Adds conversational RAG example to contrib

This shows how you can incorporate chat history
into a RAG setting. The primary purpose is to help
contextualize the ask from the new question appropriately.

Author: skrawcz

contrib/hamilton/contrib/dagworks/conversational_rag/README.md

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+# Purpose of this module
+
+This module shows a conversational retrieval augmented generation (RAG) example using
+Hamilton. It shows you how you might structure your code with Hamilton to
+create a RAG pipeline that takes into account conversation.
+
+This example uses [FAISS](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) + and in memory vector store and the OpenAI LLM provider.
+The implementation of the FAISS vector store uses the LangChain wrapper around it.
+That's because this was the simplest way to get this example up without requiring
+someone having to host and manage a proper vector store.
+
+The "smarts" in the is pipeline are that it will take a conversation, and then a question,
+and then rewrite the question based on the conversation to be "standalone". That way
+the standalone question can be used for the vector store query, as well as a more
+specific question for the LLM given the found context.
+
+## Example Usage
+
+```python
+# import the module
+from hamilton import driver
+from hamilton import lifecycle
+dr = (
+    driver.Builder()
+    .with_modules(conversational_rag)
+    .with_config({})
+    # this prints the inputs and outputs of each step.
+    .with_adapters(lifecycle.PrintLn(verbosity=2))
+    .build()
+)
+# no chat history -- nothing to rewrite
+result = dr.execute(
+    ["conversational_rag_response"],
+    inputs={
+        "input_texts": [
+            "harrison worked at kensho",
+            "stefan worked at Stitch Fix",
+        ],
+        "question": "where did stefan work?",
+        "chat_history": []
+    },
+)
+print(result)
+
+# this will now reword the question to then be
+# used to query the vector store and the final LLM call.
+result = dr.execute(
+    ["conversational_rag_response"],
+    inputs={
+        "input_texts": [
+            "harrison worked at kensho",
+            "stefan worked at Stitch Fix",
+        ],
+        "question": "where did he work?",
+        "chat_history": [
+            "Human: Who wrote this example?",
+            "AI: Stefan"
+        ]
+    },
+)
+print(result)
+```
+
+# How to extend this module
+What you'd most likely want to do is:
+
+1. Change the vector store (and how embeddings are generated).
+2. Change the LLM provider.
+3. Change the context and prompt.
+
+With (1) you can import any vector store/library that you want. You should draw out
+the process you would like, and that should then map to Hamilton functions.
+With (2) you can import any LLM provider that you want, just use `@config.when` if you
+want to switch between multiple providers.
+With (3) you can add more functions that create parts of the prompt.
+
+# Configuration Options
+There is no configuration needed for this module.
+
+# Limitations
+
+You need to have the OPENAI_API_KEY in your environment.
+It should be accessible from your code by doing `os.environ["OPENAI_API_KEY"]`.
+
+The code does not check the context length, so it may fail if the context passed is too long
+for the LLM you send it to.

contrib/hamilton/contrib/dagworks/conversational_rag/__init__.py

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+import logging
+
+logger = logging.getLogger(__name__)
+
+from hamilton import contrib
+
+with contrib.catch_import_errors(__name__, __file__, logger):
+    import openai
+
+    # use langchain implementation of vector store
+    from langchain_community.vectorstores import FAISS
+    from langchain_core.vectorstores import VectorStoreRetriever
+
+    # use langchain embedding wrapper with vector store
+    from langchain_openai import OpenAIEmbeddings
+
+
+def standalone_question_prompt(chat_history: list[str], question: str) -> str:
+    """Prompt for getting a standalone question given the chat history.
+
+    This is then used to query the vector store with.
+
+    :param chat_history: the history of the conversation.
+    :param question: the current user question.
+    :return: prompt to use.
+    """
+    chat_history_str = "\n".join(chat_history)
+    return (
+        "Given the following conversation and a follow up question, "
+        "rephrase the follow up question to be a standalone question, "
+        "in its original language.\n\n"
+        "Chat History:\n"
+        "{chat_history}\n"
+        "Follow Up Input: {question}\n"
+        "Standalone question:"
+    ).format(chat_history=chat_history_str, question=question)
+
+
+def standalone_question(standalone_question_prompt: str, llm_client: openai.OpenAI) -> str:
+    """Asks the LLM to create a standalone question from the prompt.
+
+    :param standalone_question_prompt: the prompt with context.
+    :param llm_client: the llm client to use.
+    :return: the standalone question.
+    """
+    response = llm_client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": standalone_question_prompt}],
+    )
+    return response.choices[0].message.content
+
+
+def vector_store(input_texts: list[str]) -> VectorStoreRetriever:
+    """A Vector store. This function populates and creates one for querying.
+
+    This is a cute function encapsulating the creation of a vector store. In real life
+    you could replace this with a more complex function, or one that returns a
+    client to an existing vector store.
+
+    :param input_texts: the input "text" i.e. documents to be stored.
+    :return: a vector store that can be queried against.
+    """
+    vectorstore = FAISS.from_texts(input_texts, embedding=OpenAIEmbeddings())
+    retriever = vectorstore.as_retriever()
+    return retriever
+
+
+def context(standalone_question: str, vector_store: VectorStoreRetriever, top_k: int = 5) -> str:
+    """This function returns the string context to put into a prompt for the RAG model.
+
+    It queries the provided vector store for information.
+
+    :param standalone_question: the question to use to search the vector store against.
+    :param vector_store: the vector store to search against.
+    :param top_k: the number of results to return.
+    :return: a string with all the context.
+    """
+    _results = vector_store.invoke(standalone_question, search_kwargs={"k": top_k})
+    return "\n\n".join(map(lambda d: d.page_content, _results))
+
+
+def answer_prompt(context: str, standalone_question: str) -> str:
+    """Creates a prompt that includes the question and context for the LLM to make sense of.
+
+    :param context: the information context to use.
+    :param standalone_question: the user question the LLM should answer.
+    :return: the full prompt.
+    """
+    template = (
+        "Answer the question based only on the following context:\n"
+        "{context}\n\n"
+        "Question: {question}"
+    )
+
+    return template.format(context=context, question=standalone_question)
+
+
+def llm_client() -> openai.OpenAI:
+    """The LLM client to use for the RAG model."""
+    return openai.OpenAI()
+
+
+def conversational_rag_response(answer_prompt: str, llm_client: openai.OpenAI) -> str:
+    """Creates the RAG response from the LLM model for the given prompt.
+
+    :param answer_prompt: the prompt to send to the LLM.
+    :param llm_client: the LLM client to use.
+    :return: the response from the LLM.
+    """
+    response = llm_client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": answer_prompt}],
+    )
+    return response.choices[0].message.content
+
+
+if __name__ == "__main__":
+    import __init__ as conversational_rag
+
+    from hamilton import driver, lifecycle
+
+    dr = (
+        driver.Builder()
+        .with_modules(conversational_rag)
+        .with_config({})
+        # this prints the inputs and outputs of each step.
+        .with_adapters(lifecycle.PrintLn(verbosity=2))
+        .build()
+    )
+    dr.display_all_functions("dag.png")
+
+    # shows no question is reworded
+    print(
+        dr.execute(
+            ["conversational_rag_response"],
+            inputs={
+                "input_texts": [
+                    "harrison worked at kensho",
+                    "stefan worked at Stitch Fix",
+                ],
+                "question": "where did stefan work?",
+                "chat_history": [],
+            },
+        )
+    )
+
+    # this will now reword the question to then be
+    # used to query the vector store.
+    print(
+        dr.execute(
+            ["conversational_rag_response"],
+            inputs={
+                "input_texts": [
+                    "harrison worked at kensho",
+                    "stefan worked at Stitch Fix",
+                ],
+                "question": "where did he work?",
+                "chat_history": ["Human: Who wrote this example?", "AI: Stefan"],
+            },
+        )
+    )

contrib/hamilton/contrib/dagworks/conversational_rag/dag.png

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+from operator import itemgetter
+
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema import format_document
+from langchain_community.vectorstores import FAISS
+from langchain_core.messages import AIMessage, HumanMessage, get_buffer_string
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+vectorstore = FAISS.from_texts(["harrison worked at kensho"], embedding=OpenAIEmbeddings())
+retriever = vectorstore.as_retriever()
+
+_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
+
+Chat History:
+{chat_history}
+Follow Up Input: {question}
+Standalone question:"""
+CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
+
+template = """Answer the question based only on the following context:
+{context}
+
+Question: {question}
+"""
+ANSWER_PROMPT = ChatPromptTemplate.from_template(template)
+
+DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
+
+
+def _combine_documents(docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"):
+    doc_strings = [format_document(doc, document_prompt) for doc in docs]
+    return document_separator.join(doc_strings)
+
+
+_inputs = RunnableParallel(
+    standalone_question=RunnablePassthrough.assign(
+        chat_history=lambda x: get_buffer_string(x["chat_history"])
+    )
+    | CONDENSE_QUESTION_PROMPT
+    | ChatOpenAI(temperature=0)
+    | StrOutputParser(),
+)
+_context = {
+    "context": itemgetter("standalone_question") | retriever | _combine_documents,
+    "question": lambda x: x["standalone_question"],
+}
+conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()
+
+print(
+    conversational_qa_chain.invoke(
+        {
+            "question": "where did harrison work?",
+            "chat_history": [],
+        }
+    )
+)
+print(
+    conversational_qa_chain.invoke(
+        {
+            "question": "where did he work?",
+            "chat_history": [
+                HumanMessage(content="Who wrote this notebook?"),
+                AIMessage(content="Harrison"),
+            ],
+        }
+    )
+)

contrib/hamilton/contrib/dagworks/conversational_rag/langchain.py

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+faiss-cpu
+langchain
+langchain-community
+langchain-openai

contrib/hamilton/contrib/dagworks/conversational_rag/requirements.txt

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+{
+  "schema": "1.0",
+  "use_case_tags": ["LLM", "openai", "RAG", "retrieval augmented generation", "FAISS"],
+  "secondary_tags": {
+    "language": "English"
+  }
+}

contrib/hamilton/contrib/dagworks/conversational_rag/tags.json

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+{"description": "Default", "name": "default", "config": {}}