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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

requirements.txt

@@ -1 +1,14 @@
-huggingface_hub==0.22.2
+# requirements file
+# note which revision of python, for example 3.9.6
+# in this file, insert all the pip install needs, include revision
+
+
+python-dotenv==1.0.0
+
+llama-index==0.10.27
+llama-index-llms-openai==0.1.15
+llama-index-embeddings-openai==0.1.7
+
+gradio
+transformers
+torch>=1.8.0

utils.py

@@ -0,0 +1,82 @@
+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