diff --git a/Part 1/main.py b/Part 1/main.py
index 39094228bffbe26e7ce9faef8d208a00d9fcc66f..e42c3cc1e2fe97a2a24a7b33e176cbef983f9b26 100644
--- a/Part 1/main.py
+++ b/Part 1/main.py
@@ -1,14 +1,18 @@
import boto3
import os
+from tqdm.auto import tqdm
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import BedrockEmbeddings
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
from langchain_community.vectorstores import OpenSearchVectorSearch
+import uuid
+import json
+
## Local directory for storing PDF files
LOCAL_DIR = "pdfs"
-index_name = "cloud_lecture_test"
+index_name = "cloud_lecture_test3"
## S3_client
@@ -25,7 +29,7 @@ credentials = boto3.Session().get_credentials()
awsauth = AWSV4SignerAuth(credentials, 'us-east-1', 'aoss')
## Vector DB endpoint
-host= 'ispfynbvy6eov4efdsqd.us-east-1.aoss.amazonaws.com'
+host= 'd7gvxdj7jpz3h3bj0xq6.us-east-1.aoss.amazonaws.com'
## Opensearch Client
OpenSearch_client = OpenSearch(
@@ -34,7 +38,6 @@ OpenSearch_client = OpenSearch(
use_ssl=True,
verify_certs=True,
connection_class=RequestsHttpConnection,
- timeout=300
)
@@ -83,19 +86,38 @@ def download_documents(bucket_name,local_dir):
## Split pages/text into chunks
-def split_text(pages, chunk_size, chunk_overlap, local_dir):
- loader= PyPDFDirectoryLoader(local_dir)
- pages = loader.load_and_split()
+def split_text(docs, chunk_size, chunk_overlap):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
- chunks = text_splitter.split_documents(pages)
+ chunks = text_splitter.split_documents(docs)
+
return chunks
-## Generate embeddings and index them using Opensearch
-# def generate_embeddings():
+## Generate embeddings
+def generate_embeddings(bedrock_client, chunks):
+ embeddings_model = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1", client=bedrock_client)
+ chunks_list=[chunk.page_content for chunk in chunks]
+ embeddings = embeddings_model.embed_documents(chunks_list)
+ return embeddings
+
+# Store generated embeddings into an OpenSearch index.
+def store_embeddings(embeddings, texts, meta_data, host, awsauth, index_name):
+ docsearch = OpenSearchVectorSearch.from_embeddings(
+ embeddings,
+ texts,
+ meta_data,
+ opensearch_url=f'https://{host}:443',
+ http_auth=awsauth,
+ use_ssl=True,
+ verify_certs=True,
+ connection_class=RequestsHttpConnection,
+ index_name=index_name,
+ bulk_size=1000
+)
-# def store_embeddings():
+ return docsearch
+# Func to do both generating and storing embeddings
def generate_store_embeddings(bedrock_client, chunks,awsauth,index_name):
embeddings_model = BedrockEmbeddings(model_id="amazon.titan-embed-text-v1", client=bedrock_client)
docsearch = OpenSearchVectorSearch.from_documents(
@@ -117,13 +139,23 @@ def generate_store_embeddings(bedrock_client, chunks,awsauth,index_name):
## main
def main():
- docs= download_documents(BUCKET_NAME,LOCAL_DIR)
- chunks=split_text(docs, 1000, 100, LOCAL_DIR)
- print("Sample chunk:", chunks[0])
- create_index(index_name)
- embeddings = generate_store_embeddings(bedrock_client, chunks,awsauth,index_name)
- print("Embeddings processing completed", embeddings)
+ download_documents(BUCKET_NAME,LOCAL_DIR)
+ loader= PyPDFDirectoryLoader(LOCAL_DIR)
+ docs = loader.load()
+ print(docs[80])
+ chunks = split_text(docs, 1000, 100)
+ print(chunks[80])
+ embeddings= generate_embeddings(bedrock_client, chunks)
+ print(embeddings[80])
+ texts = [chunk.page_content for chunk in chunks]
+ # Prepare metadata for each chunk
+ meta_data = [{'source': chunk.metadata['source'], 'page': chunk.metadata['page'] + 1} for chunk in chunks]
+ print(embeddings[80])
+ print(meta_data[80])
+ store_embeddings(embeddings, texts, meta_data ,host, awsauth,index_name)
+
+
diff --git a/README.md b/README.md
index 41ca5aa094d2facf7a0323f767558d1c9d1838bc..d5fad60ee617f64464e560e92e42447d545148a0 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,36 @@
# chatbot-lab
+## Set up environment
+1. AWS CLI: Ensure AWS CLI is installed and configured on your laptop(refer to Session 1)
+2. Ensure python is installed: python 3.8 or higher
+2. Install required python libraries listed in the 'requirements.txt':
+`pip install -r requirement.txt`
-## Set up environment
-1. AWS CLI: Ensure AWS CLI is installed and configured on your laptop.
- * Install [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html).
- * Configure the CLI with your AWS credentials: 'aws configure'
-2. Ensure python is installed.
-2. Install required python libraries listed in the 'requirements.txt': pip install -r requirement.txt
+## Part 1:
+
+### Step 1: Create S3 Bucket
+Create an S3 bucket and upload a few PDF files (Detailed steps are provided in the first session).
+
+### Step 2: Vector Store Creation
+To set up the Vector Store, run the following command:
+`python Create-Vector-DB.py`
+
+This script performs the following actions:
+* Set up the security policies: Sets up encryption, network, and data access policies for collections starting with "test".
+
+* Vector Store Initialization: Creates a vector store named test1, specifically designed for vector search operations.
-##
+* Endpoint Retrieval: After the vector store is set up, the script retrieves and displays the store's endpoint for immediate use.
+### Step 3: Processing PDF Files
+After setting up the S3 bucket and Vector Store, prepare to vectorize the PDF files:
+* In main.py, update the S3 bucket name to the one you created.
+* Update the Vector Store endpoint with the one provided by the setup script.
+* Execute the processing script: `python main.py`
+The main.py script will:
+1. Download PDF files from the S3 bucket.
+2. Split them into chunks.
+3. Generate embeddings from the chunks.
+4. Store these embeddings in the OpenSearch Vector DB.
\ No newline at end of file