Streaming
Often in Q&A applications it's important to show users the sources that were used to generate the answer. The simplest way to do this is for the chain to return the Documents that were retrieved in each generation.
We'll work off of the Q&A app with sources we built over the LLM Powered Autonomous Agents blog post by Lilian Weng in the Returning sources guide.
Setup
Dependencies
We'll use an OpenAI chat model and embeddings and a Chroma vector store in this walkthrough, but everything shown here works with any ChatModel or LLM, Embeddings, and VectorStore or Retriever.
We'll use the following packages:
%pip install --upgrade --quiet langchain langchain-community langchainhub langchain-openai langchain-chroma bs4
We need to set environment variable OPENAI_API_KEY, which can be done directly or loaded from a .env file like so:
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
# import dotenv
# dotenv.load_dotenv()
LangSmith
Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with LangSmith.
Note that LangSmith is not needed, but it is helpful. If you do want to use LangSmith, after you sign up at the link above, make sure to set your environment variables to start logging traces:
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
Chain with sources
Here is Q&A app with sources we built over the LLM Powered Autonomous Agents blog post by Lilian Weng in the Returning sources guide:
import bs4
from langchain import hub
from langchain_chroma import Chroma
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
API Reference:
# Load, chunk and index the contents of the blog.
bs_strainer = bs4.SoupStrainer(class_=("post-content", "post-title", "post-header"))
loader = WebBaseLoader(
web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
bs_kwargs={"parse_only": bs_strainer},
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
# Retrieve and generate using the relevant snippets of the blog.
retriever = vectorstore.as_retriever()
prompt = hub.pull("rlm/rag-prompt")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
rag_chain_from_docs = (
RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
| prompt
| llm
| StrOutputParser()
)
rag_chain_with_source = RunnableParallel(
{"context": retriever, "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)
Streaming final outputs
With LCEL it's easy to stream final outputs:
for chunk in rag_chain_with_source.stream("What is Task Decomposition"):
print(chunk)
{'question': 'What is Task Decomposition'}
{'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='The AI assistant can parse user input to several tasks: [{"task": task, "id", task_id, "dep": dependency_task_ids, "args": {"text": text, "image": URL, "audio": URL, "video": URL}}]. The "dep" field denotes the id of the previous task which generates a new resource that the current task relies on. A special tag "-task_id" refers to the generated text image, audio and video in the dependency task with id as task_id. The task MUST be selected from the following options: {{ Available Task List }}. There is a logical relationship between tasks, please note their order. If the user input can\'t be parsed, you need to reply empty JSON. Here are several cases for your reference: {{ Demonstrations }}. The chat history is recorded as {{ Chat History }}. From this chat history, you can find the path of the user-mentioned resources for your task planning.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\nThe system comprises of 4 stages:\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\nInstruction:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})]}
{'answer': ''}
{'answer': 'Task'}
{'answer': ' decomposition'}
{'answer': ' is'}
{'answer': ' a'}
{'answer': ' technique'}
{'answer': ' used'}
{'answer': ' to'}
{'answer': ' break'}
{'answer': ' down'}
{'answer': ' complex'}
{'answer': ' tasks'}
{'answer': ' into'}
{'answer': ' smaller'}
{'answer': ' and'}
{'answer': ' simpler'}
{'answer': ' steps'}
{'answer': '.'}
{'answer': ' It'}
{'answer': ' can'}
{'answer': ' be'}
{'answer': ' done'}
{'answer': ' through'}
{'answer': ' methods'}
{'answer': ' like'}
{'answer': ' Chain'}
{'answer': ' of'}
{'answer': ' Thought'}
{'answer': ' ('}
{'answer': 'Co'}
{'answer': 'T'}
{'answer': ')'}
{'answer': ' or'}
{'answer': ' Tree'}
{'answer': ' of'}
{'answer': ' Thoughts'}
{'answer': ','}
{'answer': ' which'}
{'answer': ' involve'}
{'answer': ' dividing'}
{'answer': ' the'}
{'answer': ' task'}
{'answer': ' into'}
{'answer': ' manageable'}
{'answer': ' sub'}
{'answer': 'tasks'}
{'answer': ' and'}
{'answer': ' exploring'}
{'answer': ' multiple'}
{'answer': ' reasoning'}
{'answer': ' possibilities'}
{'answer': ' at'}
{'answer': ' each'}
{'answer': ' step'}
{'answer': '.'}
{'answer': ' Task'}
{'answer': ' decomposition'}
{'answer': ' can'}
{'answer': ' be'}
{'answer': ' performed'}
{'answer': ' by'}
{'answer': ' using'}
{'answer': ' simple'}
{'answer': ' prompts'}
{'answer': ','}
{'answer': ' task'}
