388 Commits (b8b8a138df0e70a2073682d9082f7cc6ae2acb41)

Author	SHA1	Message	Date
Kacper Łukawski	1ff5b67025	Implement async API for Qdrant vector store (#7704) ... Inspired by #5550, I implemented full async API support in Qdrant. The docs were extended to mention the existence of asynchronous operations in Langchain. I also used that chance to restructure the tests of Qdrant and provided a suite of tests for the async version. Async API requires the GRPC protocol to be enabled. Thus, it doesn't work on local mode yet, but we're considering including the support to be consistent.	1 year ago
Aarav Borthakur	210296a71f	Integrate Rockset as a document loader (#7681) ... <!-- Thank you for contributing to LangChain! Replace this comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out! If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> Integrate [Rockset](https://rockset.com/docs/) as a document loader. Issue: None Dependencies: Nothing new (rockset's dependency was already added [here](https://github.com/hwchase17/langchain/pull/6216)) Tag maintainer: @rlancemartin I have added a test for the integration and an example notebook showing its use. I ran `make lint` and everything looks good. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Leonid Kuligin	85e1c9b348	Added support for examples for VertexAI chat models. (#7636) ... #5278 Co-authored-by: Leonid Kuligin <kuligin@google.com>	1 year ago
Richy Wang	45bb414be2	Add LLM for Alibaba's Damo Academy's Tongyi Qwen API (#7477) ... - Add langchain.llms.Tonyi for text completion, in examples into the Tonyi Text API, - Add system tests. Note async completion for the Text API is not yet supported and will be included in a future PR. Dependencies: dashscope. It will be installed manually cause it is not need by everyone. Happy for feedback on any aspect of this PR @hwchase17 @baskaryan.	1 year ago
UmerHA	82f3e32d8d	[Small upgrade] Allow document limit in AzureCognitiveSearchRetriever (#7690) ... Multiple people have asked in #5081 for a way to limit the documents returned from an AzureCognitiveSearchRetriever. This PR adds the `top_n` parameter to allow that. Twitter handle: [@UmerHAdil](twitter.com/umerHAdil)	1 year ago
EllieRoseS	c087ce74f7	Added matching async load func to PlaywrightURLLoader (#5938) ... Fixes # (issue) The existing PlaywrightURLLoader load() function uses a synchronous browser which is not compatible with jupyter. This PR adds a sister function aload() which can be run insisde a notebook. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Bagatur	c17a80f11c	fix chroma updated upsert interface (#7643) ... new chroma release seems to not support empty dicts for metadata. related to #7633	1 year ago
William FH	a673a51efa	[Breaking] Update Evaluation Functionality (#7388) ... - Migrate from deprecated langchainplus_sdk to `langsmith` package - Update the `run_on_dataset()` API to use an eval config - Update a number of evaluators, as well as the loading logic - Update docstrings / reference docs - Update tracer to share single HTTP session	1 year ago
Bagatur	b08f903755	fix chroma init bug (#7639)	1 year ago
Jason Fan	8effd90be0	Add new types of document transformers (#7379) ... - Description: Add two new document transformers that translates documents into different languages and converts documents into q&a format to improve vector search results. Uses OpenAI function calling via the [doctran](https://github.com/psychic-api/doctran/tree/main) library. - Issue: N/A - Dependencies: `doctran = "^0.0.5"` - Tag maintainer: @rlancemartin @eyurtsev @hwchase17 - Twitter handle: @psychicapi or @jfan001 Notes - Adheres to the `DocumentTransformer` abstraction set by @dev2049 in #3182 - refactored `EmbeddingsRedundantFilter` to put it in a file under a new `document_transformers` module - Added basic docs for `DocumentInterrogator`, `DocumentTransformer` as well as the existing `EmbeddingsRedundantFilter` --------- Co-authored-by: Lance Martin <lance@langchain.dev> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Yaroslav Halchenko	0d92a7f357	codespell: workflow, config + some (quite a few) typos fixed (#6785) ... Probably the most boring PR to review ;) Individual commits might be easier to digest --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	1 year ago
Bagatur	2babe3069f	Revert pinecone v4 support (#7566) ... Revert 9d13dcd	1 year ago
Kacper Łukawski	1f83b5f47e	Reuse the existing collection if configured properly in Qdrant.from_texts (#7530) ... This PR changes the behavior of `Qdrant.from_texts` so the collection is reused if not requested to recreate it. Previously, calling `Qdrant.from_texts` or `Qdrant.from_documents` resulted in removing the old data which was confusing for many.	1 year ago
Leonid Kuligin	6674b33cf5	Added support for chat_history (#7555) ... #7469 Co-authored-by: Leonid Kuligin <kuligin@google.com>	1 year ago
