diff --git a/examples/langchain-langserve/.gitignore b/examples/langchain-langserve/.gitignore deleted file mode 100644 index bee8a64..0000000 --- a/examples/langchain-langserve/.gitignore +++ /dev/null @@ -1 +0,0 @@ -__pycache__ diff --git a/examples/langchain-langserve/README.md b/examples/langchain-langserve/README.md deleted file mode 100644 index b596ac6..0000000 --- a/examples/langchain-langserve/README.md +++ /dev/null @@ -1,139 +0,0 @@ -# LangServe Example - -This README provides a comprehensive guide to setting up and running the Zep LangServe Example project. - -## Installation - -This project uses [Poetry](https://python-poetry.org/) for managing dependencies. - -To get started with the installation, follow these steps: - -1. **Install Poetry** - - Haven't got Poetry on your machine yet? You can install it by following the instructions on the [Poetry website](https://python-poetry.org/docs/#installation). - -2. **Create the Poetry Environment and Install Dependencies** - Run the command below to create a new virtual environment for the project and install dependencies: - ```bash - poetry install - ``` - -3. **Activate the Poetry Environment** - - Use the following command to activate the Poetry environment. - ```bash - poetry shell - ``` - - -## Setting Up Environment Variables - -To run the LangServe Sample App, please set the following environment variables: - -```dotenv -# Please use examples/.env.example as a template for .env file - -# Required -ZEP_API_KEY=# Your Zep Project API Key -ZEP_COLLECTION=# used in ingestion script and in vector store examples -OPENAI_API_KEY=# Your OpenAI API Key - -# Optional (If you want to use langsmith with LangServe Sample App) -LANGCHAIN_TRACING_V2=true -LANGCHAIN_API_KEY= -LANGCHAIN_PROJECT=# If not specified, defaults to "default" -``` - -## Data Ingestion - -The `ingest.py` script creates Document Collections and populates them with content from a web article. To use this script, simply run the following command: - -```bash -python app/ingest.py -``` - -## Starting the Server - -To start the server, navigate to the application directory and run the following command: - -```bash -python app/server.py -``` - -This will start the server and listen for incoming connections. - -## Running the Message History Chain - -The `memory.py` script is used to create a session and populate it with a series of Messages. - -1. **Execute the script** - - ```bash - python ../chat_history/memory.py - ``` - -2. **Copy the Session ID** - Once you've executed the script, you'll see an output that looks something like this: - - ```bash - ---Add Memory for Session: 0d1c45eaa52b4a2ba51988329c07e2eb - ``` - The alphanumeric sequence following "Session:" is your Session ID. Make sure to copy it for later use. - - This Session ID will be needed when you're working in the LangServe Playground. - - -## Running the Message History Chain - -Here's how to get the Message History chain up and running: - -1. **Start the Server** - - First, make sure your server is up and running. If it isn't, start it with the command provided in the setup instructions. - -2. **Head to the Playground** - - Next, open your web browser and go to: - http://localhost:8050/message_history/playground - - You'll land on the playground page, where you can start interacting with the Message History chain. - Just a heads-up, you'll need the server to be live and reachable at `http://localhost:8050` for this to work. - -## Running the RAG Vector Store Chain - -To get the RAG Vector Store chain up and running, follow these steps: - -1. **Start the Server** - - Make sure your server is up and running. If not, start it with the command provided in the setup instructions. - -2. **Head to the Playground** - - Pop open your web browser and navigate to: - http://localhost:8050/rag_vector_store/playground - - You'll land in the playground, ready to interact with the RAG Vector Store chain. Remember, your server needs to be live and accessible at `http://localhost:8050` for this to work. - - This RAG uses the content we ingested above, which is an article on Leonard Bernstein found here: https://en.wikipedia.org/wiki/Leonard_Bernstein. - - Try asking: "Who was a famous American conductor?" - -## Running the RAG Vector Store + Message History Chain - -To get the message history chain up and running, just follow these simple steps: - -1. **Start the Server** - - Make sure your server is up and running. If not, go ahead and start it with the command provided in the setup instructions. - -2. **Head to the Playground** - - Launch your web browser and head over to: - http://localhost:8050/message_history_vector_store/playground - Here, you'll need to input a Session ID and a question into the playground. - - This step will bring you to the playground, where you can interact with the RAG Vector Store chain. Remember, your server needs to be live and reachable at `http://localhost:8050` for this to work. - - This RAG uses the content we ingested above, which is an article on Leonard Bernstein found here: https://en.wikipedia.org/wiki/Leonard_Bernstein. - - Give it a try by asking, "Who was a famous American conductor?" Then, dive into a conversation about Leonard Bernstein's life. You'll be able to view the message history and the summary in the [Zep web app's](https://app.getzep.com/) session details page. diff --git a/examples/langchain-langserve/app/__init__.py b/examples/langchain-langserve/app/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/langchain-langserve/app/ingest.py b/examples/langchain-langserve/app/ingest.py deleted file mode 100644 index cd9a0f8..0000000 --- a/examples/langchain-langserve/app/ingest.py +++ /dev/null @@ -1,49 +0,0 @@ -# Ingest Documents into a Zep Collection -import os - -from dotenv import find_dotenv, load_dotenv -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain_community.document_loaders import WebBaseLoader - -from zep_cloud.client import Zep -from zep_cloud.langchain import ZepVectorStore - -load_dotenv(dotenv_path=find_dotenv()) - -SOURCE = "https://en.wikipedia.org/wiki/Leonard_Bernstein" # noqa: E501 - -ZEP_API_KEY = os.environ.get("ZEP_API_KEY") # Required for Zep Cloud -if ZEP_API_KEY is None: - raise ValueError( - "ZEP_API_KEY is required for Zep Cloud. " - "Remove this check if using Zep Open Source." - ) - -ZEP_COLLECTION_NAME = os.environ.get("ZEP_COLLECTION_NAME") -if ZEP_COLLECTION_NAME is None: - raise ValueError("ZEP_COLLECTION_NAME is required for ingestion. ") - -zep = Zep( - api_key=ZEP_API_KEY, -) - -# Load -loader = WebBaseLoader(SOURCE) -data = loader.load() - -print(f"Loaded: {len(data)} documents") - -# Split -text_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=200) -all_splits = text_splitter.split_documents(data) - -print(f"Adding {len(all_splits)} documents to {ZEP_COLLECTION_NAME}...") - -# Add to vectorDB -vectorstore = ZepVectorStore.from_documents( - documents=all_splits, - collection_name=ZEP_COLLECTION_NAME, - api_key=ZEP_API_KEY, -) - -print(f"Added {len(all_splits)} documents to {ZEP_COLLECTION_NAME}...") diff --git a/examples/langchain-langserve/app/message_history_chain.py b/examples/langchain-langserve/app/message_history_chain.py deleted file mode 100644 index ef3bc67..0000000 --- a/examples/langchain-langserve/app/message_history_chain.py +++ /dev/null @@ -1,67 +0,0 @@ -import os -from typing import List, Tuple - -from langchain.callbacks.tracers import ConsoleCallbackHandler -from langchain_core.output_parsers import StrOutputParser -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.pydantic_v1 import BaseModel, Field -from langchain_core.runnables import ( - RunnableParallel, -) -from langchain_core.runnables.history import RunnableWithMessageHistory -from langchain_openai import ChatOpenAI - -from zep_cloud.client import Zep -from zep_cloud.langchain import ZepChatMessageHistory -from dotenv import find_dotenv, load_dotenv - -load_dotenv( - dotenv_path=find_dotenv() -) # load environment variables from .env file, if present - -ZEP_API_KEY = os.environ.get("ZEP_API_KEY") or "YOUR_API_KEY" - -if ZEP_API_KEY is None: - raise ValueError( - "ZEP_API_KEY is required for Zep Cloud. " - "Remove this check if using Zep Open Source." - ) -zep = Zep( - api_key=ZEP_API_KEY, -) - -# RAG answer synthesis prompt -template = """Answer the user's question below. Be polite and helpful:""" -ANSWER_PROMPT = ChatPromptTemplate.from_messages( - [ - ("system", template), - MessagesPlaceholder(variable_name="chat_history"), - ("user", "{question}"), - ] -) - - -# User input -class ChatHistory(BaseModel): - chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}}) - question: str - session_id: str - - -_inputs = RunnableParallel( - { - "question": lambda x: x["question"], - "chat_history": lambda x: x["chat_history"], - }, -).with_types(input_type=ChatHistory) - -chain = RunnableWithMessageHistory( - _inputs | ANSWER_PROMPT | ChatOpenAI() | StrOutputParser(), - lambda session_id: ZepChatMessageHistory( - session_id=session_id, # This uniquely identifies the conversation - zep_client=zep, - memory_type="perpetual", - ), - input_messages_key="question", - history_messages_key="chat_history", -).with_config(callbacks=[ConsoleCallbackHandler()]) diff --git a/examples/langchain-langserve/app/message_history_vector_store_chain.py b/examples/langchain-langserve/app/message_history_vector_store_chain.py deleted file mode 100644 index 7f269c3..0000000 --- a/examples/langchain-langserve/app/message_history_vector_store_chain.py +++ /dev/null @@ -1,158 +0,0 @@ -import os -from typing import Any, List - -from dotenv import load_dotenv -from langchain.callbacks.tracers import ConsoleCallbackHandler -from langchain.schema import format_document -from langchain_core.documents import Document -from langchain_core.output_parsers import StrOutputParser -from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.runnables import ( - ConfigurableField, - RunnableLambda, - RunnableParallel, - RunnablePassthrough, -) -from langchain_core.runnables.history import RunnableWithMessageHistory -from langchain_core.runnables.utils import ConfigurableFieldSingleOption -from langchain_openai import ChatOpenAI - -from zep_cloud.client import AsyncZep -from zep_cloud.langchain import ZepChatMessageHistory, ZepVectorStore - -load_dotenv() - -ZEP_API_KEY = os.environ.get("ZEP_API_KEY") # Required for Zep Cloud - -ZEP_COLLECTION_NAME = os.environ.get("ZEP_COLLECTION_NAME") -if ZEP_COLLECTION_NAME is None: - raise ValueError("ZEP_COLLECTION_NAME is required for ingestion. ") - -if ZEP_API_KEY is None: - raise ValueError( - "ZEP_API_KEY is required for Zep Cloud. " - "Remove this check if using Zep Open Source." - ) - - -zep = AsyncZep( - api_key=ZEP_API_KEY, -) - -# Initialize ZepVectorStore -vectorstore = ZepVectorStore( - collection_name=ZEP_COLLECTION_NAME, - api_key=ZEP_API_KEY, -) - -# Zep offers native, hardware-accelerated MMR. Enabling this will improve -# the diversity of results, but may also reduce relevance. You can tune -# the lambda parameter to control the tradeoff between relevance and diversity. -# Enabling is a good default. -retriever = vectorstore.as_retriever().configurable_fields( - search_type=ConfigurableFieldSingleOption( - id="search_type", - options={ - "Similarity": "similarity", - "Similarity with MMR Reranking": "mmr", - }, - default="Similarity with MMR Reranking", - name="Search Type", - description="Type of search to perform: 'similarity' or 'mmr'", - ), - search_kwargs=ConfigurableField( - id="search_kwargs", - name="Search kwargs", - description=( - "Specify 'k' for number of results to return and 'lambda_mult' for" - " tuning MMR relevance vs diversity." - ), - ), -) - -# RAG answer synthesis prompt -template = """Answer the question based only on the following context: - - {context} - """ -answer_prompt = ChatPromptTemplate.from_messages( - [ - ("system", template), - MessagesPlaceholder(variable_name="chat_history"), - ("user", "{question}"), - ] -) - -DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}") - - -# User input -class UserInput(BaseModel): - question: str - session_id: str - - -def _combine_documents( - docs: List[Document], - document_prompt: PromptTemplate = DEFAULT_DOCUMENT_PROMPT, - document_separator: str = "\n\n", -): - doc_strings = [format_document(doc, document_prompt) for doc in docs] - return document_separator.join(doc_strings) - - -async def _search_query(x: Any) -> str: - sq_result = await zep.memory.synthesize_question( - session_id=x["session_id"] - ) - - synthesized_question = sq_result.question - - documents = await retriever.ainvoke( - input=x["question"] if synthesized_question == "" else synthesized_question, - session_id=x["session_id"], - ) - - return _combine_documents(documents) - - -_inputs = RunnableParallel( - { - "question": lambda x: x["question"], - "session_id": lambda x: x["session_id"], - "chat_history": lambda x: x["chat_history"], - "context": RunnableLambda(_search_query), - }, -) - - -async def invoke_chain(user_input: UserInput): - result_chain = RunnableWithMessageHistory( - RunnablePassthrough.assign(session_id=lambda x: user_input["session_id"]) - | _inputs - | answer_prompt - | ChatOpenAI() - | StrOutputParser(), - lambda session_id: ZepChatMessageHistory( - session_id=session_id, - api_key=ZEP_API_KEY, - memory_type="perpetual", - ), - input_messages_key="question", - history_messages_key="chat_history", - ) - - result = await result_chain.ainvoke( - user_input, - config={"configurable": {"session_id": user_input["session_id"]}}, - ) - return result - - -chain = ( - RunnableLambda(invoke_chain) - .with_types(input_type=UserInput) - .with_config(callbacks=[ConsoleCallbackHandler()]) -) diff --git a/examples/langchain-langserve/app/rag_vector_store_chain.py b/examples/langchain-langserve/app/rag_vector_store_chain.py