Cannot pass document by retriever and throws "text.replace is not a function" error

lynicis commented 1 month ago

Checked other resources

[X] I added a very descriptive title to this issue.
[X] I searched the LangChain.js documentation with the integrated search.
[X] I used the GitHub search to find a similar question and didn't find it.
[X] I am sure that this is a bug in LangChain.js rather than my code.
[X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code

const serializeDocuments = (documents: Array<Document>) =>
        documents.map((document) => JSON.stringify(document)).toString();

const chain = RunnableSequence.from([
    RunnableLambda.from((input: { foo: string; bar: string }) => input),
    {
        context: retriever.pipe(serializeDocuments),
        foo: new RunnablePassthrough(),
        bar: new RunnablePassthrough(),
    },
    prompt,
    this.llm,
]);

const output = await chain.invoke({ foo, bar });

Error Message and Stack Trace (if applicable)

TypeError: text.replace is not a function at OpenAIEmbeddings.embedQuery at PineconeStore.similaritySearch at VectorStoreRetriever.getRelevantDocuments

Description

I send the document pageContent and metadata with some inputs to the prompt but it throws error: text.replace is not a function

I've tried different ways to send the document and other fields but they don't work either, I've also looked for other issues similar to my case but they don't work for me.

System Info

yarn info v1.22.22 { name: 'langchain', 'dist-tags': { next: '0.3.2-rc.0', latest: '0.3.2', 'tag-for-publishing-older-releases': '0.2.20' }, versions: [ '0.0.4', '0.0.5', '0.0.6', '0.0.7', '0.0.8', '0.0.9', '0.0.10', '0.0.11', '0.0.12', '0.0.13', '0.0.14', '0.0.15', '0.0.16', '0.0.17', '0.0.18', '0.0.19', '0.0.20', '0.0.21', '0.0.22', '0.0.23', '0.0.24', '0.0.25', '0.0.26', '0.0.27', '0.0.28', '0.0.29', '0.0.30', '0.0.31', '0.0.32', '0.0.33', '0.0.34', '0.0.35', '0.0.36', '0.0.37', '0.0.38', '0.0.39', '0.0.40', '0.0.41', '0.0.42', '0.0.43', '0.0.44', '0.0.45', '0.0.46', '0.0.47', '0.0.48', '0.0.49-0', '0.0.49-1', '0.0.49', '0.0.50', '0.0.51', '0.0.52-0', '0.0.52', '0.0.53', '0.0.54', '0.0.55', '0.0.56', '0.0.57', '0.0.58', '0.0.59', '0.0.60', '0.0.61', '0.0.62', '0.0.63', '0.0.64', '0.0.65', '0.0.66', '0.0.67', '0.0.68', '0.0.69', '0.0.70', '0.0.71', '0.0.72', '0.0.73', '0.0.74', '0.0.75', '0.0.76', '0.0.77', '0.0.78', '0.0.79', '0.0.80', '0.0.81', '0.0.82', '0.0.83', '0.0.84', 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'./chains/query_constructor.cjs' }, './document_loaders/fs/json': { types: { import: './document_loaders/fs/json.d.ts', default: './document_loaders/fs/json.d.ts', require: './document_loaders/fs/json.d.cts' }, import: './document_loaders/fs/json.js', require: './document_loaders/fs/json.cjs' }, './document_loaders/fs/text': { types: { import: './document_loaders/fs/text.d.ts', default: './document_loaders/fs/text.d.ts', require: './document_loaders/fs/text.d.cts' }, import: './document_loaders/fs/text.js', require: './document_loaders/fs/text.cjs' }, './retrievers/time_weighted': { types: { import: './retrievers/time_weighted.d.ts', default: './retrievers/time_weighted.d.ts', require: './retrievers/time_weighted.d.cts' }, import: './retrievers/time_weighted.js', require: './retrievers/time_weighted.cjs' }, './schema/query_constructor': { types: { import: './schema/query_constructor.d.ts', default: './schema/query_constructor.d.ts', require: './schema/query_constructor.d.cts' }, import: './schema/query_constructor.js', require: './schema/query_constructor.cjs' }, './stores/message/in_memory': { types: { import: './stores/message/in_memory.d.ts', default: './stores/message/in_memory.d.ts', require: './stores/message/in_memory.d.cts' }, import: './stores/message/in_memory.js', require: './stores/message/in_memory.cjs' }, './experimental/openai_files': { types: { import: './experimental/openai_files.d.ts', default: './experimental/openai_files.d.ts', require: './experimental/openai_files.d.cts' }, import: './experimental/openai_files.js', require: './experimental/openai_files.cjs' }, './output_parsers/expression': { types: { import: './output_parsers/expression.d.ts', default: './output_parsers/expression.d.ts', require: './output_parsers/expression.d.cts' }, import: './output_parsers/expression.js', require: './output_parsers/expression.cjs' }, './agents/react/output_parser': { types: { import: './agents/react/output_parser.d.ts', default: 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'./agents/openai/output_parser': { types: { import: './agents/openai/output_parser.d.ts', default: './agents/openai/output_parser.d.ts', require: './agents/openai/output_parser.d.cts' }, import: './agents/openai/output_parser.js', require: './agents/openai/output_parser.cjs' }, './chains/query_constructor/ir': { types: { import: './chains/query_constructor/ir.d.ts', default: './chains/query_constructor/ir.d.ts', require: './chains/query_constructor/ir.d.cts' }, import: './chains/query_constructor/ir.js', require: './chains/query_constructor/ir.cjs' }, './agents/format_scratchpad/log': { types: { import: './agents/format_scratchpad/log.d.ts', default: './agents/format_scratchpad/log.d.ts', require: './agents/format_scratchpad/log.d.cts' }, import: './agents/format_scratchpad/log.js', require: './agents/format_scratchpad/log.cjs' }, './agents/format_scratchpad/xml': { types: { import: './agents/format_scratchpad/xml.d.ts', default: './agents/format_scratchpad/xml.d.ts', require: 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dosubot[bot] commented 1 month ago

