Merge branch 'master' into cc/retriever_score

libs/community/langchain_community/vectorstores/redis/base.py

@@ -1444,7 +1444,11 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):
         arbitrary_types_allowed = True
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
@@ -1472,7 +1476,11 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):
         return docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = await self.vectorstore.asimilarity_search(

libs/community/tests/unit_tests/vectorstores/test_databricks_vector_search.py

@@ -1,10 +1,11 @@
 import itertools
 import random
 import uuid
-from typing import Dict, List, Optional, Set
+from typing import Dict, List, Optional, Set, cast
 from unittest.mock import MagicMock, patch
 
 import pytest
+from langchain_core.documents import DocumentSearchHit
 
 from langchain_community.vectorstores import DatabricksVectorSearch
 from tests.integration_tests.vectorstores.fake_embeddings import (
@@ -598,6 +599,12 @@ def test_similarity_score_threshold(index_details: dict, threshold: float) -> No
         assert len(search_result) == len(fake_texts)
     else:
         assert len(search_result) == 0
+    result_with_scores = cast(
+        List[DocumentSearchHit], retriever.invoke(query, include_score=True)
+    )
+    for idx, result in enumerate(result_with_scores):
+        assert result.score >= threshold
+        assert result.page_content == search_result[idx].page_content
 
 
 @pytest.mark.requires("databricks", "databricks.vector_search")

libs/core/langchain_core/documents/__init__.py

@@ -2,8 +2,13 @@
 and their transformations.
 
 """
-from langchain_core.documents.base import Document
+from langchain_core.documents.base import Document, DocumentSearchHit
 from langchain_core.documents.compressor import BaseDocumentCompressor
 from langchain_core.documents.transformers import BaseDocumentTransformer
 
-__all__ = ["Document", "BaseDocumentTransformer", "BaseDocumentCompressor"]
+__all__ = [
+    "Document",
+    "DocumentSearchHit",
+    "BaseDocumentTransformer",
+    "BaseDocumentCompressor",
+]

libs/core/langchain_core/documents/base.py

@@ -30,3 +30,21 @@ class Document(Serializable):
     def get_lc_namespace(cls) -> List[str]:
         """Get the namespace of the langchain object."""
         return ["langchain", "schema", "document"]
+
+
+class DocumentSearchHit(Document):
+    """Class for storing a document and fields associated with retrieval."""
+
+    score: float
+    """Score associated with the document's relevance to a query."""
+    type: Literal["DocumentSearchHit"] = "DocumentSearchHit"  # type: ignore[assignment] # noqa: E501
+
+    @classmethod
+    def is_lc_serializable(cls) -> bool:
+        """Return whether this class is serializable."""
+        return True
+
+    @classmethod
+    def get_lc_namespace(cls) -> List[str]:
+        """Get the namespace of the langchain object."""
+        return ["langchain", "schema", "document_search_hit"]

libs/core/langchain_core/load/mapping.py

@@ -157,6 +157,12 @@ SERIALIZABLE_MAPPING: Dict[Tuple[str, ...], Tuple[str, ...]] = {
         "base",
         "Document",
     ),
+    ("langchain", "schema", "document_search_hit", "DocumentSearchHit"): (
+        "langchain_core",
+        "documents",
+        "base",
+        "DocumentSearchHit",
+    ),
     ("langchain", "output_parsers", "fix", "OutputFixingParser"): (
         "langchain",
         "output_parsers",
@@ -666,6 +672,12 @@ OLD_CORE_NAMESPACES_MAPPING: Dict[Tuple[str, ...], Tuple[str, ...]] = {
         "base",
         "Document",
     ),
+    ("langchain_core", "documents", "base", "DocumentSearchHit"): (
+        "langchain_core",
+        "documents",
+        "base",
+        "DocumentSearchHit",
+    ),
     ("langchain_core", "prompts", "chat", "AIMessagePromptTemplate"): (
         "langchain_core",
         "prompts",

libs/core/langchain_core/vectorstores.py

@@ -39,6 +39,7 @@ from typing import (
     TypeVar,
 )
 
+from langchain_core.documents import DocumentSearchHit
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.retrievers import BaseRetriever
@@ -690,8 +691,17 @@ class VectorStoreRetriever(BaseRetriever):
         return values
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
+        if include_score and self.search_type != "similarity_score_threshold":
+            raise ValueError(
+                "include_score is only supported "
+                "for search_type=similarity_score_threshold"
+            )
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
         elif self.search_type == "similarity_score_threshold":
@@ -700,6 +710,11 @@ class VectorStoreRetriever(BaseRetriever):
                     query, **self.search_kwargs
                 )
             )
+            if include_score:
+                return [
+                    DocumentSearchHit(page_content=doc.page_content, score=score)
+                    for doc, score in docs_and_similarities
+                ]
             docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
             docs = self.vectorstore.max_marginal_relevance_search(
@@ -710,8 +725,17 @@ class VectorStoreRetriever(BaseRetriever):
         return docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
+        if include_score and self.search_type != "similarity_score_threshold":
+            raise ValueError(
+                "include_score is only supported "
+                "for search_type=similarity_score_threshold"
+            )
         if self.search_type == "similarity":
             docs = await self.vectorstore.asimilarity_search(
                 query, **self.search_kwargs
@@ -722,6 +746,11 @@ class VectorStoreRetriever(BaseRetriever):
                     query, **self.search_kwargs
                 )
             )
+            if include_score:
+                return [
+                    DocumentSearchHit(page_content=doc.page_content, score=score)
+                    for doc, score in docs_and_similarities
+                ]
             docs = [doc for doc, _ in docs_and_similarities]
         elif self.search_type == "mmr":
             docs = await self.vectorstore.amax_marginal_relevance_search(

