Langchain print prompt template A PipelinePrompt consists of two main parts: final_prompt: This is the final prompt that is returned. async aformat (** kwargs: Any) → BaseMessage # Async format A partial of the prompt template. # We can param input_types: Dict [str, Any] [Optional] #. param example_selector: Any = None ¶ param input_types: Dict [str, Any] [Optional] #. A prompt template consists of a string template. A prompt for a language model is a set of instructions or input provided by a user to guide the model's response, helping it understand the context and generate relevant and coherent language-based output, such as answering questions, completing sentences, or engaging in a Few-shot prompt templates. py pip install python-dotenv langchain langchain-openai You can also clone the below code from GitHub using This code snippet shows how to create an image prompt using ImagePromptTemplate by specifying an image through a template URL, a direct URL, or a local path. These include a text string or template that takes inputs and produces a prompt for the LLM, instructions to train the LLM, few-shot examples to enhance the model’s response, and a question to guide the language model. Returns: Overview: Under the hood, MultiPromptChain routed the query by instructing the LLM to generate JSON-formatted text, and parses out the intended destination. ChatPromptTemplate¶ class langchain_core. 5. The primary template format for LangChain prompts is the simple and versatile f-string. param example_prompt: PromptTemplate [Required] ¶ PromptTemplate used to format an individual example. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. System message prompt template. A message can be represented using the following formats: (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of (message type, template); e. Partial variables populate the template so that you don’t need to pass them in every time you call the prompt. content) param input_types: Dict [str, Any] [Optional] #. param prompt: Union [StringPromptTemplate, List [Union [StringPromptTemplate, ImagePromptTemplate]]] [Required] ¶ Prompt template. Use to create flexible templated prompts for chat models. Human message prompt template. multi_prompt module. It accepts a set of parameters from the user that can be used to generate a prompt At its core, a PromptTemplate is just a string template we can pass variables to in order to generate our final string. Bases: _StringImageMessagePromptTemplate AI message prompt template This is the recommended way to use LangChain with PromptLayer. Ensuring Uniformity: LangChain prompt templates help maintain a consistent structure across different Human message prompt template. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in System message prompt template. Prompt templates are a concept in LangChain designed to assist with this transformation. In this comprehensive guide for beginners, we‘ll learn prompt templating from the ground up with hands-on code prompt = FewShotPromptTemplate (example_selector = example_selector, example_prompt = example_prompt, prefix = "You are a Neo4j expert. async aformat_prompt (** kwargs: Any) → PromptValue # Async format the prompt with the inputs. async aformat_messages (** kwargs: Any) → list [BaseMessage] [source] #. Create a custom prompt template# The only two requirements for all prompt templates are: class langchain_core. LangChain supports this in Here we demonstrate how to use prompt templates to format multimodal inputs to models. from langchain_core. Base class for chat prompt templates. versionchanged:: 0. I embedded a PDF file locally, uploaded it to Pinecone, and all is good. In this case there are two lookups from as many tables: the prompt template takes care of everything, provided you pass all the primary key columns required across tables. Given an input question, create a syntactically correct Cypher query to run. param input_variables: list [str] [Required] #. async aformat (** kwargs: Any) → BaseMessage [source] ¶ Async format the . *Security warning*: As of LangChain 0. It took a registry of string prompt templates as input. Parameters:. pretty_repr (html: bool = False) → str [source] # Return a pretty representation of the prompt template. param pipeline_prompts: List [Tuple [str, BasePromptTemplate]] [Required] ¶ A list of tuples, consisting of a string (name) and a Prompt Template. 