Boris	9129318466	CPAL (#6255) ... # Causal program-aided language (CPAL) chain ## Motivation This builds on the recent [PAL](https://arxiv.org/abs/2211.10435) to stop LLM hallucination. The problem with the [PAL](https://arxiv.org/abs/2211.10435) approach is that it hallucinates on a math problem with a nested chain of dependence. The innovation here is that this new CPAL approach includes causal structure to fix hallucination. For example, using the below word problem, PAL answers with 5, and CPAL answers with 13. "Tim buys the same number of pets as Cindy and Boris." "Cindy buys the same number of pets as Bill plus Bob." "Boris buys the same number of pets as Ben plus Beth." "Bill buys the same number of pets as Obama." "Bob buys the same number of pets as Obama." "Ben buys the same number of pets as Obama." "Beth buys the same number of pets as Obama." "If Obama buys one pet, how many pets total does everyone buy?" The CPAL chain represents the causal structure of the above narrative as a causal graph or DAG, which it can also plot, as shown below.  . The two major sections below are: 1. Technical overview 2. Future application Also see [this jupyter notebook](https://github.com/borisdev/langchain/blob/master/docs/extras/modules/chains/additional/cpal.ipynb) doc. ## 1. Technical overview ### CPAL versus PAL Like [PAL](https://arxiv.org/abs/2211.10435), CPAL intends to reduce large language model (LLM) hallucination. The CPAL chain is different from the PAL chain for a couple of reasons. * CPAL adds a causal structure (or DAG) to link entity actions (or math expressions). * The CPAL math expressions are modeling a chain of cause and effect relations, which can be intervened upon, whereas for the PAL chain math expressions are projected math identities. PAL's generated python code is wrong. It hallucinates when complexity increases. ```python def solution(): """Tim buys the same number of pets as Cindy and Boris.Cindy buys the same number of pets as Bill plus Bob.Boris buys the same number of pets as Ben plus Beth.Bill buys the same number of pets as Obama.Bob buys the same number of pets as Obama.Ben buys the same number of pets as Obama.Beth buys the same number of pets as Obama.If Obama buys one pet, how many pets total does everyone buy?""" obama_pets = 1 tim_pets = obama_pets cindy_pets = obama_pets + obama_pets boris_pets = obama_pets + obama_pets total_pets = tim_pets + cindy_pets + boris_pets result = total_pets return result # math result is 5 ``` CPAL's generated python code is correct. ```python story outcome data name code value depends_on 0 obama pass 1.0 [] 1 bill bill.value = obama.value 1.0 [obama] 2 bob bob.value = obama.value 1.0 [obama] 3 ben ben.value = obama.value 1.0 [obama] 4 beth beth.value = obama.value 1.0 [obama] 5 cindy cindy.value = bill.value + bob.value 2.0 [bill, bob] 6 boris boris.value = ben.value + beth.value 2.0 [ben, beth] 7 tim tim.value = cindy.value + boris.value 4.0 [cindy, boris] query data { "question": "how many pets total does everyone buy?", "expression": "SELECT SUM(value) FROM df", "llm_error_msg": "" } # query result is 13 ``` Based on the comments below, CPAL's intended location in the library is `experimental/chains/cpal` and PAL's location is`chains/pal`. ### CPAL vs Graph QA Both the CPAL chain and the Graph QA chain extract entity-action-entity relations into a DAG. The CPAL chain is different from the Graph QA chain for a few reasons. * Graph QA does not connect entities to math expressions * Graph QA does not associate actions in a sequence of dependence. * Graph QA does not decompose the narrative into these three parts: 1. Story plot or causal model 4. Hypothetical question 5. Hypothetical condition ### Evaluation Preliminary evaluation on simple math word problems shows that this CPAL chain generates less hallucination than the PAL chain on answering questions about a causal narrative. Two examples are in [this jupyter notebook](https://github.com/borisdev/langchain/blob/master/docs/extras/modules/chains/additional/cpal.ipynb) doc. ## 2. Future application ### "Describe as Narrative, Test as Code" The thesis here is that the Describe as Narrative, Test as Code approach allows you to represent a causal mental model both as code and as a narrative, giving you the best of both worlds. #### Why describe a causal mental mode as a narrative? The narrative form is quick. At a consensus building meeting, people use narratives to persuade others of their causal mental model, aka. plan. You can share, version control and index a narrative. #### Why test a causal mental model as a code? Code is testable, complex narratives are not. Though fast, narratives are problematic as their complexity increases. The problem is LLMs and humans are prone to hallucination when predicting the outcomes of a narrative. The cost of building a consensus around the validity of a narrative outcome grows as its narrative complexity increases. Code does not require tribal knowledge or social power to validate. Code is composable, complex narratives are not. The answer of one CPAL chain can be the hypothetical conditions of another CPAL Chain. For stochastic simulations, a composable plan can be integrated with the [DoWhy library](https://github.com/py-why/dowhy). Lastly, for the futuristic folk, a composable plan as code allows ordinary community folk to design a plan that can be integrated with a blockchain for funding. An explanation of a dependency planning application is [here.](https://github.com/borisdev/cpal-llm-chain-demo) --- Twitter handle: @boris_dev --------- Co-authored-by: Boris Dev <borisdev@Boriss-MacBook-Air.local>	1 year ago
