Multi retriever langchain python from_llm (retriever = vectordb. SearchType¶ class langchain. This tutorial will show how to build a simple Q&A application over a text data source. ***> wrote: *🤖* To configure Azure Cosmos DB as the docstore while setting up a Multi Vector retriever using the LangChain Python framework, you can follow the steps below: 1. milvus_hybrid_search. The Hybrid search in Weaviate uses sparse and dense vectors to LangChain Python API Reference; langchain-core: 0. com The MultiQueryRetriever automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. abc import Hashable from itertools import chain from typing import (Any, Callable, Dict, Iterable, Iterator, List, Optional, TypeVar, cast,) from langchain_core On Tue, Dec 12, 2023 at 2:30 PM dosubot[bot] ***@***. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the Asynchronously get documents relevant to a query. How to handle multiple retrievers when doing query analysis. Due to this limitation, LangChain cannot automatically propagate the RunnableConfig down the call chain in certain scenarios. from langchain_core. You can use them with the simple invoke method: Source code for langchain. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. These tags will be Multi-modal eval: GPT-4 w/ multi-modal embeddings and multi-vector retriever# Multi-modal slide decks is a public dataset that contains a dataset of question-answer pairs from slide decks with visual content. class ElasticsearchRetriever (BaseRetriever): """ Elasticsearch retriever Args: es_client: Elasticsearch client connection. MultiVectorRetriever. I am loading the retrievers from the """ Ensemble retriever that ensemble the results of multiple retrievers by using weighted Reciprocal Rank Fusion """ import asyncio from collections import defaultdict from collections. """ vectorstore: VectorStore """The underlying vectorstore python. index_name – The name of the index to query. If multiple indices are queried, specify a dict {index_name: field_name} here. retrievers As we can see our LLM generated arguments to a tool! You can look at the docs for bind_tools() to learn about all the ways to customize how your LLM selects tools, as well as this guide on how to force the LLM to call a tool rather than letting it decide. tags (Optional[List[str]]) – Optional list of tags associated with the retriever. Setup Install dependencies class langchain. It enables you to look up smaller chunks while returning larger context, making it ideal for complex queries. custom events will only be To create your own retriever, you need to extend the BaseRetriever class and implement a _getRelevantDocuments method that takes a string as its first parameter (and an optional runManager for tracing). import asyncio from typing import List from langchain_core. co Reply reply nanosmith123 • may I know why you use js and python, instead of just one? Ready to go live with voice + chat bot and play around with multiple retriever and chain calls. from typing import Any, Dict, List, Optional, Union from langchain_core. pageContent: The content of this document. I think you're looking for the You’ve now learned some techniques for handling multiple retrievers in a query analysis system. , vector stores or databases). You can also pass a custom output parser to parse and split the results of the LLM call into a list of queries. These tags will be MultiQueryRetriever# class langchain. inputs (Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. A lot of the complexity lies in how to create the multiple vectors per document. __call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain. multi_vector. This article is a follow-up to my previous article on the Multi-Vector Retriever. Create a new model by parsing and validating input data from class MultiQueryRetriever (BaseRetriever): """Given a query, use an LLM to write a set of queries. BaseRetriever [source] #. Alternatively you can use the `from_es_params` method with parameters to initialize the client. config (Optional[RunnableConfig]) – The config to use for the Runnable. input_keys except for inputs that will be set by the chain’s memory. This includes all inner runs of LLMs, Retrievers, Tools, etc. 9 or 3. Commits. MultiPromptChain does not support common chat model features, such as message roles and tool calling. It can often be beneficial to store multiple vectors per document. By leveraging the strengths of different algorithms, the EnsembleRetriever can achieve better performance than any single algorithm. """ Asynchronously get documents relevant to a query. 's Dense X Retrieval: What Retrieval Granularity Should We Use?. Now I am following below class MultiRetrievalQAChain (MultiRouteChain): """A multi-route chain that uses an LLM router chain to choose amongst retrieval qa chains. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well. It uses the best features of both keyword-based search algorithms with vector search techniques. Defaults to None. MultiVectorRetriever¶ class langchain. custom events will only be Asynchronously get documents relevant to a query. 