary",
+        "title",
+        "science",
         "ppt_impact",
-        "figure_caption", 
-        "figure_choice", 
+        "figure_caption",
+        "figure_choice",
         "citation",
-        "funding"
+        "funding",
     ):
         prompt = prompts.prompt_queue[prompt_name].format(content)
 
@@ -348,5 +361,5 @@ def generate_prompt(
         max_tokens=max_tokens,
         temperature=temperature,
         max_allowable_tokens=max_allowable_tokens,
-        model=model
+        model=model,
     )
diff --git a/pyproject.toml b/pyproject.toml
index 2bf3b17..e807c9b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,11 +24,14 @@ dependencies = [
     'pypdf>=3.0.1',
     'tiktoken>=0.7.0',
     'tqdm>=4.66.1',
+    'langchain_openai>=0.2.2',
+    "tomli; python_version < '3.11'"
 ]
 classifiers = [
   "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
 ]
 
 [project.optional-dependencies]
@@ -40,6 +43,25 @@ test = [
 deploy = [
     "twine>=4.0.1",
 ]
+ollama = [
+    "langchain_ollama>=0.2.0"
+]
+
+dartmouth = [
+    "langchain_dartmouth>=0.2.11; python_version > '3.9'"
+]
+
+anthropic = [
+    "langchain_anthropic>=0.2.3"
+]
+
+google = [
+    "langchain-google-genai>=2.0.1"
+]
+
+mistral = [
+    "langchain_mistralai>=0.2.0"
+]
 
 [project.urls]
 Repository = "https://github.com/crvernon/highlight"
diff --git a/tests/test-config.toml b/tests/test-config.toml
new file mode 100644
index 0000000..2c12e3d
--- /dev/null
+++ b/tests/test-config.toml
@@ -0,0 +1,3 @@
+[llm]
+[llm.openai]
+models = ["gpt-4o", "gpt-4", "gpt-3.5-turbo-16k", "gpt-3.5-turbo"]
\ No newline at end of file
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 3af5c80..0ef9c7a 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,6 +1,7 @@
 import unittest
 from unittest.mock import patch
 from io import BytesIO
+from pathlib import Path
 
 import tiktoken
 from pypdf import PdfWriter, PdfReader
@@ -26,6 +27,26 @@ def test_get_token_count_empty_text(self):
         self.assertEqual(hlt.get_token_count(text), expected_token_count)
 
 
+class TestReadConfig(unittest.TestCase):
+    def test_read_config(self):
+        config = hlt.read_config(Path(__file__).parent.resolve() / "test-config.toml")
+        self.assertDictEqual(
+            {
+                "llm": {
+                    "openai": {
+                        "models": [
+                            "gpt-4o",
+                            "gpt-4",
+                            "gpt-3.5-turbo-16k",
+                            "gpt-3.5-turbo",
+                        ]
+                    }
+                }
+            },
+            config,
+        )
+
+
 class TestReadPdf(unittest.TestCase):
     def test_read_pdf_without_reference_indicator(self):
         # Create a sample PDF file using pypdf
@@ -44,7 +65,9 @@ def test_read_pdf_without_reference_indicator(self):
         buffer.seek(0)
 
         # Mock the PdfReader's extract_text method to return "Hello World"
-        with patch.object(PdfReader, 'pages', new_callable=unittest.mock.PropertyMock) as mock_pages:
+        with patch.object(
+            PdfReader, "pages", new_callable=unittest.mock.PropertyMock
+        ) as mock_pages:
             mock_page = unittest.mock.Mock()
             mock_page.extract_text.return_value = "Hello World"
             mock_pages.return_value = [mock_page]
@@ -64,7 +87,7 @@ class TestReadText(unittest.TestCase):
     def test_read_text(self):
         # Simulate a text file using BytesIO
         sample_text = "Hello World!\nThis is a test file."
-        text_file = BytesIO(sample_text.encode('utf-8'))
+        text_file = BytesIO(sample_text.encode("utf-8"))
 
         result = hlt.read_text(text_file)
 
@@ -72,4 +95,8 @@ def test_read_text(self):
         self.assertEqual(result["content"], sample_text)
         self.assertEqual(result["n_pages"], 1)
         self.assertEqual(result["n_characters"], len(sample_text))
-        self.assertEqual(result["n_words"], 7)  
+        self.assertEqual(result["n_words"], 7)
+
+
+if __name__ == "__main__":
+    unittest.main()