started work on the loader

hw1/app.py

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+import os
+import sys
+import time
+import math
+import numpy
+from dotenv import load_dotenv
+from bs4 import BeautifulSoup
+from nltk.tokenize import WordPunctTokenizer, RegexpTokenizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+from langchain import hub
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import (
+    MessagesPlaceholder,
+    HumanMessagePromptTemplate,
+    ChatPromptTemplate,
+    PromptTemplate,
+)
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_google_genai import (
+    GoogleGenerativeAI,
+    GoogleGenerativeAIEmbeddings,
+    ChatGoogleGenerativeAI,
+    HarmCategory,
+    HarmBlockThreshold,
+)
+from langchain_community.document_loaders import AsyncHtmlLoader, RecursiveUrlLoader
+from langchain_community.document_transformers import BeautifulSoupTransformer
+from langchain_community.vectorstores import Chroma
+
+from langchain_openai import ChatOpenAI
+from langchain_openai import OpenAI
+
+from langchain_core.messages import HumanMessage
+
+load_dotenv()
+llm = OpenAI()
+chat_model = ChatOpenAI(model="gpt-4")
+
+text = "What is a good question to put here?"
+messages = [HumanMessage(content=text)]
+
+llm.invoke(text)
+# >> Feetful of Fun
+
+chat_model.invoke(messages)
+# >> AIMessage(content="Socks O'Color")

hw1/loader.py

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+from langchain_community.document_loaders import AsyncHtmlLoader, DirectoryLoader, TextLoader, PyPDFDirectoryLoader, Docx2txtLoader, UnstructuredMarkdownLoader, WikipediaLoader, ArxivLoader, CSVLoader
+from langchain_community.vectorstores import Chroma
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.document_loaders import WebBaseLoader
+import bs4
+
+"""
+Loader attempting to load documents for the game Kerbal Space program two, both from wikipedia, as well as details from 
+the games own fan-run wiki, using GPT4
+
+Code adapted from 
+1) https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_1_to_4.ipynb
+2) https://codelabs.cs.pdx.edu/labs/G2.3_LangChainRAG
+"""
+
+
+# vectorstore = Chroma(
+#     embedding_function=GoogleGenerativeAIEmbeddings(model="models/embedding-001", task_type="retrieval_query"),
+#     persist_directory="./rag_data/.chromadb"
+# )
+
+# Load Documents
+loader = WebBaseLoader(
+    web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
+    bs_kwargs=dict(
+        parse_only=bs4.SoupStrainer(
+            class_=("post-content", "post-title", "post-header")
+        )
+    ),
+)
+
+docs = loader.load()
+
+# Split
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(docs)
+
+# Embed
+vectorstore = Chroma.from_documents(documents=splits, 
+                                    embedding=OpenAIEmbeddings())
+
+def load_docs(docs):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=10)
+    splits = text_splitter.split_documents(docs)
+    vectorstore.add_documents(documents=splits)
+
+def load_wikipedia(query):
+    load_docs(WikipediaLoader(query=query, load_max_docs=1).load())
+
+def load_urls(urls):
+    load_docs(AsyncHtmlLoader(urls).load())
+
+
+wiki_query = "Kerbel Space Program"
+print(f"Loading Wikipedia pages on: {wiki_query}")
+load_wikipedia(wiki_query)
+
+urls = ["https://wiki.kerbalspaceprogram.com/wiki/Kerbin", "https://wiki.kerbalspaceprogram.com/wiki/Eve"]
+print(f"Loading: {urls}")
+load_urls(urls)
+
+print("RAG database initialized with the following sources.")
+retriever = vectorstore.as_retriever()
+document_data_sources = set()
+for doc_metadata in retriever.vectorstore.get()['metadatas']:
+    document_data_sources.add(doc_metadata['source']) 
+for doc in document_data_sources:
+    print(f"  {doc}")