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mongodb: Add Hybrid and Full-Text Search Retrievers, release 0.2.0 (#25057)
## Description This pull-request extends the existing vector search strategies of MongoDBAtlasVectorSearch to include Hybrid (Reciprocal Rank Fusion) and Full-text via new Retrievers. There is a small breaking change in the form of the `prefilter` kwarg to search. For this, and because we have now added a great deal of features, including programmatic Index creation/deletion since 0.1.0, we plan to bump the version to 0.2.0. ### Checklist * Unit tests have been extended * formatting has been applied * One mypy error remains which will either go away in CI or be simplified. --------- Signed-off-by: Casey Clements <casey.clements@mongodb.com> Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Casey Clements
GitHub
parent
f337408b0f
commit
6e9a8b188f
@ -18,7 +18,7 @@ MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")
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DB_NAME = "langchain_db"
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COLLECTION_NAME = "test"
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ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
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MONGODB_COLLECTION = client[DB_NAME][COLLECITON_NAME]
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MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
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# Create the vector search via `from_connection_string`
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vector_search = MongoDBAtlasVectorSearch.from_connection_string(
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@ -1,8 +1,16 @@
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"""
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Integrate your operational database and vector search in a single, unified,
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fully managed platform with full vector database capabilities on MongoDB Atlas.
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Store your operational data, metadata, and vector embeddings in oue VectorStore,
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MongoDBAtlasVectorSearch.
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Insert into a Chain via a Vector, FullText, or Hybrid Retriever.
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"""
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from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
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from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
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from langchain_mongodb.vectorstores import (
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MongoDBAtlasVectorSearch,
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)
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from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
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__all__ = [
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"MongoDBAtlasVectorSearch",
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@ -1,10 +1,4 @@
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"""
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LangChain MongoDB Caches
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Functions "_loads_generations" and "_dumps_generations"
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are duplicated in this utility from modules:
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- "libs/community/langchain_community/cache.py"
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"""
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"""LangChain MongoDB Caches."""
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import json
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import logging
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@ -27,100 +21,6 @@ from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
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logger = logging.getLogger(__file__)
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def _generate_mongo_client(connection_string: str) -> MongoClient:
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return MongoClient(
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connection_string,
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driver=DriverInfo(name="Langchain", version=version("langchain-mongodb")),
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)
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def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
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"""
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Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
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Args:
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generations (RETURN_VAL_TYPE): A list of language model generations.
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Returns:
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str: a single string representing a list of generations.
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This function (+ its counterpart `_loads_generations`) rely on
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the dumps/loads pair with Reviver, so are able to deal
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with all subclasses of Generation.
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Each item in the list can be `dumps`ed to a string,
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then we make the whole list of strings into a json-dumped.
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"""
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return json.dumps([dumps(_item) for _item in generations])
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def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
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"""
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Deserialization of a string into a generic RETURN_VAL_TYPE
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(i.e. a sequence of `Generation`).
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See `_dumps_generations`, the inverse of this function.
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Args:
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generations_str (str): A string representing a list of generations.
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Compatible with the legacy cache-blob format
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Does not raise exceptions for malformed entries, just logs a warning
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and returns none: the caller should be prepared for such a cache miss.
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Returns:
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RETURN_VAL_TYPE: A list of generations.
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"""
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try:
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generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
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return generations
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except (json.JSONDecodeError, TypeError):
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# deferring the (soft) handling to after the legacy-format attempt
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pass
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try:
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gen_dicts = json.loads(generations_str)
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# not relying on `_load_generations_from_json` (which could disappear):
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generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
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logger.warning(
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f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
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)
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return generations
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except (json.JSONDecodeError, TypeError):
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logger.warning(
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f"Malformed/unparsable cached blob encountered: '{generations_str}'"
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)
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return None
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def _wait_until(
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predicate: Callable, success_description: Any, timeout: float = 10.0
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) -> None:
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"""Wait up to 10 seconds (by default) for predicate to be true.
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E.g.:
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wait_until(lambda: client.primary == ('a', 1),
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'connect to the primary')
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If the lambda-expression isn't true after 10 seconds, we raise
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AssertionError("Didn't ever connect to the primary").
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Returns the predicate's first true value.
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"""
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start = time.time()
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interval = min(float(timeout) / 100, 0.1)
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while True:
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retval = predicate()
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if retval:
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return retval
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if time.time() - start > timeout:
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raise TimeoutError("Didn't ever %s" % success_description)
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time.sleep(interval)
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class MongoDBCache(BaseCache):
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"""MongoDB Atlas cache
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@ -216,7 +116,7 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
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collection_name: str = "default",
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database_name: str = "default",
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index_name: str = "default",
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wait_until_ready: bool = False,
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wait_until_ready: Optional[float] = None,
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score_threshold: Optional[float] = None,
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**kwargs: Dict[str, Any],
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):
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@ -233,8 +133,8 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
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Defaults to "default".
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index_name: Name of the Atlas Search index.
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defaults to 'default'
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wait_until_ready (bool): Block until MongoDB Atlas finishes indexing
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the stored text. Hard timeout of 10 seconds. Defaults to False.
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wait_until_ready (float): Wait this time for Atlas to finish indexing
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the stored text. Defaults to None.
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"""
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client = _generate_mongo_client(connection_string)
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self.collection = client[database_name][collection_name]
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@ -272,7 +172,7 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
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prompt: str,
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llm_string: str,
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return_val: RETURN_VAL_TYPE,
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wait_until_ready: Optional[bool] = None,
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wait_until_ready: Optional[float] = None,
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) -> None:
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"""Update cache based on prompt and llm_string."""
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self.add_texts(
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@ -290,7 +190,7 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
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return self.lookup(prompt, llm_string) == return_val
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if wait:
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_wait_until(is_indexed, return_val)
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_wait_until(is_indexed, return_val, timeout=wait)
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def clear(self, **kwargs: Any) -> None:
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"""Clear cache that can take additional keyword arguments.
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@ -302,3 +202,107 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
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self.clear(llm_string="fake-model")
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"""
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self.collection.delete_many({**kwargs})
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def _generate_mongo_client(connection_string: str) -> MongoClient:
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return MongoClient(
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connection_string,
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driver=DriverInfo(name="Langchain", version=version("langchain-mongodb")),
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)
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def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
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"""
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Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
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Args:
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generations (RETURN_VAL_TYPE): A list of language model generations.
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Returns:
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str: a single string representing a list of generations.
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This, and "_dumps_generations" are duplicated in this utility
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from modules: "libs/community/langchain_community/cache.py"
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This function and its counterpart rely on
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the dumps/loads pair with Reviver, so are able to deal
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with all subclasses of Generation.
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Each item in the list can be `dumps`ed to a string,
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then we make the whole list of strings into a json-dumped.
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"""
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return json.dumps([dumps(_item) for _item in generations])
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def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
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"""
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Deserialization of a string into a generic RETURN_VAL_TYPE
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(i.e. a sequence of `Generation`).
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Args:
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generations_str (str): A string representing a list of generations.
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Returns:
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RETURN_VAL_TYPE: A list of generations.
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This function and its counterpart rely on
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the dumps/loads pair with Reviver, so are able to deal
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with all subclasses of Generation.
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See `_dumps_generations`, the inverse of this function.
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Compatible with the legacy cache-blob format
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Does not raise exceptions for malformed entries, just logs a warning
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and returns none: the caller should be prepared for such a cache miss.
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"""
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try:
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generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
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return generations
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except (json.JSONDecodeError, TypeError):
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# deferring the (soft) handling to after the legacy-format attempt
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pass
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try:
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gen_dicts = json.loads(generations_str)
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# not relying on `_load_generations_from_json` (which could disappear):
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generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
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logger.warning(
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f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
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)
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return generations
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except (json.JSONDecodeError, TypeError):
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logger.warning(
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f"Malformed/unparsable cached blob encountered: '{generations_str}'"
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)
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return None
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def _wait_until(
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predicate: Callable, success_description: Any, timeout: float = 10.0
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) -> None:
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"""Wait up to 10 seconds (by default) for predicate to be true.
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E.g.:
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wait_until(lambda: client.primary == ('a', 1),
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'connect to the primary')
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If the lambda-expression isn't true after 10 seconds, we raise
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AssertionError("Didn't ever connect to the primary").
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Returns the predicate's first true value.
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"""
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start = time.time()
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interval = min(float(timeout) / 100, 0.1)
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while True:
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retval = predicate()
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if retval:
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return retval
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if time.time() - start > timeout:
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raise TimeoutError("Didn't ever %s" % success_description)
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time.sleep(interval)
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@ -1,3 +1,5 @@
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"""Search Index Commands"""
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import logging
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from time import monotonic, sleep
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from typing import Any, Callable, Dict, List, Optional
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@ -8,8 +10,6 @@ from pymongo.operations import SearchIndexModel
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logger = logging.getLogger(__file__)
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_DELAY = 0.5 # Interval between checks for index operations
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def _search_index_error_message() -> str:
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return (
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@ -25,19 +25,24 @@ def _vector_search_index_definition(
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dimensions: int,
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path: str,
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similarity: str,
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filters: Optional[List[Dict[str, str]]],
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filters: Optional[List[str]] = None,
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**kwargs: Any,
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) -> Dict[str, Any]:
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return {
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"fields": [
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# https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/
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fields = [
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{
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"numDimensions": dimensions,
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"path": path,
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"similarity": similarity,
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"type": "vector",
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},
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*(filters or []),
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]
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}
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if filters:
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for field in filters:
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fields.append({"type": "filter", "path": field})
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definition = {"fields": fields}
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definition.update(kwargs)
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return definition
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def create_vector_search_index(
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@ -46,9 +51,10 @@ def create_vector_search_index(
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dimensions: int,
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path: str,
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similarity: str,
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filters: Optional[List[Dict[str, str]]] = None,
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filters: Optional[List[str]] = None,
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*,
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wait_until_complete: Optional[float] = None,
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**kwargs: Any,
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) -> None:
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"""Experimental Utility function to create a vector search index
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@ -58,9 +64,10 @@ def create_vector_search_index(
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dimensions (int): Number of dimensions in embedding
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path (str): field with vector embedding
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similarity (str): The similarity score used for the index
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filters (List[Dict[str, str]]): additional filters for index definition.
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filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch
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wait_until_complete (Optional[float]): If provided, number of seconds to wait
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until search index is ready.
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kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
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"""
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logger.info("Creating Search Index %s on %s", index_name, collection.name)
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@ -72,6 +79,7 @@ def create_vector_search_index(
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path=path,
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similarity=similarity,
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filters=filters,
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**kwargs,
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),
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name=index_name,
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type="vectorSearch",
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@ -83,7 +91,7 @@ def create_vector_search_index(
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if wait_until_complete:
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_wait_for_predicate(
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predicate=lambda: _is_index_ready(collection, index_name),
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err=f"Index {index_name} creation did not finish in {wait_until_complete}!",
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err=f"{index_name=} did not complete in {wait_until_complete}!",
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timeout=wait_until_complete,
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)
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logger.info(result)
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@ -127,9 +135,10 @@ def update_vector_search_index(
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dimensions: int,
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path: str,
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similarity: str,
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filters: List[Dict[str, str]],
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filters: Optional[List[str]] = None,
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*,
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wait_until_complete: Optional[float] = None,
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**kwargs: Any,
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) -> None:
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"""Update a search index.
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@ -138,12 +147,13 @@ def update_vector_search_index(
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Args:
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collection (Collection): MongoDB Collection
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index_name (str): Name of Index
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dimensions (int): Number of dimensions in embedding.
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path (str): field with vector embedding.
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dimensions (int): Number of dimensions in embedding
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path (str): field with vector embedding
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similarity (str): The similarity score used for the index.
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filters (List[Dict[str, str]]): additional filters for index definition.
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filters (List[str]): Fields/paths to index to allow filtering in $vectorSearch
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wait_until_complete (Optional[float]): If provided, number of seconds to wait
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until search index is ready.
