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5,776
fix intermediate step over length
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2023-06-06T09:09:01
2023-08-11T02:15:30
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<!-- fix the problem of agent_scratchpad over length --> fix the problem of agent_scratchpad over length, during using 'CONVERSATIONAL_REACT_DESCRIPTION' agent. This problem is caused by the simple concatenation of intermediate_steps in Function '_construct_scratchpad', so I changed this to ignore the earliest intermediate_steps. Note: This fix add two parameters in Agent class: - scratchpad_max_size: the max size of constructed agent_scratchpad. - length_function: the length function used to count the length of agent_scratchpad. #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Agents / Tools / Toolkits - @vowelparrot -->
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ConversationalRetrievalChain is not robust to default conversation memory configurations.
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2023-06-06T07:42:49
2023-09-20T16:08:51
2023-09-20T16:08:50
CONTRIBUTOR
null
### System Info Langchain 0.0.184 Python 3.11.3 Mac OS Ventura 13.3.1 ### Who can help? @hwchase17 @agola11 I was reading [this](https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html?highlight=conversationalretriever) and changed one part of the code to `return_messages=False` when instantiating ConversationBufferMemory. We have two lines of code below that ask a question. The first line is `qa({'question': "what is a?"})`. From the error below it seems that messages should be a list. If this is the case, shouldn't it be documented, and perhaps the default keyword argument for `return_messages` should be `True` instead of `False` in `ConversationBufferMemory`, `ConversationBufferWindowMemory` and any other relevant memory intended to be used in `Conversation`. This also would've been circumvented entirely if the docstring for these memory classes contained helpful information about the parameters and what to expect. I also suppose a good solve for this would be to just change the default kwargs for all conversation memory to `memory_key="chat_history"` and `return_messages=True`. ```python import langchain langchain.__version__ ``` '0.0.184' ```python from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.llms import OpenAI from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationBufferMemory chars = ['a', 'b', 'c', 'd', '1', '2', '3'] texts = [4*c for c in chars] metadatas = [{'title': c, 'source': f'source_{c}'} for c in chars] vs = FAISS.from_texts(texts, embedding=OpenAIEmbeddings(), metadatas=metadatas) retriever = vs.as_retriever(search_kwargs=dict(k=5)) memory = ConversationBufferMemory(memory_key='chat_history', return_messages=False) qa = ConversationalRetrievalChain.from_llm(OpenAI(), retriever, memory=memory) print(qa({'question': "what is a?"})) print(qa({'question': "what is b?"})) ``` {'question': 'what is a?', 'chat_history': '', 'answer': ' a is aaaaa.'} --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[2], line 17 15 qa = ConversationalRetrievalChain.from_llm(OpenAI(), retriever, memory=memory) 16 print(qa({'question': "what is a?"})) ---> 17 print(qa({'question': "what is b?"})) File ~/code/langchain/langchain/chains/base.py:140, in Chain.__call__(self, inputs, return_only_outputs, callbacks) 138 except (KeyboardInterrupt, Exception) as e: 139 run_manager.on_chain_error(e) --> 140 raise e 141 run_manager.on_chain_end(outputs) 142 return self.prep_outputs(inputs, outputs, return_only_outputs) File ~/code/langchain/langchain/chains/base.py:134, in Chain.__call__(self, inputs, return_only_outputs, callbacks) 128 run_manager = callback_manager.on_chain_start( 129 {"name": self.__class__.__name__}, 130 inputs, 131 ) 132 try: 133 outputs = ( --> 134 self._call(inputs, run_manager=run_manager) 135 if new_arg_supported 136 else self._call(inputs) 137 ) 138 except (KeyboardInterrupt, Exception) as e: 139 run_manager.on_chain_error(e) File ~/code/langchain/langchain/chains/conversational_retrieval/base.py:100, in BaseConversationalRetrievalChain._call(self, inputs, run_manager) 98 question = inputs["question"] 99 get_chat_history = self.get_chat_history or _get_chat_history --> 100 chat_history_str = get_chat_history(inputs["chat_history"]) 102 if chat_history_str: 103 callbacks = _run_manager.get_child() File ~/code/langchain/langchain/chains/conversational_retrieval/base.py:45, in _get_chat_history(chat_history) 43 buffer += "\n" + "\n".join([human, ai]) 44 else: ---> 45 raise ValueError( 46 f"Unsupported chat history format: {type(dialogue_turn)}." 47 f" Full chat history: {chat_history} " 48 ) 49 return buffer ValueError: Unsupported chat history format: <class 'str'>. Full chat history: Human: what is a? AI: a is aaaaa. ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [X] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.llms import OpenAI from langchain.chains import ConversationalRetrievalChain from langchain.memory import ConversationBufferMemory chars = ['a', 'b', 'c', 'd', '1', '2', '3'] texts = [4*c for c in chars] metadatas = [{'title': c, 'source': f'source_{c}'} for c in chars] vs = FAISS.from_texts(texts, embedding=OpenAIEmbeddings(), metadatas=metadatas) retriever = vs.as_retriever(search_kwargs=dict(k=5)) memory = ConversationBufferMemory(memory_key='chat_history', return_messages=False) qa = ConversationalRetrievalChain.from_llm(OpenAI(), retriever, memory=memory) print(qa({'question': "what is a?"})) print(qa({'question': "what is b?"})) ``` ### Expected behavior In this code I would've expected it to be error free, but it has an error when executing the second call of the `qa` object asking `"what is b?"`.
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5,774
Dynamic loading of tools and custom tools
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2023-06-06T05:57:15
2023-09-19T18:34:06
2023-09-12T16:09:58
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### Feature request Maybe I didn't get the idea: Some tools can be loaded like 'google-search' other tools can't be loaded like duckduckgo-search by the load_tools: langchain/agents/load_tools.py E.g. specified in _EXTRA_OPTIONAL_TOOLS. As for custom tools they can't be loaded at all it seems in that way. It would be great to register custom tools so that they can be loaded by load_tools as well. I've done something like this for the moment, so I have Class-Names for my own tools and the lower-case would be standard: ``` standard_tools = [] for toolStr in tool_names: if toolStr[0].islower(): standard_tools.append(toolStr) self.tools = load_tools(standard_tools, **tool_kwargs) for toolStr in tool_names: if toolStr[0].isupper(): toolClass = globals()[toolStr] toolObject = toolClass() self.tools.append(copy.deepcopy(toolObject)) ``` ### Motivation When assigning agents a set of tools it's very handy to do it through a list. ### Your contribution I'm not familiar enough with the architecture of langchain so I'd better not. But I'm happy to test it.
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Issue: Source code issues
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2023-06-06T05:49:53
2023-06-06T06:04:21
2023-06-06T06:04:21
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### Issue you'd like to raise. when i read your code ,i had something don‘t understand,https://github.com/hwchase17/langchain/blob/master/langchain/agents/loading.py#L80, in here ":=" is not a python syntax,how could it run in python(when i run langchain,it run well !) ### Suggestion: _No response_
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YoutubeAudioLoader and updates to OpenAIWhisperParser
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2023-06-06T05:12:37
2023-06-06T22:15:09
2023-06-06T22:15:09
COLLABORATOR
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This introduces the `YoutubeAudioLoader`, which will load blobs from a YouTube url and write them. Blobs are then parsed by `OpenAIWhisperParser()`, as show in this [PR](https://github.com/hwchase17/langchain/pull/5580), but we extend the parser to split audio such that each chuck meets the 25MB OpenAI size limit. As shown in the notebook, this enables a very simple UX: ``` # Transcribe the video to text loader = GenericLoader(YoutubeAudioLoader([url],save_dir),OpenAIWhisperParser()) docs = loader.load() ``` Tested on full set of Karpathy lecture videos: ``` # Karpathy lecture videos urls = ["https://youtu.be/VMj-3S1tku0" "https://youtu.be/PaCmpygFfXo", "https://youtu.be/TCH_1BHY58I", "https://youtu.be/P6sfmUTpUmc", "https://youtu.be/q8SA3rM6ckI", "https://youtu.be/t3YJ5hKiMQ0", "https://youtu.be/kCc8FmEb1nY"] # Directory to save audio files save_dir = "~/Downloads/YouTube" # Transcribe the videos to text loader = GenericLoader(YoutubeAudioLoader(urls,save_dir),OpenAIWhisperParser()) docs = loader.load() ```
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I am a professional UX & UI designer and I want to contribute to this project.
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### Feature request Vux.design - How can I be part of this project? ### Motivation Vux.design - How can I be part of this project? ### Your contribution Vux.design - How can I be part of this project?
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ML flow call backs fails at missing keys
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2023-06-06T04:53:33
2023-10-09T16:07:22
2023-10-09T16:07:21
NONE
null
### System Info Langchain version 0.0.191 , python version 3.9.15 ### Who can help? @agola ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [X] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction following the notebook given at https://python.langchain.com/en/latest/integrations/mlflow_tracking.html?highlight=mlflow I just changed the chain from LLM chain to retrieval chain and OpenAI to Azure Open AI i continuously faced the error KeyError: "None of [Index(['step', 'prompt', 'name'], dtype='object')] are in the [columns]" But some data is getting logged into ML_flow experiments. like chain_1_start and chain_2_start and their ends respectively. ### Expected behavior To have the necessary data in the ML flow experiment logs and should work without throwing any error.
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Fix IndexError when input_list is Empty in prep_prompts
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2023-06-06T04:49:03
2023-08-11T05:50:48
2023-08-11T05:50:40
CONTRIBUTOR
null
This MR corrects the IndexError arising in prep_prompts method when no documents are returned from a similarity search. Fixes #1733 #### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 - project lead VectorStores / Retrievers / Memory - @dev2049
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fix(langchain): fix reading a list of dict from json file
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2023-06-06T04:41:19
2023-08-11T06:42:18
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This commit allows a list of dictionaries to be read from a json file. Resolves #5706 No additional tests implemented as I'm not very familiar with the tests yet, can suggest creating a list of dictionaries then asserting the values - @vowelparrot
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Observability Feature Request: Metrics, Data Monitoring, and Graph Visualization
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2023-06-06T04:09:20
2023-06-25T08:41:58
2023-06-25T08:41:57
CONTRIBUTOR
null
### Feature request I would like to propose the addition of an **observability feature** to LangChain, enabling developers to monitor and analyze their applications more effectively. This feature aims to provide metrics, data tracking, and graph visualization capabilities to enhance observability. Key aspects of the proposed feature include: - **Metrics Tracking**: Capture time taken by LLM model to handle request, errors, number of tokens, and costing indication for the particular LLM. - **Data Tracking**: Log and store prompt, request, and response data for each LangChain interaction. - **Graph Visualization**: Generate basic graphs over time, depicting metrics such as request duration, error occurrences, token count, and cost. This feature request aims to improve the development and debugging experience by providing developers with better insights into the performance, behavior, and cost implications of their LangChain applications. ### Motivation Last week, while I was creating an application using OpenAI's GPT 3.5 APIs to develop a chat-based solution for answering SEO-related queries, I encountered a significant challenge. During the launch, I realized that I had no means to monitor the responses suggested by the language model (GPT, in my case). Additionally, I had no insights into its performance, such as speed or token usage. To check the token count, I had to repeatedly log in to the OpenAI dashboard, which was time-consuming and cumbersome. It was frustrating not having a clear picture of user interactions and the effectiveness of the system's responses. I realised that I needed a way to understand the system's performance in handling different types of queries and identify areas that required improvement. I strongly believe that incorporating an observability feature in LangChain would be immensely valuable. It would empower developers like me to track user interactions, analyze the quality of responses, and measure the performance of the underlying LLM requests. Having these capabilities would not only provide insights into user behavior but also enable us to continuously improve the system's accuracy, response time, and overall user experience. ### Your contribution Yes, I am planning to raise a PR along with a couple of my friends to add an observability feature to Langchain. I would be more than happy to take suggestions from the community, on what we could add to make it more usable!
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2023-06-06T03:43:29
2023-06-06T03:54:09
2023-06-06T03:54:08
COLLABORATOR
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feature: add `split_wrap_text` method to Recursive/CharacterTextSplitter
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2023-06-06T03:28:44
2023-06-11T20:44:39
2023-06-11T20:44:39
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This method allows adding a `prefix` and a `suffix` to each split text. This is useful for wrapping the text with special characters, tokens or formatting tags. - This method implemented in `CharacterTextSplitter` and `RecursiveCharacterTextSplitter` - The method `split_wrap_text` calls the existing `split_text` method internally and then adds the prefix and suffix to each element of the output list. - The method has an optional parameters for the prefix and suffix, which default to empty strings. <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: @eyurtsev, @vowelparrot <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Wheynelau/json double quote
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2023-06-06T02:54:15
2023-08-11T06:39:58
2023-08-11T06:39:57
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In the "langchain/tools/json/tool.py" file, fixed the _parse_input function to properly handle single quotes. I've added a second .replace() to replace the single quotes. This is a workaround I can suggest. Resolves issue #5759 Test: 1. Added different orderings of quotes in test_json.py 2. Was unable to run poetry but tested in notebook - @vowelparrot
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Improving Custom Agent Speed?
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2023-06-06T02:44:39
2023-11-08T16:08:55
2023-11-08T16:08:54
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### Discussed in https://github.com/hwchase17/langchain/discussions/5730 <div type='discussions-op-text'> <sup>Originally posted by **Radvian** June 5, 2023</sup> Hello, not sure if I should ask this in Discussion or Issue, so apologies beforehand. I'm doing a passion project in which I try to create a knowledge base for a website, and create a Conversational Retrieval QA Chain so we can ask questions about the knowledge base. This is very much akin to this: https://python.langchain.com/en/latest/modules/chains/index_examples/chat_vector_db.html And I have no problem to implement it. Now, I want to go to the next step. Instead of creating a chain, I want to create an agent. I want to do something like this: - First, always ask the user for their name. - If the user asks a particular question that is not in the knowledge base, don't reply with "I don't know", but search Wikipedia about it - If the user asks about X, send an image link (from google search images) of 'X' item - and few other custom tools And, I also don't have any problem in creating tools and combining them into a unified agent. However, my problem is this (and this is expected): the agent performs slower, much slower than if I just use the chain. If I only use Retrieval QA Chain, and enables _streaming_, it can start typing the output in less than 5 seconds. If I use the complete agent, with a few custom tools, it starts to type the answer / output within 10-20 seconds. I know this is to be expected, since the agent needs to think about which tools to use. But any ideas on how to make this faster? In the end, I want to deploy this whole project in a streamlit and wants this to perform like a 'functional' chatbot, with almost-instant reply, and not having to wait 10-20 seconds before the 'agent' starts typing. Any ideas are welcome, apologies if this is a stupid question. Thank you!</div>
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fix(langchain): fix _parse_input function
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2023-06-06T02:35:54
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In the "langchain/tools/json/tool.py" file, fixed the _parse_input function to properly handle single quotes. Resolves issue #5759. #### Before submitting <!-- If you're adding a new integration, please include: 1. I've added some tests but I couldn't get poetry to work, still troubleshooting #### Who can review? Tag maintainers/contributors who might be interested: Agents / Tools / Toolkits - @vowelparrot
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5,761
Update tutorials.md
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2023-06-06T02:00:49
2023-06-06T03:37:11
2023-06-06T03:37:11
CONTRIBUTOR
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# Added an overview of LangChain modules Aimed at introducing newcomers to LangChain's main modules :) Twitter handle is @edrick_dch ## Who can review? @eyurtsev
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Modify ElasticKnnSearch to return Document object
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2023-06-06T01:24:07
2023-09-12T16:10:09
2023-09-12T16:10:08
CONTRIBUTOR
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### Feature request When performing knnSearch or hybridKnnSearch return `Document` object ### Motivation Currently the return a `dict` object. It would be more useful to return [langchain.schema.Document](https://github.com/hwchase17/langchain/blob/d5b160821641df77df447e6dfce21b58fbb13d75/langchain/schema.py#LL266C10-L266C10) ### Your contribution I will make the canges
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Issue: Json Agent tool fails when dict key is surrounded by single quotes
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2023-09-12T16:10:14
2023-09-12T16:10:13
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### Issue you'd like to raise. The Json Agent tools (json_spec_list_keys, json_spec_get_value) fails when dict key of Action Input is surrounded by single quotes. It only accepts double quotes. Here is a simple example. ```python import pathlib from langchain.tools.json.tool import JsonSpec # [test.json] # # { # "name": "Andrew Lee", # "number": 1234 # } json_spec = JsonSpec.from_file(pathlib.Path("test.json")) print(json_spec.keys("data")) # => ['name', 'number'] print(json_spec.value('data["name"]')) # => Andrew Lee print(json_spec.value("data['name']")) # => KeyError("'name'") ``` To make matters worse, the json_spec_list_keys returns keys with **single quote** at Observation. So the LLM follow it to Action input and endless error happens like below. ``` Action: json_spec_list_keys Action Input: data Observation: ['name', 'number'] Thought: I should look at the values associated with the "name" key Action: json_spec_get_value Action Input: "data['name']" Observation: KeyError("'name'") ``` ### Suggestion: The Json Agent tools (json_spec_list_keys, json_spec_get_value) should accept both single/double quotes for dict key like general python dict access.
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2023-06-05T23:24:10
2023-06-05T23:28:59
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…719) A minor update to retry Cohore API call in case of errors using tenacity as it is done for OpenAI LLMs. #### Who can review? @hwchase17, @agola11 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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DOC: PrompTemplate format with docs
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2023-06-05T21:33:49
2023-11-07T16:26:16
2023-11-07T16:26:07
NONE
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### Issue with current documentation: Hi, how can I see what my final prompt looks like when retrieving docs? For example, I'm not quite sure how this will end up looking: ```python prompt_template = """Answer based on context:\n\n{context}\n\n{question}""" PROMPT = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) qa = RetrievalQA.from_chain_type( llm=sm_llm, chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs={"prompt": PROMPT}, ) result = qa.run(question) ``` ### Idea or request for content: _No response_
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5,754
Batching added for ChromaDB Similarity Search
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2023-06-05T21:09:07
2023-09-19T18:24:36
null
CONTRIBUTOR
null
ChromaDB supports batching queries for efficient searching. Currently, the APIs used for OpenAI also support batching, so I have added batching functions for OpenAI embeddings. However, the batching implementation may vary depending on the device or API used for HuggingFace, so I haven't made any additions for that. Therefore, the functions check the embedding being used. **_Current Situation:_** Since there is no batching, each query is sent and processed individually. **_Test Case:_** I have a query set consisting of 1500 questions. In the current situation, when I retrieve the answers without batching, it takes 8.5 minutes to process all 1500 questions. **_With Batching:_** ChromaDB already has a query function that supports batching. Utilizing that, I have added `similarity_search_by_batch` and `similarity_search_with_score_by_batch` functions in addition to `similarity_search` and `similarity_search_with_score` functions. Since the output types of the functions will change, I didn't want to write them within the same function. If you want, we can also update it that way. **_Test Case:_** **_After Implementing Batching:_** when I retrieve the answers with a batch size of 256, all 1500 questions are processed in 12 seconds. When using a batch size of 512, the answers are returned in 6 seconds. _It is also important for decreasing request number for OpenAI API._ Additionally, I updated the `_results_to_docs_and_scores` function to `_results_to_docs_and_scores_for_batches`. Now the function works appropriately for batching and returns the results. Since the returned value has changed from `List[Tuple[Document, float]]` to `List[List[Tuple[Document, float]]],` I have written a separate function for handling this conversion. my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu) _**TEST AND DOCUMENTS**_ I think it will be necessary to add test cases for these functions, but I would greatly appreciate your help since I haven't written tests before :) If you're okay with these changes, I can add the necessary examples to the documentation! Thank you very much! Berke VectorStores / Retrievers / Memory - @dev2049, @hwchase17
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docs: updated `ecosystem/dependents`
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2023-06-05T21:03:29
2023-06-05T23:15:30
2023-06-05T23:09:55
COLLABORATOR
null
updated `ecosystem/dependents` data (it was updated 2+ weeks ago) #### Who can review? @hwchase17 @eyurtsev @dev2049
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docs: `ecosystem/integrations` update 5
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2023-06-05T20:27:35
2023-06-05T23:15:17
2023-06-05T23:08:56
COLLABORATOR
null
- added missed integration to `docs/ecosystem/integrations/` - updated notebooks to consistent format: changed titles, file names; added descriptions #### Who can review? @hwchase17 @dev2049
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Removes unnecessary backslash escaping for backticks in python
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2023-06-05T19:54:53
2023-06-09T02:48:44
2023-06-05T20:30:11
CONTRIBUTOR
null
Fixed python deprecation warning: DeprecationWarning: invalid escape sequence '`' backticks (`) do not have special meaning in python strings and should not be escaped. -- @spazm on twitter ### Who can review: @nfcampos ported this change from javascript, @hwchase17 wrote the original STRUCTURED_FORMAT_INSTRUCTIONS,
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Base RunEvaluator Chain
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2023-06-05T19:36:36
2023-06-06T23:42:16
2023-06-06T23:42:15
CONTRIBUTOR
null
Clean up a bit and only implement the QA and reference free implementations from https://github.com/hwchase17/langchain/pull/5618
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Add class attribute "return_generated_question" to class "BaseConversationalRetrievalChain"
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2023-06-05T19:23:03
2023-06-05T23:10:12
2023-06-05T23:10:12
CONTRIBUTOR
null
<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> Adding a class attribute "return_generated_question" to class "BaseConversationalRetrievalChain". If set to `True`, the chain's output has a key "generated_question" with the question generated by the sub-chain `question_generator` as the value. This way the generated question can be logged. <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> @dev2049 @vowelparrot
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Pinecone.from_documents() does not create the index if it does not exist
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### System Info langchain == 0.0.190 Google Colab ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction docsearch = Pinecone.from_documents(chunks, embeddings, index_name=index_name) calls from_text which returns ValueError: No active indexes found in your Pinecone project, are you sure you're using the right API key and environment? if the index does not exist. `` if index_name in indexes: index = pinecone.Index(index_name) elif len(indexes) == 0: raise ValueError( "No active indexes found in your Pinecone project, " "are you sure you're using the right API key and environment?" ) `` ### Expected behavior The method should create the index if it does not exist. This is the expected behavior from the documentation and the official examples. Creating a new index if it does not exist is the only recommended behavior as it implies that the dimension of the index is always consistent with the dimension of the embedding model used. Thanks Best regards Jerome
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base implementation for azureml foundational model
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2023-06-05T18:24:55
2023-06-05T18:26:10
2023-06-05T18:26:10
CONTRIBUTOR
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Fix a typo in the documentation for the Slack document loader
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2023-06-05T17:38:53
2023-06-05T20:30:25
2023-06-05T20:30:24
CONTRIBUTOR
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Fixes a typo I noticed while reading the docs.
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json parser for when 3 backtiks are missing
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2023-06-05T16:34:13
2023-09-29T16:27:03
2023-09-29T16:27:03
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Fixes #3455 and #2276. The parse_json_markdown function expects a JSON string within triple backticks. However, even though it's instructed this way, LLM's responses sometimes come with fewer that 3 backticks (as also described in the issue 3455. You can try using this string as input to test: json_string = '\n\nRESPONSE\n--------------------\n```json\n{\n "action": "Current Search",\n "action_input": "Atividades de lazer em Recife"\n}\n``' #### Who can review? Tag maintainers/contributors who might be interested: @vowelparrot
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Addresses GPT4All wrapper model_type attribute issues #5720.
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2023-06-05T16:27:35
2023-06-05T19:45:30
2023-06-05T19:45:30
CONTRIBUTOR
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Fixes #5720. A more in-depth discussion is in my comment here: https://github.com/hwchase17/langchain/issues/5720#issuecomment-1577047018 In a nutshell, there has been a subtle change in the latest version of GPT4Alls Python bindings. The change I submitted yesterday is compatible with this version, however, this version is as of yet unreleased and thus the code change breaks Langchain's wrapper under the currently released version of GPT4All. This pull request proposes a backwards-compatible solution.
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Zep Hybrid Search
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2023-06-05T15:54:59
2023-06-06T04:34:33
2023-06-05T19:59:28
CONTRIBUTOR
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Zep now supports persisting custom metadata with messages and hybrid search across both message embeddings and structured metadata. This PR implements custom metadata and enhancements to the `ZepChatMessageHistory` and `ZepRetriever` classes to implement this support. Tag maintainers/contributors who might be interested: VectorStores / Retrievers / Memory - @dev2049
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DOC: SageMaker JumpStart Embeddings
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2023-06-05T15:52:39
2023-09-11T16:58:07
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### Issue with current documentation: I saw in a recent notebook demo that a custom `SagemakerEndpointEmbeddingsJumpStart` class was needed to deploy a SageMaker JumpStart text embedding model. I couldn't find any langchain docs for this. ```python from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler class SagemakerEndpointEmbeddingsJumpStart(SagemakerEndpointEmbeddings): def embed_documents(self, texts: List[str], chunk_size: int = 5) -> List[List[float]]: """Compute doc embeddings using a SageMaker Inference Endpoint. Args: texts: The list of texts to embed. chunk_size: The chunk size defines how many input texts will be grouped together as request. If None, will use the chunk size specified by the class. Returns: List of embeddings, one for each text. """ results = [] _chunk_size = len(texts) if chunk_size > len(texts) else chunk_size for i in range(0, len(texts), _chunk_size): #print(f"input:", texts[i : i + _chunk_size]) response = self._embedding_func(texts[i : i + _chunk_size]) results.extend(response) return results class ContentHandler(EmbeddingsContentHandler): content_type = "application/json" accepts = "application/json" def transform_input(self, prompt: str, model_kwargs={}) -> bytes: input_str = json.dumps({"text_inputs": prompt, **model_kwargs}) return input_str.encode("utf-8") def transform_output(self, output: bytes) -> str: response_json = json.loads(output.read().decode("utf-8")) embeddings = response_json["embedding"] return embeddings content_handler = ContentHandler() embeddings = SagemakerEndpointEmbeddingsJumpStart( endpoint_name=_MODEL_CONFIG_["huggingface-textembedding-gpt-j-6b"]["endpoint_name"], region_name=aws_region, content_handler=content_handler, ) ``` ### Idea or request for content: _No response_
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Issue: TypeError: issubclass() arg 1 must be a class
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2023-06-05T15:24:37
2023-11-29T15:01:38
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### Issue you'd like to raise. Getting the following error TypeError: issubclass() arg 1 must be a class when i execute: from langchain.embeddings import HuggingFaceEmbeddings #from transformers import pipeline #Download model from Hugging face model_name = "sentence-transformers/all-mpnet-base-v2" hf_embed = HuggingFaceEmbeddings(model_name=model_name) ### Suggestion: _No response_
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Persistence layer arrangment
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2023-06-05T15:11:51
2023-07-26T16:56:49
2023-07-26T16:56:49
COLLABORATOR
null
An option for persistence layer that can cache intermediate artifacts
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Add tests for QdrantTranslator
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2023-06-05T15:02:10
2023-09-29T16:27:37
2023-09-29T16:27:37
CONTRIBUTOR
null
This is just a simple suite of unit tests for #5567 #### Who can review? VectorStores / Retrievers / Memory - @dev2049
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add_user_message() fails. called with List instead of a string.
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2023-06-05T14:32:00
2023-09-13T03:54:38
2023-09-13T03:52:08
NONE
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### System Info Here is the error call stack. The code is attached to this submission. I did a workaround to prevent the crash. However, this is not the correct fix. Hacked FIX def add_user_message(self, message: str) -> None: """Add a user message to the store""" print(type(message)) **if isinstance(message, list): message = message[0].content** self.add_message(HumanMessage(content=message)) ---------------------------- Call Stack Error -------------------- Exception has occurred: ValidationError (note: full exception trace is shown but execution is paused at: _run_module_as_main) 1 validation error for HumanMessage content str type expected (type=type_error.str) File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/site-packages/pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__ raise validation_error File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/site-packages/langchain/schema.py", line 254, in add_user_message self.add_message(HumanMessage(content=message)) File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/site-packages/langchain/memory/chat_memory.py", line 35, in save_context self.chat_memory.add_user_message(input_str) File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/site-packages/langchain/chains/base.py", line 191, in prep_outputs self.memory.save_context(inputs, outputs) File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/site-packages/langchain/chains/base.py", line 142, in __call__ return self.prep_outputs(inputs, outputs, return_only_outputs) File "/Users/randolphhill/gepetto/code/mpv1/version1/geppetto/maincontroller.py", line 96, in <module> eaes = agent(messages) File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/Users/randolphhill/anaconda3/envs/version2/lib/python3.10/runpy.py", line 196, in _run_module_as_main (Current frame) return _run_code(code, main_globals, None, pydantic.error_wrappers.ValidationError: 1 validation error for HumanMessage content str type expected (type=type_error.str) ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.chat_models import ChatOpenAI from langchain import SerpAPIWrapper #from typing import Union from langchain.agents import load_tools from langchain.agents import initialize_agent from langchain.agents import Tool from langchain.chains.conversation.memory import ConversationBufferWindowMemory from langchain.prompts.prompt import PromptTemplate from langchain.tools import DuckDuckGoSearchRun from langchain.schema import ( SystemMessage, HumanMessage, ) #Build Prompt EXAMPLES = [ "Question: Is there a Thai restaurant in San Francisco?\nThought: I need to search for Thai restaurants in San Francisco.\nservation: Yes, there are several Thai restaurants located in San Francisco. Here are five examples: 1. Lers Ros Thai, 2. Kin Khao, 3. Farmhouse Kitchen Thai Cuisine, 4. Marnee Thai, 5. Osha Thai Restaurant.\nAction: Finished" ] SUFFIX = """\nQuestion: {input} {agent_scratchpad}""" TEST_PROMPT = PromptTemplate.from_examples( EXAMPLES, SUFFIX, ["input", "agent_scratchpad"] ) #print(TEST_PROMPT) messages = [ SystemMessage(content="You are a virtual librarian who stores search results, pdf and txt files. if a message cannot save, provide a file name based on the content of the message. File extension txt."), # AIMessage(content="I'm great thank you. How can I help you?"), HumanMessage(content="List 5 Thai resturants located in San Francisco that existed before September 2021.") ] def ingestFile(filename:str): return f"{filename}ingested file cabinate middle drawer" def saveText(msg:str): print("\nSEARCH RESULTS FILER !!!!\n",msg,"\n") try: with open("/tmp/file_name.txt", 'w') as file: file.write(msg) #print(f"Successfully written") return f"{msg}Information saved to thai.txt" except Exception as e: print(f"An error occurred: {e}") #serpapi = SerpAPIWrapper() serpapi = DuckDuckGoSearchRun() search = Tool( name="Search", func=serpapi.run, description="Use to search for current events" ) save = Tool( name="Save Text", func= saveText, description="used to save results of human request. File name will be created" ) #tool_names = ["serpapi"] #tools = load_tools(tool_names,search_filer) ingestFile = Tool( name="Ingest-Save file", func= ingestFile, description="Used to store/ingest file in virtual file cabinet" ) tools = [search,save,ingestFile] type(tools) chat = ChatOpenAI( #openai_api_key=OPENAI_API_KEY, temperature=0, #model='gpt-3.5-turbo' #prompt=TEST_PROMPT, model='gpt-4' ) memory = ConversationBufferWindowMemory( memory_key='chat_history', k=20, return_messages=True ) agent = initialize_agent(tools, chat, #iagent="zero-shot-react-description", agent='chat-conversational-react-description', max_interations=5, #output_parser=output_parser, verbose=True, max_iterations=3, early_stopping_method='generate', memory=memory) eaes = agent(messages) #res =agent(O"List 5 Thai resturants in San Franciso.") #res = agent(messages) print(res['output']) res =agent("What time is it in London") print(res['output']) res =agent("Save the last TWO LAST ANSWERS PROVIDEDi. Format answer First Answer: Second Answer:") print("RESULTS OF LAST TWO\n",res['output']) print("\n") res =agent("Ingest the file nih.pdf") print(res['output']) res=agent("your state is now set to ready to analyze data") print(res['output']) res=agent("What is your current state") print(res['output']) ### Expected behavior Not to crash and return with message saved.
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nc/load
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2023-06-05T14:22:45
2023-06-11T22:51:29
2023-06-11T22:51:28
COLLABORATOR
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null
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Error in documentation: Chroma constructor
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2023-06-05T14:09:26
2023-06-05T20:30:58
2023-06-05T20:30:58
CONTRIBUTOR
null
Chroma("langchain_store", embeddings.embed_query) must be Chroma("langchain_store", embeddings)
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AttributeError: 'GPT4All' object has no attribute 'model_type'
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2023-06-05T13:20:31
2023-09-18T16:09:19
2023-09-18T16:09:19
NONE
null
### System Info run on docker image with continuumio/miniconda3 on linux langchain-0.0.190 installed ### Who can help? _No response_ ### Information - [x] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Follow the steps here: https://python.langchain.com/en/latest/modules/models/llms/integrations/gpt4all.html ``` %pip install gpt4all > /dev/null from langchain import PromptTemplate, LLMChain from langchain.llms import GPT4All from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate(template=template, input_variables=["question"]) local_path = './models/ggml-gpt4all-l13b-snoozy.bin' # replace with your desired local file path import requests from pathlib import Path from tqdm import tqdm Path(local_path).parent.mkdir(parents=True, exist_ok=True) # Example model. Check https://github.com/nomic-ai/gpt4all for the latest models. url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin' # send a GET request to the URL to download the file. Stream since it's large response = requests.get(url, stream=True) # open the file in binary mode and write the contents of the response to it in chunks # This is a large file, so be prepared to wait. with open(local_path, 'wb') as f: for chunk in tqdm(response.iter_content(chunk_size=8192)): if chunk: f.write(chunk) # Callbacks support token-wise streaming callbacks = [StreamingStdOutCallbackHandler()] # Verbose is required to pass to the callback manager llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True) ``` ### Expected behavior The expected behavior: code returns a GPT4All object. The following error was produced: ``` AttributeError Traceback (most recent call last) Cell In[9], line 4 2 callbacks = [StreamingStdOutCallbackHandler()] 3 # Verbose is required to pass to the callback manager ----> 4 llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True) File /opt/conda/lib/python3.10/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__() File /opt/conda/lib/python3.10/site-packages/pydantic/main.py:1102, in pydantic.main.validate_model() File /opt/conda/lib/python3.10/site-packages/langchain/llms/gpt4all.py:156, in GPT4All.validate_environment(cls, values) 153 if values["n_threads"] is not None: 154 # set n_threads 155 values["client"].model.set_thread_count(values["n_threads"]) --> 156 values["backend"] = values["client"].model_type 158 return values AttributeError: 'GPT4All' object has no attribute 'model_type' ```
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5,728
Strips whitespace and \n from loc before filtering urls from sitemap
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2023-06-05T13:15:09
2023-06-05T23:33:56
2023-06-05T23:33:56
CONTRIBUTOR
null
Fixes #5699 #### Who can review? Tag maintainers/contributors who might be interested: @woodworker @LeSphax @johannhartmann
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1,741,743,546
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Add Hologres data warehouse as vectorstore
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2023-06-05T13:05:12
2023-06-11T20:36:56
2023-06-11T20:36:56
CONTRIBUTOR
null
Hi, This PR adds a new vector store: [Hologres](https://www.alibabacloud.com/help/en/hologres/latest/introduction) Hologres is a product of the Alibaba Cloud Computing platform. It's a fast cloud-native data warehouse that also supports high-performance vector searching. The example use case of Hologres is already accepted by openai-cookbook. We are actively integrating our product with outstanding open-source works and we believe langchain is a great work that has a bright future. We would appreciate it if this PR can be reviewed and accepted. We followed the contribution guidelines, and added unit tests and a notebook example. The code has passed the format and lint checks. #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot - --> VectorStores / Retrievers / Memory @dev2049 --- Best, Changgeng Developer of Hologres
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1,741,740,806
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Issue: Correctness of generated SQL queries
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2023-06-05T13:04:04
2023-10-05T16:09:31
2023-10-05T16:09:30
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### Issue you'd like to raise. I have a huge database hosted in BigQuery. The recommended approach to querying this via natural language is using agents. However, this diminishes the control I have over the intermediate steps, and that leads to many problems. - The generated query isn't correct, and this cannot be reinforced at any intermediate step - Clauses like "ORDER BY" brings _NULL_ to the top, and no amount of prompt engineering can retrieve the top non-null value Are there any workarounds for this? Or is there any other approach recommended for handling databases of this scale? ### Suggestion: _No response_
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1,741,652,313
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5,725
FAISS load_local() file path issue.
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2023-06-05T12:15:19
2023-07-23T03:01:24
2023-07-12T04:30:13
NONE
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### System Info Langchain Version : [0.0.190] Python Version : 3.10.7 OS : Windows 10 ### Who can help? @eyurtsev @hwchase17 ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Error: RuntimeError: Error in __cdecl faiss::FileIOReader::FileIOReader(const char *) at D:\a\faiss-wheels\faiss-wheels\faiss\faiss\impl\io.cpp:68: Error: 'f' failed: could not open **D:\Question Answer Generative AI\Langchain\index_files\ThinkPalm☺\en\index.faiss** for reading: Invalid argument Try creating a file path : D:\Question Answer Generative AI\Langchain\index_files\ThinkPalm\1\en mainly some folder name with just a number, then try to load that file path using faiss.load_local(). The path created after passing string path to Path(path) class is causing some issue. ### Expected behavior Issue: The actual file path is : D:\Question Answer Generative AI\Langchain\index_files\ThinkPalm\1\en File Path created by PurePath : *D:\Question Answer Generative AI\Langchain\index_files\ThinkPalm@\en\index.faiss
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Issue: Getting total_tokens for each chain in sequential chains
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2023-06-05T10:20:30
2023-12-03T16:06:51
2023-12-03T16:06:50
NONE
null
### Issue you'd like to raise. Hello guys, I'm wondering how can I get total_tokens for each chain in sequential chain. I'm working on optimisation of sequential chains and want to check how much does each step cost. Unfortunately get_openai_callback returns total_tokens for the whole sequential chain rather then each one. ### Suggestion: _No response_
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1,741,458,138
I_kwDOIPDwls5nzIra
5,723
postprocessors append - Langchain and Nemoguardrails
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2023-06-05T10:18:32
2023-09-11T16:58:12
2023-09-11T16:58:11
NONE
null
### Issue you'd like to raise. I am trying to generate a chain from langchain and append it to the configuration within nemoguard rails. I am getting error message LLMChain' object has no attribute 'postprocessors I am on Langchain version 0.0.167 and nemoguardrails 0.2.0 . Nemoguard rails 0.2.0 is compatible only with the langchain version 0.0.167 and i guess postprocessor is on the latest langchain version Can you please check on this and provide a solution rails = LLMRails(llm=llm, config=rails_config) title_chain.postprocessors.append(rails) script_chain.postprocessors.append(rails) ### Suggestion: _No response_
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DOC: How to install from source?
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2023-06-05T10:06:50
2023-11-10T16:09:02
2023-11-10T16:09:01
CONTRIBUTOR
null
### Issue with current documentation: I might've not noticed but I didn't see anywhere in the document instructions to install langchain from source so developers can work/test latest updates.the ### Idea or request for content: Add setup instructions from the source in the installation section in README.md.
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Update reX Exp & Added method to return the type of the parser
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2023-06-05T09:51:27
2023-11-07T04:15:37
2023-11-07T04:15:36
NONE
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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AttributeError: 'GPT4All' object has no attribute 'model_type' (langchain 0.0.190)
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2023-06-05T09:44:08
2023-06-06T07:30:11
2023-06-05T19:45:31
NONE
null
### System Info Hi, this is related to #5651 but (on my machine ;) ) the issue is still there. ## Versions * Intel Mac with latest OSX * Python 3.11.2 * langchain 0.0.190, includes fix for #5651 * ggml-mpt-7b-instruct.bin, downloaded at June 5th from https://gpt4all.io/models/ggml-mpt-7b-instruct.bin ### Who can help? @pakcheera @bwv988 First of all: thanks for the report and the fix :). Did this issues disappear on you machines? ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ## Error message ```shell ╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ │ /Users/christoph/src/sandstorm-labs/development-tools/ai-support-chat/gpt4all/chat.py:30 in │ │ <module> │ │ │ │ 27 │ model_name="all-mpnet-base-v2") │ │ 28 │ │ 29 # see https://gpt4all.io/models/ggml-mpt-7b-instruct.bin │ │ ❱ 30 llm = GPT4All( │ │ 31 │ model="./ggml-mpt-7b-instruct.bin", │ │ 32 │ #backend='gptj', │ │ 33 │ top_p=0.5, │ │ │ │ in pydantic.main.BaseModel.__init__:339 │ │ │ │ in pydantic.main.validate_model:1102 │ │ │ │ /Users/christoph/src/sandstorm-labs/development-tools/ai-support-chat/gpt4all/venv/lib/python3.1 │ │ 1/site-packages/langchain/llms/gpt4all.py:156 in validate_environment │ │ │ │ 153 │ │ if values["n_threads"] is not None: │ │ 154 │ │ │ # set n_threads │ │ 155 │ │ │ values["client"].model.set_thread_count(values["n_threads"]) │ │ ❱ 156 │ │ values["backend"] = values["client"].model_type │ │ 157 │ │ │ │ 158 │ │ return values │ │ 159 │ ╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ ``` As you can see in _gpt4all.py:156_ contains the changed from the fix of #5651 ## Code ```python from langchain.llms import GPT4All # see https://gpt4all.io/models/ggml-mpt-7b-instruct.bin llm = GPT4All( model="./ggml-mpt-7b-instruct.bin", #backend='gptj', top_p=0.5, top_k=0, temp=0.1, repeat_penalty=0.8, n_threads=12, n_batch=16, n_ctx=2048) ``` FYI I am following [this example in a blog post](https://dev.to/akshayballal/beyond-openai-harnessing-open-source-models-to-create-your-personalized-ai-companion-1npb). ### Expected behavior I expect an instance of _GPT4All_ instead of a stacktrace.
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use tenacity to retry in cohere llm as it is done for openai llms
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2023-06-05T09:41:32
2023-06-05T23:23:23
2023-06-05T23:23:23
CONTRIBUTOR
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A minor update to retry Cohore API call in case of errors using tenacity as it is done for OpenAI LLMs. #### Who can review? @hwchase17, @agola11 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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fixing gpt4all.py to use get_model_from_type instead of model_type
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2023-06-05T09:36:05
2023-06-05T21:39:58
2023-06-05T21:39:58
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This is to fix the error : File "\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\llms\gpt4all.py", line 156, in validate_environment values["backend"] = values["client"].model_type ^^^^^^^^^^^^^^^^^^^^^^^^^^^ AttributeError: 'GPT4All' object has no attribute 'model_type' <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Regular Expression Fails to Match Newline-Starting Action Inputs
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2023-06-05T08:59:46
2023-11-01T16:06:55
2023-11-01T16:06:54
NONE
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### System Info LangChain version: `0.0.189` OS: Ubuntu `22.04.2` Python version: `3.10.7` ### Who can help? @vowelparrot ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Reproducing this issue can be challenging as it hinges on the action input generated by the LLM. Nevertheless, the issue occurs when using an agent of type `CONVERSATIONAL_REACT_DESCRIPTION`. The agent's output parser attempts to [extract](https://github.com/hwchase17/langchain/blob/d0d89d39efb5f292f72e70973f3b70c4ca095047/langchain/agents/conversational/output_parser.py#L21) both the tool's name that the agent needs to utilize for its task and the input to that tool based on the LLM prediction. In order to accomplish this, the agent's output parser employs the following regular expression on the LLM prediction: `Action: (.*?)[\n]*Action Input: (.*)`. This expression divides the LLM prediction into two components: i) the tool that needs to be utilized (Action) and ii) the tool's input (Action Input). However, the action input generated by an LLM is an arbitrary string. Hence, if the action input initiates with a newline character, the regular expression's search operation fails to find a match, resulting in the action input defaulting to an empty string. This issue stems from the fact that the `.` character in the regular expression doesn't match newline characters by default. Consequently, `(.*)` stops at the end of the line it started on. This behavior aligns with the default functionality of the Python `re` library. To rectify this corner case, we must include the [`re.S`](https://docs.python.org/3/library/re.html#:~:text=in%20version%203.11.-,re.S,-%C2%B6) flag in the `re.search()` method. This will allow the `.` special character to match any character, including a newline. > re.S > re.DOTALL > Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s). ### Expected behavior The `re.search()` method correctly matches the action input section of the LLM prediction and sets it to the relevant string, even if it starts with the new line character. I am prepared to open a new Pull Request (PR) to address this issue. The change involves modifying the following [line](https://github.com/hwchase17/langchain/blob/d0d89d39efb5f292f72e70973f3b70c4ca095047/langchain/agents/conversational/output_parser.py#L21): From: ```python match = re.search(regex, text) ``` To: ```python match = re.search(regex, text, re.S) ```
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Support chat history persistence in AutoGPT
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2023-06-05T07:59:34
2023-06-16T00:49:04
2023-06-16T00:49:03
CONTRIBUTOR
null
**Short Description** Added a new argument to AutoGPT class which allows to persist the chat history to a file. **Changes** 1. Removed the `self.full_message_history: List[BaseMessage] = []` 2. Replaced it with `chat_history_memory` which can take any subclasses of `BaseChatMessageHistory`
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feat: issue-5712 add sleep tool
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2023-06-05T07:31:54
2023-06-07T16:39:02
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CONTRIBUTOR
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Fixes # 5712 added sleep tool <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? @vowelparrot <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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2023-06-05T07:13:18
2023-10-06T16:07:55
2023-10-06T16:07:54
NONE
null
im getting this error AttributeError: module 'chromadb.errors' has no attribute 'NotEnoughElementsException' here my code ``` persist_directory = 'vectorstore' embeddings = OpenAIEmbeddings() vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings) chat_history = [] vectorstore.add_texts(["text text text"]) llm = ChatOpenAI(temperature=0.2) qa = ConversationalRetrievalChain.from_llm( llm, vectorstore.as_retriever(), ) ``` does it mean my vectorstore is damaged ?
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5,713
Inference parameters for Bedrock titan models not working
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2023-06-05T06:48:57
2023-08-16T16:31:01
2023-06-08T21:16:02
NONE
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### System Info LangChain version 0.0.190 Python 3.9 ### Who can help? @seanpmorgan @3coins ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Tried the following to provide the `temperature` and `maxTokenCount` parameters when using the `Bedrock` class for the `amazon.titan-tg1-large` model. ``` import boto3 import botocore from langchain.chains import LLMChain from langchain.llms.bedrock import Bedrock from langchain.prompts import PromptTemplate from langchain.embeddings import BedrockEmbeddings prompt = PromptTemplate( input_variables=["text"], template="{text}", ) llm = Bedrock(model_id="amazon.titan-tg1-large") llmchain = LLMChain(llm=llm, prompt=prompt) llm.model_kwargs = {'temperature': 0.3, "maxTokenCount": 512} text = "Write a blog explaining Generative AI in ELI5 style." response = llmchain.run(text=text) print(f"prompt={text}\n\nresponse={response}") ``` This results in the following exception ``` ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: The provided inference configurations are invalid ``` This happens because https://github.com/hwchase17/langchain/blob/d0d89d39efb5f292f72e70973f3b70c4ca095047/langchain/llms/bedrock.py#L20 passes these params as key value pairs rather than putting them in the `textgenerationConfig` structure as the Titan model expects them to be, The proposed fix is as follows: ``` def prepare_input( cls, provider: str, prompt: str, model_kwargs: Dict[str, Any] ) -> Dict[str, Any]: input_body = {**model_kwargs} if provider == "anthropic" or provider == "ai21": input_body["prompt"] = prompt elif provider == "amazon": input_body = dict() input_body["inputText"] = prompt input_body["textGenerationConfig] = {**model_kwargs} else: input_body["inputText"] = prompt if provider == "anthropic" and "max_tokens_to_sample" not in input_body: input_body["max_tokens_to_sample"] = 50 return input_body ``` ``` ### Expected behavior Support the inference config parameters.
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5,712
Create sleep tool
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2023-06-05T06:40:46
2023-09-05T05:32:01
2023-09-05T05:30:05
CONTRIBUTOR
null
### Feature request Create sleep tool ### Motivation Some external processes require time to be done. So agent need to just wait ### Your contribution I'll do it and create pull request
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Update test_base.py
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2023-06-05T05:57:00
2023-11-07T04:16:10
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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DOC: tracing documentation "404 Not Found"
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2023-06-05T05:27:47
2023-09-11T16:58:18
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### Issue with current documentation: I am trying to view the documentation about tracing via Use Cases --> Evaluation --> Question Answering Benchmarking: State of the Union Address --> "See [here](https://langchain.readthedocs.io/en/latest/tracing.html) for an explanation of what tracing is and how to set it up", but the link shows "404 Not Found" <img width="1247" alt="Screen Shot 2023-06-05 at 13 26 52" src="https://github.com/hwchase17/langchain/assets/72342196/35ea922e-e9ca-4e97-ba8f-304a79474771"> ### Idea or request for content: _No response_
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Issue: get intermediate df from create_pandas_dataframe_agent
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2023-06-05T04:25:03
2023-09-11T16:58:22
2023-09-11T16:58:21
NONE
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### Issue you'd like to raise. Hi, I was wondering if there is any way of getting the pandas dataframe during the execution of create_pandas_dataframe_agent. ### Suggestion: _No response_
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feat: :sparkles: Added a LLM callback to store LLM IO in-memory
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2023-06-05T03:56:16
2023-06-09T15:54:35
2023-06-09T15:54:35
CONTRIBUTOR
null
This can be useful if we want an interface to store LLM's output to some DB. I have created a very basic callback class, please guide me on how we can make it better and more extensible. Also if something like this already exists, please guide me I am very interested in how we can log these LLM inputs-outputs to a DB so that we can use the back as example prompts #### Who can review? Tag maintainers/contributors who might be interested: Tracing / Callbacks - @agola11
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ValueError: Not a valid json with Directory Loader function
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2023-06-05T03:32:38
2024-01-16T10:02:18
2023-06-12T02:53:07
NONE
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Hi, I load a folder with txt file and using Directory Loader. It keeps show this error > File "/Users/mycompany/test-venv/lib/python3.8/site-packages/unstructured/partition/json.py", line 45, in partition_json > raise ValueError("Not a valid json") > ValueError: Not a valid json When I tried with just 10 txt file before, it was fine. Now I loaded around hundred files it shows that error. After searched, probably caused by one of the documents is not valid when parsing it to json. But I struggle to track which document caused the error. Maybe any ways to do it? Here is my code > def construct_document(directory_path, persists_directory): > loader = DirectoryLoader(directory_path, glob="**/*.txt") > documents = loader.load() > text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) > texts = text_splitter.split_documents(documents) > embeddings = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"]) > vectordb = Chroma.from_documents(texts, embeddings, > persist_directory=persists_directory) > vectordb.persist() > return embeddings Thank you
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JSON Agent fails when top level of input json is List object.
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2023-06-05T02:45:49
2024-01-14T16:21:57
null
NONE
null
### Issue you'd like to raise. JsonSpec() failes to init when top level of json is List. It seems the JsonSpec only supports a top level dict object. We can avoid the error by adding a dummy key to the list, but is this an intended limitation? test.json ```json [ {"name": "Mark", "age":16}, {"name": "Gates", "age":63} ] ``` Code: ```python import pathlib from langchain.agents.agent_toolkits import JsonToolkit from langchain.tools.json.tool import JsonSpec from langchain.agents import create_json_agent from langchain.llms import OpenAI json_spec = JsonSpec.from_file(pathlib.Path("test.json")) json_toolkit = JsonToolkit(spec=json_spec) agent = create_json_agent(OpenAI(), json_toolkit, verbose=True) ``` Error: ``` Traceback (most recent call last): json_spec = JsonSpec.from_file(pathlib.Path("test.json")) File "/usr/local/lib/python3.10/dist-packages/langchain/tools/json/tool.py", line 40, in from_file return cls(dict_=dict_) File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__ pydantic.error_wrappers.ValidationError: 1 validation error for JsonSpec dict_ value is not a valid dict (type=type_error.dict) ``` Versions * Python 3.10.6 * langchain 0.0.186 ### Suggestion: _No response_
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[Draft] Add initial draft of document manager
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2023-06-05T02:41:19
2023-09-15T02:57:36
2023-09-15T02:57:36
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Adding very initial rough draft of the document manger. On a high level it supports the following operations: - Add - Update - UpdateTruncate `Update` and `UpdateTruncate` differ in that `UpdateTruncate` deletes the set of documents that are not present in the new set of documents passed in. `Update` only updates the existing documents. The document manager currently returns the a list of operations that need to be performed by the user on the vector store. This can be integrated into the existing vector store interface by adding a new method in `VectorStore` that applies add, update and delete operations. An example script is included in the changes which is an example of the interface. It is also runnable, and proves the correctness of the document manager. @hwchase17
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bump version to 190
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2023-06-05T03:04:52
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COLLABORATOR
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feat: Support for `Tigris` Vector Database for vector search
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2023-06-05T01:41:34
2023-06-06T03:39:17
2023-06-06T03:39:16
CONTRIBUTOR
null
### Changes - New vector store integration - [Tigris](https://tigrisdata.com) - Adds [tigrisdb](https://pypi.org/project/tigrisdb/) optional dependency - Example notebook demonstrating usage Fixes #5535 Closes tigrisdata/tigris-client-python#40 #### Twitter handles We'd love a shoutout on our [@TigrisData](https://twitter.com/TigrisData) and [@adilansari](https://twitter.com/adilansari) twitter handles #### Who can review? @dev2049
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5,702
Support mailman in document loader
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2023-06-05T00:41:41
2023-09-11T16:58:27
2023-09-11T16:58:27
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### Feature request It would be great if mailman could be supported in document loader, reference: https://list.org/ ### Motivation For the purpose of digesting content in mailman and quering. ### Your contribution Not sure.
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DOC: Type hint mismatch indicating that chain.apply should return Dict[str, str] instead of Dict[str, Any]
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2023-06-04T23:31:01
2023-09-15T22:13:02
2023-09-04T07:59:47
NONE
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### Issue with current documentation: Not sure if this is actually a documentation error, but at least the error is in the code's typehints in `langchain.chain.base.py`, where: ``` def __call__( self, inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Callbacks = None, ) -> Dict[str, Any]: ``` indicates that it returns a dict with Any values while the .apply is specified as follows: ``` def apply( self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None ) -> List[Dict[str, str]]: """Call the chain on all inputs in the list.""" return [self(inputs, callbacks=callbacks) for inputs in input_list] ``` This seems to be a consistent error also applying to e.g. `.run`. Langchain version: 0.0.187 ### Idea or request for content: Change the type hints to consistently be dict[str, Any]. This however, will open up the output space considerably. If desired I can open PR.
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Sitemap filters not working due to lack of stripping whitespace and newlines
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2023-06-04T22:49:54
2023-06-05T23:33:57
2023-06-05T23:33:57
CONTRIBUTOR
null
https://github.com/hwchase17/langchain/blob/8d9e9e013ccfe72d839dcfa37a3f17c340a47a88/langchain/document_loaders/sitemap.py#L83 if ``` <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9" xmlns:xhtml="http://www.w3.org/1999/xhtml"> <url> <loc> https://tatum.com/ </loc> <xhtml:link rel="alternate" hreflang="x-default" href="https://tatum.com/"/> </url> ``` then ` re.match(r, loc.text) for r in self.filter_urls` Comparison to filter here will be comparing against a value that includes those whitespaces and newlines. What worked for me: ``` def parse_sitemap(self, soup: Any) -> List[dict]: """Parse sitemap xml and load into a list of dicts.""" els = [] for url in soup.find_all("url"): loc = url.find("loc") if not loc: continue loc_text = loc.text.strip() if self.filter_urls and not any( re.match(r, loc_text) for r in self.filter_urls ): continue```
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DOC: Update Helicone docs to include requirement for API key
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2023-06-04T21:30:25
2023-09-10T16:07:47
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### Issue with current documentation: Currently the [Helicone integration docs](https://python.langchain.com/en/latest/integrations/helicone.html) don't include the fact that an [API key now must be used by passing this header](https://docs.helicone.ai/quickstart/integrate-in-one-minute): ``` Helicone-Auth: Bearer $HELICONE_API_KEY ``` ### Idea or request for content: Add this to the docs.
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5,696
Add metadata filter capability to similarity search functions for Supabase
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2023-06-04T21:16:22
2023-08-11T22:51:33
2023-08-11T22:51:33
NONE
null
Title: Add metadata filtering capabilities to similarity search functions Description: This PR enhances the functionality of the similarity search methods in our codebase. Specifically, we now support optional metadata-based filtering in our search functions. Details: The affected functions include: similarity_search similarity_search_by_vector similarity_search_with_relevance_scores similarity_search_by_vector_with_relevance_scores similarity_search_by_vector_returning_embeddings For each of these functions, a new filter argument has been added. This argument accepts a dictionary that specifies metadata fields and their desired values. When the filter dictionary is provided, the search functions will return documents that not only match the query based on similarity but also satisfy the metadata criteria defined by the filter. Impact: This change provides greater flexibility for users when conducting similarity searches. They can now refine their search results based on specific metadata properties, leading to more accurate and targeted results. Tests: Testing will involve invoking the modified functions with various filter criteria and verifying the returned documents meet both the similarity and metadata filter criteria. VectorStores / Retrievers / Memory - @dev2049 may be interested
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Use client from LCP-SDK
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2023-06-04T21:08:32
2023-06-06T13:51:06
2023-06-06T13:51:05
CONTRIBUTOR
null
- Remove the client implementation (this breaks backwards compatibility for existing testers. I could keep the stub in that file if we want, but not many people are using it yet - Add SDK as dependency - Update the 'run_on_dataset' method to be a function that optionally accepts a client as an argument - Remove the langchain plus server implementation (you get it for free with the SDK now) We could make the SDK optional for now, but the plan is to use w/in the tracer so it would likely become a hard dependency at some point.
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Add session-based context storage to VectorStoreRetrieverMemory
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2023-06-04T20:34:41
2023-08-11T22:49:44
2023-08-11T22:49:43
NONE
null
### Title: Add Session-Based Context Storage to SessionVectorStoreRetrieverMemory #### Description: This PR introduces a new feature to the `SessionVectorStoreRetrieverMemory` class that allows developers to store and retrieve the history of a particular chat session. It proposes a new class, `SessionVectorStoreRetrieverMemory`, which extends `VectorStoreRetrieverMemory` and adds a `session_id` field. The key changes include: - The addition of a `session_id` field, which will be used to uniquely identify a chat session. This will allow us to store and retrieve the history of a particular session. - Modifying the `_form_documents` method to attach the `session_id` to each document created. This means that each document in our vector store will now include information about the session it belongs to. This feature requires the `VectorStoreRetriever` to support metadata filtering. For instance, you can create a retriever that filters on the `session_id` field as follows: ```python import uuid from langchain.embeddings.openai import OpenAIEmbeddings from langchain.llms import OpenAI from langchain.memory import SessionVectorStoreRetrieverMemory # generate a random session id session_id = uuid.uuid4().hex embedding_fn = OpenAIEmbeddings() vec_store = Pinecone.from_existing_index(PINECONE_INDEX, embedding_fn, text_key="chat_memory") # include session_id as param in retriever and memory object retriever = vec_store.as_retriever(search_kwargs={'k': 1, 'filter': {'session_id': session_id}}) memory = SessionVectorStoreRetrieverMemory(retriever=retriever, session_id=session_id) memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"}) memory.save_context({"input": "My favorite sport is soccer"}, {"output": "..."}) memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"}) print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"]) ``` This will ensure that only documents with the specified session_id are returned when a query is made. The rationale behind these changes is to enable a finer granularity of data retrieval. Currently, the SessionVectorStoreRetrieverMemory class retrieves relevant documents based on a query without considering the context of a chat session. By adding the session_id, we can retrieve documents that are not only relevant to the query but also belong to the same chat session. This makes it possible to have more context-aware conversations and could improve the quality of our chatbot's responses, while maintaining privacy between sessions of different users. The change is backward-compatible and does not affect the existing functionality of the SessionVectorStoreRetrieverMemory class. Developers can choose to use the session-based version or the original version depending on their needs. Contributor: Adi Sidapara (Github: @asidapara, Twitter: @AdiSidapara) Could you review? @hwchase17 @dev2049 Thanks in advance!
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Fix serialization issue with W&B
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2023-06-04T19:02:35
2023-06-08T03:44:59
2023-06-08T03:44:59
CONTRIBUTOR
null
The chain input_documents are not displaying properly in W&B, due to serialization issue: <img width="1164" alt="Screenshot 2023-06-04 at 11 58 26 AM" src="https://github.com/hwchase17/langchain/assets/134809928/f31f14f6-0935-4cca-9913-6760cd40eadf">
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feat: make YoutubeLoader support multi language transcript
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2023-06-04T18:10:55
2023-11-17T21:56:50
2023-06-05T23:25:37
CONTRIBUTOR
null
Adding support multi language transcript for YoutubeLoader. #### Example <img width="1033" alt="Screenshot 2023-06-05 at 01 10 31" src="https://github.com/hwchase17/langchain/assets/42396310/34b99e7e-4e19-4946-85fe-20c3bde04007"> #### Who can review? Tag maintainers/contributors who might be interested: @eyurtsev
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prompt template not reset when reinstantiating SQLDatabase object
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2023-06-04T17:06:40
2023-09-10T16:07:54
2023-09-10T16:07:53
NONE
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### System Info python 3.8, langchain 0.0.187 ### Who can help? @hwchase17 @vowelparrot ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python from langchain import SQLDatabase, SQLDatabaseChain rom langchain.llms.openai import OpenAI db_fp = 'test.db' db = SQLDatabase.from_uri(f"sqlite:///{db_fp}") llm = OpenAI(temperature=0, max_tokens=1000) db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True) # reading default prompt db_chain.llm_chain.prompt.template # set to sth else db_chain.llm_chain.prompt.template = 0 # do something, prompt not good, so want to reset db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True) db_chain.llm_chain.prompt.template # BUG: returns 0, but should be default ``` ### Expected behavior should reinstantiate SQL chain object, but state from template reassignment is kept
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support local GPU server without runhouse !!!
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2023-06-04T15:56:15
2023-12-13T16:09:13
2023-12-13T16:09:12
NONE
null
### Feature request Hi guys, can we just launch SelfHostedEmbedding without runhouse, as a matter of fact, runhouse its self is still in the early development stage and has some issues, why does langchain depends on it for selfhosted instead of call cuda directly? for example: device = torch.device('cuda') embeddings = SelfHostedEmbeddings( model_load_fn=get_pipeline, hardware=device, model_reqs=["./", "torch", "transformers"], inference_fn=inference_fn ) ### Motivation use GPU local server directly instead of through runhouse. ### Your contribution *
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Add Support for Hugging Face Question Answering Models
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2023-06-04T15:44:22
2023-09-22T16:32:44
2023-09-22T16:32:44
NONE
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Working on Adding HuggingFace Question Answering Model support to Langchain #5660 First Commit is just a proof on concept, that simply adds the functionality to the HuggingFacePipeline Class and its _call method. This implementation is however in conflict with its Base Classes, since Q&A Models require an additional input (at least before the input gets tokenized). Since this is about adding new models it might be of interest to you: - @hwchase17 - @agola11 I'm not sure if it's best to modify the base classes to add the support for an additional argument (the question / context), or to add a new class for question answering models. I am happy to discuss the best way forward / your ideas.
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add awadb vectorstore
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2023-06-04T14:48:30
2023-06-08T01:17:17
2023-06-08T01:17:16
CONTRIBUTOR
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@dev2049 Add a new vectorstore-AwaDB
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testing chain generics
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2023-06-04T12:24:50
2023-08-11T18:31:25
2023-08-11T18:31:25
COLLABORATOR
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Testing using generics in a few places to see what breaks/improves
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ValueError: fix_text not implemented for this agent.
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2023-06-04T11:25:01
2023-06-15T02:25:14
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### System Info langchain==0.0.100 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [X] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Hey, I am working on a Toolformer Agent similar to ReAct. The prompts given to the agent is a few-shot prompt with tool usage. An example can be seen on this website: https://tsmatz.wordpress.com/2023/03/07/react-with-openai-gpt-and-langchain/#comments I can define the agent without any bugs: ```python SUFFIX = """\nQuestion: {input} {agent_scratchpad}""" TEST_PROMPT = PromptTemplate.from_examples( EXAMPLES, SUFFIX, ["input", "agent_scratchpad"] ) class ReActTestAgent(Agent): @classmethod def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate: return TEST_PROMPT @classmethod def _validate_tools(cls, tools: Sequence[BaseTool]) -> None: if len(tools) != 4: raise ValueError("The number of tools is invalid.") tool_names = {tool.name for tool in tools} if tool_names != {"GetInvoice", "Diff", "Total", "python_repl"}: raise ValueError("The name of tools is invalid.") @property def _agent_type(self) -> str: return "react-test" @property def finish_tool_name(self) -> str: return "Finish" @property def observation_prefix(self) -> str: return f"Observation : " @property def llm_prefix(self) -> str: return f"Thought : " # This method is called by framework to parse text def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]: action_prefix = f"Action : " if not text.split("\n")[1].startswith(action_prefix): return None action_block = text.split("\n")[1] action_str = action_block[len(action_prefix) :] re_matches = re.search(r"(.*?)\[(.*?)\]", action_str) if re_matches is None: raise ValueError(f"Could not parse action directive: {action_str}") return re_matches.group(1), re_matches.group(2) ########## # run agent ########## # llm = AzureOpenAI( # deployment_name="davinci003-deploy", # model_name="text-davinci-003", # temperature=0, # ) llm = OpenAI(temperature=0, model_name="gpt-3.5-turbo") agent = ReActTestAgent.from_llm_and_tools( llm, tools, ) agent_executor = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, verbose=True, ) ``` but When I give instruction to the agent ```python question = "hi" agent_executor.run(question) ``` Even the simplest one "hi" will return me the error saying: **ValueError: fix_text not implemented for this agent.** So my question is Why did this error emerge? How to avoid the error? ### Expected behavior The expected output should be similar to the few-shot prompts
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Index not found when trying to use ChromaDB instead of FAISS in the AutoGPT example
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2023-06-04T11:23:19
2023-11-09T16:11:30
2023-11-09T16:11:29
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### System Info Python 3.10.10 (virtual env) on macos 11.7.6 langchain 0.0.187 duckdb 0.7.1 duckduckgo-search 3.7.1 chromadb 0.3.21 tiktoken 0.3.3 torch 2.0.0 ### Who can help? @hwchase17 @eyur ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [X] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Dear community, I am now playing a bit with the AutoGPT example notebook found in the Langchain documentation, in which I already replaced the search tool for `DuckDuckGoSearchRun()` instead `SerpAPIWrapper()`. So far this works seamlessly. I am now trying to use ChromaDB as vectorstore (in persistent mode), instead of FAISS..however I cannot find how to properly initialize Chroma in this case. I have seen plenty of examples with ChromaDB for documents and/or specific web-page contents, using the `loader` class and then the `Chroma.from_documents()` method. But in the AutoGPT example, they actually use FAISS in the following way, just initializing as an empty vectorstore with fixed embedding size: ``` # Define your embedding model embeddings_model = OpenAIEmbeddings() # Initialize the vectorstore as empty import faiss FAISS() embedding_size = 1536 index = faiss.IndexFlatL2(embedding_size) vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {}) ``` I have tried the following approach without success (after creating the `chroma` subdirectory). Apparently both `chroma-collections.parquet` and `chroma-embeddings.parquet` are created, and I get the confirmation message `Using embedded DuckDB with persistence: data will be stored in: chroma`, but when doing `agent.run(["[here goes my query"])`, I get the error `NoIndexException: Index not found, please create an instance before querying`. The code I have used in order to create the Chroma vectorstore is: ``` persist_directory = "chroma" embeddings_model = OpenAIEmbeddings() chroma_client = chromadb.Client( settings=chromadb.config.Settings( chroma_db_impl="duckdb+parquet", persist_directory=persist_directory, ) ) vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings_model, client=chroma_client, collection_metadata={}) vectorstore.persist() ``` I also cannot find how to replicate the same approach used with FAISS, i.e. initializing as an empty store with a given `embedding_size` (1536 in this case). Any suggestions, or maybe I am overdoing things? It was just a matter of playing that particular example but using a different vectorstore, so to get familiar with the use of Indexes and so. Many thanks! ### Expected behavior Being able to reproduce the [AutoGPT Tutorial](https://python.langchain.com/en/latest/use_cases/autonomous_agents/autogpt.html) , making use of LangChain primitives but using ChromaDB (in persistent mode) instead of FAISS
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Added Dependencies Status, Open issues and releases badges in Readme.md
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2023-06-04T21:30:53
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CONTRIBUTOR
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[![Dependency Status](https://img.shields.io/librariesio/github/hwchase17/langchain)](https://libraries.io/github/hwchase17/langchain) [![Open Issues](https://img.shields.io/github/issues-raw/hwchase17/langchain)](https://github.com/hwchase17/langchain/issues) [![Release Notes](https://img.shields.io/github/release/hwchase17/langchain)](https://github.com/hwchase17/langchain/releases)
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Add args_schema to google_places tool
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2023-06-04T08:41:43
2023-06-04T21:28:47
2023-06-04T21:28:47
CONTRIBUTOR
null
Tiny change to actually add the args_schema to the tool. @vowelparrot
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the first paragraph of code in the quick start can not run in jupyter
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2023-06-04T07:53:47
2023-09-15T16:08:52
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### System Info Hi everyone, I am a beginner of Python and I can not run the first part of the code ![1685862362254](https://github.com/hwchase17/langchain/assets/114320010/0cd40666-fc4a-431d-8c45-5a2fa3956921) but those code is supposed to output feetful of fun ![image](https://github.com/hwchase17/langchain/assets/114320010/439335dc-d3de-4f7d-8e8d-a859f8601750) Is there anybody who knows why? ### Who can help? _No response_ ### Information - [X] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction set the environment variable with openai API key and then run the code in the picture ### Expected behavior it supposed to output something OR why is this problem? what should I add into the code?
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option to limit response length
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2023-06-04T06:57:45
2023-12-18T23:50:07
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### Feature request Provide an option to limit response length. ### Motivation Developers sometimes would like to reduce answer length to reduce API usage cost. ### Your contribution Can we implement the feature by customizing user's prompt in framework(e.g. append "answer in 50 words" to the prompt).
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Improve the recursive text splitting algorithm to minimize the number of chunks
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2023-06-04T03:29:50
2023-08-11T22:54:21
2023-08-11T22:54:20
CONTRIBUTOR
null
I notice `RecursiveCharacterTextSplitter` often produce chunks that are much smaller than the specified chunk_size. The problem happens because we merge the `_good_splits` and add it to the `final_chunks` as soon as we encounter a large next chunk. We can improve chunk space utilization by putting `splits` in a stack, so that during iteration we can further split the next chunk and push the smaller chunks into the stack. @hwchase17 What do you think about this approach?
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sqlalchemy MovedIn20Warning declarative_base DEPRICATION fix
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2023-06-04T02:12:11
2023-06-04T23:52:53
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CONTRIBUTOR
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fix for the sqlalchemy deprecated declarative_base import : ``` MovedIn20Warning: The ``declarative_base()`` function is now available as sqlalchemy.orm.declarative_base(). (deprecated since: 2.0) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9) Base = declarative_base() # type: Any ``` Import is wrapped in an try catch Block to fallback to the old import if needed.
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Fix code issue
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2023-06-04T06:54:22
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<!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes code issue <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? - @hwchase17 - @agola11 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
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Feature: Allows for structured output parser to parse lists of json objects
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# Description Enables structured output parser to parse lists of json objects Prior it would throw an error when parsing a list of json objects about missing keys. Now it evaluates if the parsed json object is a list then uses a difference in the set of keys from each record in the json list and the set of original expected keys. This allows for the `StructuredOutputParser` to parse multiple objects of the same schema at once #### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 @eyurtsev @vowelparrot
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top_k and top_p transposed in vertexai
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2023-06-03T23:56:00
2023-06-06T15:36:20
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CONTRIBUTOR
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Fix transposed properties in vertexai model Models - @hwchase17 - @agola11
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SQL agent : Improved prompt engineering prevents agent guessing database column names.
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2023-06-03T23:52:36
2023-06-04T21:39:00
2023-06-04T21:39:00
CONTRIBUTOR
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@vowelparrot: Minor change to the SQL agent: Tells agent to introspect the schema of the most relevant tables, I found this to dramatically decrease the chance that the agent wastes times guessing column names.
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UnstructuredURLLoader hangs on some URLs
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2023-06-03T23:46:59
2023-09-13T16:07:07
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### System Info Python 3.10.6 Win 11 WSL running: Distributor ID: Ubuntu Description: Ubuntu 22.04.2 LTS Release: 22.04 Codename: jammy AMD 5950x CPU w/128GB mem Python packages installed: Package Version ----------------------- ----------- aiohttp 3.8.4 aiosignal 1.3.1 altair 4.2.2 anyio 3.7.0 appdirs 1.4.4 argilla 1.8.0 asttokens 2.2.1 async-timeout 4.0.2 attrs 23.1.0 backcall 0.2.0 backoff 2.2.1 beautifulsoup4 4.12.2 blinker 1.6.2 cachetools 5.3.0 certifi 2023.5.7 cffi 1.15.1 chardet 5.1.0 charset-normalizer 3.1.0 click 8.1.3 comm 0.1.3 commonmark 0.9.1 contourpy 1.0.7 cryptography 40.0.2 cvxpy 1.3.1 cycler 0.11.0 dataclasses-json 0.5.7 debugpy 1.6.7 decorator 5.1.1 Deprecated 1.2.14 ecos 2.0.12 empyrical 0.5.5 entrypoints 0.4 et-xmlfile 1.1.0 exceptiongroup 1.1.1 executing 1.2.0 fastjsonschema 2.17.1 fonttools 4.39.4 fredapi 0.5.0 frozendict 2.3.8 frozenlist 1.3.3 gitdb 4.0.10 GitPython 3.1.31 greenlet 2.0.2 h11 0.14.0 html5lib 1.1 httpcore 0.16.3 httpx 0.23.3 idna 3.4 importlib-metadata 6.6.0 inflection 0.5.1 install 1.3.5 ipykernel 6.23.1 ipython 8.13.2 jedi 0.18.2 Jinja2 3.1.2 joblib 1.2.0 jsonschema 4.17.3 jupyter_client 8.2.0 jupyter_core 5.3.0 kaleido 0.2.1 kiwisolver 1.4.4 langchain 0.0.174 lxml 4.9.2 Markdown 3.4.3 markdown-it-py 2.2.0 MarkupSafe 2.1.2 marshmallow 3.19.0 marshmallow-enum 1.5.1 matplotlib 3.7.1 matplotlib-inline 0.1.6 mdurl 0.1.2 monotonic 1.6 more-itertools 9.1.0 msg-parser 1.2.0 multidict 6.0.4 multitasking 0.0.11 mypy-extensions 1.0.0 Nasdaq-Data-Link 1.0.4 nbformat 5.9.0 nest-asyncio 1.5.6 nltk 3.8.1 numexpr 2.8.4 numpy 1.23.5 olefile 0.46 openai 0.27.7 openapi-schema-pydantic 1.2.4 openpyxl 3.1.2 osqp 0.6.2.post9 packaging 23.1 pandas 1.5.3 pandas-datareader 0.10.0 parso 0.8.3 pdf2image 1.16.3 pdfminer.six 20221105 pexpect 4.8.0 pickleshare 0.7.5 Pillow 9.5.0 pip 22.0.2 platformdirs 3.5.1 plotly 5.14.1 prompt-toolkit 3.0.38 protobuf 3.20.3 psutil 5.9.5 ptyprocess 0.7.0 pure-eval 0.2.2 pyarrow 12.0.0 pycparser 2.21 pydantic 1.10.7 pydeck 0.8.1b0 pyfolio 0.9.2 Pygments 2.15.1 Pympler 1.0.1 pypandoc 1.11 pyparsing 3.0.9 pyportfolioopt 1.5.5 pyrsistent 0.19.3 pytesseract 0.3.10 python-dateutil 2.8.2 python-docx 0.8.11 python-dotenv 1.0.0 python-magic 0.4.27 python-pptx 0.6.21 pytz 2023.3 PyYAML 6.0 pyzmq 25.1.0 qdldl 0.1.7 Quandl 3.7.0 regex 2023.5.5 requests 2.30.0 rfc3986 1.5.0 rich 13.0.1 scikit-learn 1.2.2 scipy 1.10.1 scs 3.2.3 seaborn 0.12.2 setuptools 67.7.2 six 1.16.0 smmap 5.0.0 sniffio 1.3.0 soupsieve 2.4.1 SQLAlchemy 2.0.14 stack-data 0.6.2 stqdm 0.0.5 streamlit 1.22.0 ta 0.10.2 TA-Lib 0.4.26 tabulate 0.9.0 tenacity 8.2.2 threadpoolctl 3.1.0 tiktoken 0.4.0 toml 0.10.2 toolz 0.12.0 tornado 6.3.2 tqdm 4.65.0 traitlets 5.9.0 typer 0.9.0 typing_extensions 4.5.0 typing-inspect 0.8.0 tzdata 2023.3 tzlocal 5.0.1 unstructured 0.7.1 urllib3 2.0.2 validators 0.20.0 watchdog 3.0.0 wcwidth 0.2.6 webencodings 0.5.1 wikipedia 1.4.0 wrapt 1.14.1 xlrd 2.0.1 XlsxWriter 3.1.2 yarl 1.9.2 yfinance 0.2.18 zipp 3.15.0 ### Who can help? @MthwRobinson https://github.com/hwchase17/langchain/commits?author=MthwRobinson ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [X] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction url = "https://www.nasdaq.com/articles/apple-denies-surveillance-claims-made-by-russias-fsb" loader = UnstructuredURLLoader(urls=[url], continue_on_failure=False) data = loader.load() stopping the process shows this stack: --------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) Cell In[2], line 4 1 url = "https://www.nasdaq.com/articles/apple-denies-surveillance-claims-made-by-russias-fsb" 3 loader = UnstructuredURLLoader(urls=[url], continue_on_failure=False) ----> 4 data = loader.load() File [~/development/public/portfolio-analysis-app/.venv/lib/python3.10/site-packages/langchain/document_loaders/url.py:90](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jaws/development/public/portfolio-analysis-app/notebooks/~/development/public/portfolio-analysis-app/.venv/lib/python3.10/site-packages/langchain/document_loaders/url.py:90), in UnstructuredURLLoader.load(self) 88 if self.__is_non_html_available(): 89 if self.__is_headers_available_for_non_html(): ---> 90 elements = partition( 91 url=url, headers=self.headers, **self.unstructured_kwargs 92 ) 93 else: 94 elements = partition(url=url, **self.unstructured_kwargs) File [~/development/public/portfolio-analysis-app/.venv/lib/python3.10/site-packages/unstructured/partition/auto.py:96](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jaws/development/public/portfolio-analysis-app/notebooks/~/development/public/portfolio-analysis-app/.venv/lib/python3.10/site-packages/unstructured/partition/auto.py:96), in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, ssl_verify, ocr_languages, pdf_infer_table_structure, xml_keep_tags) 93 exactly_one(file=file, filename=filename, url=url) 95 if url is not None: ---> 96 file, filetype = file_and_type_from_url( 97 url=url, 98 content_type=content_type, 99 headers=headers, 100 ssl_verify=ssl_verify, 101 ) ... -> 1130 return self._sslobj.read(len, buffer) 1131 else: 1132 return self._sslobj.read(len) KeyboardInterrupt: ### Expected behavior expect that it does not hang
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[rfc] add cleanup method in callback handler
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2023-06-03T23:41:55
2023-06-06T17:07:34
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Fix transposed properties
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2023-06-03T23:32:54
2023-06-03T23:53:14
2023-06-03T23:48:40
CONTRIBUTOR
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~Args given to ChatVertexAI were not being transmitted to the google library.~ In the course of writing the unit test, I found that top_k and top_p were transposed. ~The unit tests uses pytest's monkeypatch because I could not get mock.patch to work (I'm not used to using it, the Python doc, and gpt4 were all unhelpful, sorry).~ ~If you would like to rewrite the monkeypatch using patch: by all means. I would love to see how it's done!~ ~If you would like me to rewrite the patch with monkeypatch (for consistency) I would be happy to do that.~ ~Fixes #5667~ #### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 @agola11 Thanks for the amazing work you've all done on this project. I've been using it every day for months and I'm incredibly impressed!
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Model settings (temp, max tokens, topk, topp) ignored with vertexai
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2023-06-03T23:26:24
2023-06-03T23:50:17
2023-06-03T23:50:17
CONTRIBUTOR
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### System Info langchain-0.0.189, windows, python 3.11.3 ### Who can help? Will submit PR to resolve. ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [ ] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction When calling ConversationChain.predict with VertexAI, the settings to the LLM are overriden. To repro, run the following (adapted from tests.integration_tests.chat_models.test_vertexai.test_vertexai_single_call) : ```python from langchain.chat_models import ChatVertexAI from langchain.schema import ( HumanMessage, ) model = ChatVertexAI( temperature=1, max_output_tokens=1, top_p=1, top_k=1, ) message = HumanMessage(content="Tell me everything you know about science.") response = model([message]) assert len(response.content) < 50 ``` ### Expected behavior Expected: the assertion should be true if only 1 token was sent back. Allowing for mistakes in the LLM, I set very generous threshold of 50 characters.
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1,739,953,737
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support returning run info for llms, chat models and chains
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2023-06-03T22:17:37
2023-06-06T17:07:48
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returning the run id is important for accessing the run later on
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Harrison/update azure nb
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2023-06-03T23:25:09
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