2. param additional_kwargs: dict [Optional] # Additional keyword arguments to pass to the prompt template. This is a message that is not sent to the user. The from_template() function ; The PromptTemplate() function; Let us discuss both approaches to In this article, we give an overview on how LangChain prompt templates work and provide examples of these. FewShotPromptWithTemplates [source] ¶ Bases: StringPromptTemplate. prompt模板定义 prompt_template = PromptTemplate. Type Parameters. few_shot_with_templates. , if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. async aformat (** kwargs: Any) → BaseMessage [source] # Async format the prompt template param input_types: Dict [str, Any] [Optional] #. Prompt templates help to translate user input and parameters into instructions for a language model. param role: str [Required] # Role of the message. This is a message sent from the AI. These templates include instructions, few-shot examples, and specific context In LangChain, a Prompt Template is a structured way to define prompts that are sent to language models. get_input_schema. chains. Returns. A chat prompt template class langchain_core. Create a new model by parsing and validating input data from keyword arguments. a chat prompt template. With the LangChain library, we can easily create reusable templates and dynamically generate prompts from within Python. param input_variables: List [str] [Required] ¶. 24 You can pass any Message-like formats supported by ``ChatPromptTemplate. param input_variables: List [str] [Required] #. input_types – A dictionary of the types of the variables the prompt template expects. chat_models import ChatOllama from langchain_core. string. param suffix: str [Required] # A prompt template string to put after the examples. from_template (""" 你是一个翻译助手，你擅长将中文翻译为英文，请将我发送给你的question的内容翻译为英文，不要返回无关的内容，只需返回最终翻译结果，下面的history examples中提供了一些具体的案例，为你提供一些 System message prompt template. prompts import PromptTemplate # 1. Chains are compositions of predictable steps. 12", removal = "1. Returns: A partial of the prompt template. Return type: A dictionary of the partial variables the prompt template carries. venv touch prompt-templates. Then we explain how prompting works in Mirascope, and highlight In this tutorial, we will show you how to save and load prompt templates. base module. mustache module. Prompt template that contains few shot examples. RunInput extends InputValues = any; Load a prompt template from a json-like object describing it. param prompt: StringPromptTemplate [Required] # String prompt template. These placeholders are keys in the input dictionary fed to the langchain chain instance. Return type: ChatPromptTemplate. Imagine you have a prompt which you always want to have the current date. Do not accept jinja2 templates from untrusted sources as they may lead Chat message prompt template. Like other methods, it can make sense to "partial" a prompt template - eg pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values. prompt. BasePromptTemplate [source] ¶ Bases: RunnableSerializable [Dict, PromptValue], Generic [FormatOutputType], ABC. Take a look at the current set of default prompt templates here. LangChain simplifies the creation and customization of prompt templates. pretty_repr (html: bool = False) → class langchain_core. pretty_print → None # Print a pretty representation of the prompt. prompts. async aformat (** kwargs: Any) → BaseMessage # Prompt templates are a powerful tool in LangChain for crafting dynamic and reusable prompts for large language models (LLMs). debug=True will print every prompt agent is executing with all the details possible. In this guide, we will go Chat message prompt template. , (“human”, “{user_input}”), (4) 2 LangChain provides a user friendly interface for composing different parts of prompts together. \n\nHere is @deprecated (since = "0. A chat prompt template langchain_core. LangChainにはExampleSelectorという機能があり、ExampleSelectorをFew-shot prompt templatesと組み合わせることで、ユーザーが提示する少数の例（“few-shot”）のうち、言語モデルへの入力に類似した例を選んで、その例を与えるプロンプトを作ることができます。 The concepts in langchain. você pode usar a classe PromptTemplate da biblioteca 'langchain'. llms import OpenAI from langchain. . Here is an example of how you can do it: Stream all output from a runnable, as reported to the callback system. async aformat_messages (** kwargs: Any) → List [BaseMessage] [source] #. If not provided, all variables are assumed to be strings. They take in raw user input and return data (a prompt) that is ready to pass into a language model. In LangChain, we can use the PromptTemplate() function and the from_template() function defined in the PromptTemplate module to generate prompt templates. Anything you are writing to an LLM is a prompt. As an example, if I were to use the prompt template in the original post to create an instance To print the content of a template after inputting values into it, you can use the mustache_formatter function provided in the langchain_core. What is a prompt? The first question that comes in mind is, what exactly is a prompt? Well prompts are basically the text input to the LLMs. , “ You are a knowledgeable historian ”). Defaults to OpenAI and PineconeVectorStore. Retrieval Augmented Generation Chatbot: Build a chatbot over your data. ChatPromptTemplate [source] ¶. Here is an example of how you can do it: you can use the format_document function from