#can you help me to improve my RAG

1 messages · Page 1 of 1 (latest)

reef umbra
#

i need help in RAG

whole heronBOT
#

Hi @reef umbra. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

#

Absolutely, I can help you improve your Retrieval-Augmented Generation (RAG) system! Here are some practical steps and best practices, based on OpenAI’s official resources and cookbooks:

1. Optimize Your Retrieval Pipeline

  • Chunking & Embedding: Break your documents into logical chunks (e.g., paragraphs) and embed each chunk using OpenAI’s embedding models. Store these embeddings in a vector database for efficient semantic search.
  • Semantic Search: When a user asks a question, embed the query and retrieve the most relevant chunks using semantic similarity, not just keyword matching. This ensures the model gets the most contextually relevant information to answer the question Retrieval Augmented Generation (RAG) and Semantic Search for GPTs.

2. Context Window Management

  • Be Mindful of Context Size: Don’t overload the model’s context window. Too much content can increase latency, cost, and risk the “lost in the middle” problem, where important information is overlooked. Balance the amount of retrieved context for accuracy and efficiency How to parse PDF docs for RAG.

3. Prompt Engineering & Few-Shot Learning

  • Prompt Engineering: Experiment with different prompt formats and instructions to guide the model’s behavior.
  • Few-Shot Examples: If the model’s output isn’t as expected, provide a few examples of the desired input/output pairs in your prompt. This can help the model generalize better, especially for edge cases Optimizing LLM Accuracy.

4. Fine-Tuning (If Needed)

#
  • If prompt engineering and few-shot examples aren’t enough, consider fine-tuning the model with more examples. This is especially useful for achieving specific output formats or handling complex tasks Fine-Tuned QA with Qdrant.

5. Evaluation & Safety

  • Systematic Evaluation: Use frameworks like RAGAS to evaluate both the retrieval and generation steps. Assess faithfulness, answer relevancy, context precision, and recall.
  • Safety Checks: Always double-check outputs for accuracy and reliability, especially in critical applications How to parse PDF docs for RAG.

6. Advanced: Multimodal RAG

  • If your documents contain images, tables, or graphics, consider using vision models (like GPT-4o Vision) to extract and interpret visual content, not just text. This can significantly improve answer quality for visually rich documents Optimizing RAG using GPT-4o Vision Modality.

If you share more about your current RAG setup or specific challenges, I can provide more targeted advice!

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron can you please check this code for RAG and analysis it help me to improve

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

I'm happy to help analyze and improve your RAG code! However, I don't see any code attached or included in your message. Please provide the code you'd like me to review. Once you share it, I’ll analyze it using the best practices and optimization strategies from the OpenAI knowledge sources, and suggest concrete improvements.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron import os
import json
import glob
import zipfile
import re
from typing import TypedDict, List, Dict, Optional
from IPython.display import display, Markdown

LangChain imports

from langchain.document_loaders import TextLoader, PyPDFLoader, UnstructuredPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
from langchain.schema import Document
from langchain.retrievers import EnsembleRetriever, ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CrossEncoderReranker
from langchain_community.cross_encoders import HuggingFaceCrossEncoder
from langchain.retrievers.multi_query import MultiQueryRetriever

LangGraph imports

from langgraph.graph import StateGraph, START, END
from google.generativeai.types import HarmCategory, HarmBlockThreshold

NLP imports

import spacy
from spacy import displacy

Initialize spaCy for NLP

nlp = spacy.load("en_core_web_sm")

Define state for the agentic RAG

class AgenticRAGState(TypedDict):
question: str
context: str
answer: str
retrieved_docs: List[Document]
sources: List[Dict]
processing_notes: List[str]
intent: str
tool_choice: str
feedback: str
fail_count: int
query_analysis: Dict
expanded_queries: List[str]
conversation_history: List[Dict] # New: Store conversation history

==================== CONVERSATION MEMORY FUNCTIONS ====================

class ConversationMemory:
def init(self, max_history=10):
self.history = []
self.max_history = max_history

please check this

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Thank you for sharing the initial part of your RAG pipeline code. Here’s an analysis and suggestions for improvement, strictly based on the OpenAI knowledge sources you provided:

Analysis

Strengths

  • Document Loading & Chunking: You are using LangChain’s document loaders and a recursive character text splitter, which aligns with best practices for chunking documents into manageable pieces for embedding and retrieval. Chunking is essential for reducing cost, latency, and improving retrieval accuracy How to parse PDF docs for RAG.
  • Vector Store: You’re using Chroma as a vector store, which is a common and effective choice for semantic search in RAG pipelines.
  • Retrievers & Re-ranking: The use of ensemble retrievers, contextual compression, and cross-encoder rerankers is a strong approach for improving retrieval quality and reducing irrelevant context, which helps minimize hallucinations Optimizing LLM Accuracy.
  • Conversation Memory: Implementing conversation history is important for multi-turn interactions, as conversation context contributes to the overall context window How to parse PDF docs for RAG.

