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DOC: Small typo in the docs, "and" should be removed, and maybe a period in the end would be ok.
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2023-05-30T15:43:50
2023-10-31T16:06:50
2023-10-31T16:06:49
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### Issue with current documentation: In https://docs.langchain.com/docs/components/agents/agent-executor the statement: The agent executor is responsible for calling the agent, getting back **and** action and action input, calling the tool that the action references with the corresponding input, getting the output of the tool, and then passing all that information back into the Agent to get the next action it should take ### Idea or request for content: The agent executor is responsible for calling the agent, getting back action and action input, calling the tool that the action references with the corresponding input, getting the output of the tool, and then passing all that information back into the Agent to get the next action it should take.
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Feature: Qdrant filters supports
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2023-05-30T15:28:29
2023-05-31T09:26:17
2023-05-31T09:26:17
CONTRIBUTOR
null
# Support Qdrant filters Qdrant has an [extensive filtering system](https://qdrant.tech/documentation/concepts/filtering/) with rich type support. This PR makes it possible to use the filters in Langchain by passing an additional param to both the `similarity_search_with_score` and `similarity_search` methods. ## Who can review? @dev2049 @hwchase17
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5,445
Allow setting explicit _id when indexing in elasticsearch
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2023-05-30T15:17:03
2023-05-31T08:08:24
2023-05-31T08:08:24
NONE
null
This PR improves the method `add_texts` of `ElasticVectorSearch`. If a key `_id` is available in metadata it will be used as unique identifier when indexing in elasticsearch. I makes the method idempotent and prevents duplicates. Fixes #5437 cc: @jeffvestal @derickson @hwchase17
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Support for Meta ToT
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2023-05-30T15:15:18
2023-09-10T16:10:00
2023-09-10T16:09:59
NONE
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### Feature request An interesting takeway for Meta TOT - Meta Tree of Thoughts. aims to enhance the Tree of Thoughts (TOT) language algorithm by using a secondary agent to critique and improve the primary agent's prompts. This innovative approach allows the primary agent to generate more accurate and relevant responses based on the feedback from the secondary agent. https://github.com/kyegomez/Meta-Tree-Of-Thoughts I would like add it to the overall offering. If ok, I can pick it up. ### Motivation Optimization on the continuous feedback loop. ### Your contribution I will like to work on this issue.
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Feat: Add batching to Qdrant
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2023-05-30T15:12:20
2023-05-30T22:33:54
2023-05-30T22:33:54
CONTRIBUTOR
null
# Add batching to Qdrant Several people requested a batching mechanism while uploading data to Qdrant. It is important, as there are some limits for the maximum size of the request payload, and without batching implemented in Langchain, users need to implement it on their own. This PR exposes a new optional `batch_size` parameter, so all the documents/texts are loaded in batches of the expected size (64, by default). The integration tests of Qdrant are extended to cover two cases: 1. Documents are sent in separate batches. 2. All the documents are sent in a single request. ## Who can review? @dev2049 @hwchase17
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bump 185
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Adds async call for fake llm list model
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2023-05-30T14:25:25
2023-05-30T20:56:20
2023-05-30T20:56:19
NONE
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# Async call for fake llm list Simple additional to allow the use of FakeLLMList in async tests. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @agola11
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Fixed docstring in faiss.py for load_local
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2023-05-30T14:24:51
2023-05-30T18:41:00
2023-05-30T18:41:00
CONTRIBUTOR
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# Fix for docstring in faiss.py vectorstore (load_local) The doctring should reflect that load_local loads something FROM the disk. @dev2049
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Added async _acall to FakeListLLM
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2023-05-30T13:47:15
2023-05-30T21:34:36
2023-05-30T21:34:36
CONTRIBUTOR
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# Added Async _acall to FakeListLLM FakeListLLM is handy when unit testing apps built with langchain. This allows the use of FakeListLLM inside concurrent code with [asyncio](https://docs.python.org/3/library/asyncio.html). I also changed the pydocstring which was out of date. ## Who can review? @hwchase17 - project lead @agola11 - async
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Harrison/condense q llm
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2023-05-30T13:43:43
2023-05-30T14:15:39
2023-05-30T14:15:38
COLLABORATOR
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I_kwDOIPDwls5nQNp5
5,437
Allow ElasticVectorSearch#add_texts to explicitely set the _ids
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2023-05-30T13:40:49
2023-09-10T16:10:05
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### Issue you'd like to raise. ElasticVectorSearch currently assigns a uuid as identifier while indexing documents. This is not idempotent: if we run the code twice duplicates are created. Also it would be beneficial to be able to insert new docs, update existing ones and ignore unchanged. ### Suggestion: I propose to check first if _id or id is present in metadata before setting it to a UUID.
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LLM from hugging face not working
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2023-05-30T13:05:46
2023-09-10T16:10:18
2023-09-10T16:10:11
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### Issue with current documentation: I am trying to use LLM from hugging face as shown in the documentation below, it is working only when write the same prompt in the documentation but when I am changing it I don't have responses. https://github.com/hwchase17/langchain/blob/master/docs/modules/models/llms/integrations/huggingface_hub.ipynb ### Idea or request for content: _No response_
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DuckDuckGo search always returns "No good DuckDuckGo Search Result was found"
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2023-05-30T12:40:28
2023-09-15T16:09:38
2023-09-15T16:09:37
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### System Info LangChain version : 0.0.177 Python version : 3.10.8 Platform : WSL 2 ### Who can help? @vowelparrot ### Information - [X] 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 - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.tools import DuckDuckGoSearchRun search = DuckDuckGoSearchRun(verbose=True) search.run("What is Captial of Ireland?") /home/vishal_kesti/miniconda3/envs/video_audio_env/lib/python3.10/site-packages/duckduckgo_search/compat.py:20: UserWarning: ddg is deprecated. Use DDGS().text() generator warnings.warn("ddg is deprecated. Use DDGS().text() generator") /home/vishal_kesti/miniconda3/envs/video_audio_env/lib/python3.10/site-packages/duckduckgo_search/compat.py:22: UserWarning: parameter time is deprecated, use parameter timelimit warnings.warn("parameter time is deprecated, use parameter timelimit") /home/vishal_kesti/miniconda3/envs/video_audio_env/lib/python3.10/site-packages/duckduckgo_search/compat.py:24: UserWarning: parameter page is deprecated, use DDGS().text() generator warnings.warn("parameter page is deprecated, use DDGS().text() generator") /home/vishal_kesti/miniconda3/envs/video_audio_env/lib/python3.10/site-packages/duckduckgo_search/compat.py:26: UserWarning: parameter max_results is deprecated, use DDGS().text() warnings.warn("parameter max_results is deprecated, use DDGS().text()") "No good DuckDuckGo Search Result was found" ### Expected behavior There is a change in the duckduckgo python library where in they have specifically mentioned to use DDGS instead of ddg and more specifically the "text" if we want to use the api. They also do not support time, page and max_result parameter directly but there is a way to do it too. For eg: from duckduckgo_search import DDGS ddgs = DDGS() keywords = 'live free or die' ddgs_text_gen = ddgs.text(keywords, region='wt-wt', safesearch='Off', timelimit='y') for r in ddgs_text_gen: print(r) # Using lite backend and limit the number of results to 10 from itertools import islice ddgs_text_gen = DDGS().text("notes from a dead house", backend="lite") for r in islice(ddgs_text_gen, 10): print(r) I got it working by making the following code changes: ========================================= """Util that calls DuckDuckGo Search. No setup required. Free. https://pypi.org/project/duckduckgo-search/ """ from typing import Dict, List, Optional from pydantic import BaseModel, Extra from pydantic.class_validators import root_validator class DuckDuckGoSearchAPIWrapper(BaseModel): """Wrapper for DuckDuckGo Search API. Free and does not require any setup """ region: Optional[str] = "wt-wt" safesearch: str = "moderate" **timelimit: Optional[str] = "y"** **backend: str = "api"** class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that python package exists in environment.""" try: from duckduckgo_search import DDGS # noqa: F401 except ImportError: raise ValueError( "Could not import duckduckgo-search python package. " "Please install it with `pip install duckduckgo-search`." ) return values def run(self, query: str) -> str: from duckduckgo_search import **DDGS** """Run query through DuckDuckGo and return results.""" **ddgs = DDGS()** results = **ddgs.text**( query, region=self.region, safesearch=self.safesearch, time=self.**timelimit** ) if len(results) == 0: return "No good DuckDuckGo Search Result was found" snippets = [result["body"] for result in results] return " ".join(snippets) def results(self, query: str, num_results: int) -> List[Dict]: """Run query through DuckDuckGo and return metadata. Args: query: The query to search for. num_results: The number of results to return. Returns: A list of dictionaries with the following keys: snippet - The description of the result. title - The title of the result. link - The link to the result. """ from duckduckgo_search import **DDGS ddgs = DDGS()** results = ddgs.text( query, region=self.region, safesearch=self.safesearch, time=self.timelimit ) if len(results) == 0: return [{"Result": "No good DuckDuckGo Search Result was found"}] def to_metadata(result: Dict) -> Dict: return { "snippet": result["body"], "title": result["title"], "link": result["href"], } return [to_metadata(result) for result in results]
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Memory with multi input
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2023-05-30T11:17:09
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### Issue you'd like to raise. I cant seem to add memory to llmChain which takes in 3 inputs as the input_key param only takes in one input so the program looses context template = """" As a helpful chatbot agent of the company, provide a answer to the customer query. Strictly limit to the information provided. {chat_history} calculate out of pocket or cost if deductable is available in the user's plan info Question: {query} f"Information from user's plan: {plan_info} f"Information from company database: {faq_info} """ prompt = PromptTemplate( input_variables=["chat_history","query", "plan_info", "faq_info"], template=template ) memory = ConversationBufferMemory(memory_key="chat_history", input_key=["query","plan_info","faq_info"]) chain = LLMChain(llm=model, prompt=prompt,memory=memory) return chain.predict(query=query, plan_info=plan_info,faq_info=faq_info) ### Suggestion: is there a way to get memory working in my case or memory with more than 1 inputs isnt implemented yet?
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FinalStreamingStdOutCallbackHandler not working with ChatOpenAI LLM
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2023-07-31T22:23:44
2023-06-03T22:05:59
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### System Info Hi :) I tested the new callback stream handler `FinalStreamingStdOutCallbackHandler` and noticed an issue with it. I copied the code from the documentation and made just one change - use `ChatOpenAI` instead of `OpenAI` ### Who can help? @hwchase17 ### 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 - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction `llm = ChatOpenAI(streaming=True, callbacks=[FinalStreamingStdOutCallbackHandler()], temperature=0)` here is my only change `tools = load_tools(["wikipedia", "llm-math"], llm=llm)` `agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)` `agent.run("It's 2023 now. How many years ago did Konrad Adenauer become Chancellor of Germany.")` ### Expected behavior The code above returns the response from the agent but does not stream it. In my project, I must use the `ChatOpenAI` LLM, so I would appreciate it if someone could fix this issue, please.
