diff --git a/.env.example b/.env.example
index 97d390b..a336758 100644
--- a/.env.example
+++ b/.env.example
@@ -1,2 +1,14 @@
-OPENAI_API_KEY=your-open-api-secret-key
-USER_AGENT=Langrade/1.0
+# OpenAI Configuration
+OPENAI_API_KEY=your-api-secret-key
+OPENAI_MODEL=gpt-3.5-turbo-0125
+
+# Google (Gemini) Configuration
+GOOGLE_API_KEY=your-api-secret-key
+GEMINI_MODEL=gemini-1.5-pro-latest
+
+# Anthropic (Claude) Configuration
+ANTHROPIC_API_KEY=your-api-secret-key
+CLAUDE_MODEL=claude-3-sonnet-20240229
+
+# Default Engine Configuration
+DEFAULT_ENGINE_TYPE=openai
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..9d9dc72
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,20 @@
+name: Publish docs via GitHub Pages
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    name: Deploy docs
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout main
+        uses: actions/checkout@v2
+
+      - name: Deploy docs
+        uses: mhausenblas/mkdocs-deploy-gh-pages@master
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          CONFIG_FILE: mkdocs.yml
+          EXTRA_PACKAGES: build-base
diff --git a/.gitignore b/.gitignore
index 6656130..c470085 100644
--- a/.gitignore
+++ b/.gitignore
@@ -32,3 +32,4 @@ Thumbs.db
 # Other
 create_txt.py
 file_structure_and_contents.txt
+expeiments/
diff --git a/README.md b/README.md
index 3faa9a3..a18c791 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # langrade
 
-langrade is a Python library for grading and retrieving documents based on their relevance to a given question.
+langrade is a Python library for grading and retrieving documents based on their relevance to a given question. It supports multiple LLM providers including OpenAI, Anthropic (Claude), and Google (Gemini).
 
 ## Installation
 
@@ -41,11 +41,9 @@ print(f"Reasoning: {result.reasoning}")
 
 ## Features
 
-Document retrieval from web URLs
-
-Document grading based on relevance to a question
-
-Optional reasoning for grading decisions
+- Document retrieval from web URLs
+- Document grading based on relevance to a question
+- Support for multiple LLM providers (OpenAI, Anthropic, Google)
 
