Add example

examples/rag.py

@@ -0,0 +1,61 @@
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import OpenAIEmbeddings
+
+texts = ["harrison went to kensho"]
+embeddings = OpenAIEmbeddings()
+db = FAISS.from_texts(texts, embeddings)
+
+retriever = db.as_retriever()
+
+from langchain.prompts import PromptTemplate
+
+prompt = PromptTemplate.from_template(
+    """Answer the question "{question}"  based on the following context: {context}"""
+)
+
+from langchain.schema.messages import AIMessage, AnyMessage, FunctionMessage
+
+from permchain import Channel, Pregel
+from permchain.channels import Topic
+
+from langchain.chat_models import ChatOpenAI
+
+model = ChatOpenAI()
+
+chain = (
+    Channel.subscribe_to(["question"])
+    | {
+        "context": (lambda x: x["question"])
+        | Channel.write_to(
+            messages=lambda _input: AIMessage(
+                content="",
+                additional_kwargs={
+                    "function_call": "retrieval",
+                    "arguments": {"question": _input},
+                },
+            )
+        )
+        | retriever
+        | Channel.write_to(
+            messages=lambda documents: FunctionMessage.construct(
+                content=documents,  # function message requires content to be str
+                name="retrieval",
+            )
+        ),
+        "question": lambda x: x["question"],
+    }
+    | prompt
+    | model
+    | Channel.write_to(messages=lambda message: [message])
+)
+
+app = Pregel(
+    chains={"chain": chain},
+    channels={"messages": Topic(AnyMessage)},
+    input=["question"],
+    output=["messages"],
+)
+
+for s in app.stream({"question": "where did harrison go"}):
+    print(s)