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# rag_dspy.py
import dspy
from dspy_qdrant import QdrantRM
from qdrant_client import QdrantClient, models
from dotenv import load_dotenv
import os
load_dotenv()
# DSPy setup
lm = dspy.LM("gpt-4", max_tokens=512,api_key=os.environ.get("OPENAI_API_KEY"))
client = QdrantClient(url=os.environ.get("QDRANT_CLOUD_URL"), api_key=os.environ.get("QDRANT_API_KEY"))
collection_name = "medical_chat_bot"
rm = QdrantRM(
qdrant_collection_name=collection_name,
qdrant_client=client,
vector_name="dense", # <-- MATCHES your vector field in upsert
document_field="passage_text", # <-- MATCHES your payload field in upsert
k=20)
dspy.settings.configure(lm=lm, rm=rm)
# Manual reranker using ColBERT multivector field
# Manual reranker using Qdrant’s native prefetch + ColBERT query
def rerank_with_colbert(query_text):
from fastembed import TextEmbedding, LateInteractionTextEmbedding
# Encode query once with both models
dense_model = TextEmbedding("BAAI/bge-small-en")
colbert_model = LateInteractionTextEmbedding("colbert-ir/colbertv2.0")
dense_query = list(dense_model.embed(query_text))[0]
colbert_query = list(colbert_model.embed(query_text))[0]
# Combined query: retrieve with dense, rerank with ColBERT
results = client.query_points(
collection_name=collection_name,
prefetch=models.Prefetch(
query=dense_query,
using="dense"
),
query=colbert_query,
using="colbert",
limit=5,
with_payload=True
)
points = results.points
docs = []
for point in points:
docs.append(point.payload['passage_text'])
return docs
# DSPy Signature and Module
class MedicalAnswer(dspy.Signature):
question = dspy.InputField(desc="The medical question to answer")
context = dspy.OutputField(desc="The answer to the medical question")
final_answer = dspy.OutputField(desc="The answer to the medical question")
class MedicalRAG(dspy.Module):
def __init__(self):
super().__init__()
def forward(self, question):
reranked_docs = rerank_with_colbert(question)
context_str = "\n".join(reranked_docs)
return dspy.ChainOfThought(MedicalAnswer)(
question=question,
context=context_str
)
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