Chroma in LangChain (#1010)

Chroma is a simple to use, open-source, zero-config, zero setup
vectorstore.

Simply `pip install chromadb`, and you're good to go. 

Out-of-the-box Chroma is suitable for most LangChain workloads, but is
highly flexible. I tested to 1M embs on my M1 mac, with out issues and
reasonably fast query times.

Look out for future releases as we integrate more Chroma features with
LangChain!

tests/integration_tests/vectorstores/test_chroma.py

@@ -0,0 +1,28 @@
+"""Test Chroma functionality."""
+from langchain.docstore.document import Document
+from langchain.vectorstores import Chroma
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+
+def test_chroma() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = Chroma.from_texts(
+        collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+def test_chroma_with_metadatas() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = Chroma.from_texts(
+        collection_name="test_collection",
+        texts=texts,
+        embedding=FakeEmbeddings(),
+        metadatas=metadatas,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]