the langchain_core. Note that templates created this way cannot be added to the LangChain prompt hub and may have unexpected behavior if you're using tracing. While PromptLayer does have LLMs that integrate directly with LangChain (e. A list of the names of the variables whose values are required as inputs to the prompt. ; Once the template object is instantiated, you can use it to generate chat prompts by replacing the Async format the prompt with the inputs. For more details, you can refer to the ImagePromptTemplate class in the LangChain repository. param input_types: Dict [str, Any] [Optional] ¶ A dictionary of the types of the variables the prompt template expects. param suffix: str [Required] ¶ A prompt template string to put after the examples. Creates a chat template consisting of a single message assumed to be from the human. BasePromptTemplate¶ class langchain_core. BaseMessagePromptTemplate¶ class langchain_core. This can be used to guide a model’s response, helping it understand the context and generate relevant and coherent language I have to go to the source code to look for the prompt langchain uses, When I create the agent using verbose=True, it does not print the full prompt. llms import OpenAI from decouple import config # Define the prompt template creative_writing_template: str = """ Write the opening paragraph of Alternate prompt template formats. Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Return a partial of the prompt template. A dictionary of the types of the variables the prompt template expects. ; User, which contains the user’s specific historical question. Parameters: html (bool) – Whether or not to return an HTML formatted string. These templates make it easier to maintain consistency, save time, and To effectively create multi-prompt router templates in LangChain, it is essential to understand the structure and functionality of the langchain. This module allows developers to design complex workflows that can handle multiple prompts and route them based on specific conditions. A partial of the prompt template. Parameters: kwargs (Any) – Any arguments to be passed to the prompt template AIMessagePromptTemplate# class langchain_core. param input_variables: List [str] [Required] ¶ A list of the names of the variables whose values are required as inputs to the BaseMessagePromptTemplate# class langchain_core. Returns: LangChain是一個用來開發大型語言模型（LLM）應用程式的框架，記得曾經在「LangChain框架揭秘： 從零開始了解開發LLM模型應用」一文中提到： LangChain Partial prompt templates. base. Now let's try hooking it up to an LLM. Bases: StringPromptTemplate Prompt template for a language model. PipelinePromptTemplate [source] # Bases: BasePromptTemplate. Print a human-readable representation. Alternatively (e. While the existing Prompt templating allows us to programmatically construct the text prompts we feed into large language models (LLMs). Just a follow-up question to your answer for #3. Highlighting a few different categories of templates. In this guide, we will go Thanks for your reply. image. PromptTemplate [source] #. In such cases, you can create a custom prompt template. It is requested to run the code in your code block and see the output, then it will be more understandable. Create a chat prompt template from a variety of message formats. validate_template – Whether to validate the template. Returns: param input_types: Dict [str, Any] [Optional] #. Prompt Templates. router. Chains . ImagePromptTemplate [source] ¶ Bases: BasePromptTemplate [ImageURL] Image prompt template for a multimodal model. Verify that tune_prompt, full_prompt, and metadata_prompt are set up properly. A PipelinePrompt consists of two main parts: final_prompt: This is the final prompt that is returned String Prompt Templates: It is used to format a single string for simple inputs. pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values. Another 2 options to print out the full chain, including prompt. PromptLayerOpenAI), using a callback is the recommended way to integrate PromptLayer with LangChain. Like partially binding arguments to a function, it can make sense to "partial" a prompt template - e. param role: str [Required] ¶ Role of the message. from langchain. In my example code, where I'm using RetrievalQA, I'm passing in my prompt (QA_CHAIN_PROMPT) as an argument, however the {context} and {prompt} values are yet to be filled in (since it is passing in the original string). This is why they are specified as input_variables when the PromptTemplate instance is created. 