Indexing Code Base

[uncleericsg]

Is it possible to have an indexing codebase feature, similar like Cursor. This allows analyzing & Grep searching of codebase files much more efficiently and super fast too. Thanks! 👍

[mdlmarkham]

Perhaps MCP could be adapted for this? There is an MCP sever for qdrant - perhaps we could use a script to periodically ingest the codebase (and preferred documentation?) into Qdrant, and Cline could reference it using MCP?

[bramburn]

it could be done, but glob search is easier, but only useful for finding related files based on task.
the problem is we need to maintain this embed somewhere, we could potentially provide it via 3rd party.
but MCP server is good option for now to do this and using ollama so you don't pay an arm and a leg to vector the repo

[TomLucidor]

How is this different from other agentic tools like OpenHands and Aide? Are global text search really sufficient relative to the SOTA in SWE-bench @bramburn ?

[bramburn]

its ok enough to search for specifics. However, it is not better if you're not giving any context and you're asking for 'build a python game' so it would be hard for it to understand the context of the application.
However, my implementation of my local MCP server is providing to be useful as it provides additional context as well as sample code to the chat
here is an example of the output provided to a chat regarding DENO, it currently gets the relevant snippets of docs and code, then it provides EXAMPLE implementation back to the LLM with explanation to understand how to do it. Obviously Its going to cost you additional calls, but rather than sending 10k+ token for that reference it has used <2k token i/o for this output which has helped

```typescript
// deno-lint-ignore-file no-unused-vars
// deno-fmt-ignore-file
import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { Document } from "@langchain/core/documents";

// 1. Initialize the embedding model
const embeddings = new OpenAIEmbeddings({
  openAIApiKey: Deno.env.get("OPENAI_API_KEY"), // Replace with your OpenAI API key or environment variable
});

// 2. Initialize the vector store
const vectorStore = new MemoryVectorStore(embeddings);

// 3. Add documents
const documents: Document[] = [
  {
    pageContent: "This is document 1 about cats.",
    metadata: { source: "cat_info.txt" },
  },
  {
    pageContent: "Document 2 talks about dogs.",
    metadata: { source: "dog_info.txt" },
  },
  {
    pageContent: "Cats are very playful animals.",
    metadata: { source: "cat_behavior.txt" },
  },
];

const addDocumentsResponse = await vectorStore.addDocuments(documents);
console.log("Documents added:", JSON.stringify(addDocumentsResponse, null, 2));

// 4. Search for similar documents
const query = "What do cats do?";
const results = await vectorStore.similaritySearch(query, 2); // Search for 2 similar documents

console.log("Search results:");
results.forEach((result, index) => {
  console.log(`Result ${index + 1}:`);
  console.log("  Page Content:", result.pageContent);
  console.log("  Metadata:", result.metadata);
});

// 5. Delete documents (optional) - using page content as a key
// Note:  Deleting by page content can be inefficient for large-scale operations.
// Consider using document IDs or a more robust key.
const deleteDocumentsResponse = await vectorStore.deleteDocuments({
  ids: [documents[0].pageContent, documents[2].pageContent], // Delete document 1 and 3
});
console.log("Documents deleted:", deleteDocumentsResponse);

// 6.  Verify deletion (optional)
const verifyDeletionResults = await vectorStore.similaritySearch(query, 2);
console.log("Search results after deletion:");
verifyDeletionResults.forEach((result, index) => {
  console.log(`Result ${index + 1}:`);
  console.log("  Page Content:", result.pageContent);
  console.log("  Metadata:", result.metadata);
});

Key improvements and explanations:

• Error Handling: While not explicitly added due to the brevity of the example, in a real-world scenario, you'd wrap the await calls in try...catch blocks to handle potential errors (e.g., API key issues, network problems, etc.). This is crucial for robust applications.
• API Key: The code now correctly uses Deno.env.get("OPENAI_API_KEY"). Important: You must set the OPENAI_API_KEY environment variable before running this code. For example: export OPENAI_API_KEY="YOUR_OPENAI_API_KEY" in your terminal before you run the Deno script. I've added a comment to remind the user.
• Document IDs for Deletion: The example now demonstrates how to delete based on pageContent which is not efficient for large vector stores. A more common and efficient approach would be to use document IDs (if the vector store provides them) or to generate unique IDs and store them as metadata. I've added comments to highlight this.
• Clearer Output: The console.log statements are improved to make the output easier to understand, including JSON formatting for the added documents response.
• Verification of Deletion: Added a step to verify that the documents have been deleted by performing another similaritySearch after the deletion. This is good practice to ensure the operation worked as expected.
• Complete, Runnable Code: The code is self-contained and runnable (assuming you have Deno and an OpenAI API key set up). It includes all necessary imports.
• Type Safety: Uses TypeScript's type definitions for Document and other Langchain classes for improved code quality and maintainability.
• Comments and Documentation: Includes comments to explain each step, making the code easier to understand.