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kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
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"""
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logger.info(
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@ -157,6 +167,7 @@ def update_vector_search_index(
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path=path,
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similarity=similarity,
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filters=filters,
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**kwargs,
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),
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)
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except OperationFailure as e:
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@ -201,7 +212,7 @@ def _wait_for_predicate(
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Args:
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predicate (Callable[, bool]): A function that returns a boolean value
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err (str): Error message to raise if nothing occurs
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timeout (float, optional): wait time for predicate. Defaults to TIMEOUT.
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timeout (float, optional): Wait time for predicate. Defaults to TIMEOUT.
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interval (float, optional): Interval to check predicate. Defaults to DELAY.
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Raises:
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@ -212,3 +223,48 @@ def _wait_for_predicate(
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if monotonic() - start > timeout:
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raise TimeoutError(err)
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sleep(interval)
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def create_fulltext_search_index(
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collection: Collection,
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index_name: str,
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field: str,
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*,
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wait_until_complete: Optional[float] = None,
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**kwargs: Any,
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) -> None:
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"""Experimental Utility function to create an Atlas Search index
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Args:
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collection (Collection): MongoDB Collection
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index_name (str): Name of Index
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field (str): Field to index
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wait_until_complete (Optional[float]): If provided, number of seconds to wait
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until search index is ready
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kwargs: Keyword arguments supplying any additional options to SearchIndexModel.
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"""
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logger.info("Creating Search Index %s on %s", index_name, collection.name)
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definition = {
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"mappings": {"dynamic": False, "fields": {field: [{"type": "string"}]}}
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}
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try:
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result = collection.create_search_index(
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SearchIndexModel(
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definition=definition,
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name=index_name,
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type="search",
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**kwargs,
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)
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)
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except OperationFailure as e:
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raise OperationFailure(_search_index_error_message()) from e
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if wait_until_complete:
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_wait_for_predicate(
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predicate=lambda: _is_index_ready(collection, index_name),
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err=f"{index_name=} did not complete in {wait_until_complete}!",
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timeout=wait_until_complete,
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)
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logger.info(result)
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||||
160
libs/partners/mongodb/langchain_mongodb/pipelines.py
Normal file
160
libs/partners/mongodb/langchain_mongodb/pipelines.py
Normal file
@ -0,0 +1,160 @@
|
||||
"""Aggregation pipeline components used in Atlas Full-Text, Vector, and Hybrid Search
|
||||
|
||||
See the following for more:
|
||||
- `Full-Text Search <https://www.mongodb.com/docs/atlas/atlas-search/aggregation-stages/search/#mongodb-pipeline-pipe.-search>`_
|
||||
- `MongoDB Operators <https://www.mongodb.com/docs/atlas/atlas-search/operators-and-collectors/#std-label-operators-ref>`_
|
||||
- `Vector Search <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/>`_
|
||||
- `Filter Example <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/#atlas-vector-search-pre-filter>`_
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
|
||||
def text_search_stage(
|
||||
query: str,
|
||||
search_field: str,
|
||||
index_name: str,
|
||||
limit: Optional[int] = None,
|
||||
filter: Optional[Dict[str, Any]] = None,
|
||||
include_scores: Optional[bool] = True,
|
||||
**kwargs: Any,
|
||||
) -> List[Dict[str, Any]]: # noqa: E501
|
||||
"""Full-Text search using Lucene's standard (BM25) analyzer
|
||||
|
||||
Args:
|
||||
query: Input text to search for
|
||||
search_field: Field in Collection that will be searched
|
||||
index_name: Atlas Search Index name
|
||||
limit: Maximum number of documents to return. Default of no limit
|
||||
filter: Any MQL match expression comparing an indexed field
|
||||
include_scores: Scores provide measure of relative relevance
|
||||
|
||||
Returns:
|
||||
Dictionary defining the $search stage
|
||||
"""
|
||||
pipeline = [
|
||||
{
|
||||
"$search": {
|
||||
"index": index_name,
|
||||
"text": {"query": query, "path": search_field},
|
||||
}
|
||||
}
|
||||
]
|
||||
if filter:
|
||||
pipeline.append({"$match": filter}) # type: ignore
|
||||
if include_scores:
|
||||
pipeline.append({"$set": {"score": {"$meta": "searchScore"}}})
|
||||
if limit:
|
||||
pipeline.append({"$limit": limit}) # type: ignore
|
||||
|
||||
return pipeline # type: ignore
|
||||
|
||||
|
||||
def vector_search_stage(
|
||||
query_vector: List[float],
|
||||
search_field: str,
|
||||
index_name: str,
|
||||
top_k: int = 4,
|
||||
filter: Optional[Dict[str, Any]] = None,
|
||||
oversampling_factor: int = 10,
|
||||
**kwargs: Any,
|
||||
) -> Dict[str, Any]: # noqa: E501
|
||||
"""Vector Search Stage without Scores.
|
||||
|
||||
Scoring is applied later depending on strategy.
|
||||
vector search includes a vectorSearchScore that is typically used.
|
||||
hybrid uses Reciprocal Rank Fusion.
|
||||
|
||||
Args:
|
||||
query_vector: List of embedding vector
|
||||
search_field: Field in Collection containing embedding vectors
|
||||
index_name: Name of Atlas Vector Search Index tied to Collection
|
||||
top_k: Number of documents to return
|
||||
oversampling_factor: this times limit is the number of candidates
|
||||
filter: MQL match expression comparing an indexed field.
|
||||
Some operators are not supported.
|
||||
See `vectorSearch filter docs <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/#atlas-vector-search-pre-filter>`_
|
||||
|
||||
|
||||
Returns:
|
||||
Dictionary defining the $vectorSearch
|
||||
"""
|
||||
stage = {
|
||||
"index": index_name,
|
||||
"path": search_field,
|
||||
"queryVector": query_vector,
|
||||
"numCandidates": top_k * oversampling_factor,
|
||||
"limit": top_k,
|
||||
}
|
||||
if filter:
|
||||
stage["filter"] = filter
|
||||
return {"$vectorSearch": stage}
|
||||
|
||||
|
||||
def combine_pipelines(
|
||||
pipeline: List[Any], stage: List[Dict[str, Any]], collection_name: str
|
||||
) -> None:
|
||||
"""Combines two aggregations into a single result set in-place."""
|
||||
if pipeline:
|
||||
pipeline.append({"$unionWith": {"coll": collection_name, "pipeline": stage}})
|
||||
else:
|
||||
pipeline.extend(stage)
|
||||
|
||||
|
||||
def reciprocal_rank_stage(
|
||||
score_field: str, penalty: float = 0, **kwargs: Any
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Stage adds Reciprocal Rank Fusion weighting.
|
||||
|
||||
First, it pushes documents retrieved from previous stage
|
||||
into a temporary sub-document. It then unwinds to establish
|
||||
the rank to each and applies the penalty.
|
||||
|
||||
Args:
|
||||
score_field: A unique string to identify the search being ranked
|
||||
penalty: A non-negative float.
|
||||
extra_fields: Any fields other than text_field that one wishes to keep.
|
||||
|
||||
Returns:
|
||||
RRF score := \frac{1}{rank + penalty} with rank in [1,2,..,n]
|
||||
"""
|
||||
|
||||
rrf_pipeline = [
|
||||
{"$group": {"_id": None, "docs": {"$push": "$$ROOT"}}},
|
||||
{"$unwind": {"path": "$docs", "includeArrayIndex": "rank"}},
|
||||
{
|
||||
"$addFields": {
|
||||
f"docs.{score_field}": {
|
||||
"$divide": [1.0, {"$add": ["$rank", penalty, 1]}]
|
||||
},
|
||||
"docs.rank": "$rank",
|
||||
"_id": "$docs._id",
|
||||
}
|
||||
},
|
||||
{"$replaceRoot": {"newRoot": "$docs"}},
|
||||
]
|
||||
|
||||
return rrf_pipeline # type: ignore
|
||||
|
||||
|
||||
def final_hybrid_stage(
|
||||
scores_fields: List[str], limit: int, **kwargs: Any
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Sum weighted scores, sort, and apply limit.
|
||||
|
||||
Args:
|
||||
scores_fields: List of fields given to scores of vector and text searches
|
||||
limit: Number of documents to return
|
||||
|
||||
Returns:
|
||||
Final aggregation stages
|
||||
"""
|
||||
|
||||
return [
|
||||
{"$group": {"_id": "$_id", "docs": {"$mergeObjects": "$$ROOT"}}},
|
||||
{"$replaceRoot": {"newRoot": "$docs"}},
|
||||
{"$set": {score: {"$ifNull": [f"${score}", 0]} for score in scores_fields}},
|
||||
{"$addFields": {"score": {"$add": [f"${score}" for score in scores_fields]}}},
|
||||
{"$sort": {"score": -1}},
|
||||
{"$limit": limit},
|
||||
]
|
||||
@ -0,0 +1,15 @@
|
||||
"""Search Retrievers of various types.
|
||||
|
||||
Use ``MongoDBAtlasVectorSearch.as_retriever(**)``
|
||||
to create MongoDB's core Vector Search Retriever.
|
||||
"""
|
||||
|
||||
from langchain_mongodb.retrievers.full_text_search import (
|
||||
MongoDBAtlasFullTextSearchRetriever,
|
||||
)
|
||||
from langchain_mongodb.retrievers.hybrid_search import MongoDBAtlasHybridSearchRetriever
|
||||
|
||||
__all__ = [
|
||||
"MongoDBAtlasHybridSearchRetriever",
|
||||
"MongoDBAtlasFullTextSearchRetriever",
|
||||
]
|
||||
@ -0,0 +1,59 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb.pipelines import text_search_stage
|
||||
from langchain_mongodb.utils import make_serializable
|
||||
|
||||
|
||||
class MongoDBAtlasFullTextSearchRetriever(BaseRetriever):
|
||||
"""Hybrid Search Retriever performs full-text searches
|
||||
using Lucene's standard (BM25) analyzer.
|
||||
"""
|
||||
|
||||
collection: Collection
|
||||
"""MongoDB Collection on an Atlas cluster"""
|
||||
search_index_name: str
|
||||
"""Atlas Search Index name"""
|
||||
search_field: str
|
||||
"""Collection field that contains the text to be searched. It must be indexed"""
|
||||
top_k: Optional[int] = None
|
||||
"""Number of documents to return. Default is no limit"""
|
||||
filter: Optional[Dict[str, Any]] = None
|
||||
"""(Optional) List of MQL match expression comparing an indexed field"""
|
||||
show_embeddings: float = False
|
||||
"""If true, returned Document metadata will include vectors"""
|
||||
|
||||
def _get_relevant_documents(
|
||||
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
||||
) -> List[Document]:
|
||||
"""Retrieve documents that are highest scoring / most similar to query.
|
||||
|
||||
Args:
|
||||
query: String to find relevant documents for
|
||||
run_manager: The callback handler to use
|
||||
Returns:
|
||||
List of relevant documents
|
||||
"""
|
||||
|
||||
pipeline = text_search_stage( # type: ignore
|
||||
query=query,
|
||||
search_field=self.search_field,
|
||||
index_name=self.search_index_name,
|
||||
limit=self.top_k,
|
||||
filter=self.filter,
|
||||
)
|
||||
|
||||
# Execution
|
||||
cursor = self.collection.aggregate(pipeline) # type: ignore[arg-type]
|
||||
|
||||
# Formatting
|
||||
docs = []
|
||||
for res in cursor:
|
||||
text = res.pop(self.search_field)
|
||||
make_serializable(res)
|
||||
docs.append(Document(page_content=text, metadata=res))
|
||||
return docs
|
||||
@ -0,0 +1,126 @@
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb import MongoDBAtlasVectorSearch
|
||||
from langchain_mongodb.pipelines import (
|
||||
combine_pipelines,
|
||||
final_hybrid_stage,
|
||||
reciprocal_rank_stage,
|
||||
text_search_stage,
|
||||
vector_search_stage,
|
||||
)
|
||||
from langchain_mongodb.utils import make_serializable
|
||||
|
||||
|
||||
class MongoDBAtlasHybridSearchRetriever(BaseRetriever):
|
||||
"""Hybrid Search Retriever combines vector and full-text searches
|
||||
weighting them the via Reciprocal Rank Fusion (RRF) algorithm.
