Switch to Langchain's LCEL

example-apps/chatbot-rag-app/api/app.py

@@ -1,9 +1,7 @@
 from flask import Flask, jsonify, request, Response
 from flask_cors import CORS
-from queue import Queue
 from uuid import uuid4
-from chat import ask_question, parse_stream_message
-import threading
+from chat import ask_question
 import os
 import sys
 
@@ -23,18 +21,8 @@ def api_chat():
     if question is None:
         return jsonify({"msg": "Missing question from request JSON"}), 400
 
-    stream_queue = Queue()
     session_id = request.args.get("session_id", str(uuid4()))
-
-    print("Chat session ID: ", session_id)
-
-    threading.Thread(
-        target=ask_question, args=(question, stream_queue, session_id)
-    ).start()
-
-    return Response(
-        parse_stream_message(session_id, stream_queue), mimetype="text/event-stream"
-    )
+    return Response(ask_question(question, session_id), mimetype="text/event-stream")
 
 
 @app.cli.command()

example-apps/chatbot-rag-app/api/chat.py

@@ -1,18 +1,10 @@
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.chains import ConversationalRetrievalChain
-from langchain.prompts.chat import (
-    HumanMessagePromptTemplate,
-    SystemMessagePromptTemplate,
-    ChatPromptTemplate,
-)
-from langchain.prompts.prompt import PromptTemplate
 from langchain.vectorstores import ElasticsearchStore
-from queue import Queue
 from llm_integrations import get_llm
 from elasticsearch_client import (
     elasticsearch_client,
     get_elasticsearch_chat_message_history,
 )
+from flask import render_template, stream_with_context, current_app
 import json
 import os
 
@@ -21,135 +13,50 @@
     "ES_INDEX_CHAT_HISTORY", "workplace-app-docs-chat-history"
 )
 ELSER_MODEL = os.getenv("ELSER_MODEL", ".elser_model_2")
-POISON_MESSAGE = "~~~END~~~"
 SESSION_ID_TAG = "[SESSION_ID]"
 SOURCE_TAG = "[SOURCE]"
 DONE_TAG = "[DONE]"
 
-
-class QueueCallbackHandler(BaseCallbackHandler):
-    def __init__(
-        self,
-        queue: Queue,
-    ):
-        self.queue = queue
-        self.in_human_prompt = True
-
-    def on_retriever_end(self, documents, *, run_id, parent_run_id=None, **kwargs):
-        if len(documents) > 0:
-            for doc in documents:
-                source = {
-                    "name": doc.metadata["name"],
-                    "page_content": doc.page_content,
-                    "url": doc.metadata["url"],
-                    "icon": doc.metadata["category"],
-                    "updated_at": doc.metadata.get("updated_at", None),
-                }
-                self.queue.put(f"{SOURCE_TAG} {json.dumps(source)}")
-
-    def on_llm_new_token(self, token, **kwargs):
-        if not self.in_human_prompt:
-            self.queue.put(token)
-
-    def on_llm_start(
-        self,
-        serialized,
-        prompts,
-        *,
-        run_id,
-        parent_run_id=None,
-        tags=None,
-        metadata=None,
-        **kwargs,
-    ):
-        self.in_human_prompt = prompts[0].startswith("Human:")
-
-    def on_llm_end(self, response, *, run_id, parent_run_id=None, **kwargs):
-        if not self.in_human_prompt:
-            self.queue.put(POISON_MESSAGE)
-
-
 store = ElasticsearchStore(
     es_connection=elasticsearch_client,
     index_name=INDEX,
     strategy=ElasticsearchStore.SparseVectorRetrievalStrategy(model_id=ELSER_MODEL),
 )
 
-general_system_template = """
-Human: Use the following passages to answer the user's question. 
-Each passage has a SOURCE which is the title of the document. When answering, give the source name of the passages you are answering from, put them in a comma seperated list, prefixed at the start with SOURCES: $sources then print an empty line.
-
-Example:
-
-Question: What is the meaning of life?
-Response:
-The meaning of life is 42. \n
 
-SOURCES: Hitchhiker's Guide to the Galaxy \n
-
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-
-----
-{context}
-----
+@stream_with_context
+def ask_question(question, session_id):
+    yield f"data: {SESSION_ID_TAG} {session_id}\n\n"
+    current_app.logger.debug("Chat session ID: %s", session_id)
 
-"""
-general_user_template = "Question: {question}"
-qa_prompt = ChatPromptTemplate.from_messages(
-    [
-        SystemMessagePromptTemplate.from_template(general_system_template),
-        HumanMessagePromptTemplate.from_template(general_user_template),
-    ]
-)
+    chat_history = get_elasticsearch_chat_message_history(
+        INDEX_CHAT_HISTORY, session_id
+    )
 
-document_prompt = PromptTemplate(
-    input_variables=["page_content", "name"],
-    template="""
----
-NAME: "{name}"
-PASSAGE:
-{page_content}
----
-""",
-)
+    if len(chat_history.messages) > 0:
+        # create a condensed question
+        condense_question_prompt = render_template(
+            'condense_question_prompt.txt', question=question,
+            chat_history=chat_history.messages)
+        question = get_llm().invoke(condense_question_prompt).content
 
-retriever = store.as_retriever()
-llm = get_llm()
-chat = ConversationalRetrievalChain.from_llm(
-    llm=llm,
-    retriever=store.as_retriever(),
-    return_source_documents=True,
-    combine_docs_chain_kwargs={"prompt": qa_prompt, "document_prompt": document_prompt},
-    verbose=True,
-)
+    current_app.logger.debug('Question: %s', question)
 
