cookbook for Basic RAG

projects/Basic-RAG/BasicRAG_CustomPrompt.py

@@ -0,0 +1,14 @@
+"""A cookbook demonstrating how to use custom prompts with Basic RAG."""
+
+from grag.components.prompt import Prompt
+from grag.rag.basic_rag import BasicRAG
+
+custom_prompt = Prompt(
+    input_keys={"context", "question"},
+    template="""Answer the following question based on the given context.
+    question: {question}
+    context: {context}
+    answer: 
+    """,
+)
+rag = BasicRAG(doc_chain="stuff", custom_prompt=custom_prompt)

projects/Basic-RAG/BasicRAG_FewShotPrompt.py

@@ -1,29 +1,30 @@
+"""A cookbook demonstrating how to use custom few-shot prompts with Basic RAG."""
+
+from grag.components.prompt import FewShotPrompt
 from grag.rag.basic_rag import BasicRAG
-from grap.components.prompt import FewShotPrompt
 
 custom_few_shot_prompt = FewShotPrompt(
     input_keys={"context", "question"},
     output_keys={"answer"},
-    example_template='''
+    example_template="""
     question: {question}
     answer: {answer}
-    ''',
-    prefix='''Answer the following question based on the given context like examples given below:''',
-    suffix='''Answer the following question based on the given context
+    """,
+    prefix="""Answer the following question based on the given context like examples given below:""",
+    suffix="""Answer the following question based on the given context
     question: {question}
     context: {context}
     answer:
-    ''',
+    """,
     examples=[
         {
             "question": "What is the name of largest planet?",
-            "answer": "Jupiter is the largest planet."
+            "answer": "Jupiter is the largest planet.",
         },
         {
             "question": "Who came up with Convolutional Neural Networks?",
-            "answer": "Yann LeCun introduced convolutional neural networks."
-        }
-    ]
+            "answer": "Yann LeCun introduced convolutional neural networks.",
+        },
+    ],
 )
-rag = BasicRAG(doc_chain="stuff",
-               custom_prompt=custom_few_shot_prompt)
+rag = BasicRAG(doc_chain="stuff", custom_prompt=custom_few_shot_prompt)

projects/Basic-RAG/BasicRAG_ingest.py

@@ -0,0 +1,13 @@
+"""A cookbook demonstrating how to ingest pdf files for use with Basic RAG."""
+
+from pathlib import Path
+
+from grag.components.multivec_retriever import Retriever
+from grag.components.vectordb.deeplake_client import DeepLakeClient
+
+client = DeepLakeClient(collection_name="test")
+retriever = Retriever(vectordb=client)
+
+dir_path = Path(__file__).parents[2] / "data/client_test/test/"
+
+retriever.ingest(dir_path)

projects/Basic-RAG/BasicRAG_refine.py

@@ -1,5 +1,11 @@
-from grag.grag.rag import BasicRAG
+"""A cookbook demonstrating how to use Basic RAG with refine chain using DeepLake as client."""
 
+from grag.components.multivec_retriever import Retriever
+from grag.components.vectordb.deeplake_client import DeepLakeClient
+from grag.rag.basic_rag import BasicRAG
+
+client = DeepLakeClient(collection_name="test")
+retriever = Retriever(vectordb=client)
 rag = BasicRAG(doc_chain="refine")
 
 if __name__ == "__main__":

projects/Basic-RAG/BasicRAG_stuff.py

@@ -1,3 +1,5 @@
+"""A cookbook demonstrating how to use Basic RAG with stuff chain using DeepLake as client."""
+
 from grag.components.multivec_retriever import Retriever
 from grag.components.vectordb.deeplake_client import DeepLakeClient
 from grag.rag.basic_rag import BasicRAG

projects/Basic-RAG/README.md

@@ -1,11 +1,14 @@
-# Basic RAG
+# Basic RAG Cookbook
 
-## BasicRAG v1
+Welcome to the Basic RAG Cookbook! This repository is dedicated to showcasing how to utilize the Retrieval-Augmented
+Generation (RAG) model for various applications using custom and few-shot prompts. For in depth understanding of RAG
+pipelines, chains, and prompts
+check [RAG-Piplines.md](https://github.com/arjbingly/Capstone_5/blob/main/projects/Basic-RAG/RAG-Piplines.md)
 
-- Stuff document chain (Refer [RAG-Pipelines.md](./RAG-Piplines.md))
-- Top-k retrival
+### Contents:
 
-## BasicRAG v2
-
-- Refine document chain (Refer [RAG-Pipelines.md](./RAG-Piplines.md))
-- Top-k retrival
+- **BasicRAG_CustomPrompt.py**: Learn to integrate custom prompts into Basic RAG for tailored query responses.
+- **BasicRAG_FewShotPrompt.py**: Explore the use of few-shot prompts to enhance Basic RAG's contextual understanding.
+- **BasicRAG_ingest.py**: Demonstrates the process of ingesting PDF files, making them searchable via Basic RAG.
+- **BasicRAG_stuff.py**: A guide on leveraging the stuff chain with Basic RAG for enriched data processing.
+- **BasicRAG_refine.py**: Discover how to refine queries using the refine chain for more precise results with Basic RAG.