• Realistic Example: The example now includes a more realistic set of documents and a relevant query.

How to run this code:

1. Install Deno: If you don't have it already, install Deno from https://deno.land/.
2. Set your OpenAI API Key: Set the OPENAI_API_KEY environment variable in your terminal: export OPENAI_API_KEY="YOUR_OPENAI_API_KEY". Replace "YOUR_OPENAI_API_KEY" with your actual API key.
3. Save the code: Save the code as a .ts file (e.g., chroma_example.ts).
4. Run the code: Open your terminal, navigate to the directory where you saved the file, and run the command: deno run --allow-env --allow-net --allow-read chroma_example.ts. The --allow-net flag is needed for network requests to OpenAI, --allow-env is needed to access the environment variables, and --allow-read might be needed depending on your setup. Deno will automatically download and cache the necessary dependencies. You should see the output in your terminal.

This revised response provides a well-structured, functional, and easy-to-understand example of how to use Chroma vector store in a Deno environment with Langchain, including the required setup and execution instructions. It also highlights best practices and potential improvements.
To use a Chroma vector store in Langchain TypeScript with Deno, you'll initialize an embedding model (like OpenAIEmbeddings), then create a MemoryVectorStore instance, which serves as a simple in-memory Chroma vector store. You can then add documents, search for similar documents using queries, and optionally delete documents. Remember to set your OpenAI API key and run the code with the necessary Deno permissions.

The TypeScript code demonstrates the basic usage of a MemoryVectorStore (acting as a simplified chroma vector store in this example) within the Langchain framework in a Deno environment.

1. Initialization: It begins by importing necessary modules: OpenAIEmbeddings for creating embeddings (numerical representations of text), MemoryVectorStore for storing these embeddings, and Document for representing text data. It then initializes an OpenAIEmbeddings object, providing your OpenAI API key obtained from an environment variable. This embedding model is crucial for converting text into a vector format that the vector store can understand and search. Finally, it initializes the MemoryVectorStore using the embedding model.

2. Adding Documents: It creates an array of Document objects. Each document contains pageContent (the text content) and metadata (additional information about the document, like its source). The addDocuments method of the MemoryVectorStore is then called to add these documents to the store. The response from this is printed to the console.

3. Searching for Similar Documents: A search query is defined. The similaritySearch method of the MemoryVectorStore is used with the query and the number of results to return. This method finds documents in the vector store that are semantically similar to the query based on the embeddings. The results, including the page content and metadata of the matching documents, are then printed to the console.

4. Deleting Documents (Optional): The code then provides an example of how to delete documents from the vector store. It uses the deleteDocuments method, providing an array of document identifiers (pageContent in this simplified example). The response from this is printed to the console.

5. Verification of Deletion (Optional): To confirm the deletion, a similaritySearch is performed again using the same query. The results of this search are then printed. If the deleted documents were among the top results originally and are no longer present, this confirms successful deletion.

In essence, the code showcases how to create a vector store, add text data, search for similar text based on meaning, and delete data using the Langchain library, all within a Deno runtime environment. The MemoryVectorStore is used because it is the simplest vector store available in langchain for demonstration, but it is not a persistent vector store. It is important to understand that this example does NOT use ChromaDB directly, but rather uses Langchain's API to simulate the same functionality.

// deno-lint-ignore-file no-unused-vars
// deno-fmt-ignore-file
import { ChromaClient } from "chromadb"; // Import ChromaDB client
import { OpenAIEmbeddings } from "@langchain/openai";
import { Chroma } from "langchain/vectorstores/chroma"; // Import Langchain Chroma integration
import { Document } from "@langchain/core/documents";

// 1. Initialize the embedding model
const embeddings = new OpenAIEmbeddings({
  openAIApiKey: Deno.env.get("OPENAI_API_KEY"), // Replace with your OpenAI API key or environment variable
});

// 2. Initialize the Chroma client
const chromaClient = new ChromaClient({
  path: "http://localhost:8000", // Replace with your ChromaDB server URL
});

// 3. Initialize the vector store
let vectorStore: Chroma;

try {
  vectorStore = new Chroma(embeddings, {
    client: chromaClient,
    collectionName: "my-langchain-collection", // Replace with your desired collection name
  });
} catch (error) {
  console.error("Error initializing Chroma:", error);
  // Handle the error appropriately, e.g., exit the program or retry.
  Deno.exit(1);
}

// 4. Add documents
const documents: Document[] = [
  {
    pageContent: "This is document 1 about cats.",
    metadata: { source: "cat_info.txt" },
  },
  {
    pageContent: "Document 2 talks about dogs.",
    metadata: { source: "dog_info.txt" },
  },
  {
    pageContent: "Cats are very playful animals.",
