langchain-ai/streamlit-agent
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Code Issues 8 Packages Projects Releases Wiki Activity

Add memory example and information (#18)

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* Add memory example and information

* Fix dependencies back
This commit is contained in:
Joshua Carroll
2023-08-04 13:41:50 -07:00
committed by GitHub
parent 24f3d54e52
commit 42738ae060
4 changed files with 75 additions and 17 deletions
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README.md
+4 -2
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@@ -5,6 +5,7 @@
This repository contains reference implementations of various LangChain agents as Streamlit apps including:
- `basic_streaming.py`: Simple streaming app with `langchain.chat_models.ChatOpenAI` ([View the app](https://langchain-streaming-example.streamlit.app/))
- `basic_memory.py`: Simple app using `StreamlitChatMessageHistory` for LLM conversation memory ([View the app](https://langchain-st-memory.streamlit.app/))
- `mrkl_demo.py`: An agent that replicates the [MRKL demo](https://python.langchain.com/docs/modules/agents/how_to/mrkl) ([View the app](https://langchain-mrkl.streamlit.app))
- `minimal_agent.py`: A minimal agent with search (requires setting `OPENAI_API_KEY` env to run)
- `search_and_chat.py`: A search-enabled chatbot that remembers chat history ([View the app](https://langchain-chat-search.streamlit.app/))
@@ -13,7 +14,8 @@ This repository contains reference implementations of various LangChain agents a
- `chat_pandas_df.py`: Chatbot to ask questions about a pandas DF ([View the app](https://langchain-chat-pandas.streamlit.app/))
Apps feature LangChain 🤝 Streamlit integrations such as the
[Callback integration](https://python.langchain.com/docs/modules/callbacks/integrations/streamlit).
[Callback integration](https://python.langchain.com/docs/modules/callbacks/integrations/streamlit) and
[StreamlitChatMessageHistory](https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history).
## Setup
@@ -43,7 +45,7 @@ To generate Image with `DOCKER_BUILDKIT`, follow below command
```DOCKER_BUILDKIT=1 docker build --target=runtime . -t langchain-streamlit-agent:latest```
1. Run the docker container directly
1. Run the docker container directly
``docker run -d --name langchain-streamlit-agent -p 8051:8051 langchain-streamlit-agent:latest ``
poetry.lock
Generated
+14 -14
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@@ -1223,13 +1223,13 @@ referencing = ">=0.28.0"
[[package]]
name = "langchain"
version = "0.0.234"
version = "0.0.252"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
{file = "langchain-0.0.234-py3-none-any.whl", hash = "sha256:a287f0b944fb1b48cc107cedb8c1ad052e0559327c7658ae20e6ce2e8e851122"},
{file = "langchain-0.0.234.tar.gz", hash = "sha256:fdb5ba8176497e5bdd7cbb7594125b1149d306f8c9ed31750160271fece356ee"},
{file = "langchain-0.0.252-py3-none-any.whl", hash = "sha256:fb14d2346f453656e8f6c138da9123918796ae950a26606c91c156f2e5c0f8c4"},
{file = "langchain-0.0.252.tar.gz", hash = "sha256:1d205417793bbf9410e0eb23d14b5ce6f13946ccf26a05dcc19bb10def92b03d"},
]
[package.dependencies]
@@ -1237,39 +1237,39 @@ aiohttp = ">=3.8.3,<4.0.0"
async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
dataclasses-json = ">=0.5.7,<0.6.0"
docarray = {version = ">=0.32.0,<0.33.0", extras = ["hnswlib"], optional = true, markers = "extra == \"docarray\" or extra == \"all\""}
langsmith = ">=0.0.5,<0.0.6"
langsmith = ">=0.0.11,<0.1.0"
numexpr = ">=2.8.4,<3.0.0"
numpy = ">=1,<2"
openapi-schema-pydantic = ">=1.2,<2.0"
pydantic = ">=1,<2"
PyYAML = ">=5.4.1"
PyYAML = ">=5.3"
requests = ">=2,<3"
SQLAlchemy = ">=1.4,<3"
tenacity = ">=8.1.0,<9.0.0"
[package.extras]
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azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b6)", "openai (>=0,<1)"]
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cohere = ["cohere (>=3,<4)"]
cohere = ["cohere (>=4,<5)"]
docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
