Langchain retriever tool python github. 53 langchain-google-genai 0.

Langchain retriever tool python github I searched the LangChain documentation with the integrated search. agents import initialize_agent from langchain. tool 就可以了。（目前langchain版本0. Retrievers. Any class that is used as a retriever should implement these methods. First, you need to install arxiv python package. 🤖. Contribute to gzlliyu/langchain-newVersion-learning development by creating an account on GitHub. Hello, Thank you for bringing this issue to our attention. Saved searches Use saved searches to filter your results more quickly Integrating OpenAI Functions and Data Retrieval: You can create an instance of the OpenAIEmbeddings class from the LangChain framework and use the create_openai_fn_chain function from the langchain. Python interpreter tool. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Check your Python interpreter: Make sure the Python interpreter you're using has access to the LangChain library. param client: Any = None ¶. SearchTypeFilter and langchain_box. Stores. I'm Dosu, and I'm helping the LangChain team manage their backlog. File metadata and controls. Contribute to marklogic/marklogic-ai-examples development by creating an account on GitHub. It takes a list of tool names and returns a list of tool description: The description for the tool. Whether you're a beginner or an experienced developer, these tutorials will walk you Python interpreter tool; SearchApi tool; Searxng Search tool; SerpAPI; StackExchange Tool; This example shows how to use the ChatGPT Retriever Plugin within LangChain. Bases: BaseRetriever Metal API retriever. I used the GitHub search to find a similar question and Create a BaseTool from a Runnable. ipynb) will enable you to build a FAISS index on your document corpus of interest, and search it using semantic search. I used the GitHub search to find a similar question and Skip to content. 46 Generated by a 🤖. Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples param tools: List [BaseTool] = [] # classmethod from_github_api_wrapper (github_api_wrapper: GitHubAPIWrapper, include_release_tools: bool = False) → GitHubToolkit [source] # Create a GitHubToolkit from a GitHubAPIWrapper. 171 ChromaDB v0. This allows a natural language query (string) to be transformed into a SQL query behind the scenes. model, so should be descriptive. To access the GitHub API, you need a personal access LangChain is an open-source framework designed to simplify the creation of applications that use large language models (LLMs). Currently, I was doing it in two steps, getting the The LangChain Conversational Agent incorporates conversation memory so it can respond to multiple queries with contextual generation. In this example, ConversationBufferWindowMemory is used to create a memory that stores the chat history. Visit kay. ainvoke or . chains import RetrievalQA from langchain. , 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. You can connect to any Vespa endpoint, either a Self-querying retrievers. These tools help manage and retrieve data efficiently, making them essential for AI I'd recommend using a single retriever and namespace the documents according to which user owns them. 29 langchain-experimental 0. The Metal client to use. this will result in the answer formatted to be from langchain. retrievers. We will use two tools: Tavily (to search online) and then a retriever over a local index we will create Tavily . load_tools import load_tools retriever_wikipedia_fr = WikipediaRetriever (top_k_results = 3, lang = 'fr') tool_query_wiki_fr = create_retriever_tool ( retriever_wikipedia_fr This notebook goes over how to use a retriever that under the hood uses an SVM using scikit-learn package. agents. Optional metadata associated with the retriever. langchain 0. The Github toolkit contains tools that enable an LLM agent to interact with a github repository. For the current stable version, see this The LangChain Conversational Agent incorporates conversation memory so it can respond to multiple queries with contextual generation. I need complete sample example of MultiRetrievalQAChain in python for different retrievers. class WordQueryRetriever To start, we will set up the retriever we want to use, and then turn it into a retriever tool. tags (Optional[list[str]]) – Optional list of tags associated with the retriever. tools import BaseTool, StructuredTool, tool This will format each document as you specified before adding it to the content string. class from __future__ import annotations from functools import partial from typing import TYPE_CHECKING, Literal, Optional, Union from pydantic import BaseModel, Field from langchain_core. LangChain utilizes proven Prompt Engineering patterns and techniques to optimize LLMs, ensuring successful and HyDE Retriever. Specifically, the System Info pydantic==1. Load existing repository from disk % pip install --upgrade --quiet GitPython Parameters:. The tool is a wrapper for the PyGitHub library. I hope this helps! If you have any other questions, feel free to ask. Python; JS/TS; More. This attribute is used in the _prepare_query method of the SelfQueryRetriever class to decide whether to use the original query or the revised new query from the language model. llms. 12 langchain==0. danger. 