|
||||
|
||||
Increasing the vector_penalty will reduce the importance on the vector search.
|
||||
Increasing the fulltext_penalty will correspondingly reduce the fulltext score.
|
||||
For more on the algorithm,see
|
||||
https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking
|
||||
"""
|
||||
|
||||
vectorstore: MongoDBAtlasVectorSearch
|
||||
"""MongoDBAtlas VectorStore"""
|
||||
search_index_name: str
|
||||
"""Atlas Search Index (full-text) name"""
|
||||
top_k: int = 4
|
||||
"""Number of documents to return."""
|
||||
oversampling_factor: int = 10
|
||||
"""This times top_k is the number of candidates chosen at each step"""
|
||||
pre_filter: Optional[Dict[str, Any]] = None
|
||||
"""(Optional) Any MQL match expression comparing an indexed field"""
|
||||
post_filter: Optional[List[Dict[str, Any]]] = None
|
||||
"""(Optional) Pipeline of MongoDB aggregation stages for postprocessing."""
|
||||
vector_penalty: float = 60.0
|
||||
"""Penalty applied to vector search results in RRF: scores=1/(rank + penalty)"""
|
||||
fulltext_penalty: float = 60.0
|
||||
"""Penalty applied to full-text search results in RRF: scores=1/(rank + penalty)"""
|
||||
show_embeddings: float = False
|
||||
"""If true, returned Document metadata will include vectors."""
|
||||
|
||||
@property
|
||||
def collection(self) -> Collection:
|
||||
return self.vectorstore._collection
|
||||
|
||||
def _get_relevant_documents(
|
||||
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
||||
) -> List[Document]:
|
||||
"""Retrieve documents that are highest scoring / most similar to query.
|
||||
|
||||
Note that the same query is used in both searches,
|
||||
embedded for vector search, and as-is for full-text search.
|
||||
|
||||
Args:
|
||||
query: String to find relevant documents for
|
||||
run_manager: The callback handler to use
|
||||
Returns:
|
||||
List of relevant documents
|
||||
"""
|
||||
|
||||
query_vector = self.vectorstore._embedding.embed_query(query)
|
||||
|
||||
scores_fields = ["vector_score", "fulltext_score"]
|
||||
pipeline: List[Any] = []
|
||||
|
||||
# First we build up the aggregation pipeline,
|
||||
# then it is passed to the server to execute
|
||||
# Vector Search stage
|
||||
vector_pipeline = [
|
||||
vector_search_stage(
|
||||
query_vector=query_vector,
|
||||
search_field=self.vectorstore._embedding_key,
|
||||
index_name=self.vectorstore._index_name,
|
||||
top_k=self.top_k,
|
||||
filter=self.pre_filter,
|
||||
oversampling_factor=self.oversampling_factor,
|
||||
)
|
||||
]
|
||||
vector_pipeline += reciprocal_rank_stage("vector_score", self.vector_penalty)
|
||||
|
||||
combine_pipelines(pipeline, vector_pipeline, self.collection.name)
|
||||
|
||||
# Full-Text Search stage
|
||||
text_pipeline = text_search_stage(
|
||||
query=query,
|
||||
search_field=self.vectorstore._text_key,
|
||||
index_name=self.search_index_name,
|
||||
limit=self.top_k,
|
||||
filter=self.pre_filter,
|
||||
)
|
||||
|
||||
text_pipeline.extend(
|
||||
reciprocal_rank_stage("fulltext_score", self.fulltext_penalty)
|
||||
)
|
||||
|
||||
combine_pipelines(pipeline, text_pipeline, self.collection.name)
|
||||
|
||||
# Sum and sort stage
|
||||
pipeline.extend(
|
||||
final_hybrid_stage(scores_fields=scores_fields, limit=self.top_k)
|
||||
)
|
||||
|
||||
# Removal of embeddings unless requested.
|
||||
if not self.show_embeddings:
|
||||
pipeline.append({"$project": {self.vectorstore._embedding_key: 0}})
|
||||
# Post filtering
|
||||
if self.post_filter is not None:
|
||||
pipeline.extend(self.post_filter)
|
||||
|
||||
# Execution
|
||||
cursor = self.collection.aggregate(pipeline) # type: ignore[arg-type]
|
||||
|
||||
# Formatting
|
||||
docs = []
|
||||
for res in cursor:
|
||||
text = res.pop(self.vectorstore._text_key)
|
||||
# score = res.pop("score") # The score remains buried!
|
||||
make_serializable(res)
|
||||
docs.append(Document(page_content=text, metadata=res))
|
||||
return docs
|
||||
@ -1,6 +1,13 @@
|
||||
"""
|
||||
Tools for the Maximal Marginal Relevance (MMR) reranking.
|
||||
Duplicated from langchain_community to avoid cross-dependencies.
|
||||
"""Various Utility Functions
|
||||
|
||||
- Tools for handling bson.ObjectId
|
||||
|
||||
The help IDs live as ObjectId in MongoDB and str in Langchain and JSON.
|
||||
|
||||
|
||||
- Tools for the Maximal Marginal Relevance (MMR) reranking
|
||||
|
||||
These are duplicated from langchain_community to avoid cross-dependencies.
|
||||
|
||||
Functions "maximal_marginal_relevance" and "cosine_similarity"
|
||||
are duplicated in this utility respectively from modules:
|
||||
@ -21,11 +28,6 @@ logger = logging.getLogger(__name__)
|
||||
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
|
||||
|
||||
|
||||
class FailCode:
|
||||
INDEX_NOT_FOUND = 27
|
||||
INDEX_ALREADY_EXISTS = 68
|
||||
|
||||
|
||||
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
|
||||
"""Row-wise cosine similarity between two equal-width matrices."""
|
||||
if len(X) == 0 or len(Y) == 0:
|
||||
@ -65,7 +67,37 @@ def maximal_marginal_relevance(
|
||||
lambda_mult: float = 0.5,
|
||||
k: int = 4,
|
||||
) -> List[int]:
|
||||
"""Calculate maximal marginal relevance."""
|
||||
"""Compute Maximal Marginal Relevance (MMR).
|
||||
|
||||
MMR is a technique used to select documents that are both relevant to the query
|
||||
and diverse among themselves. This function returns the indices
|
||||
of the top-k embeddings that maximize the marginal relevance.
|
||||
|
||||
Args:
|
||||
query_embedding (np.ndarray): The embedding vector of the query.
|
||||
embedding_list (list of np.ndarray): A list containing the embedding vectors
|
||||
of the candidate documents.
|
||||
lambda_mult (float, optional): The trade-off parameter between
|
||||
relevance and diversity. Defaults to 0.5.
|
||||
k (int, optional): The number of embeddings to select. Defaults to 4.
|
||||
|
||||
Returns:
|
||||
list of int: The indices of the embeddings that maximize the marginal relevance.
|
||||
|
||||
Notes:
|
||||
The Maximal Marginal Relevance (MMR) is computed using the following formula:
|
||||
|
||||
MMR = argmax_{D_i ∈ R \ S} [λ * Sim(D_i, Q) - (1 - λ) * max_{D_j ∈ S} Sim(D_i, D_j)]
|
||||
|
||||
where:
|
||||
- R is the set of candidate documents,
|
||||
- S is the set of selected documents,
|
||||
- Q is the query embedding,
|
||||
- Sim(D_i, Q) is the similarity between document D_i and the query,
|
||||
- Sim(D_i, D_j) is the similarity between documents D_i and D_j,
|
||||
- λ is the trade-off parameter.
|
||||
"""
|
||||
|
||||
if min(k, len(embedding_list)) <= 0:
|
||||
return []
|
||||
if query_embedding.ndim == 1:
|
||||
@ -137,6 +169,7 @@ def make_serializable(
|
||||
obj: Dict[str, Any],
|
||||
) -> None:
|
||||
"""Recursively cast values in a dict to a form able to json.dump"""
|
||||
|
||||
from bson import ObjectId
|
||||
|
||||
for k, v in obj.items():
|
||||
|
||||
@ -29,6 +29,7 @@ from langchain_mongodb.index import (
|
||||
create_vector_search_index,
|
||||
update_vector_search_index,
|
||||
)
|
||||
from langchain_mongodb.pipelines import vector_search_stage
|
||||
from langchain_mongodb.utils import (
|
||||
make_serializable,
|
||||
maximal_marginal_relevance,
|
||||
@ -36,7 +37,6 @@ from langchain_mongodb.utils import (
|
||||
str_to_oid,
|
||||
)
|
||||
|
||||
MongoDBDocumentType = TypeVar("MongoDBDocumentType", bound=Dict[str, Any])
|
||||
VST = TypeVar("VST", bound=VectorStore)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -47,17 +47,38 @@ DEFAULT_INSERT_BATCH_SIZE = 100_000
|
||||
class MongoDBAtlasVectorSearch(VectorStore):
|
||||
"""MongoDB Atlas vector store integration.
|
||||
|
||||
MongoDBAtlasVectorSearch performs data operations on
|
||||
text, embeddings and arbitrary data. In addition to CRUD operations,
|
||||
the VectorStore provides Vector Search
|
||||
based on similarity of embedding vectors following the
|
||||
Hierarchical Navigable Small Worlds (HNSW) algorithm.
|
||||
|
||||
This supports a number of models to ascertain scores,
|
||||
"similarity" (default), "MMR", and "similarity_score_threshold".
|
||||
These are described in the search_type argument to as_retriever,
|
||||
which provides the Runnable.invoke(query) API, allowing
|
||||
MongoDBAtlasVectorSearch to be used within a chain.
|
||||
|
||||
Setup:
|
||||
Install ``langchain-mongodb`` and ``pymongo`` and setup a MongoDB Atlas cluster (read through [this guide](https://www.mongodb.com/docs/manual/reference/connection-string/) to do so).
|
||||
* Set up a MongoDB Atlas cluster. The free tier M0 will allow you to start.
|
||||
Search Indexes are only available on Atlas, the fully managed cloud service,
|
||||
not the self-managed MongoDB.
|
||||
Follow [this guide](https://www.mongodb.com/basics/mongodb-atlas-tutorial)
|
||||
|
||||
* Create a Collection and a Vector Search Index.The procedure is described
|
||||
[here](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#procedure).
|
||||
|
||||
* Install ``langchain-mongodb``
|
||||
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install -qU langchain-mongodb pymongo
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import getpass
|
||||
|
||||
MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
|
||||
|
||||
Key init args — indexing params:
|
||||
@ -127,7 +148,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
Search with filter:
|
||||
.. code-block:: python
|
||||
|
||||
results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
|
||||
results = vector_store.similarity_search(query="thud",k=1,post_filter=[{"bar": "baz"]})
|
||||
for doc in results:
|
||||
print(f"* {doc.page_content} [{doc.metadata}]")
|
||||
|
||||
@ -184,29 +205,24 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
collection: Collection[MongoDBDocumentType],
|
||||
collection: Collection[Dict[str, Any]],
|
||||
embedding: Embeddings,
|
||||
*,
|
||||
index_name: str = "default",
|
||||
index_name: str = "vector_index",
|
||||
text_key: str = "text",
|
||||
embedding_key: str = "embedding",
|
||||
relevance_score_fn: str = "cosine",
|
||||
**kwargs: Any,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
collection: MongoDB collection to add the texts to.
|
||||
embedding: Text embedding model to use.
|
||||
text_key: MongoDB field that will contain the text for each
|
||||
document.
|
||||
defaults to 'text'
|
||||
embedding_key: MongoDB field that will contain the embedding for
|
||||
each document.
|
||||
defaults to 'embedding'
|
||||
index_name: Name of the Atlas Search index.
|
||||
defaults to 'default'
|
||||
relevance_score_fn: The similarity score used for the index.
|
||||
defaults to 'cosine'
|
||||
Currently supported: 'euclidean', 'cosine', and 'dotProduct'.