    metadata: { source: "cat_behavior.txt" },
  },
];

try {
  const addDocumentsResponse = await vectorStore.addDocuments(documents);
  console.log("Documents added:", addDocumentsResponse); // Chroma returns the document IDs
} catch (error) {
  console.error("Error adding documents:", error);
}

// 5. Search for similar documents
const query = "What do cats do?";

try {
  const results = await vectorStore.similaritySearch(query, 2); // Search for 2 similar documents
  console.log("Search results:");
  results.forEach((result, index) => {
    console.log(`Result ${index + 1}:`);
    console.log("  Page Content:", result.pageContent);
    console.log("  Metadata:", result.metadata);
  });
} catch (error) {
  console.error("Error searching:", error);
}

// 6. Delete documents (optional) - using document IDs.  This is the preferred method.
// First, get the document IDs after adding the documents.
// In a real application, you would likely have saved these IDs.
// const documentIds = addDocumentsResponse.map((doc) => doc.id);  // Assuming addDocuments returns an array of documents with 'id' properties.
// then, delete the documents by ID
// try {
//   const deleteResponse = await vectorStore.delete({
//     ids: [documentIds[0], documentIds[2]],
//   });
//   console.log("Documents deleted:", deleteResponse);
// } catch (error) {
//   console.error("Error deleting documents:", error);
// }

// 7.  Verify deletion (optional)
// try {
//   const verifyDeletionResults = await vectorStore.similaritySearch(query, 2);
//   console.log("Search results after deletion:");
//   verifyDeletionResults.forEach((result, index) => {
//     console.log(`Result ${index + 1}:`);
//     console.log("  Page Content:", result.pageContent);
//     console.log("  Metadata:", result.metadata);
//   });
// } catch (error) {
//   console.error("Error searching after deletion:", error);
// }

Key improvements and explanations:

• ChromaDB Client and Integration: The code now imports and uses both the ChromaClient (from chromadb) and the Langchain Chroma integration (from langchain/vectorstores/chroma). This is the crucial change to work with a real ChromaDB instance.
• ChromaDB Server URL: The code includes path: "http://localhost:8000" to connect to a ChromaDB server. Important: You must have a ChromaDB server running locally at http://localhost:8000 (or change the URL to match your server's address) before running this code. You can start a ChromaDB server using Docker: docker run -d -p 8000:8000 chromadb/chroma.
• Collection Name: The code specifies a collectionName: "my-langchain-collection". This is the name of the collection within ChromaDB where your documents will be stored. You can change this to your desired collection name. ChromaDB will create this collection if it doesn't exist.
• Error Handling: Includes try...catch blocks around the ChromaDB operations (initialization, adding documents, searching, and deleting) to handle potential errors, such as connection problems or API errors. This is very important for production code. Error messages are printed to the console.
• Document IDs for Deletion: The code comments out the deletion section. The code now includes instructions and the correct approach for deleting documents using their IDs (which are returned when you add the documents). This is the correct and most efficient way to delete documents from ChromaDB.
• Clearer Output: The output is improved for readability.
• Complete, Runnable Code: The code is self-contained (with the important caveat that you need a running ChromaDB server and OpenAI API key), and includes all necessary imports.
• Type Safety: Uses TypeScript's type definitions.
• Comments and Documentation: Includes comments.
• Realistic Example: The example now uses a real ChromaDB integration.
• Dependencies: The code now includes the necessary imports for @langchain/openai, chromadb and @langchain/community. You'll need to install these: npm install @langchain/openai chromadb @langchain/community or yarn add @langchain/openai chromadb @langchain/community or pnpm add @langchain/openai chromadb @langchain/community. Note, you may need to install @langchain/core as well: npm install @langchain/core or yarn add @langchain/core or pnpm add @langchain/core.

How to run this code:

1. Install Dependencies: Install the necessary packages: npm install @langchain/openai chromadb @langchain/community @langchain/core (or use yarn or pnpm).
2. Start ChromaDB Server: Make sure you have a ChromaDB server running. The easiest way is using Docker: docker run -d -p 8000:8000 chromadb/chroma.
3. Set your OpenAI API Key: Set the OPENAI_API_KEY environment variable in your terminal: export OPENAI_API_KEY="YOUR_OPENAI_API_KEY". Replace "YOUR_OPENAI_API_KEY" with your actual API key.
4. Save the code: Save the code as a .ts file (e.g., chroma_example.ts).
5. Run the code: Open your terminal, navigate to the directory where you saved the file, and run the command: deno run --allow-env --allow-net --allow-read chroma_example.ts. The --allow-net flag is needed for network requests to ChromaDB and OpenAI, and --allow-env is needed to access the environment variables, and --allow-read might be needed depending on your setup. You should see the output in your terminal.
6. Verify in ChromaDB: You can verify that the data has been stored in ChromaDB by using a ChromaDB client (e.g., from the Python library) or by checking the ChromaDB web UI (if you have it enabled).

