feat: Add ChromaDB integration for vector storage

- Implemented `ChromaVectorDBStorage` class in `lightrag/kg/chroma_impl.py` to support ChromaDB as a vector storage backend.
- Updated `lightrag.py` to include `ChromaVectorDBStorage` in the storage class mapping.
- Added a test script `test_chromadb.py` to demonstrate the usage of ChromaDB with LightRAG, including configuration for embedding functions and ChromaDB connection settings.
- fix lazy import function to support package context for dynamic class loading.
  288d4b8355

test_chromadb.py

@@ -0,0 +1,113 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import gpt_4o_mini_complete, openai_embedding
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+
+#########
+# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
+# import nest_asyncio
+# nest_asyncio.apply()
+#########
+WORKING_DIR = "./chromadb_test_dir"
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+# ChromaDB Configuration
+CHROMADB_HOST = os.environ.get("CHROMADB_HOST", "localhost")
+CHROMADB_PORT = int(os.environ.get("CHROMADB_PORT", 8000))
+CHROMADB_AUTH_TOKEN = os.environ.get("CHROMADB_AUTH_TOKEN", "secret-token")
+CHROMADB_AUTH_PROVIDER = os.environ.get(
+    "CHROMADB_AUTH_PROVIDER", "chromadb.auth.token_authn.TokenAuthClientProvider"
+)
+CHROMADB_AUTH_HEADER = os.environ.get("CHROMADB_AUTH_HEADER", "X-Chroma-Token")
+
+# Embedding Configuration and Functions
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
+EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
+
+# ChromaDB requires knowing the dimension of embeddings upfront when
+# creating a collection. The embedding dimension is model-specific
+# (e.g. text-embedding-3-large uses 3072 dimensions)
+# we dynamically determine it by running a test embedding
+# and then pass it to the ChromaDBStorage class
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await openai_embedding(
+        texts,
+        model=EMBEDDING_MODEL,
+    )
+
+
+async def get_embedding_dimension():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    return embedding.shape[1]
+
+
+async def create_embedding_function_instance():
+    # Get embedding dimension
+    embedding_dimension = await get_embedding_dimension()
+    # Create embedding function instance
+    return EmbeddingFunc(
+        embedding_dim=embedding_dimension,
+        max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+        func=embedding_func,
+    )
+
+
+async def initialize_rag():
+    embedding_func_instance = await create_embedding_function_instance()
+
+    return LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=gpt_4o_mini_complete,
+        embedding_func=embedding_func_instance,
+        vector_storage="ChromaVectorDBStorage",
+        log_level="DEBUG",
+        embedding_batch_num=32,
+        vector_db_storage_cls_kwargs={
+            "host": CHROMADB_HOST,
+            "port": CHROMADB_PORT,
+            "auth_token": CHROMADB_AUTH_TOKEN,
+            "auth_provider": CHROMADB_AUTH_PROVIDER,
+            "auth_header_name": CHROMADB_AUTH_HEADER,
+            "collection_settings": {
+                "hnsw:space": "cosine",
+                "hnsw:construction_ef": 128,
+                "hnsw:search_ef": 128,
+                "hnsw:M": 16,
+                "hnsw:batch_size": 100,
+                "hnsw:sync_threshold": 1000,
+            },
+        },
+    )
+
+
+# Run the initialization
+rag = asyncio.run(initialize_rag())
+
+# with open("./dickens/book.txt", "r", encoding="utf-8") as f:
+#     rag.insert(f.read())
+
+# Perform naive search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+)
+
+# Perform local search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+)
+
+# Perform global search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+)
+
+# Perform hybrid search
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)