initial Commit

Pineconeprac.py

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+from scipy.spatial.distance import cosine
+import pinecone
+from sentence_transformers import SentenceTransformer
+import openai
+
+# Initialize Pinecone
+pinecone.init(api_key='3d6a95f6-e9b7-4e87-b96d-ec90392220a8',
+              environment='us-west4-gcp')
+
+# Initialize the embedding model
+model = SentenceTransformer(
+    'sentence-transformers/distilbert-base-nli-mean-tokens')
+
+# Define department data
+departments = ["design", "video_production", "marketing"]
+
+# Generate embeddings for the departments
+vectors = model.encode(departments)
+
+# Create a Pinecone index
+index_name = "mojosolo"
+if index_name in pinecone.list_indexes():
+    pinecone.delete_index(name=index_name)
+
+pinecone.create_index(name=index_name, dimension=768, metric='cosine')
+
+# Insert department vectors into the Pinecone index
+index = pinecone.Index(index_name)
+upsert_response = index.upsert(
+    vectors=list(zip(departments, [vector.tolist() for vector in vectors])),
+    namespace="example-namespace"
+)
+
+
+def get_department(message):
+    query_vector = model.encode([message])[0]
+    min_distance = 1.0
+    best_department = None
+
+    for department, vector in zip(departments, vectors):
+        distance = cosine(query_vector, vector)
+        print(f"DEBUG: Department: {department}, Distance: {distance}")
+        if distance < min_distance:
+            min_distance = distance
+            best_department = department
+
+    if best_department is not None:
+        return best_department
+    else:
+        print("DEBUG: No department found")
+        return None
+
+
+
+
+openai.api_key = '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'
+
+
+def chatbot(message):
+    department = get_department(message)
+    if department is not None:
+        response = openai.Completion.create(
+            engine="text-davinci-002",
+            prompt=f"[{department}] {message}",
+            max_tokens=50,
+            n=1,
+            stop=None,
+            temperature=0.7,
+            top_p=0.95,
+        )
+        return response.choices[0].text.strip()
+    else:
+        return "Sorry, I couldn't understand your query."
+
+while True:
+    user_input = input("You:")
+    if user_input.lower() == "exit":
+        break
+    response = chatbot(user_input)
+    print(f"Bot: {response}")
+
+
+# Query the Pinecone index using an example sentence
+query_sentence = "We need a new video advertisement campaign."
+query_vector = model.encode([query_sentence])[0]
+query_response = index.query(
+    namespace="example-namespace",
+    top_k=1,
+    vector=query_vector.tolist()
+)
+
+# Print the query results
+print("Query results:")
+if query_response.results:
+    for result in query_response.results:
+        print(f"ID: {result.id}, Distance: {result.distance}")
+else:
+    print("No results found.")