Suggestions for Improvement

#

1. Chunking Strategy

  • Chunk Size & Overlap: Make sure your chunk size and overlap are tuned for your use case. Too large chunks can overload the context window, while too small may lose coherence. Consider sentence-aware chunking if your documents are complex Model Selection Guide.
  • Metadata: When chunking, add metadata (e.g., source, section headers) to each chunk to improve retrieval and citation How to parse PDF docs for RAG.

2. Embedding and Retrieval

  • Embed Metadata: Besides text, consider embedding relevant metadata (like titles, tags, or keywords) to improve retrieval precision How to parse PDF docs for RAG.
  • Re-ranking: You are already using cross-encoder rerankers, which is excellent. You can further experiment with rule-based re-ranking using metadata (e.g., recency, document type) How to parse PDF docs for RAG.

3. Context Window Management

  • Limit Retrieved Chunks: Be careful not to exceed the model’s context window. Too much context can lead to the “lost in the middle” problem, where important information is ignored How to parse PDF docs for RAG.
#

4. Prompt Engineering

  • System & User Prompts: Use clear system and user prompts to guide the model. If you want specific answer formats or citations, include those instructions in your prompt Optimizing LLM Accuracy.

5. Evaluation & Safety

  • Automated Evaluation: Use frameworks like RAGAS to evaluate faithfulness, answer relevancy, context precision, and recall How to parse PDF docs for RAG.
  • Guardrails: Implement guardrails to ensure outputs are safe and reliable, especially if your application is user-facing How to parse PDF docs for RAG.

6. Fine-Tuning (Optional)

  • If prompt engineering and retrieval optimization are not enough, consider fine-tuning the model with examples from your domain Fine-Tuned QA with Qdrant.

Summary Table of Key RAG Steps

#
Step Best Practice
Chunking Tune size/overlap, add metadata
Embedding Embed both text and metadata
Retrieval Use semantic search, re-ranking, and limit context window size
Prompting Engineer prompts for clarity and format
Evaluation Use RAGAS or similar frameworks
Safety Add guardrails and double-check outputs

If you share more of your code (especially the retrieval and answer generation logic), I can provide even more targeted feedback!

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron # Initialize conversation memory
conversation_memory = ConversationMemory()

==================== QUERY REWRITING FUNCTIONS ====================

def rewrite_query_with_context(question, conversation_history):
"""Rewrite query to include context from conversation history"""
if not conversation_history:
return question

history_text = conversation_memory.get_formatted_history(3)

rewrite_prompt = """
Based on the following conversation history, rewrite the current question to be more clear and contextual.
If the question is a follow-up, make sure it includes necessary context from previous exchanges.

{history}

Current question: {question}

Rewritten contextual question:
"""

try:
    response = llm.invoke(rewrite_prompt.format(history=history_text, question=question))
    return response.content.strip()
except Exception as e:
    print(f"Query rewriting failed: {e}")
    return question

def is_follow_up_question(question, conversation_history):
"""Determine if the question is a follow-up to previous conversation"""
if not conversation_history:
return False

# Check for follow-up indicators
follow_up_indicators = [
    "about that", "regarding that", "as you mentioned", "you said",
    "following up", "earlier you", "previously", "based on that"
]

question_lower = question.lower()
return any(indicator in question_lower for indicator in follow_up_indicators)   please check this
whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Your code for query rewriting and follow-up detection in a RAG pipeline is well-aligned with best practices described in OpenAI’s documentation. Here’s an analysis and suggestions for improvement, strictly based on the provided knowledge sources:

Analysis

Query Rewriting with Context

  • Purpose: Your function rewrites user questions to include relevant context from conversation history, which is important for multi-turn interactions and helps the model generate more accurate responses.
  • Approach: This matches OpenAI’s recommended approach for prompt rewriting, especially in scenarios where the model (or API) does not automatically clarify or expand user queries. The documentation suggests using a lightweight model to rewrite or clarify prompts before passing them to the main model, which is exactly what your function does Clarifying Questions in ChatGPT vs. the Deep Research API.

