id stringlengths 14 15 | text stringlengths 17 2.72k | source stringlengths 47 115 |
|---|---|---|
fe8b8a398485-0 | BiliBili
Bilibili is one of the most beloved long-form video sites in China.
Installation and Setup
pip install bilibili-api-python
Document Loader
See a usage example.
from langchain.document_loaders import BiliBiliLoader | https://python.langchain.com/docs/integrations/providers/bilibili |
2d6cfd9a6df4-0 | Brave Search
Brave Search is a search engine developed by Brave Software.
Brave Search uses its own web index. As of May 2022, it covered over 10 billion pages and was used to serve 92% of search results without relying on any third-parties, with the remainder being retrieved server-side from the Bing API or (on an opt... | https://python.langchain.com/docs/integrations/providers/brave_search |
3e3c877900b9-0 | Blackboard Learn (previously the Blackboard Learning Management System) is a web-based virtual learning environment and learning management system developed by Blackboard Inc. The software features course management, customizable open architecture, and scalable design that allows integration with student information sy... | https://python.langchain.com/docs/integrations/providers/blackboard |
571c68dcd681-0 | This page covers how to use the BittensorLLM inference runtime within LangChain. It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.
llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt")
print(llm('Write a fibonacci ... | https://python.langchain.com/docs/integrations/providers/bittensor |
89256329e1e7-0 | Apache Cassandra® is a free and open-source, distributed, wide-column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers support for clusters spanning multiple datacenters, with asy... | https://python.langchain.com/docs/integrations/providers/cassandra |
cdcbc04f4e3b-0 | This page covers how to use the CerebriumAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
from langchain.llms import CerebriumAI | https://python.langchain.com/docs/integrations/providers/cerebriumai |
62a2759c8a01-0 | We need to sign up for Chaindesk, create a datastore, add some data and get your datastore api endpoint url. We need the API Key.
from langchain.retrievers import ChaindeskRetriever | https://python.langchain.com/docs/integrations/providers/chaindesk |
d06c8054c115-0 | There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorstores import Chroma | https://python.langchain.com/docs/integrations/providers/chroma |
2375687c9b4e-0 | Clarifai
Clarifai is one of first deep learning platforms having been founded in 2013. Clarifai provides an AI platform with the full AI lifecycle for data exploration, data labeling, model training, evaluation and inference around images, video, text and audio data. In the LangChain ecosystem, as far as we're aware, C... | https://python.langchain.com/docs/integrations/providers/clarifai |
2375687c9b4e-1 | There is a Clarifai Embedding model in LangChain, which you can access with:
from langchain.embeddings import ClarifaiEmbeddings
embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID)
For more details, the docs on the Clarifai Embeddings wrapper provide a detailed walkthrou... | https://python.langchain.com/docs/integrations/providers/clarifai |
f36540c74399-0 | In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. We use the ClearML Experiment Manager that neatly tracks and organizes all your experiment runs.
We'll be using quite some APIs in this notebook, here is a list and where to get them:
First, let's ju... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-1 | {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}
{'action': 'on_llm_start', 'name': 'OpenAI', '... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-2 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-3 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-4 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-5 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-6 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-7 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-8 | 5 on_llm_start OpenAI 1 1 0 0 0 0
6 on_llm_end NaN 2 1 1 0 0 0
7 on_llm_end NaN 2 1 1 0 0 0
8 on_llm_end NaN 2 1 1 0 0 0
9 on_llm_end NaN 2 1 1 0 0 0
10 on_llm_end NaN 2 1 1 0 0 0
11 on_llm_end NaN 2 1 1 0 0 0
12 on_llm_start OpenAI 3 2 1 0 0 0
13 on_llm_start OpenAI 3 2 1 0 0 0
14 on_llm_start OpenAI 3 2 1 0 ... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-9 | chain_ends llm_starts ... difficult_words linsear_write_formula \
0 0 1 ... NaN NaN
1 0 1 ... NaN NaN
2 0 1 ... NaN NaN
3 0 1 ... NaN NaN
4 0 1 ... NaN NaN
5 0 1 ... NaN NaN
6 0 1 ... 0.0 5.5
7 0 1 ... 2.0 6.5
8 0 1 ... 0.0 5.5
9 0 1 ... 2.0 6.5
10 0 1 ... 0.0 5.5
11 0 1 ... 2.0 6.5
12 0 2 ... NaN NaN
13 0... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-10 | gunning_fog text_standard fernandez_huerta szigriszt_pazos \
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 5.20 5th and 6th grade 133.58 131.54
7 8.28 6th and 7th grade 115.58 112.37
8 5.20 5th and 6th grade 133.58 131.54
9 8.28 6th and 7th grade ... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-11 | gutierrez_polini crawford gulpease_index osman
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 62.30 -0.2 79.8 116.91
7 54.83 1.4 72.1 100.17
8 62.30 -0.2 79.8 116.91
9 54.83 1.4 72.1 100.17
10 62.30 -0.2 79.8 116.91
11 54.83 1.4 72.1 100.17
12 ... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-12 | output \
0 \n\nQ: What did the fish say when it hit the w...
1 \n\nRoses are red,\nViolets are blue,\nSugar i...
2 \n\nQ: What did the fish say when it hit the w...
3 \n\nRoses are red,\nViolets are blue,\nSugar i...
4 \n\nQ: What did the fish say when it hit the w...
5 \n\nRoses are red,\nViolets are blue,\nSugar... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-13 | ... difficult_words linsear_write_formula gunning_fog \
0 ... 0 5.5 5.20
1 ... 2 6.5 8.28
2 ... 0 5.5 5.20
3 ... 2 6.5 8.28
4 ... 0 5.5 5.20
5 ... 2 6.5 8.28
6 ... 0 5.5 5.20
7 ... 2 6.5 8.28
8 ... 0 5.5 5.20
9 ... 2 6.5 8.28
10 ... 0 5.5 5.20
11 ... 2 6.5 8.28
text_standard fernandez_huerta szigriszt_pazo... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-14 | crawford gulpease_index osman
0 -0.2 79.8 116.91
1 1.4 72.1 100.17
2 -0.2 79.8 116.91
3 1.4 72.1 100.17
4 -0.2 79.8 116.91
5 1.4 72.1 100.17
6 -0.2 79.8 116.91
7 1.4 72.1 100.17
8 -0.2 79.8 116.91
9 1.4 72.1 100.17
10 -0.2 79.8 116.91
11 1.4 72.1 100.17
[12 rows x 24 columns]}
2023-03-29 14:00:25,948 - cl... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-15 | # SCENARIO 2 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callbacks=callbacks,
)
agent.run("Who is the wife of the person who sang summer of 69?")
clearml_callback.flush_tracker(
langchain_... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-16 | > Entering new AgentExecutor chain...
{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-17 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-18 | Action: Search
Action Input: "Who sang summer of 69"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'step': 4, 'starts': 3, 'ends': 1, 'e... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-19 | Observation: Bryan Adams - Summer Of 69 (Official Music Video).
Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_sta... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-20 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_st... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-21 | I need to find out who Bryan Adams is married to.
Action: Search
Action Input: "Who is Bryan Adams married to"{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams marri... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-22 | Observation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...
Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011,... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-23 | {'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have acces... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-24 | {'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_s... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-25 | I now know the final answer.
Final Answer: Bryan Adams has never been married.
{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-26 | > Finished chain.
{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}
{'action_records': actio... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-27 | tool tool_input log \
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
.. ... ... ...
66 NaN NaN NaN
67 NaN NaN NaN
68 NaN NaN NaN
69 NaN NaN I now know the final answer.\nFinal Answer: B...
70 NaN NaN NaN
input_str description output \
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
f36540c74399-28 | text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \
0 3rd and 4th grade 121.07 119.50 54.91
1 4th and 5th grade 124.13 119.20 52.26
2 3rd and 4th grade 115.70 110.84 49.79
crawford gulpease_index osman
0 0.9 72.7 92.16
1 0.7 74.7 84.20
2 0.7 85.4 83.14
[3 rows x 24 columns]}
Could not update la... | https://python.langchain.com/docs/integrations/providers/clearml_tracking |
4d945e8d0f90-0 | We need to install clickhouse-connect python package.
from langchain.vectorstores import Clickhouse, ClickhouseSettings | https://python.langchain.com/docs/integrations/providers/clickhouse |
0c7c8308a6bb-0 | CnosDB
CnosDB is an open source distributed time series database with high performance, high compression rate and high ease of use.
Installation and Setup
pip install cnos-connector
Connecting to CnosDB
You can connect to CnosDB using the SQLDatabase.from_cnosdb() method.
Syntax
def SQLDatabase.from_cnosdb(url: str ... | https://python.langchain.com/docs/integrations/providers/cnosdb |
0c7c8308a6bb-1 | db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run(
"What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?"
)
> Entering new chain...
What is the average temperature of air at station XiaoMaiDao between October 19, 2022 and Occtober 20, 2022?... | https://python.langchain.com/docs/integrations/providers/cnosdb |
0c7c8308a6bb-2 | /*
3 rows from air table:
pressure station temperature time visibility
75.0 XiaoMaiDao 67.0 2022-10-19T03:40:00 54.0
77.0 XiaoMaiDao 69.0 2022-10-19T04:40:00 56.0
76.0 XiaoMaiDao 68.0 2022-10-19T05:40:00 55.0
*/
Thought:The "temperature" column in the "air" table is relevant to the question. I can query the average tem... | https://python.langchain.com/docs/integrations/providers/cnosdb |
6e15d8889591-0 | There exists an Cohere LLM wrapper, which you can access with See a usage example.
from langchain.llms import Cohere | https://python.langchain.com/docs/integrations/providers/cohere |
03397be0ea5a-0 | There isn't any special setup for it.
from langchain.document_loaders import CollegeConfidentialLoader | https://python.langchain.com/docs/integrations/providers/college_confidential |
18dde185e89e-0 | Confluence
Confluence is a wiki collaboration platform that saves and organizes all of the project-related material. Confluence is a knowledge base that primarily handles content management activities.
Installation and Setup
pip install atlassian-python-api
We need to set up username/api_key or Oauth2 login. See inst... | https://python.langchain.com/docs/integrations/providers/confluence |
759c7fdf49a6-0 | C Transformers
This page covers how to use the C Transformers library within LangChain. It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.
Installation and Setup
Install the Python package with pip install ctransformers
Download a supported GGML model (see Sup... | https://python.langchain.com/docs/integrations/providers/ctransformers |
493d2f934021-0 | Comet
In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with Comet.
Example Project: Comet with LangChain
Install Comet and Dependencies
import sys
{sys.executable} -m spacy download en_core_web_sm
Initialize Comet and Set your Credentials
You can grab yo... | https://python.langchain.com/docs/integrations/providers/comet_tracking |
493d2f934021-1 | template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, c... | https://python.langchain.com/docs/integrations/providers/comet_tracking |
493d2f934021-2 | def compute_metric(self, generation, prompt_idx, gen_idx):
prediction = generation.text
results = self.scorer.score(target=self.reference, prediction=prediction)
return {
"rougeLsum_score": results["rougeLsum"].fmeasure,
"reference": self.reference,
}
reference = """
The tower is 324 metres (1,063 ft) tall, about th... | https://python.langchain.com/docs/integrations/providers/comet_tracking |
493d2f934021-3 | Excluding transmitters, the Eiffel Tower is the second tallest
free-standing structure in France after the Millau Viaduct.
"""
}
]
print(synopsis_chain.apply(test_prompts, callbacks=callbacks))
comet_callback.flush_tracker(synopsis_chain, finish=True) | https://python.langchain.com/docs/integrations/providers/comet_tracking |
36918dec224b-0 | This document demonstrates to leverage DashVector within the LangChain ecosystem. In particular, it shows how to install DashVector, and how to use it as a VectorStore plugin in LangChain. It is broken into two parts: installation and setup, and then references to specific DashVector wrappers.
A DashVector Collection i... | https://python.langchain.com/docs/integrations/providers/dashvector |
be63c62feaa4-0 | Databricks
This notebook covers how to connect to the Databricks runtimes and Databricks SQL using the SQLDatabase wrapper of LangChain. It is broken into 3 parts: installation and setup, connecting to Databricks, and examples.
Installation and Setup
pip install databricks-sql-connector
Connecting to Databricks
You c... | https://python.langchain.com/docs/integrations/providers/databricks |
be63c62feaa4-1 | llm = ChatOpenAI(temperature=0, model_name="gpt-4")
SQL Chain example
This example demonstrates the use of the SQL Chain for answering a question over a Databricks database.
from langchain import SQLDatabaseChain
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run(
"What is the average duration o... | https://python.langchain.com/docs/integrations/providers/databricks |
be63c62feaa4-2 | /*
3 rows from trips table:
tpep_pickup_datetime tpep_dropoff_datetime trip_distance fare_amount pickup_zip dropoff_zip
2016-02-14 16:52:13+00:00 2016-02-14 17:16:04+00:00 4.94 19.0 10282 10171
2016-02-04 18:44:19+00:00 2016-02-04 18:46:00+00:00 0.28 3.5 10110 10110
2016-02-17 17:13:57+00:00 2016-02-17 17:17:55+00:00 0... | https://python.langchain.com/docs/integrations/providers/databricks |
29823a3fe212-0 | Datadog Tracing
ddtrace is a Datadog application performance monitoring (APM) library which provides an integration to monitor your LangChain application.
Key features of the ddtrace integration for LangChain:
Traces: Capture LangChain requests, parameters, prompt-completions, and help visualize LangChain operations.
M... | https://python.langchain.com/docs/integrations/providers/datadog |
29823a3fe212-1 | Additionally, the LangChain integration can be enabled programmatically by adding patch_all() or patch(langchain=True) before the first import of langchain in your application.
Note that using ddtrace-run or patch_all() will also enable the requests and aiohttp integrations which trace HTTP requests to LLM providers, a... | https://python.langchain.com/docs/integrations/providers/datadog |
29823a3fe212-2 | # Note: be sure to configure the integration before calling ``patch()``!
# e.g. config.langchain["logs_enabled"] = True
patch(langchain=True)
# to trace synchronous HTTP requests
# patch(langchain=True, requests=True)
# to trace asynchronous HTTP requests (to the OpenAI library)
# patch(langchain=True, aiohttp=True)... | https://python.langchain.com/docs/integrations/providers/datadog |
141b5be90095-0 | pip install datadog_api_client
We must initialize the loader with the Datadog API key and APP key, and we need to set up the query to extract the desired logs. | https://python.langchain.com/docs/integrations/providers/datadog_logs |
272e9ac1845f-0 | Google Serper
This page covers how to use the Serper Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. It is broken into two parts: setup, and then references to the specific Google Serper wrapper.... | https://python.langchain.com/docs/integrations/providers/google_serper |
aec21f29d4da-0 | We need to install several python packages.
pip install tensorflow \
google-cloud-aiplatform \
tensorflow-hub \
tensorflow-text | https://python.langchain.com/docs/integrations/providers/google_vertex_ai_matchingengine |
9ce6a78edf02-0 | This page covers how to use the GooseAI ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers.
import os
os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY" | https://python.langchain.com/docs/integrations/providers/gooseai |
92763b5c6f7f-0 | This page covers how to use the GPT4All wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.
from langchain.llms import GPT4All
#... | https://python.langchain.com/docs/integrations/providers/gpt4all |
256dd53729e3-0 | Graphsignal
This page covers how to use Graphsignal to trace and monitor LangChain. Graphsignal enables full visibility into your application. It provides latency breakdowns by chains and tools, exceptions with full context, data monitoring, compute/GPU utilization, OpenAI cost analytics, and more.
Installation and Set... | https://python.langchain.com/docs/integrations/providers/graphsignal |
14079651fadf-0 | GROBID is a machine learning library for extracting, parsing, and re-structuring raw documents.
It is designed and expected to be used to parse academic papers, where it works particularly well.
Note: if the articles supplied to Grobid are large documents (e.g. dissertations) exceeding a certain number of elements, the... | https://python.langchain.com/docs/integrations/providers/grobid |
d9efe7a3dff1-0 | Gutenberg
Project Gutenberg is an online library of free eBooks.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.document_loaders import GutenbergLoader | https://python.langchain.com/docs/integrations/providers/gutenberg |
7db294497d70-0 | This page covers how to use the Hazy Research ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
There exists an LLM wrapper around Hazy Research's manifest library. manifest is a python library which is itself a wrapper around many m... | https://python.langchain.com/docs/integrations/providers/hazy_research |
223310cb2769-0 | Hacker News
Hacker News (sometimes abbreviated as HN) is a social news website focusing on computer science and entrepreneurship. It is run by the investment fund and startup incubator Y Combinator. In general, content that can be submitted is defined as "anything that gratifies one's intellectual curiosity."
Installat... | https://python.langchain.com/docs/integrations/providers/hacker_news |
b6b746a1dadd-0 | Helicone is an open source observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.
With your LangChain environment you can just add the following parameter.
Now head over to helicone.ai to create your account, and add your OpenAI API key within our dashb... | https://python.langchain.com/docs/integrations/providers/helicone |
4fade3a5e3c1-0 | Hologres is a unified real-time data warehousing service developed by Alibaba Cloud. You can use Hologres to write, update, process, and analyze large amounts of data in real time. Hologres supports standard SQL syntax, is compatible with PostgreSQL, and supports most PostgreSQL functions. Hologres supports online anal... | https://python.langchain.com/docs/integrations/providers/hologres |
6f77d14da490-0 | iFixit
iFixit is the largest, open repair community on the web. The site contains nearly 100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is licensed under CC-BY-NC-SA 3.0.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.do... | https://python.langchain.com/docs/integrations/providers/ifixit |
f92e91d6a3e1-0 | Hugging Face
This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) within LangChain. It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers.
Installation and Setup
If you want to work with the Hugging Face Hub:
Install the Hub client... | https://python.langchain.com/docs/integrations/providers/huggingface |
1b99acb73d3a-0 | IMSDb
IMSDb is the Internet Movie Script Database.
Installation and Setup
There isn't any special setup for it.
Document Loader
See a usage example.
from langchain.document_loaders import IMSDbLoader | https://python.langchain.com/docs/integrations/providers/imsdb |
05c6a229c34f-0 | Run the following in your terminal:
docker run --rm --detach --name infino-example -p 3000:3000 infinohq/infino:latest | https://python.langchain.com/docs/integrations/providers/infino |
0b8ec941a739-0 | This page covers how to use the Jina ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
Langchain-serve, powered by Jina, helps take LangChain apps to production with easy to use REST/WebSocket APIs and Slack bots.
Install the package from Py... | https://python.langchain.com/docs/integrations/providers/jina |
99e3e35b3238-0 | This page covers how to use LanceDB within LangChain. It is broken into two parts: installation and setup, and then references to specific LanceDB wrappers.
