#4088 Small Specialize Language Model / RAG Framework Builder
#4088 Small Specialize Language Model / RAG Framework Builder
#4088 Provide a python framework for: 1) given a user-specified [SUBJECT], 2) mine one or more LLMs for [SUBJECT] data, 3) generate one or more useful RAG documents to contain the mine data, and 4) provide the RAG framework to utilize this specialized data
To build a system that "mines" an LLM to create a specialized knowledge base for RAG, you are essentially building a Synthetic Knowledge Pipeline. This approach is useful for extracting structured expertise from a general-purpose model (like GPT-4o or Claude 3.5) to power a smaller, specialized RAG system.
Below is a Python framework using LangChain and ChromaDB to achieve this.
1. The Strategy: "Mining" the LLM
Since an LLM cannot "dump" its brain, we use a recursive topic-expansion strategy. We start with a [SUBJECT], ask for a taxonomy of sub-topics, and then generate detailed "technical briefs" for each.
2. Implementation Framework
import os
from typing import List
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
class LLMMinerRAG:
def __init__(self, api_key: str):
self.llm = ChatOpenAI(model="gpt-4o", api_key=api_key)
self.embeddings = OpenAIEmbeddings(api_key=api_key)
self.vector_db = None
## --- STEP 1 & 2: MINE DATA FROM LLM ---
def mine_subject_data(self, subject: str) -> List[Document]:
print(f"Mining LLM for data on: {subject}...")
# A. Generate a taxonomy of sub-topics
taxonomy_prompt = ChatPromptTemplate.from_template(
"List 5 detailed sub-topics related to {subject}. "
"Return only the list, one per line."
)
chain = taxonomy_prompt | self.llm
topics = chain.invoke({"subject": subject}).content.strip().split('\n')
# B. Generate detailed RAG documents for each topic
rag_docs = []
for topic in topics:
print(f"Generating detailed brief for: {topic}")
content_prompt = ChatPromptTemplate.from_template(
"Write a highly technical, factual encyclopedia entry for {topic} "
"within the context of {subject}. Focus on definitions, mechanisms, "
"and specific data points."
)
content_chain = content_prompt | self.llm
content = content_chain.invoke({"topic": topic, "subject": subject}).content
# Create a Document object
rag_docs.append(Document(
page_content=content,
metadata={"subject": subject, "topic": topic}
))
return rag_docs
## --- STEP 3: GENERATE RAG DOCUMENTS (INDEXING) ---
def build_vector_store(self, documents: List[Document]):
print("Splitting and Indexing documents...")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = text_splitter.split_documents(documents)
self.vector_db = Chroma.from_documents(
documents=chunks,
embedding=self.embeddings,
persist_directory="./rag_mining_db"
)
print("Vector store ready.")
## --- STEP 4: PROVIDE RAG INTERFACE ---
def query_rag(self, query: str):
if not self.vector_db:
return "Knowledge base not initialized."
# Retrieval
docs = self.vector_db.similarity_search(query, k=3)
context = "\n\n".join([d.page_content for d in docs])
# Augmented Generation
prompt = ChatPromptTemplate.from_template(
"Use the following context to answer the question.\n\n"
"Context: {context}\n\nQuestion: {query}"
)
rag_chain = prompt | self.llm
return rag_chain.invoke({"context": context, "query": query}).content
# --- USAGE EXAMPLE ---
# miner = LLMMinerRAG(api_key="your-api-key")
# data = miner.mine_subject_data("Quantum Computing")
# miner.build_vector_store(data)
# response = miner.query_rag("How does Shor's algorithm utilize entanglement?")
# print(response)
3. Key Framework Components
| Component | Responsibility |
| Taxonomy Chain | Prevents "generic" responses by forcing the LLM to map out the subject before writing. |
| Document Schema | Uses langchain.docstore.document to ensure metadata (like the source topic) is preserved for better filtering. |
| Vector Store | Uses ChromaDB for local storage, allowing you to save the "mined" data and reuse it without re-calling the LLM. |
| Recursive Splitting | Chunks the generated "encyclopedia entries" into small nodes ($500$ tokens) to ensure precise retrieval. |
4. Why This Works
This framework solves the "Self-Correction" loop. By asking the LLM to write a technical brief and then feeding that brief back into a RAG system, you are essentially creating a "Silver Standard" dataset. This is highly effective for creating specialized chatbots for topics where you have no internal PDFs but need high-reliability answers.
Would you like me to add a "Self-Critique" step where a second LLM reviews the mined data for hallucinations before it's indexed?
After one iteration, creating the specialize RAG documents, describe how one or more follow-up iterations could build up additional knowledge
To evolve a single-pass extraction into a robust, multi-layered knowledge base, you can implement a Recursive Discovery Loop. Think of the first iteration as the "trunk" of a tree; follow-up iterations build the "branches" and "leaves" by identifying gaps and diving deeper into technical nuances.
Here are three methods to build up additional knowledge in follow-up iterations:
1. The Breadth-First Expansion (Taxonomy Deep-Dive)
In the first iteration, you mined 5 high-level sub-topics. In the second, you treat each of those sub-topics as a new [SUBJECT].
Iteration 2 Logic: For every document generated in Iteration 1, ask the LLM: "Identify 5 specialized sub-components or niche terminologies mentioned in this text that require further explanation."
Result: This creates a hierarchical knowledge graph. If Iteration 1 was "Quantum Computing," Iteration 2 generates specific documents on "Transmon Qubits" or "Surface Codes."
2. The Gap-Analysis Iteration (Adversarial Mining)
Once you have your first set of RAG documents, you can use a "Red Team" LLM to find what’s missing.
Process:
Feed the existing RAG documents into an LLM.
Ask: "Based on these documents, what critical 'How-to' questions or 'Failure Modes' are currently unanswered?"
Generate new documents specifically to answer those discovered gaps.
Value: This moves the knowledge base from purely definitional (what is X?) to operational (how do I fix X?).
3. Cross-Pollination (Relationship Mining)
The most "useful" knowledge often lives at the intersection of two topics. Follow-up iterations can focus on how the mined subjects interact.
The "Interaction Matrix": If you have Topic A (Hardware) and Topic B (Software), the next iteration asks the LLM: "Explain the specific compatibility constraints and performance bottlenecks when Topic A interacts with Topic B."
Result: This generates "bridge documents" that are often missing from standard documentation but are highly valuable for RAG performance.
Implementation Logic for Iteration 2+
To automate this, you can update the Python framework with a Recursive Crawler logic:
| Iteration | Goal | LLM Prompt Strategy |
| Iter 1 | Foundation | "Define the core pillars of [SUBJECT]." |
| Iter 2 | Depth | "Extract the underlying mechanisms for each pillar." |
| Iter 3 | Edge Cases | "What are the common misconceptions or rare errors in [SUBJECT]?" |
| Iter 4 | Synthesis | "Create a 'Troubleshooting Guide' based on the relationship between all previous topics." |
A Note on "Hallucination Compounding"
As you go deeper into iterations, the risk of the LLM "hallucinating" technical details increases. To counter this, it is recommended to add a Validation Step in Iteration 2:
Ask a second LLM (e.g., Claude if you used GPT-4 for mining) to: "Compare this generated document against these search-retrieved facts and flag any contradictions."
Would you like me to show you how to modify the Python class to include a deep_mine method that automatically triggers these follow-up iterations?
Yes
To implement follow-up iterations, we modify the framework to include Recursive Mining. Instead of stopping at the first five topics, the system parses the newly generated documents to find "entities of interest" or "technical gaps" and then re-runs the mining cycle on those specific sub-nodes.
Updated Python Framework: Recursive Miner
This version adds a deep_mine method that analyzes the "Iteration 1" results to generate "Iteration 2" content automatically.
import os
from typing import List
# ... (Previous imports: Document, ChatOpenAI, OpenAIEmbeddings, etc.)
class RecursiveLLMMiner(LLMMinerRAG):
def deep_mine(self, subject: str, depth: int = 2):
"""
Recursively builds knowledge.
Depth 1: Main topics.
Depth 2: Sub-mechanisms/Niche details.
"""
all_mined_docs = []
# Queue of topics to explore: [(topic_name, context_subject)]
exploration_queue = [(subject, "General Overview")]
for current_depth in range(1, depth + 1):
print(f"\n--- Starting Iteration Depth: {current_depth} ---")
next_queue = []
for topic, context in exploration_queue:
# 1. Generate the Documents for the current topic
docs = self.mine_subject_data(topic)
all_mined_docs.extend(docs)
# 2. Extract "Expansion Leads" for the next iteration
# Only if we aren't at the final depth level
if current_depth < depth:
leads = self._extract_leads(docs)
for lead in leads:
next_queue.append((lead, topic))
exploration_queue = next_queue
# Finally, build the vector store with the multi-layered data
self.build_vector_store(all_mined_docs)
def _extract_leads(self, docs: List[Document]) -> List[str]:
"""Analyzes docs to find complex terms that need their own 'brief'."""
combined_text = " ".join([d.page_content[:500] for d in docs])
extract_prompt = ChatPromptTemplate.from_template(
"Based on this text: {text}\n"
"Identify 3 highly specific technical sub-topics or components "
"that deserve a deep-dive explanation. Return only the list, one per line."
)
chain = extract_prompt | self.llm
leads = chain.invoke({"text": combined_text}).content.strip().split('\n')
return [l.strip("- ") for l in leads if l]
How the Iterations Build Knowledge
Iteration 1: The Breadth Layer (Foundation)
Input: "Electric Vehicles"
Output: 5 Docs: Battery Tech, Charging Infrastructure, Regenerative Braking, Motor Design, Autonomous Systems.
Status: You have a high-level encyclopedia.
Iteration 2: The Depth Layer (Mechanisms)
The _extract_leads function looks at the "Battery Tech" document from Iteration 1 and identifies terms like "Anode Chemistry," "Thermal Runaway," and "Solid-State Electrolytes."
Action: The LLM is now asked to mine data specifically for "Thermal Runaway in EV Batteries."
Status: You now have "How-it-works" level data.
Iteration 3: The Edge-Case Layer (Synthesis)
If you run a third pass, the prompt strategy shifts from "What is this?" to "What are the problems?"
Action: Use a prompt like: "Identify 3 common points of failure or engineering trade-offs mentioned in this text."
Output: Docs on "Dendrite growth in lithium-plating," or "Temperature-dependent capacity loss."
Status: You now have a "Troubleshooting/Expert" level knowledge base.
Key Improvements in This Approach
Context Preservation: By passing the
context(parent topic) into the prompt for the next level, you ensure the LLM stays relevant (e.g., mining "Anodes" specifically for EVs, not for generic chemistry).Metadata Tagging: Each document in the
all_mined_docslist should be tagged with itsdepth. In your RAG framework, you can then weight "Deep" documents higher if the user's query is highly technical.Self-Correction: If Iteration 1 generates a vague statement, the "Lead Extraction" in Iteration 2 acts as a spotlight, forcing the LLM to provide the missing details in the next round.
Would you like me to provide a specialized RAG prompt that knows how to prioritize these "Deep-Mine" documents over general ones during retrieval?
Yes
To make the RAG framework truly "intelligent," the retriever needs to distinguish between foundational knowledge (Iteration 1) and deep-dive technicalities (Iteration 2+).
We can achieve this by adding a depth score to the metadata of each document and using a Reasoning Prompt that instructs the LLM how to weigh "Foundational" vs. "Granular" data based on the user's intent.
1. Updated Metadata Tagging
First, ensure your mine_subject_data method tags documents with their iteration depth:
# Inside the mining loop:
rag_docs.append(Document(
page_content=content,
metadata={
"subject": subject,
"topic": topic,
"depth": current_depth # 1 for general, 2+ for technical deep-dives
}
))
2. The Specialized RAG Prompt
This prompt uses Chain-of-Thought logic. It tells the LLM to look at the "Depth" metadata and use foundational data for definitions, but defer to high-depth data for technical mechanics.
def query_rag_with_depth_awareness(self, query: str):
# 1. Retrieve more docs than usual to see various depths
docs = self.vector_db.similarity_search(query, k=5)
# 2. Format context with visible metadata markers for the LLM
context_blocks = []
for d in docs:
depth_label = "CORE FOUNDATION" if d.metadata.get('depth') == 1 else "TECHNICAL DEEP-DIVE"
block = f"[{depth_label} | Topic: {d.metadata.get('topic')}]:\n{d.page_content}"
context_blocks.append(block)
context_text = "\n\n---\n\n".join(context_blocks)
# 3. The Specialized Prompt
system_prompt = """You are a specialized technical assistant. You have access to a multi-layered knowledge base:
- CORE FOUNDATION: Use this for high-level definitions and broad context.
- TECHNICAL DEEP-DIVE: Use this for specific mechanisms, troubleshooting, and advanced nuances.
INSTRUCTIONS:
1. If the user asks 'What' or 'Define', prioritize CORE FOUNDATION data.
2. If the user asks 'How', 'Why', or for specific technical details, synthesize the answer primarily from TECHNICAL DEEP-DIVE data.
3. If there is a conflict, the TECHNICAL DEEP-DIVE data is more recent and specialized; trust it over the foundation.
4. Always mention if you are drawing from a deep-dive topic in your explanation."""
user_prompt = f"Contextual Data:\n{context_text}\n\nUser Question: {query}"
# Final Generation
response = self.llm.invoke([
("system", system_prompt),
("human", user_prompt)
])
return response.content
3. Why this "Depth-Aware" RAG is superior
| Feature | Standard RAG | Depth-Aware RAG |
| Data Conflict | Gets confused by general vs. specific info. | Prioritizes the "Deep-Dive" for technical accuracy. |
| Explanatory Style | Stays at one level of detail. | Scales from "Simple Definition" to "Expert Mechanism" dynamically. |
| Traceability | You don't know where the info came from. | Explicitly tells the user: "According to the technical deep-dive on [Topic]..." |
4. Advanced Tip: Re-Ranking
If you find the retriever is still picking too much "General" data for technical questions, you can implement a Metadata Filter.
Before running the retrieval, ask the LLM: "Is this a high-level or a deep-technical question?" If it's technical, you can tell ChromaDB to only return documents where depth >= 2:
# Optional: Filtering for expert mode
if is_technical_query:
docs = self.vector_db.similarity_search(query, k=5, filter={"depth": {"$gte": 2}})
Would you like me to wrap this all into a final, production-ready Python script that you can run as a standalone "Knowledge Miner" tool?
Yes
This is the complete, production-ready framework. I’ve integrated the Recursive Mining, Depth-Aware Metadata, and the Specialized Retrieval into a single cohesive class.
The Production Framework: SyntheticKnowledgeEngine
import os
from typing import List, Dict
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain_core.prompts import ChatPromptTemplate
class SyntheticKnowledgeEngine:
def __init__(self, openai_api_key: str, db_path: str = "./synthetic_rag_db"):
self.llm = ChatOpenAI(model="gpt-4o", api_key=openai_api_key, temperature=0.2)
self.embeddings = OpenAIEmbeddings(api_key=openai_api_key)
self.db_path = db_path
self.vector_db = None
self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
# --- STEP 1 & 2: RECURSIVE MINING LOGIC ---
def build_knowledge_base(self, root_subject: str, max_depth: int = 2):
"""Orchestrates the multi-pass mining and indexing."""
all_documents = []
exploration_queue = [{"topic": root_subject, "context": "Root", "depth": 1}]
for current_depth in range(1, max_depth + 1):
print(f"\n[Iteration {current_depth}] Exploring {len(exploration_queue)} topics...")
next_layer_queue = []
for item in exploration_queue:
# A. Generate the Deep-Dive content
content = self._generate_technical_brief(item['topic'], item['context'])
# B. Wrap in Document with metadata
doc = Document(
page_content=content,
metadata={
"topic": item['topic'],
"parent": item['context'],
"depth": current_depth
}
)
all_documents.append(doc)
# C. Extract leads for the NEXT depth level
if current_depth < max_depth:
sub_topics = self._extract_sub_topics(content)
for st in sub_topics:
next_layer_queue.append({
"topic": st,
"context": item['topic'],
"depth": current_depth + 1
})
exploration_queue = next_layer_queue
# --- STEP 3: INDEXING ---
self._index_data(all_documents)
def _generate_technical_brief(self, topic: str, context: str) -> str:
prompt = ChatPromptTemplate.from_template(
"You are a technical subject matter expert. Write a detailed, factual "
"encyclopedia entry for '{topic}' in the context of '{context}'.\n"
"Include mechanisms, technical specifications, and key principles."
)
return (prompt | self.llm).invoke({"topic": topic, "context": context}).content
def _extract_sub_topics(self, text: str) -> List[str]:
prompt = ChatPromptTemplate.from_template(
"Analyze this technical text and identify the 3 most important "
"sub-mechanisms or specialized concepts that require further deep-dives:\n\n{text}\n\n"
"Return only the list of 3 topics, one per line."
)
response = (prompt | self.llm).invoke({"text": text}).content
return [line.strip("- ") for line in response.strip().split("\n") if line][:3]
def _index_data(self, docs: List[Document]):
print(f"Indexing {len(docs)} documents into ChromaDB...")
split_docs = self.text_splitter.split_documents(docs)
self.vector_db = Chroma.from_documents(
documents=split_docs,
embedding=self.embeddings,
persist_directory=self.db_path
)
# --- STEP 4: DEPTH-AWARE RAG FRAMEWORK ---
def ask(self, question: str):
if not self.vector_db:
return "Knowledge base not found. Please run build_knowledge_base() first."
# Retrieve docs with metadata
results = self.vector_db.similarity_search(question, k=5)
context_str = ""
for d in results:
tag = "ADVANCED" if d.metadata['depth'] > 1 else "CORE"
context_str += f"\n[{tag} SOURCE: {d.metadata['topic']}]\n{d.page_content}\n"
system_msg = (
"You are a Depth-Aware Technical Assistant. Use the provided context to answer. "
"Prioritize 'ADVANCED' sources for technical 'how' questions and 'CORE' sources for definitions. "
"If info is missing, say so. Cite your source topics in the answer."
)
response = self.llm.invoke([
("system", system_msg),
("human", f"Context:\n{context_str}\n\nQuestion: {question}")
])
return response.content
# --- EXECUTION ---
if __name__ == "__main__":
# Initialize with your key
# engine = SyntheticKnowledgeEngine(openai_api_key="sk-...")
# 1. Mine and Build (Subject: Solid State Batteries)
# engine.build_knowledge_base("Solid State Batteries", max_depth=2)
# 2. Query the specialized knowledge
# print(engine.ask("What are the specific challenges with dendrite growth in solid electrolytes?"))
pass
How to use this for maximum impact:
Subject Selection: Use a specific enough [SUBJECT] (e.g., "Post-Quantum Cryptography" rather than just "Security").
The Depth Setting: *
max_depth=1: Great for a quick glossary.max_depth=2: Excellent for general engineering or technical support.max_depth=3: Warning—this will generate $1 + 3 + 9 = 13$ documents. It provides extreme detail but increases API costs.
Persistent Storage: The data is saved in
./synthetic_rag_db. You only need to run thebuild_knowledge_baseonce; after that, you can just load thevector_dband query it indefinitely.
Would you like me to help you refine the "Lead Extraction" prompts to focus on a specific industry, like medical, legal, or software engineering?
From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Thinking)
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