End-to-end RAG using Facebook Marketing, PyAirbyte, Milvus (Zilliz), and Langchain

%pip install airbyte pymilvus openai milvus-model

import json
import os
import airbyte as ab
from openai import OpenAI
from pymilvus import MilvusClient


# in production, you might want to avoid putting the key here.
os.environ["OPENAI_API_KEY"] = "sk-proj-****"
openai_client = OpenAI()

source = ab.get_source(
    "source-facebook-marketing",
    config={
        "start_date": "2024-06-01T00:00:00Z",
        "account_id": "account",
        "access_token": "token"
    },
    install_if_missing=True,
)
source.check()
source.select_all_streams()
result = source.read()

for name, records in result.streams.items():
    print(f"Streams of {name} has {len(records)} records.")

milvus_client = MilvusClient("./milvus_source_fake.db")

milvus_client = MilvusClient("./milvus_source_fake.db")

data = []
for record in result.streams["products"]:
    make = record["make"]
    model = record["model"]
    year = record["year"]

    text = f"{make} {model} {year}"
    data.append(text)

data[:5]

['Mazda MX-5 2023',
 'Mercedes-Benz C-Class 2023',
 'Honda Accord Crosstour 2023',
 'GMC Jimmy 2023',
 'Infiniti FX 2023']

from pymilvus import model

openai_ef = model.dense.OpenAIEmbeddingFunction(
    model_name='text-embedding-3-large', # Specify the model name
    api_key=os.environ["OPENAI_API_KEY"], # Provide your OpenAI API key
    dimensions=512 # Set the embedding dimensionality
)

embedded_data = openai_ef.encode_documents(data)

milvus_client.create_collection("products", dimension=512)

{"type": "DEBUG", "message": "Successfully created collection: products", "data": {}}
{"type": "DEBUG", "message": "Successfully created an index on collection: products", "data": {}}

embedded_docs = []
for _id, embedded_text in enumerate(embedded_data):
    embedded_docs.append({"id": _id+1, "vector": embedded_text, "text": data[_id]})

milvus_client.insert(collection_name="products", data=embedded_docs)

{'insert_count': 50,
 'ids': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],
 'cost': 0}

question = "Give list of products from Suzuki"

search_res = milvus_client.search(
    collection_name="products",
    data=[
        openai_ef.encode_documents([question])[0]
    ],  # Use the `emb_text` function to convert the question to an embedding vector
    limit=3,  # Return top 3 results
    search_params={"metric_type": "COSINE", "params": {}},  # Inner product distance
    output_fields=["text"],  # Return the text field
)

retrieved_lines_with_distances = [
    (res["entity"]["text"], res["distance"]) for res in search_res[0]
]
print(json.dumps(retrieved_lines_with_distances, indent=4))

[
    [
        "Suzuki SJ 410 2023",
        0.5219288468360901
    ],
    [
        "Isuzu VehiCROSS 2023",
        0.38782158493995667
    ],
    [
        "Jaguar S-Type 2023",
        0.35628464818000793
    ]
]

context = "\n".join(
    [line_with_distance[0] for line_with_distance in retrieved_lines_with_distances]
)

SYSTEM_PROMPT = """
Human: You are an AI assistant. You are able to find answers to the questions from the contextual passage snippets provided.
"""
USER_PROMPT = f"""
Use the following pieces of information enclosed in  tags to provide an answer to the question enclosed in  tags.

{context}


{question}

"""

response = openai_client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": USER_PROMPT},
    ],
)
print(response.choices[0].message.content)

Suzuki SJ 410 2023