feat: ssm.rag() w load, split, embed, store

README.md

@@ -1,6 +1,12 @@
 # Netec Large Language Models
 
-A Python [LangChain](https://www.langchain.com/) - [Pinecone](https://docs.pinecone.io/docs/python-client) proof of concept LLM to manage sales support inquiries on the Netec course catalogue.
+A Python [LangChain](https://www.langchain.com/) - [Pinecone](https://docs.pinecone.io/docs/python-client) proof of concept Retrieval Augmented Generation (RAG) models using sales support PDF documents.
+
+See:
+
+- [LangChain RAG](https://python.langchain.com/docs/use_cases/question_answering/)
+- [LangChain Document Loaders](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf)
+- [LanchChain Caching](https://python.langchain.com/docs/modules/model_io/llms/llm_caching)
 
 ## Installation
 
@@ -28,6 +34,9 @@ python3 -m models.examples.training_services "Microsoft certified Azure AI engin
 
 # example 4 - prompted assistant
 python3 -m models.examples.training_services_oracle "Oracle database administrator"
+
+# example 5 - RAG
+python3 -m models.examples.rag "./data/" "What is Accounting Based Valuation?"
 ```
 
 ## Requirements

models/examples/rag.py

@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+"""Sales Support Model (SSM) Retrieval Augmented Generation (RAG)"""
+import argparse
+
+from ..ssm import SalesSupportModel
+
+
+ssm = SalesSupportModel()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="RAG example")
+    parser.add_argument("filepath", type=str, help="Location of PDF documents")
+    parser.add_argument("prompt", type=str, help="A question about the PDF contents")
+    args = parser.parse_args()
+
+    result = ssm.rag(filepath=args.filepath, prompt=args.prompt)
+    print(result)

models/prompt_templates.py

@@ -35,7 +35,7 @@ def oracle_training_services(self) -> PromptTemplate:
         template = (
             self.sales_role
             + """
-        Note that Netec is the exclusive provide of Oracle training services
+        Note that Netec is the exclusive provider in Latin America of Oracle training services
         for the 6 levels of Oracle Certification credentials: Oracle Certified Junior Associate (OCJA),
         Oracle Certified Associate (OCA), Oracle Certified Professional (OCP),
         Oracle Certified Master (OCM), Oracle Certified Expert (OCE) and

models/ssm.py

@@ -1,24 +1,48 @@
 # -*- coding: utf-8 -*-
 # pylint: disable=too-few-public-methods
-"""Sales Support Model (SSM) for the LangChain project."""
-
+"""
+Sales Support Model (SSM) for the LangChain project.
+See: https://python.langchain.com/docs/modules/model_io/llms/llm_caching
+     https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf
+"""
+
+import glob
+import os
 from typing import ClassVar, List
 
 import pinecone
+from langchain import hub
+from langchain.cache import InMemoryCache
+
+# prompting and chat
 from langchain.chat_models import ChatOpenAI
+
+# document loading
+from langchain.document_loaders import PyPDFLoader
+
+# embedding
 from langchain.embeddings import OpenAIEmbeddings
+
+# vector database
+from langchain.globals import set_llm_cache
 from langchain.llms.openai import OpenAI
 from langchain.prompts import PromptTemplate
-from langchain.schema import HumanMessage, SystemMessage  # AIMessage (not used)
+from langchain.schema import HumanMessage, StrOutputParser, SystemMessage
+from langchain.schema.runnable import RunnablePassthrough
 from langchain.text_splitter import Document, RecursiveCharacterTextSplitter
 from langchain.vectorstores.pinecone import Pinecone
 from pydantic import BaseModel, ConfigDict, Field  # ValidationError
 
+# this project
 from models.const import Credentials
 
 
+###############################################################################
+# initializations
+###############################################################################
 DEFAULT_MODEL_NAME = "text-davinci-003"
 pinecone.init(api_key=Credentials.PINECONE_API_KEY, environment=Credentials.PINECONE_ENVIRONMENT)
+set_llm_cache(InMemoryCache())
 
 
 class SalesSupportModel(BaseModel):
@@ -31,24 +55,17 @@ class SalesSupportModel(BaseModel):
