How to filter documents by metadata using SupabaseHybridSearch and RunnableSequence? #4719
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Example Codeimport { SupabaseVectorStore } from '@langchain/community/vectorstores/supabase';
import { SupabaseHybridSearch } from '@langchain/community/retrievers/supabase';
import {
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
} from '@langchain/core/prompts';
import {
RunnablePassthrough,
RunnableSequence,
} from '@langchain/core/runnables';
import { formatDocumentsAsString } from 'langchain/util/document';
import { CharacterTextSplitter } from 'langchain/text_splitter';
import { StringOutputParser } from '@langchain/core/output_parsers';
import { IChatbotConfig, IChatbotLeadConfig } from '@/interfaces/chatbot';
import { LLM } from '.';
class Langchain extends LLM {
async uploadContext(chatbotId: string, text: string) {
const splitter = new CharacterTextSplitter({
separator: ' ',
chunkSize: 1000,
chunkOverlap: 0,
});
const docs = await splitter.createDocuments([text], [{ chatbotId }]);
return await SupabaseVectorStore.fromDocuments(docs, this.embeddings, {
client: this.database,
tableName: 'documents',
queryName: 'match_documents',
filter: { chatbotId },
});
}
async queryData(query: string, config: IChatbotConfig) {
const retriever = new SupabaseHybridSearch(this.embeddings, {
client: this.database,
metadata: {
chatbotId: config.chatbotId,
},
// verbose: true,
similarityK: 2,
keywordK: 2,
tableName: 'documents',
similarityQueryName: 'match_documents',
keywordQueryName: 'kw_match_documents',
});
const SYSTEM_TEMPLATE = `Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}`;
const messages = [
SystemMessagePromptTemplate.fromTemplate(
`System name is ${config.botName}.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System gender is ${config.gender}.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System only speaks ${config.language} and doesn't speak any other language.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System personality is: ${config.personality}.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System must follow these instructions: ${config.instructions}.`,
),
SystemMessagePromptTemplate.fromTemplate(SYSTEM_TEMPLATE),
HumanMessagePromptTemplate.fromTemplate('{question}'),
];
const prompt = ChatPromptTemplate.fromMessages(messages);
const chain = RunnableSequence.from([
{
context: retriever.pipe(formatDocumentsAsString),
question: new RunnablePassthrough(),
},
prompt,
this.llm,
new StringOutputParser(),
]);
return await chain.invoke(query);
}
}
export default new Langchain();DescriptionI'm creating a nodejs application to create conversational bots, I have created these two methods In method Someone can help me on how to filter by metadata using the retriever System Infoyarn info v1.22.19
{
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},
name: 'langchain',
'dist-tags': {
latest: '0.1.26',
next: '0.1.19-rc.2'
},
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],
maintainers: [
{
name: 'nfcampos',
email: '[email protected]'
},
{
name: 'jacoblee93',
email: '[email protected]'
},
{
name: 'davidduong',
email: '[email protected]'
},
{
name: 'hwchase17',
email: '[email protected]'
},
{
name: 'basproul',
email: '[email protected]'
}
],
description: 'Typescript bindings for langchain',
author: {
name: 'LangChain'
},
license: 'MIT',
readmeFilename: 'README.md',
keywords: [
'llm',
'ai',
'gpt3',
'chain',
'prompt',
'prompt engineering',
'chatgpt',
'machine learning',
'ml',
'openai',
'embeddings',
'vectorstores'
],
repository: {
type: 'git',
url: 'git+ssh://[email protected]/langchain-ai/langchainjs.git'
},
homepage: 'https://github.com/langchain-ai/langchainjs/tree/main/langchain/',
bugs: {
url: 'https://github.com/langchain-ai/langchainjs/issues'
},
version: '0.1.26',
type: 'module',
engines: {
node: '>=18'
},
main: './index.js',
types: './index.d.ts',
scripts: {
build: 'yarn run build:deps && yarn clean && yarn build:esm && yarn build:cjs && yarn build:scripts',
'build:deps': 'yarn run turbo:command build --filter=@langchain/core --filter=@langchain/anthropic --filter=@langchain/openai --filter=@langchain/community --concurrency=1',