Hashem Alsaket	1dd4236177	Fix HF endpoint returns blank for text-generation (#7386) ... Description: Current `_call` function in the `langchain.llms.HuggingFaceEndpoint` class truncates response when `task=text-generation`. Same error discussed a few days ago on Hugging Face: https://huggingface.co/tiiuae/falcon-40b-instruct/discussions/51 Issue: Fixes #7353 Tag maintainer: @hwchase17 @baskaryan @hinthornw --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Raymond Yuan	5171c3bcca	Refactor vector storage to correctly handle relevancy scores (#6570) ... Description: This pull request aims to support generating the correct generic relevancy scores for different vector stores by refactoring the relevance score functions and their selection in the base class and subclasses of VectorStore. This is especially relevant with VectorStores that require a distance metric upon initialization. Note many of the current implenetations of `_similarity_search_with_relevance_scores` are not technically correct, as they just return `self.similarity_search_with_score(query, k, **kwargs)` without applying the relevant score function Also includes changes associated with: https://github.com/hwchase17/langchain/pull/6564 and https://github.com/hwchase17/langchain/pull/6494 See more indepth discussion in thread in #6494 Issue: https://github.com/hwchase17/langchain/issues/6526 https://github.com/hwchase17/langchain/issues/6481 https://github.com/hwchase17/langchain/issues/6346 Dependencies: None The changes include: - Properly handling score thresholding in FAISS `similarity_search_with_score_by_vector` for the corresponding distance metric. - Refactoring the `_similarity_search_with_relevance_scores` method in the base class and removing it from the subclasses for incorrectly implemented subclasses. - Adding a `_select_relevance_score_fn` method in the base class and implementing it in the subclasses to select the appropriate relevance score function based on the distance strategy. - Updating the `__init__` methods of the subclasses to set the `relevance_score_fn` attribute. - Removing the `_default_relevance_score_fn` function from the FAISS class and using the base class's `_euclidean_relevance_score_fn` instead. - Adding the `DistanceStrategy` enum to the `utils.py` file and updating the imports in the vector store classes. - Updating the tests to import the `DistanceStrategy` enum from the `utils.py` file. --------- Co-authored-by: Hanit <37485638+hanit-com@users.noreply.github.com>	1 year ago
Stanko Kuveljic	9d13dcd17c	Pinecone: Add V4 support (#7473)	1 year ago
Adilkhan Sarsen	5debd5043e	Added deeplake use case examples of the new features (#6528) ... <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @hwchase17 VectorStores / Retrievers / Memory - @dev2049 --> 1. Added use cases of the new features 2. Done some code refactoring --------- Co-authored-by: Ivo Stranic <istranic@gmail.com>	1 year ago
Yifei Song	7d29bb2c02	Add Xorbits Dataframe as a Document Loader (#7319) ... - [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source computing framework that makes it easy to scale data science and machine learning workloads in parallel. Xorbits can leverage multi cores or GPUs to accelerate computation on a single machine, or scale out up to thousands of machines to support processing terabytes of data. - This PR added support for the Xorbits document loader, which allows langchain to leverage Xorbits to parallelize and distribute the loading of data. - Dependencies: This change requires the Xorbits library to be installed in order to be used. `pip install xorbits` - Request for review: @rlancemartin, @eyurtsev - Twitter handle: https://twitter.com/Xorbitsio Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Sergio Moreno	21a353e9c2	feat: ctransformers support async chain (#6859) ... - Description: Adding async method for CTransformers - Issue: I've found impossible without this code to run Websockets inside a FastAPI micro service and a CTransformers model. - Tag maintainer: Not necessary yet, I don't like to mention directly - Twitter handle: @_semoal	1 year ago
Paul-Emile Brotons	d2cf0d16b3	adding max_marginal_relevance_search method to MongoDBAtlasVectorSearch (#7310) ... Adding a maximal_marginal_relevance method to the MongoDBAtlasVectorSearch vectorstore enhances the user experience by providing more diverse search results Issue: #7304	1 year ago