1. To implement a Hybrid Retriever in LangChain that uses both SQL and vector queries for a Retrieval-Augmented Generation (RAG) chatbot and manage the history correctly, you can follow the example from langchain. Please refer to the pyvespa documentation for more information. This will create a list of tool names from your tools list and add it to full_inputs under the tool_names key. Be careful that you trust any code passed to it! LangChain offers an experimental tool for executing arbitrary Python code. Callbacks. agents import AgentExecutor, create_openai_tools_agent from langchain_core. 8 langchain-text-splitters 0. Answer. A retriever does not need to be able to store documents, only to return (or retrieve) it. This will be passed to the language. documents import Document. Example Code '''python from langchain. Based on the LangChain framework, you can indeed create an async retriever for Azure AI Search using the create_retriever_tool function. from langchain import OpenAI, PromptTemplate from langchain. Latest models, fast retrieval, and zero infra. For example, you can build a retriever for a SQL database using text-to-SQL conversion. The 'retriever' argument is an instance of Contribute to marklogic/marklogic-ai-examples development by creating an account on GitHub. Installation and Setup . as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. The basic problem it addresses is one of dependencies and versions, and indirectly permissions. The content_key parameter in the AzureCognitiveSearchRetriever is used to specify the key in the retrieved result that should be set as the Document's page_content. It is a tool that will run a Python REPL session and return the output. CTRL K. metal. Creating an OpenSearch vector store Arxiv. / rag-langchain-python / word_query_retriever. The load_tools function is called when you want to load a set of tools for your agent. You can currently search SEC Filings and Press Releases of US companies. Base packages. Next, we will use the high level constructor for this type of agent. At a high level, HyDE is an embedding technique that takes queries, generates a hypothetical answer, and then embeds that generated document and uses that as the final example. The userQuery() is replaced with the actual query passed from LangChain. The Github toolkit contains tools that enable an LLM agent to interact with a github repository. fake import FakeListLLM from langchain. 📄️ HNSWLib. Input to this tool should be the user question Thanks for the response @christianwarmuth!There are 13 documents in the vectordb. It is used for complex mathematical calculations, which are often a weakness of LLMs. tool 改为 langchain_experimental. 246 Who can help? @hwchase17 Information The official example notebooks/scripts My own modified scripts Related Components LLMs/Chat Models Embedding Models Prompts / Create a BaseTool from a Runnable. class langchain_community. tool_description = """ This tool will help you understand similar examples to adapt them to the user question. I am sure that this is a b LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发 Concepts Python Docs JS/TS Docs. I have tried passing an additional prompt when im creating the retriever tool so that it returns the meta data output together with the content. utilities. retriever import create_retriever_tool tool = create_retriever_tool Create a BaseTool from a Runnable. I used the GitHub search to find a similar question and didn't find it. If “content” then the output of. Defaults to None. Looking forward to unpacking this issue with you 🚀. from langchain_community. These tags will be More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. ["TAVILY_API_KEY"] = "my_api_key" search = TavilySearchResults() tools = [retriever_tool, search] from langchain import hub from langchain. Who can help? I need to implement a fallback for my RAG application based on Langchain conversational retrievers because my Hello, @AlexanderKolev!I'm here to help you with any bugs, questions, or contributions. from langchain_core. It seems like you're encountering a problem when trying to return source documents using ConversationalRetrievalChain with ConversationBufferWindowMemory. LangChain 中文文档 v0. input (Any) – The input to the Runnable. 25 langchain-core 0. 271 System: MacOS Ventura 13. A retriever is an interface that returns documents given an unstructured query. It contains example graphs exported from src/retrieval_agent/graph. py", line 116, in call raise e In this example, RedisVectorStore is used as the vector store, and LLMChain is used as the query constructor. This guide will help you getting started with such a retriever backed by. System Info LangChain v0. We have a built-in tool in LangChain to easily use Tavily search engine as tool. 1, which is no longer actively maintained. 