From 1682a5b6f55a589afa0d083f2ad9accf2f16b75f Mon Sep 17 00:00:00 2001
From: Omar Santos <santosomar@gmail.com>
Date: Tue, 3 Oct 2023 22:46:50 -0400
Subject: [PATCH] Create rag_basic_example_with_chromadb.py

---
 .../rag_basic_example_with_chromadb.py        | 53 +++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 ai_research/LangChain/rag_basic_example_with_chromadb.py

diff --git a/ai_research/LangChain/rag_basic_example_with_chromadb.py b/ai_research/LangChain/rag_basic_example_with_chromadb.py
new file mode 100644
index 0000000..6df6740
--- /dev/null
+++ b/ai_research/LangChain/rag_basic_example_with_chromadb.py
@@ -0,0 +1,53 @@
+from langchain.document_loaders import TextLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.retrievers import SemanticRetriever
+from langchain.prompts import ChatPromptTemplate
+from langchain.chat_models import ChatOpenAI
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+# Step 1: Load the document and split it into chunks
+loader = TextLoader("path/to/document.txt")
+documents = loader.load()
+
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+chunks = text_splitter.split_documents(documents)
+
+# Step 2: Create embeddings
+embedding_model = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+embeddings = embedding_model.embed(chunks)
+
+# Step 3: Store embeddings in ChromaDB
+db = Chroma.from_embeddings(embeddings)
+
+# Step 4: Create a retriever
+retriever = SemanticRetriever(db)
+
+# Step 5: Define the prompt template
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# Step 6: Create the language model
+model = ChatOpenAI()
+
+# Step 7: Define the output parser
+output_parser = StrOutputParser()
+
+# Step 8: Define the RAG pipeline
+pipeline = {
+    "context": retriever,
+    "question": RunnablePassthrough(),
+} | prompt | model | output_parser
+
+# Step 9: Invoke the RAG pipeline with a question
+question = "What is the main theme of the document?"
+answer = pipeline.invoke({"question": question})
+
+# Step 10: Print the answer
+print(answer)