5-turbo-0613"), df, verbose=True, agent_type=AgentType. Async format messages from kwargs. The roles in this class are: System for a system chat message setting the stage (e. messages. classmethod from_template (template: str, ** kwargs: Any) → ChatPromptTemplate # Create a chat prompt template from a template string. Great! We've got a SQL database that we can query. print( prompt_template. Return type: None. pretty_repr (html: bool = False) → A partial of the prompt template. For example, you may want to create a prompt template with specific dynamic instructions for your language model. It is not recommended Data Mastery Series — Episode 27: LangChain Website (Part 2) langchain_core. 0", message = ("Please see migration guide here for recommended implementation: ""https://python. pipeline. async aformat (** kwargs: Any class langchain_core. Using an example set param input_types: Dict [str, Any] [Optional] ¶. async aformat (** kwargs: Any) → BaseMessage # Async PromptLayer. String prompt composition When working with string prompts, each template is joined together. ``` agent = create_pandas_dataframe_agent(ChatOpenAI(temperature=0, model="gpt-3. format( query="Which libraries and model providers offer LLMs?" ) ) Out[9]: Answer the question based on the context below. Providing the LLM with a few such examples is called few-shotting, and is a simple yet powerful way to guide generation and in some cases drastically improve model performance. AI message prompt template. async aformat (** kwargs: Any) → BaseMessage [source] # Async format the prompt template from langchain import PromptTemplate from langchain. A PipelinePrompt consists of two main parts: final_prompt: This is the final prompt that is returned context and question are placeholders that are set when the LLM agent is run with an input. messages – sequence of message representations. With LangChain’s prompt management system, users are not limited to static prompts but can create more dynamic, customized, and goal-oriented prompts. A message can be represented using the following formats: (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of (message type, template); Um prompt template, em portugues: modelo de prompt, ou template de prompt, refere-se a uma maneira reproduzível de gerar um prompt. class langchain_core. First, let’s start with a simple prompt template: template = 'What is a good name for a company that makes {product}?') We can see the template PromptTemplate# class langchain_core. Prompt template; chains; Models; Retrivers; Agent; Tools; Output; Memory; Prompt templates. Parameters: template (str) – template string Base class for message prompt templates that use a string prompt template. PromptTemplate¶ class langchain_core. AIMessagePromptTemplate [source] #. This includes all inner runs of LLMs, Retrievers, Tools, etc. Prompts. param prompt: StringPromptTemplate | list [StringPromptTemplate | ImagePromptTemplate] [Required] # Prompt template. async aformat (** kwargs: Any) → BaseMessage [source] # Async format the prompt template from langchain_community. Return a partial of the prompt template. You can do this with either string prompts or chat prompts. This is a message sent from the user. param prompt: StringPromptTemplate [Required] ¶ String prompt template. Check the Prompt Template: Ensure your prompt templates are correctly defined with placeholders for inputs. prompts langchain_core. callbacks import StdOutCallbackHandler handler = StdOutCallbackHandler() qa_with_sources_chain = param input_types: Dict [str, Any] [Optional] #. For debugging your prompt templates in agent_executor, you can follow these steps:. from_chain_type and fed it user queries which were then sent to GPT-3. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. At the moment I’m writing this post, the langchain documentation is a bit lacking in providing simple examples of how to pass custom prompts to some of the built-in chains. Prompt template for composing multiple prompt templates together. BaseMessagePromptTemplate [source] #. PromptLayer is a platform for prompt engineering. Chat message prompt template. This can be useful when you want to reuse parts of prompts. Enable verbose and debug; from langchain. # string A dictionary of the partial variables the prompt template carries. ChatMessage'>, and its __repr__ value is: ChatMessage(content='Please give me flight options for New Delhi to Mumbai', role='travel Chat message prompt template. Examples:. pretty_print → None # Print a human-readable representation. LangChain supports this in class langchain_core. Returns: class ChatPromptTemplate (BaseChatPromptTemplate): """Prompt template for chat models. The output is: The type of Prompt Message Template is <class 'langchain_core. param prompt: StringPromptTemplate | List [StringPromptTemplate | ImagePromptTemplate] [Required] # Prompt template. async aformat (** kwargs: Any) → BaseMessage # param input_types: Dict [str, Any] [Optional] #. Stream all output from a runnable, as reported to the callback system. Parameters: kwargs (str | Callable[[], str]) – Union[str, Callable[[], str], partial variables to set. When using a local path, the image is converted to a data URL. 1. It is simpler and more extendible than the other method below. prompts