|
||||
collection: MongoDB collection to add the texts to
|
||||
embedding: Text embedding model to use
|
||||
text_key: MongoDB field that will contain the text for each document
|
||||
index_name: Existing Atlas Vector Search Index
|
||||
embedding_key: Field that will contain the embedding for each document
|
||||
vector_index_name: Name of the Atlas Vector Search index
|
||||
relevance_score_fn: The similarity score used for the index
|
||||
Currently supported: 'euclidean', 'cosine', and 'dotProduct'
|
||||
"""
|
||||
self._collection = collection
|
||||
self._embedding = embedding
|
||||
@ -412,69 +428,32 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
start = end
|
||||
return result_ids
|
||||
|
||||
def _similarity_search_with_score(
|
||||
self,
|
||||
embedding: List[float],
|
||||
k: int = 4,
|
||||
pre_filter: Optional[Dict] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
include_embedding: bool = False,
|
||||
include_ids: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Core implementation."""
|
||||
params = {
|
||||
"queryVector": embedding,
|
||||
"path": self._embedding_key,
|
||||
"numCandidates": k * 10,
|
||||
"limit": k,
|
||||
"index": self._index_name,
|
||||
}
|
||||
if pre_filter:
|
||||
params["filter"] = pre_filter
|
||||
query = {"$vectorSearch": params}
|
||||
|
||||
pipeline = [
|
||||
query,
|
||||
{"$set": {"score": {"$meta": "vectorSearchScore"}}},
|
||||
]
|
||||
|
||||
# Exclude the embedding key from the return payload
|
||||
if not include_embedding:
|
||||
pipeline.append({"$project": {self._embedding_key: 0}})
|
||||
|
||||
if post_filter_pipeline is not None:
|
||||
pipeline.extend(post_filter_pipeline)
|
||||
cursor = self._collection.aggregate(pipeline) # type: ignore[arg-type]
|
||||
docs = []
|
||||
|
||||
for res in cursor:
|
||||
text = res.pop(self._text_key)
|
||||
score = res.pop("score")
|
||||
make_serializable(res)
|
||||
docs.append((Document(page_content=text, metadata=res), score))
|
||||
return docs
|
||||
|
||||
def similarity_search_with_score(
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
pre_filter: Optional[Dict] = None,
|
||||
pre_filter: Optional[Dict[str, Any]] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
oversampling_factor: int = 10,
|
||||
include_embeddings: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
) -> List[Tuple[Document, float]]: # noqa: E501
|
||||
"""Return MongoDB documents most similar to the given query and their scores.
|
||||
|
||||
Uses the vectorSearch operator available in MongoDB Atlas Search.
|
||||
For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
|
||||
Atlas Vector Search eliminates the need to run a separate
|
||||
search system alongside your database.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
k: (Optional) number of documents to return. Defaults to 4.
|
||||
pre_filter: (Optional) dictionary of argument(s) to prefilter document
|
||||
fields on.
|
||||
post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
|
||||
following the vectorSearch stage.
|
||||
query: Input text of semantic query
|
||||
k: Number of documents to return. Also known as top_k.
|
||||
pre_filter: List of MQL match expressions comparing an indexed field
|
||||
post_filter_pipeline: (Optional) Arbitrary pipeline of MongoDB
|
||||
aggregation stages applied after the search is complete.
|
||||
oversampling_factor: This times k is the number of candidates chosen
|
||||
at each step in the in HNSW Vector Search
|
||||
include_embeddings: If True, the embedding vector of each result
|
||||
will be included in metadata.
|
||||
kwargs: Additional arguments are specific to the search_type
|
||||
|
||||
Returns:
|
||||
List of documents most similar to the query and their scores.
|
||||
@ -485,6 +464,8 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
k=k,
|
||||
pre_filter=pre_filter,
|
||||
post_filter_pipeline=post_filter_pipeline,
|
||||
oversampling_factor=oversampling_factor,
|
||||
include_embeddings=include_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
return docs
|
||||
@ -493,36 +474,46 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
self,
|
||||
query: str,
|
||||
k: int = 4,
|
||||
pre_filter: Optional[Dict] = None,
|
||||
pre_filter: Optional[Dict[str, Any]] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
oversampling_factor: int = 10,
|
||||
include_scores: bool = False,
|
||||
include_embeddings: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
) -> List[Document]: # noqa: E501
|
||||
"""Return MongoDB documents most similar to the given query.
|
||||
|
||||
Uses the vectorSearch operator available in MongoDB Atlas Search.
|
||||
For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
|
||||
Atlas Vector Search eliminates the need to run a separate
|
||||
search system alongside your database.
|
||||
|
||||
Args:
|
||||
query: Text to look up documents similar to.
|
||||
query: Input text of semantic query
|
||||
k: (Optional) number of documents to return. Defaults to 4.
|
||||
pre_filter: (Optional) dictionary of argument(s) to prefilter document
|
||||
fields on.
|
||||
pre_filter: List of MQL match expressions comparing an indexed field
|
||||
post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
|
||||
following the vectorSearch stage.
|
||||
to filter/process results after $vectorSearch.
|
||||
oversampling_factor: Multiple of k used when generating number of candidates
|
||||
at each step in the HNSW Vector Search,
|
||||
include_scores: If True, the query score of each result
|
||||
will be included in metadata.
|
||||
include_embeddings: If True, the embedding vector of each result
|
||||
will be included in metadata.
|
||||
kwargs: Additional arguments are specific to the search_type
|
||||
|
||||
Returns:
|
||||
List of documents most similar to the query and their scores.
|
||||
"""
|
||||
additional = kwargs.get("additional")
|
||||
docs_and_scores = self.similarity_search_with_score(
|
||||
query,
|
||||
k=k,
|
||||
pre_filter=pre_filter,
|
||||
post_filter_pipeline=post_filter_pipeline,
|
||||
oversampling_factor=oversampling_factor,
|
||||
include_embeddings=include_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
if additional and "similarity_score" in additional:
|
||||
if include_scores:
|
||||
for doc, score in docs_and_scores:
|
||||
doc.metadata["score"] = score
|
||||
return [doc for doc, _ in docs_and_scores]
|
||||
@ -533,7 +524,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
pre_filter: Optional[Dict] = None,
|
||||
pre_filter: Optional[Dict[str, Any]] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
@ -549,18 +540,15 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
algorithm. Defaults to 20.
|
||||
lambda_mult: Number between 0 and 1 that determines the degree
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
pre_filter: (Optional) dictionary of argument(s) to prefilter on document
|
||||
fields.
|
||||
to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
|
||||
pre_filter: List of MQL match expressions comparing an indexed field
|
||||
post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
|
||||
following the vectorSearch stage.
|
||||
following the $vectorSearch stage.
|
||||
Returns:
|
||||
List of documents selected by maximal marginal relevance.
|
||||
"""
|
||||
query_embedding = self._embedding.embed_query(query)
|
||||
return self.max_marginal_relevance_search_by_vector(
|
||||
embedding=query_embedding,
|
||||
embedding=self._embedding.embed_query(query),
|
||||
k=k,
|
||||
fetch_k=fetch_k,
|
||||
lambda_mult=lambda_mult,
|
||||
@ -575,7 +563,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[Dict]] = None,
|
||||
collection: Optional[Collection[MongoDBDocumentType]] = None,
|
||||
collection: Optional[Collection] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> MongoDBAtlasVectorSearch:
|
||||
@ -588,6 +576,9 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
|
||||
This is intended to be a quick way to get started.
|
||||
|
||||
See `MongoDBAtlasVectorSearch` for kwargs and further description.
|
||||
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
from pymongo import MongoClient
|
||||
@ -649,8 +640,9 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
pre_filter: Optional[Dict] = None,
|
||||
pre_filter: Optional[Dict[str, Any]] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
oversampling_factor: int = 10,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]: # type: ignore
|
||||
"""Return docs selected using the maximal marginal relevance.
|
||||
@ -666,10 +658,13 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
of diversity among the results with 0 corresponding
|
||||
to maximum diversity and 1 to minimum diversity.
|
||||
Defaults to 0.5.
|
||||
pre_filter: (Optional) dictionary of argument(s) to prefilter on document
|
||||
fields.
|
||||
pre_filter: (Optional) dictionary of arguments to filter document fields on.
|
||||
post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
|
||||
following the vectorSearch stage.
|
||||
oversampling_factor: Multiple of k used when generating number
|
||||
of candidates in HNSW Vector Search,
|
||||
kwargs: Additional arguments are specific to the search_type
|
||||
|
||||
Returns:
|
||||
List of Documents selected by maximal marginal relevance.
|
||||
"""
|
||||
@ -678,7 +673,8 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
k=fetch_k,
|
||||
pre_filter=pre_filter,
|
||||
post_filter_pipeline=post_filter_pipeline,
|
||||
include_embedding=kwargs.pop("include_embedding", True),
|
||||
include_embeddings=True,
|
||||
oversampling_factor=oversampling_factor,
|
||||
**kwargs,
|
||||
)
|
||||
mmr_doc_indexes = maximal_marginal_relevance(
|
||||
@ -696,31 +692,82 @@ class MongoDBAtlasVectorSearch(VectorStore):
|
||||
k: int = 4,
|
||||
fetch_k: int = 20,
|
||||
lambda_mult: float = 0.5,
|
||||
pre_filter: Optional[Dict[str, Any]] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
oversampling_factor: int = 10,
|
||||
**kwargs: Any,
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance."""
|
||||
return await run_in_executor(
|
||||
None,
|
||||
self.max_marginal_relevance_search_by_vector,
|
||||
self.max_marginal_relevance_search_by_vector, # type: ignore[arg-type]
|
||||
embedding,
|
||||
k=k,
|
||||
fetch_k=fetch_k,
|
||||
lambda_mult=lambda_mult,
|
||||
pre_filter=pre_filter,
|
||||
post_filter_pipeline=post_filter_pipeline,
|
||||
oversampling_factor=oversampling_factor,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def _similarity_search_with_score(
|
||||
self,
|
||||
query_vector: List[float],
|
||||
k: int = 4,
|
||||
pre_filter: Optional[Dict[str, Any]] = None,
|
||||
post_filter_pipeline: Optional[List[Dict]] = None,
|
||||
oversampling_factor: int = 10,
|
||||
include_embeddings: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> List[Tuple[Document, float]]:
|
||||
"""Core search routine. See external methods for details."""
|
||||
|
||||
# Atlas Vector Search, potentially with filter
|
||||
pipeline = [
|
||||
vector_search_stage(
|
||||
query_vector,
|
||||
self._embedding_key,
|
||||
self._index_name,
|
||||
k,
|
||||
pre_filter,
|
||||
oversampling_factor,
|
||||
**kwargs,
|
||||
),
|
||||
{"$set": {"score": {"$meta": "vectorSearchScore"}}},
|
||||
]
|
||||
|
||||
# Remove embeddings unless requested.
|
||||
if not include_embeddings:
|
||||
pipeline.append({"$project": {self._embedding_key: 0}})
|
||||
# Post-processing
|
||||
if post_filter_pipeline is not None:
|
||||
pipeline.extend(post_filter_pipeline)
|
||||
|
||||
# Execution
|
||||
cursor = self._collection.aggregate(pipeline) # type: ignore[arg-type]
|
||||
docs = []
|
||||
|
||||
# Format
|
||||
for res in cursor:
|
||||
text = res.pop(self._text_key)
|
||||
score = res.pop("score")
|
||||
make_serializable(res)
|
||||
docs.append((Document(page_content=text, metadata=res), score))
|
||||
return docs
|
||||
|
||||
def create_vector_search_index(
|
||||
self,
|
||||
dimensions: int,
|
||||
filters: Optional[List[Dict[str, str]]] = None,
|
||||
filters: Optional[List[str]] = None,
|
||||
update: bool = False,
|
||||
) -> None:
|
||||
"""Creates a MongoDB Atlas vectorSearch index for the VectorStore
|
||||
|
||||
Note**: This method may fail as it requires a MongoDB Atlas with
|
||||
these pre-requisites:
|
||||
- M10 cluster or higher
|
||||
- https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#prerequisites
|
||||
Note**: This method may fail as it requires a MongoDB Atlas with these
|
||||
`pre-requisites <https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#prerequisites>`.