Hey @lynicis! I'm here to help you with any bugs, questions, or to guide you on becoming a contributor. Let's tackle this issue together!

I found a similar issue that might help you resolve the "text.replace is not a function" error. The error often occurs when a non-string value is processed where a string is expected, particularly in the OpenAIEmbeddings.embedQuery method.

Solution:

Ensure Correct Input Types: The embedQuery method expects a string input. Make sure that the input being passed is a string and not undefined or any other type.

Filter Undefined Content: If your documents might have undefined pageContent, filter them out before processing:

const newDocs = documents
   .filter((doc) => doc.pageContent)
   .map((doc) => ({
       ...doc,
       metadata: {
           ...doc.metadata,
           s3Key: key,
       },
   }));

Check Retriever Implementation: Ensure that the retriever is correctly implemented and returns the expected types. It should be a subclass of BaseRetrieverInterface or implement RunnableInterface.

Invoke Chain Correctly: When using RunnableSequence, ensure that the input is structured correctly. Here's an example of how to set up the chain:

const ragChain = RunnableSequence.from([
     RunnableLambda.from((input) => input.question),
     {
       context: retriever.pipe(formatDocumentsAsString),
       question: new RunnablePassthrough(),
     },
     prompt,
     llm,
     new StringOutputParser(),
   ]);

const response = ragChain.invoke({ question });

These steps should help you address the error by ensuring that the inputs are correctly formatted and processed ^[1]^[2].

To continue talking to Dosu, mention @dosu.

jacoblee93 commented 1 month ago

Hey @lynicis, I believe this was fixed here:

https://github.com/langchain-ai/langchainjs/pull/6920

Please reopen if upgrading to the latest @langchain/pinecone doesn't resolve this.

lynicis commented 1 month ago

Hey @lynicis, I believe this was fixed here:

6920

Please reopen if upgrading to the latest @langchain/pinecone doesn't resolve this.

