add chat with memory tutorial

2025-03-21 15:28:55 -04:00
parent a0a1a4cadd
commit 154e37528b
13 changed files with 555 additions and 309 deletions
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+
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+import os
+from openai import OpenAI
+
+def call_llm(messages):
+    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "your-api-key"))
+    
+    response = client.chat.completions.create(
+        model="gpt-4o",
+        messages=messages,
+        temperature=0.7
+    )
+    
+    return response.choices[0].message.content
+
+if __name__ == "__main__":
+    # Test the LLM call
+    messages = [{"role": "user", "content": "In a few words, what's the meaning of life?"}]
+    response = call_llm(messages)
+    print(f"Prompt: {messages[0]['content']}")
+    print(f"Response: {response}") 
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+import os
+import numpy as np
+from openai import OpenAI
+
+def get_embedding(text):
+    client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "YOUR_API_KEY"))
+    
+    response = client.embeddings.create(
+        model="text-embedding-ada-002",
+        input=text
+    )
+    
+    # Extract the embedding vector from the response
+    embedding = response.data[0].embedding
+    
+    # Convert to numpy array for consistency with other embedding functions
+    return np.array(embedding, dtype=np.float32)
+
+
+if __name__ == "__main__":
+    # Test the embedding function
+    text1 = "The quick brown fox jumps over the lazy dog."
+    text2 = "Python is a popular programming language for data science."
+    
+    emb1 = get_embedding(text1)
+    emb2 = get_embedding(text2)
+    
+    print(f"Embedding 1 shape: {emb1.shape}")
+    print(f"Embedding 2 shape: {emb2.shape}")
+    
+    # Calculate similarity (dot product)
+    similarity = np.dot(emb1, emb2)
+    print(f"Similarity between texts: {similarity:.4f}") 
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+import numpy as np
+import faiss
+
+def create_index(dimension=1536):
+    return faiss.IndexFlatL2(dimension)
+
+def add_vector(index, vector):
+    # Make sure the vector is a numpy array with the right shape for FAISS
+    vector = np.array(vector).reshape(1, -1).astype(np.float32)
+    
+    # Add the vector to the index
+    index.add(vector)
+    
+    # Return the position (index.ntotal is the total number of vectors in the index)
+    return index.ntotal - 1
+
+def search_vectors(index, query_vector, k=1):
+    """Search for the k most similar vectors to the query vector
+    
+    Args:
+        index: The FAISS index
+        query_vector: The query vector (numpy array or list)
+        k: Number of results to return (default: 1)
+        
+    Returns:
+        tuple: (indices, distances) where:
+            - indices is a list of positions in the index
+            - distances is a list of the corresponding distances
+    """
+    # Make sure we don't try to retrieve more vectors than exist in the index
+    k = min(k, index.ntotal)
+    if k == 0:
+        return [], []
+        
+    # Make sure the query is a numpy array with the right shape for FAISS
+    query_vector = np.array(query_vector).reshape(1, -1).astype(np.float32)
+    
+    # Search the index
+    distances, indices = index.search(query_vector, k)
+    
+    return indices[0].tolist(), distances[0].tolist()
+
+# Example usage
+if __name__ == "__main__":
+    # Create a new index
+    index = create_index(dimension=3)
+    
+    # Add some random vectors and track them separately
+    items = []
+    for i in range(5):
+        vector = np.random.random(3)
+        position = add_vector(index, vector)
+        items.append(f"Item {i}")
+        print(f"Added vector at position {position}")
+        
+    print(f"Index contains {index.ntotal} vectors")
+    
+    # Search for a similar vector
+    query = np.random.random(3)
+    indices, distances = search_vectors(index, query, k=2)
+    
+    print("Query:", query)
+    print("Found indices:", indices)
+    print("Distances:", distances)
+    print("Retrieved items:", [items[idx] for idx in indices])