|
||||
Currently, vector and full-text search index operations need to be
|
||||
performed manually on the Atlas UI for shared M0 clusters.
|
||||
|
||||
Args:
|
||||
dimensions (int): Number of dimensions in embedding
|
||||
|
||||
600
libs/partners/mongodb/poetry.lock
generated
600
libs/partners/mongodb/poetry.lock
generated
@ -1,91 +1,103 @@
|
||||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
version = "2.3.4"
|
||||
description = "Happy Eyeballs for asyncio"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "aiohappyeyeballs-2.3.4-py3-none-any.whl", hash = "sha256:40a16ceffcf1fc9e142fd488123b2e218abc4188cf12ac20c67200e1579baa42"},
|
||||
{file = "aiohappyeyeballs-2.3.4.tar.gz", hash = "sha256:7e1ae8399c320a8adec76f6c919ed5ceae6edd4c3672f4d9eae2b27e37c80ff6"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.9.5"
|
||||
version = "3.10.1"
|
||||
description = "Async http client/server framework (asyncio)"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
|
||||
{file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"},
|
||||
{file = "aiohttp-3.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ae79c1bc12c34082d92bf9422764f799aee4746fd7a392db46b7fd357d4a17a"},
|
||||
{file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d3ebb9e1316ec74277d19c5f482f98cc65a73ccd5430540d6d11682cd857430"},
|
||||
{file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84dabd95154f43a2ea80deffec9cb44d2e301e38a0c9d331cc4aa0166fe28ae3"},
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohappyeyeballs = ">=2.3.0"
|
||||
aiosignal = ">=1.1.2"
|
||||
async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
|
||||
attrs = ">=17.3.0"
|
||||
@ -94,7 +106,7 @@ multidict = ">=4.5,<7.0"
|
||||
yarl = ">=1.0,<2.0"
|
||||
|
||||
[package.extras]
|
||||
speedups = ["Brotli", "aiodns", "brotlicffi"]
|
||||
speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
|
||||
|
||||
[[package]]
|
||||
name = "aiosignal"
|
||||
@ -124,6 +136,28 @@ files = [
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.4.0"
|
||||
description = "High level compatibility layer for multiple asynchronous event loop implementations"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
{file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
|
||||
idna = ">=2.8"
|
||||
sniffio = ">=1.1"
|
||||
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
|
||||
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
|
||||
trio = ["trio (>=0.23)"]
|
||||
|
||||
[[package]]
|
||||
name = "async-timeout"
|
||||
version = "4.0.3"
|
||||
@ -137,22 +171,22 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "23.2.0"
|
||||
version = "24.1.0"
|
||||
description = "Classes Without Boilerplate"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
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|
||||
]
|
||||
|
||||
[package.extras]
|
||||
cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
|
||||
dev = ["attrs[tests]", "pre-commit"]
|
||||
docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
|
||||
tests = ["attrs[tests-no-zope]", "zope-interface"]
|
||||
tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
|
||||
tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
|
||||
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
|
||||
tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
|
||||
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
@ -292,6 +326,17 @@ files = [
|
||||
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "distro"
|
||||
version = "1.9.0"
|
||||
description = "Distro - an OS platform information API"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
|
||||
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "dnspython"
|
||||
version = "2.6.1"
|
||||
@ -497,6 +542,62 @@ files = [
|
||||
docs = ["Sphinx", "furo"]
|
||||
test = ["objgraph", "psutil"]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.14.0"
|
||||
description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.5"
|
||||
description = "A minimal low-level HTTP client."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
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|
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|
||||
[package.dependencies]
|
||||
certifi = "*"
|
||||
h11 = ">=0.13,<0.15"
|
||||
|
||||
[package.extras]
|
||||
asyncio = ["anyio (>=4.0,<5.0)"]
|
||||
http2 = ["h2 (>=3,<5)"]
|
||||
socks = ["socksio (==1.*)"]
|
||||
trio = ["trio (>=0.22.0,<0.26.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "httpx"
|
||||
version = "0.27.0"
|
||||
description = "The next generation HTTP client."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
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files = [
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]
|
||||
|
||||
[package.dependencies]
|
||||
anyio = "*"
|
||||
certifi = "*"
|
||||
httpcore = "==1.*"
|
||||
idna = "*"
|
||||
sniffio = "*"
|
||||
|
||||
[package.extras]
|
||||
brotli = ["brotli", "brotlicffi"]
|
||||
cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
|
||||
http2 = ["h2 (>=3,<5)"]
|
||||
socks = ["socksio (==1.*)"]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.7"
|
||||
@ -546,7 +647,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "langchain"
|
||||
version = "0.2.9"
|
||||
version = "0.2.12"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -556,7 +657,7 @@ develop = true
|
||||
[package.dependencies]
|
||||
aiohttp = "^3.8.3"
|
||||
async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
|
||||
langchain-core = "^0.2.20"
|
||||
langchain-core = "^0.2.27"
|
||||
langchain-text-splitters = "^0.2.0"
|
||||
langsmith = "^0.1.17"
|
||||
numpy = [
|
||||
@ -575,7 +676,7 @@ url = "../../langchain"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.2.21"
|
||||
version = "0.2.28"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -592,14 +693,33 @@ pydantic = [
|
||||
]
|
||||
PyYAML = ">=5.3"
|
||||
tenacity = "^8.1.0,!=8.4.0"
|
||||
typing-extensions = ">=4.7"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
url = "../../core"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-openai"
|
||||
version = "0.1.20"
|
||||
description = "An integration package connecting OpenAI and LangChain"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = []
|
||||
develop = true
|
||||
|
||||
[package.dependencies]
|
||||
langchain-core = "^0.2.26"
|
||||
openai = "^1.32.0"
|
||||
tiktoken = ">=0.7,<1"
|
||||
|
||||
[package.source]
|
||||
type = "directory"
|
||||
url = "../openai"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-text-splitters"
|
||||
version = "0.2.2"
|
||||
version = "0.2.3"
|
||||
description = "LangChain text splitting utilities"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
@ -615,13 +735,13 @@ url = "../../text-splitters"
|
||||
|
||||
[[package]]
|
||||
name = "langsmith"
|
||||
version = "0.1.90"
|
||||
version = "0.1.96"
|
||||
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
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|
||||
|
||||
[package.dependencies]
|
||||
@ -733,44 +853,44 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "mypy"
|
||||
version = "1.10.1"
|
||||
version = "1.11.1"
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||||
description = "Optional static typing for Python"
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|
||||
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||||
[package.dependencies]
|
||||
mypy-extensions = ">=1.0.0"
|
||||
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
|
||||
typing-extensions = ">=4.1.0"
|
||||
typing-extensions = ">=4.6.0"
|
||||
|
||||
[package.extras]
|
||||
dmypy = ["psutil (>=4.0)"]
|
||||
@ -871,6 +991,29 @@ files = [
|
||||
{file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
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|
||||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "1.38.0"
|
||||
description = "The official Python library for the openai API"
|
||||
optional = false
|
||||
python-versions = ">=3.7.1"
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||||
|
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[package.dependencies]
|
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distro = ">=1.7.0,<2"
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httpx = ">=0.23.0,<1"
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pydantic = ">=1.9.0,<3"
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sniffio = "*"
|
||||
tqdm = ">4"
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typing-extensions = ">=4.7,<5"
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|
||||
[package.extras]
|
||||
datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
|
||||
|
||||
[[package]]
|
||||
name = "orjson"
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@ -1298,6 +1441,94 @@ files = [
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||||
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||||
[[package]]
|
||||
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|
||||
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||||
@ -1321,29 +1552,29 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
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||||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
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||||
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||||
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||||
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|
||||
python-versions = ">=3.7"
|
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[[package]]
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||||
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||||
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[[package]]
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||||
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||||
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|
||||
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||||
python-versions = ">=3.7"
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[[package]]
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||||
name = "sqlalchemy"
|
||||
version = "2.0.31"
|
||||
@ -1473,6 +1715,58 @@ files = [
|
||||
doc = ["reno", "sphinx"]
|
||||
test = ["pytest", "tornado (>=4.5)", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "tiktoken"
|
||||
version = "0.7.0"
|
||||
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
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||||
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|
||||
{file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:d6d73ea93e91d5ca771256dfc9d1d29f5a554b83821a1dc0891987636e0ae226"},
|
||||
{file = "tiktoken-0.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:2bcb28ddf79ffa424f171dfeef9a4daff61a94c631ca6813f43967cb263b83b9"},
|
||||
{file = "tiktoken-0.7.0.tar.gz", hash = "sha256:1077266e949c24e0291f6c350433c6f0971365ece2b173a23bc3b9f9defef6b6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
regex = ">=2022.1.18"
|
||||
requests = ">=2.26.0"
|
||||
|
||||
[package.extras]
|
||||
blobfile = ["blobfile (>=2)"]
|
||||
|
||||
[[package]]
|
||||
name = "tomli"
|
||||
version = "2.0.1"
|
||||
@ -1484,6 +1778,26 @@ files = [
|
||||
{file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.5"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
|
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{file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[package.extras]
|
||||
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
|
||||
notebook = ["ipywidgets (>=6)"]
|
||||
slack = ["slack-sdk"]
|
||||
telegram = ["requests"]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
@ -1662,4 +1976,4 @@ multidict = ">=4.0"
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "e456e34ed87e1dbec96547f5a124cb408b4c557e5e14c61e9cb5ebf3a7e07ef5"
|
||||
content-hash = "0118e4cc42983fecdb592157b956a4036d666ff4fc06de95bc964195f97f39bf"
|
||||
|
||||
@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
|
||||
|
||||
[tool.poetry]
|
||||
name = "langchain-mongodb"
|
||||
version = "0.1.7"
|
||||
version = "0.1.8"
|
||||
description = "An integration package connecting MongoDB and LangChain"
|
||||
authors = []
|
||||
readme = "README.md"
|
||||
@ -67,7 +67,9 @@ pytest-asyncio = "^0.21.1"
|
||||
[tool.poetry.group.codespell.dependencies]
|
||||
codespell = "^2.2.0"
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
[tool.poetry.group.test_integration.dependencies.langchain-openai]
|
||||
path = "../openai"
|
||||
develop = true
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.5"
|
||||
|
||||
@ -10,7 +10,8 @@ from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
|
||||
from langchain_core.outputs import ChatGeneration, Generation, LLMResult
|
||||
|
||||
from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
|
||||
from tests.utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM
|
||||
|
||||
from ..utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM
|
||||
|
||||
CONN_STRING = os.environ.get("MONGODB_ATLAS_URI")
|
||||
INDEX_NAME = "langchain-test-index-semantic-cache"
|
||||
@ -31,7 +32,7 @@ def llm_cache(cls: Any) -> BaseCache:
|
||||
database_name=DATABASE,
|
||||
index_name=INDEX_NAME,
|
||||
score_threshold=0.5,
|
||||
wait_until_ready=True,
|
||||
wait_until_ready=15.0,
|
||||
)
|
||||
)
|
||||
assert get_llm_cache()
|
||||
|
||||
@ -0,0 +1,144 @@
|
||||
"Demonstrates MongoDBAtlasVectorSearch.as_retriever() invoked in a chain" ""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from time import sleep
|
||||
|
||||
import pytest # type: ignore[import-not-found]
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.output_parsers.string import StrOutputParser
|
||||
from langchain_core.prompts.chat import ChatPromptTemplate
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
from pymongo import MongoClient
|
||||
from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb import index
|
||||
|
||||
from ..utils import PatchedMongoDBAtlasVectorSearch
|
||||
|
||||
CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
|
||||
DB_NAME = "langchain_test_db"
|
||||
COLLECTION_NAME = "langchain_test_chain_example"
|
||||
INDEX_NAME = "vector_index_with_filter"
|
||||
DIMENSIONS = 1536
|
||||
TIMEOUT = 60.0
|
||||
INTERVAL = 0.5
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def collection() -> Collection:
|
||||
"""A Collection with both a Vector and a Full-text Search Index"""
|
||||
client: MongoClient = MongoClient(CONNECTION_STRING)
|
||||
if COLLECTION_NAME not in client[DB_NAME].list_collection_names():
|
||||
clxn = client[DB_NAME].create_collection(COLLECTION_NAME)
|
||||
else:
|
||||
clxn = client[DB_NAME][COLLECTION_NAME]
|
||||
|
||||
clxn.delete_many({})
|
||||
|
||||
if all([INDEX_NAME != ix["name"] for ix in clxn.list_search_indexes()]):
|
||||
index.create_vector_search_index(
|
||||
collection=clxn,
|
||||
index_name=INDEX_NAME,
|
||||
dimensions=DIMENSIONS,
|
||||
path="embedding",
|
||||
similarity="cosine",
|
||||
filters=None,
|
||||
wait_until_complete=TIMEOUT,
|
||||
)
|
||||
|
||||
return clxn
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
"OPENAI_API_KEY" not in os.environ, reason="Requires OpenAI for chat responses."