Follow-up Question Detection

  • Purpose: Detecting follow-up questions allows you to decide when to include conversation history for context, which is a good practice for conversational RAG systems.
  • Approach: Using keyword indicators is a simple and effective heuristic, though not foolproof.

Suggestions for Improvement

#

1. Query Rewriting Prompt

  • Explicitness: The prompt you use for rewriting is clear and instructs the model to include necessary context. This is in line with OpenAI’s guidance to maximize specificity and detail in rewritten prompts Clarifying Questions in ChatGPT vs. the Deep Research API.
  • Error Handling: You already have a try/except block, which is good for robustness.

2. Conversation History Formatting

  • History Length: You use the last 3 turns, which is reasonable. Be mindful of the context window size to avoid overloading the model How to parse PDF docs for RAG.

3. Follow-up Detection

  • Heuristics: While keyword-based detection is a good start, consider using semantic similarity or a lightweight classifier for more robust detection if you find the heuristic approach insufficient. However, the current method is acceptable for many use cases.

4. General Best Practices

  • Evaluation: Regularly evaluate if the rewritten queries actually improve retrieval and answer quality. This is recommended in the best practices for RAG optimization How to parse PDF docs for RAG.
  • Prompt Engineering: Continue to iterate on your rewrite prompt as you observe real user interactions, as prompt engineering is a key lever for improving RAG performance Optimizing LLM Accuracy.

Summary Table

#
Component Best Practice Reference
Query Rewriting Use a prompt to clarify and expand user queries with context [Deep Research API]
Conversation History Limit history to avoid context window overflow [Parse PDF docs for RAG]
Follow-up Detection Use heuristics or classifiers as needed
Evaluation & Iteration Regularly test and refine prompts and logic

In summary:
Your approach is well-aligned with OpenAI’s recommendations for conversational RAG systems. Continue to monitor and iterate based on real-world usage, and consider more advanced follow-up detection if needed. If you have specific issues or want to optimize further, please provide more details!

If you need more targeted advice, let me know which part of the pipeline you want to focus on next.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron can you arrange it and porper and make it more powerful here

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Certainly! Here’s a revised, well-structured, and more robust version of your query rewriting and follow-up detection functions, strictly following OpenAI’s best practices for RAG systems as described in the knowledge sources:

# ==================== CONVERSATION MEMORY FUNCTIONS ====================

class ConversationMemory:
    def __init__(self, max_history=10):
        self.history = []
        self.max_history = max_history

    def add(self, user_message, assistant_message):
        self.history.append({"user": user_message, "assistant": assistant_message})
        if len(self.history) > self.max_history:
            self.history = self.history[-self.max_history:]

    def get_formatted_history(self, n=3):
        # Returns the last n exchanges as a formatted string
        selected = self.history[-n:]
        formatted = ""
        for turn in selected:
            formatted += f"User: {turn['user']}\nAssistant: {turn['assistant']}\n"
        return formatted.strip()

# ==================== QUERY REWRITING FUNCTIONS ====================

def rewrite_query_with_context(question, conversation_memory, llm, n_history=3):
    """
    Rewrites the user's question to include necessary context from recent conversation history.
    Uses an LLM to generate a more self-contained query, as recommended for RAG and deep research APIs.
    """
    history_text = conversation_memory.get_formatted_history(n_history)
    if not history_text:
        return question
#

rewrite_prompt = (
"Given the following conversation history, rewrite the current user question so it is fully self-contained. "
"If the question is a follow-up, ensure it includes all necessary context from previous exchanges.\n\n"
f"Conversation history:\n{history_text}\n\n"
f"Current question: {question}\n\n"
"Rewritten contextual question:"
)

try:
    response = llm.invoke(rewrite_prompt)
    return response.content.strip()
except Exception as e:
    print(f"Query rewriting failed: {e}")
    return question

def is_follow_up_question(question, conversation_memory):
"""
Determines if the current question is a follow-up, based on conversation history and common follow-up indicators.
"""
if not conversation_memory.history:
return False

follow_up_indicators = [
    "about that", "regarding that", "as you mentioned", "you said",
    "following up", "earlier you", "previously", "based on that"
]
question_lower = question.lower()
return any(indicator in question_lower for indicator in follow_up_indicators)

Example usage:

conversation_memory = ConversationMemory()

rewritten = rewrite_query_with_context(user_question, conversation_memory, llm)


---

### **Key Improvements & Rationale**
#
  • History Formatting: The get_formatted_history method ensures conversation history is presented in a clear, structured way, which helps the LLM understand context—this is recommended for prompt rewriting in RAG and deep research scenarios Clarifying Questions in ChatGPT vs. the Deep Research API.
  • Prompt Clarity: The rewrite prompt is explicit about making the question self-contained, following OpenAI’s guidance to maximize specificity and detail in rewritten prompts.
  • Robustness: Error handling ensures the system falls back gracefully if the LLM call fails.
  • Follow-up Detection: The function checks for follow-up indicators in the question, as a lightweight heuristic, which is a practical approach for conversational RAG systems.

If you want to further enhance follow-up detection, consider using semantic similarity or a lightweight classifier, but the current approach is effective for many use cases.

Let me know if you need further customization or want to focus on another part of your RAG pipeline!

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron can you gave me full code for RAG can you make it more powerful

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

I'm sorry, but the knowledge sources do not contain a single, complete, production-ready RAG (Retrieval-Augmented Generation) codebase that can simply be copied and used as a "full code for RAG." However, I can summarize the key components and best practices for building a powerful RAG pipeline, and provide code snippets and structure based strictly on the official OpenAI cookbooks and documentation.

Key Steps for a Powerful RAG Pipeline

  1. Document Loading & Chunking

    • Load documents (PDFs, text, etc.)
    • Chunk them into manageable pieces, adding metadata as needed

  2. Embedding

    • Use OpenAI embedding models to convert chunks into vectors

  3. Vector Store

    • Store embeddings in a vector database (e.g., Chroma, Pinecone, Elasticsearch)

  4. Retrieval

    • At query time, embed the user question and retrieve the most relevant chunks

  5. Re-ranking (Optional)

    • Use cross-encoder or rule-based re-ranking to improve retrieval quality

  6. Prompt Construction

    • Construct a prompt for the LLM that includes the user question and retrieved context

  7. Answer Generation

    • Use an OpenAI model (e.g., GPT-4o) to generate an answer using the prompt

  8. Evaluation & Guardrails

    • Evaluate outputs for faithfulness and relevancy, and implement safety checks

Example: RAG Pipeline Structure (Based on OpenAI Cookbook)

Below is a high-level structure with code snippets, as found in the OpenAI Cookbook and documentation. You will need to adapt this to your specific stack and requirements.

# 1. Load and chunk documents
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
#

loader = PyPDFLoader("your_file.pdf")
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)

2. Embed chunks

from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY")
embeddings = [client.embeddings.create(input=chunk.page_content, model="text-embedding-3-small").data[0].embedding for chunk in chunks]

3. Store in vector database (example: Chroma)

from langchain.vectorstores import Chroma
vectorstore = Chroma.from_documents(chunks, embedding_function=...)

4. At query time: embed user question and retrieve

query = "What is the return policy?"
query_embedding = client.embeddings.create(input=query, model="text-embedding-3-small").data[0].embedding
retrieved_docs = vectorstore.similarity_search_by_vector(query_embedding, k=3)

5. (Optional) Re-rank retrieved_docs here

6. Construct prompt

context = "\n\n".join([doc.page_content for doc in retrieved_docs])
prompt = f"Answer the following question using the provided context.\n\nContext:\n{context}\n\nQuestion: {query}\nAnswer:"

7. Generate answer

response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "system", "content": prompt}]
)
print(response.choices[0].message.content)


---

### **Best Practices and Optimizations**
#
  • Chunking: Tune chunk size and overlap for your use case. Add metadata (e.g., source, page number) to each chunk for better retrieval and citation.
  • Re-ranking: Use cross-encoder or rule-based re-ranking to improve the relevance of retrieved chunks.
  • Prompt Engineering: Be explicit in your prompt about the answer format and citation requirements.
  • Evaluation: Use frameworks like RAGAS to evaluate faithfulness, answer relevancy, context precision, and recall.
  • Guardrails: Implement safety checks to ensure outputs are reliable and safe.

Note:
For a full, production-ready RAG system, you will need to integrate these components, handle edge cases, and possibly add features like conversation memory, query rewriting, and advanced evaluation. The OpenAI Cookbook provides detailed examples for each step, but does not provide a single, monolithic "full code" for RAG. You can find more details and code samples in the OpenAI Cookbook RAG example.