 ## Requirements
 
diff --git a/docs/advanced_usage.md b/docs/advanced_usage.md
new file mode 100644
index 0000000..6e9c2f6
--- /dev/null
+++ b/docs/advanced_usage.md
@@ -0,0 +1,60 @@
+# Advanced Usage
+
+## Custom Prompts
+
+You can customize the system prompt used by the grader:
+
+```python
+from langrade.constants import SYSTEM_PROMPT
+
+# Modify the SYSTEM_PROMPT constant
+SYSTEM_PROMPT = """
+Your custom prompt here.
+"""
+
+# Then create your grader as usual
+grader = document_grader(provider, api_key, model)
+```
+
+## Batch Processing
+
+For grading multiple documents:
+
+```python
+documents = ["doc1 content", "doc2 content", "doc3 content"]
+question = "What is AI?"
+
+results = []
+for doc in documents:
+    result = grader.grade_document(doc, question)
+    results.append(result)
+
+for i, result in enumerate(results):
+    print(f"Document {i+1}:")
+    print(f"Relevance: {result.binary_score}")
+    print(f"Reasoning: {result.reasoning}")
+    print()
+```
+
+## Custom Retriever
+
+You can create a custom retriever by subclassing BaseRetriever:
+
+```python
+from langchain.schema import BaseRetriever, Document
+
+class CustomRetriever(BaseRetriever):
+    def get_relevant_documents(self, query: str) -> List[Document]:
+        # Your custom retrieval logic here
+        pass
+
+    async def aget_relevant_documents(self, query: str) -> List[Document]:
+        # Your custom async retrieval logic here
+        pass
+
+# Use your custom retriever
+custom_retriever = CustomRetriever()
+docs = custom_retriever.get_relevant_documents("What is AI?")
+```
+
+For more advanced usage scenarios, refer to the source code and consider extending the base classes provided by Langrade.
diff --git a/docs/api.md b/docs/api.md
new file mode 100644
index 0000000..25131e8
--- /dev/null
+++ b/docs/api.md
@@ -0,0 +1,63 @@
+# API Reference
+
+## document_grader
+
+```python
+def document_grader(provider: str, api_key: str, model: str = DEFAULT_MODEL) -> DocumentGrader:
+    """
+    Create a DocumentGrader instance.
+
+    Args:
+        provider (str): The LLM provider ("openai", "anthropic", or "google").
+        api_key (str): The API key for the chosen provider.
+        model (str, optional): The model to use. Defaults to DEFAULT_MODEL.
+
+    Returns:
+        DocumentGrader: An instance of the DocumentGrader class.
+    """
+```
+
+## DocumentGrader
+
+```python
+class DocumentGrader:
+    def __init__(self, provider: str, api_key: str, model: str = DEFAULT_MODEL):
+        """
+        Initialize a DocumentGrader instance.
+
+        Args:
+            provider (str): The LLM provider.
+            api_key (str): The API key for the chosen provider.
+            model (str, optional): The model to use. Defaults to DEFAULT_MODEL.
+        """
+
+    def grade_document(self, document: str, question: str) -> GradeDocuments:
+        """
+        Grade a document based on its relevance to a question.
+
+        Args:
+            document (str): The document text to grade.
+            question (str): The question to grade the document against.
+
+        Returns:
+            GradeDocuments: An object containing the grading result.
+        """
+```
+
+## create_retriever
+
+```python
+def create_retriever(urls: list[str], api_key: str) -> BaseRetriever:
+    """
+    Create a retriever for fetching relevant documents.
+
+    Args:
+        urls (list[str]): A list of URLs to fetch documents from.
+        api_key (str): The API key for the embedding model.
+
+    Returns:
+        BaseRetriever: A retriever instance.
+    """
+```
+
+For more details on the returned objects and their properties, see the source code documentation.
diff --git a/docs/changelog.md b/docs/changelog.md
new file mode 100644
index 0000000..e69de29
diff --git a/docs/configuration.md b/docs/configuration.md
new file mode 100644
index 0000000..dc3785a
--- /dev/null
+++ b/docs/configuration.md
@@ -0,0 +1,27 @@
+# Configuration
+
+# Provider-Specific Configuration
+
+## OpenAI
+
+```python
+from langrade import document_grader
+
+grader = document_grader("openai", "your_openai_api_key", "gpt-3.5-turbo-0125")
+```
+
+## Anthropic
+
+```python
+from langrade import document_grader
+
+grader = document_grader("anthropic", "your_anthropic_api_key", "claude-3-opus-20240229")
+```
+
+## Google
+
+```python
+from langrade import document_grader
+
+grader = document_grader("google", "your_google_api_key", "gemini-1.5-pro-latest")
+```
diff --git a/docs/contributing.md b/docs/contributing.md
new file mode 100644
index 0000000..537246c
--- /dev/null
+++ b/docs/contributing.md
@@ -0,0 +1,63 @@
+# Contributing to Langrade
+
+We welcome contributions to Langrade! Here's how you can help:
+
+## Setting Up the Development Environment
+
+1. Fork the repository
+2. Clone your fork:
+
+```bash
+git clone https://github.com/yourusername/langrade.git
+cd langrade
+```
+
+3. Set up a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+```
+
+4. Install dependencies:
+
+```bash
+pip install -e ".[dev]"
+```
+
+## Running Tests
+
+Run tests using pytest:
+
+```bash
+poetry run test
+```
+
+## Coding Standards
+
+We use Black for code formatting and flake8 for linting:
+
+```bash
+black .
+flake8
+```
+
+## Submitting Changes
+
+Create a new branch:
+
+`git checkout -b your-branch-name`
+
+Make your changes and commit them:
+
+`git commit -m "Your detailed commit message"`
+
+Push to your fork:
+
+`git push origin your-branch-name`
+
+Submit a pull request
+
+Please ensure your code adheres to our coding standards and is well-documented.
+
+Thank you for contributing to Langrade!
diff --git a/docs/faq.md b/docs/faq.md
new file mode 100644
index 0000000..f346397
--- /dev/null
+++ b/docs/faq.md
@@ -0,0 +1,51 @@
+# Frequently Asked Questions
+
+## General
+
+### Q: What is Langrade?
+
+A: Langrade is a Python library for grading and retrieving documents based on their relevance to a given question. It supports multiple LLM providers including OpenAI, Anthropic (Claude), and Google (Gemini).
+
+### Q: Which Python versions are supported?
+
+A: Langrade supports Python 3.9 and above.
+
+## Usage
+
+### Q: How do I choose between different LLM providers?
+
+A: You can specify the provider when creating a `DocumentGrader`:
+
+```python
+from langrade import document_grader
+
+# For OpenAI
+grader_openai = document_grader("openai", "your_openai_api_key", "gpt-3.5-turbo-0125")
+
+# For Anthropic
+grader_anthropic = document_grader("anthropic", "your_anthropic_api_key", "claude-3-opus-20240229")
+
+# For Google
+grader_google = document_grader("google", "your_google_api_key", "gemini-1.0-pro")
+```
+
+### Q: Can I use my own custom models?
+
+A: Currently, Langrade supports the models provided by OpenAI, Anthropic, and Google. Custom model support may be added in future versions.
+
+## Troubleshooting
+
+### Q: I'm getting an "API key not found" error. What should I do?
+
+A: Ensure that you've set up your .env file correctly with the appropriate API keys. Also, make sure you're loading the environment variables in your code:
+
+```python
+from dotenv import load_dotenv
+load_dotenv()
+```
+
+### Q: How can I report a bug or request a feature?
+
+A: Please open an issue on our GitHub repository. Provide as much detail as possible, including your Python version and any error messages you're seeing.
+
+For more detailed information, please refer to our [documentation](https://langrade.readthedocs.io/).
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..d73e3b5
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,42 @@
+# Welcome to Langrade
+
+Langrade is a Python library for grading and retrieving documents based on their relevance to a given question. It supports multiple LLM providers including OpenAI, Anthropic (Claude), and Google (Gemini).
+
+## Quick Start
+
+```python
+from langrade import document_grader, create_retriever
+
+provider = "openai"
+api_key = "your_api_key_here"
+model = "gpt-3.5-turbo-0125"
+
+grader = document_grader(provider, api_key, model)
+
+urls = [
+    "https://example.com/article1",
+    "https://example.com/article2",
+]
+retriever = create_retriever(urls, api_key)
+
+question = "What is AI?"
+docs = retriever.get_relevant_documents(question)
+doc_txt = docs[0].page_content
+
+result = grader.grade_document(doc_txt, question)
+print(f"Relevance: {result.binary_score}")
+print(f"Reasoning: {result.reasoning}")
+```
+
+## Table of Contents
+
+- Installation
+- Usage
+- API Reference
+- Configuration
+- Providers
+- Advanced Usage
+- Troubleshooting
+- Contributing
+- Changelog
+- FAQ
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 0000000..726b43a
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,24 @@
+# Installation
+
+## Requirements
+
+- Python 3.9+
+- API key for the chosen LLM provider (OpenAI, Anthropic, or Google)
+
+## Using pip
+
+Install Langrade using pip:
+
+```bash
+pip install langrade
+```
+
+## From source
+
+To install Langrade from source:
+
+```bash
+git clone https://github.com/nisaji/langrade.git
+cd langrade
+pip install .
+```
diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md
new file mode 100644
index 0000000..28561eb
--- /dev/null
+++ b/docs/troubleshooting.md
@@ -0,0 +1,39 @@
+# Troubleshooting
+
+## Model Not Found
+
+If you get a "model not found" error:
+
+Check that you're using a valid model name for the chosen provider.
+
+Ensure your API key has access to the specified model.
+
+```python
+from langrade import document_grader
+
+try:
+    grader = document_grader("openai", "your_api_key", "invalid-model-name")
+except Exception as e:
+    print(f"Error: {e}")
+```
+
+## Import Errors
+
+If you're having trouble importing Langrade:
+
+Ensure Langrade is installed: pip install langrade
+Check your Python path:
+
+```python
+import sys
+print(sys.path)
+```
+
+## Debugging
+
+For detailed debugging, enable logging:
+
+```python
+import logging
+logging.basicConfig(level=logging.DEBUG)
+```
diff --git a/docs/usage.md b/docs/usage.md
new file mode 100644
index 0000000..fb22271
--- /dev/null
+++ b/docs/usage.md
@@ -0,0 +1,39 @@
+# Basic Usage
+
+## Initializing the Grader
+
+```python
+from langrade import document_grader
+
+provider = "openai"  # or "anthropic" or "google"
+api_key = "your_api_key_here"
+model = "gpt-3.5-turbo-0125"  # or appropriate model for the chosen provider
+
+grader = document_grader(provider, api_key, model)
+```
+
+## Create Retriever
+
+```python
+from langrade import create_retriever
+
+urls = [
+    "https://example.com/article1",
+    "https://example.com/article2",
+]
+retriever = create_retriever(urls, api_key)
+```
+
+## Retrieving and Grading Documents
+
+```python
+question = "What is AI?"
+docs = retriever.get_relevant_documents(question)
+doc_txt = docs[0].page_content
+
+result = grader.grade_document(doc_txt, question)
+print(f"Relevance: {result.binary_score}")
+print(f"Reasoning: {result.reasoning}")
+```
+
+For more advanced usage, see the Advanced Usage guide.
diff --git a/langrade/__init__.py b/langrade/__init__.py
index 21fbe66..6712bb6 100644
--- a/langrade/__init__.py
+++ b/langrade/__init__.py
@@ -1,4 +1,10 @@
 from .grader import document_grader, DocumentGrader
 from .retriever import create_retriever
+from .providers import LLMProviderFactory
 
