Remove unnecessary langsmith dependency

README.md

@@ -173,7 +173,6 @@ A `Microservices` can be created by using the decorator `register_microservice`.
 
 ```python
 from langchain_community.embeddings import HuggingFaceHubEmbeddings
-from langsmith import traceable
 
 from comps import register_microservice, EmbedDoc, ServiceType, TextDoc
 
@@ -187,7 +186,6 @@ from comps import register_microservice, EmbedDoc, ServiceType, TextDoc
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 def embedding(input: TextDoc) -> EmbedDoc:
     embed_vector = embeddings.embed_query(input.text)
     res = EmbedDoc(text=input.text, embedding=embed_vector)

comps/agent/langchain/requirements.txt

@@ -11,7 +11,6 @@ langchain-openai
 langchain_community
 langchainhub
 langgraph
-langsmith
 numpy
 
 # used by cloud native

comps/dataprep/milvus/prepare_doc_milvus.py

@@ -21,7 +21,6 @@
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings, OpenAIEmbeddings
 from langchain_milvus.vectorstores import Milvus
 from langchain_text_splitters import HTMLHeaderTextSplitter
-from langsmith import traceable
 from pyspark import SparkConf, SparkContext
 
 from comps import DocPath, opea_microservices, register_microservice
@@ -160,7 +159,6 @@ def ingest_link_to_milvus(link_list: List[str]):
 
 
 @register_microservice(name="opea_service@prepare_doc_milvus", endpoint="/v1/dataprep", host="0.0.0.0", port=6010)
-@traceable(run_type="tool")
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
     link_list: Optional[str] = Form(None),

comps/dataprep/milvus/requirements.txt

@@ -9,7 +9,6 @@ langchain
 langchain-community
 langchain-text-splitters
 langchain_milvus
-langsmith
 markdown
 numpy
 openai

comps/dataprep/pgvector/langchain/prepare_doc_pgvector.py

@@ -12,7 +12,6 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import PGVector
-from langsmith import traceable
 
 from comps import DocPath, ServiceType, opea_microservices, register_microservice, register_statistics
 from comps.dataprep.utils import document_loader, get_separators, parse_html
@@ -101,7 +100,6 @@ def ingest_link_to_pgvector(link_list: List[str]):
     host="0.0.0.0",
     port=6007,
 )
-@traceable(run_type="tool")
 @register_statistics(names=["opea_service@dataprep_pgvector"])
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None), link_list: Optional[str] = Form(None)

comps/dataprep/pgvector/langchain/requirements.txt

@@ -7,7 +7,6 @@ fastapi
 huggingface_hub
 langchain
 langchain-community
-langsmith
 markdown
 numpy
 opentelemetry-api

comps/dataprep/pinecone/requirements.txt

@@ -6,7 +6,6 @@ huggingface_hub
 langchain
 langchain-community
 langchain-pinecone
-langsmith
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp

comps/dataprep/redis/langchain/prepare_doc_redis.py

@@ -16,7 +16,6 @@
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import Redis
 from langchain_text_splitters import HTMLHeaderTextSplitter
-from langsmith import traceable
 from redis.commands.search.field import TextField
 from redis.commands.search.indexDefinition import IndexDefinition, IndexType
 
@@ -206,7 +205,6 @@ async def ingest_link_to_redis(link_list: List[str]):
 
 
 @register_microservice(name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep", host="0.0.0.0", port=6007)
-@traceable(run_type="tool")
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
     link_list: Optional[str] = Form(None),
@@ -279,7 +277,6 @@ async def ingest_documents(
 @register_microservice(
     name="opea_service@prepare_doc_redis_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ dataprep - get file ] start to get file structure")
 
@@ -294,7 +291,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_redis_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 

comps/dataprep/redis/langchain/requirements.txt

@@ -8,7 +8,6 @@ huggingface_hub
 langchain
 langchain-community
 langchain-text-splitters
-langsmith
 markdown
 numpy
 opentelemetry-api

comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py

@@ -26,7 +26,6 @@
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
 from langchain_community.vectorstores import Redis
-from langsmith import traceable
 
 cur_path = pathlib.Path(__file__).parent.resolve()
 comps_path = os.path.join(cur_path, "../../../../")
@@ -322,7 +321,6 @@ async def ingest_documents(files: List[UploadFile] = File(None), link_list: str
 @register_microservice(
     name="opea_service@prepare_doc_redis_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ get_file_structure] ")
 
@@ -337,7 +335,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_redis_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 

comps/dataprep/redis/langchain_ray/requirements.txt

@@ -7,7 +7,6 @@ fastapi
 huggingface_hub
 langchain
 langchain-community
-langsmith
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp

comps/dataprep/redis/llama_index/prepare_doc_redis.py

@@ -8,7 +8,6 @@
 
 from config import EMBED_MODEL, INDEX_NAME, REDIS_URL
 from fastapi import Body, File, HTTPException, UploadFile
-from langsmith import traceable
 from llama_index.core import SimpleDirectoryReader, StorageContext, VectorStoreIndex
 from llama_index.core.settings import Settings
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
@@ -55,7 +54,6 @@ async def ingest_data_to_redis(doc_path: DocPath):
 
 
 @register_microservice(name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep", host="0.0.0.0", port=6007)
-@traceable(run_type="tool")
 # llama index only support upload files now
 async def ingest_documents(files: Optional[Union[UploadFile, List[UploadFile]]] = File(None)):
     print(f"files:{files}")
@@ -81,7 +79,6 @@ async def ingest_documents(files: Optional[Union[UploadFile, List[UploadFile]]]
 @register_microservice(
     name="opea_service@prepare_doc_redis_file", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6008
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ get_file_structure] ")
 
@@ -96,7 +93,6 @@ async def rag_get_file_structure():
 @register_microservice(
     name="opea_service@prepare_doc_redis_del", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6009
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 

comps/dataprep/redis/llama_index/requirements.txt

@@ -1,7 +1,6 @@
 docarray[full]
 fastapi
 huggingface_hub
-langsmith
 llama-index 
 llama-index-embeddings-huggingface==0.2.0
 llama-index-readers-file

comps/embeddings/langchain-mosec/embedding_mosec.py

@@ -6,7 +6,6 @@
 from typing import List, Optional
 
 from langchain_community.embeddings import OpenAIEmbeddings
-from langsmith import traceable
 
 from comps import (
     EmbedDoc,
@@ -53,7 +52,6 @@ def empty_embedding() -> List[float]:
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 @register_statistics(names=["opea_service@embedding_mosec"])
 def embedding(input: TextDoc) -> EmbedDoc:
     start = time.time()

comps/embeddings/langchain/embedding_tei.py

@@ -5,7 +5,6 @@
 import time
 
 from langchain_community.embeddings import HuggingFaceHubEmbeddings
-from langsmith import traceable
 
 from comps import (
     EmbedDoc,
@@ -27,7 +26,6 @@
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 @register_statistics(names=["opea_service@embedding_tei_langchain"])
 def embedding(input: TextDoc) -> EmbedDoc:
     start = time.time()
@@ -40,5 +38,5 @@ def embedding(input: TextDoc) -> EmbedDoc:
 if __name__ == "__main__":
     tei_embedding_endpoint = os.getenv("TEI_EMBEDDING_ENDPOINT", "http://localhost:8080")
     embeddings = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
-    print("TEI Gaudi Embedding initialized.")
+    print("TEI Embedding initialized.")
     opea_microservices["opea_service@embedding_tei_langchain"].start()

comps/embeddings/langchain/requirements.txt

@@ -2,7 +2,6 @@ docarray[full]
 fastapi
 huggingface_hub
 langchain
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk

comps/embeddings/llama_index/embedding_tei.py

@@ -3,7 +3,6 @@
 
 import os
 
-from langsmith import traceable
 from llama_index.embeddings.text_embeddings_inference import TextEmbeddingsInference
 
 from comps import EmbedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
@@ -18,7 +17,6 @@
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 def embedding(input: TextDoc) -> EmbedDoc:
     embed_vector = embeddings._get_query_embedding(input.text)
     res = EmbedDoc(text=input.text, embedding=embed_vector)

comps/embeddings/llama_index/local_embedding.py

@@ -1,7 +1,6 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-from langsmith import traceable