If you have a specific part of the pipeline you want to focus on, or need help integrating these steps, please let me know!

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Sorry @reef umbra, I seem to be having a bit of trouble. Don't worry though, I've pinged my support and they will try to fix me up! :technologist:

Perhaps try again in 15mins?

reef umbra
#

@whole heron when i run the same cell with same question its gave different answer due to this code can you check this # ==================== QUERY ANALYSIS FUNCTIONS ====================

def analyze_query(query):
"""Advanced query analysis using spaCy NLP"""
doc = nlp(query)

# Extract entities
entities = [(ent.text, ent.label_) for ent in doc.ents]

# Extract key components
verbs = [token.lemma_ for token in doc if token.pos_ == "VERB"]
nouns = [token.lemma_ for token in doc if token.pos_ == "NOUN"]
adjectives = [token.lemma_ for token in doc if token.pos_ == "ADJ"]

# Determine query type
query_type = "informational"
if any(word in query.lower() for word in ["how", "install", "configure", "setup", "troubleshoot"]):
    query_type = "procedural"
elif any(word in query.lower() for word in ["compare", "vs", "versus", "difference", "similar"]):
    query_type = "comparative"
elif any(word in query.lower() for word in ["why", "reason", "cause", "because"]):
    query_type = "causal"

# Determine complexity
complexity = "simple"
if len(doc) > 8 or len(entities) > 2:
    complexity = "complex"

return {
    "entities": entities,
    "verbs": verbs,
    "nouns": nouns,
    "adjectives": adjectives,
    "query_type": query_type,
    "complexity": complexity,
    "word_count": len(doc)
}
whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

The code you provided for analyze_query uses spaCy to extract entities, verbs, nouns, adjectives, and to classify the query type and complexity. However, this function itself does not introduce randomness—it is deterministic, meaning that given the same input, it will always return the same output.

If you are seeing different answers when running the same cell with the same question, the cause is not this analyze_query function. Instead, the variability is likely coming from the language model (LLM) you are using to generate answers. By default, OpenAI models (and most LLMs) are non-deterministic unless you explicitly control for randomness.

To get consistent outputs from OpenAI models, you should:

  • Set the temperature parameter to 0 (for deterministic output).
  • Set the seed parameter (if supported by the model and API version you are using).
  • Ensure all other parameters and the prompt are exactly the same for each run.

This is confirmed in OpenAI’s documentation:

#

If your temperature is set above 0, the model will generate outputs with some randomness, so seeing different completions is expected. When you want consistent, repeatable results, use temperature = 0.
...
To receive mostly deterministic outputs across API calls:

Summary:
Your analyze_query function is not the source of answer variability. To make your RAG system return the same answer for the same question, control the randomness in your LLM calls by setting temperature=0 and, if possible, a fixed seed parameter.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron my llm looks like this llm = ChatGoogleGenerativeAI(
model="gemini-2.0-flash-lite",
temperature=0.1, # Lower temperature for factual accuracy
top_p=0.8, # Focused sampling
api_key=GOOGLE_API_KEY
)

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Based on your code, you are using ChatGoogleGenerativeAI with the Gemini model, not an OpenAI model. The knowledge sources provided do not contain information about Gemini, Google Generative AI, or the ChatGoogleGenerativeAI class. They only cover OpenAI models and APIs.

However, I can confirm from the knowledge sources that for OpenAI models, output variability is controlled by parameters like temperature, top_p, and (for supported models) seed. Setting temperature=0 and a fixed seed is recommended for reproducible outputs with OpenAI models How to make your completions outputs consistent with the new seed parameter, Why am I getting different completions on Playground vs. the API?.

Since you are using a non-OpenAI model, I’m sorry, but there’s no information in the knowledge sources about how to achieve deterministic outputs or control randomness with Gemini or ChatGoogleGenerativeAI. For OpenAI models, you would use the seed parameter and set temperature=0 for maximum determinism, but I cannot confirm if or how this applies to your current setup.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron can you provide me complete code for llm to control

whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

I'm sorry, but the knowledge sources do not contain information or code for controlling randomness or determinism for non-OpenAI LLMs such as Gemini or ChatGoogleGenerativeAI. The sources only provide guidance and code for controlling outputs with OpenAI models (e.g., GPT-3.5, GPT-4) using parameters like temperature and seed.

