大模型（LLMs）langchain面
1.什么是 LangChain?
LangChain是一个基于语言模型的框架，用于构建聊天机器人、生成式问答（GQA）、摘要等功 能.它的核心思想是将不同的组件“链在一起，以创建更高级的语言模型应用.LangChain的起源可以追溯到2022年10月，由创造者HarrisonChase在那时提交了第一个版本.与Bitcoin不同，Bitcoin是在2009年由一位使用化名SatoshiNakamoto的未知人士创建的，它是一种去中心化的加密货币.而LangChain是围绕语言模型构建的框架.
2.LangChain包含哪些核心概念？
1. LangChain 中 Components and Chains 是什么?
Components and Chains are key concepts in the LangChain framework.
Components refer to the individual building blocks or modules that make up thepreprocessors response generators and other functionalities. Each ponent is LangChain framework These ponents can include language models dataresponsible for a specific task or functionality within the language model application.
Chains on the other hand are the connections or links between these ponents.They define the flow of data and information within the language model application.Chains allow the output of one ponent to serve as the input for anotherponent enabling the creation of more advanced language models.
In summary Components are the individual modules or fuinctionalities within the LangChain framework while Chains define the connections and flow of data betweenthese ponents.
Here's an example to ilustrate the concept of Components and Chains in LangChain:
from 1angchain import Component Chain
Define ponents
preprocessor = Component("Preprocessor")1anguage_model = Component("Language Mode1") response_generator = Component("Response Generator")
Define chains
chain1 = Chain(preprocessor 1anguage_model)chain2 = Chain(Tanguage_model response_generator)
Execute chains
input_data ="Hello how are you?"processed_data = chain1.execute(input_data)response = chain2.execute(processed_data)
print(response)
In the above example we have three ponents: Preprocessor Language Model andResponse Generator. We create two chains: chain1 connects the Preprocessor andLanguage Model and chain2 connects the Language Model and Response Generator. The input data is passed through chain1 to preprocess it and then passed throughchain2 to generate a response.
This is a simplified example to demonstrate the concept of Components and Chains inponents and data transformations.
LangChain. In a real-world scenario you would have more plex chains with multiple
2. LangChain 中 Prompt Templates and Values 是什么?
Prompt Templates and Values are key concepts in the LangChain framework.
Prompt Templates refer to predefined structures or formats that guide the generationpue puasisuo e apiod saedas asau sapou atenue joj sduod go ndyno pue qndu! paujsap au uyads Aq sdwoud onujsuo o Aem pazpuepueasformats. Prompt templates can include placeholders or variables that are later filledwith specific values.
Values on the other hand are the specific data or information that is used to fill in the placeholders or variables in prompt templates. These values can be dynamicallyinput for the language model to generate the desired output. generated or retrieved from external sources. They provide the necessary context or
Here's an example to illustrate the concept of Prompt Templates and Values inLangChain:
from 1angchain import PromptTemplate value
Define prompt template
template = PromptTemplate("what is the capital of {country}?")
Define values
country_value = Value("country” “France")
Generate prompt
prompt = template.generate_prompt(values=[country_value])
print(prompt)
no eo ede au sse ea d e ay a axa oe a The template includes a placeholder {country] that will be filled with the actualthe value "France". We then generate the prompt by passing the value object to the template's generate_prompt method.
The generated prompt will be "What is the capital of France?".
Prompt templates and values allow for flexible and dynamic generation of prompts inthe LangChain framework. They enable the customization and adaptation of promptsbased on specific requirements or scenarios.
3. LangChain 中 Example Selectors 是什么？
io/prompts/example_selectors/
Example Selectors are a feature in the LangChain framework that allow users to specifyand retrieve specific examples or data points from a dataset. These selectors help incustomizing the training or inference process by selecting specific examples that meetcertain criteria or conditions.
Example Selectors can be used in various scenarios such as:
1. Training data selection: Users can use example selectors to filter and select specific examples from a large training dataset. This can be useful when working withlimited putational resources or when focusing on specific subsets of the data.2. Inference customization: Example selectors can be used to retrieve specificgenerate responses or predictions based on specific conditions or criteria. examples from a dataset during the inference process. This allows users to
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from 1angchain import ExampleSelector
Define an example selector
selector = ExampleSelector(condition="1abel==′positive'")
selected_examples = selector.select_examples(dataset) Retrieve examples based on the selector
Use the selected examples for training or inference for example in selected_examples:#Perform training or inference on the selected example
In the above example we define an example selector with a condition that selectsexamples with a label equal to *positive*. We then use the selector to retrieve theselected examples from a dataset. These selected examples can be used for training orinference purposes.
framework. They allow users to focus on specific subsets of the data or apply specific Example Selectors provide a flexible way to customize the data used in the LangChaincriteria to select examples that meet their requirements.