embeddings = ["sentence-transformers (>=2,<3)"]
extended-testing = ["atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.0.7,<0.0.8)", "chardet (>=5.1.0,<6.0.0)", "esprima (>=4.0.1,<5.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "openai (>=0,<1)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tqdm (>=4.48.0)", "zep-python (>=0.32)"]
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javascript = ["esprima (>=4.0.1,<5.0.0)"]
llms = ["anthropic (>=0.3,<0.4)", "clarifai (>=9.1.0)", "cohere (>=3,<4)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openllm (>=0.1.19)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
llms = ["anthropic (>=0.3,<0.4)", "clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openllm (>=0.1.19)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)", "xinference (>=0.0.6,<0.0.7)"]
openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.4.0)"]
qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
[[package]]
name = "langsmith"
version = "0.0.5"
version = "0.0.18"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
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{file = "langsmith-0.0.5.tar.gz", hash = "sha256:ffad2fc638cfee8c9d27c9eae2fa3c3f9ec423bf443b1dc44cc8184fa34cd6b2"},
{file = "langsmith-0.0.18-py3-none-any.whl", hash = "sha256:58f3eb029495e3696c3c94c95adfb2dd15043d82928ef932a7829fe020ad5522"},
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]
[package.dependencies]
@@ -3853,4 +3853,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<4.0"
content-hash = "dfae9493fa40f783e1d68bab86183e8be896459dc0057d7f3a290a2aeb4e8d7b"
content-hash = "2f25d17358c896166837cb621947de1497d7c1f58b890c3730734c801efc8aff"
pyproject.toml
+1 -1
View File
@@ -10,7 +10,7 @@ packages = [{include = "streamlit_agent"}]
[tool.poetry.dependencies]
python = ">=3.10,<4.0"
streamlit = ">=1.25"
langchain = {extras = ["docarray"], version = "^0.0.234"}
langchain = {version = ">=0.0.252", extras = ["docarray"]}
openai = "^0.27.8"
duckduckgo-search = "^3.8.3"
pypdf = "^3.12.2"
streamlit_agent/basic_memory.py
+56
View File
@@ -0,0 +1,56 @@
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain.prompts import PromptTemplate
import streamlit as st
st.set_page_config(page_title="StreamlitChatMessageHistory", page_icon="📖")
st.title("📖 StreamlitChatMessageHistory")
"""
A basic example of using StreamlitChatMessageHistory to help LLMChain remember messages in a conversation.
The messages are stored in Session State across re-runs automatically. You can view the contents of Session State
in the expander below. View the [source code for this app]().
"""
# Set up memory
msgs = StreamlitChatMessageHistory(key="chat_messages")
memory = ConversationBufferMemory(chat_memory=msgs)
if len(msgs.messages) == 0:
msgs.add_ai_message("How can I help you?")
view_messages = st.expander("View the message contents in session state")
# Get an OpenAI API Key before continuing
if "openai_api_key" in st.secrets:
openai_api_key = st.secrets.openai_api_key
else:
openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
if not openai_api_key:
st.info("Enter an OpenAI API Key to continue")
st.stop()
# Set up the LLMChain, passing in memory
template = """You are an AI chatbot having a conversation with a human.
{history}
Human: {human_input}
AI: """
prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
llm_chain = LLMChain(llm=OpenAI(openai_api_key=openai_api_key), prompt=prompt, memory=memory)
# Render current messages from StreamlitChatMessageHistory
for msg in msgs.messages:
st.chat_message(msg.type).write(msg.content)
# If user inputs a new prompt, generate and draw a new response
if prompt := st.chat_input():
st.chat_message("human").write(prompt)
# Note: new messages are saved to history automatically by Langchain during run
response = llm_chain.run(prompt)
st.chat_message("ai").write(response)
# Draw the messages at the end, so newly generated ones show up immediately
view_messages.write("Contents of `st.session_state.chat_messages`:")
view_messages.json(st.session_state.chat_messages)