deleted file mode 100644 index 1ddbf47..0000000 --- a/examples/langchain-langserve/app/rag_vector_store_chain.py +++ /dev/null @@ -1,108 +0,0 @@ -import os -from typing import List - -from langchain.callbacks.tracers import ConsoleCallbackHandler -from langchain.schema import format_document -from langchain_core.documents import Document -from langchain_core.output_parsers import StrOutputParser -from langchain_core.prompts import ChatPromptTemplate -from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.runnables import ( - ConfigurableField, - RunnableLambda, - RunnableParallel, -) -from langchain_core.runnables.utils import ConfigurableFieldSingleOption -from langchain_openai import ChatOpenAI - -from zep_cloud.client import Zep -from zep_cloud.langchain import ZepVectorStore - -ZEP_COLLECTION_NAME = os.environ.get("ZEP_COLLECTION_NAME") -if ZEP_COLLECTION_NAME is None: - raise ValueError("ZEP_COLLECTION_NAME is required for ingestion. ") - -ZEP_API_KEY = os.environ.get("ZEP_API_KEY") # Required for Zep Cloud -if ZEP_API_KEY is None: - raise ValueError( - "ZEP_API_KEY is required for Zep Cloud. " - "Remove this check if using Zep Open Source." - ) - -zep = Zep( - api_key=ZEP_API_KEY, -) - -# Initialize ZepVectorStore -vectorstore = ZepVectorStore( - collection_name=ZEP_COLLECTION_NAME, - api_key=ZEP_API_KEY, -) - -# Zep offers native, hardware-accelerated MMR. Enabling this will improve -# the diversity of results, but may also reduce relevance. You can tune -# the lambda parameter to control the tradeoff between relevance and diversity. -# Enabling is a good default. -retriever = vectorstore.as_retriever().configurable_fields( - search_type=ConfigurableFieldSingleOption( - id="search_type", - options={ - "Similarity": "similarity", - "Similarity with MMR Reranking": "mmr", - }, - default="Similarity with MMR Reranking", - name="Search Type", - description="Type of search to perform: 'similarity' or 'mmr'", - ), - search_kwargs=ConfigurableField( - id="search_kwargs", - name="Search kwargs", - description=( - "Specify 'k' for number of results to return and 'lambda_mult' for" - " tuning MMR relevance vs diversity." - ), - ), -) - -# RAG answer synthesis prompt -template = """Answer the question based only on the following context: - - {context} - """ -answer_prompt = ChatPromptTemplate.from_messages( - [ - ("system", template), - ("user", "{question}"), - ] -) - -DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}") - - -# User input -class UserInput(BaseModel): - question: str - - -def _combine_documents( - docs: List[Document], - document_prompt: PromptTemplate = DEFAULT_DOCUMENT_PROMPT, - document_separator: str = "\n\n", -): - doc_strings = [format_document(doc, document_prompt) for doc in docs] - return document_separator.join(doc_strings) - - -extract_question = RunnableLambda(lambda x: x["question"]) - -_inputs = RunnableParallel( - { - "question": lambda x: x["question"], - "context": extract_question | retriever | _combine_documents, - }, -).with_types(input_type=UserInput) - -chain = (_inputs | answer_prompt | ChatOpenAI() | StrOutputParser()).with_config( - callbacks=[ConsoleCallbackHandler()] -) diff --git a/examples/langchain-langserve/app/server.py b/examples/langchain-langserve/app/server.py deleted file mode 100644 index db4f432..0000000 --- a/examples/langchain-langserve/app/server.py +++ /dev/null @@ -1,33 +0,0 @@ -from dotenv import find_dotenv, load_dotenv - -load_dotenv(dotenv_path=find_dotenv()) - -from fastapi import FastAPI -from fastapi.responses import RedirectResponse -from langserve import add_routes -from message_history_chain import chain as message_history_chain -from message_history_vector_store_chain import ( - chain as message_history_vector_store_chain, -) -from rag_vector_store_chain import chain as rag_vector_store_chain - -app = FastAPI() - - -@app.get("/") -async def redirect_root_to_docs(): - return RedirectResponse("/docs") - - -add_routes(app, message_history_chain, path="/message_history") -add_routes(app, rag_vector_store_chain, path="/rag_vector_store") -add_routes( - app, - message_history_vector_store_chain, - path="/message_history_vector_store", -) - -if __name__ == "__main__": - import uvicorn - - uvicorn.run(app, host="0.0.0.0", port=8050) diff --git a/examples/langchain-langserve/poetry.lock b/examples/langchain-langserve/poetry.lock deleted file mode 100644 index c854472..0000000 --- a/examples/langchain-langserve/poetry.lock +++ /dev/null @@ -1,1950 +0,0 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. - 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