templates

rag.py

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+# +
+import streamlit as st
+
+# Set up the document loader
+from langchain_community.document_loaders import PyPDFLoader
+loader = PyPDFLoader("ca30x30-2024.pdf")
+docs = loader.load()
+
+# Set up the language model
+from langchain_openai import ChatOpenAI
+llm = ChatOpenAI(model = "llama3", api_key=st.secrets["LITELLM_KEY"], base_url = "https://llm.nrp-nautilus.io",  temperature=0)
+
+# Set up the embedding model
+from langchain_openai import OpenAIEmbeddings
+embedding = OpenAIEmbeddings(
+    model = "embed-mistral", 
+    api_key=st.secrets["LITELLM_KEY"], 
+    base_url = "https://llm.nrp-nautilus.io"
+)
+
+# Build a retrival agent
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(docs)
+vectorstore = InMemoryVectorStore.from_documents(documents=splits, embedding=embedding)
+retriever = vectorstore.as_retriever()
+
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+system_prompt = (
+    "You are an assistant for question-answering tasks. "
+    "Use the following pieces of retrieved context to answer "
+    "the question. If you don't know the answer, say that you "
+    "don't know. Use three sentences maximum and keep the "
+    "answer concise."
+    "\n\n"
+    "{context}"
+)
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+question_answer_chain = create_stuff_documents_chain(llm, prompt)
+rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+# agent is ready to test:
+#results = rag_chain.invoke({"input": "What is the goal of CA 30x30?"})
+#results['answer']
+
+
+# Place agent inside a streamlit application:
+st.title("RAG Demo")
+
+if prompt := st.chat_input("What is the goal of CA 30x30?"):
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        results = rag_chain.invoke({"input": prompt})
+        st.write(results['answer'])
+
+        st.write('**Context metadata:**\n')
+        st.write(results['context'][0]['metadata'])
+

sql.py

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+import streamlit as st
+
+from langchain_community.utilities import SQLDatabase
+from langchain_openai import ChatOpenAI
+from langchain.chains import create_sql_query_chain
+
+# Set up Langchain SQL access
+db = SQLDatabase.from_uri("duckdb:///tmp.db", view_support=True)
+parquet = "s3://us-west-2.opendata.source.coop/cboettig/gbif/2024-10-01/**"
+db.run(f"create or replace view gbif_h3 as select * from read_parquet('{parquet}');")
+llm = ChatOpenAI(model="llama3", temperature=0, api_key=st.secrets["LITELLM_KEY"], base_url = "https://llm.nrp-nautilus.io")
+chain = create_sql_query_chain(llm, db)
+