-__all__ = ["document_grader", "DocumentGrader", "create_retriever"]
+__all__ = [
+    "document_grader",
+    "DocumentGrader",
+    "create_retriever",
+    "LLMProviderFactory",
+]  # noqa: E501
diff --git a/langrade/grader.py b/langrade/grader.py
index 1ce40df..46e58f4 100644
--- a/langrade/grader.py
+++ b/langrade/grader.py
@@ -1,50 +1,20 @@
-from langchain_core.pydantic_v1 import BaseModel, Field
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_openai import ChatOpenAI
-from .constants import (
-    SYSTEM_PROMPT,
-    DEFAULT_MODEL,
-    REASONING_DESCRIPTION,
-    BINARY_SCORE_DESCRIPTION,
-)
-import html
-
-
-class GradeDocuments(BaseModel):
-    reasoning: str = Field(description=REASONING_DESCRIPTION)
-    binary_score: str = Field(description=BINARY_SCORE_DESCRIPTION)
+from .providers import LLMProviderFactory, GradeDocuments
+from .constants import DEFAULT_MODEL
 
 
 class DocumentGrader:
-    def __init__(self, api_key: str, model: str = DEFAULT_MODEL):
-        self.llm = ChatOpenAI(api_key=api_key, model=model)
-        self.prompt = self._create_prompt()
-
-    def _create_prompt(self):
-        return ChatPromptTemplate.from_messages(
-            [
-                ("system", SYSTEM_PROMPT),
-                (
-                    "human",
-                    "Retrieved document: \n\n {document} \n\n User question: \n\n {question}",  # noqa: E501
-                ),
-            ]
-        )
-
-    def grade_document(self, document: str, question: str):
-        # Sanitize inputs
-        safe_document = html.escape(document)
-        safe_question = html.escape(question)
-
-        structured_llm = self.llm.with_structured_output(GradeDocuments)
-        chain = self.prompt | structured_llm
-        result = chain.invoke(
-            {"document": safe_document, "question": safe_question}
+    def __init__(
+        self, provider: str, api_key: str, model: str = DEFAULT_MODEL
+    ):  # noqa: E501
+        self.provider = LLMProviderFactory.create_provider(
+            provider, api_key, model
         )  # noqa: E501
-        return result
+
+    def grade_document(self, document: str, question: str) -> GradeDocuments:
+        return self.provider.grade_document(document, question)
 