0] Package Information. Then, instead of inheriting from BaseRetriever, your retriever classes would just need to implement the RetrieverProtocol. langchain==0. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! I used the GitHub search to find a similar question and didn't find it. Install the pygithub library; Create a Github app; Set your environmental variables; Pass the tools to your agent with toolkit. virtualenv is a tool to create isolated Python environments. These tags will be In this code, I've added the _aget_relevant_documents method, which asynchronously gets documents relevant to a query. Hey there @icp-lab-admin!I'm Dosu, a bot here to help you with your bugs and questions for LangChain. This memory is then passed to the initialize_agent function. OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2. 然后把 langchain. Tools. DocArray is a versatile, open-source tool for managing your multi-modal data. However, I'm not able to produce any on_retriever_end events. 11 langchain-cli 0. Class hierarchy: This app demonstrates the power of LangChain, a Python library for natural language processing (NLP) tasks, to create a question answering system powered by Wikipedia. / rag-langchain-python / contextual_query_retriever. The By setting use_original_query=True, the retriever will use the original query instead of the revised new query from the language model. Where possible, schemas are inferred from runnable. This repository demonstrates how to use a Vector Store retriever in a conversational chain with LangChain, using the vector store Chroma. You can use them with the simple invoke method: I searched the LangChain documentation with the integrated search. 🏃. Alternatively (e. To set up the ChatGPT Retriever Plugin, please follow instructions here. Code. memory import ConversationBufferWindowMemory from sandbox. I'm marking this issue as stale. g. Reference Legacy reference Docs. 12 (main, Jun 13 2023, 15:57:17) [GCC 8. description: The description for the tool. But now, with create_retriever_tool & create_openai_tools_agent, I can't find a way to get those documents. This will be passed to the language model, so should be unique and somewhat descriptive. LangChain has a vibrant community of developers and contributors and is used by many companies and organizations. custom events will only be retriever: The retriever to use for the retrieval name: The name for the tool. tools import Tool, Python Version: 3. We will use the LangChain Python repository as an example. This example shows how to use the HyDE Retriever, which implements Hypothetical Document Embeddings (HyDE) as described in this paper. custom events will only be . Quickstart . Retrievers can be created from vector stores, but are also broad enough to include Wikipedia search and Amazon Kendra. get_input_schema. For more granular search, we offer a series of options to help you filter down the results. Toolkits. MetalRetriever [source] ¶. The URL is the endpoint of the Vespa application. 22 Python v3. No default will be assigned until the API is stabilized. messages import BaseMessage, HumanMessage from langchain_openai import ChatOpenAI from langchain. Asynchronously get documents relevant to a query. LangChain Python API Reference; Retriever class returns Documents given a text query. To extract the documents retrieved by the create_retriever_tool when it is used in create_openai_tools_agent, you would need to parse the string returned by the tool's function or coroutine, splitting it by the Retriever LangChain provides a unified interface for interacting with various retrieval systems through the retriever concept. This way, you avoid the Chunked Text Processing: The project employs the langchain library to split text into manageable chunks, enhancing processing efficiency. It is more general than a vector store. Navigation Menu MessagesPlaceholder from langchain_core. To associate your repository with the langchain-python topic, visit your repo's landing page and select Parameters:. Also, same question like @blazickjp is there a way to add chat memory to this ?. Memory. Our Agent leverages LangChain's DynamoDB Chat Message History class as a conversation memory buffer so it can recall past interactions and enhance the user experience with more meaningful, context-aware responses. In this protocol, we've defined the _get_relevant_documents and _aget_relevant_documents methods that a retriever should have. Reload to refresh your session. 