Langchain and Chroma Vector Embedding to nada

quickstart/client_code/payments.txt

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+yuvaraj sent 1

quickstart/client_code/requirements.txt

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+langchain==0.2.6
+langchain-chroma==0.1.2
+langchain-community==0.2.6
+langchain-core==0.2.11
+langchain-huggingface==0.0.3
+langchain-text-splitters==0.2.2
+langsmith==0.1.83
+py-nillion-client
+nada-dsl
+nillion-python-helpers
+python-dotenv==1.0.0
+cosmpy>=0.9.2

quickstart/client_code/run_my_first_program.py

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+from langchain_community.document_loaders import TextLoader
+from langchain_huggingface import HuggingFaceEmbeddings
+
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_chroma import Chroma
+from nada_dsl import *
+
+# Load the document and split it into chunks
+
+
+def nada_main():
+    loader = TextLoader("payments.txt")
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    docs = text_splitter.split_documents(documents)
+    embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = Chroma.from_documents(docs, embedding_function)
+    print(docs[0].page_content) 
+    vector = embedding_function.embed_query(docs[0].page_content)
+    storage_party = Party(name=vector) 
+    secret_vector = SecretInteger(Input(name="vector", party=storage_party))
+    stored_vector = Output(secret_vector, "stored_vector", storage_party)
+
+    return [stored_vector]
+
+# Execute the nada_main function to store the vector 
+stored_results = nada_main()
+print(stored_results)
+

quickstart/client_code/vector.py

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+from langchain_community.document_loaders import TextLoader
+from langchain_huggingface import HuggingFaceEmbeddings
+
+from langchain_text_splitters import CharacterTextSplitter
+from langchain_chroma import Chroma
+from nada_dsl import *
+
+# Load the document and split it into chunks
+
+
+def nada_main():
+    loader = TextLoader("payments.txt")
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    docs = text_splitter.split_documents(documents)
+    embedding_function = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    db = Chroma.from_documents(docs, embedding_function)
+    print(docs[0].page_content) 
+    vector = embedding_function.embed_query(docs[0].page_content)
+    storage_party = Party(name=vector) 
+    secret_vector = SecretInteger(Input(name="vector", party=storage_party))
+    stored_vector = Output(secret_vector, "stored_vector", storage_party)
+
+    return [stored_vector]
+
+# Execute the nada_main function to store the vector 
+stored_results = nada_main()
+print(stored_results)
+