         default_factory=lambda: ChatOpenAI(
             api_key=Credentials.OPENAI_API_KEY,
             organization=Credentials.OPENAI_API_ORGANIZATION,
+            cache=True,
             max_retries=3,
             model="gpt-3.5-turbo",
-            temperature=0.3,
+            temperature=0.0,
         )
     )
 
     # embeddings
-    text_splitter: RecursiveCharacterTextSplitter = Field(
-        default_factory=lambda: RecursiveCharacterTextSplitter(
-            chunk_size=100,
-            chunk_overlap=0,
-        )
-    )
-
     texts_splitter_results: List[Document] = Field(None, description="Text splitter results")
     pinecone_search: Pinecone = Field(None, description="Pinecone search")
-    pinecone_index_name: str = Field(default="netec-ssm", description="Pinecone index name")
-    openai_embedding: OpenAIEmbeddings = Field(default_factory=lambda: OpenAIEmbeddings(model="ada"))
+    openai_embedding: OpenAIEmbeddings = Field(OpenAIEmbeddings())
     query_result: List[float] = Field(None, description="Vector database query result")
 
     def cached_chat_request(self, system_message: str, human_message: str) -> SystemMessage:
@@ -68,24 +85,72 @@ def prompt_with_template(self, prompt: PromptTemplate, concept: str, model: str
 
     def split_text(self, text: str) -> List[Document]:
         """Split text."""
-        # pylint: disable=no-member
-        retval = self.text_splitter.create_documents([text])
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=100,
+            chunk_overlap=0,
+        )
+        retval = text_splitter.create_documents([text])
         return retval
 
     def embed(self, text: str) -> List[float]:
         """Embed."""
-        texts_splitter_results = self.split_text(text)
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=100,
+            chunk_overlap=0,
+        )
+        texts_splitter_results = text_splitter.create_documents([text])
         embedding = texts_splitter_results[0].page_content
         # pylint: disable=no-member
         self.openai_embedding.embed_query(embedding)
 
         self.pinecone_search = Pinecone.from_documents(
             texts_splitter_results,
             embedding=self.openai_embedding,
-            index_name=self.pinecone_index_name,
+            index_name=Credentials.PINECONE_INDEX_NAME,
         )
 
+    def rag(self, filepath: str, prompt: str):
+        """
+        Embed PDF.
+        1. Load PDF document text data
+        2. Split into pages
+        3. Embed each page
+        4. Store in Pinecone
+        """
+
+        # pylint: disable=unused-variable
+        def format_docs(docs):
+            """Format docs."""
+            return "\n\n".join(doc.page_content for doc in docs)
+
+        for pdf_file in glob.glob(os.path.join(filepath, "*.pdf")):
+            loader = PyPDFLoader(file_path=pdf_file)
+            docs = loader.load()
+            for doc in docs:
+                self.embed(doc.page_content)
+
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_documents(docs)
+        vectorstore = Pinecone.from_documents(documents=splits, embedding=self.openai_embedding)
+        retriever = vectorstore.as_retriever()
+        prompt = hub.pull("rlm/rag-prompt")
+
+        rag_chain = (
+            {"context": retriever | self.format_docs, "question": RunnablePassthrough()}
+            | prompt
+            | self.chat
+            | StrOutputParser()
+        )
+
+        return rag_chain.invoke(prompt)
+
     def embedded_prompt(self, prompt: str) -> List[Document]:
-        """Embedded prompt."""
+        """
+        Embedded prompt.
+        1. Retrieve prompt: Given a user input, relevant splits are retrieved
+           from storage using a Retriever.
+        2. Generate: A ChatModel / LLM produces an answer using a prompt that includes
+           the question and the retrieved data
+        """
         result = self.pinecone_search.similarity_search(prompt)
         return result

requirements.txt

@@ -22,4 +22,7 @@ codespell==2.2.6
 python-dotenv==1.0.0
 pydantic==2.5.2
 langchain==0.0.343
+openai==1.3.5
 pinecone-client==2.2.4
+pypdf==3.17.1
+tiktoken==0.5.1