{'answer': '-specific'}
{'answer': ' instructions'}
{'answer': ','}
{'answer': ' or'}
{'answer': ' human'}
{'answer': ' inputs'}
{'answer': '.'}
{'answer': ''}
We can add some logic to compile our stream as it's being returned:
output = {}
curr_key = None
for chunk in rag_chain_with_source.stream("What is Task Decomposition"):
for key in chunk:
if key not in output:
output[key] = chunk[key]
else:
output[key] += chunk[key]
if key != curr_key:
print(f"\n\n{key}: {chunk[key]}", end="", flush=True)
else:
print(chunk[key], end="", flush=True)
curr_key = key
output
question: What is Task Decomposition
context: [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='The AI assistant can parse user input to several tasks: [{"task": task, "id", task_id, "dep": dependency_task_ids, "args": {"text": text, "image": URL, "audio": URL, "video": URL}}]. The "dep" field denotes the id of the previous task which generates a new resource that the current task relies on. A special tag "-task_id" refers to the generated text image, audio and video in the dependency task with id as task_id. The task MUST be selected from the following options: {{ Available Task List }}. There is a logical relationship between tasks, please note their order. If the user input can\'t be parsed, you need to reply empty JSON. Here are several cases for your reference: {{ Demonstrations }}. The chat history is recorded as {{ Chat History }}. From this chat history, you can find the path of the user-mentioned resources for your task planning.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}), Document(page_content='Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\nThe system comprises of 4 stages:\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\nInstruction:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})]
answer: Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It can be done through methods like Chain of Thought (CoT) or Tree of Thoughts, which involve dividing the task into manageable subtasks and exploring multiple reasoning possibilities at each step. Task decomposition can be performed by using simple prompts, task-specific instructions, or human inputs.
{'question': 'What is Task Decomposition',
'context': [Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),
Document(page_content='Tree of Thoughts (Yao et al. 2023) extends CoT by exploring multiple reasoning possibilities at each step. It first decomposes the problem into multiple thought steps and generates multiple thoughts per step, creating a tree structure. The search process can be BFS (breadth-first search) or DFS (depth-first search) with each state evaluated by a classifier (via a prompt) or majority vote.\nTask decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),
Document(page_content='The AI assistant can parse user input to several tasks: [{"task": task, "id", task_id, "dep": dependency_task_ids, "args": {"text": text, "image": URL, "audio": URL, "video": URL}}]. The "dep" field denotes the id of the previous task which generates a new resource that the current task relies on. A special tag "-task_id" refers to the generated text image, audio and video in the dependency task with id as task_id. The task MUST be selected from the following options: {{ Available Task List }}. There is a logical relationship between tasks, please note their order. If the user input can\'t be parsed, you need to reply empty JSON. Here are several cases for your reference: {{ Demonstrations }}. The chat history is recorded as {{ Chat History }}. From this chat history, you can find the path of the user-mentioned resources for your task planning.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),
Document(page_content='Fig. 11. Illustration of how HuggingGPT works. (Image source: Shen et al. 2023)\nThe system comprises of 4 stages:\n(1) Task planning: LLM works as the brain and parses the user requests into multiple tasks. There are four attributes associated with each task: task type, ID, dependencies, and arguments. They use few-shot examples to guide LLM to do task parsing and planning.\nInstruction:', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})],
'answer': 'Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It can be done through methods like Chain of Thought (CoT) or Tree of Thoughts, which involve dividing the task into manageable subtasks and exploring multiple reasoning possibilities at each step. Task decomposition can be performed by using simple prompts, task-specific instructions, or human inputs.'}
Streaming intermediate steps
Suppose we want to stream not only the final outputs of the chain, but also some intermediate steps. As an example let's take our Chat history chain. Here we reformulate the user question before passing it to the retriever. This reformulated question is not returned as part of the final output. We could modify our chain to return the new question, but for demonstration purposes we'll leave it as is.
