📝 Basic Usage Example

This guide demonstrates a simple workflow using EmbeddingFramework.

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1️⃣ Install the package

pip install embeddingframework

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2️⃣ Import required modules

from embeddingframework.adapters.openai_embedding_adapter import OpenAIEmbeddingAdapter
from embeddingframework.adapters.vector_dbs import ChromaDBAdapter

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3️⃣ Initialize components

embedding_provider = OpenAIEmbeddingAdapter(api_key="YOUR_OPENAI_API_KEY")
vector_db = ChromaDBAdapter(persist_directory="./chroma_store")

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4️⃣ Generate embeddings

texts = ["Hello world", "EmbeddingFramework is awesome!"]
embeddings = embedding_provider.embed_texts(texts)

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5️⃣ Store embeddings

vector_db.add_texts(texts, embeddings)

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📊 Example Output

Input:

texts = ["Hello world", "EmbeddingFramework is awesome!"]

Output (example embeddings):

[
  [0.0123, -0.2345, 0.9876, ...],
  [-0.5432, 0.1234, 0.4567, ...]
]

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🧩 Additional Examples

Example 1: Using a Different Vector DB

from embeddingframework.adapters.vector_dbs import WeaviateAdapter

vector_db = WeaviateAdapter(url="http://localhost:8080")
vector_db.add_texts(["Sample text"], [[0.1, 0.2, 0.3]])

Example 2: Storing Metadata

vector_db.add_texts(
    ["Document 1", "Document 2"],
    embeddings,
    metadatas=[{"source": "file1.txt"}, {"source": "file2.txt"}]
)

Example 3: Querying the Database

results = vector_db.query("Hello", top_k=2)
print(results)

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✅ Summary

You have successfully: - Initialized an embedding provider - Generated embeddings - Stored them in a vector database - Queried the database - Stored metadata alongside embeddings