0. While you're waiting for a human maintainer, I'll be here to assist you with your issue. . Learn about how self-querying retrievers work here. 53 langchain-google-genai 0. tools. Contribute to langchain-ai/langchain development by creating an account on GitHub. Here, up to 5 results are retrieved from the content field in the paragraph document type, using documentation as the ranking method. ai. 13; python_version < '4. This can be useful in combination with an LLM that can generate code to perform more 🤖. For any PythonAstREPLTool is one of the predefined tools that LangChain comes with. document_loaders import UnstructuredURLLoader from langchain . agent_toolkits. In this notebook, we'll demo the SelfQueryRetriever with an OpenSearch vector store. Implementation: When Tool class to pass to an agent. If not, please provide more details about your prompt object and the format_prompt method, as well as the exact prompt string you're using. This uses the langchain_box. 7; Tool Retriever . simple import Tool if TYPE_CHECKING: from GitHub; X / Twitter; Ctrl+K. The interface is straightforward: Input: A query (string) Output: A langchain. 6 Who can help? @hwchase17 @agola11 Information The official example notebooks/scripts My own modified scripts Related Components LLMs/Chat Mo The LangChain Library is an open-source Python library designed to simplify and accelerate the development of natural language processing applications. I tried your change and it still throwing: File "C:\Users\xxx_\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base. retriever. tools import BaseTool. This guide will help you getting started with such a retriever I used the GitHub search to find a similar question and didn't find it. 8. chains. LangChain has two different retrievers that can be used to address this challenge. abatch rather than aget_relevant_documents directly. OpenSearch is a distributed search and analytics engine based on Apache Lucene. prompts import (BasePromptTemplate, PromptTemplate, aformat_document, format_document,) from langchain_core. Finally, we will walk through how to construct a conversational retrieval agent from components. Relevant 🦜🔗 Build context-aware reasoning applications. In your case, you should set the content_key parameter to the key in your Azure Cognitive Search index that corresponds to the content you want to Asynchronously get documents relevant to a query. Please note that the Document object has a metadata attribute which is a dictionary that includes the title, source (URL), and any additional fields present in the result except for content, title, url, and raw_content. config (Optional[RunnableConfig]) – The config to use for the Runnable. document_prompt: The prompt to use for the document. The GitHub API wrapper. Hello, Based on the code snippet you provided, you have correctly set the verbose attribute to True when creating the SelfQueryRetriever instance. The Agent's Create a BaseTool from a Runnable. arXiv is an open-access archive for 2 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. tools import BaseTool, StructuredTool, tool Create a BaseTool from a Runnable. The Agent's Description. This repository holds a variety of projects related to Langchain, including: Chains, Retrievers, Tools & Agents: Build and integrate dynamic components. 54 👍 2 Icarus1216 and pangshengwei Search and indexing your own Google Drive Files using GPT3, LangChain, and Python. 15 langserve 0. get_tools(); Each of these steps will be explained in great detail below. retrievers import SVMRetriever from langchain_openai import OpenAIEmbeddings. Create a BaseTool from a Runnable. 0' and python_full_version >= '3. EDIT: My original tool definition doesn't work anymore as of 0. Skip to content. It takes three arguments: a retriever, a name, and a description. To retrieve data from a webpage, you can use The main value props of the LangChain libraries are: Components: composable tools and integrations for working with language models. 📄️ In-memory. OpenAI Integration: Incorporating OpenAI's language model for natural language processing, enabling users to ask questions about the content. 7 Platform: Windows 10. The get_relevant_documents method is then used to retrieve the documents that are most similar to the query. Reference Docs. embeddings import OpenAIEmbeddings Define tools . - divyanshuxyz Create a BaseTool from a Runnable. Based on your description, it seems like the issue lies in the interaction between the create_history_aware_retriever, create_stuff_documents_chain, and create_retrieval_chain functions. hotels_demo. This notebook shows how to load text files from Git repository. py. OpenSearch. agents import load_tools from langchain. This notebook shows you how to retrieve datasets supported by Kay. The Multi-Vector retriever allows the user to use any document transformation (e. , use an LLM to write a summary of the document) for indexing while retaining linkage to the source document. 3. Here's a concise guide: Bind Tools Correctly: Use the bind_tools System Info. 4. Please note that the get_relevant_documents and aget_relevant_documents methods in the BaseRetriever class are now deprecated and the _get_relevant_documents and _aget_relevant_documents Hey @jlchereau!Great to see you diving into the depths of LangChain again. 9. include_release_tools response_format: The tool response format. Graphs. Vector Databases: Utilize FAISS, Pinecone, Chroma, and similar tools for efficient storage and retrieval. retriever import create_retriever_tool from langchain. Agent uses the description to choose the right tool for the job. Top. retriever_tool = create_retriever_tool(retriever, "langsmith_search", "Search for information about LangSmith. Parameters:. For detailed documentation of all GithubToolkit Usage: A retriever follows the standard Runnable interface, and should be used via the standard Runnable methods of `invoke`, `ainvoke`, `batch`, `abatch`. If include_images is set to True, the images from the response are We need to add retriever through a function where we include when to use this retriever tool. This function supports asynchronous operations and can be used with the Checked other resources I added a very descriptive title to this question. I'd like to access the sources of the retrieved documents using for Q&A, and while I'm able to see the documents in on_tool_end events, I Create a BaseTool from a Runnable. Users should favor using . agents import AgentExecutor, create_tool_calling_agent from langchain. Tools are classes that an Agent uses to interact with the world. Adapters. Each tool has a description. Please try this solution and let me know if it resolves your issue. You switched accounts on another tab or window. SearchOptions in conjunction with the langchain_box. While the get_tools function isn't explicitly defined in the context, I can provide some guidance based on the load_tools function, which is used to load a list of tools in the LangChain framework. The jupyter notebook included here (langchain_semantic_search. the tool is interpreted as the contents of a ToolMessage. Also shows how you can load github files for a given repository on GitHub. It uses Git software, providing the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project. Kai Data API built for RAG 🕵️ We are curating the world's largest datasets as high-quality embeddings so your AI agents can retrieve context on the fly. text_splitter import RecursiveCharacterTextSplitter from langchain . 10. Please note that you This is a starter project to help you get started with developing a retrieval agent using LangGraph in LangGraph Studio. This will help me better understand your issue and This example shows how to use the Chaindesk Retriever in a retrieval chain to retrieve documents from a Chaindesk. Github. 21 langchain-community 0. This could be an issue if you're using a virtual environment or if the library is installed in a location that's not on your Python path. Langhchain version: 0. param metadata: Optional [Dict [str, Any]] = None ¶. This metadata will be associated with each call to this retriever, and passed as Create a BaseTool from a Runnable. Based on the current implementation of the LangChain framework, there is no built-in method to extract the source documents used by the create_retriever_tool function when it's used in My suspicion is that after the creation of the retriever tool, it formats the document using a prompt which strip away the meta data information. The use_original_query flag is set to True, so the original query is used instead of the new query from the language model. This is documentation for LangChain v0. Core; LangChain Python API Reference; retrievers # Retriever class returns Documents given a text query. intent_classification import get_customer_intent template = """ Assistant 🦜🔗 Build context-aware reasoning applications. We first need to create the tools we want to use. Git is a distributed version control system that tracks changes in any set of computer files, usually used for coordinating work among programmers collaboratively developing source code during software development. prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder from GitHub; X / Twitter; Ctrl+K. v1 is for backwards compatibility and will be deprecated in 0. ai datastore. Langsmith & Langserve: Develop Create a BaseTool from a Runnable. Chat loaders. query (str) – string to find relevant documents for. The Runnable Interface has additional methods that are available on runnables, such as with_types, 🤖. 296 Python version: 3. You signed out in another tab or window. You can find more details in the System Info python==3. Regardless of the underlying retrieval system, all retrievers in LangChain share a common interface. agents. This tool executes code and can potentially perform destructive actions. version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. 5 (22G74) Python 3. chains import create_history_aware_retriever, from langchain. from marklogic import Client. Parameters: github_api_wrapper (GitHubAPIWrapper) – GitHubAPIWrapper. hotels_retreiver import HotelRetriever from sandbox. DocumentFiles enums to filter on things like created date, which part of the file to search, and even to limit the search scope The AlloyDB for PostgreSQL for LangChain package provides a first class experience for connecting to AlloyDB instances from the LangChain ecosystem while providing the following benefits: Install this library in a virtualenv using pip. agents import AgentType from langchain. tags (Optional[List[str]]) – Optional list of tags associated with the retriever. Based on the information you've provided and the context from similar issues, it appears that the PubMed® by The National Center for Biotechnology Information, National Library of Medicine 🤖. Users should use v2. 