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MRKL output parser no longer breaks well formed queries
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2023-05-30T10:43:05
2023-05-30T19:58:48
2023-05-30T19:58:48
CONTRIBUTOR
null
# Handles the edge scenario in which the action input is a well formed SQL query which ends with a quoted column There may be a cleaner option here (or indeed other edge scenarios) but this seems to robustly determine if the action input is likely to be a well formed SQL query in which we don't want to arbitrarily trim off `"` characters Fixes #5423 ## Who can review? Community members can review the PR once tests pass. 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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I_kwDOIPDwls5nOyis
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`RetrievalQA` and `HuggingFacePipeline` lead to `TypeError: cannot pickle '_thread.lock' object`
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2023-11-06T16:07:59
2023-11-06T16:07:58
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### System Info Ubuntu 20.04.6 Python 3.8.5 Langchain 0.0.184 ### Who can help? _No response_ ### 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 - [X] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ```python import torch from langchain.vectorstores import Qdrant from langchain.embeddings import HuggingFaceInstructEmbeddings from langchain.chains import RetrievalQA from langchain.llms import HuggingFacePipeline from transformers import ( AutoModelForCausalLM, AutoTokenizer, pipeline, TextIteratorStreamer, ) # embeddings embeddings_model_name = "hkunlp/instructor-base" embeddings_model = HuggingFaceInstructEmbeddings( model_name=embeddings_model_name, model_kwargs={"device": "cuda"}, ) contents = ["bla", "blabla", "blablabla"] vector_store = Qdrant.from_texts( contents, embeddings_model, location=":memory:", collection_name="test", ) retriever = vector_store.as_retriever() # llm chatbot_model_name = ""togethercomputer/RedPajama-INCITE-Chat-3B-v1" model = AutoModelForCausalLM.from_pretrained( chatbot_model_name, load_in_8bit=True, torch_dtype=torch.float16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name) streamer = TextIteratorStreamer( tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True, ) pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, streamer=streamer, max_length=1024, temperature=0, top_p=0.95, repetition_penalty=1.15, ) pipe = HuggingFacePipeline(pipeline=pipe) # qa qa = RetrievalQA.from_chain_type( llm=pipe, chain_type="stuff", retriever=retriever, return_source_documents=False, ) qa.run("What is the capital of France") ``` which lead to ```python TypeError: cannot pickle '_thread.lock' object ```` ### Expected behavior I should be able to get the `streamer` outputs
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Add maximal relevance search to SKLearnVectorStore
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2023-05-30T09:51:58
2023-05-30T23:13:34
2023-05-30T23:13:33
CONTRIBUTOR
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# Add maximal relevance search to SKLearnVectorStore This PR implements the maximum relevance search in SKLearnVectorStore. Twitter handle: jtolgyesi (I submitted also the original implementation of SKLearnVectorStore) ## Before submitting Unit tests are included. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @hwchase17 - project lead VectorStores / Retrievers / Memory - @dev2049
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parse_json_markdown is unable to parse json strings with nested triple backticks
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2023-12-14T16:08:19
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### System Info Langchain version 0.0.184, python 3.9.13 Function `parse_json_markdown` in langchain/output_parsers/json.py fails with input text string: \`\`\`json { "action": "Final Answer", "action_input": "Here's a Python script to remove backticks at the beginning and end of a string:\n\n\`\`\`python\ndef remove_backticks(s):\n return s.strip('\`')\n\nstring_with_backticks = '\`example string\`'\nresult = remove_backticks(string_with_backticks)\nprint(result)\n\`\`\`\n\nThis script defines a function called \`remove_backticks\` that takes a string as input and returns a new string with backticks removed from the beginning and end. It then demonstrates how to use the function with an example string." } \`\`\` Potential case of error: `match.group(2)` in the function `parse_json_markdown` contains only the string up to the first occurrence of the second triple backticks: { "action": "Final Answer", "action_input": "Here's a Python script to remove backticks at the beginning and end of a string:\n\n ### 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 - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [X] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Called function `parse_json_markdown` in langchain/output_parsers/json.py with input text string: \`\`\`json { "action": "Final Answer", "action_input": "Here's a Python script to remove backticks at the beginning and end of a string:\n\n\`\`\`python\ndef remove_backticks(s):\n return s.strip('\`')\n\nstring_with_backticks = '\`example string\`'\nresult = remove_backticks(string_with_backticks)\nprint(result)\n\`\`\`\n\nThis script defines a function called \`remove_backticks\` that takes a string as input and returns a new string with backticks removed from the beginning and end. It then demonstrates how to use the function with an example string." } \`\`\` ### Expected behavior Function `parse_json_markdown` should return the following json string { "action": "Final Answer", "action_input": "Here's a Python script to remove backticks at the beginning and end of a string:\n\n\`\`\`python\ndef remove_backticks(s):\n return s.strip('\`')\n\nstring_with_backticks = '\`example string\`'\nresult = remove_backticks(string_with_backticks)\nprint(result)\n\`\`\`\n\nThis script defines a function called \`remove_backticks\` that takes a string as input and returns a new string with backticks removed from the beginning and end. It then demonstrates how to use the function with an example string." }
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added n_threads functionality for gpt4all
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2023-05-30T08:24:42
2023-05-30T23:31:30
2023-05-30T23:31:30
CONTRIBUTOR
null
# Added support for modifying the number of threads in the GPT4All model I have added the capability to modify the number of threads used by the GPT4All model. This allows users to adjust the model's parallel processing capabilities based on their specific requirements. ## Changes Made - Updated the `validate_environment` method to set the number of threads for the GPT4All model using the `values["n_threads"]` parameter from the `GPT4All` class constructor. ## Context Useful in scenarios where users want to optimize the model's performance by leveraging multi-threading capabilities. Please note that the `n_threads` parameter was included in the `GPT4All` class constructor but was previously unused. This change ensures that the specified number of threads is utilized by the model . ## Dependencies There are no new dependencies introduced by this change. It only utilizes existing functionality provided by the GPT4All package. ## Testing Since this is a minor change testing is not required. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: Models - @hwchase17 - @agola11
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Change _parse_input implementation in BaseTool
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2023-05-30T07:42:35
2023-08-23T07:33:52
2023-08-23T07:33:52
CONTRIBUTOR
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- Change to respect the BaseModel more than the results of the agent. # Change _parse_input implementation in BaseTool It's hard for me to figure out the intent of the original author of the old code. The original code tends to prioritize the agent's output over the results of pydantic's BaseModel parse_obj. For example, the old code does not honor pydantic's default value or alias. (If they are not included in the key of the agent's output, they are removed.) I was a bit curious about why this implementation was needed, so I created a pull request. <!-- 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? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @vowelparrot
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# Fix typo in LanceDB notebook filename <!-- 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! --> ## 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? Community members can review the PR once tests pass. 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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SQLDatabaseToolkit doesn't work well with Postgresql, it will truncate the last double quotation marks in the SQL
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2023-05-30T04:02:36
2023-06-01T01:25:23
2023-05-30T19:58:50
NONE
null
### System Info Langchain: 0.0.184 Python: 3.10.9 Platform: Windows 10 with Jupyter lab ### Who can help? @vowelparrot ### 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 - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction SQLDatabaseToolkit works well if the SQL doesn't include the double quotation marks at the end, if there is, it will truncate the last double quotation marks, resulting in an endless loop. Below is the initial code snapshot. ![image](https://github.com/hwchase17/langchain/assets/38554198/6a444508-4774-4962-8ae2-e5513c756535) And when I executed it. ![image](https://github.com/hwchase17/langchain/assets/38554198/5d3c76fe-8151-4caf-9970-03c84473f925) The LLM generates the correct SQL, but the toolkit truncats the last double quotation marks. ### Expected behavior Won't truncate the last double quotation marks for PostgreSql.
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1,731,462,914
I_kwDOIPDwls5nNAcC
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Can not Use OpenAI and AzureChatOpenAI together
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2023-05-30T03:52:01
2023-09-18T16:09:59
2023-09-18T16:09:59
NONE
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### System Info Dear Developer: I have encounter an error that I am not able run OpenAI and AzureChatOpenAI together.Here is how to reproduce the error langchain Version: 0.0.184 pyython: 3.9.12 ```python from langchain.llms import OpenAI, AzureOpenAI from langchain.chat_models import ChatOpenAI, AzureChatOpenAI openai_params = { "openai_api_key" : "key", "openai_api_base": "url" } openaiazure_params = { "deployment_name" : "db", "openai_api_base" : "https://azure.com/", "openai_api_version" : "2023-03-15-preview", "openai_api_type" : "azure", "openai_api_key" : "key" } llm = OpenAI(temperature=0.5, max_tokens=1024, **openai_params) print(llm("tell me joke")). # note that this line works fun it wiil call the api without any error llmazure = AzureChatOpenAI(**openaiazure_params) print(llm("tell me joke")). # now it seems that running the AzureChatOpenAI would somehow change the class attribute of the OpenAI. If I rerun this line it would give the following error ``` ```text File "/Users/xyn/anaconda3/lib/python3.9/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 83, in __prepare_create_request raise error.InvalidRequestError( openai.error.InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'> ``` ### Who can help? _No response_ ### 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 ```python from langchain.llms import OpenAI, AzureOpenAI from langchain.chat_models import ChatOpenAI, AzureChatOpenAI openai_params = { "openai_api_key" : "key", "openai_api_base": "url" } openaiazure_params = { "deployment_name" : "db", "openai_api_base" : "https://azure.com/", "openai_api_version" : "2023-03-15-preview", "openai_api_type" : "azure", "openai_api_key" : "key" } llm = OpenAI(temperature=0.5, max_tokens=1024, **openai_params) print(llm("tell me joke")). # note that this line works fun it wiil call the api without any error llmazure = AzureChatOpenAI(**openaiazure_params) print(llm("tell me joke")) # now it seems that running the AzureChatOpenAI would somehow change the class attribute of the OpenAI. If I rerun this line it would give the following error ``` ### Expected behavior ```python print(llm("tell me joke")) # still gives the result after using the AzureChatOpenAI from langchain.schema import HumanMessage llmazure([HumanMessage(content="tell me joke")]) # could also do appropriate calls # was worried attributes would be changed back, so what if I reset the OpenAI and test AzureChatOpenAI again llm = OpenAI(temperature=0.5, max_tokens=1024, **openai_params) # then test AzureChatOpenAI llmazure([HumanMessage(content="tell me joke")]) # do appropriate calls print(llm("tell me joke")) # do appropriate calls ```
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5,421
Implement other Language version of LangChain
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2023-05-30T03:23:46
2023-12-24T12:34:38
null
NONE
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### Issue you'd like to raise. LangChain framework mostly aims to build applications which interact with LLM. Many online applications themselves are implements by other languages such as Java,C++, but LangChain only supports Python and JS by now. How about implement other language version of LangChain? ### Suggestion: _No response_
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5,420
Issue: How to merge two vector in HNSWLib
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2023-05-30T02:44:14
2023-05-30T08:52:51
2023-05-30T08:52:51
NONE
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### Issue you'd like to raise. I try, but don't work. my code: ``` let { HNSWLib } = await import('langchain/vectorstores/hnswlib') let { OpenAIEmbeddings } = await import('langchain/embeddings/openai') let vectors1 = await HNSWLib.load( "D:/workcode/nodejs/chatgpt_server/vectors/32202", new OpenAIEmbeddings() ) let vectors2 = await HNSWLib.load( "D:/workcode/nodejs/chatgpt_server/vectors/60551", new OpenAIEmbeddings() ) let vectors3 = await vectors1.addVectors(vectors2, vectors2.docstore._docs) ``` ### Suggestion: _No response_
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1,731,329,471
I_kwDOIPDwls5nMf2_
5,419
Add AzureCosmosDBVectorSearch VectorStore
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2023-05-29T23:58:25
2023-09-18T16:10:05
2023-09-18T16:10:04
CONTRIBUTOR
null
### Feature request Azure Cosmos DB for MongoDB vCore enables users to efficiently store, index, and query high dimensional vector data stored directly in Azure Cosmos DB for MongoDB vCore. It contains similarity measures such as COS (cosine distance), L2 (Euclidean distance) or IP (inner product) which measures the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically and retrieved during query time. The accompanying PR would add support for Langchain Python users to store vectors from document embeddings generated from APIs such as Azure OpenAI Embeddings or Hugging Face on Azure. [Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search) ### Motivation This capability described in the feature request is currently not available for Langchain Python. ### Your contribution I will be submitting a PR for this feature request.
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Issue: Unsupported OpenAI-Version header provided: 2023-05-15. (HINT: you can provide any of the following support ed versions: 2020-10-01, 2020-11-07.'
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2023-05-29T23:56:40
2023-09-10T16:10:21
2023-09-10T16:10:21
CONTRIBUTOR
null
### Issue you'd like to raise. My code: ``` from langchain.embeddings.openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings( openai_api_base='https://api.openai.com/v1', openai_api_type='open_ai', model='text-embedding-ada-002', openai_api_version='2023-05-15', openai_api_key=openai_api_key, max_retries=3, ) ``` And I got `2023-05-30 07:28:40,163 INFO error_code=None error_message='Unsupported OpenAI-Version header provided: 2023-05-15. (HINT: you can provide any of the following supported versions: 2020-10-01, 2020-11-07. Alternatively, you can simply omit this header to use the default version associated with your account.)' error_param=headers:openai-version error_type=invalid_request_error message='OpenAI API error received' stream_error=False ` Why this is supported versions so old (they are 2020-10-01, 2020-11-07)? Is this normal ? Anybody got this error ? ### Suggestion: _No response_
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Harrison/text splitter
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2023-05-29T23:41:50
2023-05-29T23:56:32
2023-05-29T23:56:31
COLLABORATOR
null
adds support for keeping separators around when using recursive text splitter
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Is it possible to return similarity score from RetrievalQA/docsearch
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2023-05-29T21:02:00
2023-12-21T10:06:28
2023-12-18T23:50:12
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### Feature request It will be very nice to have similarity score from docsearch. Now I have list of documents even if model says "I'm sorry, but I don't have enough information to answer your question". ``` from langchain.chains import RetrievalQA qa = RetrievalQA.from_chain_type( llm=llm, chain_type= "stuff", retriever = docsearch.as_retriever( search_type="similarity", search_kwargs={"k":3} ), return_source_documents=True ) ``` ### Motivation Now it returns list of documents even if model says "I'm sorry, but I don't have enough information to answer your question." And also I got list of not really relevant documents. ### Your contribution Not sure here. For example in `chroma.py` I can add output of similarity: ``` def similarity_search( self, query: str, k: int = DEFAULT_K, filter: Optional[Dict[str, str]] = None, **kwargs: Any, ) -> List[Document]: docs_and_scores = self.similarity_search_with_score(query, k, filter=filter) result = [] for doc, score in docs_and_scores: doc.metadata['score'] = score result.append(doc) return result ``` **BUT** it's not relevant to the output - I got similarity 0.3-0.4 for correct answers and at the same time - for "he context does not provide any information about what...". So this method is good to return score, but not good to solve all my questions.
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Chroma integration improvement
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2023-08-16T23:46:53
2023-08-16T23:46:53
NONE
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# Chroma Integration Improvement [Langchain love](https://github.com/chroma-core/chroma/issues/560): deepened the integration of langchain and chromadb. - [x] upsert - [x] delete - [x] get_all_ids - [x] docstrings improvement - [x] Examples <!-- Remove if not applicable --> Fixes # (issue) - #3011 - #1714 ## Who can review? - @dev2049 - @jeffchuber
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FLARE | Azure open Ai doesn't work
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2023-05-29T17:50:06
2023-10-10T12:40:52
2023-09-21T16:09:08
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### System Info Langchain 0.0.182, python 3.11.3, mac os ### Who can help? _No response_ ### 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 - [x] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [x] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction FLARE with azure open ai causes this issue. InvalidRequestError Traceback (most recent call last) in <cell line: 1>() ----> 1 flare.run(query) 20 frames [/usr/local/lib/python3.10/dist-packages/openai/api_resources/abstract/engine_api_resource.py](https://localhost:8080/#) in __prepare_create_request(cls, api_key, api_base, api_type, api_version, organization, **params) 81 if typed_api_type in (util.ApiType.AZURE, util.ApiType.AZURE_AD): 82 if deployment_id is None and engine is None: ---> 83 raise error.InvalidRequestError( 84 "Must provide an 'engine' or 'deployment_id' parameter to create a %s" 85 % cls, InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'> ### Expected behavior > Entering new FlareChain chain... Current Response: Prompt after formatting: Respond to the user message using any relevant context. If context is provided, you should ground your answer in that context. Once you're done responding return FINISHED. >>> CONTEXT: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> RESPONSE: > Entering new QuestionGeneratorChain chain... Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " decentralized platform for natural language processing" is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " uses a blockchain" is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " distributed ledger to" is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " process data, allowing for secure and transparent data sharing." is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " set of tools" is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " help developers create" is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " create an AI system" is: Prompt after formatting: Given a user input and an existing partial response as context, ask a question to which the answer is the given term/entity/phrase: >>> USER INPUT: explain in great detail the difference between the langchain framework and baby agi >>> EXISTING PARTIAL RESPONSE: The Langchain Framework is a decentralized platform for natural language processing (NLP) applications. It uses a blockchain-based distributed ledger to store and process data, allowing for secure and transparent data sharing. The Langchain Framework also provides a set of tools and services to help developers create and deploy NLP applications. Baby AGI, on the other hand, is an artificial general intelligence (AGI) platform. It uses a combination of deep learning and reinforcement learning to create an AI system that can learn and adapt to new tasks. Baby AGI is designed to be a general-purpose AI system that can be used for a variety of applications, including natural language processing. In summary, the Langchain Framework is a platform for NLP applications, while Baby AGI is an AI system designed for The question to which the answer is the term/entity/phrase " NLP applications" is:
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docs: cleaning
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2023-05-29T17:36:44
2023-05-31T22:28:40
2023-05-30T20:58:17
COLLABORATOR
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# docs cleaning Changed docs to consistent format (probably, we need an official doc integration template): - ClearML - added product descriptions; changed title/headers - Rebuff - added product descriptions; changed title/headers - WhyLabs - added product descriptions; changed title/headers - Docugami - changed title/headers/structure - Airbyte - fixed title - Wolfram Alpha - added descriptions, fixed title - OpenWeatherMap - - added product descriptions; changed title/headers - Unstructured - changed description ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @hwchase17 @dev2049
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Early Stopping Exception
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2023-05-29T17:02:26
2023-09-10T16:10:25
2023-09-10T16:10:25
NONE
null
### Feature request It would be useful to introduce an EarlyStoppingException for capture by the AgentExecutor. ### Motivation It would be nice to throw a `EarlyStoppingException` in a tool used by an Agent when certain pre-conditions are met (e.g. a validation of something has failed). The `EarlyStoppingException` would be caught by the AgentExector where it would immediately return the error message of the tool as the output. My current work around to achieve something like this without customising the AgentExecutor requires me to throw the error message as part of the JSON output of the tool in the output format the agent expects from the tool. The agent would then take this information and run it through an LLM. This is not as ideal as an immediate return of the error message as the LLM doesn't return the exact message and it has to pass through a LLM again. The `return_immediate_results` is not ideal in this case as I would like the Agent to summarise the output of the tool on most passes, except when the condition is met where a `EarlyStoppingException` is thrown and caught by the `AgentExector`. ### Your contribution Happy to create a PR for this if it is wanted.
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1,731,026,192
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MultiRetrievalQAChain requires ChatModel... but should it?
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2023-05-29T16:47:37
2023-09-20T16:09:16
2023-09-20T16:09:15
NONE
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### System Info ![Screen Shot 2023-05-29 at 9 33 50 AM](https://github.com/hwchase17/langchain/assets/36281899/25d386c4-1769-486c-a664-baf235068b97) ![Screen Shot 2023-05-29 at 9 33 25 AM](https://github.com/hwchase17/langchain/assets/36281899/1750f2be-a049-4b7b-a555-0505ecfcfb51) ![Screen Shot 2023-05-29 at 9 33 00 AM](https://github.com/hwchase17/langchain/assets/36281899/ede64632-7098-4c87-aeca-43463cc2a40a) python 3.10. Langchain 0.0.184 Seems that MultiRetrievalQAChain is requiring a chat model to run, but feels like this shouldn't be required. Other QA pipelines don't require a chat model, and I don't see why they should. I'm guessing this is just a symptom of some parts of the system being upgraded to use chatmodels, since OpenAI is used for a lot of little things under the hood with langchain, like deciding which vectorDB to use based on the retriever_infos dict. However, if you are trying to use another LLM aside from openAI, you cannot use MultiRetrievalQA. Further, I've run into some issues with this sort of thing across otherQA pipelines when using huggingface LLMs. Generally, better support for huggingface is important to me, and I think would be important to many others in the near future as the open source model ecosystem continues to grow. ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [X] 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 import langchain from langchain.llms import HuggingFacePipeline, HuggingFaceHub from langchain.embeddings import HuggingFaceEmbeddings from langchain.schema import Document from langchain.document_loaders import DirectoryLoader, TextLoader from langchain.indexes import VectorstoreIndexCreator from langchain.vectorstores import Chroma from langchain.text_splitter import CharacterTextSplitter from langchain.chains import RetrievalQA from langchain.chains.router import MultiRetrievalQAChain from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, GPTJForCausalLM, AutoModelForQuestionAnswering, GPTJForQuestionAnswering from abc import ABC, abstractmethod from typing import List, Dict, Optional import os import requests import shutil #device object device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # hf embeddings model embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # Cerebras GPT2.7B model = AutoModelForCausalLM.from_pretrained( "cerebras/Cerebras-GPT-2.7B", torch_dtype=torch.float16, ).to(device) tokenizer = AutoTokenizer.from_pretrained("cerebras/Cerebras-GPT-2.7B") # hf pipeline gen_pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=200, early_stopping=True, no_repeat_ngram_size=2, device=0 ) # define llm gen_llm = HuggingFacePipeline(pipeline=gen_pipe) # load documents and create retrievers docs1 = TextLoader('docs/dir1/mydoc1.txt').load_and_split() retreiver1 = Chroma.from_documents(docs1, embeddings).as_retriever() docs2 = TextLoader('docs/dir2/mydoc2.txt').load_and_split() retriever2 = Chroma.from_documents(docs2, embeddings).as_retriever() retriever_infos = [ { "name": "space-knowledge", "description": "Good for answering general questions about outer space.", "retriever": retriever1 }, { "name": "earth-knowledge", "description": "Good for answer questions about planet earth", "retriever": retriever2 } ] chain = MultiRetrievalQAChain.from_retrievers(llm=gen_llm, retriever_infos=retriever_infos, verbose=True) ### Expected behavior I expect this to work the same way it does when using an openAI model. i.e., the model chooses the vectorDB to use based on the question, searches, and returns a response.
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Issue: Display metadata after streaming a response with FastAPI
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2023-05-29T15:31:49
2023-09-18T16:10:20
2023-09-18T16:10:19
NONE
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### Issue you'd like to raise. I have sucessfully set up streaming in HTTP call with FastApi and OpenAI + ConversationalRetrievalChain If I don't use streaming and just return the whole response, like I was doing previously, I also get metadata displayed with the answer. If I enable streaming, i get displayed only the answer and the '%' at the end of response. Like: ......dummytext.% Code, which is responsible for streaming: ``` from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler import queue class ThreadedGenerator: def __init__(self): self.queue = queue.Queue() def __iter__(self): return self def __next__(self): item = self.queue.get() if item is StopIteration: raise item return item def send(self, data): self.queue.put(data) def close(self): self.queue.put(StopIteration) class ChainStreamHandler(StreamingStdOutCallbackHandler): def __init__(self, gen): super().__init__() self.gen = gen def on_llm_new_token(self, token: str, **kwargs): self.gen.send(token) def on_llm_new_token(self, token: str, **kwargs): self.gen.send(token) ``` Ask question function: ``` def askQuestion(self, generator, collection_id, question): try: collection_name = "collection-" + str(collection_id) self.llm = ChatOpenAI(model_name=self.model_name, temperature=self.temperature, openai_api_key=settings.OPENAI_API_KEY, streaming=True, verbose=VERBOSE, callback_manager=CallbackManager([ChainStreamHandler(generator)])) self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, output_key='answer') self.chain = ConversationalRetrievalChain.from_llm(self.llm, chroma_Vectorstore.as_retriever(similarity_search_with_score=True), return_source_documents=True,verbose=VERBOSE, memory=self.memory) result = self.chain({"question": question}) res_dict = { "answer": result["answer"], } res_dict["source_documents"] = [] for source in result["source_documents"]: res_dict["source_documents"].append({ "page_content": source.page_content, "metadata": source.metadata }) return res_dict finally: generator.close() ``` And the API route itself ``` def stream(question, collection_id): generator = ThreadedGenerator() threading.Thread(target=thread_handler.askQuestion, args=(generator, collection_id, question)).start() return generator @router.post("/collection/{collection_id}/ask_question") async def ask_question(collection_id: str, request: Request): form_data = await request.form() question = form_data["question"] return StreamingResponse(stream(question, collection_id), media_type='text/event-stream') ``` In askQuestion function I am creating the res_dict object, which has answer and also source from metadata stored. How can I also display the source after the answer is stopped being streamable? (I have source already in metadata) Is for this a better way to make separate API call, or are there some other practices or,..? Thanks for everyone for advice!
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DocumentLoader for GitHub
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2023-05-29T14:50:53
2023-05-30T17:43:57
2023-05-30T03:11:21
CONTRIBUTOR
null
# Creates GitHubLoader (#5257) GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub. Fixes #5257 Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: DataLoaders - @eyurtsev Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord: RicChilligerDude#7589
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2023-05-29T14:47:13
2023-05-29T14:53:33
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add idea of foreign_id to enable easier langchain-vector store intera…
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2023-05-29T14:31:46
2023-11-07T04:23:16
2023-11-07T04:23:16
CONTRIBUTOR
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@dev2049 @hwchase17 - what do you all think about add a `foreign_id` to `Document`. See the test for Update included here is included to demonstrate why this is useful. It enables users to pass lists of `Documents` to vector store operations like `update`, `delete`, etc and handle the location of those records correctly. This is set up so that if the user sets the `foreign_id` before adding the records to the database, the database will use those `foreign_id`s as the ids. *** Concerns - the name `foreign_id` implies that it is not something the user should set, where the user setting it is valid. - if the user changes `foreign_id` after it has been passed to the database, the "link" will be lost. - I am not entirely familiar with the lifecycle of `Documents` inside LangChain and I don't know if this makes sense. The alternative to adding a `foreign_id` would be to match on the `page_content` - but I think this is far worse. *** I couldn't get tests to run locally. Perhaps someone can point me in the right direction.
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Adds the option to pass the original prompt into the AgentExecutor for PlanAndExecute agents
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CONTRIBUTOR
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# Adds the option to pass the original prompt into the AgentExecutor for PlanAndExecute agents This PR allows the user to optionally specify that they wish for the original prompt/objective to be passed into the Executor agent used by the PlanAndExecute agent. This solves a potential problem where the plan is formed referring to some context contained in the original prompt, but which is not included in the current prompt. Currently, the prompt format given to the Executor is: ``` System: Respond to the human as helpfully and accurately as possible. You have access to the following tools: <Tool and Action Description> <Output Format Description> Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:. Thought: Human: <Previous steps> <Current step> ``` This PR changes the final part after `Human:` to optionally insert the objective: ``` Human: <objective> <Previous steps> <Current step> ``` I have given a specific example in #5400 where the context of a database path is lost, since the plan refers to the "given path". The PR has been linted and formatted. So that existing behaviour is not changed, I have defaulted the argument to `False` and added it as the last argument in the signature, so it does not cause issues for any users passing args positionally as opposed to using keywords. Happy to take any feedback or make required changes! Fixes #5400 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @vowelparrot
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Add the ability to pass the prompt through to Executor Agents for enrichment during PlanAndExecute
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2023-06-03T21:59:11
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CONTRIBUTOR
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### Feature request Add the ability to pass the original prompt through to the ExecutorAgent so that the original explicit context is not lost during a PlanAndExecute run. ### Motivation PlanAndExecute agents can create a plan of steps dependent on context given in the original prompt. However, this context is lost after the plan is created and is being executed. However, often the plan is formed in a way which refers to the prior context, losing information. For example, I gave the following prompt, and gave the agent access only to the PythonREPL tool: ```py prompt = ( f"Task: Analyse the customer data available in the database with path '{db_path}'. Tell me the average " "sales by month." ) ``` In the above example, `db_path` is a fully formed string which can be passed directly to `sqlalchemy.create_engine`. The first step in the plan formed was: `Connect to the database using the given path`. This would ordinarily be fine, however, the context of the "given path" was lost, as it was not part of the reformed prompt passed to the executor. Optionally including the original prompt in the template should assist with this. ### Your contribution I will be submitting a PR shortly with a proposed solution :)
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GraphCypherQAChain Authentication error
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2023-09-12T16:12:01
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### System Info langchain 0.0.183 , Platform Anaconda, Python version 3.10.9 ![Capture](https://github.com/hwchase17/langchain/assets/42122075/b09e3f03-d63a-4aa6-94bf-79722f2118ab) ### Who can help? @hwchase17 ### 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 - [X] Chains - [X] Callbacks/Tracing - [ ] Async ### Reproduction [issue.txt](https://github.com/hwchase17/langchain/files/11591485/issue.txt) ### Expected behavior Cypher Query along with Explanation
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convert the parameter 'text' to uppercase in the function 'parse' of the class BooleanOutputParser
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when the LLMs output 'yes|no',BooleanOutputParser can parse it to 'True|False', fix the ValueError in parse(). <!-- when use the BooleanOutputParser in the chain_filter.py, the LLMs output 'yes|no',the function 'parse' will throw ValueError。 --> Fixes # (issue) #5396 https://github.com/hwchase17/langchain/issues/5396 @hwchase17
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Issue: value error in BooleanOutputParser
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2023-05-29T10:56:23
2023-09-10T16:10:36
2023-09-10T16:10:35
CONTRIBUTOR
null
### Issue you'd like to raise. when use the BooleanOutputParser in the chain_filter.py, the LLMs output 'yes|no',the function 'parse' will throw ValueError。 ### Suggestion: I fix it,as follows: def parse(self, text: str) -> bool: """Parse the output of an LLM call to a boolean. Args: text: output of language model Returns: boolean """ cleaned_text = text.upper().strip() if cleaned_text not in (self.true_val, self.false_val): raise ValueError( f"BooleanOutputParser expected output value to either be " f"{self.true_val} or {self.false_val}. Received {cleaned_text}." ) return cleaned_text == self.true_val
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fix: Blob.from_data mimetype is lost
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2023-05-29T10:03:50
2023-05-29T13:36:50
2023-05-29T13:36:50
CONTRIBUTOR
null
# Fix lost mimetype when using Blob.from_data method The mimetype is lost due to a typo in the class attribue name Fixes # - (no issue opened but I can open one if needed) ## Changes * Fixed typo in name * Added unit-tests to validate the output Blob ## Review @eyurtsev
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Allow for async use of SelfAskWithSearchChain
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2023-05-29T08:53:59
2023-05-31T00:02:40
2023-05-31T00:02:40
CONTRIBUTOR
null
# Allow for async use of SelfAskWithSearchChain ## Who can review? Async - @agola11
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Parser output may not always produce a string (based on what GPT returns), any valid json construct is possible
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2023-05-29T08:50:13
2023-09-10T16:10:41
2023-09-10T16:10:40
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### System Info langchain 0.183 ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [X] My own modified scripts ### Related Components - [X] LLMs/Chat Models - [ ] Embedding Models - [ ] Prompts / Prompt Templates / Prompt Selectors - [X] Output Parsers - [ ] Document Loaders - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction If GPT returns a message formatted this way: Thought: I will produce a list of things. Action: ``` { "action": "Final Answer", "action_input": [ { "thing" : "A", "attribute" : "X" }, { "thing" : "B", "attribute" : "Y" } ] } ``` Where action_input is a list instead of a string, the output parser will recognize it as valid and return it all the way back. output_parser.py: line 23->32: ``` def parse(self, text: str) -> Union[AgentAction, AgentFinish]: try: action_match = re.search(r"```(.*?)```?", text, re.DOTALL) if action_match is not None: response = json.loads(action_match.group(1).strip(), strict=False) if isinstance(response, list): # gpt turbo frequently ignores the directive to emit a single action logger.warning("Got multiple action responses: %s", response) response = response[0] if response["action"] == "Final Answer": ``` reponse = json.loads only checks that the response is a valid json, not that the type of "reponse['action']" is a string. While not necessarily an issue functionally speaking, this means that the function prototype in base.py:225 ``` def run(self, *args: Any, callbacks: Callbacks = None, **kwargs: Any) -> str: """Run the chain as text in, text out or multiple variables, text out.""" ``` is incorrect. the return value usually is a string, but could be a dictionary or a list based on the output of gpt. ### Expected behavior Either force GPT to reformulate, or change the function prototype
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Allow for async use of SelfAskWithSearchChain
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2023-05-29T08:46:16
2023-05-29T08:51:11
2023-05-29T08:51:01
CONTRIBUTOR
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# Allow for async use of SelfAskWithSearchChain ## Who can review? @agola11
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Incorrect prompt formatting when initializing ReActDocstoreAgent with a chat model
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2023-09-10T16:10:46
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### System Info Langchain v0.0.183 ### Who can help? @hwchase17 @agola11 ### Information - [X] 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 Wikipedia from langchain.chat_models import ChatOpenAI from langchain.agents import initialize_agent, Tool from langchain.agents import AgentType from langchain.agents.react.base import DocstoreExplorer docstore=DocstoreExplorer(Wikipedia()) tools = [ Tool( name="Search", func=docstore.search, description="useful for when you need to ask with search" ), Tool( name="Lookup", func=docstore.lookup, description="useful for when you need to ask with lookup" ) ] llm = ChatOpenAI() react = initialize_agent(tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True) question = 'Question' react.run(question) ``` Running this snippet initializes a ReActDocStoreAgent and runs it on the given question. [initialize_agent( )](https://github.com/hwchase17/langchain/blob/master/langchain/agents/initialize.py#L12) returns an AgentExecutor. During this call an agent is created using the [from_llm_and_tools( )](https://github.com/hwchase17/langchain/blob/master/langchain/agents/agent.py#L528) function. This function creates a new llm_chain for the agent and sets the prompt using agent_cls.create_prompt( ). Since we are using a ReActDocStoreAgent, [the function](https://github.com/hwchase17/langchain/blob/master/langchain/agents/react/base.py#L35) simply returns the WIKI_PROMPT, which is a PromptTemplate object. [run( )](https://github.com/hwchase17/langchain/blob/master/langchain/agents/agent.py#L934) from AgentExecutor calls [_take_next_step( )](https://github.com/hwchase17/langchain/blob/master/langchain/agents/agent.py#L748) which calls agent.plan( ). [plan( )](https://github.com/hwchase17/langchain/blob/master/langchain/agents/agent.py#L425) eventually calls llm_chain.predict( ). Eventually llm_chain calls [generate( )](https://github.com/hwchase17/langchain/blob/master/langchain/chains/llm.py#L72). This function creates a prompt by calling [prep_prompt( )](https://github.com/hwchase17/langchain/blob/master/langchain/chains/llm.py#L94). The prompt is a StringPromptValue since it is created using [format_prompt( )](https://github.com/hwchase17/langchain/blob/master/langchain/prompts/base.py#L230). This variable is then used as an argument for the function llm.generate_prompt( ). [generate_prompt( )](https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/base.py#L136) converts the prompt to message format using [to_messages( )](https://github.com/hwchase17/langchain/blob/master/langchain/prompts/base.py#L98), which treats the entire prompt as a HumanMessage. ### Expected behavior The issue here is that the prompt includes both human and AI messages. Ideally, the questions and observations should be human messages whereas the thoughts and actions should be AI messages. Treating the entire prompt as a human message may decrease prompt quality and lead to suboptimal performance.
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DeepSpeed Support And Device Map Fix For HuggingFacePipeline
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2023-11-08T18:09:18
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# DeepSpeed Support For HuggingFacePipeline This PR adds support for DeepSpeed with the Huggingface pipelines for local models. DeepSpeed allows over 2x faster model inference for a wide variety of models. To use this, you need a modern GPU and have CUDA installed that is the same version as the one used to compile Pytorch You also need to install deepspeed with the following command: ```pip install deepspeed``` Fixes # (issue) A test has been added for this new feature. As a side note, some existing tests seem to not be working due to the fact that ```max_new_tokens``` is not a supported keyword. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @hwchase17 - project lead Models - @hwchase17 - @agola11
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RFC: Use wasm-exec package to sandbox code execution by the Python REPL tool
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2023-05-29T05:39:42
2023-09-18T16:10:26
2023-09-18T16:10:25
CONTRIBUTOR
null
### Issue you'd like to raise. Issues #1026 and #5294 raise legitimate concerns with the security of executing LLM-generated code via the `exec()` function. Out of that discussion came a link to [this blog](https://til.simonwillison.net/webassembly/python-in-a-wasm-sandbox) that demonstrated a way to use Wasm to execute Python code in an isolated interpreter. I have developed a library based on that code called [wasm-exec](https://github.com/Jflick58/wasm_exec) to provide a clean interface to this solution. I’d like to next make a PR in LangChain replacing instances of `exec()` with `wasm_exec()` but wanted to get some feedback on this solution before doing so. Right now, the largest unknown is the extent of support for arbitrary packages, which may make running something like Pandas in the sandbox untenable until a solution is found. I believe I have a path forward on that (via installing when configuring the wasm runtime), but will need to continue to experiment. ### Suggestion: _No response_
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Failure when running pip install langchain[all]
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2023-05-29T05:23:41
2023-12-08T20:31:23
2023-11-17T16:07:08
NONE
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### System Info Langchain version: NA Python 3.10.9 WSL2 ### 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 - [ ] Vector Stores / Retrievers - [ ] Memory - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction 1. pip install langchain[all] 2. Wait 3. Observe: ``` Preparing metadata (setup.py) ... done Downloading openai-0.0.2.tar.gz (741 bytes) Preparing metadata (setup.py) ... error error: subprocess-exited-with-error × python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [6 lines of output] Traceback (most recent call last): File "<string>", line 2, in <module> File "<pip-setuptools-caller>", line 34, in <module> File "/tmp/pip-install-9c_7ozkg/openai_9e8b55b2ec17406d8b64d964c29099f3/setup.py", line 6, in <module> raise RuntimeError( RuntimeError: This package is a placeholder package on the public PyPI instance, and is not the correct version to install. If you are having trouble figuring out the correct package to install, please contact us. [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed × Encountered error while generating package metadata. ╰─> See above for output. note: This is an issue with the package mentioned above, not pip. hint: See above for details. ``` ### Expected behavior Installation complete.
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fix: compatibility with old version FAISS vector store file
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2023-05-31T01:16:55
2023-05-31T01:16:55
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# fix: compatibility with old version FAISS vector store file This PR addresses the compatibility issues with langchain when processing vector files generated by older versions of langchain and FAISS. The older files lack the required internal member variables (`deployment, allowed_special, disallowed_special, request_timeout`) that are needed by the new version, leading to compatibility issues as reported in #3251. In addition, this PR also adds a new environment variable `OPENAI_EMBEDDINGS_DEPLOYMENT` which enables langchain to dynamically set the deployment id for older versions of the data files. This fix does not introduce any functional changes. Instead, it sets default values for these new variables so that they can handle older data files and prevent compatibility bugs from occurring. Fixes #3251 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @hwchase17 - project lead Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 VectorStores / Retrievers / Memory - @dev2049
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Implements support for Personal Access Token Authentication in the ConfluenceLoader
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2023-06-03T21:57:50
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CONTRIBUTOR
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# Implements support for Personal Access Token Authentication in the ConfluenceLoader Fixes #5191 Implements a new optional parameter for the ConfluenceLoader: `token`. This allows the use of personal access authentication when using the on-prem server version of Confluence. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @eyurtsev @Jflick58 Twitter Handle: felipe_yyc
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Running into this error while creating embeddings out of pdf file
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2023-11-18T16:06:43
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### System Info File "d:\langchain\pdfqa-app.py", line 46, in _upload_data Pinecone.from_texts(self.doc_chunk,embeddings,batch_size=16,index_name=self.index_name) File "E:\anaconda\envs\langchain\lib\site-packages\langchain\vectorstores\pinecone.py", line 232, in from_texts embeds = embedding.embed_documents(lines_batch) File "E:\anaconda\envs\langchain\lib\site-packages\langchain\embeddings\openai.py", line 297, in embed_documents return self._get_len_safe_embeddings(texts, engine=self.deployment) File "E:\anaconda\envs\langchain\lib\site-packages\langchain\embeddings\openai.py", line 221, in _get_len_safe_embeddings token = encoding.encode( File "E:\anaconda\envs\langchain\lib\site-packages\tiktoken\core.py", line 117, in encode if match := _special_token_regex(disallowed_special).search(text): TypeError: expected string or buffer ### Who can help? _No response_ ### Information - [ ] The official example notebooks/scripts - [ ] My own modified scripts ### Related Components - [ ] LLMs/Chat Models - [X] 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 def _load_docs(self): loader=PyPDFLoader("D:\langchain\data_source\\1706.03762.pdf") self.doc= loader.load() def _split_docs (self): text_splitter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap,separators= ["\n\n", ""]) self.doc_chunk = text_splitter.split_documents(self.doc) def _upload_data(self): embeddings = OpenAIEmbeddings() list_of_index = pinecone.list_indexes() if self.index_name in list_of_index: Pinecone.from_texts(self.doc_chunk,embeddings,batch_size=16,index_name=self.index_name) else: pinecone.create_index(self.index_name, dimension=1024) # for open AI Pinecone.from_texts(self.doc_chunk,embeddings,batch_size=16,index_name=self.index_name) def dataloader(self): self._load_docs() self._split_docs() self._upload_data() ### Expected behavior Please help with the solution
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Update confluence.py to return spaces between elements
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2023-05-29T01:05:33
2023-06-03T21:57:26
2023-06-03T21:57:25
CONTRIBUTOR
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# Update confluence.py to return spaces between elements like headers and links. Please see https://stackoverflow.com/questions/48913975/how-to-return-nicely-formatted-text-in-beautifulsoup4-when-html-text-is-across-m Given: ```html <address> 183 Main St<br>East Copper<br>Massachusetts<br>U S A<br> MA 01516-113 </address> ``` The document loader currently returns: ``` '183 Main StEast CopperMassachusettsU S A MA 01516-113' ``` After this change, the document loader will return: ``` 183 Main St East Copper Massachusetts U S A MA 01516-113 ``` @eyurtsev would you prefer this to be an option that can be passed in?
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Update PR template with Twitter handle request
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2023-05-29T00:33:58
2023-05-29T13:23:17
2023-05-29T13:23:17
CONTRIBUTOR
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# Updates PR template to request Twitter handle for shoutouts! Makes it easier for maintainers to show their appreciation 😄
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5,381
Set old LCTracer to default to port 8000
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2023-05-29T00:11:35
2023-05-30T05:42:54
2023-05-30T05:42:54
CONTRIBUTOR
null
Issue from: https://discord.com/channels/1038097195422978059/1069478035918688346/1112445980466483222
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5,380
Use Default Factory
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2023-05-28T23:14:51
2023-05-29T13:22:36
2023-05-29T13:22:35
CONTRIBUTOR
null
We shouldn't be calling a constructor for a default value - should use default_factory instead. This is especially ad in this case since it requires an optional dependency and an API key to be set. Resolves #5361
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Supabase similarity search with metadata filtering
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2023-05-28T23:11:23
2023-08-11T22:56:58
2023-08-11T22:56:58
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# Supabase similarity search with metadata filtering <!-- 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. --> <!-- Remove if not applicable --> From what I understood, `SupabaseVectorStore`'s similarity searches did not implement any sort of filtering based on the metadata stored in Supabase. Furthermore, I could not find any way of using `metadata_filter` to limit the results given by a `RetrievalQA`. This is my first open source contribution so if I happened to overlook anything please let me know! ### Usage Metadata can be defined in the constructor. ``` vectorStore = SupabaseVectorStore(client=supabase, embedding=embeddings, table_name="documents", metadatas=[{"key": "value"}]) # unchanged qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), chain_type="stuff", retriever=vectorStore.as_retriever()) res = qa.run("What is langchain?") ``` @dev2049
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BaseConversationalRetrievalChain error on 0 docs found
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2023-05-28T23:03:16
2023-09-10T16:10:51
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### System Info BaseConversationalRetrievalChain._call() and ._acall() run into errors eventually in LLMChain.prep_prompts() on referencing input_list[0]. This will trigger an index error with an empty input_list, which happens when there are no Documents retrieved during _call() by BaseConversationalRetrievalChain._get_docs(). There should be a check for this. ### 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 - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Set up a qa call to ConversationalRetrievalChain.from_llm() on an retriever to which a filter has been applied that will result in no Documents being matched. ### Expected behavior If 0 docs are found, don't bother running the rest of the process.
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py tracer fixes
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2023-05-28T23:02:46
2023-05-31T01:47:08
2023-05-31T01:47:07
COLLABORATOR
null
Not merging yet due to two issues: * LLM/Chat Models inputs and outputs not showing up correctly rendered * LLM/Chat Models extra showing up twice (one in rendered format, one in json format) cc @vowelparrot
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Post with child runs
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2023-05-28T22:56:15
2023-05-28T23:49:19
2023-05-28T23:49:19
CONTRIBUTOR
null
Don't send a post for each run all at once
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error thrown when trying to implement BabyAGI with tools with multiple inputs (requiring Structured Tool Chat Agent)
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2023-05-28T20:52:13
2023-09-10T16:10:56
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Windows 11, Anaconda, python 3.9.16 LanchChain 0.0.183 My goal is to extend the tools used by baby AGI, more specifically to use At least the basic WriteFileTool() and ReadFileTool(). they use two inputs though, so I cannot stick with the vanilla ZeroShotAgent. forgive my poor understanding of Python but I have tried to use AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION and replace it in the code. Agent initialization is the only modification I've made to the code in the link below, though it throws an error. Could anyone kindly help updating the code in order to be able to leverage multiple input tools or provide guidance or resources? https://python.langchain.com/en/latest/use_cases/agents/baby_agi_with_agent.html?highlight=babyagi%20with%20tools ```python from langchain.agents import AgentType from langchain.agents import initialize_agent @classmethod def from_llm( cls, llm: BaseLLM, vectorstore: VectorStore, verbose: bool = False, **kwargs ) -> "BabyAGI": """Initialize the BabyAGI Controller.""" task_creation_chain = TaskCreationChain.from_llm(llm, verbose=verbose) task_prioritization_chain = TaskPrioritizationChain.from_llm( llm, verbose=verbose ) llm_chain = LLMChain(llm=llm, prompt=prompt) tool_names = [tool.name for tool in tools] agent = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,llm=llm_chain, tools=tool_names) agent_executor = AgentExecutor.from_agent_and_tools( agent=agent, tools=tools, verbose=True ) return cls( task_creation_chain=task_creation_chain, task_prioritization_chain=task_prioritization_chain, execution_chain=agent_executor, vectorstore=vectorstore, **kwargs, ) ``` I get the error ```python File ~\anaconda3\envs\aagi\lib\site-packages\langchain\agents\structured_chat\base.py:83 in create_prompt args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args))) AttributeError: 'str' object has no attribute 'args' ``` ### Suggestion: _No response_
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Formatter chain
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2023-05-28T19:35:23
2023-11-05T16:07:09
2023-11-05T16:07:09
CONTRIBUTOR
null
### Feature request Creation of a chain or wrapper that uses two LLMs with different models to force correct formatting: - do the "difficult" work with a model like GPT-4: output format is free text. - have a second LLM (davinci-003 or a specialized model for this purpose) that is good at following formatting instructions to convert the output of the first model into (say) JSON. ### Motivation When a model is set up to be creative, or the use case demands that the output is consistently formatted correctly, we are often out of luck, and the output breaks an app. This proposed use of two models will most likely increase the accuracy of the output a lot. ### Your contribution If maintainers think this is a good idea, please add your input, and I will be happy to provide a PR.
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PR_kwDOIPDwls5Ri9bt
5,373
Add async support to routing chains
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2023-05-28T19:15:03
2023-05-29T13:37:26
2023-05-29T13:37:26
CONTRIBUTOR
null
# Add async support for (LLM) routing chains <!-- 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. --> <!-- Remove if not applicable --> Add asynchronous LLM calls support for the routing chains. More specifically: - Add async `aroute` function (i.e. async version of `route`) to the `RouterChain` which calls the routing LLM asynchronously - Implement the async `_acall` for the `LLMRouterChain` - Implement the async `_acall` function for `MultiRouteChain` which first calls asynchronously the routing chain with its new `aroute` function, and then calls asynchronously the relevant destination chain. <!-- 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? - @agola11 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Async - @agola11 -->
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bump to 183
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2023-05-28T17:23:53
2023-05-28T18:42:59
2023-05-28T18:42:58
COLLABORATOR
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null
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handle json parsing errors
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2023-05-28T17:18:48
2023-06-05T08:48:33
2023-05-29T13:18:20
COLLABORATOR
null
adds tests cases, consolidates a lot of PRs
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PR_kwDOIPDwls5Riyub
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revert bad json
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2023-05-28T17:17:25
2023-05-28T17:22:03
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COLLABORATOR
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HtmlTextSplitter
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2023-05-28T17:14:24
2023-05-30T22:18:33
2023-05-30T22:18:33
CONTRIBUTOR
null
# HtmlTextSplitter I am submitting a new HtmlTextSplitter class, which attempts to split text along HTML layout elements. This PR addresses the need for HTML text splitting functionality in the LangChain library. There are no additional dependencies required for this change. ## Examples An [example notebook](docs/modules/indexes/text_splitters/examples/html.ipynb) showing its use ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @vowelparrot
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5,368
Revert "standardize json parsing (#5168)"
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2023-06-01T08:02:06
2023-05-29T13:37:37
CONTRIBUTOR
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Fixes iter error in FAISS add_embeddings call
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2023-05-28T16:31:26
2023-05-28T16:59:30
2023-05-28T16:59:30
CONTRIBUTOR
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# Remove re-use of iter within add_embeddings causing error As reported in https://github.com/hwchase17/langchain/issues/5336 there is an issue currently involving the atempted re-use of an iterator within the FAISS vectorstore adapter Fixes # https://github.com/hwchase17/langchain/issues/5336 ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: VectorStores / Retrievers / Memory - @dev2049
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ImportError: DLL load failed while importing interpreter: The specified module could not be found.
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I cannot figure out why this is happening: File "c:\Users\Yaseen\Documents\AVA\main.py", line 2, in <module> import langchain File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\__init__.py", line 6, in <module> from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\__init__.py", line 2, in <module> from langchain.agents.agent import ( File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\agent.py", line 16, in <module> from langchain.agents.tools import InvalidTool File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\tools.py", line 8, in <module> from langchain.tools.base import BaseTool, Tool, tool File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\tools\__init__.py", line 45, in <module> from langchain.tools.powerbi.tool import ( File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\tools\powerbi\tool.py", line 10, in <module> from langchain.chains.llm import LLMChain File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\__init__.py", line 18, in <module> from langchain.chains.llm_math.base import LLMMathChain File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\llm_math\base.py", line 9, in <module> import numexpr File "C:\Users\Yaseen\AppData\Local\Programs\Python\Python311\Lib\site-packages\numexpr\__init__.py", line 24, in <module> from numexpr.interpreter import MAX_THREADS, use_vml, __BLOCK_SIZE1__ ImportError: DLL load failed while importing interpreter: The specified module could not be found.
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AssertionError when using AutoGPT with Huggingface
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2023-05-28T16:16:57
2023-09-10T03:09:29
2023-08-04T00:18:16
NONE
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# This code: `from langchain.experimental import AutoGPT from langchain import HuggingFaceHub repo_id = "google/flan-t5-xl" # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options agent = AutoGPT.from_llm_and_tools( ai_name="Tom", ai_role="Assistant", tools=tools, llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature":0, "max_length":64}), memory=vectorstore.as_retriever() ) agent.chain.verbose = True` agent.run(["write a weather report for SF today"]) # outputs the error: `AssertionError Traceback (most recent call last) Cell In[21], line 1 ----> 1 agent.run(["write a weather report for SF today"]) File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\experimental\autonomous_agents\autogpt\agent.py:91, in AutoGPT.run(self, goals) 88 loop_count += 1 90 # Send message to AI, get response ---> 91 assistant_reply = self.chain.run( 92 goals=goals, 93 messages=self.full_message_history, 94 memory=self.memory, 95 user_input=user_input, 96 ) 98 # Print Assistant thoughts 99 print(assistant_reply) File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\base.py:239, in Chain.run(self, callbacks, *args, **kwargs) 236 return self(args[0], callbacks=callbacks)[self.output_keys[0]] 238 if kwargs and not args: --> 239 return self(kwargs, callbacks=callbacks)[self.output_keys[0]] 241 if not kwargs and not args: 242 raise ValueError( 243 "`run` supported with either positional arguments or keyword arguments," 244 " but none were provided." 245 ) File ~\anaconda3\envs\langchain\Lib\site-packages\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 ~\anaconda3\envs\langchain\Lib\site-packages\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 ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\llm.py:69, in LLMChain._call(self, inputs, run_manager) 64 def _call( 65 self, 66 inputs: Dict[str, Any], 67 run_manager: Optional[CallbackManagerForChainRun] = None, 68 ) -> Dict[str, str]: ---> 69 response = self.generate([inputs], run_manager=run_manager) 70 return self.create_outputs(response)[0] File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\llm.py:78, in LLMChain.generate(self, input_list, run_manager) 72 def generate( 73 self, 74 input_list: List[Dict[str, Any]], 75 run_manager: Optional[CallbackManagerForChainRun] = None, 76 ) -> LLMResult: 77 """Generate LLM result from inputs.""" ---> 78 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager) 79 return self.llm.generate_prompt( 80 prompts, stop, callbacks=run_manager.get_child() if run_manager else None 81 ) File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\chains\llm.py:106, in LLMChain.prep_prompts(self, input_list, run_manager) 104 for inputs in input_list: 105 selected_inputs = {k: inputs[k] for k in self.prompt.input_variables} --> 106 prompt = self.prompt.format_prompt(**selected_inputs) 107 _colored_text = get_colored_text(prompt.to_string(), "green") 108 _text = "Prompt after formatting:\n" + _colored_text File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\prompts\chat.py:144, in BaseChatPromptTemplate.format_prompt(self, **kwargs) 143 def format_prompt(self, **kwargs: Any) -> PromptValue: --> 144 messages = self.format_messages(**kwargs) 145 return ChatPromptValue(messages=messages) File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\experimental\autonomous_agents\autogpt\prompt.py:51, in AutoGPTPrompt.format_messages(self, **kwargs) 49 memory: VectorStoreRetriever = kwargs["memory"] 50 previous_messages = kwargs["messages"] ---> 51 relevant_docs = memory.get_relevant_documents(str(previous_messages[-10:])) 52 relevant_memory = [d.page_content for d in relevant_docs] 53 relevant_memory_tokens = sum( 54 [self.token_counter(doc) for doc in relevant_memory] 55 ) File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\base.py:377, in VectorStoreRetriever.get_relevant_documents(self, query) 375 def get_relevant_documents(self, query: str) -> List[Document]: 376 if self.search_type == "similarity": --> 377 docs = self.vectorstore.similarity_search(query, **self.search_kwargs) 378 elif self.search_type == "similarity_score_threshold": 379 docs_and_similarities = ( 380 self.vectorstore.similarity_search_with_relevance_scores( 381 query, **self.search_kwargs 382 ) 383 ) File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\faiss.py:255, in FAISS.similarity_search(self, query, k, **kwargs) 243 def similarity_search( 244 self, query: str, k: int = 4, **kwargs: Any 245 ) -> List[Document]: 246 """Return docs most similar to query. 247 248 Args: (...) 253 List of Documents most similar to the query. 254 """ --> 255 docs_and_scores = self.similarity_search_with_score(query, k) 256 return [doc for doc, _ in docs_and_scores] File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\faiss.py:225, in FAISS.similarity_search_with_score(self, query, k) 215 """Return docs most similar to query. 216 217 Args: (...) 222 List of Documents most similar to the query and score for each 223 """ 224 embedding = self.embedding_function(query) --> 225 docs = self.similarity_search_with_score_by_vector(embedding, k) 226 return docs File ~\anaconda3\envs\langchain\Lib\site-packages\langchain\vectorstores\faiss.py:199, in FAISS.similarity_search_with_score_by_vector(self, embedding, k) 197 if self._normalize_L2: 198 faiss.normalize_L2(vector) --> 199 scores, indices = self.index.search(vector, k) 200 docs = [] 201 for j, i in enumerate(indices[0]): File ~\anaconda3\envs\langchain\Lib\site-packages\faiss\class_wrappers.py:329, in handle_Index.<locals>.replacement_search(self, x, k, params, D, I) 327 n, d = x.shape 328 x = np.ascontiguousarray(x, dtype='float32') --> 329 assert d == self.d 331 assert k > 0 333 if D is None: AssertionError: ` # How can I resolve this behaviour?
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bump 182
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2023-05-28T16:04:44
2023-05-28T16:16:19
2023-05-28T16:16:18
CONTRIBUTOR
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1,729,341,841
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5,363
DOC: the example on setting model_kwrgs in HuggingFaceEmbeddings does not work
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2023-05-28T09:35:29
2023-09-18T16:10:30
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### Issue with current documentation: The example in https://python.langchain.com/en/latest/reference/modules/embeddings.html ``` from langchain.embeddings import HuggingFaceEmbeddings model_name = "sentence-transformers/all-mpnet-base-v2" model_kwargs = {'device': 'cpu'} hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs) ``` does not work. I get an error: > pydantic.error_wrappers.ValidationError: 1 validation error for HuggingFaceEmbeddings model_kwargs extra fields not permitted (type=value_error.extra) ### Idea or request for content: fixed example
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fix: agent CHAT_ZERO_SHOT_REACT can not work normally
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2023-05-28T09:20:05
2023-05-29T13:38:12
2023-05-29T13:38:11
CONTRIBUTOR
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# Fix agent CHAT_ZERO_SHOT_REACT can not work normally Fixes #5299 In this problem, `ChatOutputParser` must handle text similar to the the following: ``` Thought: I need to use the search tool to find the current price of EPAM in NYSE and then use the calculator tool to raise it to the 0.23 power. Action: \``` { "action": "Search", "action_input": "EPAM NYSE price" } \``` ``` Initially, `ChatOutputParser` used `parse_json_markdown` to solve the problem but it doesn't work and resulted in #5299 . A better solution is to use `re` to handle text to obtain value for `action` and `action_input` . The optimized program output content is as follows. --- > Entering new AgentExecutor chain... Thought: I need to use the search tool to find the current price of EPAM in NYSE and then use the calculator tool to raise it to the 0.23 power. Action: ``` { "action": "Search", "action_input": "EPAM NYSE price" } ``` Observation: EPAM Systems, Inc. is an American company that specializes in software engineering services, digital platform engineering, and digital product design, operating out of Newtown, Pennsylvania. EPAM is a founding member of the MACH Alliance. Thought:I need to refine my search query to find the current stock price of EPAM on NYSE. Action: ``` { "action": "Search", "action_input": "EPAM stock price NYSE" } ``` Observation: EPAM Systems, Inc. is an American company that specializes in software engineering services, digital platform engineering, and digital product design, operating out of Newtown, Pennsylvania. EPAM is a founding member of the MACH Alliance. Thought:I need to check the search results to find the current stock price of EPAM on NYSE. Action: ``` { "action": "Search", "action_input": "EPAM stock price NYSE" } ``` Observation: EPAM Systems, Inc. is an American company that specializes in software engineering services, digital platform engineering, and digital product design, operating out of Newtown, Pennsylvania. EPAM is a founding member of the MACH Alliance. Thought:I found the current stock price of EPAM on NYSE, now I need to use the calculator tool to raise it to the 0.23 power. Action: ``` { "action": "Calculator", "action_input": "pow(123.45, 0.23)" } ``` Observation: Answer: 3.027212804346112 Final Answer: 3.027212804346112 > Finished chain.
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Validation Error importing OpenAPI planner when OpenAI credentials not in environment
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2023-05-28T08:18:12
2023-05-29T13:22:37
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### System Info Name: langchain, Version: 0.0.180 Name: openai, Version: 0.27.7 macOS Mojave 10.14.6 ### Who can help? @vowelparrot ### Information - [X] 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 - [ ] Agents / Agent Executors - [X] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction Steps: 1. Do _not_ load open ai key into env with the intention of wanting to pass it as a parameter when instantiating the llm ``` from dotenv import dotenv_values openai_api_key = dotenv_values('.env')['OPENAI_API_KEY'] ``` 2. Load the planner: ``` from langchain.llms.openai import OpenAI from langchain.agents.agent_toolkits.openapi import planner ``` ### Expected behavior A validation error should not be raised during the importing of the module. We should be able to pass the open api key as an argument. That is, the following should work: ``` from langchain.llms.openai import OpenAI from langchain.agents.agent_toolkits.openapi import planner llm = OpenAI(model_name="gpt-4", temperature=0.0, open_api_key=open_api_key) ```
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change in the template string is causing the SSL connection or the server response to hang
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2023-05-28T02:31:02
2023-09-10T16:11:01
2023-09-10T16:11:00
NONE
null
### System Info Pycharm in macOS ### 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 # change in the template string will cause the SSL connection or the server response to hang,code here: `question = "Who won the FIFA World Cup in the year 1994? " template = """Question: {question} Answer: Let's think step by step.""" def execute_llm(repo_id:str,template:str,question:str): prompt = PromptTemplate(template=template, input_variables=["question"]) llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0, "max_length": 64}) llm_chain = LLMChain(prompt=prompt, llm=llm) print(llm_chain.run(question))` execute_llm(repo_id="google/flan-t5-xl",template=template,question=question) Upon executing llm_chain.run(question), it accesses the HuggingFaceHub service. This results in the code getting stuck at the following line in ssl.py, which is part of the SSLSocket class: self._sslobj.read(len, buffer). This method of the SSLSocket class holds a _sslobj object, which is also of SSLSocket class. The SSLSocket class defines a method read(self, len=1024, buffer=None) which internally calls _sslobj's read(self, len=1024, buffer=None) method. The problem is that any modification to the template (whether it's adding spaces, newline characters, or changing the string) will cause self._sslobj.read(len, buffer) to block until it times out. Since it's completely impossible to step into it for tracking and debugging, I'm unable to determine the specific reason. ### Expected behavior Could you explain what might be causing this issue? It seems to be related to the SSL network connection, not the content of the template string. However, it's puzzling that the change in the template string is causing the SSL connection or the server response to hang. This behavior is not entirely reasonable and may be a bug or a problem caused by other factors.
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5,359
Fix update_document function, add test and documentation.
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2023-05-27T23:41:36
2023-05-29T14:32:12
2023-05-29T13:39:25
CONTRIBUTOR
null
# Fix for `update_document` Function in Chroma ## Summary This pull request addresses an issue with the `update_document` function in the Chroma class, as described in [#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947). The issue was identified as an `AttributeError` raised when calling `update_document` due to a missing corresponding method in the `Collection` object. This fix refactors the `update_document` method in `Chroma` to correctly interact with the `Collection` object. ## Changes 1. Fixed the `update_document` method in the `Chroma` class to correctly call methods on the `Collection` object. 2. Added the corresponding test `test_chroma_update_document` in `tests/integration_tests/vectorstores/test_chroma.py` to reflect the updated method call. 3. Added an example and explanation of how to use the `update_document` function in the Jupyter notebook tutorial for Chroma. ## Test Plan All existing tests pass after this change. In addition, the `test_chroma_update_document` test case now correctly checks the functionality of `update_document`, ensuring that the function works as expected and updates the content of documents correctly. ## Reviewers @dev2049 This fix will ensure that users are able to use the `update_document` function as expected, without encountering the previous `AttributeError`. This will enhance the usability and reliability of the Chroma class for all users. Thank you for considering this pull request. I look forward to your feedback and suggestions.
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DOC: SteamshipImageGenerationTool returns Config Error in multi_modal_output_agent.ipynb
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2023-05-27T22:42:18
2023-11-15T16:08:24
2023-11-15T16:08:24
NONE
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### Issue with current documentation: When running multi_modal_output_agent.ipynb: https://python.langchain.com/en/latest/use_cases/agents/multi_modal_output_agent.html, I get ConfigError: field "steamship" not yet prepared so type is still a ForwardRef, you might need to call SteamshipImageGenerationTool.update_forward_refs(). tools = [ SteamshipImageGenerationTool(model_name= "dall-e") ] --------------------------------------------------------------------------- ConfigError Traceback (most recent call last) Cell In[7], line 2 1 tools = [ ----> 2 SteamshipImageGenerationTool(model_name= "dall-e") 3 ] File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__() File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/main.py:1076, in pydantic.main.validate_model() File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/fields.py:860, in pydantic.fields.ModelField.validate() ConfigError: field "steamship" not yet prepared so type is still a ForwardRef, you might need to call SteamshipImageGenerationTool.update_forward_refs(). After I call SteamshipImageGenerationTool.update_forward_refs(), I get another error. SteamshipImageGenerationTool.update_forward_refs() --------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[10], line 1 ----> 1 SteamshipImageGenerationTool.update_forward_refs() File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/main.py:815, in pydantic.main.BaseModel.update_forward_refs() File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/typing.py:562, in pydantic.typing.update_model_forward_refs() File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/typing.py:528, in pydantic.typing.update_field_forward_refs() File ~/miniconda3/envs/langchain/lib/python3.11/site-packages/pydantic/typing.py:66, in pydantic.typing.evaluate_forwardref() File ~/miniconda3/envs/langchain/lib/python3.11/typing.py:864, in ForwardRef._evaluate(self, globalns, localns, recursive_guard) 859 if self.__forward_module__ is not None: 860 globalns = getattr( 861 sys.modules.get(self.__forward_module__, None), '__dict__', globalns 862 ) 863 type_ = _type_check( --> 864 eval(self.__forward_code__, globalns, localns), 865 "Forward references must evaluate to types.", 866 is_argument=self.__forward_is_argument__, 867 allow_special_forms=self.__forward_is_class__, 868 ) 869 self.__forward_value__ = _eval_type( 870 type_, globalns, localns, recursive_guard | {self.__forward_arg__} 871 ) 872 self.__forward_evaluated__ = True File <string>:1 NameError: name 'Steamship' is not defined ### Idea or request for content: _No response_
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Merging to branch
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2023-05-27T22:38:54
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"parse_json_markdown" function improved / fixed.
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2023-05-27T22:11:00
2023-06-14T06:38:04
2023-05-29T13:38:37
NONE
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# "parse_json_markdown" function improved / fixed. I've run into an LLm output parsing issue with an agent (type: conversational-react-description), it always raises llm output error/exception with this type of agent. I couldn't wait for a fix to be released, so I fixed/improved the "parse_json_markdown" function to ensure parsing the json from LLM output, now it's working great for me, and I would like to contribute the fix :D Note: I'm a Python expert, and I would like to contribute more, is there anyone that can help/guide me to do the testing for more complex contributions (any articles/blogs may help)? ## Before submitting Thanks in advance :D ## Who can review? @hwchase17 @vowelparrot
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fix output parsing for chat agent
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2023-05-27T21:31:19
2023-05-29T13:38:24
2023-05-29T13:38:23
COLLABORATOR
null
Confirmed that this allows people to run `ChatAgents` with LLMs and ChatModels with no more parsing errors.
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Move chat agent prompt formatting instructions into human message
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2023-05-27T20:46:19
2023-05-31T07:27:52
2023-05-27T21:12:17
CONTRIBUTOR
null
# Move chat agent prompt formatting instructions into human message Partially addresses #5353 around chat agent reliability. OpenAI's 3.5 turbo model is documented as "not pay[ing] strong attention to the system message, and therefore important instructions are often better placed in a user message.". Could use a bit of testing, opening for discussion at the moment. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @vowelparrot @agola11
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5,353
Issue: Chat agents should put format instructions and other important information in a human message
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2023-05-27T20:44:27
2023-05-27T21:21:10
2023-05-27T21:21:10
CONTRIBUTOR
null
### Chat agent reliability fix: put format instructions and other important information in a human message There have been a few raised issues specifically around agent reliability when using chat models. @emilsedgh brought up in the JS Discord that OpenAI's 3.5 turbo model is documented as "not pay[ing] strong attention to the system message, and therefore important instructions are often better placed in a user message.": https://platform.openai.com/docs/guides/chat/instructing-chat-models The `ConversationalChatAgent` is implemented this way: https://github.com/hwchase17/langchain/blob/master/langchain/agents/conversational_chat/base.py#L90 But the base `ChatAgent` and the `StructuredChatAgent` are not: https://github.com/hwchase17/langchain/blob/master/langchain/agents/chat/base.py#L81 https://github.com/hwchase17/langchain/blob/master/langchain/agents/structured_chat/base.py#L88 Need to do a little bit more experimenting, but moving things into the human message may help with reliability issues. ### Suggestion: _No response_
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5,352
Add llama.cpp get_num_tokens support
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2023-05-27T20:26:43
2023-05-29T13:40:25
2023-05-29T13:40:24
CONTRIBUTOR
null
# Adds support for counting tokens using the llama.cpp python interface rather than the default huggingface transformers library The current implementation of the `LlamaCpp` LLM defaults to the base `LLM` for token counting. This results in the need for the huggingface transformers library to be loaded. The Llama.cpp python interface provides a method for tokenizing a given string. This PR overloads the `get_num_tokens` method of the base class to use that instead. Using the native tokenizer should yield more accurate token counts dependent on the loaded model. For llama.cpp workflows this PR reduces dependencies. <!-- 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. --> ## Before submitting Wasn't sure how to setup a test for this without spinning up a particular model. But I have tested it in a project. ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: @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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Sample Notebook for DynamoDB Chat Message History
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2023-05-27T20:15:50
2023-05-28T04:29:34
2023-05-28T04:16:25
CONTRIBUTOR
null
# Sample Notebook for DynamoDB Chat Message History @dev2049 Adding a sample notebook for the DynamoDB Chat Message History class. <!-- 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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5,348
implement `Multiagent Debate`
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2023-05-27T19:13:41
2023-12-11T17:05:44
2023-12-11T17:05:39
COLLABORATOR
null
### Feature request [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://arxiv.org/pdf/2305.14325.pdf) - looks very promising ### Motivation This method is orthogonal to other methods like CoT. It looks like, this method is beneficial almost in any case when we need the highest quality answer. ### Your contribution I can help with testing. Not sure about quick implementation.
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5,347
Using OpenLM example giving error: "ValueError: OPENAI_API_KEY is not set or passed as an argument"
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2023-05-27T18:13:32
2023-09-12T16:12:17
2023-09-12T16:12:16
NONE
null
### System Info Running latest versions of langchain, openai, openlm, python 3.10, mac M1, trying this example I saw on Twitter https://python.langchain.com/en/latest/modules/models/llms/integrations/openlm.html?highlight=openlm ``` from langchain.llms import OpenLM from langchain.llms import OpenAI from langchain.llms import openai from langchain import PromptTemplate, LLMChain openai.api_key = os.getenv("OPENAI_API_KEY") question = "What is the capital of France?" template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate(template=template, input_variables=["question"]) llm1 = OpenAI() llm2 = OpenLM(model="text-davinci-003") llm_chain1 = LLMChain(prompt=prompt, llm=llm1) llm_chain2 = LLMChain(prompt=prompt, llm=llm2) result1 = llm_chain1.run(question) result2 = llm_chain2.run(question) ``` result 1 runs, result 2 gives ValueError: OPENAI_API_KEY is not set or passed as an argument ### Who can help? _No response_ ### Information - [ ] 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 - [X] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction ``` from langchain.llms import OpenLM from langchain.llms import OpenAI from langchain.llms import openai from langchain import PromptTemplate, LLMChain openai.api_key = os.getenv("OPENAI_API_KEY") question = "What is the capital of France?" template = """Question: {question} Answer: Let's think step by step.""" prompt = PromptTemplate(template=template, input_variables=["question"]) llm1 = OpenAI() llm2 = OpenLM(model="text-davinci-003") llm_chain1 = LLMChain(prompt=prompt, llm=llm1) llm_chain2 = LLMChain(prompt=prompt, llm=llm2) result1 = llm_chain1.run(question) result2 = llm_chain2.run(question) ``` ### Expected behavior Run without error
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Extend elastic_vector_search.py to allow for kNN indexing/searching
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2023-05-27T17:12:57
2023-06-02T19:49:19
2023-06-02T19:49:19
CONTRIBUTOR
null
### Feature request Extend `langchain/embeddings/elasticsearch.py` to support kNN indexing and searching. The high-level objectives will be: 1. Allow for the [creation of an index with the correct mapping](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#index-vectors-knn-search) to store documents including dense_vectors so they can be used for kNN search 2. Store embeddings in elasticsearch in [dense_vector](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html) field type 3. Perform [kNN search](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html) 4. Perform [Hybrid](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html#_combine_approximate_knn_with_other_features) BM25 (query) + kNN search ### Motivation Elasticsearch support approximate k-nearest neighbor search with dense vectors. The current module only support script score / exact match vector search. ### Your contribution I will work on the code and create the pull request
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FLARE | Azure open Ai doesn't work
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2023-05-27T16:08:32
2023-09-15T16:10:19
2023-09-15T16:10:18
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--------------------------------------------------------------------------- InvalidRequestError Traceback (most recent call last) [<ipython-input-33-f877209e86e7>](https://localhost:8080/#) in <cell line: 1>() ----> 1 flare.run(query) 20 frames [/usr/local/lib/python3.10/dist-packages/openai/api_resources/abstract/engine_api_resource.py](https://localhost:8080/#) in __prepare_create_request(cls, api_key, api_base, api_type, api_version, organization, **params) 81 if typed_api_type in (util.ApiType.AZURE, util.ApiType.AZURE_AD): 82 if deployment_id is None and engine is None: ---> 83 raise error.InvalidRequestError( 84 "Must provide an 'engine' or 'deployment_id' parameter to create a %s" 85 % cls, InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'>
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Update llamacpp demonstration notebook
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2023-05-27T15:39:34
2023-05-29T13:43:27
2023-05-29T13:43:27
CONTRIBUTOR
null
# Update llamacpp demonstration notebook Add instructions to install with BLAS backend, and update the example of model usage. Fixes #5071. However, it is more like a prevention of similar issues in the future, not a fix, since there was no problem in the framework functionality ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @hwchase17 - @agola11
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5,343
Removed deprecated llm attribute for load_chain
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2023-05-27T15:22:22
2023-05-29T13:44:47
2023-05-29T13:44:47
CONTRIBUTOR
null
# Removed deprecated llm attribute for load_chain Currently `load_chain` for some chain types expect `llm` attribute to be present but `llm` is deprecated attribute for those chains and might not be persisted during their `chain.save`. Fixes #5224 [(issue)](https://github.com/hwchase17/langchain/issues/5224) ## Who can review? @hwchase17 @dev2049
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UnstructuredURLLoader can't load data from url
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2023-05-27T13:55:09
2023-11-08T13:29:53
2023-05-28T04:07:03
NONE
null
### System Info I operate according to the official documentation (https://python.langchain.com/en/latest/modules/indexes/document_loaders/examples/url.html), but I can't get the information through loader.load(), but the output is still [], the language I use is Python, and the version of langchain is 0.0.181, I tried reinstalling the dependent environment but it didn't work well, hopefully it can be solved soon ### 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 - [ ] Agents / Agent Executors - [ ] Tools / Toolkits - [ ] Chains - [ ] Callbacks/Tracing - [ ] Async ### Reproduction from langchain.document_loaders import UnstructuredURLLoader urls = [ "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023", "https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023" ] loader = UnstructuredURLLoader(urls=urls) data = loader.load() ### Expected behavior data not empty
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5,340
Implement a way to abort / cancel request
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2023-05-27T12:07:10
2023-11-19T18:56:36
null
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### Feature request I've been playing around with OpenAI GPT-4 and ran into situation when response generation might take quite some time - say 5 minutes. I switched over to streaming, but often I can immediately see the response is not what want, and therefore I'd like to cancel request. Now here is the part that is unclear to me: **is there an official way to cancel request in Python's version of LangChain?** I have found this [described](https://js.langchain.com/docs/modules/models/chat/additional_functionality#cancelling-requests) in JS/TS version of the framework, however scanning docs, sources and issues yields nothing for this repo. For now I simply terminate process, which works good enough for something like Jupyter notebooks, but quite problematic for say web application. Besides termination, it's also unclear if I may incur any unwanted costs or not for the abandoned request. Should some sort of feature parity be made with JS LangChain? ### Motivation Provide a documented way to cancel long-running requests ### Your contribution At this point I have capacity only to test out potential implementation. May work on the implementation in later weeks.
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