Commit Graph

1671 Commits

Author SHA1 Message Date
Zander Chase
9b9b231e10
Update some Tools Docs (#3913)
Haven't gotten to all of them, but this:
- Updates some of the tools notebooks to actually instantiate a tool
(many just show a 'utility' rather than a tool. More changes to come in
separate PR)
- Move the `Tool` and decorator definitions to `langchain/tools/base.py`
(but still export from `langchain.agents`)
- Add scene explain to the load_tools() function
- Add unit tests for public apis for the langchain.tools and langchain.agents modules
2023-05-01 19:07:26 -07:00
Zander Chase
84ea17b786
Move Tool Validation (#3923)
Move tool validation to each implementation of the Agent.

Another alternative would be to adjust the `_validate_tools()` signature
to accept the output parser (and format instructions) and add logic
there. Something like

`parser.outputs_structured_actions(format_instructions)`

But don't think that's needed right now.
2023-05-01 18:44:24 -07:00
Eugene Yurtsev
7cce68a051
Add minimal file system blob loader (#3669)
This adds a minimal file system blob loader.

If looks good, this PR will be merged and a few additional enhancements will be made.
2023-05-01 21:37:26 -04:00
Bank Natchapol
487d4aeebd
Motorhead Memory messages come in reversed order. (#3835)
History from Motorhead memory return in reversed order
It should be Human: 1, AI:..., Human: 2, Ai...

```
You are a chatbot having a conversation with a human.
AI: I'm sorry, I'm still not sure what you're trying to communicate. Can you please provide more context or information?
Human: 3
AI: I'm sorry, I'm not sure what you mean by "1" and "2". Could you please clarify your request or question?
Human: 2
AI: Hello, how can I assist you today?
Human: 1
Human: 4
AI:
```

So, i `reversed` the messages before putting in chat_memory.
2023-05-01 17:02:34 -07:00
Davis Chase
900ad106d3
Update google palm model signatures (#3920)
Signatures out of date after callback refactors
2023-05-01 16:19:31 -07:00
sherylZhaoCode
145ff23fb1
correct the llm type of AzureOpenAI (#3721)
The llm type of AzureOpenAI was previously set to default, which is
openai. But since AzureOpenAI has different API from openai, it creates
problems when doing chain saving and loading. This PR corrected the llm
type of AzureOpenAI to "azure"
2023-05-01 15:51:34 -07:00
engkheng
21335d43b2
Minor LLMChain docs correction (#3791)
`LLMChain` run method can take multiple input variables.
2023-05-01 15:50:57 -07:00
Rafal Wojdyla
039b672f46
Fixup OpenAI Embeddings - fix the weighted mean (#3778)
Re: https://github.com/hwchase17/langchain/issues/3777

Copy pasting from the issue:

While working on https://github.com/hwchase17/langchain/issues/3722 I
have noticed that there might be a bug in the current implementation of
the OpenAI length safe embeddings in `_get_len_safe_embeddings`, which
before https://github.com/hwchase17/langchain/issues/3722 was actually
the **default implementation** regardless of the length of the context
(via https://github.com/hwchase17/langchain/pull/2330).

It appears the weights used are constant and the length of the embedding
vector (1536) and NOT the number of tokens in the batch, as in the
reference implementation at
https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb

<hr>

Here's some debug info:

<img width="1094" alt="image"
src="https://user-images.githubusercontent.com/1419010/235286595-a8b55298-7830-45df-b9f7-d2a2ad0356e0.png">

<hr>

We can also validate this against the reference implementation:

<details>

<summary>Reference implementation (click to unroll)</summary>

This implementation is copy pasted from
https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb

```py
import openai
from itertools import islice
import numpy as np
from tenacity import retry, wait_random_exponential, stop_after_attempt, retry_if_not_exception_type


EMBEDDING_MODEL = 'text-embedding-ada-002'
EMBEDDING_CTX_LENGTH = 8191
EMBEDDING_ENCODING = 'cl100k_base'

# let's make sure to not retry on an invalid request, because that is what we want to demonstrate
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(openai.InvalidRequestError))
def get_embedding(text_or_tokens, model=EMBEDDING_MODEL):
    return openai.Embedding.create(input=text_or_tokens, model=model)["data"][0]["embedding"]

def batched(iterable, n):
    """Batch data into tuples of length n. The last batch may be shorter."""
    # batched('ABCDEFG', 3) --> ABC DEF G
    if n < 1:
        raise ValueError('n must be at least one')
    it = iter(iterable)
    while (batch := tuple(islice(it, n))):
        yield batch
        
def chunked_tokens(text, encoding_name, chunk_length):
    encoding = tiktoken.get_encoding(encoding_name)
    tokens = encoding.encode(text)
    chunks_iterator = batched(tokens, chunk_length)
    yield from chunks_iterator


def reference_safe_get_embedding(text, model=EMBEDDING_MODEL, max_tokens=EMBEDDING_CTX_LENGTH, encoding_name=EMBEDDING_ENCODING, average=True):
    chunk_embeddings = []
    chunk_lens = []
    for chunk in chunked_tokens(text, encoding_name=encoding_name, chunk_length=max_tokens):
        chunk_embeddings.append(get_embedding(chunk, model=model))
        chunk_lens.append(len(chunk))

    if average:
        chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens)
        chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings)  # normalizes length to 1
        chunk_embeddings = chunk_embeddings.tolist()
    return chunk_embeddings
```