import PromptTemplate # Define your prompt templates summary_template = """Write a summary of the following podcast text: {text} SUMMARY :""" guest_template = """Write a summary of the following podcast text as if you are Modifying langchain. This feature is deprecated and will be removed in the future. BaseMessagePromptTemplate [source] ¶. LangChain offers a range of features to make this process more efficient and flexible. example_selector import LengthBasedExampleSelector example_selector = LengthBasedExampleSelector( examples=examples, example_prompt=example_prompt, print (dynamic_prompt_template. Return type: str. 329, this method uses Jinja2's SandboxedEnvironment by default. Prompt Templates. The PromptLayer request ID is used to tag requests with metadata, scores, associated prompt templates from langchain. com/docs param input_types: Dict [str, Any] [Optional] #. Hi team! I'm building a document QA application. Why are How to Create Prompt Templates in LangChain? The PromptTemplate module in LangChain provides two ways to create prompt templates. Extraction with OpenAI Functions: Do extraction of structured data from unstructured param input_types: Dict [str, Any] [Optional] #. You can't hard code it in the prompt, and passing it along with the other input variables can be tedious. globals import set_verbose, set_debug set_debug(True) set_verbose(True) Use StdOutCallbackHandler; from langchain. prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI model = ChatOpenAI (model = "gpt-4o") API Reference: print (response. Parameters: kwargs (Any) – Any arguments to be passed to the prompt template. param additional_kwargs: dict [Optional] ¶ Additional keyword arguments to pass to the prompt template. ⭐ Popular These are some of the more popular templates to get started with. async aformat (** kwargs: Any) → BaseMessage # Async System message prompt template. Common transformations include adding a system message or formatting a template with the user input. Os templates de prompt podem receber qualquer número de variáveis de entrada e podem ser formatados para gerar um prompt. This can be used to guide a model's response, helping it understand the context and generate relevant and coherent language-based output. param prefix: str = '' # A prompt template string to put before the examples. pipeline_prompts: This is a list of tuples, consisting of a string (name) and a Prompt Template. param prefix: str = '' ¶ A prompt template string to put before the examples. prompts import PromptTemplate prompt_template = PromptTemplate. The prompt template classes in Langchain are built to make constructing prompts with dynamic inputs easier. OPENAI_FUNCTIONS,) ``` Thanks for the help. Photo by Conor Brown on Unsplash. Base class for message prompt templates that use a string prompt template. Like other methods, it can make sense to "partial" a prompt template - e. import { PromptTemplate} from "langchain/prompts"; const prompt = new PromptTemplate ({inputVariables: ["foo"], template: "Say {foo}",}); Copy. format (query=query)) Start coding or generate with AI. In LangChain, a Prompt Template is a structured way to define prompts that are sent to language models. And we can then pass these PromptTemplate’s to LLM’s in order to create Prompt templates in LangChain are predefined recipes for generating language model prompts. async aformat (** kwargs: Any) → BaseMessage [source] ¶ Async format the prompt template PromptLayer. Let's create a prompt template here. Structured prompt template for a language model. Should return a list of BaseMessages. Details param input_types: Dict [str, Any] [Optional] #. Returns: class langchain_core. In this case, it's very handy to be able to partial the prompt with a function that always returns the current date. 上一篇中，我們簡要了解了 LangChain 的優勢和學習資源，同時完成了套件的安裝。今天，我們將嘗試實作 LangChain 中的 Prompt Template 與 Output Parser。這兩個模組代表了LLM服務的輸入與輸出，因此在部署LLM服務中扮演著相當重要的角色。 整個運算流程如下：首先，使用者輸入的語句會通過 Prompt Template param input_types: Dict [str, Any] [Optional] #. prompts import FewShotPromptTemplate, PromptTemplate example_prompt = PromptTemplate. We recommend you experiment with the code and create prompt templates with different contexts, instructions, and input variables to understand how they can help you create generative AI プロンプトテンプレートのシリアライズ. Partial prompt templates. String prompt that exposes the format method, returning a prompt. Code cell output actions. Where possible, schemas are inferred from runnable. Use this code: import langchain # Define a partial variable for the chatbot to use my_partial_variable = """APPLE SAUCE""" # Initialize your chat template with partial variables prompt_messages = [ # System message SystemMessage (content class ChatPromptTemplate (BaseChatPromptTemplate): """Prompt template for chat models. Consistency and Standardization. LangChain supports this in from langchain_core. as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Base class for all prompt templates, returning a prompt. async aformat (** kwargs: Any) → BaseMessage # A partial of the prompt template. It also helps with the LLM observability to visualize requests, version prompts, and track usage. In LangGraph, we can represent a chain via simple sequence of nodes. This is where LangChain prompt templates come into play. LangChainは、大規模な言語モデルを使用したアプリケーションの作成を簡素化するためのフレームワークです。