Use LCP Client in Tracer (#5908)

Move the LCP calls to the client.

langchain/callbacks/tracers/langchain.py

@@ -8,61 +8,16 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional, Union
 from uuid import UUID
 
-import requests
+from langchainplus_sdk import LangChainPlusClient
-from requests.exceptions import HTTPError
-from tenacity import (
-    before_sleep_log,
-    retry,
-    retry_if_exception_type,
-    stop_after_attempt,
-    wait_exponential,
-)
 
 from langchain.callbacks.tracers.base import BaseTracer
-from langchain.callbacks.tracers.schemas import (
+from langchain.callbacks.tracers.schemas import Run, RunTypeEnum, TracerSession
-    Run,
+from langchain.env import get_runtime_environment
-    RunCreate,
-    RunTypeEnum,
-    RunUpdate,
-    TracerSession,
-)
 from langchain.schema import BaseMessage, messages_to_dict
 
 logger = logging.getLogger(__name__)
 
 
-def get_headers() -> Dict[str, Any]:
-    """Get the headers for the LangChain API."""
-    headers: Dict[str, Any] = {"Content-Type": "application/json"}
-    if os.getenv("LANGCHAIN_API_KEY"):
-        headers["x-api-key"] = os.getenv("LANGCHAIN_API_KEY")
-    return headers
-
-
-def get_endpoint() -> str:
-    return os.getenv("LANGCHAIN_ENDPOINT", "http://localhost:1984")
-
-
-class LangChainTracerAPIError(Exception):
-    """An error occurred while communicating with the LangChain API."""
-
-
-class LangChainTracerUserError(Exception):
-    """An error occurred while communicating with the LangChain API."""
-
-
-class LangChainTracerError(Exception):
-    """An error occurred while communicating with the LangChain API."""
-
-
-retry_decorator = retry(
-    stop=stop_after_attempt(3),
-    wait=wait_exponential(multiplier=1, min=4, max=10),
-    retry=retry_if_exception_type(LangChainTracerAPIError),
-    before_sleep=before_sleep_log(logger, logging.WARNING),
-)
-
-
 class LangChainTracer(BaseTracer):
     """An implementation of the SharedTracer that POSTS to the langchain endpoint."""
 
@@ -70,19 +25,19 @@ class LangChainTracer(BaseTracer):
         self,
         example_id: Optional[Union[UUID, str]] = None,
         session_name: Optional[str] = None,
+        client: Optional[LangChainPlusClient] = None,
         **kwargs: Any,
     ) -> None:
         """Initialize the LangChain tracer."""
         super().__init__(**kwargs)
         self.session: Optional[TracerSession] = None
-        self._endpoint = get_endpoint()
-        self._headers = get_headers()
         self.example_id = (
             UUID(example_id) if isinstance(example_id, str) else example_id
         )
         self.session_name = session_name or os.getenv("LANGCHAIN_SESSION", "default")
         # set max_workers to 1 to process tasks in order
         self.executor = ThreadPoolExecutor(max_workers=1)
+        self.client = client or LangChainPlusClient()
 
     def on_chat_model_start(
         self,
@@ -114,60 +69,19 @@ class LangChainTracer(BaseTracer):
     def _persist_run(self, run: Run) -> None:
         """The Langchain Tracer uses Post/Patch rather than persist."""
 
-    @retry_decorator
     def _persist_run_single(self, run: Run) -> None:
         """Persist a run."""
         if run.parent_run_id is None:
             run.reference_example_id = self.example_id
-        run_dict = run.dict()
+        run_dict = run.dict(exclude={"child_runs"})
-        del run_dict["child_runs"]
+        extra = run_dict.get("extra", {})
-        run_create = RunCreate(**run_dict, session_name=self.session_name)
+        extra["runtime"] = get_runtime_environment()
-        response = None
+        run_dict["extra"] = extra
-        try:
+        run = self.client.create_run(**run_dict, session_name=self.session_name)
-            # TODO: Add retries when async
+
-            response = requests.post(
-                f"{self._endpoint}/runs",
-                data=run_create.json(),
-                headers=self._headers,
-            )
-            response.raise_for_status()
-        except HTTPError as e:
-            if response is not None and response.status_code == 500:
-                raise LangChainTracerAPIError(
-                    f"Failed to upsert persist run to LangChain API. {e}"
-                )
-            else:
-                raise LangChainTracerUserError(
-                    f"Failed to persist run to LangChain API. {e}"
-                )
-        except Exception as e:
-            raise LangChainTracerError(
-                f"Failed to persist run to LangChain API. {e}"
-            ) from e
-
-    @retry_decorator
     def _update_run_single(self, run: Run) -> None:
         """Update a run."""
-        run_update = RunUpdate(**run.dict())
+        self.client.update_run(run.id, **run.dict())
-        response = None
-        try:
-            response = requests.patch(
-                f"{self._endpoint}/runs/{run.id}",
-                data=run_update.json(),
-                headers=self._headers,
-            )
-            response.raise_for_status()
-        except HTTPError as e:
-            if response is not None and response.status_code == 500:
-                raise LangChainTracerAPIError(
-                    f"Failed to update run to LangChain API. {e}"
-                )
-            else:
-                raise LangChainTracerUserError(f"Failed to run to LangChain API. {e}")
-        except Exception as e:
-            raise LangChainTracerError(
-                f"Failed to update run to LangChain API. {e}"
-            ) from e
 
     def _on_llm_start(self, run: Run) -> None:
         """Persist an LLM run."""

langchain/callbacks/tracers/langchain_v1.py

@@ -2,12 +2,11 @@ from __future__ import annotations
 
 import logging
 import os
-from typing import Any, Optional, Union
+from typing import Any, Dict, Optional, Union
 
 import requests
 
 from langchain.callbacks.tracers.base import BaseTracer
-from langchain.callbacks.tracers.langchain import get_headers
 from langchain.callbacks.tracers.schemas import (
     ChainRun,
     LLMRun,
@@ -21,6 +20,14 @@ from langchain.schema import get_buffer_string
 from langchain.utils import raise_for_status_with_text
 
 
+def get_headers() -> Dict[str, Any]:
+    """Get the headers for the LangChain API."""
+    headers: Dict[str, Any] = {"Content-Type": "application/json"}
+    if os.getenv("LANGCHAIN_API_KEY"):
+        headers["x-api-key"] = os.getenv("LANGCHAIN_API_KEY")
+    return headers
+
+
 def _get_endpoint() -> str:
     return os.getenv("LANGCHAIN_ENDPOINT", "http://localhost:8000")
 

langchain/env.py

@@ -10,6 +10,7 @@ def get_runtime_environment() -> dict:
 
     return {
         "library_version": __version__,
+        "library": "langchain",
         "platform": platform.platform(),
         "runtime": "python",
         "runtime_version": platform.python_version(),

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -4005,14 +4005,14 @@ tests = ["pytest", "pytest-mock"]
 
 [[package]]
 name = "langchainplus-sdk"
-version = "0.0.6"
+version = "0.0.7"
 description = "Client library to connect to the LangChainPlus LLM Tracing and Evaluation Platform."
 category = "main"
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langchainplus_sdk-0.0.6-py3-none-any.whl", hash = "sha256:43fe01c66442b88403c969b8812f6be81e023c0d2a6d5d3572a8d87961438658"},
+    {file = "langchainplus_sdk-0.0.7-py3-none-any.whl", hash = "sha256:aefc471058648bf9fc51f659117d33ef905d25a304d5a021f7e32c30f5921076"},
-    {file = "langchainplus_sdk-0.0.6.tar.gz", hash = "sha256:c911a98fd2d02baa48f742b7d700fd6a55f11c9a545ee5d66b08825940c9a32e"},
+    {file = "langchainplus_sdk-0.0.7.tar.gz", hash = "sha256:b58565bdcaf301d2e6e7dd8898f0b8ccf549a35476258e0c14d871d6de02d210"},
 ]
 
 [package.dependencies]
@@ -11340,13 +11340,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
 cffi = ["cffi (>=1.11)"]
 
 [extras]
-all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "pymongo", "weaviate-client", "redis", "google-api-python-client", "google-auth", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "langkit", "lark", "pexpect", "pyvespa", "O365", "jq", "docarray", "steamship", "pdfminer-six", "lxml", "requests-toolbelt", "neo4j", "openlm", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "momento", "singlestoredb", "tigrisdb", "nebula3-python"]
+all = ["O365", "aleph-alpha-client", "anthropic", "arxiv", "atlassian-python-api", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jina", "jinja2", "jq", "lancedb", "langkit", "lark", "lxml", "manifest-ml", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "pyvespa", "qdrant-client", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "spacy", "steamship", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
-azure = ["azure-identity", "azure-cosmos", "openai", "azure-core", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech"]
+azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "openai"]
 cohere = ["cohere"]
 docarray = ["docarray"]
 embeddings = ["sentence-transformers"]
-extended-testing = ["beautifulsoup4", "bibtexparser", "chardet", "jq", "pdfminer-six", "pypdf", "pymupdf", "pypdfium2", "tqdm", "lxml", "atlassian-python-api", "beautifulsoup4", "pandas", "telethon", "psychicapi", "zep-python", "gql", "requests-toolbelt", "html2text", "py-trello", "scikit-learn", "pyspark"]
+extended-testing = ["atlassian-python-api", "beautifulsoup4", "beautifulsoup4", "bibtexparser", "chardet", "gql", "html2text", "jq", "lxml", "pandas", "pdfminer-six", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "requests-toolbelt", "scikit-learn", "telethon", "tqdm", "zep-python"]
-llms = ["anthropic", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
+llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"]
 openai = ["openai", "tiktoken"]
 qdrant = ["qdrant-client"]
 text-helpers = ["chardet"]
@@ -11354,4 +11354,4 @@ text-helpers = ["chardet"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "0da3585d7f3216764f396c162c8f9456423b9f80a6dc9af46040c3e5eec0b79e"
+content-hash = "dbbaa2907bf2ac09ed111ce712772bba0fe56901627f41c53aef71ae5a38d1c6"

pyproject.toml

@@ -105,7 +105,7 @@ singlestoredb = {version = "^0.6.1", optional = true}
 pyspark = {version = "^3.4.0", optional = true}
 tigrisdb = {version = "^1.0.0b6", optional = true}
 nebula3-python = {version = "^3.4.0", optional = true}
-langchainplus-sdk = ">=0.0.6"
+langchainplus-sdk = ">=0.0.7"
 
 
 [tool.poetry.group.docs.dependencies]