Matt Robinson	bcab894f4e	feat: Add `UnstructuredTSVLoader` (#7367) ... ### Summary Adds an `UnstructuredTSVLoader` for TSV files. Also updates the doc strings for `UnstructuredCSV` and `UnstructuredExcel` loaders. ### Testing ```python from langchain.document_loaders.tsv import UnstructuredTSVLoader loader = UnstructuredTSVLoader( file_path="example_data/mlb_teams_2012.csv", mode="elements" ) docs = loader.load() ```	1 year ago
Jona Sassenhagen	7ffc431b3a	Add spacy sentencizer (#7442) ... `SpacyTextSplitter` currently uses spacy's statistics-based `en_core_web_sm` model for sentence splitting. This is a good splitter, but it's also pretty slow, and in this case it's doing a lot of work that's not needed given that the spacy parse is then just thrown away. However, there is also a simple rules-based spacy sentencizer. Using this is at least an order of magnitude faster than using `en_core_web_sm` according to my local tests. Also, spacy sentence tokenization based on `en_core_web_sm` can be sped up in this case by not doing the NER stage. This shaves some cycles too, both when loading the model and when parsing the text. Consequently, this PR adds the option to use the basic spacy sentencizer, and it disables the NER stage for the current approach, *which is kept as the default*. Lastly, when extracting the tokenized sentences, the `text` attribute is called directly instead of doing the string conversion, which is IMO a bit more idiomatic.	1 year ago
Delgermurun	a1603fccfb	integrate JinaChat (#6927) ... Integration with https://chat.jina.ai/api. It is OpenAI compatible API. - Twitter handle: [https://twitter.com/JinaAI_](https://twitter.com/JinaAI_) --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
William FH	4789c99bc2	Add String Distance and Embedding Evaluators (#7123) ... Add a string evaluator and pairwise string evaluator implementation for: - Embedding distance - String distance Update docs	1 year ago
William FH	c5edbea34a	Load Run Evaluator (#7101) ... Current problems: 1. Evaluating LLMs or Chat models isn't smooth. Even specifying 'generations' as the output inserts a redundant list into the eval template 2. Configuring input / prediction / reference keys in the `get_qa_evaluator` function is confusing. Unless you are using a chain with the default keys, you have to specify all the variables and need to reason about whether the key corresponds to the traced run's inputs, outputs or the examples inputs or outputs. Proposal: - Configure the run evaluator according to a model. Use the model type and input/output keys to assert compatibility where possible. Only need to specify a reference_key for certain evaluators (which is less confusing than specifying input keys) When does this work: - If you have your langchain model available (assumed always for run_on_dataset flow) - If you are evaluating an LLM, Chat model, or chain - If the LLM or chat models are traced by langchain (wouldn't work if you add an incompatible schema via the REST API) When would this fail: - Currently if you directly create an example from an LLM run, the outputs are generations with all the extra metadata present. A simple `example_key` and dumping all to the template could make the evaluations unreliable - Doesn't help if you're not using the low level API - If you want to instantiate the evaluator without instantiating your chain or LLM (maybe common for monitoring, for instance) -> could also load from run or run type though What's ugly: - Personally think it's better to load evaluators one by one since passing a config down is pretty confusing. - Lots of testing needs to be added - Inconsistent in that it makes a separate run and example input mapper instead of the original `RunEvaluatorInputMapper`, which maps a run and example to a single input. Example usage running the for an LLM, Chat Model, and Agent. ``` # Test running for the string evaluators evaluator_names = ["qa", "criteria"] model = ChatOpenAI() configured_evaluators = load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer") run_on_dataset(ds_name, model, run_evaluators=configured_evaluators) ``` <details> <summary>Full code with dataset upload</summary> ``` ## Create dataset from langchain.evaluation.run_evaluators.loading import load_run_evaluators_for_model from langchain.evaluation import load_dataset import pandas as pd lcds = load_dataset("llm-math") df = pd.DataFrame(lcds) from uuid import uuid4 from langsmith import Client client = Client() ds_name = "llm-math - " + str(uuid4())[0:8] ds = client.upload_dataframe(df, name=ds_name, input_keys=["question"], output_keys=["answer"]) ## Define the models we'll test over from langchain.llms import OpenAI from langchain.chat_models import ChatOpenAI from langchain.agents import initialize_agent, AgentType from langchain.tools import tool llm = OpenAI(temperature=0) chat_model = ChatOpenAI(temperature=0) @tool def sum(a: float, b: float) -> float: """Add two numbers""" return a + b def construct_agent(): return initialize_agent( llm=chat_model, tools=[sum], agent=AgentType.OPENAI_MULTI_FUNCTIONS, ) agent = construct_agent() # Test running for the string evaluators evaluator_names = ["qa", "criteria"] models = [llm, chat_model, agent] run_evaluators = [] for model in models: run_evaluators.append(load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer")) # Run on LLM, Chat Model, and Agent from langchain.client.runner_utils import run_on_dataset to_test = [llm, chat_model, construct_agent] for model, configured_evaluators in zip(to_test, run_evaluators): run_on_dataset(ds_name, model, run_evaluators=configured_evaluators, verbose=True) ``` </details> --------- Co-authored-by: Nuno Campos <nuno@boringbits.io>	1 year ago