'build:esm': 'NODE_OPTIONS=--max-old-space-size=4096 tsc --outDir dist/ && rimraf dist/tests dist/**/tests',
'build:cjs': 'NODE_OPTIONS=--max-old-space-size=4096 tsc --outDir dist-cjs/ -p tsconfig.cjs.json && yarn move-cjs-to-dist && rimraf dist-cjs',
'build:watch': 'yarn create-entrypoints && tsc --outDir dist/ --watch',
'build:scripts': 'yarn create-entrypoints && yarn check-tree-shaking',
'lint:eslint': 'NODE_OPTIONS=--max-old-space-size=4096 eslint --cache --ext .ts,.js src/',
'lint:dpdm': 'dpdm --exit-code circular:1 --no-warning --no-tree src/*.ts src/**/*.ts',
lint: 'yarn lint:eslint && yarn lint:dpdm',
'lint:fix': 'yarn lint:eslint --fix && yarn lint:dpdm',
precommit: 'lint-staged',
clean: 'rimraf .turbo/ dist/ && NODE_OPTIONS=--max-old-space-size=4096 yarn lc-build --config ./langchain.config.js --create-entrypoints --pre --gen-maps',
prepack: 'yarn build',
release: 'release-it --only-version --config .release-it.json',
test: 'yarn run build:deps && NODE_OPTIONS=--experimental-vm-modules jest --testPathIgnorePatterns=\\.int\\.test.ts --testTimeout 30000 --maxWorkers=50%',
'test:watch': 'yarn run build:deps && NODE_OPTIONS=--experimental-vm-modules jest --watch --testPathIgnorePatterns=\\.int\\.test.ts',
'test:integration': 'yarn run build:deps && NODE_OPTIONS=--experimental-vm-modules jest --testPathPattern=\\.int\\.test.ts --testTimeout 100000 --maxWorkers=50%',
'test:single': 'yarn run build:deps && NODE_OPTIONS=--experimental-vm-modules yarn run jest --config jest.config.cjs --testTimeout 100000',
format: 'prettier --config .prettierrc --write "src"',
'format:check': 'prettier --config .prettierrc --check "src"',
'move-cjs-to-dist': 'yarn lc-build --config ./langchain.config.js --move-cjs-dist',
'create-entrypoints': 'yarn lc-build --config ./langchain.config.js --create-entrypoints',
'check-tree-shaking': 'yarn lc-build --config ./langchain.config.js --tree-shaking'
},
devDependencies: {
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'@aws-sdk/client-sagemaker-runtime': '^3.414.0',
'@aws-sdk/client-sfn': '^3.362.0',
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'@aws-sdk/types': '^3.357.0',
'@azure/storage-blob': '^12.15.0',
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typescript: '~5.1.6',
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wikipedia: '^2.1.2',
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peerDependencies: {
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ws: '^8.14.2',
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},
peerDependenciesMeta: {
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optional: true
},
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optional: true
},
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},
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optional: true
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optional: true
},
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optional: true
},
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optional: true
},
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optional: true
},
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optional: true
},
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optional: true
},
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optional: true
},
'@pinecone-database/pinecone': {
optional: true
},
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optional: true
},
'@vercel/kv': {
optional: true
},
'@xata.io/client': {
optional: true
},
'apify-client': {
optional: true
},
assemblyai: {
optional: true
},
axios: {
optional: true
},
cheerio: {
optional: true
},
chromadb: {
optional: true
},
convex: {
optional: true
},
couchbase: {
optional: true
},
'd3-dsv': {
optional: true
},
epub2: {
optional: true
},
'faiss-node': {
optional: true
},
'fast-xml-parser': {
optional: true
},
'google-auth-library': {
optional: true
},
handlebars: {
optional: true
},
'html-to-text': {
optional: true
},
ignore: {
optional: true
},
ioredis: {
optional: true
},
jsdom: {
optional: true
},
mammoth: {
optional: true
},
mongodb: {
optional: true
},
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optional: true
},
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optional: true