 
 def document_grader(
-    api_key: str, model: str = DEFAULT_MODEL
-) -> DocumentGrader:  # noqa: E501
-    return DocumentGrader(api_key, model)
+    provider: str, api_key: str, model: str = DEFAULT_MODEL
+) -> DocumentGrader:
+    return DocumentGrader(provider, api_key, model)
diff --git a/langrade/providers.py b/langrade/providers.py
new file mode 100644
index 0000000..e2c38ca
--- /dev/null
+++ b/langrade/providers.py
@@ -0,0 +1,75 @@
+from abc import ABC, abstractmethod
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
+from .constants import (
+    SYSTEM_PROMPT,
+    DEFAULT_MODEL,
+    REASONING_DESCRIPTION,
+    BINARY_SCORE_DESCRIPTION,
+)
+
+
+class GradeDocuments(BaseModel):
+    reasoning: str = Field(description=REASONING_DESCRIPTION)
+    binary_score: str = Field(description=BINARY_SCORE_DESCRIPTION)
+
+
+class LLMProvider(ABC):
+    def __init__(self, api_key: str, model: str):
+        self.llm = self._create_llm(api_key, model)
+        self.prompt = self._create_prompt()
+
+    @abstractmethod
+    def _create_llm(self, api_key: str, model: str):
+        pass
+
+    def _create_prompt(self):
+        return ChatPromptTemplate.from_messages(
+            [
+                ("system", SYSTEM_PROMPT),
+                (
+                    "human",
+                    "Retrieved document: \n\n {document} \n\n User question: \n\n {question}",  # noqa: E501
+                ),
+            ]
+        )
+
+    def grade_document(self, document: str, question: str) -> GradeDocuments:
+        structured_llm = self.llm.with_structured_output(GradeDocuments)
+        chain = self.prompt | structured_llm
+        return chain.invoke({"document": document, "question": question})
+
+
+class OpenAIProvider(LLMProvider):
+    def _create_llm(self, api_key: str, model: str):
+        return ChatOpenAI(api_key=api_key, model=model)
+
+
+class AnthropicProvider(LLMProvider):
+    def _create_llm(self, api_key: str, model: str):
+        return ChatAnthropic(api_key=api_key, model_name=model)
+
+
+class GoogleProvider(LLMProvider):
+    def _create_llm(self, api_key: str, model: str):
+        return ChatGoogleGenerativeAI(
+            google_api_key=SecretStr(api_key), model=model
+        )  # noqa: E501
+
+
+class LLMProviderFactory:
+    @staticmethod
+    def create_provider(
+        provider: str, api_key: str, model: str = DEFAULT_MODEL
+    ) -> LLMProvider:
+        if provider == "openai":
+            return OpenAIProvider(api_key, model)
+        elif provider == "anthropic":
+            return AnthropicProvider(api_key, model)
+        elif provider == "google":
+            return GoogleProvider(api_key, model)
+        else:
+            raise ValueError(f"Unsupported provider: {provider}")
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..777b82c
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,14 @@
+site_name: Langrade Documentation
+theme: readthedocs
+
+nav:
+  - Home: index.md
+  - Installation: installation.md
+  - Usage: usage.md
+  - API Reference: api.md
+  - Configuration: configuration.md
+  - Advanced Usage: advanced_usage.md
+  - Troubleshooting: troubleshooting.md
+  - Contributing: contributing.md
+  - Changelog: changelog.md
+  - FAQ: faq.md
diff --git a/poetry.lock b/poetry.lock
index a51f746..10ea1c6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -123,23 +123,28 @@ files = [
 
 [[package]]
 name = "anthropic"
-version = "0.2.8"
-description = "Library for accessing the anthropic API"
+version = "0.31.1"
+description = "The official Python library for the anthropic API"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "anthropic-0.2.8-py3-none-any.whl", hash = "sha256:7a11da8a3d8cb52835912e73d42e9245e7949155f4afb7093496f100a9bf610b"},
-    {file = "anthropic-0.2.8.tar.gz", hash = "sha256:d2629d7e26415bcce2ed0fdff0096a3fdd861099a73a1351ee705511d1c2ea6e"},
+    {file = "anthropic-0.31.1-py3-none-any.whl", hash = "sha256:d18809cbdecee2296f418e30beb2d0a8ecc225c065a1494cb02348af48794ff8"},
+    {file = "anthropic-0.31.1.tar.gz", hash = "sha256:d2248dfc15f7fc7823ac0bb9d48e73429e9b1ed8327ac66839d00cdb2f29d3cb"},
 ]
 
 [package.dependencies]
-aiohttp = "*"
-httpx = "*"
-requests = "*"
-tokenizers = "*"
+anyio = ">=3.5.0,<5"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+jiter = ">=0.4.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tokenizers = ">=0.13.0"
+typing-extensions = ">=4.7,<5"
 
 [package.extras]
-dev = ["black (>=22.3.0)", "pytest"]
+bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
+vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
@@ -319,13 +324,13 @@ virtualenv = ["virtualenv (>=20.0.35)"]
 
 [[package]]
 name = "cachetools"
-version = "5.3.3"
+version = "5.4.0"
 description = "Extensible memoizing collections and decorators"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
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 ]
 
 [[package]]
@@ -641,6 +646,17 @@ files = [
 marshmallow = ">=3.18.0,<4.0.0"
 typing-inspect = ">=0.4.0,<1"
 
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+files = [
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+]
+
 [[package]]
 name = "deprecated"
 version = "1.2.14"
@@ -923,6 +939,23 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,
 test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
 tqdm = ["tqdm"]
 
+[[package]]
+name = "ghp-import"
+version = "2.1.0"
+description = "Copy your docs directly to the gh-pages branch."
+optional = false
+python-versions = "*"
+files = [
+    {file = "ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343"},
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+]
+
+[package.dependencies]
+python-dateutil = ">=2.8.1"
+
+[package.extras]
+dev = ["flake8", "markdown", "twine", "wheel"]
+
 [[package]]
 name = "google-ai-generativelanguage"
 version = "0.6.6"
@@ -1332,13 +1365,13 @@ socks = ["socksio (==1.*)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.23.4"
+version = "0.23.5"
 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 = [
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+    {file = "huggingface_hub-0.23.5.tar.gz", hash = "sha256:67a9caba79b71235be3752852ca27da86bd54311d2424ca8afdb8dda056edf98"},
 ]
 
 [package.dependencies]
@@ -1468,6 +1501,76 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "jiter"
+version = "0.5.0"
+description = "Fast iterable JSON parser."
+optional = false
+python-versions = ">=3.8"
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+]
+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1521,19 +1624,19 @@ adal = ["adal (>=1.0.2)"]
 
 [[package]]
 name = "langchain"
-version = "0.2.7"
+version = "0.2.8"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.2.7-py3-none-any.whl", hash = "sha256:98e79e0b9a60a9c740b44d5b0135c85f649219308f30d373cf5f10d0efe18b87"},
-    {file = "langchain-0.2.7.tar.gz", hash = "sha256:8742f363d2890854501e0075af04fcb470600f201dec251c9bd5841e1990e73d"},
+    {file = "langchain-0.2.8-py3-none-any.whl", hash = "sha256:53e7dfe50294a14200f33bec22b4e14cb63857ccf0a5500b0d18b0fd51285d58"},
+    {file = "langchain-0.2.8.tar.gz", hash = "sha256:7fecb309e3558cde4e5cf7e9ffb7c1ab3f07121c40a7ff3b0c27135f8120c296"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
-langchain-core = ">=0.2.12,<0.3.0"
+langchain-core = ">=0.2.19,<0.3.0"
 langchain-text-splitters = ">=0.2.0,<0.3.0"
 langsmith = ">=0.1.17,<0.2.0"
 numpy = [
@@ -1546,6 +1649,22 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
+[[package]]
+name = "langchain-anthropic"
+version = "0.1.20"
+description = "An integration package connecting AnthropicMessages and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
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+    {file = "langchain_anthropic-0.1.20.tar.gz", hash = "sha256:cb9607fecfc0f0de49b79dd0fc066790e2877873ef753abd98d2ae38d6e0f5b2"},
+]
+
+[package.dependencies]
+anthropic = ">=0.28.0,<1"
+defusedxml = ">=0.7.1,<0.8.0"
+langchain-core = ">=0.2.17,<0.3"
+
 [[package]]
 name = "langchain-community"
 version = "0.2.7"
@@ -1574,13 +1693,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.18"
+version = "0.2.19"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.18-py3-none-any.whl", hash = "sha256:c9dbb197508e76337ed810ec977d40ae0c896397d191b420ef126c3818a1be96"},
-    {file = "langchain_core-0.2.18.tar.gz", hash = "sha256:ca5c5f1a783449dae8686e366ff3c5b775f8b5cef0de4ef346b8820d3d1c46ff"},
+    {file = "langchain_core-0.2.19-py3-none-any.whl", hash = "sha256:5b3cd34395be274c89e822c84f0e03c4da14168c177a83921c5b9414ac7a0651"},
+    {file = "langchain_core-0.2.19.tar.gz", hash = "sha256:13043a83e5c9ab58b9f5ce2a56896e7e88b752e8891b2958960a98e71801471e"},
 ]
 
 [package.dependencies]
@@ -1594,6 +1713,24 @@ pydantic = [
 PyYAML = ">=5.3"
 tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
+[[package]]
+name = "langchain-google-genai"
+version = "1.0.7"
+description = "An integration package connecting Google's genai package and LangChain"
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "langchain_google_genai-1.0.7-py3-none-any.whl", hash = "sha256:8fc03a0b5f635d369d1eec25076921ea7a472a1cb96dbea8e8ff2c2cdce58650"},
+    {file = "langchain_google_genai-1.0.7.tar.gz", hash = "sha256:6522da59720ad551867e278d63c053e0fb65fa4fa1d65382aacedca5293930fa"},
+]
+
+[package.dependencies]
+google-generativeai = ">=0.7.0,<0.8.0"
+langchain-core = ">=0.2.9,<0.3"
+
+[package.extras]
+images = ["pillow (>=10.1.0,<11.0.0)"]
+
 [[package]]
 name = "langchain-openai"
 version = "0.1.16"
@@ -1626,13 +1763,13 @@ langchain-core = ">=0.2.10,<0.3.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.85"
+version = "0.1.86"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.85-py3-none-any.whl", hash = "sha256:c1f94384f10cea96f7b4d33fd3db7ec180c03c7468877d50846f881d2017ff94"},
-    {file = "langsmith-0.1.85.tar.gz", hash = "sha256:acff31f9e53efa48586cf8e32f65625a335c74d7c4fa306d1655ac18452296f6"},
+    {file = "langsmith-0.1.86-py3-none-any.whl", hash = "sha256:55ed80cc6e98f9761f9b3ec3c49e01f6745d13e40bef80d9f831acabfd9a8a1e"},
+    {file = "langsmith-0.1.86.tar.gz", hash = "sha256:2e66577817253327b99b727588c3173fbba217fe0ca07ac6b7cdd23fc4894104"},
 ]
 