Bagatur	4d427b2397	Base language model docstrings (#7104)	1 year ago
German Martin	3ce4e46c8c	The Fellowship of the Vectors: New Embeddings Filter using clustering. (#7015) ... Continuing with Tolkien inspired series of langchain tools. I bring to you: **The Fellowship of the Vectors**, AKA EmbeddingsClusteringFilter. This document filter uses embeddings to group vectors together into clusters, then allows you to pick an arbitrary number of documents vector based on proximity to the cluster centers. That's a representative sample of the cluster. The original idea is from [Greg Kamradt](https://github.com/gkamradt) from this video (Level4): https://www.youtube.com/watch?v=qaPMdcCqtWk&t=365s I added few tricks to make it a bit more versatile, so you can parametrize what to do with duplicate documents in case of cluster overlap: replace the duplicates with the next closest document or remove it. This allow you to use it as an special kind of redundant filter too. Additionally you can choose 2 diff orders: grouped by cluster or respecting the original retriever scores. In my use case I was using the docs grouped by cluster to run refine chains per cluster to generate summarization over a large corpus of documents. Let me know if you want to change anything! @rlancemartin, @eyurtsev, @hwchase17, --------- Co-authored-by: rlm <pexpresss31@gmail.com>	1 year ago
Jan Kubica	fed64ae060	Chroma: add vector search with scores (#6864) ... - Description: Adding to Chroma integration the option to run a similarity search by a vector with relevance scores. Fixing two minor typos. - Issue: The "lambda_mult" typo is related to #4861 - Maintainer: @rlancemartin, @eyurtsev	1 year ago
Tom	e533da8bf2	Adding Marqo to vectorstore ecosystem (#7068) ... This PR brings in a vectorstore interface for [Marqo](https://www.marqo.ai/). The Marqo vectorstore exposes some of Marqo's functionality in addition the the VectorStore base class. The Marqo vectorstore also makes the embedding parameter optional because inference for embeddings is an inherent part of Marqo. Docs, notebook examples and integration tests included. Related PR: https://github.com/hwchase17/langchain/pull/2807 --------- Co-authored-by: Tom Hamer <tom@marqo.ai> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Harrison Chase	6711854e30	Harrison/dataforseo (#7214) ... Co-authored-by: Alexander <sune357@gmail.com>	1 year ago
Richy Wang	cab7d86f23	Implement delete interface of vector store on AnalyticDB (#7170) ... Hi, there This pull request contains two commit: **1. Implement delete interface with optional ids parameter on AnalyticDB.** **2. Allow customization of database connection behavior by exposing engine_args parameter in interfaces.** - This commit adds the `engine_args` parameter to the interfaces, allowing users to customize the behavior of the database connection. The `engine_args` parameter accepts a dictionary of additional arguments that will be passed to the create_engine function. Users can now modify various aspects of the database connection, such as connection pool size and recycle time. This enhancement provides more flexibility and control to users when interacting with the database through the exposed interfaces. This commit is related to VectorStores @rlancemartin @eyurtsev Thank you for your attention and consideration.	1 year ago
Jamal	a2f191a322	Replace JIRA Arbitrary Code Execution vulnerability with finer grain API wrapper (#6992) ... This fixes #4833 and the critical vulnerability https://nvd.nist.gov/vuln/detail/CVE-2023-34540 Previously, the JIRA API Wrapper had a mode that simply pipelined user input into an `exec()` function. [The intended use of the 'other' mode is to cover any of Atlassian's API that don't have an existing interface](cc33bde74f/langchain/tools/jira/prompt.py (L24)) Fortunately all of the [Atlassian JIRA API methods are subfunctions of their `Jira` class](https://atlassian-python-api.readthedocs.io/jira.html), so this implementation calls these subfunctions directly. As well as passing a string representation of the function to call, the implementation flexibly allows for optionally passing args and/or keyword-args. These are given as part of the dictionary input. Example: ``` { "function": "update_issue_field", #function to execute "args": [ #list of ordered args similar to other examples in this JiraAPIWrapper "key", {"summary": "New summary"} ], "kwargs": {} #dict of key value keyword-args pairs } ``` the above is equivalent to `self.jira.update_issue_field("key", {"summary": "New summary"})` Alternate query schema designs are welcome to make querying easier without passing and evaluating arbitrary python code. I considered parsing (without evaluating) input python code and extracting the function, args, and kwargs from there and then pipelining them into the callable function via `*f(args, **kwargs)` - but this seemed more direct. @vowelparrot @dev2049 --------- Co-authored-by: Jamal Rahman <jamal.rahman@builder.ai>	1 year ago
