Add vector search module

src/module/vectorsearch.py

@@ -0,0 +1,38 @@
+import os
+from config import OPENAI_API_KEY, file_Directory
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain_openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+import pandas as pd
+
+os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
+
+
+# df = pd.read_excel(r"/home/vrush/Catalog-Digitization-/src/module/data/Catalog Digitization/ONDC Test Data _ Images/ONDCSampleData.xlsx")
+# df_new = pd.DataFrame(columns=["id", "name"])
+# df_new =  df['name']
+# df_new.to_csv(r"data/data.csv", index=False)
+
+def create_vector():
+    loader = CSVLoader(file_path="data/data.csv")
+    docs = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    documents = text_splitter.split_documents(docs)
+    db_path = os.path.join(file_Directory,"vectorstore")
+    embeddings = OpenAIEmbeddings()
+    os.makedirs(db_path, exist_ok=True)      
+    Chroma.from_documents(docs, embeddings, persist_directory= db_path)
+    
+def search(query):
+    embeddings = OpenAIEmbeddings()
+    db_path = os.path.join(file_Directory,"vectorstore")
+    db = Chroma(persist_directory= db_path, embedding_function= embeddings)
+    embedding_vector = OpenAIEmbeddings().embed_query(query)
+    docs = db.similarity_search_by_vector(embedding_vector)
+    print(docs[0].page_content)
+
+
+if __name__ == "__main__":
+    create_vector()
+    search("Choco Creme Wafers")