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MongoDB: improve example #546

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Mar 6, 2024
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MongoDB: improve example #546

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25 changes: 21 additions & 4 deletions integrations/mongodb_atlas/examples/example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,18 +10,24 @@

from haystack import Pipeline
from haystack.components.converters import MarkdownToDocument
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack_integrations.components.retrievers.mongodb_atlas import MongoDBAtlasEmbeddingRetriever
from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore

# Provide your connection string
connection_string = input("Enter your MongoDB Atlas connection string: ")
# To use the MongoDBAtlasDocumentStore, you must have a running MongoDB Atlas database.
# For details, see https://www.mongodb.com/docs/atlas/getting-started/

# Once your database is set, set the environment variable `MONGO_CONNECTION_STRING`
# with the connection string to your MongoDB Atlas database.
# format: "mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}".

# Initialize the document store
document_store = MongoDBAtlasDocumentStore(
database_name="haystack_test",
collection_name="test_collection",
vector_search_index="test_vector_search_index",
)

# Create the indexing Pipeline and index some documents
Expand All @@ -39,4 +45,15 @@

indexing.run({"converter": {"sources": file_paths}})

print("Indexed documents:" + document_store.count_documents() + "\n - ".join(document_store.filter_documents()))

# Create the querying Pipeline and try a query
querying = Pipeline()
querying.add_component("embedder", SentenceTransformersTextEmbedder())
querying.add_component("retriever", MongoDBAtlasEmbeddingRetriever(document_store=document_store, top_k=3))
querying.connect("embedder", "retriever")

results = querying.run({"embedder": {"text": "What is a cross-encoder?"}})

for doc in results["retriever"]["documents"]:
print(doc)
print("-" * 10)