🏃. For synchronous applications, use Asynchronously execute the chain. utilities import SQLDatabase from langchain_experimental. It is more general than a vector store. from_retrievers(OpenAI(), retriever_infos_list, verbose=True) I have used MultiRetrievalQAChain but the answer are not from the relevant documents most of the time as it dynamically selects from multiple retrievers. langchain. The final return value is a dict with the results of each value under its appropriate key. Seamless question-answering across diverse data types (images, text, tables) is one of the holy grails of RAG. The main difference between this method and Chain. , document id, file name, source, etc). These cookbooks as also present a few ideas for pairing multimodal LLMs with the multi-vector Retriever that merges the results of multiple retrievers. We add a @chain decorator to the function to create a Runnable that can be used similarly to a typical retriever. This method should return an array of Documents fetched from some source. Users should favor using . The EnsembleRetriever supports ensembling of results from multiple retrievers. query (str) – string to find relevant documents for. MultiQueryRetriever [source] ¶ Bases: BaseRetriever. If True, only new keys generated by Retrievers. Python 3. This template demonstrates the multi-vector indexing strategy proposed by Chen, et. These tags will be class langchain. How to migrate from legacy LangChain agents to LangGraph; How to generate multiple embeddings per document; How to pass multimodal data directly to models; How to use multimodal prompts; How to generate multiple queries to retrieve data for; How to try to fix errors in output parsing; How to parse JSON output; How to parse XML output Convenience method for executing chain. chains. 4. multi_query import MultiQueryRetriever from langchain_openai import ChatOpenAI question = "What are the approaches to Task Decomposition?" llm = ChatOpenAI (temperature = 0) retriever_from_llm = MultiQueryRetriever. Summarization prompt to use with multi-vector retriever. Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. sql import SQLDatabaseChain from langchain. Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on "distance". Skip to main content. issues_and_opportunities = "Launching a rocket in space is hard, but spectacular. In the context of RAG and LLM application components, LangChain's retriever interface provides a standard way to connect to many different types of data services or databases (e. Retrievers can be created from vector stores, but are also broad enough to include Wikipedia search and Amazon Kendra. Probably a smarter way to do this though idk. _async. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications. This is particularly useful when you have multiple retrievers that are good at finding different types of relevant documents. If you are experiencing issues with streaming, callbacks or tracing in async code and are using Python 3. Setup Install dependencies Asynchronously get documents relevant to a query. LineListOutputParser [source] # Bases: BaseOutputParser[List[str]] Output parser for a list of lines. 9 and 3. If tool calls are included in a LLM response, they are attached to the corresponding message or message chunk as a list of Handle Multiple Queries. """ Migrating from MultiPromptChain. By leveraging the strengths of different algorithms, the EnsembleRetriever Convenience method for executing chain. Replies: 2 ElasticsearchRetriever# class langchain_elasticsearch. callbacks import class MergerRetriever (BaseRetriever): """Retriever that merges the results of multiple retrievers. merger_retriever. Alternatively you can use the from_es_params method with parameters to initialize the client. It runs all of its values in parallel, and each value is called with the overall input of the RunnableParallel. retrievers import BaseRetriever from pymilvus import Stream all output from a runnable, as reported to the callback system. I searched the LangChain documentation with the integrated search. We use 2 retriever langchain js and python based llama index in the same application talkingsite. These tags will be MultiVector Retriever. Here are some notable types: Parent Document Retriever: This retriever allows you to create multiple embeddings per parent document. as_retriever() retriever2 = store2. Parameters. similarity_score_threshold = 'similarity_score_threshold' ¶ Similarity search with a score threshold. These tags will be Retriever that merges the results of multiple retrievers. So I am building a chatbot using user's custom data. vectorstores import VectorStore from langchain. This is documentation for LangChain v0. 