libs/core/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-core"
-version = "0.1.46"
+version = "0.1.47rc1"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"

libs/core/tests/unit_tests/documents/test_imports.py

@@ -1,6 +1,11 @@
 from langchain_core.documents import __all__
 
-EXPECTED_ALL = ["Document", "BaseDocumentTransformer", "BaseDocumentCompressor"]
+EXPECTED_ALL = [
+    "Document",
+    "DocumentSearchHit",
+    "BaseDocumentTransformer",
+    "BaseDocumentCompressor",
+]
 
 
 def test_all_imports() -> None:

libs/langchain/langchain/retrievers/self_query/base.py

@@ -33,7 +33,7 @@ from langchain_core.callbacks.manager import (
     AsyncCallbackManagerForRetrieverRun,
     CallbackManagerForRetrieverRun,
 )
-from langchain_core.documents import Document
+from langchain_core.documents import Document, DocumentSearchHit
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.retrievers import BaseRetriever
@@ -192,19 +192,43 @@ class SelfQueryRetriever(BaseRetriever):
         return new_query, search_kwargs
 
     def _get_docs_with_query(
-        self, query: str, search_kwargs: Dict[str, Any]
+        self, query: str, search_kwargs: Dict[str, Any], include_score: bool = False
     ) -> List[Document]:
-        docs = self.vectorstore.search(query, self.search_type, **search_kwargs)
+        if include_score:
+            docs_and_scores = self.vectorstore.similarity_search_with_score(
+                query, **search_kwargs
+            )
+            return [
+                DocumentSearchHit(page_content=doc.page_content, score=score)
+                for doc, score in docs_and_scores
+            ]
+        else:
+            docs = self.vectorstore.search(query, self.search_type, **search_kwargs)
         return docs
 
     async def _aget_docs_with_query(
-        self, query: str, search_kwargs: Dict[str, Any]
+        self, query: str, search_kwargs: Dict[str, Any], include_score: bool = False
     ) -> List[Document]:
-        docs = await self.vectorstore.asearch(query, self.search_type, **search_kwargs)
+        if include_score:
+            docs_and_scores = await self.vectorstore.asimilarity_search_with_score(
+                query, **search_kwargs
+            )
+            return [
+                DocumentSearchHit(page_content=doc.page_content, score=score)
+                for doc, score in docs_and_scores
+            ]
+        else:
+            docs = await self.vectorstore.asearch(
+                query, self.search_type, **search_kwargs
+            )
         return docs
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
         """Get documents relevant for a query.
 
@@ -220,11 +244,17 @@ class SelfQueryRetriever(BaseRetriever):
         if self.verbose:
             logger.info(f"Generated Query: {structured_query}")
         new_query, search_kwargs = self._prepare_query(query, structured_query)
-        docs = self._get_docs_with_query(new_query, search_kwargs)
+        docs = self._get_docs_with_query(
+            new_query, search_kwargs, include_score=include_score
+        )
         return docs
 
     async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: AsyncCallbackManagerForRetrieverRun,
+        include_score: bool = False,
     ) -> List[Document]:
         """Get documents relevant for a query.
 
@@ -240,7 +270,9 @@ class SelfQueryRetriever(BaseRetriever):
         if self.verbose:
             logger.info(f"Generated Query: {structured_query}")
         new_query, search_kwargs = self._prepare_query(query, structured_query)
-        docs = await self._aget_docs_with_query(new_query, search_kwargs)
+        docs = await self._aget_docs_with_query(
+            new_query, search_kwargs, include_score=include_score
+        )
         return docs
 
     @classmethod

libs/langchain/poetry.lock

@@ -3469,7 +3469,7 @@ files = [
 
 [[package]]
 name = "langchain-community"
-version = "0.0.32"
+version = "0.0.34"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -3479,7 +3479,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 dataclasses-json = ">= 0.5.7, < 0.7"
-langchain-core = "^0.1.41"
+langchain-core = "^0.1.45"
 langsmith = "^0.1.0"
 numpy = "^1"
 PyYAML = ">=5.3"
@@ -3489,7 +3489,7 @@ tenacity = "^8.1.0"
 
 [package.extras]
 cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpickle (>=2.0.0)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
 
 [package.source]
 type = "directory"
@@ -3497,7 +3497,7 @@ url = "../community"
 
 [[package]]
 name = "langchain-core"
-version = "0.1.42"
+version = "0.1.47rc1"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -9410,4 +9410,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "845d36b1258779b2b483ec8758070fc73adad9d94b7d4c93a4145c360d946ac2"
+content-hash = "00c1e092e378283d46a322dfb3014e30f5388f334e0a82f49acd2e8ecf5c05d3"

libs/langchain/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain"
-version = "0.1.16"
+version = "0.1.17rc1"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
@@ -12,7 +12,7 @@ langchain-server = "langchain.server:main"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain-core = "^0.1.42"
+langchain-core = { version = "^0.1.47rc1", allow-prereleases = true }
 langchain-text-splitters = ">=0.0.1,<0.1"
 langchain-community = ">=0.0.32,<0.1"
 langsmith = "^0.1.17"