Retrieval Returns 0 Results - Need Help - RAG Logic and Vector Search Specialist
Budget: $30 – $250 USD
I need help from a developer who can help me solve a retrieval problem. I need someone who is familiar with Retrieval-Augmented Generation (RAG) logic, and vector search. Using Firebase studio, and Firestore database, I have developed a chatbot that uses a combination of Google's Natural Language Processing (NLP) and RAG. The chatbot software has already been developed, the front-end, where the chatbot resides, and the back-end admin area, where files are uploaded to the knowledge base and indexed. Every step of the RAG indexing has been verified.
File upload and processing:
1. File upload to Firebase storage (success).
2. Metadata uploaded to Firestore database (level, topic, _name_) (success)
3. Text extraction to Firestore database (success)
4. Text chunking to Firestore database (success)
5. Vector embeddings to Firestore database (success)
User queries from the chatbot:
6. Data retrieval from embeddings (fail)
1. The Core Problem:
The primary issue is a failure in the Retrieval-Augmented Generation (RAG) pipeline's core retrieval step. The vector search, which is implemented using Firestore's native vector search capabilities, is not returning relevant document chunks, even when user queries directly correspond to content within the indexed knowledge base. This results in the AI frequently responding that it cannot find information, defeating the purpose of the RAG system. The root cause is believed to be a semantic mismatch between the embeddings generated for user queries and the embeddings stored for the document chunks in Firestore.
2. Key Files & Logic Flow:
The problem spans the entire indexing and retrieval process. The relevant files are:
- src/ai/flows/index-document-flow.ts: This server-side flow handles the ingestion pipeline. It receives document text, splits it into chunks, generates embeddings for each chunk using Google's text-embedding-004 model, and writes the chunk, its embedding, and associated metadata (level, topic, sourceName, etc.) to the kb_chunks_v1 Firestore collection.
- src/ai/retrieval/vector-search.ts: This file contains the searchKnowledgeBase function, which is the heart of the retrieval logic. It takes a search query, generates a query embedding (also using text-embedding-004), and executes the findNearest query against the kb_chunks_v1 collection. It also contains the logic to filter and prioritize the results based on the level metadata (High, Medium, Low).
- src/ai/flows/generate-chat-response.ts: This is the orchestrator. It takes the end-user's message, uses an NLP prompt (queryRefinementPrompt) to distill it into a cleaner search query, calls searchKnowledgeBase to get context, and then passes that context to the final LLM to generate an answer.
3. Hypothesis for the Root Cause:
The culprit (where I need help to solve) is either a misalignment, syntac coding error, or a lack of standardization in the text pre-processing stage before the embedding generation.
4. The Task for the Freelancer:
The specific task is to advise and implement a standardized text pre-processing function and apply it consistently during both indexing and searching. Look over the code and correct/change and test to success.
This involves:
1. If needed, create a shared pre-processing function (e.g., in a new src/ai/retrieval/preprocessing.ts file) that handles, at a minimum, conversion to lowercase and normalization of whitespace.
2. Integrating this function into src/ai/flows/index-document-flow.ts so that each text chunk is pre-processed before its embedding is generated and stored.
3. Integrating the same shared function into src/ai/retrieval/vector-search.ts so that the incoming user query is pre-processed before its embedding is generated for the search.
4. Ensuring all calls to ai.embed across these files are syntactically correct and do not re-introduce the schema validation errors encountered previously.
Remember, everything is already built, everything up to the very end of the RAG pipeline works except for the retrieval.
File upload and processing:
1. File upload to Firebase storage (success).
2. Metadata uploaded to Firestore database (level, topic, _name_) (success)
3. Text extraction to Firestore database (success)
4. Text chunking to Firestore database (success)
5. Vector embeddings to Firestore database (success)
User queries from the chatbot:
6. Data retrieval from embeddings (fail)
1. The Core Problem:
The primary issue is a failure in the Retrieval-Augmented Generation (RAG) pipeline's core retrieval step. The vector search, which is implemented using Firestore's native vector search capabilities, is not returning relevant document chunks, even when user queries directly correspond to content within the indexed knowledge base. This results in the AI frequently responding that it cannot find information, defeating the purpose of the RAG system. The root cause is believed to be a semantic mismatch between the embeddings generated for user queries and the embeddings stored for the document chunks in Firestore.
2. Key Files & Logic Flow:
The problem spans the entire indexing and retrieval process. The relevant files are:
- src/ai/flows/index-document-flow.ts: This server-side flow handles the ingestion pipeline. It receives document text, splits it into chunks, generates embeddings for each chunk using Google's text-embedding-004 model, and writes the chunk, its embedding, and associated metadata (level, topic, sourceName, etc.) to the kb_chunks_v1 Firestore collection.
- src/ai/retrieval/vector-search.ts: This file contains the searchKnowledgeBase function, which is the heart of the retrieval logic. It takes a search query, generates a query embedding (also using text-embedding-004), and executes the findNearest query against the kb_chunks_v1 collection. It also contains the logic to filter and prioritize the results based on the level metadata (High, Medium, Low).
- src/ai/flows/generate-chat-response.ts: This is the orchestrator. It takes the end-user's message, uses an NLP prompt (queryRefinementPrompt) to distill it into a cleaner search query, calls searchKnowledgeBase to get context, and then passes that context to the final LLM to generate an answer.
3. Hypothesis for the Root Cause:
The culprit (where I need help to solve) is either a misalignment, syntac coding error, or a lack of standardization in the text pre-processing stage before the embedding generation.
4. The Task for the Freelancer:
The specific task is to advise and implement a standardized text pre-processing function and apply it consistently during both indexing and searching. Look over the code and correct/change and test to success.
This involves:
1. If needed, create a shared pre-processing function (e.g., in a new src/ai/retrieval/preprocessing.ts file) that handles, at a minimum, conversion to lowercase and normalization of whitespace.
2. Integrating this function into src/ai/flows/index-document-flow.ts so that each text chunk is pre-processed before its embedding is generated and stored.
3. Integrating the same shared function into src/ai/retrieval/vector-search.ts so that the incoming user query is pre-processed before its embedding is generated for the search.
4. Ensuring all calls to ai.embed across these files are syntactically correct and do not re-introduce the schema validation errors encountered previously.
Remember, everything is already built, everything up to the very end of the RAG pipeline works except for the retrieval.