For OpenAI models, you can control output determinism as follows:

import openai

client = openai.OpenAI(api_key="YOUR_API_KEY")
response = client.chat.completions.create(
    model="gpt-3.5-turbo-1106",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Your question here."}
    ],
    temperature=0,  # For deterministic output
    seed=123,       # For reproducibility (supported on some models)
)
print(response.choices[0].message.content)

This approach is described in the official OpenAI documentation and cookbooks How to make your completions outputs consistent with the new seed parameter, Why am I getting different completions on Playground vs. the API?.

If you are using a non-OpenAI LLM (like Gemini), the knowledge sources do not provide code or guidance for controlling output determinism.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

#

@reef umbra Kapa.ai is still learning and improving, please let me know how I did by reacting below.

reef umbra
#

@whole heron can you pleases check this intial query so when user asked this should be able to answer properly

NLP imports

import spacy
from spacy import displacy

Initialize spaCy for NLP

nlp = spacy.load("en_core_web_sm")

Define state for the agentic RAG

class AgenticRAGState(TypedDict):
question: str
context: str
answer: str
retrieved_docs: List[Document]
sources: List[Dict]
processing_notes: List[str]
intent: str
tool_choice: str
feedback: str
fail_count: int
query_analysis: Dict
expanded_queries: List[str]

==================== QUERY ANALYSIS FUNCTIONS ====================

def analyze_query(query):
"""Advanced query analysis using spaCy NLP"""
doc = nlp(query)

# Extract entities
entities = [(ent.text, ent.label_) for ent in doc.ents]

# Extract key components
verbs = [token.lemma_ for token in doc if token.pos_ == "VERB"]
nouns = [token.lemma_ for token in doc if token.pos_ == "NOUN"]
adjectives = [token.lemma_ for token in doc if token.pos_ == "ADJ"]

# Determine query type
query_type = "informational"
if any(word in query.lower() for word in ["how", "install", "configure", "setup", "troubleshoot"]):
    query_type = "procedural"
elif any(word in query.lower() for word in ["compare", "vs", "versus", "difference", "similar"]):
    query_type = "comparative"
elif any(word in query.lower() for word in ["why", "reason", "cause", "because"]):
    query_type = "causal"

# Determine complexity
complexity = "simple"
if len(doc) > 8 or len(entities) > 2:
    complexity = "complex"

return {
    "entities": entities,
    "verbs": verbs,
    "nouns": nouns,
    "adjectives": adjectives,
    "query_type": query_type,
    "complexity": complexity,
    "word_count": len(doc)
}
whole heronBOT
#

Let me check the knowledge base for you again. One moment! :books:

#

Your initial query analysis function using spaCy is a solid approach for extracting entities, verbs, nouns, adjectives, and for classifying the query type and complexity. However, based on the OpenAI knowledge sources, this kind of NLP-based query analysis is not a standard or required step for effective RAG pipelines. The OpenAI-recommended RAG workflows focus on:

  • Chunking and embedding documents for semantic search,
  • Retrieving relevant chunks using vector similarity,
  • Prompt engineering to ensure the LLM has the right context,
  • (Optionally) Rewriting user queries to include necessary context in multi-turn conversations,
  • Evaluating and re-ranking retrieved results for answer quality.

There is no mention in the knowledge sources of using spaCy or similar NLP libraries for query analysis as a core part of RAG. Instead, OpenAI recommends using LLMs themselves for tasks like entity extraction, query rewriting, and intent detection, as these models are generally more robust and context-aware for such tasks in the RAG context. For example, you can prompt the LLM to extract entities or rewrite queries directly, as shown in the RAG with a Graph Database example.

If your goal is to ensure the system answers user questions properly, focus on:

  • Ensuring your retrieval step is robust and returns the most relevant context for the LLM to answer the question.
  • Using prompt engineering to clarify the user’s intent and provide the LLM with all necessary information.
  • (If needed) Using the LLM itself to extract entities or rewrite queries, rather than relying on external NLP tools.
#

Summary:
Your spaCy-based query analysis is not harmful, but according to OpenAI’s RAG best practices, it is not necessary and may not improve answer quality. Instead, rely on LLM-based entity extraction and query rewriting, and focus on retrieval and prompt construction for best results in a RAG pipeline. If you want to extract entities for structured queries (e.g., for a graph database), use the LLM as shown in the official examples, not spaCy.

If you need a code example for LLM-based entity extraction or query rewriting, let me know!

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon: