Using Knowledge Graph with LLM-RAG

 Background.

Knowledge Graph.

A knowledge graph represents information using a graph structure where entities are nodes and relationships between entities are edges. Each piece of information is typically represented as a triplet (subject, predicate, object).

Knowledge Graph Triplets.

Each triplet involves a single subject, predicate, and object. For eg.

• (Bears, are, Carnivoran mammals)
• (Bears, belong to, Family Ursidae)
• (Bears, classified as, Caniforms)
• (Caniforms, also known as, Doglike carnivorans)
• (Bears, number of species, Eight)

Video Tutorial.

Code. 

1. Main code for using Knowledge Graph with LLM-RAG

import ollama
import chromadb

documents = [
  "Bears are carnivoran mammals of the family Ursidae.",
  "They are classified as caniforms, or doglike carnivorans.",
  "Although only eight species of bears are extant, they are widespread, appearing in a wide variety of habitats throughout most of the Northern Hemisphere and partially in the Southern Hemisphere.",
  "Bears are found on the continents of North America, South America, and Eurasia.",
  "Common characteristics of modern bears include large bodies with stocky legs, long snouts, small rounded ears, shaggy hair, plantigrade paws with five nonretractile claws, and short tails.",
  "With the exception of courting individuals and mothers with their young, bears are typically solitary animals.",
  "They may be diurnal or nocturnal and have an excellent sense of smell.",
  "Despite their heavy build and awkward gait, they are adept runners, climbers, and swimmers.",
  "Bears use shelters, such as caves and logs, as their dens; most species occupy their dens during the winter for a long period of hibernation, up to 100 days.",
]
Kg_triplets = [["Bears", "are", "carnivoran mammals"],
["Bears", "belong to", "family Ursidae"],
["Bears", "are classified as", "caniforms"],
["Caniforms", "are", "doglike carnivorans"],
["Bears", "have", "eight species"],
["Bears", "are", "widespread"],
["Bears", "appear in", "a wide variety of habitats"],
["Bears", "are found in", "Northern Hemisphere"],
["Bears", "are partially found in", "Southern Hemisphere"],
["Bears", "are found on", "North America"],
["Bears", "are found on", "South America"],
["Bears", "are found on", "Eurasia"],
["Modern bears", "have", "large bodies"],
["Modern bears", "have", "stocky legs"],
["Modern bears", "have", "long snouts"],
["Modern bears", "have", "small rounded ears"],
["Modern bears", "have", "shaggy hair"],
["Modern bears", "have", "plantigrade paws with five nonretractile claws"],
["Modern bears", "have", "short tails"],
["Bears", "are typically", "solitary animals"],
["Bears", "can be", "diurnal"],
["Bears", "can be", "nocturnal"],
["Bears", "have", "an excellent sense of smell"],
["Bears", "are", "adept runners"],
["Bears", "are", "adept climbers"],
["Bears", "are", "adept swimmers"],
["Bears", "use", "shelters"],
["Shelters", "include", "caves"],
["Shelters", "include", "logs"],
["Bears", "use", "shelters as dens"],
["Most species", "occupy", "dens during winter"],
["Bears", "hibernate for", "up to 100 days"],]
# Convert the triplets to text
def triplet_to_text(triplet):
  txt = str(triplet[0]) +" "+str(triplet[1]) +" "+str(triplet[2])
  # print(txt)
  return txt
# triplet_texts = [triplet_to_text(triplet) for triplet in Kg_triplets]
# Create database
client = chromadb.PersistentClient(path="E:\\Niraj_Work\\DL_Projects\\llm_projects\\database_tmp")
collection = client.create_collection(name="bear_kg")
metadata = {"hnsw:space":"cosine"}
# store each document in a vector embedding database

for d in range(0,len(Kg_triplets)):
  triplet_txt = triplet_to_text(Kg_triplets[d])
  response = ollama.embeddings(model="mxbai-embed-large", prompt=triplet_txt)
  embedding = response["embedding"]
  collection.add(
    ids=[str(d)],
    embeddings=[embedding],
    documents=[triplet_txt]
  )

# an example prompt
prompt = "How does the bear's body looks?"

# generate an embedding for the prompt and retrieve the most relevant doc
response = ollama.embeddings(
  prompt=prompt,
  model="mxbai-embed-large"
)
results = collection.query(
  query_embeddings=[response["embedding"]],
  n_results=3
)
print("result = ",results)
print(collection.get(include=['embeddings','documents','metadatas']))

# data = results['documents'][0][0]
data = ""
supported_docs = results['documents']
if len(supported_docs)==1:
  data = results['documents'][0][0]
else:
  for i in range(0, len(supported_docs)):
    data = data+" "+str(supported_docs[i])
    data = data.strip()
# generate a response combining the prompt and data we retrieved in step 2
output = ollama.generate(
  model="llama3",
  prompt=f"Using this data: {data}. Respond to this prompt: {prompt}"
)

print(output['response'])

2. Code to Construct Knowledge Graph.

import ollama
import chromadb

documents = [
  "Bears are carnivoran mammals of the family Ursidae.",
  "They are classified as caniforms, or doglike carnivorans.",
  "Although only eight species of bears are extant, they are widespread, appearing in a wide variety of habitats throughout most of the Northern Hemisphere and partially in the Southern Hemisphere.",
  "Bears are found on the continents of North America, South America, and Eurasia.",
  "Common characteristics of modern bears include large bodies with stocky legs, long snouts, small rounded ears, shaggy hair, plantigrade paws with five nonretractile claws, and short tails.",
  "With the exception of courting individuals and mothers with their young, bears are typically solitary animals.",
  "They may be diurnal or nocturnal and have an excellent sense of smell.",
  "Despite their heavy build and awkward gait, they are adept runners, climbers, and swimmers.",
  "Bears use shelters, such as caves and logs, as their dens; most species occupy their dens during the winter for a long period of hibernation, up to 100 days.",
]
single_doc = ' '.join(documents)
# an example prompt
prompt = "Give the list of all Knowledge Graph triplets for the following text" +"\n"+single_doc

# generate a response combining the prompt and data we retrieved in step 2
output = ollama.generate(
  model="llama3",
  # prompt=f"Using this data: {data}. Respond to this prompt: {prompt}"
  prompt=prompt
)

print(output['response'])