1. A QA application that routes between different domain-specific retrievers given a user question. Hybrid search retriever that uses Milvus Collection to retrieve documents based on multiple fields. version (Literal['v1', 'v2']) – The version of the schema to use either v2 or v1. You can also supply a custom prompt to tune what types of questions are generated. pydantic_v1 import Field, root_validator from langchain_core. Sometimes, a query analysis technique may allow for selection of which retriever to use. abc import Hashable from itertools import chain from typing import (Any, Callable, Dict, Iterable, Iterator, List, Optional, TypeVar, cast,) from Source code for langchain. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the LOTR (Merger Retriever) Lord of the Retrievers (LOTR), also known as MergerRetriever, takes a list of retrievers as input and merges the results of their get_relevant_documents() methods into a single list. MilvusCollectionHybridSearchRetriever. This notebook covers some of the common ways to create those Handle Multiple Retrievers. How to: use a vector store to retrieve data; How to: generate multiple queries to retrieve data for; How to: use contextual compression to compress the data retrieved; How to: write a custom retriever class; How to: add similarity scores to retriever results Customization . MultiQueryRetriever. We will show a simple example (using mock data) of how to do that. You can use these to eg identify a langchain. Should contain all inputs specified in Chain. " business_goals = "Get investors. SearchType (value) [source] ¶ Enumerator of the types of search to perform. from langchain. ainvoke or . Hello, @AlexanderKolev!I'm here to help you with any bugs, questions, or contributions. param docstore: BaseStore [str, Document] [Required] ¶ Source code for langchain_elasticsearch. ensemble""" Ensemble retriever that ensemble the results of multiple retrievers by using weighted Reciprocal Rank Fusion """ import asyncio from collections import defaultdict from collections. LangChain offers a range of retrievers that cater to different needs. """ retriever: BaseRetriever llm_chain: Runnable verbose: bool = True parser_key: str = "lines" """DEPRECATED. similarity_search_with_score method in a short function that packages scores into the associated document's metadata. return_only_outputs (bool) – Whether to return only outputs in the response. langchain. LangChain Python API Reference; langchain: 0. merger_retriever import MergerRetriever // the rest of your existing code goes here retriever1 = store1. retrievers import BaseRetriever from langchain_core. """ langchain. metadata: Arbitrary metadata associated with this document (e. Create a new model by parsing and validating input data from Applying Multiple 'OR' Filters with FAISS Retriever. document_mapper: Function to map Elasticsearch hits to LangChain Documents. custom events will only be class langchain. To use this, you will need to add Retrievers return a list of Document objects, which have two attributes:. Retrieve from a set of multiple embeddings for the EnsembleRetriever# class langchain. Note. router import MultiRetrievalQAChain chain = MultiRetrievalQAChain. retrievers. callbacks import langchain. input (Any) – The input to the Runnable. ElasticsearchRetriever [source] ¶. Defaults to equal weighting for all retrievers. weights – A list of weights corresponding to the retrievers. This guide assumes familiarity with the following concepts: Convenience method for executing chain. If True, only new Convenience method for executing chain. stores import BaseStore, ByteStore from langchain_core. INFO) # Define the prompt template for generating multiple query versions QUERY_PROMPT = PromptTemplate( input_variables=["question"], template="""You are an AI language model assistant. Bases: BaseRetriever Elasticsearch retriever. similarity = 'similarity' ¶ Similarity search. Vector stores can be used as the backbone of a retriever, but there are other In Python 3. """ class langchain_elasticsearch. v1 is for backwards compatibility and will be deprecated in 0. Bases: RunnableSerializable[str, list[Document]], ABC Abstract base class for a Document retrieval system. Retriever that merges the results of multiple retrievers. param metadata: Optional [Dict [str, Any]] = None ¶ Optional metadata associated with the retriever. es_client – Elasticsearch client connection. The environment provides the documents and the retriever How to add scores to retriever results. body_func: Function to create an Elasticsearch DSL query body from a Summary. They are important for applications that fetch data to be reasoned over as part of model inference, as in the case of retrieval-augmented generation, Convenience method for executing chain. """ destination_chains: Mapping [str, BaseRetrievalQA] """Map of name to candidate chains that Asynchronously get documents relevant to a query. tags (Optional[list[str]]) – Optional list of tags associated with the retriever. Parameters *args (Any) – If the chain expects a single input, it can be