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 
 from comps import EmbedDoc, ServiceType, TextDoc, opea_microservices, register_microservice
@@ -16,7 +15,6 @@
     input_datatype=TextDoc,
     output_datatype=EmbedDoc,
 )
-@traceable(run_type="embedding")
 def embedding(input: TextDoc) -> EmbedDoc:
     embed_vector = embeddings.get_text_embedding(input.text)
     res = EmbedDoc(text=input.text, embedding=embed_vector)

comps/embeddings/llama_index/requirements.txt

@@ -1,7 +1,6 @@
 docarray[full]
 fastapi
 huggingface_hub
-langsmith
 llama-index-embeddings-text-embeddings-inference
 opentelemetry-api
 opentelemetry-exporter-otlp

comps/guardrails/langchain/guardrails_tgi_gaudi.py

@@ -6,7 +6,6 @@
 from langchain_community.utilities.requests import JsonRequestsWrapper
 from langchain_huggingface import ChatHuggingFace
 from langchain_huggingface.llms import HuggingFaceEndpoint
-from langsmith import traceable
 
 from comps import ServiceType, TextDoc, opea_microservices, register_microservice
 
@@ -62,7 +61,6 @@ def get_tgi_service_model_id(endpoint_url, default=DEFAULT_MODEL):
     input_datatype=TextDoc,
     output_datatype=TextDoc,
 )
-@traceable(run_type="llm")
 def safety_guard(input: TextDoc) -> TextDoc:
     response_input_guard = llm_engine_hf.invoke([{"role": "user", "content": input.text}]).content
     if "unsafe" in response_input_guard:

comps/guardrails/pii_detection/pii_detection.py

@@ -8,7 +8,6 @@
 from pathlib import Path
 
 from fastapi import File, Form, HTTPException, UploadFile
-from langsmith import traceable
 
 cur_path = pathlib.Path(__file__).parent.resolve()
 comps_path = os.path.join(cur_path, "../../../")

comps/guardrails/pii_detection/requirements.txt

@@ -7,7 +7,6 @@ gibberish-detector
 huggingface_hub
 langchain
 langchain-community
-langsmith
 numpy
 opentelemetry-api
 opentelemetry-exporter-otlp

comps/guardrails/requirements.txt

@@ -3,7 +3,6 @@ fastapi
 huggingface_hub
 langchain-community
 langchain-huggingface
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk

comps/knowledgegraphs/langchain/knowledge_graph.py

@@ -22,7 +22,6 @@
 from langchain_community.graphs import Neo4jGraph
 from langchain_community.llms import HuggingFaceEndpoint
 from langchain_community.vectorstores.neo4j_vector import Neo4jVector
-from langsmith import traceable
 
 from comps import GeneratedDoc, GraphDoc, ServiceType, opea_microservices, register_microservice
 

comps/knowledgegraphs/requirements.txt

@@ -8,7 +8,6 @@ langchain
 langchain_community==0.2.5
 langchain_openai
 langchainhub
-langsmith
 neo4j
 numpy
 opentelemetry-api

comps/llms/faq-generation/tgi/llm.py

@@ -9,12 +9,10 @@
 from langchain.prompts import PromptTemplate
 from langchain.text_splitter import CharacterTextSplitter
 from langchain_community.llms import HuggingFaceEndpoint
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
 
-@traceable(run_type="tool")
 def post_process_text(text: str):
     if text == " ":
         return "data: @#$\n\n"
@@ -33,7 +31,6 @@ def post_process_text(text: str):
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
     llm = HuggingFaceEndpoint(

comps/llms/faq-generation/tgi/requirements.txt

@@ -3,7 +3,6 @@ fastapi
 huggingface_hub
 langchain==0.1.16
 langserve
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk

comps/llms/summarization/tgi/llm.py

@@ -8,12 +8,10 @@
 from langchain.docstore.document import Document
 from langchain.text_splitter import CharacterTextSplitter
 from langchain_huggingface import HuggingFaceEndpoint
-from langsmith import traceable
 
 from comps import GeneratedDoc, LLMParamsDoc, ServiceType, opea_microservices, register_microservice
 
 
-@traceable(run_type="tool")
 def post_process_text(text: str):
     if text == " ":
         return "data: @#$\n\n"
@@ -32,7 +30,6 @@ def post_process_text(text: str):
     host="0.0.0.0",
     port=9000,
 )
-@traceable(run_type="llm")
 def llm_generate(input: LLMParamsDoc):
     llm_endpoint = os.getenv("TGI_LLM_ENDPOINT", "http://localhost:8080")
     llm = HuggingFaceEndpoint(

comps/llms/summarization/tgi/requirements.txt

@@ -6,7 +6,6 @@ langchain-huggingface
 langchain-openai
 langchain_community
 langchainhub
-langsmith
 opentelemetry-api
 opentelemetry-exporter-otlp
 opentelemetry-sdk