Ankush Gola	4c1c05c2c7	support adding custom metadata to runs (#7120) ... - [x] wire up tools - [x] wire up retrievers - [x] add integration test <!-- Thank you for contributing to LangChain! Replace this comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out! If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @baskaryan - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @baskaryan - Memory: @hwchase17 - Agents / Tools / Toolkits: @hinthornw - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
Nuno Campos	81e5b1ad36	Add serialized object to retriever start callback (#7074) ... <!-- Thank you for contributing to LangChain! Replace this comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out! If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @dev2049 - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @dev2049 - Memory: @hwchase17 - Agents / Tools / Toolkits: @vowelparrot - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
felixocker	db98c44f8f	Support for SPARQL (#7165) ... # [SPARQL](https://www.w3.org/TR/rdf-sparql-query/) for [LangChain](https://github.com/hwchase17/langchain) ## Description LangChain support for knowledge graphs relying on W3C standards using RDFlib: SPARQL/ RDF(S)/ OWL with special focus on RDF \ * Works with local files, files from the web, and SPARQL endpoints * Supports both SELECT and UPDATE queries * Includes both a Jupyter notebook with an example and integration tests ## Contribution compared to related PRs and discussions * [Wikibase agent](https://github.com/hwchase17/langchain/pull/2690) - uses SPARQL, but specifically for wikibase querying * [Cypher qa](https://github.com/hwchase17/langchain/pull/5078) - graph DB question answering for Neo4J via Cypher * [PR 6050](https://github.com/hwchase17/langchain/pull/6050) - tries something similar, but does not cover UPDATE queries and supports only RDF * Discussions on [w3c mailing list](mailto:semantic-web@w3.org) related to the combination of LLMs (specifically ChatGPT) and knowledge graphs ## Dependencies * [RDFlib](https://github.com/RDFLib/rdflib) ## Tag maintainer Graph database related to memory -> @hwchase17	1 year ago
Simon Cheung	81eebc4070	Add HugeGraphQAChain to support gremlin generating chain (#7132) ... [Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a convenient, efficient, and adaptable graph database, compatible with the Apache TinkerPop3 framework and the Gremlin query language. In this PR, the HugeGraph and HugeGraphQAChain provide the same functionality as the existing integration with Neo4j and enables query generation and question answering over HugeGraph database. The difference is that the graph query language supported by HugeGraph is not cypher but another very popular graph query language [Gremlin](https://tinkerpop.apache.org/gremlin.html). A notebook example and a simple test case have also been added. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Nuno Campos	696886f397	Use serialized format for messages in tracer (#6827) ... <!-- Thank you for contributing to LangChain! Replace this comment with: - Description: a description of the change, - Issue: the issue # it fixes (if applicable), - Dependencies: any dependencies required for this change, - Tag maintainer: for a quicker response, tag the relevant maintainer (see below), - Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out! If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. Maintainer responsibilities: - General / Misc / if you don't know who to tag: @dev2049 - DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev - Models / Prompts: @hwchase17, @dev2049 - Memory: @hwchase17 - Agents / Tools / Toolkits: @vowelparrot - Tracing / Callbacks: @agola11 - Async: @agola11 If no one reviews your PR within a few days, feel free to @-mention the same people again. See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md -->	1 year ago
Mike Salvatore	d0c7f7c317	Remove `None` default value for FAISS relevance_score_fn (#7085) ... ## Description The type hint for `FAISS.__init__()`'s `relevance_score_fn` parameter allowed the parameter to be set to `None`. However, a default function is provided by the constructor. This led to an unnecessary check in the code, as well as a test to verify this check. **ASSUMPTION**: There's no reason to ever set `relevance_score_fn` to `None`. This PR changes the type hint and removes the unnecessary code.	1 year ago
Ofer Mendelevitch	153b56d19b	Vectara upd2 (#6506) ... Update to Vectara integration - By user request added "add_files" to take advantage of Vectara capabilities to process files on the backend, without the need for separate loading of documents and chunking in the chain. - Updated vectara.ipynb example notebook to be broader and added testing of add_file() @hwchase17 - project lead --------- Co-authored-by: rlm <pexpresss31@gmail.com>	1 year ago
Bagatur	7acd524210	Rm retriever kwargs (#7013) ... Doesn't actually limit the Retriever interface but hopefully in practice it does	1 year ago
skspark	e5f6f0ffc4	Support params on GoogleSearchApiWrapper (#6810) (#7014) ... ## Description Support search params in GoogleSearchApiWrapper's result call, for the extra filtering on search, to support extra query parameters that google cse provides: https://developers.google.com/custom-search/v1/reference/rest/v1/cse/list?hl=ko ## Issue #6810	1 year ago
Stefano Lottini	8d2281a8ca	Second Attempt - Add concurrent insertion of vector rows in the Cassandra Vector Store (#7017) ... Retrying with the same improvements as in #6772, this time trying not to mess up with branches. @rlancemartin doing a fresh new PR from a branch with a new name. This should do. Thank you for your help! --------- Co-authored-by: Jonathan Ellis <jbellis@datastax.com> Co-authored-by: rlm <pexpresss31@gmail.com>	1 year ago
Harrison Chase	3bfe7cf467	Harrison/split schema dir (#7025) ... should be no functional changes also keep __init__ exposing a lot for backwards compat --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago
Matt Robinson	0498dad562	feat: enable `UnstructuredEmailLoader` to process attachments (#6977) ... ### Summary Updates `UnstructuredEmailLoader` so that it can process attachments in addition to the e-mail content. The loader will process attachments if the `process_attachments` kwarg is passed when the loader is instantiated. ### Testing ```python file_path = "fake-email-attachment.eml" loader = UnstructuredEmailLoader( file_path, mode="elements", process_attachments=True ) docs = loader.load() docs[-1] ``` ### Reviewers - @rlancemartin - @eyurtsev - @hwchase17	1 year ago
Kacper Łukawski	140ba682f1	Support named vectors in Qdrant (#6871) ... # Description This PR makes it possible to use named vectors from Qdrant in Langchain. That was requested multiple times, as people want to reuse externally created collections in Langchain. It doesn't change anything for the existing applications. The changes were covered with some integration tests and included in the docs. ## Example ```python Qdrant.from_documents( docs, embeddings, location=":memory:", collection_name="my_documents", vector_name="custom_vector", ) ``` ### Issue: #2594 Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.	1 year ago
Stefano Lottini	75fb9d2fdc	Cassandra support for chat history using CassIO library (#6771) ... ### Overview This PR aims at building on #4378, expanding the capabilities and building on top of the `cassIO` library to interface with the database (as opposed to using the core drivers directly). Usage of `cassIO` (a library abstracting Cassandra access for ML/GenAI-specific purposes) is already established since #6426 was merged, so no new dependencies are introduced. In the same spirit, we try to uniform the interface for using Cassandra instances throughout LangChain: all our appreciation of the work by @jj701 notwithstanding, who paved the way for this incremental work (thank you!), we identified a few reasons for changing the way a `CassandraChatMessageHistory` is instantiated. Advocating a syntax change is something we don't take lighthearted way, so we add some explanations about this below. Additionally, this PR expands on integration testing, enables use of Cassandra's native Time-to-Live (TTL) features and improves the phrasing around the notebook example and the short "integrations" documentation paragraph. We would kindly request @hwchase to review (since this is an elaboration and proposed improvement of #4378 who had the same reviewer). ### About the __init__ breaking changes There are [many](https://docs.datastax.com/en/developer/python-driver/3.28/api/cassandra/cluster/) options when creating the `Cluster` object, and new ones might be added at any time. Choosing some of them and exposing them as `__init__` parameters `CassandraChatMessageHistory` will prove to be insufficient for at least some users. On the other hand, working through `kwargs` or adding a long, long list of arguments to `__init__` is not a desirable option either. For this reason, (as done in #6426), we propose that whoever instantiates the Chat Message History class provide a Cassandra `Session` object, ready to use. This also enables easier injection of mocks and usage of Cassandra-compatible connections (such as those to the cloud database DataStax Astra DB, obtained with a different set of init parameters than `contact_points` and `port`). We feel that a breaking change might still be acceptable since LangChain is at `0.