from operator import itemgetter
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.tracers.log_stream import LogEntry, LogStreamCallbackHandler
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is."""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
contextualize_q_chain = (contextualize_q_prompt | llm | StrOutputParser()).with_config(
tags=["contextualize_q_chain"]
)
qa_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, just say that you don't know. \
Use three sentences maximum and keep the answer concise.\
{context}"""
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", qa_system_prompt),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
def contextualized_question(input: dict):
if input.get("chat_history"):
return contextualize_q_chain
else:
return input["question"]
rag_chain = (
RunnablePassthrough.assign(context=contextualize_q_chain | retriever | format_docs)
| qa_prompt
| llm
)
To stream intermediate steps we'll use the astream_log method. This is an async method that yields JSONPatch ops that when applied in the same order as received build up the RunState:
from typing import Any, Dict, List, Optional, TypedDict
class RunState(TypedDict):
id: str
"""ID of the run."""
streamed_output: List[Any]
"""List of output chunks streamed by Runnable.stream()"""
final_output: Optional[Any]
"""Final output of the run, usually the result of aggregating (`+`) streamed_output.
Only available after the run has finished successfully."""
logs: Dict[str, LogEntry]
"""Map of run names to sub-runs. If filters were supplied, this list will
contain only the runs that matched the filters."""
You can stream all steps (default) or include/exclude steps by name, tags or metadata. In this case we'll only stream intermediate steps that are part of the contextualize_q_chain and the final output. Notice that when defining the contextualize_q_chain we gave it a corresponding tag, which we can now filter on.
We only show the first 20 chunks of the stream for readability:
# Needed for running async functions in Jupyter notebook:
import nest_asyncio
nest_asyncio.apply()
from langchain_core.messages import HumanMessage
chat_history = []
question = "What is Task Decomposition?"
ai_msg = rag_chain.invoke({"question": question, "chat_history": chat_history})
chat_history.extend([HumanMessage(content=question), ai_msg])
second_question = "What are common ways of doing it?"
ct = 0
async for jsonpatch_op in rag_chain.astream_log(
{"question": second_question, "chat_history": chat_history},
include_tags=["contextualize_q_chain"],
):
print(jsonpatch_op)
print("\n" + "-" * 30 + "\n")
ct += 1
if ct > 20:
break
API Reference:
RunLogPatch({'op': 'replace',
'path': '',
'value': {'final_output': None,
'id': 'df0938b3-3ff2-451b-a233-6c882b640e4d',
'logs': {},
'streamed_output': []}})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/RunnableSequence',
'value': {'end_time': None,
'final_output': None,
'id': '2e2af851-9e1f-4260-b004-c30dea4affe9',
'metadata': {},
'name': 'RunnableSequence',
'start_time': '2023-12-29T20:08:28.923',
'streamed_output': [],
'streamed_output_str': [],
'tags': ['seq:step:1', 'contextualize_q_chain'],
'type': 'chain'}})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatPromptTemplate',
'value': {'end_time': None,
'final_output': None,
'id': '7ad34564-337c-4362-ae7a-655d79cf0ab0',
'metadata': {},
'name': 'ChatPromptTemplate',
'start_time': '2023-12-29T20:08:28.926',
'streamed_output': [],
'streamed_output_str': [],
'tags': ['seq:step:1', 'contextualize_q_chain'],
'type': 'prompt'}})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatPromptTemplate/final_output',
'value': ChatPromptValue(messages=[SystemMessage(content='Given a chat history and the latest user question which might reference context in the chat history, formulate a standalone question which can be understood without the chat history. Do NOT answer the question, just reformulate it if needed and otherwise return it as is.'), HumanMessage(content='What is Task Decomposition?'), AIMessage(content='Task decomposition is a technique used to break down complex tasks into smaller and more manageable subtasks. It involves dividing a task into multiple steps or subgoals, allowing an agent or model to better understand and plan for the overall task. Task decomposition can be done through various methods, such as using prompting techniques like Chain of Thought or Tree of Thoughts, task-specific instructions, or human inputs.'), HumanMessage(content='What are common ways of doing it?')])},
{'op': 'add',
'path': '/logs/ChatPromptTemplate/end_time',
'value': '2023-12-29T20:08:28.926'})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI',
'value': {'end_time': None,
'final_output': None,
'id': '228792d6-1d76-4209-8d25-08c484b6df57',
'metadata': {},
'name': 'ChatOpenAI',
'start_time': '2023-12-29T20:08:28.931',
'streamed_output': [],
'streamed_output_str': [],
'tags': ['seq:step:2', 'contextualize_q_chain'],
'type': 'llm'}})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/StrOutputParser',