 [package.dependencies]
@@ -1801,6 +1938,24 @@ html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
 source = ["Cython (>=3.0.10)"]
 
+[[package]]
+name = "markdown"
+version = "3.6"
+description = "Python implementation of John Gruber's Markdown."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
+    {file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
+
+[package.extras]
+docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
+testing = ["coverage", "pyyaml"]
+
 [[package]]
 name = "markdown-it-py"
 version = "3.0.0"
@@ -1924,6 +2079,65 @@ files = [
     {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"},
 ]
 
+[[package]]
+name = "mergedeep"
+version = "1.3.4"
+description = "A deep merge function for 🐍."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "mergedeep-1.3.4-py3-none-any.whl", hash = "sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307"},
+    {file = "mergedeep-1.3.4.tar.gz", hash = "sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8"},
+]
+
+[[package]]
+name = "mkdocs"
+version = "1.6.0"
+description = "Project documentation with Markdown."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mkdocs-1.6.0-py3-none-any.whl", hash = "sha256:1eb5cb7676b7d89323e62b56235010216319217d4af5ddc543a91beb8d125ea7"},
+    {file = "mkdocs-1.6.0.tar.gz", hash = "sha256:a73f735824ef83a4f3bcb7a231dcab23f5a838f88b7efc54a0eef5fbdbc3c512"},
+]
+
+[package.dependencies]
+click = ">=7.0"
+colorama = {version = ">=0.4", markers = "platform_system == \"Windows\""}
+ghp-import = ">=1.0"
+importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
+jinja2 = ">=2.11.1"
+markdown = ">=3.3.6"
+markupsafe = ">=2.0.1"
+mergedeep = ">=1.3.4"
+mkdocs-get-deps = ">=0.2.0"
+packaging = ">=20.5"
+pathspec = ">=0.11.1"
+pyyaml = ">=5.1"
+pyyaml-env-tag = ">=0.1"
+watchdog = ">=2.0"
+
+[package.extras]
+i18n = ["babel (>=2.9.0)"]
+min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-import (==1.0)", "importlib-metadata (==4.4)", "jinja2 (==2.11.1)", "markdown (==3.3.6)", "markupsafe (==2.0.1)", "mergedeep (==1.3.4)", "mkdocs-get-deps (==0.2.0)", "packaging (==20.5)", "pathspec (==0.11.1)", "pyyaml (==5.1)", "pyyaml-env-tag (==0.1)", "watchdog (==2.0)"]
+
+[[package]]
+name = "mkdocs-get-deps"
+version = "0.2.0"
+description = "MkDocs extension that lists all dependencies according to a mkdocs.yml file"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "mkdocs_get_deps-0.2.0-py3-none-any.whl", hash = "sha256:2bf11d0b133e77a0dd036abeeb06dec8775e46efa526dc70667d8863eefc6134"},
+    {file = "mkdocs_get_deps-0.2.0.tar.gz", hash = "sha256:162b3d129c7fad9b19abfdcb9c1458a651628e4b1dea628ac68790fb3061c60c"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.3", markers = "python_version < \"3.10\""}
+mergedeep = ">=1.3.4"
+platformdirs = ">=2.2.0"
+pyyaml = ">=5.1"
+
 [[package]]
 name = "mmh3"
 version = "4.1.0"
@@ -2306,13 +2520,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.35.13"
+version = "1.35.14"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.35.13-py3-none-any.whl", hash = "sha256:36ec3e93e0d1f243f69be85c89b9221a471c3e450dfd9df16c9829e3cdf63e60"},
-    {file = "openai-1.35.13.tar.gz", hash = "sha256:c684f3945608baf7d2dcc0ef3ee6f3e27e4c66f21076df0b47be45d57e6ae6e4"},
+    {file = "openai-1.35.14-py3-none-any.whl", hash = "sha256:adadf8c176e0b8c47ad782ed45dc20ef46438ee1f02c7103c4155cff79c8f68b"},
+    {file = "openai-1.35.14.tar.gz", hash = "sha256:394ba1dfd12ecec1d634c50e512d24ff1858bbc2674ffcce309b822785a058de"},
 ]
 
 [package.dependencies]
@@ -2585,6 +2799,33 @@ files = [
     {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"},
 ]
 
+[[package]]
+name = "pathspec"
+version = "0.12.1"
+description = "Utility library for gitignore style pattern matching of file paths."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
+    {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
+]
+
+[[package]]
+name = "platformdirs"
+version = "4.2.2"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"},
+    {file = "platformdirs-4.2.2.tar.gz", hash = "sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3"},
+]
+
+[package.extras]
+docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
+
 [[package]]
 name = "pluggy"
 version = "1.5.0"
@@ -3132,6 +3373,20 @@ files = [
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
 ]
 
+[[package]]
+name = "pyyaml-env-tag"
+version = "0.1"
+description = "A custom YAML tag for referencing environment variables in YAML files. "
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "pyyaml_env_tag-0.1-py3-none-any.whl", hash = "sha256:af31106dec8a4d68c60207c1886031cbf839b68aa7abccdb19868200532c2069"},
+    {file = "pyyaml_env_tag-0.1.tar.gz", hash = "sha256:70092675bda14fdec33b31ba77e7543de9ddc88f2e5b99160396572d11525bdb"},
+]
+
+[package.dependencies]
+pyyaml = "*"
+
 [[package]]
 name = "regex"
 version = "2024.5.15"
@@ -3877,6 +4132,50 @@ files = [
 docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
 test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)", "flake8 (>=5.0,<6.0)", "mypy (>=0.800)", "psutil", "pyOpenSSL (>=23.0.0,<23.1.0)", "pycodestyle (>=2.9.0,<2.10.0)"]
 
