Update pages/21_GraphRag.py

pages/21_GraphRag.py

@@ -1,60 +1,70 @@
-import torch
-from transformers import AutoTokenizer, AutoModel
-from sentence_transformers import SentenceTransformer
-import networkx as nx
-import matplotlib.pyplot as plt
-
-# Load pre-trained model and tokenizer
-model_name = "bert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModel.from_pretrained(model_name)
-
-# Function to get embeddings
-def get_embeddings(texts):
-    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    return outputs.last_hidden_state[:, 0, :].numpy()
-
-# Sample data (replace with your own data import)
-documents = [
-    "The quick brown fox jumps over the lazy dog.",
-    "A journey of a thousand miles begins with a single step.",
-    "To be or not to be, that is the question.",
-    "All that glitters is not gold.",
-]
-
-# Get embeddings for documents
-embeddings = get_embeddings(documents)
-
-# Create graph
-G = nx.Graph()
-
-# Add nodes and edges based on cosine similarity
-threshold = 0.5  # Adjust this threshold as needed
-for i in range(len(documents)):
-    G.add_node(i, text=documents[i])
-    for j in range(i+1, len(documents)):
-        similarity = torch.cosine_similarity(torch.tensor(embeddings[i]), torch.tensor(embeddings[j]), dim=0)
-        if similarity > threshold:
-            G.add_edge(i, j, weight=similarity.item())
-
-# Visualize the graph
-pos = nx.spring_layout(G)
-nx.draw(G, pos, with_labels=True, node_color='lightblue', node_size=500, font_size=8, font_weight='bold')
-edge_labels = nx.get_edge_attributes(G, 'weight')
-nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
-plt.title("Document Similarity Graph")
-plt.show()
-
-# Example of querying the graph
-query = "What is the meaning of life?"
-query_embedding = get_embeddings([query])[0]
-
-# Find most similar document
-similarities = [torch.cosine_similarity(torch.tensor(query_embedding), torch.tensor(emb), dim=0) for emb in embeddings]
-most_similar_idx = max(range(len(similarities)), key=similarities.__getitem__)
-
-print(f"Most similar document to the query: {documents[most_similar_idx]}")
-
-# You can extend this to implement more complex graph-based retrieval algorithms
+import streamlit as st
+import sys
+import subprocess
+import importlib
+
+st.title("GraphRAG Module Explorer")
+
+# Function to install a package
+def install_package(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+# Check and install required packages
+required_packages = ['graphrag', 'sentence_transformers']
+for package in required_packages:
+    try:
+        importlib.import_module(package)
+    except ImportError:
+        st.write(f"Installing {package}...")
+        install_package(package)
+        st.write(f"{package} installed successfully.")
+
+# Now try to import graphrag
+try:
+    import graphrag
+    import inspect
+
+    # Display all attributes and functions in the graphrag module
+    st.header("GraphRAG Module Contents")
+    graphrag_contents = dir(graphrag)
+
+    for item in graphrag_contents:
+        attr = getattr(graphrag, item)
+        st.subheader(f"{item}")
+        st.write(f"Type: {type(attr)}")
+        
+        if inspect.isclass(attr):
+            st.write("Class Methods:")
+            for name, method in inspect.getmembers(attr, predicate=inspect.isfunction):
+                st.write(f"- {name}")
+                st.write(f"  Signature: {inspect.signature(method)}")
+                st.write(f"  Docstring: {method.__doc__}")
+        
+        elif inspect.isfunction(attr):
+            st.write("Function:")
+            st.write(f"Signature: {inspect.signature(attr)}")
+            st.write(f"Docstring: {attr.__doc__}")
+        
+        elif isinstance(attr, (int, float, str, bool)):
+            st.write(f"Value: {attr}")
+        
+        st.write("---")
+
+    # Display the module's docstring if available
+    if graphrag.__doc__:
+        st.header("GraphRAG Module Documentation")
+        st.write(graphrag.__doc__)
+
+    st.header("Next Steps")
+    st.write("""
+    Based on the information above, we need to determine:
+    1. How to create a graph representation of text using graphrag.
+    2. How to process this graph representation for analysis.
+    3. Whether graphrag provides any built-in analysis tools or if we need to integrate it with other libraries.
+
+    Please review the module contents and let me know which components seem most relevant for our text analysis task.
+    """)
+
+except Exception as e:
+    st.error(f"An error occurred while exploring the graphrag module: {str(e)}")
+    st.write("Please check the installation of graphrag and its dependencies, and try running the app again.")