|
||||
)
|
||||
def test_chain(
|
||||
collection: Collection,
|
||||
) -> None:
|
||||
"""Demonstrate usage of MongoDBAtlasVectorSearch in a realistic chain
|
||||
|
||||
Follows example in the docs: https://python.langchain.com/v0.2/docs/how_to/hybrid/
|
||||
|
||||
Requires OpenAI_API_KEY for embedding and chat model.
|
||||
Requires INDEX_NAME to have been set up on MONGODB_ATLAS_URI
|
||||
"""
|
||||
|
||||
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
|
||||
|
||||
embedding_openai = OpenAIEmbeddings(
|
||||
openai_api_key=os.environ["OPENAI_API_KEY"], # type: ignore # noqa
|
||||
model="text-embedding-3-small",
|
||||
)
|
||||
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
collection=collection,
|
||||
embedding=embedding_openai,
|
||||
index_name=INDEX_NAME,
|
||||
text_key="page_content",
|
||||
)
|
||||
|
||||
texts = [
|
||||
"In 2023, I visited Paris",
|
||||
"In 2022, I visited New York",
|
||||
"In 2021, I visited New Orleans",
|
||||
"In 2019, I visited San Francisco",
|
||||
"In 2020, I visited Vancouver",
|
||||
]
|
||||
vectorstore.add_texts(texts)
|
||||
|
||||
# Give the index time to build (For CI)
|
||||
sleep(TIMEOUT)
|
||||
|
||||
query = "In the United States, what city did I visit last?"
|
||||
# One can do vector search on the vector store, using its various search types.
|
||||
k = len(texts)
|
||||
|
||||
store_output = list(vectorstore.similarity_search(query=query, k=k))
|
||||
assert len(store_output) == k
|
||||
assert isinstance(store_output[0], Document)
|
||||
|
||||
# Unfortunately, the VectorStore output cannot be given to a Chat Model
|
||||
# If we wish Chat Model to answer based on our own data,
|
||||
# we have to give it the right things to work with.
|
||||
# The way that Langchain does this is by piping results along in
|
||||
# a Chain: https://python.langchain.com/v0.1/docs/modules/chains/
|
||||
|
||||
# Now, we can turn our VectorStore into something Runnable in a Chain
|
||||
# by turning it into a Retriever.
|
||||
# For the simple VectorSearch Retriever, we can do this like so.
|
||||
|
||||
retriever = vectorstore.as_retriever(search_kwargs=dict(k=k))
|
||||
|
||||
# This does not do much other than expose our search function
|
||||
# as an invoke() method with a a certain API, a Runnable.
|
||||
retriever_output = retriever.invoke(query)
|
||||
assert len(retriever_output) == len(texts)
|
||||
assert retriever_output[0].page_content == store_output[0].page_content
|
||||
|
||||
# To get a natural language response to our question,
|
||||
# we need ChatOpenAI, a template to better frame the question as a prompt,
|
||||
# and a parser to send the output to a string.
|
||||
# Together, these become our Chain!
|
||||
# Here goes:
|
||||
|
||||
template = """Answer the question based only on the following context.
|
||||
Answer in as few words as possible.
|
||||
{context}
|
||||
Question: {question}
|
||||
"""
|
||||
prompt = ChatPromptTemplate.from_template(template)
|
||||
|
||||
model = ChatOpenAI()
|
||||
|
||||
chain = (
|
||||
{"context": retriever, "question": RunnablePassthrough()} # type: ignore
|
||||
| prompt
|
||||
| model
|
||||
| StrOutputParser()
|
||||
)
|
||||
|
||||
answer = chain.invoke("What city did I visit last?")
|
||||
|
||||
assert "Paris" in answer
|
||||
@ -1,6 +1,7 @@
|
||||
"""Search index commands are only supported on Atlas Clusters >=M10"""
|
||||
|
||||
import os
|
||||
from typing import Generator, List, Optional
|
||||
|
||||
import pytest
|
||||
from pymongo import MongoClient
|
||||
@ -8,23 +9,28 @@ from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb import index
|
||||
|
||||
TIMEOUT = 120
|
||||
DIMENSIONS = 10
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def collection() -> Collection:
|
||||
def collection() -> Generator:
|
||||
"""Depending on uri, this could point to any type of cluster."""
|
||||
uri = os.environ.get("MONGODB_ATLAS_URI")
|
||||
client: MongoClient = MongoClient(uri)
|
||||
clxn = client["db"].create_collection("collection")
|
||||
return clxn
|
||||
clxn = client["db"]["collection"]
|
||||
clxn.insert_one({"foo": "bar"})
|
||||
yield clxn
|
||||
clxn.drop()
|
||||
|
||||
|
||||
def test_search_index_commands(collection: Collection) -> None:
|
||||
index_name = "vector_index"
|
||||
dimensions = 1536
|
||||
dimensions = DIMENSIONS
|
||||
path = "embedding"
|
||||
similarity = "cosine"
|
||||
filters: list = []
|
||||
wait_until_complete = 120
|
||||
filters: Optional[List[str]] = None
|
||||
wait_until_complete = TIMEOUT
|
||||
|
||||
for index_info in collection.list_search_indexes():
|
||||
index.drop_vector_search_index(
|
||||
@ -52,10 +58,10 @@ def test_search_index_commands(collection: Collection) -> None:
|
||||
index.update_vector_search_index(
|
||||
collection,
|
||||
index_name,
|
||||
1536,
|
||||
DIMENSIONS,
|
||||
"embedding",
|
||||
new_similarity,
|
||||
[],
|
||||
filters=[],
|
||||
wait_until_complete=wait_until_complete,
|
||||
)
|
||||
|
||||
|
||||
176
libs/partners/mongodb/tests/integration_tests/test_retrievers.py
Normal file
176
libs/partners/mongodb/tests/integration_tests/test_retrievers.py
Normal file
@ -0,0 +1,176 @@
|
||||
import os
|
||||
from time import sleep
|
||||
from typing import List
|
||||
|
||||
import pytest
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from pymongo import MongoClient
|
||||
from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb import index
|
||||
from langchain_mongodb.retrievers import (
|
||||
MongoDBAtlasFullTextSearchRetriever,
|
||||
MongoDBAtlasHybridSearchRetriever,
|
||||
)
|
||||
|
||||
from ..utils import ConsistentFakeEmbeddings, PatchedMongoDBAtlasVectorSearch
|
||||
|
||||
CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
|
||||
DB_NAME = "langchain_test_db"
|
||||
COLLECTION_NAME = "test_retrievers"
|
||||
VECTOR_INDEX_NAME = "vector_index"
|
||||
EMBEDDING_FIELD = "embedding"
|
||||
PAGE_CONTENT_FIELD = "text"
|
||||
SEARCH_INDEX_NAME = "text_index"
|
||||
|
||||
DIMENSIONS = 1536
|
||||
TIMEOUT = 60.0
|
||||
INTERVAL = 0.5
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def example_documents() -> List[Document]:
|
||||
return [
|
||||
Document(page_content="In 2023, I visited Paris"),
|
||||
Document(page_content="In 2022, I visited New York"),
|
||||
Document(page_content="In 2021, I visited New Orleans"),
|
||||
Document(page_content="Sandwiches are beautiful. Sandwiches are fine."),
|
||||
]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def embedding_openai() -> Embeddings:
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
|
||||
try:
|
||||
return OpenAIEmbeddings(
|
||||
openai_api_key=os.environ["OPENAI_API_KEY"], # type: ignore # noqa
|
||||
model="text-embedding-3-small",
|
||||
)
|
||||
except Exception:
|
||||
return ConsistentFakeEmbeddings(DIMENSIONS)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def collection() -> Collection:
|
||||
"""A Collection with both a Vector and a Full-text Search Index"""
|
||||
client: MongoClient = MongoClient(CONNECTION_STRING)
|
||||
if COLLECTION_NAME not in client[DB_NAME].list_collection_names():
|
||||
clxn = client[DB_NAME].create_collection(COLLECTION_NAME)
|
||||
else:
|
||||
clxn = client[DB_NAME][COLLECTION_NAME]
|
||||
|
||||
clxn.delete_many({})
|
||||
|
||||
if not any([VECTOR_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
|
||||
index.create_vector_search_index(
|
||||
collection=clxn,
|
||||
index_name=VECTOR_INDEX_NAME,
|
||||
dimensions=DIMENSIONS,
|
||||
path="embedding",
|
||||
similarity="cosine",
|
||||
wait_until_complete=TIMEOUT,
|
||||
)
|
||||
|
||||
if not any([SEARCH_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
|
||||
index.create_fulltext_search_index(
|
||||
collection=clxn,
|
||||
index_name=SEARCH_INDEX_NAME,
|
||||
field=PAGE_CONTENT_FIELD,
|
||||
wait_until_complete=TIMEOUT,
|
||||
)
|
||||
|
||||
return clxn
|
||||
|
||||
|
||||
def test_hybrid_retriever(
|
||||
embedding_openai: Embeddings,
|
||||
collection: Collection,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test basic usage of MongoDBAtlasHybridSearchRetriever"""
|
||||
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
collection=collection,
|
||||
embedding=embedding_openai,
|
||||
index_name=VECTOR_INDEX_NAME,
|
||||
text_key=PAGE_CONTENT_FIELD,
|
||||
)
|
||||
|
||||
vectorstore.add_documents(example_documents)
|
||||
|
||||
sleep(TIMEOUT) # Wait for documents to be sync'd
|
||||
|
||||
retriever = MongoDBAtlasHybridSearchRetriever(
|
||||
vectorstore=vectorstore,
|
||||
search_index_name=SEARCH_INDEX_NAME,
|
||||
top_k=3,
|
||||
)
|
||||
|
||||
query1 = "What was the latest city that I visited?"
|
||||
results = retriever.invoke(query1)
|
||||
assert len(results) == 3
|
||||
assert "Paris" in results[0].page_content
|
||||
|
||||
query2 = "When was the last time I visited new orleans?"
|
||||
results = retriever.invoke(query2)
|
||||
assert "New Orleans" in results[0].page_content
|
||||
|
||||
|
||||
def test_fulltext_retriever(
|
||||
collection: Collection,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test result of performing fulltext search
|
||||
|
||||
Independent of the VectorStore, one adds documents
|
||||
via MongoDB's Collection API
|
||||
"""
|
||||
#
|
||||
|
||||
collection.insert_many(
|
||||
[{PAGE_CONTENT_FIELD: doc.page_content} for doc in example_documents]
|
||||
)
|
||||
sleep(TIMEOUT) # Wait for documents to be sync'd
|
||||
|
||||
retriever = MongoDBAtlasFullTextSearchRetriever(
|
||||
collection=collection,
|
||||
search_index_name=SEARCH_INDEX_NAME,
|
||||
search_field=PAGE_CONTENT_FIELD,
|
||||
)
|
||||
|
||||
query = "When was the last time I visited new orleans?"
|
||||
results = retriever.invoke(query)
|
||||
assert "New Orleans" in results[0].page_content
|
||||
assert "score" in results[0].metadata
|
||||
|
||||
|
||||
def test_vector_retriever(
|
||||
embedding_openai: Embeddings,
|
||||
collection: Collection,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test VectorStoreRetriever"""
|
||||
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
collection=collection,
|
||||
embedding=embedding_openai,
|
||||
index_name=VECTOR_INDEX_NAME,
|
||||
text_key=PAGE_CONTENT_FIELD,
|
||||
)
|
||||
|
||||
vectorstore.add_documents(example_documents)
|
||||
|
||||
sleep(TIMEOUT) # Wait for documents to be sync'd
|
||||
|
||||
retriever = vectorstore.as_retriever()
|
||||
|
||||
query1 = "What was the latest city that I visited?"
|
||||
results = retriever.invoke(query1)
|
||||
assert len(results) == 4
|
||||
assert "Paris" in results[0].page_content
|
||||
|
||||
query2 = "When was the last time I visited new orleans?"
|
||||
results = retriever.invoke(query2)
|
||||
assert "New Orleans" in results[0].page_content
|
||||
@ -4,7 +4,7 @@ from __future__ import annotations
|
||||
|
||||
import os
|
||||
from time import monotonic, sleep
|
||||
from typing import Any, Dict, Generator, Iterable, List, Optional, Union
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import pytest # type: ignore[import-not-found]
|
||||
from bson import ObjectId
|
||||
@ -14,11 +14,10 @@ from pymongo import MongoClient
|
||||
from pymongo.collection import Collection
|
||||
from pymongo.errors import OperationFailure
|
||||
|
||||
from langchain_mongodb import MongoDBAtlasVectorSearch
|
||||
from langchain_mongodb.index import drop_vector_search_index
|
||||
from langchain_mongodb.utils import oid_to_str
|
||||
|
||||
from ..utils import ConsistentFakeEmbeddings
|
||||
from ..utils import ConsistentFakeEmbeddings, PatchedMongoDBAtlasVectorSearch
|
||||
|
||||
INDEX_NAME = "langchain-test-index-vectorstores"
|
||||
INDEX_CREATION_NAME = "langchain-test-index-vectorstores-create-test"
|
||||
@ -32,41 +31,14 @@ TIMEOUT = 120.0
|
||||
INTERVAL = 0.5
|
||||
|
||||
|
||||
class PatchedMongoDBAtlasVectorSearch(MongoDBAtlasVectorSearch):
|
||||
def bulk_embed_and_insert_texts(
|
||||
self,
|
||||
texts: Union[List[str], Iterable[str]],
|
||||
metadatas: Union[List[dict], Generator[dict, Any, Any]],
|
||||
ids: Optional[List[str]] = None,
|
||||
) -> List:
|
||||
"""Patched insert_texts that waits for data to be indexed before returning"""
|
||||
ids_inserted = super().bulk_embed_and_insert_texts(texts, metadatas, ids)
|
||||
start = monotonic()
|
||||
while len(ids_inserted) != len(self.similarity_search("sandwich")) and (
|
||||
monotonic() - start <= TIMEOUT
|
||||
):
|
||||
sleep(INTERVAL)
|
||||
return ids_inserted
|
||||
|
||||
def create_vector_search_index(
|
||||
self,
|
||||
dimensions: int,
|
||||
filters: Optional[List[Dict[str, str]]] = None,
|
||||
update: bool = False,
|
||||
) -> None:
|
||||
result = super().create_vector_search_index(
|
||||
dimensions=dimensions, filters=filters, update=update
|
||||
)
|
||||
start = monotonic()
|
||||
while monotonic() - start <= TIMEOUT:
|
||||
if indexes := list(
|
||||
self._collection.list_search_indexes(name=self._index_name)
|
||||
):
|
||||
if indexes[0].get("status") == "READY":
|
||||
return result
|
||||
sleep(INTERVAL)
|
||||
|
||||
raise TimeoutError(f"{self._index_name} never reached 'status: READY'")
|
||||
@pytest.fixture
|
||||
def example_documents() -> List[Document]:
|
||||
return [
|
||||
Document(page_content="Dogs are tough.", metadata={"a": 1}),
|
||||
Document(page_content="Cats have fluff.", metadata={"b": 1}),
|
||||
Document(page_content="What is a sandwich?", metadata={"c": 1}),
|
||||
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
|
||||
]
|
||||
|
||||
|
||||
def _await_index_deletion(coll: Collection, index_name: str) -> None:
|
||||
@ -137,40 +109,46 @@ class TestMongoDBAtlasVectorSearch:
|
||||
|
||||
@pytest.fixture
|
||||
def embeddings(self) -> Embeddings:
|
||||
try:
|
||||
from langchain_openai import OpenAIEmbeddings
|
||||
|
||||
return OpenAIEmbeddings(
|
||||
openai_api_key=os.environ["OPENAI_API_KEY"], # type: ignore # noqa
|
||||
model="text-embedding-3-small",
|
||||
)
|
||||
except Exception:
|
||||
return ConsistentFakeEmbeddings(DIMENSIONS)
|
||||
|
||||
def test_from_documents(self, embeddings: Embeddings, collection: Any) -> None:
|
||||
def test_from_documents(
|
||||
self,
|
||||
embeddings: Embeddings,
|
||||
collection: Any,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
documents = [
|
||||
Document(page_content="Dogs are tough.", metadata={"a": 1}),
|
||||
Document(page_content="Cats have fluff.", metadata={"b": 1}),
|
||||
Document(page_content="What is a sandwich?", metadata={"c": 1}),
|
||||
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
|
||||
]
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
|
||||
documents,
|
||||
embeddings,
|
||||
example_documents,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
output = vectorstore.similarity_search("Sandwich", k=1)
|
||||
assert len(output) == 1
|
||||
# Check for the presence of the metadata key
|
||||
assert any([key.page_content == output[0].page_content for key in documents])
|
||||
assert any(
|
||||
[key.page_content == output[0].page_content for key in example_documents]
|
||||
)
|
||||
|
||||
def test_from_documents_no_embedding_return(
|
||||
self, embeddings: Embeddings, collection: Any
|
||||
self,
|
||||
embeddings: Embeddings,
|
||||
collection: Any,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
documents = [
|
||||
Document(page_content="Dogs are tough.", metadata={"a": 1}),
|
||||
Document(page_content="Cats have fluff.", metadata={"b": 1}),
|
||||
Document(page_content="What is a sandwich?", metadata={"c": 1}),
|
||||
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
|
||||
]
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
|
||||
documents,
|
||||
embeddings,
|
||||
example_documents,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
@ -179,37 +157,38 @@ class TestMongoDBAtlasVectorSearch:
|
||||
# Check for presence of embedding in each document
|
||||
assert all(["embedding" not in key.metadata for key in output])
|
||||
# Check for the presence of the metadata key
|
||||
assert any([key.page_content == output[0].page_content for key in documents])
|
||||
assert any(
|
||||
[key.page_content == output[0].page_content for key in example_documents]
|
||||
)
|
||||
|
||||
def test_from_documents_embedding_return(
|
||||
self, embeddings: Embeddings, collection: Any
|
||||
self,
|
||||
embeddings: Embeddings,
|
||||
collection: Any,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test end to end construction and search."""
|
||||
documents = [
|
||||
Document(page_content="Dogs are tough.", metadata={"a": 1}),
|
||||
Document(page_content="Cats have fluff.", metadata={"b": 1}),
|
||||
Document(page_content="What is a sandwich?", metadata={"c": 1}),
|
||||
Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
|
||||
]
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
|
||||
documents,
|
||||
embeddings,
|
||||
example_documents,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
output = vectorstore.similarity_search("Sandwich", k=1, include_embedding=True)
|
||||
output = vectorstore.similarity_search("Sandwich", k=1, include_embeddings=True)
|
||||
assert len(output) == 1
|
||||
# Check for presence of embedding in each document
|
||||
assert all([key.metadata.get("embedding") for key in output])
|
||||
# Check for the presence of the metadata key
|
||||
assert any([key.page_content == output[0].page_content for key in documents])
|
||||
assert any(
|
||||
[key.page_content == output[0].page_content for key in example_documents]
|
||||
)
|
||||
|
||||
def test_from_texts(
|
||||
self, embeddings: Embeddings, collection: Collection, texts: List[str]
|
||||
) -> None:
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
|
||||
texts,
|
||||
embeddings,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
@ -226,7 +205,7 @@ class TestMongoDBAtlasVectorSearch:
|
||||
metakeys = ["a", "b", "c", "d", "e"]
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
|
||||
texts,
|
||||
embeddings,
|
||||
embedding=embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
@ -242,21 +221,26 @@ class TestMongoDBAtlasVectorSearch:
|
||||
metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
|
||||
texts,
|
||||
embeddings,
|
||||
embedding=embeddings,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
output = vectorstore.similarity_search(
|
||||
does_not_match_filter = vectorstore.similarity_search(
|
||||
"Sandwich", k=1, pre_filter={"c": {"$lte": 0}}
|
||||
)
|
||||
assert output == []
|
||||
assert does_not_match_filter == []
|
||||
|
||||
matches_filter = vectorstore.similarity_search(
|
||||
"Sandwich", k=3, pre_filter={"c": {"$gt": 0}}
|
||||
)
|
||||
assert len(matches_filter) == 1
|
||||
|
||||
def test_mmr(self, embeddings: Embeddings, collection: Any) -> None:
|
||||
texts = ["foo", "foo", "fou", "foy"]
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
|
||||
texts,
|
||||
embeddings,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
@ -266,10 +250,57 @@ class TestMongoDBAtlasVectorSearch:
|
||||
assert output[0].page_content == "foo"
|
||||
assert output[1].page_content != "foo"
|
||||
|
||||
def test_retriever(
|
||||
self,
|
||||
embeddings: Embeddings,
|
||||
collection: Any,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Demonstrate usage and parity of VectorStore similarity_search
|
||||
with Retriever.invoke."""
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
|
||||
example_documents,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
query = "sandwich"
|
||||
|
||||
retriever_default_kwargs = vectorstore.as_retriever()
|
||||
result_retriever = retriever_default_kwargs.invoke(query)
|
||||
result_vectorstore = vectorstore.similarity_search(query)
|
||||
assert all(
|
||||
[
|
||||
result_retriever[i].page_content == result_vectorstore[i].page_content
|
||||
for i in range(len(result_retriever))
|
||||
]
|
||||
)
|
||||
|
||||
def test_include_embeddings(
|
||||
self,
|
||||
embeddings: Embeddings,
|
||||
collection: Any,
|
||||
example_documents: List[Document],
|
||||
) -> None:
|
||||
"""Test explicitly passing vector kwarg matches default."""
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
|
||||
documents=example_documents,
|
||||
embedding=embeddings,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
)
|
||||
|
||||
output_with = vectorstore.similarity_search(
|
||||
"Sandwich", include_embeddings=True, k=1
|
||||
)
|
||||
assert vectorstore._embedding_key in output_with[0].metadata
|
||||
output_without = vectorstore.similarity_search("Sandwich", k=1)
|
||||
assert vectorstore._embedding_key not in output_without[0].metadata
|
||||
|
||||
def test_delete(
|
||||
self, embeddings: Embeddings, collection: Any, texts: List[str]
|
||||
) -> None:
|
||||
vectorstore = MongoDBAtlasVectorSearch( # PatchedMongoDBAtlasVectorSearch(
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
collection=collection,
|
||||
embedding=embeddings,
|
||||
index_name=INDEX_NAME,
|
||||
@ -296,7 +327,10 @@ class TestMongoDBAtlasVectorSearch:
|
||||
collection: Collection,
|
||||
texts: List[str],
|
||||
) -> None:
|
||||
"""Tests API of add_texts, focussing on id treatment"""
|
||||
"""Tests API of add_texts, focussing on id treatment
|
||||
|
||||
Warning: This is slow because of the number of cases
|
||||
"""
|
||||
metadatas: List[Dict[str, Any]] = [
|
||||
{"a": 1},
|
||||
{"b": 1},
|
||||
@ -380,7 +414,6 @@ class TestMongoDBAtlasVectorSearch:
|
||||
self,
|
||||
embeddings: Embeddings,
|
||||
collection: Collection,
|
||||
index_name: str = INDEX_NAME,
|
||||
) -> None:
|
||||
"""Tests add_documents."""
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
@ -428,13 +461,13 @@ class TestMongoDBAtlasVectorSearch:
|
||||
self, embeddings: Embeddings, index_collection: Any
|
||||
) -> None:
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
index_collection, embeddings, index_name=INDEX_CREATION_NAME
|
||||
index_collection, embedding=embeddings, index_name=INDEX_CREATION_NAME
|
||||
)
|
||||
vectorstore.create_vector_search_index(dimensions=1536)
|
||||
|
||||
def test_index_update(self, embeddings: Embeddings, index_collection: Any) -> None:
|
||||
vectorstore = PatchedMongoDBAtlasVectorSearch(
|
||||
index_collection, embeddings, index_name=INDEX_CREATION_NAME
|
||||
index_collection, embedding=embeddings, index_name=INDEX_CREATION_NAME
|
||||
)
|
||||
vectorstore.create_vector_search_index(dimensions=1536)
|
||||
vectorstore.create_vector_search_index(dimensions=1536, update=True)
|
||||
|
||||
@ -12,7 +12,8 @@ from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
|
||||
from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
|
||||
from tests.utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM, MockCollection
|
||||
|
||||
from ..utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM, MockCollection
|
||||
|
||||
CONN_STRING = "MockString"
|
||||
COLLECTION = "default"
|
||||
@ -74,7 +75,7 @@ def llm_cache(cls: Any) -> BaseCache:
|
||||
connection_string=CONN_STRING,
|
||||
collection_name=COLLECTION,
|
||||
database_name=DATABASE,
|
||||
wait_until_ready=True,
|
||||
wait_until_ready=15.0,
|
||||
)
|
||||
)
|
||||
assert get_llm_cache()
|
||||
@ -207,7 +208,7 @@ def test_mongodb_atlas_cache_matrix(
|
||||
for prompt_i, llm_generations_i in zip(prompts, llm_generations):
|
||||
_execute_test(prompt_i, llm_string, llm_generations_i)
|
||||
|
||||
get_llm_cache()._collection._simluate_cache_aggregation_query = True # type: ignore
|
||||
get_llm_cache()._collection._simulate_cache_aggregation_query = True # type: ignore
|
||||
assert llm.generate(prompts) == LLMResult(
|
||||
generations=llm_generations, llm_output={}
|
||||
)
|
||||
|
||||
@ -4,7 +4,8 @@ from langchain.memory import ConversationBufferMemory # type: ignore[import-not
|
||||
from langchain_core.messages import message_to_dict
|
||||
|
||||
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
|
||||
from tests.utils import MockCollection
|
||||
|
||||
from ..utils import MockCollection
|
||||
|
||||
|
||||
class PatchedMongoDBChatMessageHistory(MongoDBChatMessageHistory):
|
||||
|
||||
@ -10,6 +10,9 @@ from pymongo.errors import OperationFailure, ServerSelectionTimeoutError
|
||||
|
||||
from langchain_mongodb import index
|
||||
|
||||
DIMENSION = 10
|
||||
TIMEOUT = 10
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def collection() -> Collection:
|
||||
@ -25,19 +28,33 @@ def collection() -> Collection:
|
||||
def test_create_vector_search_index(collection: Collection) -> None:
|
||||
with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
|
||||
index.create_vector_search_index(
|
||||
collection, "index_name", 1536, "embedding", "cosine", []
|
||||
collection,
|
||||
"index_name",
|
||||
DIMENSION,
|
||||
"embedding",
|
||||
"cosine",
|
||||
[],
|
||||
wait_until_complete=TIMEOUT,
|
||||
)
|
||||
|
||||
|
||||
def test_drop_vector_search_index(collection: Collection) -> None:
|
||||
with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
|
||||
index.drop_vector_search_index(collection, "index_name")
|
||||
index.drop_vector_search_index(
|
||||
collection, "index_name", wait_until_complete=TIMEOUT
|
||||
)
|
||||
|
||||
|
||||
def test_update_vector_search_index(collection: Collection) -> None:
|
||||
with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
|
||||
index.update_vector_search_index(
|
||||
collection, "index_name", 1536, "embedding", "cosine", []
|
||||
collection,
|
||||
"index_name",
|
||||
DIMENSION,
|
||||
"embedding",
|
||||
"cosine",
|
||||
[],
|
||||
wait_until_complete=TIMEOUT,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -2,14 +2,13 @@ from json import dumps, loads
|
||||
from typing import Any, Optional
|
||||
|
||||
import pytest # type: ignore[import-not-found]
|
||||
from bson import ObjectId, json_util
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.embeddings import Embeddings
|
||||
from pymongo.collection import Collection
|
||||
|
||||
from langchain_mongodb import MongoDBAtlasVectorSearch
|
||||
from langchain_mongodb.utils import str_to_oid
|
||||
from tests.utils import ConsistentFakeEmbeddings, MockCollection
|
||||
|
||||
from ..utils import ConsistentFakeEmbeddings, MockCollection
|
||||
|
||||
INDEX_NAME = "langchain-test-index"
|
||||
NAMESPACE = "langchain_test_db.langchain_test_collection"
|
||||
@ -81,8 +80,7 @@ class TestMongoDBAtlasVectorSearch:
|
||||
# Validate the ObjectId provided is json serializable
|
||||
assert loads(dumps(output[0].page_content)) == output[0].page_content
|
||||
assert loads(dumps(output[0].metadata)) == output[0].metadata
|
||||
json_metadata = dumps(output[0].metadata) # normal json.dumps
|
||||
isinstance(str_to_oid(json_util.loads(json_metadata)["_id"]), ObjectId)
|
||||
assert isinstance(output[0].metadata["_id"], str)
|
||||
|
||||
def test_from_documents(
|
||||
self, embedding_openai: Embeddings, collection: MockCollection
|
||||
@ -98,7 +96,7 @@ class TestMongoDBAtlasVectorSearch:
|
||||
documents,
|
||||
embedding_openai,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
vector_index_name=INDEX_NAME,
|
||||
)
|
||||
self._validate_search(
|
||||
vectorstore, collection, metadata=documents[2].metadata["c"]
|
||||
@ -117,7 +115,7 @@ class TestMongoDBAtlasVectorSearch:
|
||||
texts,
|
||||
embedding_openai,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
vector_index_name=INDEX_NAME,
|
||||
)
|
||||
self._validate_search(vectorstore, collection, metadata=None)
|
||||
|
||||
@ -136,7 +134,7 @@ class TestMongoDBAtlasVectorSearch:
|
||||
embedding_openai,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
vector_index_name=INDEX_NAME,
|
||||
)
|
||||
self._validate_search(vectorstore, collection, metadata=metadatas[2]["c"])
|
||||
|
||||
@ -155,7 +153,7 @@ class TestMongoDBAtlasVectorSearch:
|
||||
embedding_openai,
|
||||
metadatas=metadatas,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
vector_index_name=INDEX_NAME,
|
||||
)
|
||||
collection._aggregate_result = list(
|
||||
filter(
|
||||
@ -175,9 +173,9 @@ class TestMongoDBAtlasVectorSearch:
|
||||
texts = ["foo", "foo", "fou", "foy"]
|
||||
vectorstore = MongoDBAtlasVectorSearch.from_texts(
|
||||
texts,
|
||||
embedding_openai,
|
||||
embedding=embedding_openai,
|
||||
collection=collection,
|
||||
index_name=INDEX_NAME,
|
||||
vector_index_name=INDEX_NAME,
|
||||
)
|
||||
query = "foo"
|
||||
self._validate_search(
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict, List, Mapping, Optional, cast
|
||||
from time import monotonic, sleep
|
||||
from typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Union, cast
|
||||
|
||||
from bson import ObjectId
|
||||
from langchain_core.callbacks.manager import (
|
||||
@ -20,8 +21,47 @@ from langchain_core.pydantic_v1 import validator
|
||||
from pymongo.collection import Collection
|
||||
from pymongo.results import DeleteResult, InsertManyResult
|
||||
|
||||
from langchain_mongodb import MongoDBAtlasVectorSearch
|
||||
from langchain_mongodb.cache import MongoDBAtlasSemanticCache
|
||||
|
||||
TIMEOUT = 120
|
||||
INTERVAL = 0.5
|
||||
|
||||
|
||||
class PatchedMongoDBAtlasVectorSearch(MongoDBAtlasVectorSearch):
|
||||
def bulk_embed_and_insert_texts(
|
||||
self,
|
||||
texts: Union[List[str], Iterable[str]],
|
||||
metadatas: Union[List[dict], Generator[dict, Any, Any]],
|
||||
ids: Optional[List[str]] = None,
|
||||
) -> List:
|
||||
"""Patched insert_texts that waits for data to be indexed before returning"""
|
||||
ids_inserted = super().bulk_embed_and_insert_texts(texts, metadatas, ids)
|
||||
start = monotonic()
|
||||
while len(ids_inserted) != len(self.similarity_search("sandwich")) and (
|
||||
monotonic() - start <= TIMEOUT
|
||||
):
|
||||
sleep(INTERVAL)
|
||||
return ids_inserted
|
||||
|
||||
def create_vector_search_index(
|
||||
self,
|
||||
dimensions: int,
|
||||
filters: Optional[List[str]] = None,
|
||||
update: bool = False,
|
||||
) -> None:
|
||||
result = super().create_vector_search_index(
|
||||
dimensions=dimensions, filters=filters, update=update
|
||||
)
|
||||
start = monotonic()
|
||||
while monotonic() - start <= TIMEOUT:
|
||||
if indexes := list(
|
||||
self._collection.list_search_indexes(name=self._index_name)
|
||||
):
|
||||
if indexes[0].get("status") == "READY":
|
||||
return result
|
||||
sleep(INTERVAL)
|
||||
|
||||
|
||||
class ConsistentFakeEmbeddings(Embeddings):
|
||||
"""Fake embeddings functionality for testing."""
|
||||
@ -147,13 +187,13 @@ class MockCollection(Collection):
|
||||
_aggregate_result: List[Any]
|
||||
_insert_result: Optional[InsertManyResult]
|
||||
_data: List[Any]
|
||||
_simluate_cache_aggregation_query: bool
|
||||
_simulate_cache_aggregation_query: bool
|
||||
|
||||
def __init__(self) -> None:
|
||||
self._data = []
|
||||
self._aggregate_result = []
|
||||
self._insert_result = None
|
||||
self._simluate_cache_aggregation_query = False
|
||||
self._simulate_cache_aggregation_query = False
|
||||
|
||||
def delete_many(self, *args, **kwargs) -> DeleteResult: # type: ignore
|
||||
old_len = len(self._data)
|
||||
@ -201,7 +241,7 @@ class MockCollection(Collection):
|
||||
elif upsert:
|
||||
self._data.append({**find_query, **set_options})
|
||||
|
||||
def _execute_cache_aggreation_query(self, *args, **kwargs) -> List[Dict[str, Any]]: # type: ignore
|
||||
def _execute_cache_aggregation_query(self, *args, **kwargs) -> List[Dict[str, Any]]: # type: ignore
|
||||
"""Helper function only to be used for MongoDBAtlasSemanticCache Testing
|
||||
|
||||
Returns:
|
||||
@ -223,12 +263,12 @@ class MockCollection(Collection):
|
||||
return acc
|
||||
|
||||
def aggregate(self, *args, **kwargs) -> List[Any]: # type: ignore
|
||||
if self._simluate_cache_aggregation_query:
|
||||
return deepcopy(self._execute_cache_aggreation_query(*args, **kwargs))
|
||||
if self._simulate_cache_aggregation_query:
|
||||
return deepcopy(self._execute_cache_aggregation_query(*args, **kwargs))
|
||||
return deepcopy(self._aggregate_result)
|
||||
|
||||
def count_documents(self, *args, **kwargs) -> int: # type: ignore
|
||||
return len(self._data)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return "FakeCollection"
|
||||
return "MockCollection"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user