passed in retrievers. Note: adding documentation and/or support for multiple users for retrievers that do not support it (or document it) is retrievers. SVMRetriever. Retrieve from a set of multiple embeddings for the Qdrant (read: quadrant ) is a vector similarity search engine. I looked through lot of documentation but got confused on the retriever part. 1 You must be logged in to vote. setLevel(logging. getLogger("langchain. We’re releasing three new cookbooks that showcase the multi-vector retriever for RAG on documents that contain a mixture of content types. pull ("rlm/multi-vector-retriever-summarization") Details. SearchType (value, names = None, *, module = None, qualname = None, type = None, start = 1, boundary = None) [source] # Enumerator of the types of search to perform. Next, check out some of the other query analysis guides in this section, like how to deal with cases where no query is generated . How to get a RAG application to add citations. How to handle cases where no queries are generated. Source code for langchain. param byte_store: Optional [BaseStore [str, bytes]] = None ¶ The lower-level backing storage layer for the parent documents. Currently is a string. 2. Retrieve from a set of multiple embeddings for the Example: retrievers . Just waiting for a human maintainer to join the conversation. llms import OpenAI from langchain. Retrieve from a set of multiple embeddings for the same document. Bases: BaseRetriever Given a query, use an LLM to write a set of queries. index_name: The name of the index to query. mmr = 'mmr' ¶ How to migrate from legacy LangChain agents to LangGraph; How to generate multiple embeddings per document; How to pass multimodal data directly to models; How to use multimodal prompts; How to generate multiple queries to retrieve data for; How to try to fix errors in output parsing; How to parse JSON output; How to parse XML output Handle Multiple Retrievers. custom Retriever that merges the results of multiple retrievers. MultiVectorRetriever [source] ¶ Bases: BaseRetriever. You can see the full definition in Source code for langchain. It is initialized with a list of BaseRetriever objects. Given a query, use an LLM to write a set of queries. chat_models import ChatOpenAI from langchain. Part 2 extends the implementation to accommodate conversation-style interactions and multi-step retrieval processes. Next, check out some of the other query analysis guides in this section, like how to deal with [docs] class MultiVectorRetriever(BaseRetriever): """Retrieve from a set of multiple embeddings for the same document. Convenience method for executing chain. This template performs RAG using Pinecone and OpenAI with a multi-query retriever. 10, asyncio's tasks did not accept a context parameter. 28; retrievers; retrievers # Retriever class returns Documents given a text query. In these cases, we need to remember to run all queries and then to combine the results. abatch rather than aget_relevant_documents directly. How to recursively split text by Retriever that merges the results of multiple retrievers. MultiQueryRetriever implements the standard Runnable Interface. These The RunnableParallel primitive is essentially a dict whose values are runnables (or things that can be coerced to runnables, like functions). Retrieve from a set of multiple embeddings for the class langchain. RAG with Multiple Indexes (Routing) propositional-retrieval. """ def _get_relevant_documents . as_retriever (), llm = llm) class ElasticsearchRetriever (BaseRetriever): """ Elasticsearch retriever Args: es_client: Elasticsearch client connection. Bases: BaseRetriever Retriever that ensembles the multiple retrievers. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the This is a multi-part tutorial: Part 1 (this guide) introduces RAG and walks through a minimal implementation. Output parser for a list of lines. Return the unique union of all retrieved docs. body_func: Function to create an Elasticsearch DSL query body from a search string. We generate summaries of table How to handle multiple retrievers when doing query analysis. Setup Install dependencies from langchain. parser_key is no longer used and should not be specified. These tags will be Retrievers. al. 1 commit. Retriever that ensembles the multiple retrievers. " Convenience method for executing chain. """ retrievers: List [BaseRetriever] """A list of retrievers to merge. StringPromptTemplate. multi_query"). How to get your RAG application to return sources. memory import ConversationBufferMemory from Execute the chain. Try it and share your feedback. First, you The EnsembleRetriever supports ensembling of results from multiple retrievers. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. class MultiRetrievalQAChain (MultiRouteChain): """A multi-route chain that uses an LLM router chain to choose amongst retrieval qa chains. retrievers. How to combine results from multiple retrievers. For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis. multi_query. These tags will be Retrievers Retrievers are responsible for taking a query and returning relevant documents. Handle Multiple Retrievers. . This process can involve calls to a database, to the web using fetch, or any other source. The merged results will be a list of documents that are relevant to the query and that have been ranked by the different retrievers. basicConfig() logging. These tags will be From the Langchain documentation, you should call invoke() on a dictionary. EnsembleRetriever [source] ¶ Bases: BaseRetriever. To use this, you will need to add some logic to select the retriever to do. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. It uses an LLM to generate multiple queries from different perspectives based on the user's input query. svm. Hybrid search is a technique that combines multiple search algorithms to improve the accuracy and relevance of search results. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the This tutorial will familiarize you with LangChain's vector store and retriever abstractions. """ router_chain: LLMRouterChain """Chain for deciding a destination chain and the input to it. Regardless of the underlying retrieval system, all retrievers in LangChain share a common interface. import os from dotenv import load_dotenv import langchain from langchain. prompt = hub. How to handle multiple retrievers when doing query analysis; Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. LangChain API retriever. All reactions. A vector store retriever is a retriever that uses a vector store to retrieve documents. Components Integrations Guides API Reference. MultiQueryRetriever [source] #. The best way to do this is with LangSmith. ElasticsearchRetriever# class langchain_elasticsearch. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. These abstractions are designed to support retrieval of data-- from (vector) databases and other sources-- for integration with LLM workflows. Parameters:. config (RunnableConfig | None) – The config to use for the Runnable. Retriever To obtain scores from a vector store retriever, we wrap the underlying vector store's . These Parameters. ensemble. For example, you can build a retriever for a SQL database using text-to-SQL conversion. 9. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the Parameters:. A retriever does not need to be able to store documents, only to return (or retrieve) them. A retrieval system is defined as something that can take string queries and return the most ‘relevant’ Documents from some source. Weaviate is an open-source vector database. storage LangChain Python API Reference; retrievers; SearchType; SearchType# class langchain. embeddings import Embeddings from langchain_core. 0. Checked other resources I added a very descriptive title to this question. Retrieve from a set of multiple embeddings for the Retrievers. LangChain messages are Python objects that subclass from a BaseMessage. The MultiQueryRetriever automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. 2. Parameters: *args (Any) – If the chain expects a single input, it can be passed in as the Weaviate Hybrid Search. retrievers – A list of retrievers to ensemble. similarity = 'similarity' # Convenience method for executing chain. Retrieve docs for each query. A retriever is an interface that returns documents given an unstructured query. The combination of Unstructured file parsing and multi-vector retriever can support RAG on semi-structured data, which is a challenge for naive chunking strategies that may spit tables. Asynchronously get documents relevant to a query. vectorstores import VectorStore from pydantic import Field, model_validator from langchain. 8; retrievers; LineListOutputParser; LineListOutputParser# class langchain. The methods to create multiple vectors per document include: Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). Check out the docs for the latest version here. storage. How to handle multiple queries when doing query analysis. Type. MultiQueryRetriever¶ class langchain. Tool calls . g. Sometimes, a query analysis technique may allow for multiple queries to be generated. as_retriever() lotr = MergerRetriever(retrievers=[retriever1, retriever2]) LangChain Messages LangChain provides a unified message format that can be used across all chat models, allowing users to work with different chat models without worrying about the specific details of the message format used by each model provider. The Runnable Interface has additional methods that are available on runnables, such as with_types, My workaround for this is to put everything in one collection and use metadata to filter when I need to. _lc_store import EnsembleRetriever# class langchain. from enum import Enum from typing import Dict, List, Optional from langchain_core. Create a new model by parsing and Dynamically selecting from multiple retrievers. """ destination_chains: Mapping [str, BaseRetrievalQA] """Map of name to candidate chains that inputs can be routed to. These tags will be Asynchronously get documents relevant to a query. So, assuming that your variables issues_and_opportunities, business_goals, description are strings defined in your code, this should work:. documents import Document from langchain_core. """ MultiQuery Retriever. 