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https://github.com/qdrant/landing_page.git
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code formatting
This commit is contained in:
Derrick Mwiti
@@ -49,35 +49,34 @@ To enable high-quality retrieval, we will embed each medical passage with two mo
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* **ColBERT Multivectors**: These provide more fine-grained representations, enabling precise ranking of results.
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```python
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dense_documents = [
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models.Document(text=doc, model="BAAI/bge-small-en")
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for doc in ds['passage_text']
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dense_documents = [
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models.Document(text=doc, model="BAAI/bge-small-en") for doc in ds["passage_text"]
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]
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colbert_documents = [
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models.Document(text=doc, model="colbert-ir/colbertv2.0")
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for doc in ds['passage_text']
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colbert_documents = [
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models.Document(text=doc, model="colbert-ir/colbertv2.0")
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for doc in ds["passage_text"]
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]
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collection_name = "miriad"
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# Create collection
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if not client.collection_exists(collection_name):
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client.create_collection(
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collection_name=collection_name,
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vectors_config={
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"dense": models.VectorParams(size=384,
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distance=models.Distance.COSINE),
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"colbert": models.VectorParams(
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size=128,
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distance=models.Distance.COSINE,
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multivector_config=models.MultiVectorConfig(
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comparator=models.MultiVectorComparator.MAX_SIM
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),
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hnsw_config=models.HnswConfigDiff(m=0) # reranker: no indexing
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)
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}
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)
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# Create collection
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if not client.collection_exists(collection_name):
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client.create_collection(
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collection_name=collection_name,
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vectors_config={
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"dense": models.VectorParams(size=384, distance=models.Distance.COSINE),
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"colbert": models.VectorParams(
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size=128,
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distance=models.Distance.COSINE,
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multivector_config=models.MultiVectorConfig(
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comparator=models.MultiVectorComparator.MAX_SIM
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),
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hnsw_config=models.HnswConfigDiff(m=0), # reranker: no indexing
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),
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},
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)
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```
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We disable indexing for the ColBERT multivector since it will only be used for reranking. To learn more about this, check out the [How to Effectively Use Multivector Representations in Qdrant for Reranking](https://qdrant.tech/documentation/advanced-tutorials/using-multivector-representations/) article.
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@@ -90,34 +89,31 @@ To avoid hitting API limits, we upload the data in batches, each batch containin
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* `year` and `specialty` metadata fields.
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```python
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BATCH_SIZE = 3
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BATCH_SIZE = 3
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points_batch = []
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for i in range(len(ds['passage_text'])):
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point = models.PointStruct(
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id=i,
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vector={
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"dense": dense_documents[i],
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"colbert": colbert_documents[i]
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},
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payload={
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"passage_text": ds['passage_text'][i],
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"year": ds['year'][i],
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"specialty": ds['specialty'][i],
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}
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)
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for i in range(len(ds["passage_text"])):
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point = models.PointStruct(
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id=i,
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vector={"dense": dense_documents[i], "colbert": colbert_documents[i]},
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payload={
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"passage_text": ds["passage_text"][i],
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"year": ds["year"][i],
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"specialty": ds["specialty"][i],
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},
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)
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points_batch.append(point)
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if len(points_batch) == BATCH_SIZE:
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client.upsert(collection_name=collection_name,
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points=points_batch)
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print(f"Uploaded batch ending at index {i}")
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if len(points_batch) == BATCH_SIZE:
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client.upsert(collection_name=collection_name, points=points_batch)
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print(f"Uploaded batch ending at index {i}")
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points_batch = []
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# Final flush
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if points_batch:
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client.upsert(collection_name=collection_name, points=points_batch)
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print("Uploaded final batch.")
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# Final flush
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if points_batch:
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client.upsert(collection_name=collection_name, points=points_batch)
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print("Uploaded final batch.")
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```
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## Retrieval-Augmented Generation (RAG) Pipeline
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@@ -133,47 +129,44 @@ At the heart of the application is the Qdrant vector database that provides the
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* DSPy uses these passages to guide the language model through a chain-of-thought reasoning to generate the most accurate answer.
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```python
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def rerank_with_colbert(query_text, min_year, max_year, specialty):
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def rerank_with_colbert(query_text, min_year, max_year, specialty):
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from fastembed import TextEmbedding, LateInteractionTextEmbedding
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# Encode query once with both models
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dense_model = TextEmbedding("BAAI/bge-small-en")
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# Encode query once with both models
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dense_model = TextEmbedding("BAAI/bge-small-en")
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colbert_model = LateInteractionTextEmbedding("colbert-ir/colbertv2.0")
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dense_query = list(dense_model.embed(query_text))[0]
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dense_query = list(dense_model.embed(query_text))[0]
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colbert_query = list(colbert_model.embed(query_text))[0]
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# Combined query: retrieve with dense,
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# rerank with ColBERT
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results = client.query_points(
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collection_name=collection_name,
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prefetch=models.Prefetch(
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query=dense_query,
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using="dense"
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),
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query=colbert_query,
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using="colbert",
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limit=5,
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with_payload=True,
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query_filter=Filter(
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must=[
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FieldCondition(key="specialty",
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match=MatchValue(value=specialty)),
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FieldCondition(key="year",
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range=models.Range(gt=None,gte=min_year,
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lt=None,lte=max_year))
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# Combined query: retrieve with dense,
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# rerank with ColBERT
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results = client.query_points(
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collection_name=collection_name,
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prefetch=models.Prefetch(query=dense_query, using="dense"),
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query=colbert_query,
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using="colbert",
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limit=5,
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with_payload=True,
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query_filter=Filter(
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must=[
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FieldCondition(key="specialty", match=MatchValue(value=specialty)),
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FieldCondition(
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key="year",
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range=models.Range(gt=None, gte=min_year, lt=None, lte=max_year),
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),
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]
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),
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)
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)
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)
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points = results.points
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points = results.points
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docs = []
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for point in points:
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docs.append(point.payload['passage_text'])
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for point in points:
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docs.append(point.payload["passage_text"])
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return docs
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return docs
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```
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The pipeline ensures that each response is grounded in real and recent medical literature and is aligned with the user's needs.
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@@ -185,25 +178,25 @@ Since this is a medical chatbot, we can introduce a simple guardrail to ensure i
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The chatbot checks if every question is medical-related before attempting to answer it. This is achieved by a DSPy module that classifies each incoming query as medical or not. If the question is not medical-related, the chatbot declines to answer, reducing the risk of misinformation or inappropriate responses.
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```python
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class MedicalGuardrail(dspy.Module):
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def forward(self, question):
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prompt = (
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"Is the following question a medical
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question? Answer with 'Yes' or 'No'.n"
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f"Question: {question}n"
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"Answer:"
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)
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response = dspy.settings.lm(prompt)
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answer = response[0].strip().lower()
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class MedicalGuardrail(dspy.Module):
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def forward(self, question):
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prompt = (
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"Is the following question a medical question? Answer with 'Yes' or 'No'.n"
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f"Question: {question}n"
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"Answer:"
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)
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response = dspy.settings.lm(prompt)
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answer = response[0].strip().lower()
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return answer.startswith("yes")
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if not self.guardrail.forward(question):
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class DummyResult:
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final_answer = "Sorry,
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I can only answer medical questions.
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Please ask a question related to medicine
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or healthcare."
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return DummyResult()
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if not self.guardrail.forward(question):
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class DummyResult:
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final_answer = "Sorry, I can only answer medical questions. Please ask a question related to medicine or healthcare"
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return DummyResult()
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```
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By combining this guardrail with specialty and year filtering, we ensure that the chatbot:
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