</details>

```py
long_text = 'foo bar' * 5000

reference_safe_get_embedding(long_text, average=True)[:10]

# Here's the first 10 floats from the reference embeddings:
[0.004407593824276758,
 0.0017611146161865465,
 -0.019824815970984996,
 -0.02177626039794025,
 -0.012060967454897886,
 0.0017955296329155309,
 -0.015609168983609643,
 -0.012059823076681351,
 -0.016990468527792825,
 -0.004970484452089445]


# and now langchain implementation
from langchain.embeddings.openai import OpenAIEmbeddings
OpenAIEmbeddings().embed_query(long_text)[:10]

[0.003791506184693747,
 0.0025310066579390025,
 -0.019282322699514628,
 -0.021492679249899803,
 -0.012598522213242891,
 0.0022181168611315662,
 -0.015858940621301307,
 -0.011754004130791204,
 -0.016402944319627515,
 -0.004125287485127554]

# clearly they are different ^
```
2023-05-01 15:47:38 -07:00
Younis Shah
22a1896c30
[docs]: updates connecting_to_a_feature_store.ipynb (#3776)
* fixes `FeastPromptTemplate.format` example to use `driver_id`
2023-05-01 15:45:59 -07:00
Harrison Chase
e28c6403aa
Harrison/cohere reranker (#3904) 2023-05-01 15:40:16 -07:00
Zura Isakadze
647bbf61c1
Add SQLiteChatMessageHistory (#3534)
It's based on already existing `PostgresChatMessageHistory`

Use case somewhere in between multiple files and Postgres storage.
2023-05-01 15:40:00 -07:00
James Brotchie
921894960b
Add ChatModel, LLM, and Embeddings for Google's PaLM APIs (#3575)
- Add langchain.llms.GooglePalm for text completion,
 - Add langchain.chat_models.ChatGooglePalm for chat completion,
- Add langchain.embeddings.GooglePalmEmbeddings for sentence embeddings,
- Add example field to HumanMessage and AIMessage so that users can feed
in examples into the PaLM Chat API,
 - Add system and unit tests.

Note async completion for the Text API is not yet supported and will be
included in a future PR.

Happy for feedback on any aspect of this PR, especially our choice of
adding an example field to Human and AI Message objects to enable
passing example messages to the API.
2023-05-01 15:23:16 -07:00
Roma
d15f481352
Add unit test to output parsers (#3911)
This pull request adds unit tests for various output parsers
(BooleanOutputParser, CommaSeparatedListOutputParser, and
StructuredOutputParser) to ensure their correct functionality and to
increase code reliability and maintainability. The tests cover both
valid and invalid input cases.

Changes:

Added unit tests for BooleanOutputParser.
Added unit tests for CommaSeparatedListOutputParser.
Added unit tests for StructuredOutputParser.

Testing:

All new unit tests have been executed, and they pass successfully.
The overall test suite has been run, and all tests pass.
Notes:

These tests cover both successful parsing scenarios and error handling
for invalid inputs.
If any new output parsers are added in the future, corresponding unit
tests should also be created to maintain coverage.
2023-05-01 14:53:08 -07:00
Tim Asp
9c89ff8bd9
Increase request_timeout on ChatOpenAI (#3910)
With longer context and completions, gpt-3.5-turbo and, especially,
gpt-4, will more times than not take > 60seconds to respond.

