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@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Ehlers-Danlos-1/2024_EDS_2.pdf filter=lfs diff=lfs merge=lfs -text
+Ehlers-Danlos-1/2024_EDS_3.pdf filter=lfs diff=lfs merge=lfs -text
+Ehlers-Danlos-1/2024_EDS_4.pdf filter=lfs diff=lfs merge=lfs -text
+Ehlers-Danlos-1/2024_EDS_5.pdf filter=lfs diff=lfs merge=lfs -text
+Ehlers-Danlos-1/Unknown_EDS_1.pdf filter=lfs diff=lfs merge=lfs -text
+Ehlers-Danlos-1/Unknown_EDS_5.pdf filter=lfs diff=lfs merge=lfs -text

Ehlers-Danlos-1/2024_EDS_2.pdf

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Ehlers-Danlos-1/2024_EDS_3.pdf

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Ehlers-Danlos-1/2024_EDS_4.pdf

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Ehlers-Danlos-1/2024_EDS_5.pdf

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Ehlers-Danlos-1/Unknown_EDS_1.pdf

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Ehlers-Danlos-1/Unknown_EDS_5.pdf

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helper.py

@@ -0,0 +1,13 @@
+# Add your utilities or helper functions to this file.
+
+import os
+from dotenv import load_dotenv, find_dotenv
+
+# these expect to find a .env file at the directory above the lesson.                                                                                                                     # the format for that file is (without the comment)                                                                                                                                       #API_KEYNAME=AStringThatIsTheLongAPIKeyFromSomeService                                                                                                                                     
+def load_env():
+    _ = load_dotenv(find_dotenv())
+
+def get_openai_api_key():
+    load_env()
+    openai_api_key = os.getenv("OPENAI_API_KEY")
+    return openai_api_key

ragas_eval.py

@@ -0,0 +1,36 @@
+import os
+import sys
+from helper import get_openai_api_key
+
+venv_path = os.path.join(os.path.dirname(__file__), 'venv', 'Lib', 'python3.12', 'site-packages')
+sys.path.append(venv_path)
+
+os.environ["OPENAI_API_KEY"] = get_openai_api_key()
+
+from langchain_community.document_loaders import DirectoryLoader
+loader = DirectoryLoader("Ehlers-Danlos-1")
+documents = loader.load()
+
+for document in documents:
+    document.metadata['filename'] = document.metadata['source']
+
+from ragas.testset.generator import TestsetGenerator
+from ragas.testset.evolutions import simple, reasoning, multi_context
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+# generator with openai models
+generator_llm = ChatOpenAI(model="gpt-3.5-turbo")
+critic_llm = ChatOpenAI(model="gpt-4")
+embeddings = OpenAIEmbeddings()
+
+generator = TestsetGenerator.from_langchain(
+    generator_llm,
+    critic_llm,
+    embeddings
+)
+
+# generate testset
+testset = generator.generate_with_langchain_docs(documents, test_size=10, distributions={simple: 0.5, reasoning: 0.25, multi_context: 0.25})
+print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+print(testset)
+testset.to_pandas()

test.py

@@ -0,0 +1,26 @@
+import sys
+import os
+
+# Add the virtual environment's site-packages to sys.path
+# Replace 'pythonX.Y' with your Python version, e.g., 'python3.8'
+venv_path = os.path.join(os.path.dirname(__file__), 'venv', 'lib', 'site-packages')
+sys.path.append(venv_path)
+
+# Ensure the directory structure is recognized as a package
+# You can verify by listing the contents of the directory
+print("sys.path:", sys.path)
+print("Contents of venv_path:", os.listdir(venv_path))
+
+# Now import the TestsetGenerator
+try:
+    from ragas.testset.generator import TestsetGenerator
+    print("Successfully imported TestsetGenerator.")
+except ImportError as e:
+    print("ImportError:", e)
+
+# Use the imported function or class
+try:
+    generator = TestsetGenerator()
+    print("Successfully created a TestsetGenerator instance.")
+except Exception as e:
+    print("Error creating TestsetGenerator instance:", e)

utils.py

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+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import Settings
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.core import SummaryIndex, VectorStoreIndex
+from llama_index.core.tools import QueryEngineTool
+from llama_index.core.query_engine.router_query_engine import RouterQueryEngine
+from llama_index.core.selectors import LLMSingleSelector
+
+from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, SummaryIndex
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core.tools import FunctionTool, QueryEngineTool
+from llama_index.core.vector_stores import MetadataFilters, FilterCondition
+from typing import List, Optional
+
+
+
+def get_doc_tools(
+    file_path: str,
+    name: str,
+) -> str:
+    """Get vector query and summary query tools from a document."""
+
+    # load documents
+    documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
+    splitter = SentenceSplitter(chunk_size=1024)
+    nodes = splitter.get_nodes_from_documents(documents)
+    vector_index = VectorStoreIndex(nodes)
+    
+    def vector_query(
+        query: str, 
+        page_numbers: Optional[List[str]] = None
+    ) -> str:
+        """Use to answer questions over a given paper.
+    
+        Useful if you have specific questions over the paper.
+        Always leave page_numbers as None UNLESS there is a specific page you want to search for.
+    
+        Args:
+            query (str): the string query to be embedded.
+            page_numbers (Optional[List[str]]): Filter by set of pages. Leave as NONE 
+                if we want to perform a vector search
+                over all pages. Otherwise, filter by the set of specified pages.
+        
+        """
+    
+        page_numbers = page_numbers or []
+        metadata_dicts = [
+            {"key": "page_label", "value": p} for p in page_numbers
+        ]
+        
+        query_engine = vector_index.as_query_engine(
+            similarity_top_k=2,
+            filters=MetadataFilters.from_dicts(
+                metadata_dicts,
+                condition=FilterCondition.OR
+            )
+        )
+        response = query_engine.query(query)
+        return response
+        
+    
+    vector_query_tool = FunctionTool.from_defaults(
+        name=f"vector_tool_{name}",
+        fn=vector_query
+    )
+    
+    summary_index = SummaryIndex(nodes)
+    summary_query_engine = summary_index.as_query_engine(
+        response_mode="tree_summarize",
+        use_async=True,
+    )
+    summary_tool = QueryEngineTool.from_defaults(
+        name=f"summary_tool_{name}",
+        query_engine=summary_query_engine,
+        description=(
+            f"Useful for summarization questions related to {name}"
+        ),
+    )
+
+    return vector_query_tool, summary_tool