Create utils.py

utils.py

@@ -0,0 +1,108 @@
+import requests
+
+def google_search(query, api_key, cx):
+    url = f"https://www.googleapis.com/customsearch/v1?q={query}&key={api_key}&cx={cx}"
+    
+    response = requests.get(url)
+
+    if response.status_code == 200:
+        search_results = response.json() 
+        return search_results
+    else:
+        print(f"Error: {response.status_code}")
+        return None
+
+
+
+
+def generate_embedding_for_user_resume(data,user_id):
+    from sentence_transformers import SentenceTransformer
+
+    model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+
+
+    def get_embedding(data, precision="float32"):
+        return model.encode(data, precision=precision)
+
+
+    from pinecone import Vector
+    def create_docs_with_vector_embeddings(bson_float32, data):
+        docs = []
+        for i, (bson_f32_emb, text) in enumerate(zip(bson_float32, data)):
+                doc =Vector(
+                id=f"{i}",
+                values= bson_f32_emb.tolist(),
+                metadata={"text":text,"user_id":user_id},
+                )
+                docs.append(doc)
+        return docs
+    float32_embeddings = get_embedding(data, "float32")
+
+
+
+
+    docs = create_docs_with_vector_embeddings(float32_embeddings,  data)
+    return docs
+
+
+def insert_embeddings_into_pinecone_database(doc,api_key,name_space):
+    from pinecone import Pinecone
+    pc = Pinecone(api_key=api_key)
+    index_name = "resumes"
+    index = pc.Index(index_name)
+    upsert_response = index.upsert(namespace=name_space,vectors=doc)
+    return upsert_response
+
+
+
+
+def query_vector_database(query,api_key,name_space):
+    from pinecone import Pinecone
+    from sentence_transformers import SentenceTransformer
+    model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
+    ret=[]
+    pc = Pinecone(api_key=api_key)
+    index_name = "resumes"
+
+
+    index = pc.Index(index_name)
+    
+    # Define a function to generate embeddings in multiple precisions
+    def get_embedding(data, precision="float32"):
+        return model.encode(data, precision=precision)
+    
+    query_embedding = get_embedding(query, precision="float32")
+
+    response = index.query(
+        namespace=name_space,
+        vector=query_embedding.tolist(),
+        top_k=3,
+        include_metadata=True
+        )
+
+
+    for doc in response['matches']:
+        ret.append(doc['metadata']['text'])
+    return ret
+
+
+def delete_vector_namespace(name_space,api_key):
+    from pinecone import Pinecone
+    pc = Pinecone(api_key=api_key)
+    index_name = "resumes"
+
+
+    index = pc.Index(index_name)
+    response = index.delete(delete_all=True,namespace=name_space)
+    return response
+
+
+
+def split_text_into_chunks(text, chunk_size=400):
+    # Split the text into words using whitespace.
+    words = text.split()
+
+    # Group the words into chunks of size 'chunk_size'.
+    chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+    return chunks
+