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officeparser: {
optional: true
},
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optional: true
},
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optional: true
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optional: true
},
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optional: true
},
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optional: true
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optional: true
},
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optional: true
},
'srt-parser-2': {
optional: true
},
typeorm: {
optional: true
},
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optional: true
},
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optional: true
},
ws: {
optional: true
},
'youtube-transcript': {
optional: true
},
'youtubei.js': {
optional: true
}
},
dependencies: {
'@anthropic-ai/sdk': '^0.9.1',
'@langchain/community': '~0.0.36',
'@langchain/core': '~0.1.44',
'@langchain/openai': '~0.0.19',
'binary-extensions': '^2.2.0',
'expr-eval': '^2.0.2',
'js-tiktoken': '^1.0.7',
'js-yaml': '^4.1.0',
jsonpointer: '^5.0.1',
langchainhub: '~0.0.8',
langsmith: '~0.1.7',
'ml-distance': '^4.0.0',
'openapi-types': '^12.1.3',
'p-retry': '4',
uuid: '^9.0.0',
yaml: '^2.2.1',
zod: '^3.22.4',
'zod-to-json-schema': '^3.22.3'
},
publishConfig: {
access: 'public'
},
exports: {
'./load': {
types: {
import: './load.d.ts',
require: './load.d.cts',
default: './load.d.ts'
},
import: './load.js',
require: './load.cjs'
},
'./load/serializable': {
types: {
import: './load/serializable.d.ts',
require: './load/serializable.d.cts',
default: './load/serializable.d.ts'
},
import: './load/serializable.js',
require: './load/serializable.cjs'
},
'./agents': {
types: {
import: './agents.d.ts',
require: './agents.d.cts',
default: './agents.d.ts'
},
import: './agents.js',
require: './agents.cjs'
},
'./agents/load': {
types: {
import: './agents/load.d.ts',
require: './agents/load.d.cts',
default: './agents/load.d.ts'
},
import: './agents/load.js',
require: './agents/load.cjs'
},
'./agents/toolkits': {
types: {
import: './agents/toolkits.d.ts',
require: './agents/toolkits.d.cts',
default: './agents/toolkits.d.ts'
},
import: './agents/toolkits.js',
require: './agents/toolkits.cjs'
},
'./agents/toolkits/aws_sfn': {
types: {
import: './agents/toolkits/aws_sfn.d.ts',
require: './agents/toolkits/aws_sfn.d.cts',
default: './agents/toolkits/aws_sfn.d.ts'
},
import: './agents/toolkits/aws_sfn.js',
require: './agents/toolkits/aws_sfn.cjs'
},
'./agents/toolkits/connery': {
types: {
import: './agents/toolkits/connery.d.ts',
require: './agents/toolkits/connery.d.cts',
default: './agents/toolkits/connery.d.ts'
},
import: './agents/toolkits/connery.js',
require: './agents/toolkits/connery.cjs'
},
'./agents/toolkits/sql': {
types: {
import: './agents/toolkits/sql.d.ts',
require: './agents/toolkits/sql.d.cts',
default: './agents/toolkits/sql.d.ts'
},
import: './agents/toolkits/sql.js',
require: './agents/toolkits/sql.cjs'
},
'./agents/format_scratchpad': {
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import: './agents/format_scratchpad.d.ts',
require: './agents/format_scratchpad.d.cts',
default: './agents/format_scratchpad.d.ts'
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require: './agents/format_scratchpad.cjs'
},
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default: './agents/format_scratchpad/openai_tools.d.ts'
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require: './agents/format_scratchpad/openai_tools.cjs'
},
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default: './agents/format_scratchpad/log.d.ts'
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require: './agents/format_scratchpad/log.cjs'
},
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default: './agents/format_scratchpad/xml.d.ts'
},
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require: './agents/format_scratchpad/xml.cjs'
},
'./agents/format_scratchpad/log_to_message': {
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},
import: './agents/format_scratchpad/log_to_message.js',
require: './agents/format_scratchpad/log_to_message.cjs'
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require: './agents/react/output_parser.d.cts',