+    docs = store.as_retriever().invoke(question)
+    for doc in docs:
+        doc_source = {**doc.metadata, 'page_content': doc.page_content}
+        current_app.logger.debug('Retrieved document passage from: %s', doc.metadata['name'])
+        yield f'data: {SOURCE_TAG} {json.dumps(doc_source)}\n\n'
 
-def parse_stream_message(session_id, queue: Queue):
-    yield f"data: {SESSION_ID_TAG} {session_id}\n\n"
+    qa_prompt = render_template('rag_prompt.txt', question=question, docs=docs)
 
-    message = None
-    break_out_flag = False
-    while True:
-        message = queue.get()
-        for line in message.splitlines():
-            if line == POISON_MESSAGE:
-                break_out_flag = True
-                break
-            yield f"data: {line}\n\n"
-        if break_out_flag:
-            break
+    answer = ''
+    for chunk in get_llm().stream(qa_prompt):
+        yield f'data: {chunk.content}\n\n'
+        answer += chunk.content
 
     yield f"data: {DONE_TAG}\n\n"
+    current_app.logger.debug('Answer: %s', answer)
 
-
-def ask_question(question, queue, session_id):
-    chat_history = get_elasticsearch_chat_message_history(
-        INDEX_CHAT_HISTORY, session_id
-    )
-    result = chat(
-        {"question": question, "chat_history": chat_history.messages},
-        callbacks=[QueueCallbackHandler(queue)],
-    )
-
-    chat_history.add_user_message(result["question"])
-    chat_history.add_ai_message(result["answer"])
+    chat_history.add_user_message(question)
+    chat_history.add_ai_message(answer)

example-apps/chatbot-rag-app/api/llm_integrations.py

@@ -5,15 +5,15 @@
 
 LLM_TYPE = os.getenv("LLM_TYPE", "openai")
 
-def init_openai_chat():
+def init_openai_chat(temperature):
     OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-    return ChatOpenAI(openai_api_key=OPENAI_API_KEY, streaming=True, temperature=0.2)
-def init_vertex_chat():
+    return ChatOpenAI(openai_api_key=OPENAI_API_KEY, streaming=True, temperature=temperature)
+def init_vertex_chat(temperature):
     VERTEX_PROJECT_ID = os.getenv("VERTEX_PROJECT_ID")
     VERTEX_REGION = os.getenv("VERTEX_REGION", "us-central1")
     vertexai.init(project=VERTEX_PROJECT_ID, location=VERTEX_REGION)
-    return ChatVertexAI(streaming=True, temperature=0.2)
-def init_azure_chat():
+    return ChatVertexAI(streaming=True, temperature=temperature)
+def init_azure_chat(temperature):
     OPENAI_VERSION=os.getenv("OPENAI_VERSION", "2023-05-15")
     BASE_URL=os.getenv("OPENAI_BASE_URL")
     OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
@@ -24,8 +24,8 @@ def init_azure_chat():
         openai_api_version=OPENAI_VERSION,
         openai_api_key=OPENAI_API_KEY,
         streaming=True,
-        temperature=0.2)
-def init_bedrock():
+        temperature=temperature)
+def init_bedrock(temperature):
     AWS_ACCESS_KEY=os.getenv("AWS_ACCESS_KEY")
     AWS_SECRET_KEY=os.getenv("AWS_SECRET_KEY")
     AWS_REGION=os.getenv("AWS_REGION")
@@ -35,7 +35,7 @@ def init_bedrock():
         client=BEDROCK_CLIENT,
         model_id=AWS_MODEL_ID,
         streaming=True,
-        model_kwargs={"temperature":0.2})
+        model_kwargs={"temperature":temperature})
 
 MAP_LLM_TYPE_TO_CHAT_MODEL = {
     "azure": init_azure_chat,
@@ -44,8 +44,8 @@ def init_bedrock():
     "vertex": init_vertex_chat,
 }
 
-def get_llm():
+def get_llm(temperature=0.2):
     if not LLM_TYPE in MAP_LLM_TYPE_TO_CHAT_MODEL:
         raise Exception("LLM type not found. Please set LLM_TYPE to one of: " + ", ".join(MAP_LLM_TYPE_TO_CHAT_MODEL.keys()) + ".")
 
-    return MAP_LLM_TYPE_TO_CHAT_MODEL[LLM_TYPE]()
+    return MAP_LLM_TYPE_TO_CHAT_MODEL[LLM_TYPE](temperature=temperature)

example-apps/chatbot-rag-app/api/templates/condense_question_prompt.txt

@@ -0,0 +1,8 @@
+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:
+{% for dialogue_turn in chat_history -%}
+{% if dialogue_turn.type == 'human' %}Question: {{ dialogue_turn.content }}{% elif dialogue_turn.type == 'ai' %}Response: {{ dialogue_turn.content }}{% endif %}
+{% endfor -%}
+Follow Up Question: {{ question }}
+Standalone question:

example-apps/chatbot-rag-app/api/templates/rag_prompt.txt

@@ -0,0 +1,26 @@
+Use the following passages to answer the user's question. 
+Each passage has a NAME which is the title of the document. When answering, give the source name of the passages you are answering from at the end. Put them in a comma separated list, prefixed with SOURCES:.
+
+Example:
+
+Question: What is the meaning of life?
+Response:
+The meaning of life is 42.
+
+SOURCES: Hitchhiker's Guide to the Galaxy
+
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+----
+
+{% for doc in docs -%}
+---
+NAME: {{ doc.metadata.name }}
+PASSAGE:
+{{ doc.page_content }}
+---
+
+{% endfor -%}
+----
+Question: {{ question }}
+Response: