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197 Commits

Author SHA1 Message Date
vowelparrot
4d5ce154f7 Merge branch 'ankush/callbacks-refactor' into vwp/tools_callbacks 2023-04-28 14:58:25 -07:00
vowelparrot
67b5bb53e3 Merge branch 'ankush/callbacks-refactor' into vwp/tools_callbacks 2023-04-28 14:45:08 -07:00
Davis Chase
50f6895900 Chains callbacks refactor (#3683)
Will keep adding chains to this branch, just pushing now for visibility
2023-04-28 14:41:47 -07:00
Ankush Gola
18138c6fc1 cr 2023-04-28 14:27:01 -07:00
vowelparrot
fc402b5d61 test 2023-04-28 14:24:45 -07:00
vowelparrot
f078943a4c Add test 2023-04-28 14:24:01 -07:00
vowelparrot
fede5f75b0 Mypy not happy 2023-04-28 14:10:51 -07:00
Ankush Gola
1b48ea8d73 cr 2023-04-28 13:45:14 -07:00
Ankush Gola
eb9de308c5 merge 2023-04-28 13:35:54 -07:00
vowelparrot
9291f4a53b Merge branch 'master' into vwp/tools_callbacks 2023-04-28 11:15:59 -07:00
vowelparrot
cd7d0eadee merging 2023-04-28 11:09:49 -07:00
vowelparrot
7ee24f4a15 Merge branch 'ankush/callbacks-refactor' into vwp/tools_callbacks 2023-04-28 11:07:03 -07:00
vowelparrot
6b05934ddf Merge branch 'master' into vwp/tools_callbacks 2023-04-28 10:58:28 -07:00
Nuno Campos
0e81e83466 Nc/callbacks docs (#3717) 2023-04-28 10:37:29 -07:00
vowelparrot
baa26ab294 merge 2023-04-28 10:33:56 -07:00
vowelparrot
7af99e1abc Merge branch 'master' into vwp/tools_callbacks 2023-04-28 10:13:41 -07:00
Zach Schillaci
225963a85e Update VectorDBQA to RetrievalQA in tools (#3698)
Because `VectorDBQA` and `VectorDBQAWithSourcesChain` are deprecated
2023-04-28 10:11:04 -07:00
Harrison Chase
2e5b9389de bump version to 152 (#3695) 2023-04-28 10:11:04 -07:00
mbchang
48c7f95add Multiagent authoritarian (#3686)
This notebook showcases how to implement a multi-agent simulation where
a privileged agent decides who to speak.
This follows the polar opposite selection scheme as [multi-agent
decentralized speaker
selection](https://python.langchain.com/en/latest/use_cases/agent_simulations/multiagent_bidding.html).

We show an example of this approach in the context of a fictitious
simulation of a news network. This example will showcase how we can
implement agents that
- think before speaking
- terminate the conversation
2023-04-28 10:11:04 -07:00
Zander Chase
1848a2ca93 Add validation on agent instantiation for multi-input tools (#3681)
Tradeoffs here:
- No lint-time checking for compatibility
- Differs from JS package
- The signature inference, etc. in the base tool isn't simple
- The `args_schema` is optional 

Pros:
- Forwards compatibility retained
- Doesn't break backwards compatibility
- User doesn't have to think about which class to subclass (single base
tool or dynamic `Tool` interface regardless of input)
-  No need to change the load_tools, etc. interfaces

Co-authored-by: Hasan Patel <mangafield@gmail.com>
2023-04-28 10:11:04 -07:00
Davis Chase
76ba417413 Nit: list to sequence (#3678) 2023-04-28 10:11:04 -07:00
Davis Chase
1a6603ba1a Add query parsing unit tests (#3672) 2023-04-28 10:11:04 -07:00
Hasan Patel
0fcaf65152 Fixed some typos on deployment.md (#3652)
Fixed typos and added better formatting for easier readability
2023-04-28 10:11:04 -07:00
Zander Chase
0f9e59e093 Remove Pexpect Dependency (#3667)
Resolves #3664

Next PR will be to clean up CI to catch this earlier. Triaging this, it
looks like it wasn't caught because pexpect is a `poetry` dependency.

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2023-04-28 10:11:04 -07:00
Eugene Yurtsev
46e43e5f67 Blob: Add validator and use future annotations (#3650)
Minor changes to the Blob schema.

---------

Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
2023-04-28 10:11:04 -07:00
Eugene Yurtsev
3b14bbd8f1 Suppress duckdb warning in unit tests explicitly (#3653)
This catches the warning raised when using duckdb, asserts that it's as expected.

The goal is to resolve all existing warnings to make unit-testing much stricter.
2023-04-28 10:11:04 -07:00
Eugene Yurtsev
a8431deb32 Add lazy iteration interface to document loaders (#3659)
Adding a lazy iteration for document loaders.

Following the plan here:
https://github.com/hwchase17/langchain/pull/2833

Keeping the `load` method as is for backwards compatibility. The `load`
returns a materialized list of documents and downstream users may rely on that
fact.

A new method that returns an iterable is introduced for handling lazy
loading.

---------

Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
2023-04-28 10:11:04 -07:00
Piotr Mardziel
64891af07f update example of ConstitutionalChain.from_llm (#3630)
Example code was missing an argument and import. Fixed.
2023-04-28 10:11:04 -07:00
Eugene Yurtsev
d8dcd39f87 Add unit-test to catch changes to required deps (#3662)
This adds a unit test that can catch changes to required dependencies
2023-04-28 10:11:04 -07:00
Eugene Yurtsev
e8fbb9dc8e Fix pytest collection warning (#3651)
Fixes a pytest collection warning because the test class starts with the
prefix "Test"
2023-04-28 10:11:04 -07:00
Harrison Chase
5da0567f5f bump version to 151 (#3658) 2023-04-28 10:11:04 -07:00
Davis Chase
f7639ba150 Self-query with generic query constructor (#3607)
Alternate implementation of #3452 that relies on a generic query
constructor chain and language and then has vector store-specific
translation layer. Still refactoring and updating examples but general
structure is there and seems to work s well as #3452 on exampels

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-28 10:11:04 -07:00
plutopulp
45578e82c4 Add PipelineAI LLM integration (#3644)
Add PipelineAI LLM integration
2023-04-28 10:11:04 -07:00
Harrison Chase
e83e178252 Harrison/lancedb (#3634)
Co-authored-by: Minh Le <minhle@canva.com>
2023-04-28 10:11:04 -07:00
Nuno Campos
40ebacb3ad Update README.md (#3643) 2023-04-28 10:11:04 -07:00
Eugene Yurtsev
73ca8e9164 Introduce Blob and Blob Loader interface (#3603)
This PR introduces a Blob data type and a Blob loader interface.

This is the first of a sequence of PRs that follows this proposal: 

https://github.com/hwchase17/langchain/pull/2833

The primary goals of these abstraction are:

* Decouple content loading from content parsing code.
* Help duplicated content loading code from document loaders.
* Make lazy loading a default for langchain.
2023-04-28 10:11:04 -07:00
Matt Robinson
84a020c572 enhancement: add elements mode to UnstructuredURLLoader (#3456)
### Summary

Updates the `UnstructuredURLLoader` to include a "elements" mode that
retains additional metadata from `unstructured`. This makes
`UnstructuredURLLoader` consistent with other unstructured loaders,
which also support "elements" mode. Patched mode into the existing
`UnstructuredURLLoader` class instead of inheriting from
`UnstructuredBaseLoader` because it significantly simplified the
implementation.

### Testing

This should still work and show the url in the source for the metadata

```python
from langchain.document_loaders import UnstructuredURLLoader

urls = ["https://www.understandingwar.org/sites/default/files/Russian%20Offensive%20Campaign%20Assessment%2C%20April%2011%2C%202023.pdf"]

loader = UnstructuredURLLoader(urls=urls, headers={"Accept": "application/json"}, strategy="fast")
docs = loader.load()
print(docs[0].page_content[:1000])
docs[0].metadata
``` 

This should now work and show additional metadata from `unstructured`.

This should still work and show the url in the source for the metadata

```python
from langchain.document_loaders import UnstructuredURLLoader

urls = ["https://www.understandingwar.org/sites/default/files/Russian%20Offensive%20Campaign%20Assessment%2C%20April%2011%2C%202023.pdf"]

loader = UnstructuredURLLoader(urls=urls, headers={"Accept": "application/json"}, strategy="fast", mode="elements")
docs = loader.load()
print(docs[0].page_content[:1000])
docs[0].metadata
```
2023-04-28 10:11:04 -07:00
Eduard van Valkenburg
c971fefaab Some more PowerBI pydantic and import fixes (#3461) 2023-04-28 10:11:04 -07:00
Harrison Chase
e5f8878a82 Harrison/opensearch logic (#3631)
Co-authored-by: engineer-matsuo <95115586+engineer-matsuo@users.noreply.github.com>
2023-04-28 10:11:04 -07:00
ccw630
588bab73fa Supports async in SequentialChain/SimpleSequentialChain (#3503) 2023-04-28 10:11:04 -07:00
Ehsan M. Kermani
c58f4c504e Allow clearing cache and fix gptcache (#3493)
This PR

* Adds `clear` method for `BaseCache` and implements it for various
caches
* Adds the default `init_func=None` and fixes gptcache integtest
* Since right now integtest is not running in CI, I've verified the
changes by running `docs/modules/models/llms/examples/llm_caching.ipynb`
(until proper e2e integtest is done in CI)
2023-04-28 10:11:04 -07:00
Howard Su
a7cf0b247f Fix Invalid Request using AzureOpenAI (#3522)
This fixes the error when calling AzureOpenAI of gpt-35-turbo model.

The error is:
InvalidRequestError: logprobs, best_of and echo parameters are not
available on gpt-35-turbo model. Please remove the parameter and try
again. For more details, see
https://go.microsoft.com/fwlink/?linkid=2227346.
2023-04-28 10:11:04 -07:00
Luoyger
f057c5b118 add --no-sandbox for chrome in url_selenium (#3589)
without --no-sandbox param, load documents from url by selenium in
chrome occured error below:

```Traceback (most recent call last):
  File "/data//playgroud/try_langchain.py", line 343, in <module>
    langchain_doc_loader()
  File "/data//playgroud/try_langchain.py", line 67, in langchain_doc_loader
    documents = loader.load()
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/langchain/document_loaders/url_selenium.py", line 102, in load
    driver = self._get_driver()
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/langchain/document_loaders/url_selenium.py", line 76, in _get_driver
    return Chrome(options=chrome_options)
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/selenium/webdriver/chrome/webdriver.py", line 80, in __init__
    super().__init__(
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/selenium/webdriver/chromium/webdriver.py", line 104, in __init__
    super().__init__(
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/selenium/webdriver/remote/webdriver.py", line 286, in __init__
    self.start_session(capabilities, browser_profile)
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/selenium/webdriver/remote/webdriver.py", line 378, in start_session
    response = self.execute(Command.NEW_SESSION, parameters)
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/selenium/webdriver/remote/webdriver.py", line 440, in execute
    self.error_handler.check_response(response)
  File "/install/anaconda3-env/envs/python3.10/lib/python3.10/site-packages/selenium/webdriver/remote/errorhandler.py", line 245, in check_response
    raise exception_class(message, screen, stacktrace)
selenium.common.exceptions.WebDriverException: Message: unknown error: Chrome failed to start: exited abnormally.
  (unknown error: DevToolsActivePort file doesn't exist)
  (The process started from chrome location /usr/bin/google-chrome is no longer running, so ChromeDriver is assuming that Chrome has crashed.)
Stacktrace:
#0 0x55cf8da1bfe3 <unknown>
#1 0x55cf8d75ad36 <unknown>
#2 0x55cf8d783b20 <unknown>
#3 0x55cf8d77fa9b <unknown>
#4 0x55cf8d7c1af7 <unknown>
#5 0x55cf8d7c111f <unknown>
#6 0x55cf8d7b8693 <unknown>
#7 0x55cf8d78b03a <unknown>
#8 0x55cf8d78c17e <unknown>
#9 0x55cf8d9dddbd <unknown>
#10 0x55cf8d9e1c6c <unknown>
#11 0x55cf8d9eb4b0 <unknown>
#12 0x55cf8d9e2d63 <unknown>
#13 0x55cf8d9b5c35 <unknown>
#14 0x55cf8da06138 <unknown>
#15 0x55cf8da062c7 <unknown>
#16 0x55cf8da14093 <unknown>
#17 0x7f3da31a72de start_thread
```

add option `chrome_options.add_argument("--no-sandbox")` for chrome.
2023-04-28 10:11:04 -07:00
Shukri
6104cf4c01 Update models used for embeddings in the weaviate example (#3594)
Use text-embedding-ada-002 because it [outperforms all other
models](https://openai.com/blog/new-and-improved-embedding-model).
2023-04-28 10:11:04 -07:00
cs0lar
453e4d2ce8 Fix/issue 2695 (#3608)
## Background
fixes #2695  

## Changes
The `add_text` method uses the internal embedding function if one was
passes to the `Weaviate` constructor.
NOTE: the latest merge on the `Weaviate` class made the specification of
a `weaviate_api_key` mandatory which might not be desirable for all
users and connection methods (for example weaviate also support Embedded
Weaviate which I am happy to add support to here if people think it's
desirable). I wrapped the fetching of the api key into a try catch in
order to allow the `weaviate_api_key` to be unspecified. Do let me know
if this is unsatisfactory.

## Test Plan
added test for `add_texts` method.
2023-04-28 10:11:03 -07:00
brian-tecton-ai
f621c3cb48 Add Tecton example to the "Connecting to a Feature Store" example notebook (#3626)
This PR adds a similar example to the Feast example, using the [Tecton
Feature Platform](https://www.tecton.ai/) and features from the [Tecton
Fundamentals
Tutorial](https://docs.tecton.ai/docs/tutorials/tecton-fundamentals).
2023-04-28 10:11:03 -07:00
mbchang
81e21ba4fd new example: multiagent dialogue with decentralized speaker selection (#3629)
This notebook showcases how to implement a multi-agent simulation
without a fixed schedule for who speaks when. Instead the agents decide
for themselves who speaks. We can implement this by having each agent
bid to speak. Whichever agent's bid is the highest gets to speak.

We will show how to do this in the example below that showcases a
fictitious presidential debate.
2023-04-28 10:11:03 -07:00
leo-gan
48aa248e5c Arxiv document loader (#3627)
It makes sense to use `arxiv` as another source of the documents for
downloading.
- Added the `arxiv` document_loader, based on the
`utilities/arxiv.py:ArxivAPIWrapper`
- added tests
- added an example notebook
- sorted `__all__` in `__init__.py` (otherwise it is hard to find a
class in the very long list)
2023-04-28 10:11:03 -07:00
Tim Asp
ec060418d6 Add way to get serpapi results async (#3604)
Sometimes it's nice to get the raw results from serpapi, and we're
missing the async version of this function.
2023-04-28 10:11:03 -07:00
Zander Chase
a269024194 Align names of search tools (#3620)
Tools for Bing, DDG and Google weren't consistent even though the
underlying implementations were.
All three services now have the same tools and implementations to easily
switch and experiment when building chains.
2023-04-28 10:11:03 -07:00
Maciej Bryński
59af5c5d28 Add get_text_separator parameter to BSHTMLLoader (#3551)
By default get_text doesn't separate content of different HTML tag.
Adding option for specifying separator helps with document splitting.
2023-04-28 10:11:03 -07:00
Bhupendra Aole
43343fdfc8 Close dataframe column names are being treated as one by the LLM (#3611)
We are sending sample dataframe to LLM with df.head().
If the column names are close by, LLM treats two columns names as one,
returning incorrect results.


![image](https://user-images.githubusercontent.com/4707543/234678692-97851fa0-9e12-44db-92ec-9ad9f3545ae2.png)

In the above case the LLM uses **Org Week** as the column name instead
of **Week** if asked about a specific week.

Returning head() as a markdown separates out the columns names and thus
using correct column name.


![image](https://user-images.githubusercontent.com/4707543/234678945-c6d7b218-143e-4e70-9e17-77dc64841a49.png)
2023-04-28 10:11:03 -07:00
James O'Dwyer
2a28bb0089 add metal to ecosystem (#3613) 2023-04-28 10:11:03 -07:00
Zander Chase
bac368b1aa Persistent Bash Shell (#3580)
Clean up linting and make more idiomatic by using an output parser

---------

Co-authored-by: FergusFettes <fergusfettes@gmail.com>
2023-04-28 10:11:03 -07:00
Ilyes Bouchada
a1a795044a Update docker-compose.yaml (#3582)
The following error gets returned when trying to launch
langchain-server:

ERROR: The Compose file
'/opt/homebrew/lib/python3.11/site-packages/langchain/docker-compose.yaml'
is invalid because:
services.langchain-db.expose is invalid: should be of the format
'PORT[/PROTOCOL]'

Solution:
Change line 28 from - 5432:5432 to - 5432
2023-04-28 10:11:03 -07:00
Kátia Nakamura
6bbe43e7c7 Add docs for Fly.io deployment (#3584)
A minimal example of how to deploy LangChain to Fly.io using Flask.
2023-04-28 10:11:03 -07:00
Chirag Bhatia
54d3cdcf61 Fixed typo for HuggingFaceHub (#3612)
The current text has a typo. This PR contains the corrected spelling for
HuggingFaceHub
2023-04-28 10:11:03 -07:00
Charlie Holtz
695901c5d8 Fix Replicate llm response to handle iterator / multiple outputs (#3614)
One of our users noticed a bug when calling streaming models. This is
because those models return an iterator. So, I've updated the Replicate
`_call` code to join together the output. The other advantage of this
fix is that if you requested multiple outputs you would get them all –
previously I was just returning output[0].

I also adjusted the demo docs to use dolly, because we're featuring that
model right now and it's always hot, so people won't have to wait for
the model to boot up.

The error that this fixes:
```
> llm = Replicate(model=“replicate/flan-t5-xl:eec2f71c986dfa3b7a5d842d22e1130550f015720966bec48beaae059b19ef4c”)
>  llm(“hello”)
> Traceback (most recent call last):
  File "/Users/charlieholtz/workspace/dev/python/main.py", line 15, in <module>
    print(llm(prompt))
  File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 246, in __call__
    return self.generate([prompt], stop=stop).generations[0][0].text
  File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 140, in generate
    raise e
  File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 137, in generate
    output = self._generate(prompts, stop=stop)
  File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/base.py", line 324, in _generate
    text = self._call(prompt, stop=stop)
  File "/opt/homebrew/lib/python3.10/site-packages/langchain/llms/replicate.py", line 108, in _call
    return outputs[0]
TypeError: 'generator' object is not subscriptable
```
2023-04-28 10:11:03 -07:00
Harrison Chase
b764817c05 bump ver 150 (#3599) 2023-04-28 10:11:03 -07:00
Chirag Bhatia
d371904151 Fix broken Cerebrium link in documentation (#3554)
The current hyperlink has a typo. This PR contains the corrected
hyperlink to Cerebrium docs
2023-04-28 10:11:03 -07:00
Harrison Chase
337b55a1b9 Harrison/plugnplai (#3573)
Co-authored-by: Eduardo Reis <edu.pontes@gmail.com>
2023-04-28 10:11:03 -07:00
Zander Chase
334ddf0cb3 Confluence beautifulsoup (#3576)
Co-authored-by: Theau Heral <theau.heral@ln.email.gs.com>
2023-04-28 10:11:03 -07:00
Mike Wang
7455df7820 [simple] updated annotation in load_tools.py (#3544)
- added a few missing annotation for complex local variables.
- auto formatted.
- I also went through all other files in agent directory. no seeing any
other missing piece. (there are several prompt strings not annotated,
but I think it’s trivial. Also adding annotation will make it harder to
read in terms of indents.) Anyway, I think this is the last PR in
agent/annotation.
2023-04-28 10:11:03 -07:00
Zander Chase
f3932cdf41 Sentence Transformers Aliasing (#3541)
The sentence transformers was a dup of the HF one. 

This is a breaking change (model_name vs. model) for anyone using
`SentenceTransformerEmbeddings(model="some/nondefault/model")`, but
since it was landed only this week it seems better to do this now rather
than doing a wrapper.
2023-04-28 10:11:03 -07:00
Eric Peter
b7999329c4 Fix docs error for google drive loader (#3574) 2023-04-28 10:11:03 -07:00
CG80499
7e6fb35238 Add ReAct eval chain (#3161)
- Adds GPT-4 eval chain for arbitrary agents using any set of tools
- Adds notebook

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-28 10:11:03 -07:00
mbchang
74fd5a98e0 example: multi player dnd (#3560)
This notebook shows how the DialogueAgent and DialogueSimulator class
make it easy to extend the [Two-Player Dungeons & Dragons
example](https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html)
to multiple players.

The main difference between simulating two players and multiple players
is in revising the schedule for when each agent speaks

To this end, we augment DialogueSimulator to take in a custom function
that determines the schedule of which agent speaks. In the example
below, each character speaks in round-robin fashion, with the
storyteller interleaved between each player.
2023-04-28 10:11:03 -07:00
James Brotchie
9bc8df63dc Strip surrounding quotes from requests tool URLs. (#3563)
Often an LLM will output a requests tool input argument surrounded by
single quotes. This triggers an exception in the requests library. Here,
we add a simple clean url function that strips any leading and trailing
single and double quotes before passing the URL to the underlying
requests library.

Co-authored-by: James Brotchie <brotchie@google.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
39806c8953 add feast nb (#3565) 2023-04-28 10:11:03 -07:00
Harrison Chase
29958c0b37 Harrison/streamlit handler (#3564)
Co-authored-by: kurupapi <37198601+kurupapi@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Filip Michalsky
d644a34f6c Notebook example: Context-Aware AI Sales Agent (#3547)
I would like to contribute with a jupyter notebook example
implementation of an AI Sales Agent using `langchain`.

The bot understands the conversation stage (you can define your own
stages fitting your needs)
using two chains:

1. StageAnalyzerChain - takes context and LLM decides what part of sales
conversation is one in
2. SalesConversationChain - generate next message

Schema:

https://images-genai.s3.us-east-1.amazonaws.com/architecture2.png

my original repo: https://github.com/filip-michalsky/SalesGPT

This example creates a sales person named Ted Lasso who is trying to
sell you mattresses.

Happy to update based on your feedback.

Thanks, Filip
https://twitter.com/FilipMichalsky
2023-04-28 10:11:03 -07:00
Harrison Chase
10a7946c8e anthropic docs: deprecated LLM, add chat model (#3549) 2023-04-28 10:11:03 -07:00
mbchang
47d30c88f7 docs: simplification of two agent d&d simulation (#3550)
Simplifies the [Two Agent
D&D](https://python.langchain.com/en/latest/use_cases/agent_simulations/two_player_dnd.html)
example with a cleaner, simpler interface that is extensible for
multiple agents.

`DialogueAgent`:
- `send()`: applies the chatmodel to the message history and returns the
message string
- `receive(name, message)`: adds the `message` spoken by `name` to
message history

The `DialogueSimulator` class takes a list of agents. At each step, it
performs the following:
1. Select the next speaker
2. Calls the next speaker to send a message 
3. Broadcasts the message to all other agents
4. Update the step counter.
The selection of the next speaker can be implemented as any function,
but in this case we simply loop through the agents.
2023-04-28 10:11:03 -07:00
apurvsibal
5a2c53b978 Update Alchemy Key URL (#3559)
Update Alchemy Key URL in Blockchain Document Loader. I want to say
thank you for the incredible work the LangChain library creators have
done.

I am amazed at how seamlessly the Loader integrates with Ethereum
Mainnet, Ethereum Testnet, Polygon Mainnet, and Polygon Testnet, and I
am excited to see how this technology can be extended in the future.

@hwchase17 - Please let me know if I can improve or if I have missed any
community guidelines in making the edit? Thank you again for your hard
work and dedication to the open source community.
2023-04-28 10:11:03 -07:00
Tiago De Gaspari
f47a9e4d1f Fix agents' notebooks outputs (#3517)
Fix agents' notebooks to make the answer reflect what is being asked by
the user.
2023-04-28 10:11:03 -07:00
engkheng
1f673e703a Fix typo in Prompts Templates Getting Started page (#3514)
`from_templates` -> `from_template`
2023-04-28 10:11:03 -07:00
Vincent
ac9bcf4886 adding add_documents and aadd_documents to class RedisVectorStoreRetriever (#3419)
Ran into this issue In vectorstores/redis.py when trying to use the
AutoGPT agent with redis vector store. The error I received was

`
langchain/experimental/autonomous_agents/autogpt/agent.py", line 134, in
run
    self.memory.add_documents([Document(page_content=memory_to_add)])
AttributeError: 'RedisVectorStoreRetriever' object has no attribute
'add_documents'
`

Added the needed function to the class RedisVectorStoreRetriever which
did not have the functionality like the base VectorStoreRetriever in
vectorstores/base.py that, for example, vectorstores/faiss.py has
2023-04-28 10:11:03 -07:00
Davis Chase
bbc43cf842 Add Anthropic default request timeout (#3540)
thanks @hitflame!

---------

Co-authored-by: Wenqiang Zhao <hitzhaowenqiang@sina.com>
Co-authored-by: delta@com <delta@com>
2023-04-28 10:11:03 -07:00
Zander Chase
b4c94f05a8 Change Chain Docs (#3537)
Co-authored-by: engkheng <60956360+outday29@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Ikko Eltociear Ashimine
049181e883 fix typo in comet_tracking.ipynb (#3505)
intializing -> initializing
2023-04-28 10:11:03 -07:00
Zander Chase
b693a6a9a2 Add DDG to load_tools (#3535)
Fix linting

---------

Co-authored-by: Mike Wang <62768671+skcoirz@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Roma
3ffa814638 Add unit test for _merge_splits function (#3513)
This commit adds a new unit test for the _merge_splits function in the
text splitter. The new test verifies that the function merges text into
chunks of the correct size and overlap, using a specified separator. The
test passes on the current implementation of the function.
2023-04-28 10:11:03 -07:00
Sami Liedes
7f0edd8353 Pandas agent: Pass forward callback manager (#3518)
The Pandas agent fails to pass callback_manager forward, making it
impossible to use custom callbacks with it. Fix that.

Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>
2023-04-28 10:11:03 -07:00
mbchang
e607ba5df8 Docs: fix naming typo (#3532) 2023-04-28 10:11:03 -07:00
Harrison Chase
482348e852 bump version to 149 (#3530) 2023-04-28 10:11:03 -07:00
mbchang
1edbbedf0a docs: two_player_dnd docs (#3528) 2023-04-28 10:11:03 -07:00
yakigac
87c2564624 Add a test for cosmos db memory (#3525)
Test for #3434 @eavanvalkenburg 
Initially, I was unaware and had submitted a pull request #3450 for the
same purpose, but I have now repurposed the one I used for that. And it
worked.
2023-04-28 10:11:03 -07:00
leo-gan
a8e81f8e0e improved arxiv (#3495)
Improved `arxiv/tool.py` by adding more specific information to the
`description`. It would help with selecting `arxiv` tool between other
tools.
Improved `arxiv.ipynb` with more useful descriptions.
2023-04-28 10:11:03 -07:00
mbchang
7877088ead doc: add two player D&D game (#3476)
In this notebook, we show how we can use concepts from
[CAMEL](https://www.camel-ai.org/) to simulate a role-playing game with
a protagonist and a dungeon master. To simulate this game, we create a
`TwoAgentSimulator` class that coordinates the dialogue between the two
agents.
2023-04-28 10:11:03 -07:00
Harrison Chase
24bd1c4964 Harrison/blockchain docloader (#3491)
Co-authored-by: Jon Saginaw <saginawj@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
91032df759 Updated missing refactor in docs "return_map_steps" (#2956) (#3469)
Minor rename in the documentation that was overlooked when refactoring.

---------

Co-authored-by: Ehmad Zubair <ehmad@cogentlabs.co>
2023-04-28 10:11:03 -07:00
Harrison Chase
cc56ed88aa Harrison/prediction guard (#3490)
Co-authored-by: Daniel Whitenack <whitenack.daniel@gmail.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
46674f4a6b Harrison/tfidf parameters (#3481)
Co-authored-by: pao <go5kuramubon@gmail.com>
Co-authored-by: KyoHattori <kyo.hattori@abejainc.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
ea50abfffe openai embeddings (#3488) 2023-04-28 10:11:03 -07:00
Harrison Chase
a47b127907 Harrison/chroma update (#3489)
Co-authored-by: vyeevani <30946190+vyeevani@users.noreply.github.com>
Co-authored-by: Vineeth Yeevani <vineeth.yeevani@gmail.com>
2023-04-28 10:11:03 -07:00
Sami Liedes
3c4fdf5285 langchain-server: Do not expose postgresql port to host (#3431)
Apart from being unnecessary, postgresql is run on its default port,
which means that the langchain-server will fail to start if there is
already a postgresql server running on the host. This is obviously less
than ideal.

(Yeah, I don't understand why "expose" is the syntax that does not
expose the ports to the host...)

Tested by running langchain-server and trying out debugging on a host
that already has postgresql bound to the port 5432.

Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>
2023-04-28 10:11:03 -07:00
Harrison Chase
24dd0fcbb0 Harrison/verbose conv ret (#3492)
Co-authored-by: makretch <max.kretchmer@gmail.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
d7ac46f62b Harrison/prompt prefix (#3496)
Co-authored-by: Ian <ArGregoryIan@gmail.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
794c342457 Harrison/weaviate (#3494)
Co-authored-by: Nick Rubell <nick@rubell.com>
2023-04-28 10:11:03 -07:00
Eduard van Valkenburg
2966b50608 Azure CosmosDB memory (#3434)
Still needs docs, otherwise works.
2023-04-28 10:11:03 -07:00
Lucas Vieira
6bfd97b24d Support GCS Objects with / in GCS Loaders (#3356)
So, this is basically fixing the same things as #1517 but for GCS.

### Problem
When loading GCS Objects with `/` in the object key (eg.
folder/some-document.txt) using `GCSFileLoader`, the objects are
downloaded into a temporary directory and saved as a file.

This errors out when the parent directory does not exist within the
temporary directory.

### What this pr does
Creates parent directories based on object key.

This also works with deeply nested keys:
folder/subfolder/some-document.txt
2023-04-28 10:11:03 -07:00
Mindaugas Sharskus
e69208f362 [Fix #3365]: Changed regex to cover new line before action serious (#3367)
Fix for: [Changed regex to cover new line before action
serious.](https://github.com/hwchase17/langchain/issues/3365)
---

This PR fixes the issue where `ValueError: Could not parse LLM output:`
was thrown on seems to be valid input.

Changed regex to cover new lines before action serious (after the
keywords "Action:" and "Action Input:").

regex101: https://regex101.com/r/CXl1kB/1

---------

Co-authored-by: msarskus <msarskus@cisco.com>
2023-04-28 10:11:03 -07:00
Maxwell Mullin
22ad33a8e1 GuessedAtParserWarning from RTD document loader documentation example (#3397)
Addresses #3396 by adding 

`features='html.parser'` in example
2023-04-28 10:11:03 -07:00
engkheng
e44317878b Improve llm_chain.ipynb and getting_started.ipynb for chains docs (#3380)
My attempt at improving the `Chain`'s `Getting Started` docs and
`LLMChain` docs. Might need some proof-reading as English is not my
first language.

In LLM examples, I replaced the example use case when a simpler one
(shorter LLM output) to reduce cognitive load.
2023-04-28 10:11:03 -07:00
Zander Chase
2402bb5b57 Add retry logic for ChromaDB (#3372)
Rewrite of #3368

Mainly an issue for when people are just getting started, but still nice
to not throw an error if the number of docs is < k.

Add a little decorator utility to block mutually exclusive keyword
arguments
2023-04-28 10:11:03 -07:00
tkarper
a61aa37010 Add Databutton to list of Deployment options (#3364) 2023-04-28 10:11:03 -07:00
jrhe
6cefec4c65 Adds progress bar using tqdm to directory_loader (#3349)
Approach copied from `WebBaseLoader`. Assumes the user doesn't have
`tqdm` installed.
2023-04-28 10:11:03 -07:00
killpanda
5c8d6fa791 bug_fixes: use md5 instead of uuid id generation (#3442)
At present, the method of generating `point` in qdrant is to use random
`uuid`. The problem with this approach is that even documents with the
same content will be inserted repeatedly instead of updated. Using `md5`
as the `ID` of `point` to insert text can achieve true `update or
insert`.

Co-authored-by: mayue <mayue05@qiyi.com>
2023-04-28 10:11:03 -07:00
Jon Luo
4917c71695 Support SQLAlchemy 2.0 (#3310)
With https://github.com/executablebooks/jupyter-cache/pull/93 merged and
`MyST-NB` updated, we can now support SQLAlchemy 2. Closes #1766
2023-04-28 10:11:03 -07:00
engkheng
ca98b3e519 Update Getting Started page of Prompt Templates (#3298)
Updated `Getting Started` page of `Prompt Templates` to showcase more
features provided by the class. Might need some proof reading because
apparently English is not my first language.
2023-04-28 10:11:03 -07:00
Hasan Patel
fae3eb7223 Updated Readme.md (#3477)
Corrected some minor grammar issues, changed infra to infrastructure for
more clarity. Improved readability
2023-04-28 10:11:03 -07:00
Davis Chase
6544d2bc6f fix #3884 (#3475)
fixes mar bug #3384
2023-04-28 10:11:03 -07:00
Prakhar Agarwal
75c097dbdf pass list of strings to embed method in tf_hub (#3284)
This fixes the below mentioned issue. Instead of simply passing the text
to `tensorflow_hub`, we convert it to a list and then pass it.
https://github.com/hwchase17/langchain/issues/3282

Co-authored-by: Prakhar Agarwal <i.prakhar-agarwal@devrev.ai>
2023-04-28 10:11:03 -07:00
Beau Horenberger
6214b15d53 add LoRA loading for the LlamaCpp LLM (#3363)
First PR, let me know if this needs anything like unit tests,
reformatting, etc. Seemed pretty straightforward to implement. Only
hitch was that mmap needs to be disabled when loading LoRAs or else you
segfault.
2023-04-28 10:11:03 -07:00
Ehsan M. Kermani
a21fc19d91 Use a consistent poetry version everywhere (#3250)
Fixes the discrepancy of poetry version in Dockerfile and the GAs
2023-04-28 10:11:03 -07:00
Felipe Lopes
385d9271eb feat: add private weaviate api_key support on from_texts (#3139)
This PR adds support for providing a Weaviate API Key to the VectorStore
methods `from_documents` and `from_texts`. With this addition, users can
authenticate to Weaviate and make requests to private Weaviate servers
when using these methods.

## Motivation
Currently, LangChain's VectorStore methods do not provide a way to
authenticate to Weaviate. This limits the functionality of the library
and makes it more difficult for users to take advantage of Weaviate's
features.

This PR addresses this issue by adding support for providing a Weaviate
API Key as extra parameter used in the `from_texts` method.

## Contributing Guidelines
I have read the [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md)
and the PR code passes the following tests:

- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
2023-04-28 10:11:03 -07:00
Zzz233
0ed4b1050f ES similarity_search_with_score() and metadata filter (#3046)
Add similarity_search_with_score() to ElasticVectorSearch, add metadata
filter to both similarity_search() and similarity_search_with_score()
2023-04-28 10:11:03 -07:00
Zander Chase
312cb3fd88 Vwp/alpaca streaming (#3468)
Co-authored-by: Luke Stanley <306671+lukestanley@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Cao Hoang
38a958eb30 remove default usage of openai model in SQLDatabaseToolkit (#2884)
#2866

This toolkit used openai LLM as the default, which could incurr unwanted
cost.
2023-04-28 10:11:03 -07:00
Harrison Chase
0471854072 show how to use memory in convo chain (#3463) 2023-04-28 10:11:03 -07:00
leo-gan
1b3bf86486 added integration links to the ecosystem.rst (#3453)
Now it is hard to search for the integration points between
data_loaders, retrievers, tools, etc.
I've placed links to all groups of providers and integrations on the
`ecosystem` page.
So, it is easy to navigate between all integrations from a single
location.
2023-04-28 10:11:03 -07:00
Davis Chase
6100ad65b1 Bugfix: Not all combine docs chains takes kwargs prompt (#3462)
Generalize ConversationalRetrievalChain.from_llm kwargs

---------

Co-authored-by: shubham.suneja <shubham.suneja>
2023-04-28 10:11:03 -07:00
cs0lar
ae6bda90fc fixes #1214 (#3003)
### Background

Continuing to implement all the interface methods defined by the
`VectorStore` class. This PR pertains to implementation of the
`max_marginal_relevance_search_by_vector` method.

### Changes

- a `max_marginal_relevance_search_by_vector` method implementation has
been added in `weaviate.py`
- tests have been added to the the new method
- vcr cassettes have been added for the weaviate tests

### Test Plan

Added tests for the `max_marginal_relevance_search_by_vector`
implementation

### Change Safety

- [x] I have added tests to cover my changes
2023-04-28 10:11:03 -07:00
Zander Chase
f44e275e1e LM Requests Wrapper (#3457)
Co-authored-by: jnmarti <88381891+jnmarti@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
a83c4a7711 bump version to 148 (#3458) 2023-04-28 10:11:03 -07:00
Harrison Chase
17cbc6a5dd update notebook 2023-04-28 10:11:03 -07:00
mbchang
95fbd29353 add meta-prompt to autonomous agents use cases (#3254)
An implementation of
[meta-prompt](https://noahgoodman.substack.com/p/meta-prompt-a-simple-self-improving),
where the agent modifies its own instructions across episodes with a
user.

![figure](https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F468217b9-96d9-47c0-a08b-dbf6b21b9f49_492x384.png)
2023-04-28 10:11:03 -07:00
yunfeilu92
14a2599bd2 propogate kwargs to cls in OpenSearchVectorSearch (#3416)
kwargs shoud be passed into cls so that opensearch client can be
properly initlized in __init__(). Otherwise logic like below will not
work. as auth will not be passed into __init__

```python
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200")

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
```

Co-authored-by: EC2 Default User <ec2-user@ip-172-31-28-97.ec2.internal>
2023-04-28 10:11:03 -07:00
Eduard van Valkenburg
f17c7bbe83 small constructor change and updated notebook (#3426)
small change in the pydantic definitions, same api. 

updated notebook with right constructure and added few shot example
2023-04-28 10:11:03 -07:00
Zander Chase
b253b0b0d9 Structured Tool Bugfixes (#3324)
- Proactively raise error if a tool subclasses BaseTool, defines its
own schema, but fails to add the type-hints
- fix the auto-inferred schema of the decorator to strip the
unneeded virtual kwargs from the schema dict

Helps avoid silent instances of #3297
2023-04-28 10:11:03 -07:00
Bilal Mahmoud
a588e5a311 Do not await sync callback managers (#3440)
This fixes a bug in the math LLM, where even the sync manager was
awaited, creating a nasty `RuntimeError`
2023-04-28 10:11:03 -07:00
Dianliang233
b23e1de43b Fix NoneType has no len() in DDG tool (#3334)
Per
46ac914daa/duckduckgo_search/ddg.py (L109),
ddg function actually returns None when there is no result.
2023-04-28 10:11:03 -07:00
Davit Buniatyan
b72d9c9d77 Deep Lake mini upgrades (#3375)
Improvements
* set default num_workers for ingestion to 0
* upgraded notebooks for avoiding dataset creation ambiguity
* added `force_delete_dataset_by_path`
* bumped deeplake to 3.3.0
* creds arg passing to deeplake object that would allow custom S3

Notes
* please double check if poetry is not messed up (thanks!)

Asks
* Would be great to create a shared slack channel for quick questions

---------

Co-authored-by: Davit Buniatyan <d@activeloop.ai>
2023-04-28 10:11:03 -07:00
Haste171
4a08ffc2e0 Update unstructured_file.ipynb (#3377)
Fix typo in docs
2023-04-28 10:11:03 -07:00
张城铭
31ee671894 Optimize code (#3412)
Co-authored-by: assert <zhangchengming@kkguan.com>
2023-04-28 10:11:03 -07:00
Zander Chase
e5f184c7ba Catch all exceptions in autogpt (#3413)
Ought to be more autonomous
2023-04-28 10:11:03 -07:00
Zander Chase
6d07bafda5 Move Generative Agent definition to Experimental (#3245)
Extending @BeautyyuYanli 's #3220 to move from the notebook

---------

Co-authored-by: BeautyyuYanli <beautyyuyanli@gmail.com>
2023-04-28 10:11:03 -07:00
Zander Chase
eb47767e9e Add Sentence Transformers Embeddings (#3409)
Add embeddings based on the sentence transformers library.
Add a notebook and integration tests.

Co-authored-by: khimaros <me@khimaros.com>
2023-04-28 10:11:03 -07:00
Zander Chase
9f40c09c86 Update marathon notebook (#3408)
Fixes #3404
2023-04-28 10:11:03 -07:00
Luke Harris
b7dad1b6bf Several confluence loader improvements (#3300)
This PR addresses several improvements:

- Previously it was not possible to load spaces of more than 100 pages.
The `limit` was being used both as an overall page limit *and* as a per
request pagination limit. This, in combination with the fact that
atlassian seem to use a server-side hard limit of 100 when page content
is expanded, meant it wasn't possible to download >100 pages. Now
`limit` is used *only* as a per-request pagination limit and `max_pages`
is introduced as the way to limit the total number of pages returned by
the paginator.
- Document metadata now includes `source` (the source url), making it
compatible with `RetrievalQAWithSourcesChain`.
 - It is now possible to include inline and footer comments.
- It is now possible to pass `verify_ssl=False` and other parameters to
the confluence object for use cases that require it.
2023-04-28 10:11:03 -07:00
zz
e808444b79 Add support for wikipedia's lang parameter (#3383)
Allow to hange the language of the wikipedia API being requested.

Co-authored-by: zhuohui <zhuohui@datastory.com.cn>
2023-04-28 10:11:03 -07:00
Johann-Peter Hartmann
f400386865 Improve youtube loader (#3395)
Small improvements for the YouTube loader: 
a) use the YouTube API permission scope instead of Google Drive 
b) bugfix: allow transcript loading for single videos 
c) an additional parameter "continue_on_failure" for cases when videos
in a playlist do not have transcription enabled.
d) support automated translation for all languages, if available.

---------

Co-authored-by: Johann-Peter Hartmann <johann-peter.hartmann@mayflower.de>
2023-04-28 10:11:03 -07:00
Harrison Chase
f3ab7c2a9f Harrison/hf document loader (#3394)
Co-authored-by: Azam Iftikhar <azamiftikhar1000@gmail.com>
2023-04-28 10:11:03 -07:00
Hadi Curtay
4b071a69d1 Updated incorrect link to Weaviate notebook (#3362)
The detailed walkthrough of the Weaviate wrapper was pointing to the
getting-started notebook. Fixed it to point to the Weaviable notebook in
the examples folder.
2023-04-28 10:11:03 -07:00
Ismail Pelaseyed
0a7ca1014f Add example on deploying LangChain to Cloud Run (#3366)
## Summary

Adds a link to a minimal example of running LangChain on Google Cloud
Run.
2023-04-28 10:11:03 -07:00
Ivan Zatevakhin
326c2c2474 llamacpp wrong default value passed for f16_kv (#3320)
Fixes default f16_kv value in llamacpp; corrects incorrect parameter
passed.

See:
ba3959eafd/llama_cpp/llama.py (L33)

Fixes #3241
Fixes #3301
2023-04-28 10:11:03 -07:00
Harrison Chase
8feb416664 bump version to 147 (#3353) 2023-04-28 10:11:03 -07:00
Harrison Chase
4003a79b35 Harrison/myscale (#3352)
Co-authored-by: Fangrui Liu <fangruil@moqi.ai>
Co-authored-by: 刘 方瑞 <fangrui.liu@outlook.com>
Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>
2023-04-28 10:11:03 -07:00
Harrison Chase
994027771e Harrison/error hf (#3348)
Co-authored-by: Rui Melo <44201826+rufimelo99@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Honkware
fcf610bf31 Add ChatGPT Data Loader (#3336)
This pull request adds a ChatGPT document loader to the document loaders
module in `langchain/document_loaders/chatgpt.py`. Additionally, it
includes an example Jupyter notebook in
`docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
which uses fake sample data based on the original structure of the
`conversations.json` file.

The following files were added/modified:
- `langchain/document_loaders/__init__.py`
- `langchain/document_loaders/chatgpt.py`
- `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
-
`docs/modules/indexes/document_loaders/examples/example_data/fake_conversations.json`

This pull request was made in response to the recent release of ChatGPT
data exports by email:
https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history
2023-04-28 10:11:03 -07:00
Zander Chase
a4a4c59e97 Fix Sagemaker Batch Endpoints (#3249)
Add different typing for @evandiewald 's heplful PR

---------

Co-authored-by: Evan Diewald <evandiewald@gmail.com>
2023-04-28 10:11:03 -07:00
Johann-Peter Hartmann
3d762f2b8b Support recursive sitemaps in SitemapLoader (#3146)
A (very) simple addition to support multiple sitemap urls.

---------

Co-authored-by: Johann-Peter Hartmann <johann-peter.hartmann@mayflower.de>
2023-04-28 10:11:03 -07:00
Filip Haltmayer
69d60041af Refactor Milvus/Zilliz (#3047)
Refactoring milvus/zilliz to clean up and have a more consistent
experience.

Signed-off-by: Filip Haltmayer <filip.haltmayer@zilliz.com>
2023-04-28 10:11:03 -07:00
Harrison Chase
38c3478be4 Harrison/voice assistant (#3347)
Co-authored-by: Jaden <jaden.lorenc@gmail.com>
2023-04-28 10:11:03 -07:00
Richy Wang
c0fd62cd6f Add a full PostgresSQL syntax database 'AnalyticDB' as vector store. (#3135)
Hi there!
I'm excited to open this PR to add support for using a fully Postgres
syntax compatible database 'AnalyticDB' as a vector.
As AnalyticDB has been proved can be used with AutoGPT,
ChatGPT-Retrieve-Plugin, and LLama-Index, I think it is also good for
you.
AnalyticDB is a distributed Alibaba Cloud-Native vector database. It
works better when data comes to large scale. The PR includes:

- [x]  A new memory: AnalyticDBVector
- [x]  A suite of integration tests verifies the AnalyticDB integration

I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
- [x]  make format
- [x]  make lint
- [x]  make coverage
- [x]  make test
2023-04-28 10:11:03 -07:00
Harrison Chase
471f961c8e Harrison/power bi (#3205)
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2023-04-28 10:11:03 -07:00
Daniel Chalef
a0f76dd40b args_schema type hint on subclassing (#3323)
per https://github.com/hwchase17/langchain/issues/3297

Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
2023-04-28 10:11:03 -07:00
Zander Chase
3492a93dc4 Fix linting on master (#3327) 2023-04-28 10:11:03 -07:00
Varun Srinivas
bc76e0613c Change in method name for creating an issue on JIRA (#3307)
The awesome JIRA tool created by @zywilliamli calls the `create_issue()`
method to create issues, however, the actual method is `issue_create()`.

Details in the Documentation here:
https://atlassian-python-api.readthedocs.io/jira.html#manage-issues
2023-04-28 10:11:03 -07:00
Davis Chase
692baa797d Update docs api references (#3315) 2023-04-28 10:11:03 -07:00
Paul Garner
15535b913d Add PythonLoader which auto-detects encoding of Python files (#3311)
This PR contributes a `PythonLoader`, which inherits from
`TextLoader` but detects and sets the encoding automatically.
2023-04-28 10:11:03 -07:00
Daniel Chalef
6d55489419 Fix example match_documents fn table name, grammar (#3294)
ref
https://github.com/hwchase17/langchain/pull/3100#issuecomment-1517086472

Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
2023-04-28 10:11:03 -07:00
Davis Chase
be958d98ad Cleanup integration test dir (#3308) 2023-04-28 10:11:03 -07:00
leo-gan
0f7d997bb0 added links to the important YouTube videos (#3244)
Added links to the important YouTube videos
2023-04-28 10:11:03 -07:00
Sertaç Özercan
87c046858b fix: handle youtube TranscriptsDisabled (#3276)
handles error when youtube video has transcripts disabled

```
youtube_transcript_api._errors.TranscriptsDisabled: 
Could not retrieve a transcript for the video https://www.youtube.com/watch?v=<URL> This is most likely caused by:

Subtitles are disabled for this video

If you are sure that the described cause is not responsible for this error and that a transcript should be retrievable, please create an issue at https://github.com/jdepoix/youtube-transcript-api/issues. Please add which version of youtube_transcript_api you are using and provide the information needed to replicate the error. Also make sure that there are no open issues which already describe your problem!
```

Signed-off-by: Sertac Ozercan <sozercan@gmail.com>
2023-04-28 10:11:03 -07:00
Alexandre Pesant
eecd5795f4 Do not print openai settings (#3280)
There's no reason to print these settings like that, it just pollutes
the logs :)
2023-04-28 10:11:03 -07:00
Zander Chase
181840dcb4 Handle null action in AutoGPT Agent (#3274)
Handle the case where the command is `null`
2023-04-28 10:11:03 -07:00
Harrison Chase
d2b2772272 bump version 146 (#3272) 2023-04-28 10:11:03 -07:00
Harrison Chase
353e96cb66 gradio tools (#3255) 2023-04-28 10:11:03 -07:00
Naveen Tatikonda
a8b1bb6c4c OpenSearch: Add Support for Lucene Filter (#3201)
### Description
Add Support for Lucene Filter. When you specify a Lucene filter for a
k-NN search, the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering. This filter is supported only for approximate search
with the indexes that are created using `lucene` engine.

OpenSearch Documentation -
https://opensearch.org/docs/latest/search-plugins/knn/filter-search-knn/#lucene-k-nn-filter-implementation

Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
2023-04-28 10:11:02 -07:00
Davis Chase
c08f644d6d Hf emb device (#3266)
Make it possible to control the HuggingFaceEmbeddings and HuggingFaceInstructEmbeddings client model kwargs. Additionally, the cache folder was added for HuggingFaceInstructEmbedding as the client inherits from SentenceTransformer (client of HuggingFaceEmbeddings).

It can be useful, especially to control the client device, as it will be defaulted to GPU by sentence_transformers if there is any.

---------

Co-authored-by: Yoann Poupart <66315201+Xmaster6y@users.noreply.github.com>
2023-04-28 10:11:02 -07:00
Zach Jones
e12dc1321a Fix type annotation for QueryCheckerTool.llm (#3237)
Currently `langchain.tools.sql_database.tool.QueryCheckerTool` has a
field `llm` with type `BaseLLM`. This breaks initialization for some
LLMs. For example, trying to use it with GPT4:

```python
from langchain.sql_database import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.tools.sql_database.tool import QueryCheckerTool


db = SQLDatabase.from_uri("some_db_uri")
llm = ChatOpenAI(model_name="gpt-4")
tool = QueryCheckerTool(db=db, llm=llm)

# pydantic.error_wrappers.ValidationError: 1 validation error for QueryCheckerTool
# llm
#   Can't instantiate abstract class BaseLLM with abstract methods _agenerate, _generate, _llm_type (type=type_error)
```

Seems like much of the rest of the codebase has switched from `BaseLLM`
to `BaseLanguageModel`. This PR makes the change for QueryCheckerTool as
well

Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
2023-04-28 10:11:02 -07:00
Davis Chase
c31cdc6d8d Contextual compression retriever (#2915)
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-28 10:11:02 -07:00
Matt Robinson
7f4eb81be7 feat: add loader for rich text files (#3227)
### Summary

Adds a loader for rich text files. Requires `unstructured>=0.5.12`.

### Testing

The following test uses the example RTF file from the [`unstructured`
repo](https://github.com/Unstructured-IO/unstructured/tree/main/example-docs).

```python
from langchain.document_loaders import UnstructuredRTFLoader

loader = UnstructuredRTFLoader("fake-doc.rtf", mode="elements")
docs = loader.load()
docs[0].page_content
```
2023-04-28 10:11:02 -07:00
Harrison Chase
ba167800dd add to docs 2023-04-28 10:11:02 -07:00
Albert Castellana
96f33ed3ae Ecosystem/Yeager.ai (#3239)
Added yeagerai.md to ecosystem
2023-04-28 10:11:02 -07:00
Boris Feld
0bfa2c9216 Fixing issue link for Comet callback (#3212)
Sorry I fixed that link once but there was still a typo inside, this
time it should be good.
2023-04-28 10:11:02 -07:00
Daniel Chalef
1a6e8865bf fix error msg ref to beautifulsoup4 (#3242)
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
2023-04-28 10:11:02 -07:00
Tom Dyson
fd948bef64 Add DuckDB prompt (#3233)
Adds a prompt template for the DuckDB SQL dialect.
2023-04-28 10:11:02 -07:00
Zander Chase
bd9d9412b7 Patch Chat History Formatting (#3236)
While we work on solidifying the memory interfaces, handle common chat
history formats.

This may break linting on anyone who has been passing in
`get_chat_history` .

Somewhat handles #3077

Alternative to #3078 that updates the typing
2023-04-28 10:11:02 -07:00
Ankush Gola
2ed4649e50 fix baby agi 2023-04-27 23:23:58 -07:00
Ankush Gola
da27d8713d fix most tests 2023-04-27 23:19:09 -07:00
Ankush Gola
5dcb44ee1d fix llm chain 2023-04-27 19:48:47 -07:00
Ankush Gola
15c0fa5e7e cr 2023-04-27 18:14:51 -07:00
Ankush Gola
1fc3941430 mypy 2023-04-27 18:05:44 -07:00
Ankush Gola
8ae809af67 mypy 2023-04-27 17:50:01 -07:00
Ankush Gola
6cd653deb4 cr 2023-04-27 14:16:31 -07:00
Ankush Gola
e953d2cf93 mypy 2023-04-27 12:26:58 -07:00
Ankush Gola
50668693d7 fix execution order issue 2023-04-26 19:19:39 -07:00
Ankush Gola
6fec15b6fb write to different session 2023-04-26 11:37:36 -07:00
Ankush Gola
7bcdc66b99 fix notebook and warnings 2023-04-26 11:34:21 -07:00
Ankush Gola
4cdd19bd4e Callbacks Refactor [2/n] update tracer to work with new callbacks mechanism (#3381) 2023-04-25 18:20:16 -07:00
Ankush Gola
90cef7b53a cr 2023-04-22 21:49:34 -07:00
Ankush Gola
675e27c136 Callbacks Refactor [2/n]: refactor CallbackManager code to own file (#3341) 2023-04-22 21:40:59 -07:00
Ankush Gola
fa4a4f2940 cr 2023-04-20 17:06:00 -07:00
Ankush Gola
55c7964e4e Merge branch 'master' into ankush/callbacks-refactor 2023-04-20 17:01:32 -07:00
Ankush Gola
3cc2ce6ac9 callbacks changes 2023-04-20 16:58:04 -07:00
3255 changed files with 106716 additions and 285420 deletions

View File

@@ -1,37 +0,0 @@
# Dev container
This project includes a [dev container](https://containers.dev/), which lets you use a container as a full-featured dev environment.
You can use the dev container configuration in this folder to build and run the app without needing to install any of its tools locally! You can use it in [GitHub Codespaces](https://github.com/features/codespaces) or the [VS Code Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers).
## GitHub Codespaces
[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/hwchase17/langchain)
You may use the button above, or follow these steps to open this repo in a Codespace:
1. Click the **Code** drop-down menu at the top of https://github.com/hwchase17/langchain.
1. Click on the **Codespaces** tab.
1. Click **Create codespace on master** .
For more info, check out the [GitHub documentation](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace#creating-a-codespace).
## VS Code Dev Containers
[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/hwchase17/langchain)
If you already have VS Code and Docker installed, you can use the button above to get started. This will cause VS Code to automatically install the Dev Containers extension if needed, clone the source code into a container volume, and spin up a dev container for use.
You can also follow these steps to open this repo in a container using the VS Code Dev Containers extension:
1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the [getting started steps](https://aka.ms/vscode-remote/containers/getting-started).
2. Open a locally cloned copy of the code:
- Clone this repository to your local filesystem.
- Press <kbd>F1</kbd> and select the **Dev Containers: Open Folder in Container...** command.
- Select the cloned copy of this folder, wait for the container to start, and try things out!
You can learn more in the [Dev Containers documentation](https://code.visualstudio.com/docs/devcontainers/containers).
## Tips and tricks
* If you are working with the same repository folder in a container and Windows, you'll want consistent line endings (otherwise you may see hundreds of changes in the SCM view). The `.gitattributes` file in the root of this repo will disable line ending conversion and should prevent this. See [tips and tricks](https://code.visualstudio.com/docs/devcontainers/tips-and-tricks#_resolving-git-line-ending-issues-in-containers-resulting-in-many-modified-files) for more info.
* If you'd like to review the contents of the image used in this dev container, you can check it out in the [devcontainers/images](https://github.com/devcontainers/images/tree/main/src/python) repo.

View File

@@ -1,36 +0,0 @@
// For format details, see https://aka.ms/devcontainer.json. For config options, see the
// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-docker-compose
{
// Name for the dev container
"name": "langchain",
// Point to a Docker Compose file
"dockerComposeFile": "./docker-compose.yaml",
// Required when using Docker Compose. The name of the service to connect to once running
"service": "langchain",
// The optional 'workspaceFolder' property is the path VS Code should open by default when
// connected. This is typically a file mount in .devcontainer/docker-compose.yml
"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
// Prevent the container from shutting down
"overrideCommand": true
// Features to add to the dev container. More info: https://containers.dev/features
// "features": {
// "ghcr.io/devcontainers-contrib/features/poetry:2": {}
// }
// Use 'forwardPorts' to make a list of ports inside the container available locally.
// "forwardPorts": [],
// Uncomment the next line to run commands after the container is created.
// "postCreateCommand": "cat /etc/os-release",
// Configure tool-specific properties.
// "customizations": {},
// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
// "remoteUser": "root"
}

View File

@@ -1,32 +0,0 @@
version: '3'
services:
langchain:
build:
dockerfile: libs/langchain/dev.Dockerfile
context: ..
volumes:
# Update this to wherever you want VS Code to mount the folder of your project
- ..:/workspaces:cached
networks:
- langchain-network
# environment:
# MONGO_ROOT_USERNAME: root
# MONGO_ROOT_PASSWORD: example123
# depends_on:
# - mongo
# mongo:
# image: mongo
# restart: unless-stopped
# environment:
# MONGO_INITDB_ROOT_USERNAME: root
# MONGO_INITDB_ROOT_PASSWORD: example123
# ports:
# - "27017:27017"
# networks:
# - langchain-network
networks:
langchain-network:
driver: bridge

3
.gitattributes vendored
View File

@@ -1,3 +0,0 @@
* text=auto eol=lf
*.{cmd,[cC][mM][dD]} text eol=crlf
*.{bat,[bB][aA][tT]} text eol=crlf

View File

@@ -2,64 +2,60 @@
Hi there! Thank you for even being interested in contributing to LangChain.
As an open source project in a rapidly developing field, we are extremely open
to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
## 🗺️ Guidelines
### 👩‍💻 Contributing Code
to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
Please do not try to push directly to this repo unless you are maintainer.
Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
maintainers.
Pull requests cannot land without passing the formatting, linting and testing checks first. See
[Common Tasks](#-common-tasks) for how to run these checks locally.
It's essential that we maintain great documentation and testing. If you:
- Fix a bug
- Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
- Make an improvement
- Update any affected example notebooks and documentation. These lives in `docs`.
- Update unit and integration tests when relevant.
- Add a feature
- Add a demo notebook in `docs/modules`.
- Add unit and integration tests.
We're a small, building-oriented team. If there's something you'd like to add or change, opening a pull request is the
best way to get our attention.
## 🗺Contributing Guidelines
### 🚩GitHub Issues
Our [issues](https://github.com/hwchase17/langchain/issues) page is kept up to date
with bugs, improvements, and feature requests.
with bugs, improvements, and feature requests. There is a taxonomy of labels to help
with sorting and discovery of issues of interest. These include:
There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
organize issues.
- prompts: related to prompt tooling/infra.
- llms: related to LLM wrappers/tooling/infra.
- chains
- utilities: related to different types of utilities to integrate with (Python, SQL, etc.).
- agents
- memory
- applications: related to example applications to build
If you start working on an issue, please assign it to yourself.
If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
If two issues are related, or blocking, please link them rather than combining them.
If you are adding an issue, please try to keep it focused on a single modular bug/improvement/feature.
If the two issues are related, or blocking, please link them rather than keep them as one single one.
We will try to keep these issues as up to date as possible, though
with the rapid rate of develop in this field some may get out of date.
If you notice this happening, please let us know.
If you notice this happening, please just let us know.
### 🙋Getting Help
Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
smooth for future contributors.
Although we try to have a developer setup to make it as easy as possible for others to contribute (see below)
it is possible that some pain point may arise around environment setup, linting, documentation, or other.
Should that occur, please contact a maintainer! Not only do we want to help get you unblocked,
but we also want to make sure that the process is smooth for future contributors.
In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
we do not want these to get in the way of getting good code into the codebase.
If you are finding these difficult (or even just annoying) to work with,
feel free to contact a maintainer for help - we do not want these to get in the way of getting
good code into the codebase.
## 🚀 Quick Start
### 🏭Release process
> **Note:** You can run this repository locally (which is described below) or in a [development container](https://containers.dev/) (which is described in the [.devcontainer folder](https://github.com/hwchase17/langchain/tree/master/.devcontainer)).
As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
a developer and published to [PyPI](https://pypi.org/project/langchain/).
LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
## 🚀Quick Start
This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
@@ -69,14 +65,6 @@ This project uses [Poetry](https://python-poetry.org/) as a dependency manager.
3. Tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
4. Continue with the following steps.
There are two separate projects in this repository:
- `langchain`: core langchain code, abstractions, and use cases
- `langchain.experimental`: more experimental code
Each of these has their OWN development environment.
In order to run any of the commands below, please move into their respective directories.
For example, to contribute to `langchain` run `cd libs/langchain` before getting started with the below.
To install requirements:
```bash
@@ -89,7 +77,7 @@ This will install all requirements for running the package, examples, linting, f
Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
## ✅ Common Tasks
## ✅Common Tasks
Type `make` for a list of common tasks.
@@ -103,14 +91,6 @@ To run formatting for this project:
make format
```
Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the master branch using the format_diff command:
```bash
make format_diff
```
This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase.
### Linting
Linting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/), [isort](https://pycqa.github.io/isort/), [flake8](https://flake8.pycqa.org/en/latest/), and [mypy](http://mypy-lang.org/).
@@ -121,42 +101,8 @@ To run linting for this project:
make lint
```
In addition, you can run the linter only on the files that have been modified in your current branch as compared to the master branch using the lint_diff command:
```bash
make lint_diff
```
This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase.
We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
### Spellcheck
Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
Note that `codespell` finds common typos, so could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
To check spelling for this project:
```bash
make spell_check
```
To fix spelling in place:
```bash
make spell_fix
```
If codespell is incorrectly flagging a word, you can skip spellcheck for that word by adding it to the codespell config in the `pyproject.toml` file.
```python
[tool.codespell]
...
# Add here:
ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure'
```
### Coverage
Code coverage (i.e. the amount of code that is covered by unit tests) helps identify areas of the code that are potentially more or less brittle.
@@ -167,37 +113,8 @@ To get a report of current coverage, run the following:
make coverage
```
### Working with Optional Dependencies
Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.
If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
that most users won't have it installed.
Users that do not have the dependency installed should be able to **import** your code without
any side effects (no warnings, no errors, no exceptions).
To introduce the dependency to the pyproject.toml file correctly, please do the following:
1. Add the dependency to the main group as an optional dependency
```bash
poetry add --optional [package_name]
```
2. Open pyproject.toml and add the dependency to the `extended_testing` extra
3. Relock the poetry file to update the extra.
```bash
poetry lock --no-update
```
4. Add a unit test that the very least attempts to import the new code. Ideally the unit
test makes use of lightweight fixtures to test the logic of the code.
5. Please use the `@pytest.mark.requires(package_name)` decorator for any tests that require the dependency.
### Testing
See section about optional dependencies.
#### Unit Tests
Unit tests cover modular logic that does not require calls to outside APIs.
To run unit tests:
@@ -214,20 +131,8 @@ make docker_tests
If you add new logic, please add a unit test.
#### Integration Tests
Integration tests cover logic that requires making calls to outside APIs (often integration with other services).
**warning** Almost no tests should be integration tests.
Tests that require making network connections make it difficult for other
developers to test the code.
Instead favor relying on `responses` library and/or mock.patch to mock
requests using small fixtures.
To run integration tests:
```bash
@@ -256,55 +161,30 @@ When you run `poetry install`, the `langchain` package is installed as editable
## Documentation
While the code is split between `langchain` and `langchain.experimental`, the documentation is one holistic thing.
This covers how to get started contributing to documentation.
### Contribute Documentation
The docs directory contains Documentation and API Reference.
Docs are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
Documentation is built using [Docusaurus 2](https://docusaurus.io/).
API Reference are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
For that reason, we ask that you add good documentation to all classes and methods.
Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
### Build Documentation Locally
In the following commands, the prefix `api_` indicates that those are operations for the API Reference.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make docs_clean
make api_docs_clean
```
Next, you can build the documentation as outlined below:
```bash
make docs_build
make api_docs_build
```
Finally, you can run the linkchecker to make sure all links are valid:
Next, you can run the linkchecker to make sure all links are valid:
```bash
make docs_linkcheck
make api_docs_linkcheck
```
## 🏭 Release Process
As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
a developer and published to [PyPI](https://pypi.org/project/langchain/).
LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
### 🌟 Recognition
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
Finally, you can build the documentation as outlined below:
```bash
make docs_build
```

View File

@@ -1,106 +0,0 @@
name: "\U0001F41B Bug Report"
description: Submit a bug report to help us improve LangChain
labels: ["02 Bug Report"]
body:
- type: markdown
attributes:
value: >
Thank you for taking the time to file a bug report. Before creating a new
issue, please make sure to take a few moments to check the issue tracker
for existing issues about the bug.
- type: textarea
id: system-info
attributes:
label: System Info
description: Please share your system info with us.
placeholder: LangChain version, platform, python version, ...
validations:
required: true
- type: textarea
id: who-can-help
attributes:
label: Who can help?
description: |
Your issue will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
The core maintainers strive to read all issues, but tagging them will help them prioritize.
Please tag fewer than 3 people.
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoader Abstractions
- @eyurtsev
LLM/Chat Wrappers
- @hwchase17
- @agola11
Tools / Toolkits
- ...
placeholder: "@Username ..."
- type: checkboxes
id: information-scripts-examples
attributes:
label: Information
description: "The problem arises when using:"
options:
- label: "The official example notebooks/scripts"
- label: "My own modified scripts"
- type: checkboxes
id: related-components
attributes:
label: Related Components
description: "Select the components related to the issue (if applicable):"
options:
- label: "LLMs/Chat Models"
- label: "Embedding Models"
- label: "Prompts / Prompt Templates / Prompt Selectors"
- label: "Output Parsers"
- label: "Document Loaders"
- label: "Vector Stores / Retrievers"
- label: "Memory"
- label: "Agents / Agent Executors"
- label: "Tools / Toolkits"
- label: "Chains"
- label: "Callbacks/Tracing"
- label: "Async"
- type: textarea
id: reproduction
validations:
required: true
attributes:
label: Reproduction
description: |
Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
If you have code snippets, error messages, stack traces please provide them here as well.
Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
placeholder: |
Steps to reproduce the behavior:
1.
2.
3.
- type: textarea
id: expected-behavior
validations:
required: true
attributes:
label: Expected behavior
description: "A clear and concise description of what you would expect to happen."

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@@ -1,6 +0,0 @@
blank_issues_enabled: true
version: 2.1
contact_links:
- name: Discord
url: https://discord.gg/6adMQxSpJS
about: General community discussions

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@@ -1,19 +0,0 @@
name: Documentation
description: Report an issue related to the LangChain documentation.
title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
labels: [03 - Documentation]
body:
- type: textarea
attributes:
label: "Issue with current documentation:"
description: >
Please make sure to leave a reference to the document/code you're
referring to.
- type: textarea
attributes:
label: "Idea or request for content:"
description: >
Please describe as clearly as possible what topics you think are missing
from the current documentation.

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@@ -1,30 +0,0 @@
name: "\U0001F680 Feature request"
description: Submit a proposal/request for a new LangChain feature
labels: ["02 Feature Request"]
body:
- type: textarea
id: feature-request
validations:
required: true
attributes:
label: Feature request
description: |
A clear and concise description of the feature proposal. Please provide links to any relevant GitHub repos, papers, or other resources if relevant.
- type: textarea
id: motivation
validations:
required: true
attributes:
label: Motivation
description: |
Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
- type: textarea
id: contribution
validations:
required: true
attributes:
label: Your contribution
description: |
Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md)

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@@ -1,18 +0,0 @@
name: Other Issue
description: Raise an issue that wouldn't be covered by the other templates.
title: "Issue: <Please write a comprehensive title after the 'Issue: ' prefix>"
labels: [04 - Other]
body:
- type: textarea
attributes:
label: "Issue you'd like to raise."
description: >
Please describe the issue you'd like to raise as clearly as possible.
Make sure to include any relevant links or references.
- type: textarea
attributes:
label: "Suggestion:"
description: >
Please outline a suggestion to improve the issue here.

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@@ -1,28 +0,0 @@
<!-- 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!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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
-->

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@@ -1,78 +0,0 @@
# An action for setting up poetry install with caching.
# Using a custom action since the default action does not
# take poetry install groups into account.
# Action code from:
# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
name: poetry-install-with-caching
description: Poetry install with support for caching of dependency groups.
inputs:
python-version:
description: Python version, supporting MAJOR.MINOR only
required: true
poetry-version:
description: Poetry version
required: true
install-command:
description: Command run for installing dependencies
required: false
default: poetry install
cache-key:
description: Cache key to use for manual handling of caching
required: true
working-directory:
description: Directory to run install-command in
required: false
default: ""
runs:
using: composite
steps:
- uses: actions/setup-python@v4
name: Setup python $${ inputs.python-version }}
with:
python-version: ${{ inputs.python-version }}
- uses: actions/cache@v3
id: cache-pip
name: Cache Pip ${{ inputs.python-version }}
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
with:
path: |
~/.cache/pip
key: pip-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}
- run: pipx install poetry==${{ inputs.poetry-version }} --python python${{ inputs.python-version }}
shell: bash
- name: Check Poetry File
shell: bash
working-directory: ${{ inputs.working-directory }}
run: |
poetry check
- name: Check lock file
shell: bash
working-directory: ${{ inputs.working-directory }}
run: |
poetry lock --check
- uses: actions/cache@v3
id: cache-poetry
env:
SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
with:
path: |
~/.cache/pypoetry/virtualenvs
~/.cache/pypoetry/cache
~/.cache/pypoetry/artifacts
key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('poetry.lock') }}
- run: ${{ inputs.install-command }}
working-directory: ${{ inputs.working-directory }}
shell: bash

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@@ -1,46 +0,0 @@
name: lint
on:
workflow_call:
inputs:
working-directory:
required: true
type: string
description: "From which folder this pipeline executes"
env:
POETRY_VERSION: "1.4.2"
jobs:
build:
defaults:
run:
working-directory: ${{ inputs.working-directory }}
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.8"
- "3.9"
- "3.10"
- "3.11"
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: |
pipx install poetry==$POETRY_VERSION
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: poetry
- name: Install dependencies
run: |
poetry install
- name: Install langchain editable
if: ${{ inputs.working-directory != 'langchain' }}
run: |
pip install -e ../langchain
- name: Analysing the code with our lint
run: |
make lint

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@@ -1,51 +0,0 @@
name: release
on:
workflow_call:
inputs:
working-directory:
required: true
type: string
description: "From which folder this pipeline executes"
env:
POETRY_VERSION: "1.4.2"
jobs:
if_release:
if: |
${{ github.event.pull_request.merged == true }}
&& ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
runs-on: ubuntu-latest
defaults:
run:
working-directory: ${{ inputs.working-directory }}
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python 3.10
uses: actions/setup-python@v4
with:
python-version: "3.10"
cache: "poetry"
- name: Build project for distribution
run: poetry build
- name: Check Version
id: check-version
run: |
echo version=$(poetry version --short) >> $GITHUB_OUTPUT
- name: Create Release
uses: ncipollo/release-action@v1
with:
artifacts: "dist/*"
token: ${{ secrets.GITHUB_TOKEN }}
draft: false
generateReleaseNotes: true
tag: v${{ steps.check-version.outputs.version }}
commit: master
- name: Publish to PyPI
env:
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
run: |
poetry publish

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@@ -1,61 +0,0 @@
name: test
on:
workflow_call:
inputs:
working-directory:
required: true
type: string
description: "From which folder this pipeline executes"
test_type:
type: string
description: "Test types to run"
default: '["core", "extended"]'
env:
POETRY_VERSION: "1.4.2"
jobs:
build:
defaults:
run:
working-directory: ${{ inputs.working-directory }}
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.8"
- "3.9"
- "3.10"
- "3.11"
test_type: ${{ fromJSON(inputs.test_type) }}
name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: "./.github/actions/poetry_setup"
with:
python-version: ${{ matrix.python-version }}
working-directory: ${{ inputs.working-directory }}
poetry-version: "1.4.2"
cache-key: ${{ matrix.test_type }}
install-command: |
if [ "${{ matrix.test_type }}" == "core" ]; then
echo "Running core tests, installing dependencies with poetry..."
poetry install
else
echo "Running extended tests, installing dependencies with poetry..."
poetry install -E extended_testing
fi
- name: Install langchain editable
if: ${{ inputs.working-directory != 'langchain' }}
run: |
pip install -e ../langchain
- name: Run ${{matrix.test_type}} tests
run: |
if [ "${{ matrix.test_type }}" == "core" ]; then
make test
else
make extended_tests
fi
shell: bash

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@@ -1,22 +0,0 @@
---
name: Codespell
on:
push:
branches: [master]
pull_request:
branches: [master]
permissions:
contents: read
jobs:
codespell:
name: Check for spelling errors
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Codespell
uses: codespell-project/actions-codespell@v2

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@@ -1,27 +0,0 @@
---
name: libs/langchain CI
on:
push:
branches: [ master ]
pull_request:
paths:
- '.github/workflows/_lint.yml'
- '.github/workflows/_test.yml'
- '.github/workflows/langchain_ci.yml'
- 'libs/langchain/**'
workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI
jobs:
lint:
uses:
./.github/workflows/_lint.yml
with:
working-directory: libs/langchain
secrets: inherit
test:
uses:
./.github/workflows/_test.yml
with:
working-directory: libs/langchain
secrets: inherit

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@@ -1,29 +0,0 @@
---
name: libs/langchain-experimental CI
on:
push:
branches: [ master ]
pull_request:
paths:
- '.github/workflows/_lint.yml'
- '.github/workflows/_test.yml'
- '.github/workflows/langchain_experimental_ci.yml'
- 'libs/langchain/**'
- 'libs/experimental/**'
workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI
jobs:
lint:
uses:
./.github/workflows/_lint.yml
with:
working-directory: libs/experimental
secrets: inherit
test:
uses:
./.github/workflows/_test.yml
with:
working-directory: libs/experimental
test_type: '["core"]'
secrets: inherit

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@@ -1,20 +0,0 @@
---
name: libs/langchain-experimental Release
on:
pull_request:
types:
- closed
branches:
- master
paths:
- 'libs/experimental/pyproject.toml'
workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI
jobs:
release:
uses:
./.github/workflows/_release.yml
with:
working-directory: libs/experimental
secrets: inherit

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@@ -1,20 +0,0 @@
---
name: libs/langchain Release
on:
pull_request:
types:
- closed
branches:
- master
paths:
- 'libs/langchain/pyproject.toml'
workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI
jobs:
release:
uses:
./.github/workflows/_release.yml
with:
working-directory: libs/langchain
secrets: inherit

36
.github/workflows/linkcheck.yml vendored Normal file
View File

@@ -0,0 +1,36 @@
name: linkcheck
on:
push:
branches: [master]
pull_request:
env:
POETRY_VERSION: "1.4.2"
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.11"
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: |
pipx install poetry==$POETRY_VERSION
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: poetry
- name: Install dependencies
run: |
poetry install --with docs
- name: Build the docs
run: |
make docs_build
- name: Analyzing the docs with linkcheck
run: |
make docs_linkcheck

36
.github/workflows/lint.yml vendored Normal file
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@@ -0,0 +1,36 @@
name: lint
on:
push:
branches: [master]
pull_request:
env:
POETRY_VERSION: "1.4.2"
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.8"
- "3.9"
- "3.10"
- "3.11"
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: |
pipx install poetry==$POETRY_VERSION
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: poetry
- name: Install dependencies
run: |
poetry install
- name: Analysing the code with our lint
run: |
make lint

49
.github/workflows/release.yml vendored Normal file
View File

@@ -0,0 +1,49 @@
name: release
on:
pull_request:
types:
- closed
branches:
- master
paths:
- 'pyproject.toml'
env:
POETRY_VERSION: "1.4.2"
jobs:
if_release:
if: |
${{ github.event.pull_request.merged == true }}
&& ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python 3.10
uses: actions/setup-python@v4
with:
python-version: "3.10"
cache: "poetry"
- name: Build project for distribution
run: poetry build
- name: Check Version
id: check-version
run: |
echo version=$(poetry version --short) >> $GITHUB_OUTPUT
- name: Create Release
uses: ncipollo/release-action@v1
with:
artifacts: "dist/*"
token: ${{ secrets.GITHUB_TOKEN }}
draft: false
generateReleaseNotes: true
tag: v${{ steps.check-version.outputs.version }}
commit: master
- name: Publish to PyPI
env:
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
run: |
poetry publish

34
.github/workflows/test.yml vendored Normal file
View File

@@ -0,0 +1,34 @@
name: test
on:
push:
branches: [master]
pull_request:
env:
POETRY_VERSION: "1.4.2"
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version:
- "3.8"
- "3.9"
- "3.10"
- "3.11"
steps:
- uses: actions/checkout@v3
- name: Install poetry
run: pipx install poetry==$POETRY_VERSION
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
cache: "poetry"
- name: Install dependencies
run: poetry install
- name: Run unit tests
run: |
make test

23
.gitignore vendored
View File

@@ -1,4 +1,3 @@
.vs/
.vscode/
.idea/
# Byte-compiled / optimized / DLL files
@@ -73,7 +72,6 @@ instance/
# Sphinx documentation
docs/_build/
docs/docs/_build/
# PyBuilder
target/
@@ -150,23 +148,4 @@ wandb/
# integration test artifacts
data_map*
\[('_type', 'fake'), ('stop', None)]
# Replit files
*replit*
node_modules
docs/.yarn/
docs/node_modules/
docs/.docusaurus/
docs/.cache-loader/
docs/_dist
docs/api_reference/api_reference.rst
docs/api_reference/_build
docs/api_reference/*/
!docs/api_reference/_static/
!docs/api_reference/templates/
!docs/api_reference/themes/
docs/docs_skeleton/build
docs/docs_skeleton/node_modules
docs/docs_skeleton/yarn.lock
\[('_type', 'fake'), ('stop', None)]

4
.gitmodules vendored
View File

@@ -1,4 +0,0 @@
[submodule "docs/_docs_skeleton"]
path = docs/_docs_skeleton
url = https://github.com/langchain-ai/langchain-shared-docs
branch = main

View File

@@ -1,29 +0,0 @@
# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
# Required
version: 2
# Set the version of Python and other tools you might need
build:
os: ubuntu-22.04
tools:
python: "3.11"
jobs:
pre_build:
- python docs/api_reference/create_api_rst.py
# Build documentation in the docs/ directory with Sphinx
sphinx:
configuration: docs/api_reference/conf.py
# If using Sphinx, optionally build your docs in additional formats such as PDF
# formats:
# - pdf
# Optionally declare the Python requirements required to build your docs
python:
install:
- requirements: docs/api_reference/requirements.txt
- method: pip
path: .

View File

@@ -1,7 +1,5 @@
# This is a Dockerfile for running unit tests
ARG POETRY_HOME=/opt/poetry
# Use the Python base image
FROM python:3.11.2-bullseye AS builder
@@ -9,7 +7,7 @@ FROM python:3.11.2-bullseye AS builder
ARG POETRY_VERSION=1.4.2
# Define the directory to install Poetry to (default is /opt/poetry)
ARG POETRY_HOME
ARG POETRY_HOME=/opt/poetry
# Create a Python virtual environment for Poetry and install it
RUN python3 -m venv ${POETRY_HOME} && \
@@ -25,8 +23,6 @@ WORKDIR /app
# Use a multi-stage build to install dependencies
FROM builder AS dependencies
ARG POETRY_HOME
# Copy only the dependency files for installation
COPY pyproject.toml poetry.lock poetry.toml ./

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@@ -1,47 +0,0 @@
# Migrating to `langchain.experimental`
We are moving any experimental components of langchain, or components with vulnerability issues, into `langchain.experimental`.
This guide covers how to migrate.
## Installation
Previously:
`pip install -U langchain`
Now:
`pip install -U langchain langchain.experimental`
## PALChain
Previously:
`from langchain.chains import PALChain`
Now:
`from langchain.experimental.pal_chain import PALChain`
## SQLDatabaseChain
Previously:
`from langchain.chains import SQLDatabaseChain`
Now:
`from langchain.experimental.sql import SQLDatabaseChain`
## `load_prompt` for Python files
Note: this only applies if you want to load Python files as prompts.
If you want to load json/yaml files, no change is needed.
Previously:
`from langchain.prompts import load_prompt`
Now:
`from langchain.experimental.prompts import load_prompt`

View File

@@ -1,49 +1,62 @@
.PHONY: all clean docs_build docs_clean docs_linkcheck api_docs_build api_docs_clean api_docs_linkcheck
.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help
# Default target executed when no arguments are given to make.
all: help
coverage:
poetry run pytest --cov \
--cov-config=.coveragerc \
--cov-report xml \
--cov-report term-missing:skip-covered
######################
# DOCUMENTATION
######################
clean: docs_clean api_docs_clean
clean: docs_clean
docs_build:
docs/.local_build.sh
cd docs && poetry run make html
docs_clean:
rm -r docs/_dist
cd docs && poetry run make clean
docs_linkcheck:
poetry run linkchecker docs/_dist/docs_skeleton/ --ignore-url node_modules
poetry run linkchecker docs/_build/html/index.html
api_docs_build:
poetry run python docs/api_reference/create_api_rst.py
cd docs/api_reference && poetry run make html
format:
poetry run black .
poetry run ruff --select I --fix .
api_docs_clean:
rm -f docs/api_reference/api_reference.rst
cd docs/api_reference && poetry run make clean
PYTHON_FILES=.
lint: PYTHON_FILES=.
lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$')
api_docs_linkcheck:
poetry run linkchecker docs/api_reference/_build/html/index.html
lint lint_diff:
poetry run mypy $(PYTHON_FILES)
poetry run black $(PYTHON_FILES) --check
poetry run ruff .
spell_check:
poetry run codespell --toml pyproject.toml
test:
poetry run pytest tests/unit_tests
spell_fix:
poetry run codespell --toml pyproject.toml -w
tests:
poetry run pytest tests/unit_tests
######################
# HELP
######################
test_watch:
poetry run ptw --now . -- tests/unit_tests
integration_tests:
poetry run pytest tests/integration_tests
docker_tests:
docker build -t my-langchain-image:test .
docker run --rm my-langchain-image:test
help:
@echo '----'
@echo 'coverage - run unit tests and generate coverage report'
@echo 'docs_build - build the documentation'
@echo 'docs_clean - clean the documentation build artifacts'
@echo 'docs_linkcheck - run linkchecker on the documentation'
@echo 'coverage - run unit tests and generate coverage report'
@echo 'docs_build - build the documentation'
@echo 'docs_clean - clean the documentation build artifacts'
@echo 'docs_linkcheck - run linkchecker on the documentation'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'test_watch - run unit tests in watch mode'
@echo 'integration_tests - run integration tests'
@echo 'docker_tests - run unit tests in docker'

View File

@@ -2,19 +2,7 @@
⚡ Building applications with LLMs through composability ⚡
[![Release Notes](https://img.shields.io/github/release/hwchase17/langchain)](https://github.com/hwchase17/langchain/releases)
[![CI](https://github.com/hwchase17/langchain/actions/workflows/langchain_ci.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/langchain_ci.yml)
[![Experimental CI](https://github.com/hwchase17/langchain/actions/workflows/langchain_experimental_ci.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/langchain_experimental_ci.yml)
[![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/hwchase17/langchain)
[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/hwchase17/langchain)
[![GitHub star chart](https://img.shields.io/github/stars/hwchase17/langchain?style=social)](https://star-history.com/#hwchase17/langchain)
[![Dependency Status](https://img.shields.io/librariesio/github/hwchase17/langchain)](https://libraries.io/github/hwchase17/langchain)
[![Open Issues](https://img.shields.io/github/issues-raw/hwchase17/langchain)](https://github.com/hwchase17/langchain/issues)
[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
@@ -25,7 +13,7 @@ Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set u
`pip install langchain`
or
`pip install langsmith && conda install langchain -c conda-forge`
`conda install langchain -c conda-forge`
## 🤔 What is this?
@@ -35,22 +23,22 @@ This library aims to assist in the development of those types of applications. C
**❓ Question Answering over specific documents**
- [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/question_answering.html)
- End-to-end Example: [Question Answering over Notion Database](https://github.com/hwchase17/notion-qa)
**💬 Chatbots**
- [Documentation](https://python.langchain.com/docs/use_cases/chatbots/)
- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/chatbots.html)
- End-to-end Example: [Chat-LangChain](https://github.com/hwchase17/chat-langchain)
**🤖 Agents**
- [Documentation](https://python.langchain.com/docs/modules/agents/)
- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html)
- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
## 📖 Documentation
Please see [here](https://python.langchain.com) for full documentation on:
Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documentation on:
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
@@ -86,7 +74,7 @@ Memory refers to persisting state between calls of a chain/agent. LangChain prov
[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
For more information on these concepts, please see our [full documentation](https://python.langchain.com).
For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/).
## 💁 Contributing

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@@ -1,17 +0,0 @@
#!/usr/bin/env bash
set -o errexit
set -o nounset
set -o pipefail
set -o xtrace
SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
cd "${SCRIPT_DIR}"
mkdir -p _dist/docs_skeleton
cp -r {docs_skeleton,snippets} _dist
cp -r extras/* _dist/docs_skeleton/docs
cd _dist/docs_skeleton
poetry run nbdoc_build
yarn install
yarn start

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17
docs/_static/css/custom.css vendored Normal file
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@@ -0,0 +1,17 @@
pre {
white-space: break-spaces;
}
@media (min-width: 1200px) {
.container,
.container-lg,
.container-md,
.container-sm,
.container-xl {
max-width: 2560px !important;
}
}
#my-component-root *, #headlessui-portal-root * {
z-index: 1000000000000;
}

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@@ -30,7 +30,10 @@ document.addEventListener('DOMContentLoaded', () => {
const icon = React.createElement('p', {
style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
}, [iconSpan1, iconSpan2]);
const mendableFloatingButton = React.createElement(
MendableFloatingButton,
{
@@ -39,7 +42,6 @@ document.addEventListener('DOMContentLoaded', () => {
anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
messageSettings: {
openSourcesInNewTab: false,
prettySources: true // Prettify the sources displayed now
},
icon: icon,
}
@@ -50,7 +52,7 @@ document.addEventListener('DOMContentLoaded', () => {
loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
loadScript('https://unpkg.com/@mendable/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
loadScript('https://unpkg.com/@mendable/search@0.0.83/dist/umd/mendable.min.js', initializeMendable);
});
});
});

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@@ -1,17 +0,0 @@
pre {
white-space: break-spaces;
}
@media (min-width: 1200px) {
.container,
.container-lg,
.container-md,
.container-sm,
.container-xl {
max-width: 2560px !important;
}
}
#my-component-root *, #headlessui-portal-root * {
z-index: 10000;
}

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@@ -1,125 +0,0 @@
"""Configuration file for the Sphinx documentation builder."""
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
import toml
sys.path.insert(0, os.path.abspath("."))
sys.path.insert(0, os.path.abspath("../../libs/langchain"))
with open("../../libs/langchain/pyproject.toml") as f:
data = toml.load(f)
# -- Project information -----------------------------------------------------
project = "🦜🔗 LangChain"
copyright = "2023, Harrison Chase"
author = "Harrison Chase"
version = data["tool"]["poetry"]["version"]
release = version
html_title = project + " " + version
html_last_updated_fmt = "%b %d, %Y"
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autodoc.typehints",
"sphinx.ext.autosummary",
"sphinx.ext.napoleon",
"sphinx.ext.viewcode",
"sphinxcontrib.autodoc_pydantic",
"sphinx_copybutton",
"sphinx_panels",
"IPython.sphinxext.ipython_console_highlighting",
]
source_suffix = [".rst"]
autodoc_pydantic_model_show_json = False
autodoc_pydantic_field_list_validators = False
autodoc_pydantic_config_members = False
autodoc_pydantic_model_show_config_summary = False
autodoc_pydantic_model_show_validator_members = False
autodoc_pydantic_model_show_validator_summary = False
autodoc_pydantic_model_signature_prefix = "class"
autodoc_pydantic_field_signature_prefix = "param"
autodoc_member_order = "groupwise"
autoclass_content = "both"
autodoc_typehints_format = "short"
autodoc_default_options = {
"members": True,
"show-inheritance": True,
"inherited-members": "BaseModel",
"undoc-members": True,
"special-members": "__call__",
}
# autodoc_typehints = "description"
# Add any paths that contain templates here, relative to this directory.
templates_path = ["templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "scikit-learn-modern"
html_theme_path = ["themes"]
# redirects dictionary maps from old links to new links
html_additional_pages = {}
redirects = {
"index": "api_reference",
}
for old_link in redirects:
html_additional_pages[old_link] = "redirects.html"
html_context = {
"display_github": True, # Integrate GitHub
"github_user": "hwchase17", # Username
"github_repo": "langchain", # Repo name
"github_version": "master", # Version
"conf_py_path": "/docs/api_reference", # Path in the checkout to the docs root
"redirects": redirects,
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# These paths are either relative to html_static_path
# or fully qualified paths (eg. https://...)
html_css_files = [
"css/custom.css",
]
html_use_index = False
myst_enable_extensions = ["colon_fence"]
# generate autosummary even if no references
autosummary_generate = True

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@@ -1,96 +0,0 @@
"""Script for auto-generating api_reference.rst"""
import glob
import re
from pathlib import Path
ROOT_DIR = Path(__file__).parents[2].absolute()
PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
WRITE_FILE = Path(__file__).parent / "api_reference.rst"
def load_members() -> dict:
members: dict = {}
for py in glob.glob(str(PKG_DIR) + "/**/*.py", recursive=True):
module = py[len(str(PKG_DIR)) + 1 :].replace(".py", "").replace("/", ".")
top_level = module.split(".")[0]
if top_level not in members:
members[top_level] = {"classes": [], "functions": []}
with open(py, "r") as f:
for line in f.readlines():
cls = re.findall(r"^class ([^_].*)\(", line)
members[top_level]["classes"].extend([module + "." + c for c in cls])
func = re.findall(r"^def ([^_].*)\(", line)
afunc = re.findall(r"^async def ([^_].*)\(", line)
func_strings = [module + "." + f for f in func + afunc]
members[top_level]["functions"].extend(func_strings)
return members
def construct_doc(members: dict) -> str:
full_doc = """\
.. _api_reference:
=============
API Reference
=============
"""
for module, _members in sorted(members.items(), key=lambda kv: kv[0]):
classes = _members["classes"]
functions = _members["functions"]
if not (classes or functions):
continue
module_title = module.replace("_", " ").title()
if module_title == "Llms":
module_title = "LLMs"
section = f":mod:`langchain.{module}`: {module_title}"
full_doc += f"""\
{section}
{'=' * (len(section) + 1)}
.. automodule:: langchain.{module}
:no-members:
:no-inherited-members:
"""
if classes:
cstring = "\n ".join(sorted(classes))
full_doc += f"""\
Classes
--------------
.. currentmodule:: langchain
.. autosummary::
:toctree: {module}
:template: class.rst
{cstring}
"""
if functions:
fstring = "\n ".join(sorted(functions))
full_doc += f"""\
Functions
--------------
.. currentmodule:: langchain
.. autosummary::
:toctree: {module}
{fstring}
"""
return full_doc
def main() -> None:
members = load_members()
full_doc = construct_doc(members)
with open(WRITE_FILE, "w") as f:
f.write(full_doc)
if __name__ == "__main__":
main()

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@@ -1,8 +0,0 @@
=============
LangChain API
=============
.. toctree::
:maxdepth: 2
api_reference.rst

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@@ -1,9 +0,0 @@
Evaluation
=======================
LangChain has a number of convenient evaluation chains you can use off the shelf to grade your models' oupputs.
.. automodule:: langchain.evaluation
:members:
:undoc-members:
:inherited-members:

View File

@@ -1,27 +0,0 @@
Copyright (c) 2007-2023 The scikit-learn developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

View File

@@ -1,28 +0,0 @@
:mod:`{{module}}`.{{objname}}
{{ underline }}==============
.. currentmodule:: {{ module }}
.. autoclass:: {{ objname }}
{% block methods %}
{% if methods %}
.. rubric:: {{ _('Methods') }}
.. autosummary::
{% for item in methods %}
~{{ name }}.{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block attributes %}
{% if attributes %}
.. rubric:: {{ _('Attributes') }}
.. autosummary::
{% for item in attributes %}
~{{ name }}.{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}

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@@ -1,15 +0,0 @@
{% set redirect = pathto(redirects[pagename]) %}
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="Refresh" content="0; url={{ redirect }}" />
<meta name="Description" content="scikit-learn: machine learning in Python">
<link rel="canonical" href="{{ redirect }}" />
<title>scikit-learn: machine learning in Python</title>
</head>
<body>
<p>You will be automatically redirected to the <a href="{{ redirect }}">new location of this page</a>.</p>
</body>
</html>

View File

@@ -1,27 +0,0 @@
Copyright (c) 2007-2023 The scikit-learn developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

View File

@@ -1,67 +0,0 @@
<script>
$(document).ready(function() {
/* Add a [>>>] button on the top-right corner of code samples to hide
* the >>> and ... prompts and the output and thus make the code
* copyable. */
var div = $('.highlight-python .highlight,' +
'.highlight-python3 .highlight,' +
'.highlight-pycon .highlight,' +
'.highlight-default .highlight')
var pre = div.find('pre');
// get the styles from the current theme
pre.parent().parent().css('position', 'relative');
var hide_text = 'Hide prompts and outputs';
var show_text = 'Show prompts and outputs';
// create and add the button to all the code blocks that contain >>>
div.each(function(index) {
var jthis = $(this);
if (jthis.find('.gp').length > 0) {
var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
button.attr('title', hide_text);
button.data('hidden', 'false');
jthis.prepend(button);
}
// tracebacks (.gt) contain bare text elements that need to be
// wrapped in a span to work with .nextUntil() (see later)
jthis.find('pre:has(.gt)').contents().filter(function() {
return ((this.nodeType == 3) && (this.data.trim().length > 0));
}).wrap('<span>');
});
// define the behavior of the button when it's clicked
$('.copybutton').click(function(e){
e.preventDefault();
var button = $(this);
if (button.data('hidden') === 'false') {
// hide the code output
button.parent().find('.go, .gp, .gt').hide();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
button.css('text-decoration', 'line-through');
button.attr('title', show_text);
button.data('hidden', 'true');
} else {
// show the code output
button.parent().find('.go, .gp, .gt').show();
button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
button.css('text-decoration', 'none');
button.attr('title', hide_text);
button.data('hidden', 'false');
}
});
/*** Add permalink buttons next to glossary terms ***/
$('dl.glossary > dt[id]').append(function() {
return ('<a class="headerlink" href="#' +
this.getAttribute('id') +
'" title="Permalink to this term">¶</a>');
});
});
</script>
{%- if pagename != 'index' and pagename != 'documentation' %}
{% if theme_mathjax_path %}
<script id="MathJax-script" async src="{{ theme_mathjax_path }}"></script>
{% endif %}
{%- endif %}

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@@ -1,142 +0,0 @@
{# TEMPLATE VAR SETTINGS #}
{%- set url_root = pathto('', 1) %}
{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
{%- if not embedded and docstitle %}
{%- set titlesuffix = " &mdash; "|safe + docstitle|e %}
{%- else %}
{%- set titlesuffix = "" %}
{%- endif %}
{%- set lang_attr = 'en' %}
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="{{ lang_attr }}" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="{{ lang_attr }}" > <!--<![endif]-->
<head>
<meta charset="utf-8">
{{ metatags }}
<meta name="viewport" content="width=device-width, initial-scale=1.0">
{% block htmltitle %}
<title>{{ title|striptags|e }}{{ titlesuffix }}</title>
{% endblock %}
<link rel="canonical" href="http://scikit-learn.org/stable/{{pagename}}.html" />
{% if favicon_url %}
<link rel="shortcut icon" href="{{ favicon_url|e }}"/>
{% endif %}
<link rel="stylesheet" href="{{ pathto('_static/css/vendor/bootstrap.min.css', 1) }}" type="text/css" />
{%- for css in css_files %}
{%- if css|attr("rel") %}
<link rel="{{ css.rel }}" href="{{ pathto(css.filename, 1) }}" type="text/css"{% if css.title is not none %} title="{{ css.title }}"{% endif %} />
{%- else %}
<link rel="stylesheet" href="{{ pathto(css, 1) }}" type="text/css" />
{%- endif %}
{%- endfor %}
<link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
<script id="documentation_options" data-url_root="{{ pathto('', 1) }}" src="{{ pathto('_static/documentation_options.js', 1) }}"></script>
<script src="{{ pathto('_static/jquery.js', 1) }}"></script>
{%- block extrahead %} {% endblock %}
</head>
<body>
{% include "nav.html" %}
{%- block content %}
<div class="d-flex" id="sk-doc-wrapper">
<input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
<label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
<div id="sk-sidebar-wrapper" class="border-right">
<div class="sk-sidebar-toc-wrapper">
<div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
{%- if prev %}
<a href="{{ prev.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ prev.title|striptags }}">Prev</a>
{%- else %}
<a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Prev</a>
{%- endif %}
{%- if parents -%}
<a href="{{ parents[-1].link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ parents[-1].title|striptags }}">Up</a>
{%- else %}
<a href="#" role="button" class="btn sk-btn-rellink disabled py-1">Up</a>
{%- endif %}
{%- if next %}
<a href="{{ next.link|e }}" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="{{ next.title|striptags }}">Next</a>
{%- else %}
<a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Next</a>
{%- endif %}
</div>
{%- if pagename != "install" %}
<div class="alert alert-warning p-1 mb-2" role="alert">
<p class="text-center mb-0">
<strong>LangChain {{ release }}</strong><br/>
</p>
</div>
{%- endif %}
{%- if meta and meta['parenttoc']|tobool %}
<div class="sk-sidebar-toc">
{% set nav = get_nav_object(maxdepth=3, collapse=True, numbered=True) %}
<ul>
{% for main_nav_item in nav %}
{% if main_nav_item.active %}
<li>
<a href="{{ main_nav_item.url }}" class="sk-toc-active">{{ main_nav_item.title }}</a>
</li>
<ul>
{% for nav_item in main_nav_item.children %}
<li>
<a href="{{ nav_item.url }}" class="{% if nav_item.active %}sk-toc-active{% endif %}">{{ nav_item.title }}</a>
{% if nav_item.children %}
<ul>
{% for inner_child in nav_item.children %}
<li class="sk-toctree-l3">
<a href="{{ inner_child.url }}">{{ inner_child.title }}</a>
</li>
{% endfor %}
</ul>
{% endif %}
</li>
{% endfor %}
</ul>
{% endif %}
{% endfor %}
</ul>
</div>
{%- elif meta and meta['globalsidebartoc']|tobool %}
<div class="sk-sidebar-toc sk-sidebar-global-toc">
{{ toctree(maxdepth=2, titles_only=True) }}
</div>
{%- else %}
<div class="sk-sidebar-toc">
{{ toc }}
</div>
{%- endif %}
</div>
</div>
<div id="sk-page-content-wrapper">
<div class="sk-page-content container-fluid body px-md-3" role="main">
{% block body %}{% endblock %}
</div>
<div class="container">
<footer class="sk-content-footer">
{%- if pagename != 'index' %}
{%- if show_copyright %}
{%- if hasdoc('copyright') %}
{% trans path=pathto('copyright'), copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
{%- else %}
{% trans copyright=copyright|e %}&copy; {{ copyright }}.{% endtrans %}
{%- endif %}
{%- endif %}
{%- if last_updated %}
{% trans last_updated=last_updated|e %}Last updated on {{ last_updated }}.{% endtrans %}
{%- endif %}
{%- if show_source and has_source and sourcename %}
<a href="{{ pathto('_sources/' + sourcename, true)|e }}" rel="nofollow">{{ _('Show this page source') }}</a>
{%- endif %}
{%- endif %}
</footer>
</div>
</div>
</div>
{%- endblock %}
<script src="{{ pathto('_static/js/vendor/bootstrap.min.js', 1) }}"></script>
{% include "javascript.html" %}
</body>
</html>

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@@ -1,69 +0,0 @@
{%- if pagename != 'index' and pagename != 'documentation' %}
{%- set nav_bar_class = "sk-docs-navbar" %}
{%- set top_container_cls = "sk-docs-container" %}
{%- else %}
{%- set nav_bar_class = "sk-landing-navbar" %}
{%- set top_container_cls = "sk-landing-container" %}
{%- endif %}
{% if theme_link_to_live_contributing_page|tobool %}
{# Link to development page for live builds #}
{%- set development_link = "https://scikit-learn.org/dev/developers/index.html" %}
{# Open on a new development page in new window/tab for live builds #}
{%- set development_attrs = 'target="_blank" rel="noopener noreferrer"' %}
{%- else %}
{%- set development_link = pathto('developers/index') %}
{%- set development_attrs = '' %}
{%- endif %}
<nav id="navbar" class="{{ nav_bar_class }} navbar navbar-expand-md navbar-light bg-light py-0">
<div class="container-fluid {{ top_container_cls }} px-0">
{%- if logo_url %}
<a class="navbar-brand py-0" href="{{ pathto('index') }}">
<img
class="sk-brand-img"
src="{{ logo_url|e }}"
alt="logo"/>
</a>
{%- endif %}
<button
id="sk-navbar-toggler"
class="navbar-toggler"
type="button"
data-toggle="collapse"
data-target="#navbarSupportedContent"
aria-controls="navbarSupportedContent"
aria-expanded="false"
aria-label="Toggle navigation"
>
<span class="navbar-toggler-icon"></span>
</button>
<div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav mr-auto">
<li class="nav-item">
<a class="sk-nav-link nav-link" href="{{ pathto('api_reference') }}">API</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Python Docs</a>
</li>
{%- for title, link, link_attrs in drop_down_navigation %}
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ link }}" {{ link_attrs }}>{{ title }}</a>
</li>
{%- endfor %}
</ul>
{%- if pagename != "search"%}
<div id="searchbox" role="search">
<div class="searchformwrapper">
<form class="search" action="{{ pathto('search') }}" method="get">
<input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
<input class="sk-search-text-btn" type="submit" value="{{ _('Go') }}" />
</form>
</div>
</div>
{%- endif %}
</div>
</div>
</nav>

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@@ -1,16 +0,0 @@
{%- extends "basic/search.html" %}
{% block extrahead %}
<script type="text/javascript" src="{{ pathto('_static/underscore.js', 1) }}"></script>
<script type="text/javascript" src="{{ pathto('searchindex.js', 1) }}" defer></script>
<script type="text/javascript" src="{{ pathto('_static/doctools.js', 1) }}"></script>
<script type="text/javascript" src="{{ pathto('_static/language_data.js', 1) }}"></script>
<script type="text/javascript" src="{{ pathto('_static/searchtools.js', 1) }}"></script>
<!-- <script type="text/javascript" src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script> -->
<script type="text/javascript">
$(document).ready(function() {
if (!Search.out) {
Search.init();
}
});
</script>
{% endblock %}

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[theme]
inherit = basic
pygments_style = default
stylesheet = css/theme.css
[options]
link_to_live_contributing_page = false
mathjax_path =

112
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"""Configuration file for the Sphinx documentation builder."""
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
import toml
with open("../pyproject.toml") as f:
data = toml.load(f)
# -- Project information -----------------------------------------------------
project = "🦜🔗 LangChain"
copyright = "2023, Harrison Chase"
author = "Harrison Chase"
version = data["tool"]["poetry"]["version"]
release = version
html_title = project + " " + version
html_last_updated_fmt = "%b %d, %Y"
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autodoc.typehints",
"sphinx.ext.autosummary",
"sphinx.ext.napoleon",
"sphinx.ext.viewcode",
"sphinxcontrib.autodoc_pydantic",
"myst_nb",
"sphinx_copybutton",
"sphinx_panels",
"IPython.sphinxext.ipython_console_highlighting",
]
source_suffix = [".ipynb", ".html", ".md", ".rst"]
autodoc_pydantic_model_show_json = False
autodoc_pydantic_field_list_validators = False
autodoc_pydantic_config_members = False
autodoc_pydantic_model_show_config_summary = False
autodoc_pydantic_model_show_validator_members = False
autodoc_pydantic_model_show_field_summary = False
autodoc_pydantic_model_members = False
autodoc_pydantic_model_undoc_members = False
# autodoc_typehints = "signature"
# autodoc_typehints = "description"
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "sphinx_book_theme"
html_theme_options = {
"path_to_docs": "docs",
"repository_url": "https://github.com/hwchase17/langchain",
"use_repository_button": True,
}
html_context = {
"display_github": True, # Integrate GitHub
"github_user": "hwchase17", # Username
"github_repo": "langchain", # Repo name
"github_version": "master", # Version
"conf_py_path": "/docs/", # Path in the checkout to the docs root
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# These paths are either relative to html_static_path
# or fully qualified paths (eg. https://...)
html_css_files = [
"css/custom.css",
]
html_js_files = [
"js/mendablesearch.js",
]
nb_execution_mode = "off"
myst_enable_extensions = ["colon_fence"]

58
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# Deployments
So, you've created a really cool chain - now what? How do you deploy it and make it easily shareable with the world?
This section covers several options for that. Note that these options are meant for quick deployment of prototypes and demos, not for production systems. If you need help with the deployment of a production system, please contact us directly.
What follows is a list of template GitHub repositories designed to be easily forked and modified to use your chain. This list is far from exhaustive, and we are EXTREMELY open to contributions here.
## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
This repo serves as a template for how to deploy a LangChain with Streamlit.
It implements a chatbot interface.
It also contains instructions for how to deploy this app on the Streamlit platform.
## [Gradio (on Hugging Face)](https://github.com/hwchase17/langchain-gradio-template)
This repo serves as a template for how deploy a LangChain with Gradio.
It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
It also contains instructions for how to deploy this app on the Hugging Face platform.
This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.
## [Vercel](https://github.com/homanp/vercel-langchain)
A minimal example on how to run LangChain on Vercel using Flask.
## [Fly.io](https://github.com/fly-apps/hello-fly-langchain)
A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using Flask.
## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
A minimal example on how to deploy LangChain to DigitalOcean App Platform.
## [Google Cloud Run](https://github.com/homanp/gcp-langchain)
A minimal example on how to deploy LangChain to Google Cloud Run.
## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship. This includes: production-ready endpoints, horizontal scaling across dependencies, persistent storage of app state, multi-tenancy support, etc.
## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
## [BentoML](https://github.com/ssheng/BentoChain)
This repository provides an example of how to deploy a LangChain application with [BentoML](https://github.com/bentoml/BentoML). BentoML is a framework that enables the containerization of machine learning applications as standard OCI images. BentoML also allows for the automatic generation of OpenAPI and gRPC endpoints. With BentoML, you can integrate models from all popular ML frameworks and deploy them as microservices running on the most optimal hardware and scaling independently.
## [Databutton](https://databutton.com/home?new-data-app=true)
These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.

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.yarn/
node_modules/
.docusaurus
.cache-loader
docs/api

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# Website
This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
### Installation
```
$ yarn
```
### Local Development
```
$ yarn start
```
This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
### Build
```
$ yarn build
```
This command generates static content into the `build` directory and can be served using any static contents hosting service.
### Deployment
Using SSH:
```
$ USE_SSH=true yarn deploy
```
Not using SSH:
```
$ GIT_USER=<Your GitHub username> yarn deploy
```
If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
### Continuous Integration
Some common defaults for linting/formatting have been set for you. If you integrate your project with an open source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
```
$ yarn ci
```

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/**
* Copyright (c) Meta Platforms, Inc. and affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*
* @format
*/
module.exports = {
presets: [require.resolve("@docusaurus/core/lib/babel/preset")],
};

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/* eslint-disable prefer-template */
/* eslint-disable no-param-reassign */
// eslint-disable-next-line import/no-extraneous-dependencies
const babel = require("@babel/core");
const path = require("path");
const fs = require("fs");
/**
*
* @param {string|Buffer} content Content of the resource file
* @param {object} [map] SourceMap data consumable by https://github.com/mozilla/source-map
* @param {any} [meta] Meta data, could be anything
*/
async function webpackLoader(content, map, meta) {
const cb = this.async();
if (!this.resourcePath.endsWith(".ts")) {
cb(null, JSON.stringify({ content, imports: [] }), map, meta);
return;
}
try {
const result = await babel.parseAsync(content, {
sourceType: "module",
filename: this.resourcePath,
});
const imports = [];
result.program.body.forEach((node) => {
if (node.type === "ImportDeclaration") {
const source = node.source.value;
if (!source.startsWith("langchain")) {
return;
}
node.specifiers.forEach((specifier) => {
if (specifier.type === "ImportSpecifier") {
const local = specifier.local.name;
const imported = specifier.imported.name;
imports.push({ local, imported, source });
} else {
throw new Error("Unsupported import type");
}
});
}
});
imports.forEach((imp) => {
const { imported, source } = imp;
const moduleName = source.split("/").slice(1).join("_");
const docsPath = path.resolve(__dirname, "docs", "api", moduleName);
const available = fs.readdirSync(docsPath, { withFileTypes: true });
const found = available.find(
(dirent) =>
dirent.isDirectory() &&
fs.existsSync(path.resolve(docsPath, dirent.name, imported + ".md"))
);
if (found) {
imp.docs =
"/" + path.join("docs", "api", moduleName, found.name, imported);
} else {
throw new Error(
`Could not find docs for ${source}.${imported} in docs/api/`
);
}
});
cb(null, JSON.stringify({ content, imports }), map, meta);
} catch (err) {
cb(err);
}
}
module.exports = webpackLoader;

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pre {
white-space: break-spaces;
}
@media (min-width: 1200px) {
.container,
.container-lg,
.container-md,
.container-sm,
.container-xl {
max-width: 2560px !important;
}
}
#my-component-root *, #headlessui-portal-root * {
z-index: 10000;
}
.content-container p {
margin: revert;
}

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# Installation
import Installation from "@snippets/get_started/installation.mdx"
<Installation/>

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---
sidebar_position: 0
---
# Introduction
**LangChain** is a framework for developing applications powered by language models. It enables applications that are:
- **Data-aware**: connect a language model to other sources of data
- **Agentic**: allow a language model to interact with its environment
The main value props of LangChain are:
1. **Components**: abstractions for working with language models, along with a collection of implementations for each abstraction. Components are modular and easy-to-use, whether you are using the rest of the LangChain framework or not
2. **Off-the-shelf chains**: a structured assembly of components for accomplishing specific higher-level tasks
Off-the-shelf chains make it easy to get started. For more complex applications and nuanced use-cases, components make it easy to customize existing chains or build new ones.
## Get started
[Heres](/docs/get_started/installation.html) how to install LangChain, set up your environment, and start building.
We recommend following our [Quickstart](/docs/get_started/quickstart.html) guide to familiarize yourself with the framework by building your first LangChain application.
_**Note**: These docs are for the LangChain [Python package](https://github.com/hwchase17/langchain). For documentation on [LangChain.js](https://github.com/hwchase17/langchainjs), the JS/TS version, [head here](https://js.langchain.com/docs)._
## Modules
LangChain provides standard, extendable interfaces and external integrations for the following modules, listed from least to most complex:
#### [Model I/O](/docs/modules/model_io/)
Interface with language models
#### [Data connection](/docs/modules/data_connection/)
Interface with application-specific data
#### [Chains](/docs/modules/chains/)
Construct sequences of calls
#### [Agents](/docs/modules/agents/)
Let chains choose which tools to use given high-level directives
#### [Memory](/docs/modules/memory/)
Persist application state between runs of a chain
#### [Callbacks](/docs/modules/callbacks/)
Log and stream intermediate steps of any chain
## Examples, ecosystem, and resources
### [Use cases](/docs/use_cases/)
Walkthroughs and best-practices for common end-to-end use cases, like:
- [Chatbots](/docs/use_cases/chatbots/)
- [Answering questions using sources](/docs/use_cases/question_answering/)
- [Analyzing structured data](/docs/use_cases/tabular.html)
- and much more...
### [Guides](/docs/guides/)
Learn best practices for developing with LangChain.
### [Ecosystem](/docs/ecosystem/)
LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/ecosystem/integrations/) and [dependent repos](/docs/ecosystem/dependents.html).
### [Additional resources](/docs/additional_resources/)
Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).
<h3><span style={{color:"#2e8555"}}> Support </span></h3>
Join us on [GitHub](https://github.com/hwchase17/langchain) or [Discord](https://discord.gg/6adMQxSpJS) to ask questions, share feedback, meet other developers building with LangChain, and dream about the future of LLMs.
## API reference
Head to the [reference](https://api.python.langchain.com) section for full documentation of all classes and methods in the LangChain Python package.

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@@ -1,158 +0,0 @@
# Quickstart
## Installation
To install LangChain run:
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import Install from "@snippets/get_started/quickstart/installation.mdx"
<Install/>
For more details, see our [Installation guide](/docs/get_started/installation.html).
## Environment setup
Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.
import OpenAISetup from "@snippets/get_started/quickstart/openai_setup.mdx"
<OpenAISetup/>
## Building an application
Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
## LLMs
#### Get predictions from a language model
The basic building block of LangChain is the LLM, which takes in text and generates more text.
As an example, suppose we're building an application that generates a company name based on a company description. In order to do this, we need to initialize an OpenAI model wrapper. In this case, since we want the outputs to be MORE random, we'll initialize our model with a HIGH temperature.
import LLM from "@snippets/get_started/quickstart/llm.mdx"
<LLM/>
## Chat models
Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
<ChatModel/>
## Prompt templates
Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
import PromptTemplateLLM from "@snippets/get_started/quickstart/prompt_templates_llms.mdx"
import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_templates_chat_models.mdx"
<Tabs>
<TabItem value="llms" label="LLMs" default>
With PromptTemplates this is easy! In this case our template would be very simple:
<PromptTemplateLLM/>
</TabItem>
<TabItem value="chat_models" label="Chat models">
Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_messages` method to generate the formatted messages.
Because this is generating a list of messages, it is slightly more complex than the normal prompt template which is generating only a string. Please see the detailed guides on prompts to understand more options available to you here.
<PromptTemplateChatModel/>
</TabItem>
</Tabs>
## Chains
Now that we've got a model and a prompt template, we'll want to combine the two. Chains give us a way to link (or chain) together multiple primitives, like models, prompts, and other chains.
import ChainLLM from "@snippets/get_started/quickstart/chains_llms.mdx"
import ChainChatModel from "@snippets/get_started/quickstart/chains_chat_models.mdx"
<Tabs>
<TabItem value="llms" label="LLMs" default>
The simplest and most common type of chain is an LLMChain, which passes an input first to a PromptTemplate and then to an LLM. We can construct an LLM chain from our existing model and prompt template.
<ChainLLM/>
There we go, our first chain! Understanding how this simple chain works will set you up well for working with more complex chains.
</TabItem>
<TabItem value="chat_models" label="Chat models">
The `LLMChain` can be used with chat models as well:
<ChainChatModel/>
</TabItem>
</Tabs>
## Agents
import AgentLLM from "@snippets/get_started/quickstart/agents_llms.mdx"
import AgentChatModel from "@snippets/get_started/quickstart/agents_chat_models.mdx"
Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs.
Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer.
To load an agent, you need to choose a(n):
- LLM/Chat model: The language model powering the agent.
- Tool(s): A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. For a list of predefined tools and their specifications, see the [Tools documentation](/docs/modules/agents/tools/).
- Agent name: A string that references a supported agent class. An agent class is largely parameterized by the prompt the language model uses to determine which action to take. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see [here](/docs/modules/agents/how_to/custom_agent.html). For a list of supported agents and their specifications, see [here](/docs/modules/agents/agent_types/).
For this example, we'll be using SerpAPI to query a search engine.
You'll need to install the SerpAPI Python package:
```bash
pip install google-search-results
```
And set the `SERPAPI_API_KEY` environment variable.
<Tabs>
<TabItem value="llms" label="LLMs" default>
<AgentLLM/>
</TabItem>
<TabItem value="chat_models" label="Chat models">
Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
<AgentChatModel/>
</TabItem>
</Tabs>
## Memory
The chains and agents we've looked at so far have been stateless, but for many applications it's necessary to reference past interactions. This is clearly the case with a chatbot for example, where you want it to understand new messages in the context of past messages.
The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
There are a number of built-in memory systems. The simplest of these is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
<Tabs>
<TabItem value="llms" label="LLMs" default>
<MemoryLLM/>
</TabItem>
<TabItem value="chat_models" label="Chat models">
You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
<MemoryChatModel/>
</TabItem>
</Tabs>

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# LangSmith
import DocCardList from "@theme/DocCardList";
LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you
move from prototype to production.
Check out the [interactive walkthrough](walkthrough) below to get started.
For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/)
<DocCardList />

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# Conversational
This walkthrough demonstrates how to use an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.
import Example from "@snippets/modules/agents/agent_types/conversational_agent.mdx"
<Example/>
import ChatExample from "@snippets/modules/agents/agent_types/chat_conversation_agent.mdx"
## Using a chat model
<ChatExample/>

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---
sidebar_position: 0
---
# Agent types
## Action agents
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
Here are the agents available in LangChain.
### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
based solely on the tool's description. Any number of tools can be provided.
This agent requires that a description is provided for each tool.
**Note**: This is the most general purpose action agent.
### [Structured input ReAct](/docs/modules/agents/agent_types/structured_chat.html)
The structured tool chat agent is capable of using multi-input tools.
Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
schema to create a structured action input. This is useful for more complex tool usage, like precisely
navigating around a browser.
### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
function should to be called and respond with the inputs that should be passed to the function.
The OpenAI Functions Agent is designed to work with these models.
### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)
This agent is designed to be used in conversational settings.
The prompt is designed to make the agent helpful and conversational.
It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
This agent utilizes a single tool that should be named `Intermediate Answer`.
This tool should be able to lookup factual answers to questions. This agent
is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
where a Google search API was provided as the tool.
### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html)
This agent uses the ReAct framework to interact with a docstore. Two tools must
be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
The `Search` tool should search for a document, while the `Lookup` tool should lookup
a term in the most recently found document.
This agent is equivalent to the
original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html)
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).

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# OpenAI functions
Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.
In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.
The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.
The OpenAI Functions Agent is designed to work with these models.
import Example from "@snippets/modules/agents/agent_types/openai_functions_agent.mdx";
<Example/>

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# Plan and execute
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
The planning is almost always done by an LLM.
The execution is usually done by a separate agent (equipped with tools).
import Example from "@snippets/modules/agents/agent_types/plan_and_execute.mdx"
<Example/>

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# ReAct
This walkthrough showcases using an agent to implement the [ReAct](https://react-lm.github.io/) logic.
import Example from "@snippets/modules/agents/agent_types/react.mdx"
<Example/>
## Using chat models
You can also create ReAct agents that use chat models instead of LLMs as the agent driver.
import ChatExample from "@snippets/modules/agents/agent_types/react_chat.mdx"
<ChatExample/>

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# Structured tool chat
The structured tool chat agent is capable of using multi-input tools.
Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.
import Example from "@snippets/modules/agents/agent_types/structured_chat.mdx"
<Example/>

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label: 'How-to'
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# Custom LLM Agent
This notebook goes through how to create your own custom LLM agent.
An LLM agent consists of three parts:
- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
- LLM: This is the language model that powers the agent
- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
import Example from "@snippets/modules/agents/how_to/custom_llm_agent.mdx"
<Example/>

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# Custom LLM Agent (with a ChatModel)
This notebook goes through how to create your own custom agent based on a chat model.
An LLM chat agent consists of three parts:
- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
- ChatModel: This is the language model that powers the agent
- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
import Example from "@snippets/modules/agents/how_to/custom_llm_chat_agent.mdx"
<Example/>

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# Replicating MRKL
This walkthrough demonstrates how to replicate the [MRKL](https://arxiv.org/pdf/2205.00445.pdf) system using agents.
This uses the example Chinook database.
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.
import Example from "@snippets/modules/agents/how_to/mrkl.mdx"
<Example/>
## With a chat model
import ChatExample from "@snippets/modules/agents/how_to/mrkl_chat.mdx"
<ChatExample/>

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# Agents
Some applications require a flexible chain of calls to LLMs and other tools based on user input. The **Agent** interface provides the flexibility for such applications. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next.
There are two main types of agents:
- **Action agents**: at each timestep, decide on the next action using the outputs of all previous actions
- **Plan-and-execute agents**: decide on the full sequence of actions up front, then execute them all without updating the plan
Action agents are suitable for small tasks, while plan-and-execute agents are better for complex or long-running tasks that require maintaining long-term objectives and focus. Often the best approach is to combine the dynamism of an action agent with the planning abilities of a plan-and-execute agent by letting the plan-and-execute agent use action agents to execute plans.
For a full list of agent types see [agent types](/docs/modules/agents/agent_types/). Additional abstractions involved in agents are:
- [**Tools**](/docs/modules/agents/tools/): the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
- [**Toolkits**](/docs/modules/agents/toolkits/): wrappers around collections of tools that can be used together a specific use case. For example, in order for an agent to
interact with a SQL database it will likely need one tool to execute queries and another to inspect tables
## Action agents
At a high-level an action agent:
1. Receives user input
2. Decides which tool, if any, to use and the tool input
3. Calls the tool and records the output (also known as an "observation")
4. Decides the next step using the history of tools, tool inputs, and observations
5. Repeats 3-4 until it determines it can respond directly to the user
Action agents are wrapped in **agent executors**, which are responsible for calling the agent, getting back an action and action input, calling the tool that the action references with the generated 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.
Although an agent can be constructed in many ways, it typically involves these components:
- **Prompt template**: Responsible for taking the user input and previous steps and constructing a prompt
to send to the language model
- **Language model**: Takes the prompt with use input and action history and decides what to do next
- **Output parser**: Takes the output of the language model and parses it into the next action or a final answer
## Plan-and-execute agents
At a high-level a plan-and-execute agent:
1. Receives user input
2. Plans the full sequence of steps to take
3. Executes the steps in order, passing the outputs of past steps as inputs to future steps
The most typical implementation is to have the planner be a language model, and the executor be an action agent. Read more [here](/docs/modules/agents/agent_types/plan_and_execute.html).
## Get started
import GetStarted from "@snippets/modules/agents/get_started.mdx"
<GetStarted/>

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# Toolkits
Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
import DocCardList from "@theme/DocCardList";
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# Tools
Tools are interfaces that an agent can use to interact with the world.
## Get started
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
Currently, tools can be loaded with the following snippet:
import GetStarted from "@snippets/modules/agents/tools/get_started.mdx"
<GetStarted/>

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# Callbacks
LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
import GetStarted from "@snippets/modules/callbacks/get_started.mdx"
<GetStarted/>

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# Analyze Document
The AnalyzeDocumentChain can be used as an end-to-end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain.
import Example from "@snippets/modules/chains/additional/analyze_document.mdx"
<Example/>

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# Self-critique chain with constitutional AI
The ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.
import Example from "@snippets/modules/chains/additional/constitutional_chain.mdx"
<Example/>

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