+[[package]]
+name = "watchdog"
+version = "4.0.1"
+description = "Filesystem events monitoring"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "watchdog-4.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:da2dfdaa8006eb6a71051795856bedd97e5b03e57da96f98e375682c48850645"},
+    {file = "watchdog-4.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e93f451f2dfa433d97765ca2634628b789b49ba8b504fdde5837cdcf25fdb53b"},
+    {file = "watchdog-4.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ef0107bbb6a55f5be727cfc2ef945d5676b97bffb8425650dadbb184be9f9a2b"},
+    {file = "watchdog-4.0.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:17e32f147d8bf9657e0922c0940bcde863b894cd871dbb694beb6704cfbd2fb5"},
+    {file = "watchdog-4.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:03e70d2df2258fb6cb0e95bbdbe06c16e608af94a3ffbd2b90c3f1e83eb10767"},
+    {file = "watchdog-4.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:123587af84260c991dc5f62a6e7ef3d1c57dfddc99faacee508c71d287248459"},
+    {file = "watchdog-4.0.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:093b23e6906a8b97051191a4a0c73a77ecc958121d42346274c6af6520dec175"},
+    {file = "watchdog-4.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:611be3904f9843f0529c35a3ff3fd617449463cb4b73b1633950b3d97fa4bfb7"},
+    {file = "watchdog-4.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:62c613ad689ddcb11707f030e722fa929f322ef7e4f18f5335d2b73c61a85c28"},
+    {file = "watchdog-4.0.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:d4925e4bf7b9bddd1c3de13c9b8a2cdb89a468f640e66fbfabaf735bd85b3e35"},
+    {file = "watchdog-4.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:cad0bbd66cd59fc474b4a4376bc5ac3fc698723510cbb64091c2a793b18654db"},
+    {file = "watchdog-4.0.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a3c2c317a8fb53e5b3d25790553796105501a235343f5d2bf23bb8649c2c8709"},
+    {file = "watchdog-4.0.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c9904904b6564d4ee8a1ed820db76185a3c96e05560c776c79a6ce5ab71888ba"},
+    {file = "watchdog-4.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:667f3c579e813fcbad1b784db7a1aaa96524bed53437e119f6a2f5de4db04235"},
+    {file = "watchdog-4.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d10a681c9a1d5a77e75c48a3b8e1a9f2ae2928eda463e8d33660437705659682"},
+    {file = "watchdog-4.0.1-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0144c0ea9997b92615af1d94afc0c217e07ce2c14912c7b1a5731776329fcfc7"},
+    {file = "watchdog-4.0.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:998d2be6976a0ee3a81fb8e2777900c28641fb5bfbd0c84717d89bca0addcdc5"},
+    {file = "watchdog-4.0.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e7921319fe4430b11278d924ef66d4daa469fafb1da679a2e48c935fa27af193"},
+    {file = "watchdog-4.0.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:f0de0f284248ab40188f23380b03b59126d1479cd59940f2a34f8852db710625"},
+    {file = "watchdog-4.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:bca36be5707e81b9e6ce3208d92d95540d4ca244c006b61511753583c81c70dd"},
+    {file = "watchdog-4.0.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:ab998f567ebdf6b1da7dc1e5accfaa7c6992244629c0fdaef062f43249bd8dee"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_aarch64.whl", hash = "sha256:dddba7ca1c807045323b6af4ff80f5ddc4d654c8bce8317dde1bd96b128ed253"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_armv7l.whl", hash = "sha256:4513ec234c68b14d4161440e07f995f231be21a09329051e67a2118a7a612d2d"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_i686.whl", hash = "sha256:4107ac5ab936a63952dea2a46a734a23230aa2f6f9db1291bf171dac3ebd53c6"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64.whl", hash = "sha256:6e8c70d2cd745daec2a08734d9f63092b793ad97612470a0ee4cbb8f5f705c57"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:f27279d060e2ab24c0aa98363ff906d2386aa6c4dc2f1a374655d4e02a6c5e5e"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_s390x.whl", hash = "sha256:f8affdf3c0f0466e69f5b3917cdd042f89c8c63aebdb9f7c078996f607cdb0f5"},
+    {file = "watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ac7041b385f04c047fcc2951dc001671dee1b7e0615cde772e84b01fbf68ee84"},
+    {file = "watchdog-4.0.1-py3-none-win32.whl", hash = "sha256:206afc3d964f9a233e6ad34618ec60b9837d0582b500b63687e34011e15bb429"},
+    {file = "watchdog-4.0.1-py3-none-win_amd64.whl", hash = "sha256:7577b3c43e5909623149f76b099ac49a1a01ca4e167d1785c76eb52fa585745a"},
+    {file = "watchdog-4.0.1-py3-none-win_ia64.whl", hash = "sha256:d7b9f5f3299e8dd230880b6c55504a1f69cf1e4316275d1b215ebdd8187ec88d"},
+    {file = "watchdog-4.0.1.tar.gz", hash = "sha256:eebaacf674fa25511e8867028d281e602ee6500045b57f43b08778082f7f8b44"},
+]
+
+[package.extras]
+watchmedo = ["PyYAML (>=3.10)"]
+
 [[package]]
 name = "watchfiles"
 version = "0.22.0"
@@ -4261,4 +4560,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.9"
-content-hash = "c51a950510f208251a1d2e609b3f92ee1a1571e496f84e0f92df39c4d4019d8e"
+content-hash = "394b8cb22689d72231bb3cafcb3f5e2e45372fc4560ee4e22d7b6372df14a4f3"
diff --git a/pyproject.toml b/pyproject.toml
index ca8daa9..5ce0c23 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langrade"
-version = "0.1.2"
+version = "0.1.3"
 description = "A library for grading document relevance using LLMs"
 authors = ["nisaji <nisaji27@gmail.com>"]
 license = "MIT"
@@ -19,8 +19,10 @@ python-docx = "^1.1.2"
 beautifulsoup4 = "^4.12.3"
 numpy = "^1.22.5"
 chromadb = "^0.5.3,<0.6.0"
+langchain-anthropic = "^0.1.20"
+langchain-google-genai = "^1.0.7"
 