4. LangChain 中 Output Parsers 是什么?
Output Parsers are a feature in the LangChain framework that allow users toasau apow atenue au q pajeaua ndno a ased pue pajap (jeeoneparsers are designed to handle different types of output such as strings lists dictionaries or even Pydantic models.
Output Parsers provide a convenient way to process and manipulate the output of theextracting relevant information from the output and enable further processing oranalysis.
Here's an example to ilustrate the concept of Output Parsers in LangChain:
from 1angchain import 1lm_prompt OutputParser
Define an output parser parser = OutputParser()
011m_prompt(output_parser=parser) Apply the output parser toa functiondef generate_response(input_text):Generate response using the language mode1response = 1anguage_mode1.generate(input_text) return response
input_text ="Hello how are you?"response = generate_response(input_text)
Parse the output
parsed_output = parser .parse_output(response)
#Process the parsed output
processed_output = process_output(parsed_output)
print(processed_output)
In the above example we define an output parser and apply it to thegenerate_response function using the 1lm_prompt decorator. The output parserautomatically detects the type of the output and provides the parsed output. We canthen further process or analyze the parsed output as needed.
Output Parsers provide a flexible and efficient way to handle the output of the languageenable seamless integration with other ponents or systems. model in the LangChain framework. They simplify the post-processing of the output and
5. LangChain 中 Indexes and Retrievers 是什么?
Indexes and Retrievers are ponents in the Langchain framework.
Indexes are used to store and organize data for efficient retrieval. Langchain supports multiple types of document indexes such as InMemoryExactNNlndex HnswDocumentindex WeaviateDocumentlndex ElasticDocindex andQdirantDocumentlindex. Each index has its own characteristics and is suited for different use cases. For example InMemoryExactNNlndex is suitable for small datasets that can be stored in memory while HnswDocumentlndex is lightweight and suitable for small tomedium-sized datasets.
Retrievers on the other hand are used to retrieve relevant documents from theindexes based on a given query. Langchain provides different types of retrievers suchas MetalRetriever and DocArrayRetriever. MetalRetriever is used with the MetalDocArray tool for managing multi-modal data. platform for semantic search and retrieval while DocArrayRetriever is used with the
Overall indexes and retrievers are essential ponents in Langchain for efficient datastorage and retrieval.
6. LangChain 中 Chat Message History 是什么?
ChatMessage History是Langchain框架中的一个组件，用于存储和管理聊天消息的历史记录.它可以跟踪和保存用户和AI之间的对话，以使在需要时进行检索和分析.
Langchain 提供了不同的 Chat Message History 实现，包括StreamlitChatMessageHistory、 CassandraChatMessageHistory 和 MongoDBChatMessageHistory.
StreamlitChatMessageHistory：用于在Streamlit应用程序中存储和使用聊天消息历史记录.它使用 Streamlit会话状态来存储消息，并可以与ConversationBufferMemory和链或代理一起使用.CassandraChatMessageHistory：使用ApacheCassandra数据库存储聊天消息历史记MongoDBChatMessageHistory：使用MongoDB数据库存储聊天消息历史记录. 录.Cassandra是一种高度可扩展和高可用的NoSQL数据库，适用于存储大量数据.MongoDB是一种面向文档的NoSQL数据库，使用类似JSON的文档进行存储.
您可以根据自己的需求选择适合的ChatMessageHistory实现，并将其集成到Langchain框架中，以便记录和管理聊天消息的历史记录.
请注意，Chat Message History的具体用法和实现细节可以参考Langchain的官方文档和示例代码.
7. LangChain 中 Agents and Toolkits 是什么?
ae pue ae o pasn ae e suaodo ae ueuue ui slo pue suaaconversational agents.
Agents are responsible for determining the next action to take based on the current state of the conversation. They can be created using different approaches such asOpenAl Function Calling Plan-and-execute Agent Baby AGI and Auto GPT. Theseapproaches provide different levels of customization and functionality for buildingagents.
Toolkits on the other hand are collections of tools that can be used by agents toperform specific tasks or actions. Tools are functions or methods that take input and jo aue apm e ao pue pauap-aud jo nq-osno q ue a andno anpofunctionalities such as language processing data manipulation and external APIintegration.
By bining agents and toolkits developers can create powerful conversationalagents that can understand user inputs generate appropriate responses and perform various tasks based on the given context.
Here is an example of how to create an agent using LangChain:
from 1angchain.chat_models import ChatopenAIfrom 1angchain.agents import too1