Still same on "@langchain/pinecone": "^0.1.1"

lynicis commented 2 weeks ago

OpenAI Embeddings is broken and no one cares

jacoblee93 commented 2 weeks ago

Can you give a minimal reproducible example? Something I can paste into a file and run end to end

lynicis commented 2 weeks ago

Can you give a minimal reproducible example? Something I can paste into a file and run end to end

After invoke prompt, it just throws replace is not a function

import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { OpenAIEmbeddings, OpenAI, ChatOpenAI } from "@langchain/openai";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { PDFLoader } from "langchain/document_loaders/fs/pdf";
import { pull } from "langchain/hub";

const doc = await new PDFLoader("foo.pdf").load();

const textEmbedding = await new OpenAIEmbeddings({
  modelName: "text-embedding-3-large",
  openAIApiKey: "foobar",
});

await MemoryVectorStore.fromDocuments(doc, textEmbedding);
const retriever = vectorStore.asRetriever();

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
  apiKey: "foobar",
});

const fooBarPrompt = pull("foo-bar-prompt");

const documentChain = await createStuffDocumentsChain({
  llm,
  prompt: fooBarPrompt,
});

const ragChain = await createRetrievalChain({
  retriever,
  combineDocsChain: documentChain,
});

const game = await ragChain.invoke({
    foo: "bar",
});

jacoblee93 commented 2 weeks ago

Can you print const fooBarPrompt = pull("foo-bar-prompt");? Pretty sure that also needs to be await'ed

lynicis commented 1 week ago

Can you print const fooBarPrompt = pull("foo-bar-prompt");? Pretty sure that also needs to be await'ed

sorry it was typo but same

jacoblee93 commented 1 week ago

I can't see the prompt though

lynicis commented 1 week ago

I can't see the prompt though

import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { PDFLoader } from "langchain/document_loaders/fs/pdf";
import { pull } from "langchain/hub";

const pdf = await new PDFLoader("test.pdf").load();

const openAIEmbeddings = await new OpenAIEmbeddings({
    modelName: "text-embedding-3-large",
    openAIApiKey: "...",
});

const vectorStore = new MemoryVectorStore(openAIEmbeddings);
await vectorStore.addDocuments(pdf);

const retriever = vectorStore.asRetriever();

const llm = new ChatOpenAI({
    model: "gpt-4o-mini",
    apiKey: "...",
})

const prompt = await pull("rlm/rag-prompt");

const documentChain = await createStuffDocumentsChain({
    llm,
    prompt,
});
const ragChain = await createRetrievalChain({
    retriever,
    combineDocsChain: documentChain,
    outputParser: new StringOutputParser()
});

const result = await ragChain.invoke("what is the context about?");

console.log(result);

lynicis commented 1 week ago

is there any update?

jacoblee93 commented 1 week ago

Doesn't seem like you're using TypeScript? We generally recommend it.

Invoking looks that chain looks like this:

const result = await ragChain.invoke({ input: "what is the context about?" });

You also cannot pass arbitrary prompts.

So end to end:

import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
import { createRetrievalChain } from "langchain/chains/retrieval";
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
import { pull } from "langchain/hub";
import { ChatPromptTemplate } from "@langchain/core/prompts";

const pdf = await new PDFLoader(
  "test.pdf"
).load();

const openAIEmbeddings = await new OpenAIEmbeddings({
  model: "text-embedding-3-large",
});

const vectorStore = new MemoryVectorStore(openAIEmbeddings);
await vectorStore.addDocuments(pdf);

const retriever = vectorStore.asRetriever();

const llm = new ChatOpenAI({
  model: "gpt-4o-mini",
});

const SYSTEM_TEMPLATE = `Answer the user's questions based on the below context. 
If the context doesn't contain any relevant information to the question, don't make something up and just say "I don't know":

<context>
{context}
</context>
`;

const prompt = ChatPromptTemplate.fromMessages([
  ["system", SYSTEM_TEMPLATE],
  ["placeholder", "{messages}"],
]);

const documentChain = await createStuffDocumentsChain({
  llm,
  prompt,
});
const ragChain = await createRetrievalChain({
  retriever,
  combineDocsChain: documentChain,
});

const result = await ragChain.invoke({ input: "what is the context about?" });

console.log(result);

langchain-ai / langchainjs