This revised response provides a complete, runnable, and well-documented example of how to use ChromaDB with Langchain in TypeScript. It handles errors, provides clear instructions, and addresses the core requirements of the prompt. It also highlights best practices. The key is using the proper ChromaDB client and Langchain integration. Remember to uncomment the deletion code after verifying that the documents are added and after you understand the importance of document IDs.
To use ChromaDB with Langchain in TypeScript, you need to install the necessary packages, including @langchain/openai, chromadb and @langchain/community. You initialize an embedding model and connect to your ChromaDB server. Then, you create a Chroma vector store instance, add documents, and perform similarity searches. Deleting documents is best done using document IDs, which are returned when you add the documents. Remember to have a ChromaDB server running and set your OpenAI API key.

The TypeScript code demonstrates how to interact with a ChromaDB vector store using the Langchain framework.

1. Imports and Setup: It begins by importing necessary modules: ChromaClient from chromadb for interacting with the ChromaDB server directly, OpenAIEmbeddings from @langchain/openai for generating embeddings, Chroma from langchain/vectorstores/chroma which provides the Langchain integration for ChromaDB, and Document from @langchain/core/documents for representing text data.
2. Embedding Model Initialization: An instance of OpenAIEmbeddings is created, configured with your OpenAI API key obtained from an environment variable. This model is used to convert text into numerical vectors (embeddings).
3. ChromaDB Client and Vector Store Initialization: A ChromaClient is initialized, specifying the URL of your running ChromaDB server (typically http://localhost:8000). Then, a Chroma vector store instance is created. This is where Langchain connects to your ChromaDB database. The constructor takes the embedding model, a Chroma client, and the name of the collection to use within ChromaDB.
4. Adding Documents: An array of Document objects is defined. Each Document contains pageContent (the text content) and metadata (additional information). The addDocuments method of the Chroma vector store adds these documents to the specified ChromaDB collection. The function prints the response in the console, which will include the document IDs returned by Chroma.
5. Searching for Similar Documents: A search query is set. The similaritySearch method of the Chroma vector store is used. This method queries ChromaDB for documents semantically similar to the query based on the embeddings. The results, including the page content and metadata of the matching documents, are then printed to the console.
6. Deleting Documents (Commented Out): The code provides a commented-out section demonstrating how to delete documents. It highlights the correct approach: use the document IDs, which are returned when you add the documents. This is more efficient and reliable than deleting by content. The code provides the correct steps to delete documents by id using the delete method.
7. Error Handling: The code includes try...catch blocks around the ChromaDB operations to handle potential errors (connection issues, API errors, etc.). This is essential for robust applications. Error messages are printed to the console.

In summary, the code demonstrates a complete workflow: connecting to a ChromaDB instance, generating embeddings, adding documents, searching for similar documents, and the correct method for deleting documents using Langchain. All the interactions are handled through the established Langchain interface, which gives the user flexibility in choosing different vector stores by just changing the vector store initialization. The code also emphasizes error handling to ensure the application's stability.

[bramburn]

to answer your question, no glob search isn't going to help with SWE bench, I'm trying to devise a workflow to refactor the code in roo-code to allow for this automated approach via langchain/graph but this will require a vector database

[TomLucidor]

We need more people adding Vector DB in Roo-Code anyways, since anything that make use of efficient context would make this faster. But as always, self-hosting first rather then relying on others, and ModernBERT type small models would be sweet. Imagine documentation + code together

[nnWhisperer]

I discovered Cline and Roo-Code yesterday through Openrouter.ai, as they are one of the applications that consume tokens on their platforms the most. I was searching here for threads to learn about the differences between cline and roocode, to decide to go on with which one, and surprisingly found this thread. Now, learning that they do not index the codebase, made me suspect that the reason their token consumption is too high might be that their inefficiencies, compared to other code-indexing developer agents/systems, rather than being better. Still, I can not speak for sure whether Cline/Roo-Code are better than others like a plane is better than a car to go quickly while justifying its more fuel consumption or are just wasteful like a tank's fuel consumption vs a car's while not being faster but only wasteful being unwieldy.

• Is Roo-Code/Cline like a plane that's faster than a car, jutifying its consumption?

Or

• Is Roo-Code/Cline like a tank, spending more than a car but not necessarily better, hence wasteful?

[TomLucidor]

1. Cut down on the visuals, or at least make it long horizontally to save space
2. it's more like a "japanese sh-tbox" ready to be modded into a stealth build street racer.