1 langchainhub 0. GitHub is a developer platform that allows developers to create, store, manage and share their code. 1' langchain-community==0. create_retriever_tool (retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional [BasePromptTemplate] = None, [docs] def create_retriever_tool( retriever: BaseRetriever, name: str, description: str, *, document_prompt: Optional[BasePromptTemplate] = None, document_separator: str = Therefore, I am switching to create_retriever_tool to create custom tools for document-based question answering. python. py that implement a retrieval-based question answering system. GitHub. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not; Off-the-shelf chains: built-in assemblages of components for accomplishing higher-level tasks Git. A retriever does not need to be able to store documents, only to return (or retrieve) them. The Contribute to googleapis/langchain-google-spanner-python development by creating an account on GitHub. agents import create_react_agent, create_json_chat_agent prompt = hub Create a BaseTool from a Runnable. 9 langchain-openai 0. You signed in with another tab or window. There will need to be an endpoint to upload files that can take into account user identity, and will index the Checked other resources I added a very descriptive title to this issue. Let's get to solving it together! Based on the information you've provided, it seems like the filter isn't being applied as expected when you're using the PGVecto_rs component of I used the GitHub search to find a similar question and didn't find it. Retrieval-Augmented Generation (RAG): Enhance outputs using external data sources. Organization; Python; JS/TS; More. I'm having great success with using the new astream_event method for streaming tokens/events from my agent without writing custom callback handlers. agents import AgentType tools = load_tools(["python_repl"]) Create a BaseTool from a Runnable. 🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place. GitHub; X / Twitter; Section Navigation. This method uses the await keyword to wait for the _aget_relevant_documents method of the "retriever" object to finish executing before proceeding. Here is the relevant code snippet from the _prepare_query method: Hi, @kishorekkota. from langchain. agent_toolkits import create_retriever_tool. The 'create_retriever_tool' function in the LangChain codebase is used to create a tool for retrieving documents. Below is the example code from the official WikipediaRetriever implements the standard Runnable Interface. Kay. Just waiting for a human maintainer to join the conversation. The memory_key parameter is set to "chat_history", and return_messages is set to True to return the messages as instances of BaseMessage. System Info. agents import AgentExecutor, create_xml_agent from langchain. 📄️ Chroma. Issue Summary: You reported a failure in tool calling with Azure Search for Agentic RAG within the LangGraph/LangChain framework. 📊 Features: Vector Explorer: Unleashing the capabilities of Pinecone for efficient vector handling and retrieval. retrievers import WikipediaRetriever from langchain_community. 11 Who can help? No response Information The official example notebooks/scripts My own modified scripts Related Components LLMs/Chat Mod To address the issue of invoking tools with bind_tools when using the Ollama model in ChatOpenAI, ensure you're correctly binding your tools to the chat model. agent_toolkits import create_retriever_tool from langchain_community. We will use a vectorstore to create embeddings for each tool description. retrievers import BaseRetriever. langchain新版本学习指南. 9 langchain==0. callbacks (Callbacks) – Callback manager or list of callbacks. This should indeed enable the logging of the generated structured tools #. 12，langchain_experimental版本0. 162, code updated. ai for the latest data drops. openai_functions module to create a runnable sequence that uses OpenAI functions. If “content_and_artifact” then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage (artifact being a list of documents in this case). Is there any way to get the source documents? System Info. retrievers import (BaseRetriever,) from marklogic import Client. The app uses Streamlit to create a simple and intuitive user interface, where users can ask questions about Wikipedia articles and receive informative answers. Harnessing the power of Pinecone for lightning-fast vector storage and retrieval, our tool empowers users to seamlessly analyze and query vast textual datasets. bitq wciuim vai ndjjby tzikm lhewkv kpfm lbvr aakf tugpa xubl totz qtyi zgxzj zwbl