'value': {'end_time': None,
'final_output': None,
'id': 'f740f235-2b14-412d-9f54-53bbc4fa8fd8',
'metadata': {},
'name': 'StrOutputParser',
'start_time': '2023-12-29T20:08:29.487',
'streamed_output': [],
'streamed_output_str': [],
'tags': ['seq:step:3', 'contextualize_q_chain'],
'type': 'parser'}})
------------------------------
RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content='')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': 'What'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content='What')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' are'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' are')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' some'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' some')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' commonly'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' commonly')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' used'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' used')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' methods'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' methods')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' or'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' or')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' approaches'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' approaches')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' for'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' for')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' task'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' task')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output_str/-',
'value': ' decomposition'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content=' decomposition')})
------------------------------
RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': '?'},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content='?')})
------------------------------
RunLogPatch({'op': 'add', 'path': '/logs/ChatOpenAI/streamed_output_str/-', 'value': ''},
{'op': 'add',
'path': '/logs/ChatOpenAI/streamed_output/-',
'value': AIMessageChunk(content='')})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/ChatOpenAI/final_output',
'value': {'generations': [[{'generation_info': {'finish_reason': 'stop'},
'message': AIMessageChunk(content='What are some commonly used methods or approaches for task decomposition?'),
'text': 'What are some commonly used methods or '
'approaches for task decomposition?',
'type': 'ChatGenerationChunk'}]],
'llm_output': None,
'run': None}},
{'op': 'add',
'path': '/logs/ChatOpenAI/end_time',
'value': '2023-12-29T20:08:29.688'})
------------------------------
If we wanted to get our retrieved docs, we could filter on name "Retriever":
ct = 0
async for jsonpatch_op in rag_chain.astream_log(
{"question": second_question, "chat_history": chat_history},
include_names=["Retriever"],
with_streamed_output_list=False,
):
print(jsonpatch_op)
print("\n" + "-" * 30 + "\n")
ct += 1
if ct > 20:
break
RunLogPatch({'op': 'replace',
'path': '',
'value': {'final_output': None,
'id': '9d122c72-378c-41f8-96fe-3fd9a214e9bc',
'logs': {},
'streamed_output': []}})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/Retriever',
'value': {'end_time': None,
'final_output': None,
'id': 'c83481fb-7ca3-4125-9280-96da0c14eee9',
'metadata': {},
'name': 'Retriever',
'start_time': '2023-12-29T20:10:13.794',
'streamed_output': [],
'streamed_output_str': [],
'tags': ['seq:step:2', 'Chroma', 'OpenAIEmbeddings'],
'type': 'retriever'}})
------------------------------
RunLogPatch({'op': 'add',
'path': '/logs/Retriever/final_output',
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Document(page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#\nChain of thought (CoT; Wei et al. 2022) has become a standard prompting technique for enhancing model performance on complex tasks. The model is instructed to “think step by step” to utilize more test-time computation to decompose hard tasks into smaller and simpler steps. CoT transforms big tasks into multiple manageable tasks and shed lights into an interpretation of the model’s thinking process.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),
Document(page_content='Resources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}),
Document(page_content='Fig. 9. Comparison of MIPS algorithms, measured in recall@10. (Image source: Google Blog, 2020)\nCheck more MIPS algorithms and performance comparison in ann-benchmarks.com.\nComponent Three: Tool Use#\nTool use is a remarkable and distinguishing characteristic of human beings. We create, modify and utilize external objects to do things that go beyond our physical and cognitive limits. Equipping LLMs with external tools can significantly extend the model capabilities.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/'})]}},
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For more on how to stream intermediate steps check out the LCEL Interface docs.