Add a full PostgresSQL syntax database 'AnalyticDB' as vector store. (#3135)

Hi there！
I'm excited to open this PR to add support for using a fully Postgres
syntax compatible database 'AnalyticDB' as a vector.
As AnalyticDB has been proved can be used with AutoGPT,
ChatGPT-Retrieve-Plugin, and LLama-Index, I think it is also good for
you.
AnalyticDB is a distributed Alibaba Cloud-Native vector database. It
works better when data comes to large scale. The PR includes:

- [x]  A new memory: AnalyticDBVector
- [x]  A suite of integration tests verifies the AnalyticDB integration

I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
- [x]  make format
- [x]  make lint
- [x]  make coverage
- [x]  make test

tests/integration_tests/vectorstores/test_analyticdb.py

@@ -0,0 +1,148 @@
+"""Test PGVector functionality."""
+import os
+from typing import List
+
+from sqlalchemy.orm import Session
+
+from langchain.docstore.document import Document
+from langchain.vectorstores.analyticdb import AnalyticDB
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+CONNECTION_STRING = AnalyticDB.connection_string_from_db_params(
+    driver=os.environ.get("PG_DRIVER", "psycopg2cffi"),
+    host=os.environ.get("PG_HOST", "localhost"),
+    port=int(os.environ.get("PG_HOST", "5432")),
+    database=os.environ.get("PG_DATABASE", "postgres"),
+    user=os.environ.get("PG_USER", "postgres"),
+    password=os.environ.get("PG_PASSWORD", "postgres"),
+)
+
+
+ADA_TOKEN_COUNT = 1536
+
+
+class FakeEmbeddingsWithAdaDimension(FakeEmbeddings):
+    """Fake embeddings functionality for testing."""
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Return simple embeddings."""
+        return [
+            [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(i)] for i in range(len(texts))
+        ]
+
+    def embed_query(self, text: str) -> List[float]:
+        """Return simple embeddings."""
+        return [float(1.0)] * (ADA_TOKEN_COUNT - 1) + [float(0.0)]
+
+
+def test_analyticdb() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    docsearch = AnalyticDB.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo")]
+
+
+def test_analyticdb_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 = AnalyticDB.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search("foo", k=1)
+    assert output == [Document(page_content="foo", metadata={"page": "0"})]
+
+
+def test_analyticdb_with_metadatas_with_scores() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = AnalyticDB.from_texts(
+        texts=texts,
+        collection_name="test_collection",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1)
+    assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
+
+
+def test_analyticdb_with_filter_match() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = AnalyticDB.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "0"})
+    assert output == [(Document(page_content="foo", metadata={"page": "0"}), 0.0)]
+
+
+def test_analyticdb_with_filter_distant_match() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = AnalyticDB.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"})
+    print(output)
+    assert output == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)]
+
+
+def test_analyticdb_with_filter_no_match() -> None:
+    """Test end to end construction and search."""
+    texts = ["foo", "bar", "baz"]
+    metadatas = [{"page": str(i)} for i in range(len(texts))]
+    docsearch = AnalyticDB.from_texts(
+        texts=texts,
+        collection_name="test_collection_filter",
+        embedding=FakeEmbeddingsWithAdaDimension(),
+        metadatas=metadatas,
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "5"})
+    assert output == []
+
+
+def test_analyticdb_collection_with_metadata() -> None:
+    """Test end to end collection construction"""
+    pgvector = AnalyticDB(
+        collection_name="test_collection",
+        collection_metadata={"foo": "bar"},
+        embedding_function=FakeEmbeddingsWithAdaDimension(),
+        connection_string=CONNECTION_STRING,
+        pre_delete_collection=True,
+    )
+    session = Session(pgvector.connect())
+    collection = pgvector.get_collection(session)
+    if collection is None:
+        assert False, "Expected a CollectionStore object but received None"
+    else:
+        assert collection.name == "test_collection"
+        assert collection.cmetadata == {"foo": "bar"}