*`. However, while maintaining that the approach we propose will be more flexible in the future, room could be made for a "compatibility layer" that respects the current init method. Honestly, we would to that only if there are strong reasons for it, as that would entail an additional maintenance burden. ### Other changes We propose to remove the keyspace creation from the class code for two reasons: first, production Cassandra instances often employ RBAC so that the database user reading/writing from tables does not necessarily (and generally shouldn't) have permission to create keyspaces, and second that programmatic keyspace creation is not a best practice (it should be done more or less manually, with extra care about schema mismatched among nodes, etc). Removing this (usually unnecessary) operation from the `__init__` path would also improve initialization performance (shorter time). We suggest, likewise, to remove the `__del__` method (which would close the database connection), for the following reason: it is the recommended best practice to create a single Cassandra `Session` object throughout an application (it is a resource-heavy object capable to handle concurrency internally), so in case Cassandra is used in other ways by the app there is the risk of truncating the connection for all usages when the history instance is destroyed. Moreover, the `Session` object, in typical applications, is best left to garbage-collect itself automatically. As mentioned above, we defer the actual database I/O to the `cassIO` library, which is designed to encode practices optimized for LLM applications (among other) without the need to expose LangChain developers to the internals of CQL (Cassandra Query Language). CassIO is already employed by the LangChain's Vector Store support for Cassandra. We added a few more connection options in the companion notebook example (most notably, Astra DB) to encourage usage by anyone who cannot run their own Cassandra cluster. We surface the `ttl_seconds` option for automatic handling of an expiration time to chat history messages, a likely useful feature given that very old messages generally may lose their importance. We elaborated a bit more on the integration testing (Time-to-live, separation of "session ids", ...). ### Remarks from linter & co. We reinstated `cassio` as a dependency both in the "optional" group and in the "integration testing" group of `pyproject.toml`. This might not be the right thing do to, in which case the author of this PR offer his apologies (lack of confidence with Poetry - happy to be pointed in the right direction, though!). During linter tests, we were hit by some errors which appear unrelated to the code in the PR. We left them here and report on them here for awareness: ``` langchain/vectorstores/mongodb_atlas.py:137: error: Argument 1 to "insert_many" of "Collection" has incompatible type "List[Dict[str, Sequence[object]]]"; expected "Iterable[Union[MongoDBDocumentType, RawBSONDocument]]" [arg-type] langchain/vectorstores/mongodb_atlas.py:186: error: Argument 1 to "aggregate" of "Collection" has incompatible type "List[object]"; expected "Sequence[Mapping[str, Any]]" [arg-type] langchain/vectorstores/qdrant.py:16: error: Name "grpc" is not defined [name-defined] langchain/vectorstores/qdrant.py:19: error: Name "grpc" is not defined [name-defined] langchain/vectorstores/qdrant.py:20: error: Name "grpc" is not defined [name-defined] langchain/vectorstores/qdrant.py:22: error: Name "grpc" is not defined [name-defined] langchain/vectorstores/qdrant.py:23: error: Name "grpc" is not defined [name-defined] ``` In the same spirit, we observe that to even get `import langchain` run, it seems that a `pip install bs4` is missing from the minimal package installation path. Thank you!	1 year ago
Harrison Chase	3ac08c3de4	Harrison/octo ml (#6897) ... Co-authored-by: Bassem Yacoube <125713079+AI-Bassem@users.noreply.github.com> Co-authored-by: Shotaro Kohama <khmshtr28@gmail.com> Co-authored-by: Rian Dolphin <34861538+rian-dolphin@users.noreply.github.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com> Co-authored-by: Shashank Deshpande <shashankdeshpande18@gmail.com>	1 year ago
Rian Dolphin	2e39ede848	add with score option for max marginal relevance (#6867) ... ### Adding the functionality to return the scores with retrieved documents when using the max marginal relevance - Description: Add the method `max_marginal_relevance_search_with_score_by_vector` to the FAISS wrapper. Functionality operates the same as `similarity_search_with_score_by_vector` except for using the max marginal relevance retrieval framework like is used in the `max_marginal_relevance_search_by_vector` method. - Dependencies: None - Tag maintainer: @rlancemartin @eyurtsev - Twitter handle: @RianDolphin --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago