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0 on_llm_start OpenAI 1 1 0 0 0 0 1 on_llm_start OpenAI 1 1 0 0 0 0 2 on_llm_start OpenAI 1 1 0 0 0 0 3 on_llm_start OpenAI 1 1 0 0 0 0 ...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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14 on_llm_start OpenAI 3 2 1 0 0 0 15 on_llm_start OpenAI 3 2 1 0 0 0 16 on_llm_start OpenAI 3 2 1 0 0 0 17 on_llm_start OpenAI 3 2 1 0 0 0 ...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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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 ...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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0 NaN NaN NaN NaN 1 NaN NaN NaN NaN 2 NaN NaN NaN NaN 3 NaN NaN NaN NaN 4 NaN N...
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21 8.28 6th and 7th grade 115.58 112.37 22 5.20 5th and 6th grade 133.58 131.54 23 8.28 6th and 7th grade 115.58 112.37 gutierrez_polini crawford gulpease_index osman 0 NaN NaN NaN ...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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19 54.83 1.4 72.1 100.17 20 62.30 -0.2 79.8 116.91 21 54.83 1.4 72.1 100.17 22 62.30 -0.2 79.8 116.91 23 54.83 1.4 72.1 100.17 [24 rows x 39 columns], 'session_an...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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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 i... 6 \n\nQ: What did the fish say when it hit the w... 7 \n\nRoses are red,\nViolets are...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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1 138 83.66 4.8 2 138 109.04 1.3 3 138 83.66 4.8 4 138 109.04 1.3 ...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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10 ... 0 5.5 5.20 11 ... 2 6.5 8.28 text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \ 0 5th and 6th grade 133.58 131.54 62.30 1 6th and 7th grade 115...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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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 ...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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Finally, if you enabled visualizations, these are stored as HTML files under debug samples. Scenario 2: Creating an agent with tools# To show a more advanced workflow, let’s create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as t...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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{'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/en/latest/ecosystem/clearml_tracking.html
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I need to find out who sang summer of 69 and then find out who their wife is. 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\...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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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/en/latest/ecosystem/clearml_tracking.html
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{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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{'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/en/latest/ecosystem/clearml_tracking.html
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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/en/latest/ecosystem/clearml_tracking.html
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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, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_en...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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{'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/en/latest/ecosystem/clearml_tracking.html
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to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: 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"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThou...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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{'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/en/latest/ecosystem/clearml_tracking.html
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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/en/latest/ecosystem/clearml_tracking.html
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4 on_llm_start OpenAI 1 1 0 0 0 .. ... ... ... ... ... ... ... 66 on_tool_end NaN 11 7 4 0 0 67 on_llm_start OpenAI 12 8 4 0 0 68 on_llm_end NaN 13 8 ...
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69 1 0 3 ... NaN NaN NaN 70 1 1 3 ... NaN NaN NaN tool tool_input log \ 0 NaN NaN NaN 1 NaN ...
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0 2 Answer the following questions as best you can... OpenAI 1 7 Answer the following questions as best you can... OpenAI 2 12 Answer the following questions as best you can... OpenAI output_step output \ 0 3 I n...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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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 last created model in Task 988bd727b0e94a29a3ac0ee526813545...
https://python.langchain.com/en/latest/ecosystem/clearml_tracking.html
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.md .pdf SerpAPI Contents Installation and Setup Wrappers Utility Tool SerpAPI# This page covers how to use the SerpAPI search APIs within LangChain. It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper. Installation and Setup# Install requirements with pip install ...
https://python.langchain.com/en/latest/ecosystem/serpapi.html
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.md .pdf Google Search Wrapper Contents Installation and Setup Wrappers Utility Tool Google Search Wrapper# This page covers how to use the Google Search API within LangChain. It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper. Installation and Setup# Instal...
https://python.langchain.com/en/latest/ecosystem/google_search.html
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.md .pdf DeepInfra Contents Installation and Setup Wrappers LLM DeepInfra# This page covers how to use the DeepInfra ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers. Installation and Setup# Get your DeepInfra api key from this link he...
https://python.langchain.com/en/latest/ecosystem/deepinfra.html
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.ipynb .pdf Aim Aim# Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. With Aim, you can easily debug and examine an individual execution: Additionally, you have the option to compare multiple executions side by side: Aim ...
https://python.langchain.com/en/latest/ecosystem/aim_tracking.html
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session_group = datetime.now().strftime("%m.%d.%Y_%H.%M.%S") aim_callback = AimCallbackHandler( repo=".", experiment_name="scenario 1: OpenAI LLM", ) manager = CallbackManager([StdOutCallbackHandler(), aim_callback]) llm = OpenAI(temperature=0, callback_manager=manager, verbose=True) The flush_tracker function ...
https://python.langchain.com/en/latest/ecosystem/aim_tracking.html
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] synopsis_chain.apply(test_prompts) aim_callback.flush_tracker( langchain_asset=synopsis_chain, experiment_name="scenario 3: Agent with Tools" ) Scenario 3 The third scenario involves an agent with tools. from langchain.agents import initialize_agent, load_tools from langchain.agents import AgentType # scenario 3 ...
https://python.langchain.com/en/latest/ecosystem/aim_tracking.html
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Thought: I now know the final answer Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078. > Finished chain. previous AI21 Labs next Apify By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Apr 21, 2023.
https://python.langchain.com/en/latest/ecosystem/aim_tracking.html
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.md .pdf Apify Contents Overview Installation and Setup Wrappers Utility Loader Apify# This page covers how to use Apify within LangChain. Overview# Apify is a cloud platform for web scraping and data extraction, which provides an ecosystem of more than a thousand ready-made apps called Actors for various scraping, c...
https://python.langchain.com/en/latest/ecosystem/apify.html
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.md .pdf Cohere Contents Installation and Setup Wrappers LLM Embeddings Cohere# This page covers how to use the Cohere ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Cohere wrappers. Installation and Setup# Install the Python SDK with pip install coher...
https://python.langchain.com/en/latest/ecosystem/cohere.html
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.md .pdf Google Serper Wrapper Contents Setup Wrappers Utility Output Tool Google Serper Wrapper# 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 br...
https://python.langchain.com/en/latest/ecosystem/google_serper.html
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Yes. Follow up: Who is the reigning men's U.S. Open champion? Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion. Follow up: Where is Carlos Alcaraz from? Intermediate answer: El Palmar, Spain So the final answer is: El Palmar, Spain > Finished chain. 'El Palmar, Spain' For a more detail...
https://python.langchain.com/en/latest/ecosystem/google_serper.html
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.md .pdf AtlasDB Contents Installation and Setup Wrappers VectorStore AtlasDB# This page covers how to use Nomic’s Atlas ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Atlas wrappers. Installation and Setup# Install the Python package with pip install ...
https://python.langchain.com/en/latest/ecosystem/atlas.html
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.md .pdf AI21 Labs Contents Installation and Setup Wrappers LLM AI21 Labs# This page covers how to use the AI21 ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific AI21 wrappers. Installation and Setup# Get an AI21 api key and set it as an environment varia...
https://python.langchain.com/en/latest/ecosystem/ai21.html
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.md .pdf PGVector Contents Installation Setup Wrappers VectorStore Usage PGVector# This page covers how to use the Postgres PGVector ecosystem within LangChain It is broken into two parts: installation and setup, and then references to specific PGVector wrappers. Installation# Install the Python package with pip inst...
https://python.langchain.com/en/latest/ecosystem/pgvector.html
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.md .pdf Petals Contents Installation and Setup Wrappers LLM Petals# This page covers how to use the Petals ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Petals wrappers. Installation and Setup# Install with pip install petals Get a Hugging Face api k...
https://python.langchain.com/en/latest/ecosystem/petals.html
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.md .pdf OpenSearch Contents Installation and Setup Wrappers VectorStore OpenSearch# This page covers how to use the OpenSearch ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers. Installation and Setup# Install the Python package with ...
https://python.langchain.com/en/latest/ecosystem/opensearch.html
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.md .pdf Runhouse Contents Installation and Setup Self-hosted LLMs Self-hosted Embeddings Runhouse# This page covers how to use the Runhouse ecosystem within LangChain. It is broken into three parts: installation and setup, LLMs, and Embeddings. Installation and Setup# Install the Python SDK with pip install runhouse...
https://python.langchain.com/en/latest/ecosystem/runhouse.html
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.md .pdf PromptLayer Contents Installation and Setup Wrappers LLM PromptLayer# This page covers how to use PromptLayer within LangChain. It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers. Installation and Setup# If you want to work with PromptLayer: Install the ...
https://python.langchain.com/en/latest/ecosystem/promptlayer.html
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you can add pl_tags when instantializing to tag your requests on PromptLayer you can add return_pl_id when instantializing to return a PromptLayer request id to use while tracking requests. PromptLayer also provides native wrappers for PromptLayerChatOpenAI and PromptLayerOpenAIChat previous Pinecone next Qdrant Conte...
https://python.langchain.com/en/latest/ecosystem/promptlayer.html
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.md .pdf Pinecone Contents Installation and Setup Wrappers VectorStore Pinecone# This page covers how to use the Pinecone ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers. Installation and Setup# Install the Python SDK with pip install ...
https://python.langchain.com/en/latest/ecosystem/pinecone.html
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.md .pdf Hazy Research Contents Installation and Setup Wrappers LLM Hazy Research# 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. Installation and Setup# To use the manifest, install...
https://python.langchain.com/en/latest/ecosystem/hazy_research.html
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.md .pdf Deep Lake Contents Why Deep Lake? More Resources Installation and Setup Wrappers VectorStore Deep Lake# This page covers how to use the Deep Lake ecosystem within LangChain. Why Deep Lake?# More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models. Not only s...
https://python.langchain.com/en/latest/ecosystem/deeplake.html
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.md .pdf Zilliz Contents Installation and Setup Wrappers VectorStore Zilliz# This page covers how to use the Zilliz Cloud ecosystem within LangChain. Zilliz uses the Milvus integration. It is broken into two parts: installation and setup, and then references to specific Milvus wrappers. Installation and Setup# Instal...
https://python.langchain.com/en/latest/ecosystem/zilliz.html
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.md .pdf Yeager.ai Contents What is Yeager.ai? yAgents How to use? Creating and Executing Tools with yAgents 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 featu...
https://python.langchain.com/en/latest/ecosystem/yeagerai.html
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create a tool that returns the n-th prime number Load the tool into the toolkit: To load a tool into yAgents, simply provide a command to yAgents that says so. For example: load the tool that you just created it into your toolkit Execute the tool: To run a tool or agent, simply provide a command to yAgents that include...
https://python.langchain.com/en/latest/ecosystem/yeagerai.html
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.ipynb .pdf Weights & Biases Weights & Biases# This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to s...
https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html
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notes (str): The notes to log. visualize (bool): Whether to visualize the run. complexity_metrics (bool): Whether to log complexity metrics. stream_logs (bool): Whether to stream callback actions to W&B Default values for WandbCallbackHandler(...) visualize: bool = False, complexity_metrics: bool = False, s...
https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html
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Tracking run with wandb version 0.14.0Run data is saved locally in /Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914Syncing run llm to Weights & Biases (docs) View project at https://wandb.ai/harrison-chase/langchain_callback_demo View run at https://wandb.ai/harrison-chase/lang...
https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html
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wandb_callback.flush_tracker(llm, name="simple_sequential") Waiting for W&B process to finish... (success). View run llm at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)Find logs at: ./wandb/run-20230318_150408-e47j191...
https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html
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] synopsis_chain.apply(test_prompts) wandb_callback.flush_tracker(synopsis_chain, name="agent") Waiting for W&B process to finish... (success). View run simple_sequential at: https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7huSynced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s...
https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html
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Action: Search Action Input: "Leo DiCaprio girlfriend" Observation: DiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood. Thought: I need t...
https://python.langchain.com/en/latest/ecosystem/wandb_tracking.html
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.md .pdf Writer Contents Installation and Setup Wrappers LLM Writer# 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. Installation and Setup# Get an Writer api key and set it as an environment varia...
https://python.langchain.com/en/latest/ecosystem/writer.html
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.ipynb .pdf Comet Contents Install Comet and Dependencies Initialize Comet and Set your Credentials Set OpenAI and SerpAPI credentials Scenario 1: Using just an LLM Scenario 2: Using an LLM in a Chain Scenario 3: Using An Agent with Tools Scenario 4: Using Custom Evaluation Metrics Comet# In this guide we will demons...
https://python.langchain.com/en/latest/ecosystem/comet_tracking.html
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) manager = CallbackManager([StdOutCallbackHandler(), comet_callback]) llm = OpenAI(temperature=0.9, callback_manager=manager, verbose=True) llm_result = llm.generate(["Tell me a joke", "Tell me a poem", "Tell me a fact"] * 3) print("LLM result", llm_result) comet_callback.flush_tracker(llm, finish=True) Scenario 2: Us...
https://python.langchain.com/en/latest/ecosystem/comet_tracking.html
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from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler from langchain.callbacks.base import CallbackManager from langchain.llms import OpenAI comet_callback = CometCallbackHandler( project_name="comet-example-langchain", complexity_metrics=True, stream_logs=True, tags=["agent"], ) m...
https://python.langchain.com/en/latest/ecosystem/comet_tracking.html
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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 = ...
https://python.langchain.com/en/latest/ecosystem/comet_tracking.html
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an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, ...
https://python.langchain.com/en/latest/ecosystem/comet_tracking.html
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.md .pdf Databerry Contents What is Databerry? Quick start Databerry# This page covers how to use the Databerry within LangChain. What is Databerry?# Databerry is an open source document retrievial platform that helps to connect your personal data with Large Language Models. Quick start# Retrieving documents stored i...
https://python.langchain.com/en/latest/ecosystem/databerry.html
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.md .pdf Milvus Contents Installation and Setup Wrappers VectorStore Milvus# This page covers how to use the Milvus ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Milvus wrappers. Installation and Setup# Install the Python SDK with pip install pymilvus...
https://python.langchain.com/en/latest/ecosystem/milvus.html
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.md .pdf Chroma Contents Installation and Setup Wrappers VectorStore Chroma# This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers. Installation and Setup# Install the Python package with pip install chro...
https://python.langchain.com/en/latest/ecosystem/chroma.html
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.md .pdf RWKV-4 Contents Installation and Setup Usage RWKV Model File Rwkv-4 models -> recommended VRAM RWKV-4# This page covers how to use the RWKV-4 wrapper within LangChain. It is broken into two parts: installation and setup, and then usage with an example. Installation and Setup# Install the Python package with ...
https://python.langchain.com/en/latest/ecosystem/rwkv.html
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RWKV VRAM Model | 8bit | bf16/fp16 | fp32 14B | 16GB | 28GB | >50GB 7B | 8GB | 14GB | 28GB 3B | 2.8GB| 6GB | 12GB 1b5 | 1.3GB| 3GB | 6GB See the rwkv pip page for more information about strategies, including streaming and cuda support. previous Runhouse next SearxNG Search API Contents...
https://python.langchain.com/en/latest/ecosystem/rwkv.html
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.md .pdf Weaviate Contents Installation and Setup Wrappers VectorStore Weaviate# This page covers how to use the Weaviate ecosystem within LangChain. What is Weaviate? Weaviate in a nutshell: Weaviate is an open-source ​database of the type ​vector search engine. Weaviate allows you to store JSON documents in a class...
https://python.langchain.com/en/latest/ecosystem/weaviate.html
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To import this vectorstore: from langchain.vectorstores import Weaviate For a more detailed walkthrough of the Weaviate wrapper, see this notebook previous Weights & Biases next Wolfram Alpha Wrapper Contents Installation and Setup Wrappers VectorStore By Harrison Chase © Copyright 2023, Harrison Chase. ...
https://python.langchain.com/en/latest/ecosystem/weaviate.html
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.md .pdf Qdrant Contents Installation and Setup Wrappers VectorStore Qdrant# This page covers how to use the Qdrant ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers. Installation and Setup# Install the Python SDK with pip install qdrant-c...
https://python.langchain.com/en/latest/ecosystem/qdrant.html
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.md .pdf NLPCloud Contents Installation and Setup Wrappers LLM NLPCloud# This page covers how to use the NLPCloud ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers. Installation and Setup# Install the Python SDK with pip install nlpcloud...
https://python.langchain.com/en/latest/ecosystem/nlpcloud.html
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.md .pdf Installation Contents Official Releases Installing from source Installation# Official Releases# LangChain is available on PyPi, so to it is easily installable with: pip install langchain That will install the bare minimum requirements of LangChain. A lot of the value of LangChain comes when integrating it wi...
https://python.langchain.com/en/latest/reference/installation.html
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.md .pdf Integrations Integrations# Besides the installation of this python package, you will also need to install packages and set environment variables depending on which chains you want to use. Note: the reason these packages are not included in the dependencies by default is that as we imagine scaling this package,...
https://python.langchain.com/en/latest/reference/integrations.html
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PromptLayer: Install requirements with pip install promptlayer (be sure to be on version 0.1.62 or higher) Get an API key from promptlayer.com and set it using promptlayer.api_key=<API KEY> SerpAPI: Install requirements with pip install google-search-results Get a SerpAPI api key and either set it as an environment var...
https://python.langchain.com/en/latest/reference/integrations.html
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DeepLake: Install requirements with pip install deeplake LlamaCpp: Install requirements with pip install llama-cpp-python Download model and convert following llama.cpp instructions Milvus: Install requirements with pip install pymilvus In order to setup a local cluster, take a look here. Zilliz: Install requirements w...
https://python.langchain.com/en/latest/reference/integrations.html
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.rst .pdf Utilities Utilities# There are a lot of different utilities that LangChain provides integrations for These guides go over how to use them. These can largely be grouped into two categories: generic utilities, and then utilities for working with larger text documents. Generic Utilities Python REPL SerpAPI Searx...
https://python.langchain.com/en/latest/reference/utils.html
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.rst .pdf Prompts Prompts# The reference guides here all relate to objects for working with Prompts. PromptTemplates Example Selector previous How to serialize prompts next PromptTemplates By Harrison Chase © Copyright 2023, Harrison Chase. Last updated on Apr 21, 2023.
https://python.langchain.com/en/latest/reference/prompts.html
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.rst .pdf VectorStores VectorStores# Wrappers on top of vector stores. class langchain.vectorstores.Annoy(embedding_function: Callable, index: Any, metric: str, docstore: langchain.docstore.base.Docstore, index_to_docstore_id: Dict[int, str])[source]# Wrapper around Annoy vector database. To use, you should have the an...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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n_jobs – Number of jobs to use for indexing. Defaults to -1 This is a user friendly interface that: Creates an in memory docstore with provided embeddings Initializes the Annoy database This is intended to be a quick way to get started. Example from langchain import Annoy from langchain.embeddings import OpenAIEmbeddin...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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index = Annoy.from_texts(texts, embeddings) classmethod load_local(folder_path: str, embeddings: langchain.embeddings.base.Embeddings) → langchain.vectorstores.annoy.Annoy[source]# Load Annoy index, docstore, and index_to_docstore_id to disk. Parameters folder_path – folder path to load index, docstore, and index_to_do...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Turns annoy results into a list of documents and scores. Parameters idxs – List of indices of the documents in the index. dists – List of distances of the documents in the index. Returns List of Documents and scores. save_local(folder_path: str, prefault: bool = False) → None[source]# Save Annoy index, docstore, and in...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Return docs most similar to embedding vector. Parameters embedding – Embedding to look up documents similar to. k – Number of Documents to return. Defaults to 4. search_k – inspect up to search_k nodes which defaults to n_trees * n if not provided Returns List of Documents most similar to the embedding. similarity_sear...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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to n_trees * n if not provided Returns List of Documents most similar to the query and score for each class langchain.vectorstores.AtlasDB(name: str, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, api_key: Optional[str] = None, description: str = 'A description for your project', is_public: ...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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for full detail. classmethod from_documents(documents: List[langchain.schema.Document], embedding: Optional[langchain.embeddings.base.Embeddings] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = None, persist_directory: Optional[str] = None, description: str = 'A description...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Returns Nomic’s neural database and finest rhizomatic instrument Return type AtlasDB classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, name: Optional[str] = None, api_key: Optional[str] = No...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Run similarity search with AtlasDB Parameters query (str) – Query text to search for. k (int) – Number of results to return. Defaults to 4. Returns List of documents most similar to the query text. Return type List[Document] class langchain.vectorstores.Chroma(collection_name: str = 'langchain', embedding_function: Opt...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Return type List[str] delete_collection() → None[source]# Delete the collection. classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb....
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Otherwise, the data will be ephemeral in-memory. Parameters texts (List[str]) – List of texts to add to the collection. collection_name (str) – Name of the collection to create. persist_directory (Optional[str]) – Directory to persist the collection. embedding (Optional[Embeddings]) – Embedding function. Defaults to No...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param filter: Filter by metada...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Returns List of Documents most similar to the query vector. similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]# Run similarity search with Chroma with distance. Parameters query (str) – Query text to search...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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embeddings = OpenAIEmbeddings() vectorstore = DeepLake("langchain_store", embeddings.embed_query) add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]# Run more texts through the embeddings and add to the vectorstore. Parameters text...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Otherwise, the data will be ephemeral in-memory. Parameters path (str, pathlib.Path) – The full path to the dataset. Can be: Deep Lake cloud path of the form hub://username/dataset_name.To write to Deep Lake cloud datasets, ensure that you are logged in to Deep Lake (use ‘activeloop login’ from command line) AWS S3 pa...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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:param fetch_k: Number of Documents to fetch to pass to MMR algorithm. Returns List of Documents selected by maximal marginal relevance. max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any) → List[langchain.schema.Document][source]# Return docs selected using the...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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filter – Attribute filter by metadata example {‘key’: ‘value’}. It can also filter] (take [Deep Lake) – (https – //docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter) Defaults to None. maximal_marginal_relevance – Whether to use maximal marginal relevance. Defaults to False. fet...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Returns List of Documents most similar to the query vector. similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]# Return docs most similar to embedding vector. Parameters embedding – Embedding to look up documents similar to. k – Number of Documents to...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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embedding = OpenAIEmbeddings() elastic_vector_search = ElasticVectorSearch( elasticsearch_url="http://localhost:9200", index_name="test_index", embedding=embedding ) To connect to an Elasticsearch instance that requires login credentials, including Elastic Cloud, use the Elasticsearch URL format https://use...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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index_name (str) – The name of the Elasticsearch index for the embeddings. embedding (Embeddings) – An object that provides the ability to embed text. It should be an instance of a class that subclasses the Embeddings abstract base class, such as OpenAIEmbeddings() Raises ValueError – If the elasticsearch python packag...
https://python.langchain.com/en/latest/reference/modules/vectorstore.html
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Return docs most similar to query. Parameters query – Text to look up documents similar to. k – Number of Documents to return. Defaults to 4. Returns List of Documents most similar to the query. class langchain.vectorstores.FAISS(embedding_function: typing.Callable, index: typing.Any, docstore: langchain.docstore.base....
https://python.langchain.com/en/latest/reference/modules/vectorstore.html