Based on some other discussions, it seems like this is an increasingly
common problem, especially with summarization tasks.
- https://github.com/hwchase17/langchain/issues/3512
- https://github.com/hwchase17/langchain/issues/3005

OpenAI's max 600s timeout seems excessive, so I settled on 120, but I do
run into generations that take >240 seconds when using large prompts and
completions with GPT-4, so maybe 240 would be a better compromise?
2023-05-01 14:51:05 -07:00
Davis Chase
2451310975
Chroma fix mmr (#3897)
Fixes #3628, thanks @derekmoeller for the issue!
2023-05-01 10:47:15 -07:00
mbchang
3e1cb31f63
fix: add import for gymnasium (#3899) 2023-05-01 10:37:25 -07:00
Zander Chase
484707ad29
Add incremental messages token count (#3890) 2023-05-01 10:36:54 -07:00
Davis Chase
52e4fba897
Fix self query pinecone translation (#3892)
Enum to string conversion handled differently between python 3.9 and
3.11, currently breaking in 3.11 (see #3788). Thanks @peter-brady for
catching this!
2023-05-01 10:35:48 -07:00
Jef Packer
47a685adcf
count tokens instead of chars in autogpt prompt (#3841)
This looks like a bug. 

Overall by using len instead of token_counter the prompt thinks it has
less context window than it actually does. Because of this it adds fewer
messages. The reduced previous message context makes the agent
repetitive when selecting tasks.
2023-05-01 09:21:42 -07:00
Nikolas Garske
c4d3d74148
Fix typos in arxiv.ipynb (#3887)
Several minor typos in the doc for the arxiv document loaders were
fixed.
2023-05-01 09:17:37 -07:00
Zander Chase
f7cb2af5f4
Export StructuredTool at /tools (#3858) 2023-04-30 19:22:21 -07:00
Ankush Gola
e87f81b3ec
add more color to callbacks docs (#3856) 2023-04-30 19:13:01 -07:00
Zander Chase
19912d755e
Vwp/arxiv (#3855)
Co-authored-by: Mike Wang <62768671+skcoirz@users.noreply.github.com>
2023-04-30 18:59:22 -07:00
Zander Chase
e17858470c
Vwp/multi line input (#3854)
Co-authored-by: Paolo Rechia <paolorechia@gmail.com>
2023-04-30 18:59:11 -07:00
Harrison Chase
c896657d28
bump version to 154 (#3846) 2023-04-30 17:49:58 -07:00
Zander Chase
d7e17fc8fe
Deprecate StdInquireTool (#3850)
- Deprecate StdInInquire tool (dup of HumanInputRun)
- Expose missing tools from `langchain.tools`
2023-04-30 16:55:50 -07:00
Zander Chase
b1d69d3e7a
Vwp/fix vectorstore typing (#3851)
Co-authored-by: Jay Stakelon <stakes@users.noreply.github.com>
2023-04-30 16:45:10 -07:00
Zander Chase
fbbdf161cd
Lambda Tool (#3842)
Co-authored-by: Jason Holtkamp <holtkam2@gmail.com>
2023-04-30 15:15:09 -07:00
Ankush Gola
d3ec00b566
Callbacks Refactor [base] (#3256)
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-30 11:14:09 -07:00
Zander Chase
18ec22fe56
Remove multi-input tool section (#3810)
Moving to new notebook. Will re-intro w/ new agent
2023-04-29 15:29:08 -07:00
mbchang
adcad98bee
fix: fix filepath error in agent simulations docs (#3795) 2023-04-29 11:21:27 -07:00
Harrison Chase
20aad0bed1 stripe docs 2023-04-29 08:16:37 -07:00
Harrison Chase
378f0889eb
bump version to 153 (#3774) 2023-04-29 07:31:35 -07:00
Sheldon
399065e858
update zilliz example (#3578)
1. Now the Zilliz example can't connect to Zilliz Cloud, fixed

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-28 22:10:13 -07:00
Harrison Chase
bd7e0a534c
Harrison/csv loader (#3771)
Co-authored-by: mrT23 <tal.r@codium.ai>
2023-04-28 21:54:24 -07:00
Harrison Chase
c494ca3ad2
Harrison/doc2txt (#3772)
Co-authored-by: rishni ratnam <rishniratnam@gmail.com>
2023-04-28 21:54:16 -07:00
Mike Wang
ce4fea983b
[simple] added test case and improve self class return type annotation (#3773)
a simple follow up of https://github.com/hwchase17/langchain/pull/3748
- added test case
- improve annotation when function return type is class itself.
2023-04-28 21:54:07 -07:00
Harrison Chase
0c0f14407c
Harrison/tair (#3770)
Co-authored-by: Seth Huang <848849+seth-hg@users.noreply.github.com>
2023-04-28 21:25:33 -07:00
Aurélien SCHILTZ
502ba6a0be
Fix type annotation for SQLDatabaseToolkit.llm (#3581)
Currently `langchain.agents.agent_toolkits.SQLDatabaseToolkit` has a
field `llm` with type `BaseLLM`. This breaks initialization for some
LLMs. For example, trying to use it with GPT4:
```

from langchain.sql_database import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_toolkits import SQLDatabaseToolkit


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

# pydantic.error_wrappers.ValidationError: 1 validation error for SQLDatabaseToolkit
# 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 SQLDatabaseToolkit as
well
2023-04-28 21:19:01 -07:00
uyhcire
0a7a2b99b5
Fix Chroma integration failing when there are less than 4 items in the collection (#3674)
The code was failing to decrement the `n_results` kwarg passed to
`query(...)`
2023-04-28 21:18:19 -07:00
Rafal Wojdyla
57e028549a
Expose kwargs in LLMChainExtractor.from_llm (#3748)
Re: https://github.com/hwchase17/langchain/issues/3747
2023-04-28 21:18:05 -07:00
Mike Wang
512c24fc9c
[annotation improvement] Make AgentType->Class Conversion More Scalable (#3749)
In the current solution, AgentType and AGENT_TO_CLASS are placed in two
separate files and both manually maintained. This might cause
inconsistency when we update either of them.

— latest —
based on the discussion with hwchase17, we don’t know how to further use
the newly introduced AgentTypeConfig type, so it doesn’t make sense yet
to add it. Instead, it’s better to move the dictionary to another file
to keep the loading.py file clear. The consistency is a good point.
Instead of asserting the consistency during linting, we added a unittest
for consistency check. I think it works as auto unittest is triggered
every time with clear failure notice. (well, force push is possible, but
we all know what we are doing, so let’s show trust. :>)

~~This PR includes~~
- ~~Introduced AgentTypeConfig as the source of truth of all AgentType
related meta data.~~
- ~~Each AgentTypeConfig is a annotated class type which can be used for
annotation in other places.~~
- ~~Each AgentTypeConfig can be easily extended when we have more meta
data needs.~~
- ~~Strong assertion to ensure AgentType and AGENT_TO_CLASS are always
consistent.~~
- ~~Made AGENT_TO_CLASS automatically generated.~~

~~Test Plan:~~
- ~~since this change is focusing on annotation, lint is the major test
focus.~~
- ~~lint, format and test passed on local.~~
2023-04-28 21:17:28 -07:00
Harrison Chase
b7ae9f715d
Langchain with reddit (#3661) (#3768)
I have added a reddit document loader which fetches the text from the
Posts of Subreddits or Reddit users, using the `praw` Python package. I
have also added an example notebook reddit.ipynb in order to guide users
to use this dataloader.
This code was made in format similar to twiiter document loader. I have
run code formating, linting and also checked the code myself for
different scenarios.

This is my first contribution to an open source project and I am really
excited about this. If you want to suggest some improvements in my code,
I will be happy to do it. :)

Co-authored-by: Taaha Bajwa <taaha.s.bajwa@gmail.com>
2023-04-28 20:59:56 -07:00
Kohei Kumazaki
fa4c35e9e5
Fix encoding issue in WebBaseLoader (#3602)
The character code mismatches occurred when character information was
not included in the response header (In my case, a Japanese web page).
I solved this issue by changing the encoding setting to
apparent_encoding.
2023-04-28 20:56:33 -07:00
Harrison Chase
be7a8e0824
Harrison/redis cache (#3766)
Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>
2023-04-28 20:47:18 -07:00
Mike Wang
b588446bf9
[simple][test] Added test case for schema.py (#3692)
- added unittest for schema.py covering utility functions and token
counting.
- fixed a nit. based on huggingface doc, the tokenizer model is gpt-2.
[link](https://huggingface.co/transformers/v4.8.2/_modules/transformers/models/gpt2/tokenization_gpt2_fast.html)
- make lint && make format, passed on local
- screenshot of new test running result

<img width="1283" alt="Screenshot 2023-04-27 at 9 51 55 PM"
src="https://user-images.githubusercontent.com/62768671/235057441-c0ac3406-9541-453f-ba14-3ebb08656114.png">
2023-04-28 20:42:24 -07:00
Harrison Chase
15b92d361d
Harrison/confluence stuff (#3765)
Co-authored-by: Jelmer Borst <japborst@gmail.com>
2023-04-28 20:19:44 -07:00
SimFG
5998b53596
Use the GPTCache api interface (#3693)
Use the GPTCache api interface to reduce the possibility of
compatibility issues
2023-04-28 20:18:51 -07:00
engkheng
f37a932b24
Improve chat prompt template docs (#3719)
Add a few more explanations and examples.
2023-04-28 20:16:22 -07:00
Robert Perrotta
22770f5202
Make StuffDocumentsChain doc separator configurable (#3718)
This PR makes the `"\n\n"` string with which `StuffDocumentsChain` joins
formatted documents a property so it can be configured. The new
`document_separator` property defaults to `"\n\n"` so the change is
backwards compatible.
2023-04-28 20:14:07 -07:00