Templates (#12294)

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>

templates/rag-chroma-private/rag_chroma_private/chain.py

@@ -0,0 +1,48 @@
+from langchain.vectorstores import Chroma
+from langchain.chat_models import ChatOllama
+from langchain.prompts import ChatPromptTemplate
+from langchain.embeddings import GPT4AllEmbeddings
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough, RunnableParallel
+
+# Load
+from langchain.document_loaders import WebBaseLoader
+loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
+data = loader.load()
+
+# Split
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
+all_splits = text_splitter.split_documents(data)
+
+# Add to vectorDB
+vectorstore = Chroma.from_documents(documents=all_splits, 
+                                    collection_name="rag-private",
+                                    embedding=GPT4AllEmbeddings(),
+                                    )
+retriever = vectorstore.as_retriever()
+
+# Prompt 
+# Optionally, pull from the Hub
+# from langchain import hub
+# prompt = hub.pull("rlm/rag-prompt")
+# Or, define your own:
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+prompt = ChatPromptTemplate.from_template(template)
+
+# LLM
+# Select the LLM that you downloaded
+ollama_llm = "llama2:13b-chat"
+model = ChatOllama(model=ollama_llm)
+
+# RAG chain
+chain = (
+    RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
+    | prompt
+    | model
+    | StrOutputParser()
+)