Upload 5 files

.env

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+COHERE_API_KEY="0DWnJ9TpwKIdSKCexwhyXN2FyOOIiO4JhnWPcmRg"

.env.example

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+COHERE_API_KEY=""

app.py

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+import streamlit as st 
+import os 
+import logging 
+from dotenv import load_dotenv
+from langchain_community.embeddings import CohereEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.document_loaders.csv_loader import CSVLoader
+
+#setup logging
+logging.basicConfig(level = logging.DEBUG)
+
+#load Environemnt Variables
+load_dotenv()
+
+#ensure COHERE_API_KEY is set properly
+if not os.getenv("COHERE_API_KEY"):
+    raise ValueError("COHERE_API_KEY environment variable not set")
+
+st.set_page_config(page_title = "Educate Kids" , page_icon=":robot:")
+st.header("SIMILARITY MATCHING")
+
+model_name = "embed-english-v3.0"
+
+user_agent = "my-app/1.0" # Replace with your agent if you Want
+embeddings = CohereEmbeddings(model = model_name , user_agent = user_agent)
+# embeddings = CohereEmbeddings(model = model_name)
+
+loader = CSVLoader(file_path = "myData.csv" , csv_args={
+    'delimiter' : ',',
+    'quotechar' : '"',
+    'fieldnames' : ['words']
+})
+
+data = loader.load()
+
+logging.debug(f"loaded data : {data}")
+print(f"loaded data : {data}")
+
+#extract data from data and ensure they are valid
+texts = [doc.page_content for doc in data]
+
+#validate the databeing passed to the mebeddings
+for text in texts:
+    logging.debug(f"Document text  : {text}")
+
+#initialize FAISS database
+try:
+    db = FAISS.from_documents(data , embeddings)
+    st.write("FAISS database created successfully")
+except ValueError as e:
+    logging.error(f"Error occured : {e}")
+    for text in texts:
+        try:
+            embedding = embeddings.embed_documents([text])
+            logging.debug(f"Embeddings : {embedding}")
+        except ValueError as ve:
+            logging.error(f"Failed to embed document: {text} with error :{ve}")
+
+def get_input():
+    input_text  = st.text_input("you : " , key = "input")
+    return input_text
+
+user_input = get_input()
+submit = st.button("Find similar Things")
+
+if submit:
+    docs = db.similarity_search(user_input)
+    print(docs)
+    st.subheader("Top Matches : ")
+    st.text(docs[0].page_content)
+    st.text(docs[1].page_content)

myData.csv

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+Words
+Elephant
+Lion
+Tiger
+Dog
+Cricket
+Football
+Tennis
+Basketball
+Apple
+Orange
+Banana

requirements.txt

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+langchain
+streamlit
+cohere
+tiktoken
+python-dotenv
+faiss-cpu
+langchain-cohere