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Done in 0.63s.Platform: Linux |
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Answered by
ThiagoPereiraUFV
Dec 12, 2024
Replies: 1 comment 5 replies
-
|
Hey @ThiagoPereiraUFV! Great to see you back here with another intriguing challenge. Hope you're doing well! Based on your code, it seems like you're already passing the const retriever = new SupabaseHybridSearch(this.embeddings, {
client: this.database,
metadata: {
chatbotId: config.chatbotId,
},
// verbose: true,
similarityK: 2,
keywordK: 2,
tableName: 'documents',
similarityQueryName: 'match_documents',
keywordQueryName: 'kw_match_documents',
});This should theoretically filter the documents based on the The if (Object.keys(this.metadata ?? {}).length > 0) {
matchDocumentsParams.filter = this.metadata;
}This code snippet checks if If this isn't working as expected, it might be a bug in the LangChain JS library or a misunderstanding of how the metadata filtering is supposed to work. I recommend checking the SupabaseHybridSearch class source code and the Supabase documentation for more information on how metadata filtering is implemented and supposed to work. If you're still having trouble, it might be helpful to provide more information about the issue. For example, what is the exact output you're getting, and what output are you expecting? Are there any error messages? This could help identify the problem more accurately. Sources
|
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5 replies
-
|
After many hours of searching I found a solution my friend @codebymarcus, unfortunately I had to change the approach and instead of instantiate a async queryData(query: string, config: IChatbotConfig) {
const vectorStore = await SupabaseVectorStore.fromExistingIndex(
this.embeddings,
{
client: this.database,
tableName: 'documents',
queryName: 'match_documents',
},
);
const retriever = vectorStore.asRetriever({
filter: { chatbotId: config.chatbotId },
k: 2,
// verbose: true,
});
// const retriever = new SupabaseHybridSearch(this.embeddings, {
// client: this.database,
// metadata: {
// chatbotId: config.chatbotId,
// },
// verbose: true,
// similarityK: 2,
// keywordK: 2,
// tableName: 'documents',
// similarityQueryName: 'match_documents',
// keywordQueryName: 'kw_match_documents',
// });
const SYSTEM_TEMPLATE = `Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}`;
const messages = [
SystemMessagePromptTemplate.fromTemplate(
`System name is ${config.botName}.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System gender is ${config.gender}.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System only speaks ${config.language} and doesn't speak any other language.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System personality is: ${config.personality}.`,
),
SystemMessagePromptTemplate.fromTemplate(
`System must follow these instructions: ${config.instructions}.`,
),
SystemMessagePromptTemplate.fromTemplate(SYSTEM_TEMPLATE),
HumanMessagePromptTemplate.fromTemplate('{question}'),
];
const prompt = ChatPromptTemplate.fromMessages(messages);
const chain = RunnableSequence.from([
{
context: retriever.pipe(formatDocumentsAsString),
question: new RunnablePassthrough(),
},
prompt,
this.llm,
new StringOutputParser(),
]);
return await chain.invoke(query);
}Test it and give me a feedback. Although I think that the bug on |
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-
|
But how does this use the |
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-
|
The below approach worked for me when trying to filter by meta data " retriever.metadata = { orgId }"- |
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-
|
@alastairayliffe it seems that the old version I was running langchain (0.1.26) has not supported filter by metadata, I updated to the latest version and it worked like a charm! |
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Answer selected by
ThiagoPereiraUFV
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@alastairayliffe it seems that the old version I was running langchain (0.1.26) has not supported filter by metadata, I updated to the latest version and it worked like a charm!