1, which is no longer actively maintained. These tags will be class MultiQueryRetriever (BaseRetriever): """Given a query, use an LLM to write a set of queries. Can also be a list of names. 3. These tags will be By reading the documentation or source code, figure out whether the retriever you are using supports multiple users, and, if so, how to use it. SVM retriever. callbacks (Callbacks) – Callback manager or list of callbacks. LineListOutputParser. For each query, it LangChain has a base MultiVectorRetriever which makes querying this type of setup easy. Understanding Retrievers. Specifically we show how to use the MultiRetrievalQAChain to create a question-answering chain that selects the retrieval QA chain which is most relevant for a given question, and then Because the retriever interface is so simple, returning a list of Document objects given a search query, it is possible to combine multiple retrievers using ensembling. Questions and answers based on a snapshot of the LangChain python docs. Source code for langchain_milvus. This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects which Retrieval system to use. It provides a production-ready service with a convenient API to store, search, and manage vectors with additional payload and extended filtering support. LangChain has a base MultiVectorRetriever which makes querying this type of setup easier! Asynchronously get documents relevant to a query. EnsembleRetrievers rerank the results of the constituent retrievers based on the Reciprocal Rank Fusion algorithm. It is initialized with a list of BaseRetriever objects. These tags will be EnsembleRetriever# class langchain. This allows a natural language query (string) to be transformed into a SQL query behind the scenes. It uses a rank fusion. No default will be assigned until the API is stabilized. Users should use v2. The prompt, which you can try out on the hub, directs an LLM to generate de-contextualized "propositions" which can be vectorized to increase the retrieval accuracy. A LangGraph Retriever that merges the results of multiple retrievers. While the Multi-Vector Retriever enhances retrieval capabilities by using multiple vectors to represent different import logging logging. The underlying implementation of the retriever depends on the type of data store or database you are connecting to, but all retrievers rag-pinecone-multi-query. callbacks import CallbackManagerForRetrieverRun from langchain_core. 10, this is a likely cause. 3 › # set the LANGCHAIN_API_KEY environment variable (create key in settings) from langchain import hub. EnsembleRetriever [source] #. 13; retrievers # Retriever class returns Documents given a text query. A retriever does not need to be able to store documents, only to return (or retrieve) it. 7 langchain latest version. """ def _get_relevant_documents LangChain Python API Reference; langchain-community: 0. 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 provided below. These tags will be How to use a vectorstore as a retriever. param docstore: BaseStore [str, Document] [Required] ¶ Asynchronously get documents relevant to a query. class MultiRetrievalQAChain (MultiRouteChain): # type: ignore[override] """A multi-route chain that uses an LLM router chain to choose amongst retrieval qa chains. I think you're looking for the Lord of the Retrievers, also known as MergerRetriever. Beta Was this translation helpful? Give feedback. Python SDK . Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in Source code for langchain. **Structured Software Development**: A systematic approach to creating Python software projects is emphasized, focusing on defining core components, managing BaseRetriever# class langchain_core. SearchType ( value , names = None , * , module = None , qualname = None , type = None , start = 1 , boundary = None ) [source] # Enumerator of the types of search to perform. ElasticsearchRetriever [source] #. Retrieve from a set of multiple embeddings for the You’ve now learned some techniques for handling multiple retrievers in a query analysis system. class langchain. __call__ expects a single input dictionary with all the inputs. The MultiPromptChain routed an input query to one of multiple LLMChains-- that is, given an input query, it used a LLM to select from a list of prompts, formatted the query into the prompt, and generated a response. User will feed the data; Data should be upserted to Pinecone; Then later user can chat with their data; there can be multiple users and each user will be able to chat with their own data. baya tphz cvaeu dtidn asdur ujcx clvfq zthg lhle skhiipic