言語モデル統合フレームワークとして、LangChainの使用ケースは、文書の分析や要約、 そんな悩みを解決するのがLangChainのPrompt Templatesです。 この記事では、以下を学びます： Prompt Templatesの基礎とその必要性; 実際のPythonコードを使った活用方法; ChatPromptTemplateとの違いと応用例; また、LLMについてですがollamaの環境で行います。 In this guide, we'll learn how to create a simple prompt template that provides the model with example inputs and outputs when generating. Return type: BaseMessagePromptTemplate# class langchain_core. chains import SequentialDocumentsChain, LLMChain from langchain. The LangGraph implementation, implemented above via lower-level primitives, uses tool-calling to route to arbitrary chains. Bases: BaseChatPromptTemplate Prompt template for chat models. Prompt Templates allow you to create dynamic and flexible prompts by incorporating variables A prompt template in LangChain serves as a blueprint that defines a structure for input queries sent to a language model. Bases: Serializable, ABC Base class for message prompt templates. They provide a structured approach to define the core elements of an Templates. ChatMessagePromptTemplate'> The type of message is: <class 'langchain_core. I wanted to improve the performance and accuracy of the results by adding a prompt template, but I'm unsure on how to incorporate LLMChain + What is a Prompt Template? Generating, sharing, and reusing prompts in a reproducible manner can be achieved using a few key components. Return type: BasePromptTemplate. param input_types: Dict [str, Any] [Optional] #. In LangChain you could use prompt templates (PromptTemplate) these are very useful because they supply input data, which is useful for generating some chat models BaseMessagePromptTemplate# class langchain_core. However, this sand-boxing should be treated as a best-effort approach rather than a guarantee of security. js supports handlebars as an experimental alternative. g. async aformat_messages (** kwargs: Any) → List [BaseMessage] [source] ¶. from_messages()`` directly to ``ChatPromptTemplate()`` init code-block:: python from langchain_core. utils. In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. LangChain. Prompt Templates allow you to create dynamic and flexible prompts by incorporating To achieve this task, we will create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function. Constructing prompts this way allows for easy reuse of components. langchain. Parameters. StringPromptTemplate [source] ¶ Bases: BasePromptTemplate, ABC. PromptTemplate [source] ¶. Of these classes, the simplest is the PromptTemplate. ['My name is "']) print (resp, pl_request_id) asyncio. Prompt Templates take as input an object, where each key represents a variable in the mkdir prompt-templates cd prompt-templates python3 -m venv . It accepts a set of parameters from the user that can be used to generate a prompt for a language Prompt template for a language model. Let's create a sequence of steps that, given a def jinja2_formatter (template: str, /, ** kwargs: Any)-> str: """Format a template using jinja2. async aformat (** kwargs: Any) → A dictionary of the partial variables the prompt template carries. ; AI, which contains the LLM’s preliminary response or follow-up question. お使いのローカルファイルシステムのファイルにPromptTemplateを保存することができます。langchainは、ファイルの拡張子を通じてファイルフォーマットを自動で推定します。現時点では、langchainはYAMLかJSONファイルでのテンプレート保存をサポートしてい langchain_core. from_template(""" You are a receptionist in a hotel, You AI message prompt template. I used the RetrievalQA. run (async_generate (openai_llm)) PromptLayer Request ID. It accepts a set of parameters from the user that can be used to generate a prompt for a language model. Invoke the Agent and Observe Outputs: Use the agent_executor to run a test input When formatting the Prompt Template, you will have to specify the primary key values for the DB lookup -- the rest is done by the prompt template. A few-shot prompt template can be constructed from Prompt Templates. from_template ("User input: {input}\nSQL query: {query}") prompt = FewShotPromptTemplate (examples = examples [: 5], example_prompt = example_prompt, prefix = "You are a SQLite expert. Given an input question, create a param input_types: Dict [str, Any] [Optional] #. Create a BaseTool from a Runnable. 0. prompts A prime example of this is with date or time. This can be used to guide a model's response, helping it understand the Prompt template for a language model. Returns: A formatted string. chat. bidmvj samdl jgzuy fdqmy dfnm jrdbu ttsuykk ltrr torq dmgrgcu