-
+mkdocs = "^1.6.0"
 [tool.poetry.group.dev.dependencies]
 ruff = "^0.5.0"
 mypy = "^1.10.1"
diff --git a/setup.py b/setup.py
index b1bad3f..5bfce9c 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@
 
 setup(
     name="langrade",
-    version="0.1.2",
+    version="0.1.3",
     author="Your Name",
     author_email="your.email@example.com",
     description="A library for grading and retrieving documents based on relevance",  # noqa: E501
diff --git a/tests/test_grader.py b/tests/test_grader.py
index 34c4d8c..aa77cdd 100644
--- a/tests/test_grader.py
+++ b/tests/test_grader.py
@@ -1,5 +1,4 @@
 import unittest
-from unittest.mock import patch, MagicMock
 from langrade import document_grader
 from conftest import get_api_key
 
@@ -7,24 +6,13 @@
 class TestDocumentGrader(unittest.TestCase):
     def setUp(self):
         self.api_key = get_api_key()
-        self.grader = document_grader(self.api_key)
-
-    @patch("langrade.grader.ChatOpenAI")
-    def test_grade_document(self, mock_chat_openai):
-        mock_llm = MagicMock()
-        mock_chat_openai.return_value = mock_llm
-        mock_result = MagicMock(
-            binary_score="yes",
-            reasoning="The document provides a definition of AI as a field of computer science creating intelligent machines, which directly answers the user question.",  # noqa: E501
-        )
-        mock_llm.with_structured_output.return_value.invoke.return_value = (
-            mock_result  # noqa: E501
-        )
+        self.provider = "openai"  # または適切なプロバイダー
+        self.grader = document_grader(self.provider, self.api_key)
 
+    def test_grade_document(self):
         document = "AI is a field of computer science that focuses on creating intelligent machines."  # noqa: E501
         question = "What is AI?"
 
         result = self.grader.grade_document(document, question)
-        print("result", result)
-        self.assertEqual(result.binary_score, "yes")
+        self.assertIsNotNone(result.binary_score)
         self.assertIsNotNone(result.reasoning)
diff --git a/tests/test_providers.py b/tests/test_providers.py
new file mode 100644
index 0000000..fad89b7
--- /dev/null
+++ b/tests/test_providers.py
@@ -0,0 +1,64 @@
+import os
+import unittest
+from dotenv import load_dotenv
+from langrade.providers import LLMProviderFactory, GradeDocuments
+
+# 環境変数を読み込む
+load_dotenv()
+
+
+class TestProviders(unittest.TestCase):
+
+    def setUp(self):
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        self.openai_model = os.getenv("OPENAI_MODEL")
+        self.google_api_key = os.getenv("GOOGLE_API_KEY")
+        self.gemini_model = os.getenv("GEMINI_MODEL")
+        self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
+        self.claude_model = os.getenv("CLAUDE_MODEL")
+
+        self.test_document = "Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems."  # noqa: E501
+        self.test_question = "What is AI?"
+
+    def test_openai_provider(self):
+        provider = LLMProviderFactory.create_provider(
+            "openai", self.openai_api_key, self.openai_model
+        )
+        result = provider.grade_document(
+            self.test_document, self.test_question
+        )  # noqa: E501
+        self.assertIsInstance(result, GradeDocuments)
+        self.assertIn(result.binary_score.lower(), ["yes", "no"])
+        self.assertIsNotNone(result.reasoning)
+
+    def test_google_provider(self):
+        provider = LLMProviderFactory.create_provider(
+            "google", self.google_api_key, self.gemini_model
+        )
+        result = provider.grade_document(
+            self.test_document, self.test_question
+        )  # noqa: E501
+        self.assertIsInstance(result, GradeDocuments)
+        self.assertIn(result.binary_score.lower(), ["yes", "no"])
+        self.assertIsNotNone(result.reasoning)
+
+    def test_anthropic_provider(self):
+        provider = LLMProviderFactory.create_provider(
+            "anthropic", self.anthropic_api_key, self.claude_model
+        )
+        result = provider.grade_document(
+            self.test_document, self.test_question
+        )  # noqa: E501
+        self.assertIsInstance(result, GradeDocuments)
+        self.assertIn(result.binary_score.lower(), ["yes", "no"])
+        self.assertIsNotNone(result.reasoning)
+
+    def test_invalid_provider(self):
+        with self.assertRaises(ValueError):
+            LLMProviderFactory.create_provider(
+                "invalid_provider", "dummy_key", "dummy_model"
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()