There exists a wrapper around LanceDB databases, allowing you to use it as a vectorstore, whether for semantic search or example selection.
from langchain.vectorst... | https://python.langchain.com/docs/integrations/providers/lancedb |
5eac48461895-0 | LangChain Decorators ✨
lanchchain decorators is a layer on the top of LangChain that provides syntactic sugar 🍭 for writing custom langchain prompts and chains
For Feedback, Issues, Contributions - please raise an issue here: ju-bezdek/langchain-decorators
Main principles and benefits:
more pythonic way of writing cod... | https://python.langchain.com/docs/integrations/providers/langchain_decorators |
5eac48461895-1 | PromptTypes.AGENT_REASONING.llm = ChatOpenAI()
# Or you can just define your own ones:
class MyCustomPromptTypes(PromptTypes):
GPT4=PromptTypeSettings(llm=ChatOpenAI(model="gpt-4"))
@llm_prompt(prompt_type=MyCustomPromptTypes.GPT4)
def write_a_complicated_code(app_idea:str)->str:
...
Define the settings directly in... | https://python.langchain.com/docs/integrations/providers/langchain_decorators |
5eac48461895-2 | from langchain_decorators import StreamingContext, llm_prompt
# this will mark the prompt for streaming (useful if we want stream just some prompts in our app... but don't want to pass distribute the callback handlers)
# note that only async functions can be streamed (will get an error if it's not)
@llm_prompt(capture... | https://python.langchain.com/docs/integrations/providers/langchain_decorators |
5eac48461895-3 | Now only to code block above will be used as a prompt, and the rest of the docstring will be used as a description for developers.
(It has also a nice benefit that IDE (like VS code) will display the prompt properly (not trying to parse it as markdown, and thus not showing new lines properly))
"""
return
Chat messages... | https://python.langchain.com/docs/integrations/providers/langchain_decorators |
5eac48461895-4 | you can also place it in between the words
this too will be rendered{? , but
this block will be rendered only if {this_value} and {this_value}
is not empty?} !
"""
Output parsers
llm_prompt decorator natively tries to detect the best output parser based on the output type. (if not set, it returns the raw string)
list, ... | https://python.langchain.com/docs/integrations/providers/langchain_decorators |
5eac48461895-5 | @llm_prompt
def introduce_your_self(self)->str:
"""
``` <prompt:system>
You are an assistant named {assistant_name}.
Your role is to act as {assistant_role}
```
```<prompt:user>
Introduce your self (in less than 20 words)
```
"""
personality = AssistantPersonality(assistant_name="John", assistant_role="a pirate")
... | https://python.langchain.com/docs/integrations/providers/langchain_decorators |
111cd59a56c1-0 | This page covers how to use llama.cpp within LangChain. It is broken into two parts: installation and setup, and then references to specific Llama-cpp wrappers.
from langchain.llms import LlamaCpp | https://python.langchain.com/docs/integrations/providers/llamacpp |
91d21cf4f501-0 | Log10 is an open source proxiless LLM data management and application development platform that lets you log, debug and tag your Langchain calls.
Integration with log10 is a simple one-line log10_callback integration as shown below:
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
... | https://python.langchain.com/docs/integrations/providers/log10 |
91d21cf4f501-1 | # Log a call via Langchain
llm = OpenAI(model_name="text-ada-001", temperature=0.5)
response = llm.predict("You are a ping pong machine.\nPing?\n")
print(response) | https://python.langchain.com/docs/integrations/providers/log10 |
a443a3fdfd89-0 | Marqo
This page covers how to use the Marqo ecosystem within LangChain.
What is Marqo?
Marqo is a tensor search engine that uses embeddings stored in in-memory HNSW indexes to achieve cutting edge search speeds. Marqo can scale to hundred-million document indexes with horizontal index sharding and allows for async and... | https://python.langchain.com/docs/integrations/providers/marqo |
c53a10559a76-0 | MediaWiki XML Dumps contain the content of a wiki (wiki pages with all their revisions), without the site-related data. A XML dump does not create a full backup of the wiki database, the dump does not contain user accounts, images, edit logs, etc.
We need to install several python packages.
The mediawiki-utilities supp... | https://python.langchain.com/docs/integrations/providers/mediawikidump |
aa1012f860c0-0 | Meilisearch
Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences.
You can self-host Meilisearch or run on Meilisearch Cloud.
Meilisearch v1.3 supports vector search.
Installation and Setup
See a usage example f... | https://python.langchain.com/docs/integrations/providers/meilisearch |
930672fd9761-0 | Metal is a managed retrieval & memory platform built for production. Easily index your data into Metal and run semantic search and retrieval on it.
Then, you can easily take advantage of the MetalRetriever class to start retrieving your data for semantic search, prompting context, etc. This class takes a Metal instance... | https://python.langchain.com/docs/integrations/providers/metal |
5bb9ed75ebf3-0 | This page covers how to use the Weaviate ecosystem within LangChain.
Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Buil... | https://python.langchain.com/docs/integrations/providers/weaviate |
6f940a538898-0 | WhatsApp (also called WhatsApp Messenger) is a freeware, cross-platform, centralized instant messaging (IM) and voice-over-IP (VoIP) service. It allows users to send text and voice messages, make voice and video calls, and share images, documents, user locations, and other content.
There isn't any special setup for it.... | https://python.langchain.com/docs/integrations/providers/whatsapp |
4a39d38787e2-0 | Microsoft OneDrive
Microsoft OneDrive (formerly SkyDrive) is a file-hosting service operated by Microsoft.
Installation and Setup
First, you need to install a python package.
Then follow instructions here.
Document Loader
See a usage example.
from langchain.document_loaders import OneDriveLoader | https://python.langchain.com/docs/integrations/providers/microsoft_onedrive |
199cc6fad989-0 | Make sure to set the required API keys and config required to send telemetry to WhyLabs:
Here's a single LLM integration with OpenAI, which will log various out of the box metrics and send telemetry to WhyLabs for monitoring.
result = llm.generate(
[
"Can you give me 3 SSNs so I can understand the format?",
"Can you gi... | https://python.langchain.com/docs/integrations/providers/whylabs_profiling |
52a2630010f3-0 | Wikipedia
Wikipedia is a multilingual free online encyclopedia written and maintained by a community of volunteers, known as Wikipedians, through open collaboration and using a wiki-based editing system called MediaWiki. Wikipedia is the largest and most-read reference work in history.
Installation and Setup
Document ... | https://python.langchain.com/docs/integrations/providers/wikipedia |
469b4f2c446b-0 | This page covers how to use the Wolfram Alpha API within LangChain.
There exists a WolframAlphaAPIWrapper utility which wraps this API. To import this utility:
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
You can also easily load this wrapper as a Tool (to use with an Agent). You can do this wit... | https://python.langchain.com/docs/integrations/providers/wolfram_alpha |
c27c48a52b77-0 | This page covers how to use the Writer ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
from langchain.llms import Writer | https://python.langchain.com/docs/integrations/providers/writer |
0b5c90c7b05d-0 | Xorbits Inference (Xinference)
This page demonstrates how to use Xinference with LangChain.
Xinference is a powerful and versatile library designed to serve LLMs, speech recognition models, and multimodal models, even on your laptop. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art... | https://python.langchain.com/docs/integrations/providers/xinference |
0b5c90c7b05d-1 | Usage
For more information and detailed examples, refer to the example notebook for xinference
Embeddings
Xinference also supports embedding queries and documents. See example notebook for xinference embeddings for a more detailed demo. | https://python.langchain.com/docs/integrations/providers/xinference |
a867725aa54b-0 | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. Xata has a native vector type, which can be added to any table, and supports similarity search. LangChain inserts vectors directly to Xata, and queries it for the neares... | https://python.langchain.com/docs/integrations/providers/xata |
3548e32491aa-0 | Yeager.ai
This page covers how to use Yeager.ai to generate LangChain tools and agents.
What is Yeager.ai?
Yeager.ai is an ecosystem designed to simplify the process of creating AI agents and tools.
It features yAgents, a No-code LangChain Agent Builder, which enables users to build, test, and deploy AI solutions wit... | https://python.langchain.com/docs/integrations/providers/yeagerai |
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