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PR_kwDOIPDwls5d50vN
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update chains how to
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PR_kwDOIPDwls5d5yzo
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Wfh/json edit dist
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[] | null | 1 | 2023-10-26T19:03:29 | 2023-10-27T01:10:30 | 2023-10-27T01:10:29 |
COLLABORATOR
| null |
Compare predicted json to reference. First canonicalize (sort keys, rm whitespace separators), then return normalized string edit distance.
Not a silver bullet but maybe an easy way to capture structure differences in a less flakey way
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PR_kwDOIPDwls5d5sc5
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Wfh/tree distance
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I_kwDOIPDwls51EmBA
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Unable to extend BaseChatModel with pydantic 2
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NONE
| null |
### System Info
`langchain` = 0.0.324
`pydantic` = 2.4.2
`python` = 3.11
`platform` = macos
In the project I have class `MyChat` which inherits `BaseChatModel` and further customize/extends it. Adding additional fields works fine with both pydantic v1 and v2, however adding a field and/or model validator fails with pydantic v2, raising `TypeError`:
```
TypeError: cannot pickle 'classmethod' object
```
Full error:
```
TypeError Traceback (most recent call last)
.../test.ipynb Cell 1 line 1
9 from pydantic import ConfigDict, Extra, Field, field_validator, model_validator
11 logger = logging.getLogger(__name__)
---> 13 class MyChat(BaseChatModel):
14 client: Any #: :meta private:
15 model_name: str = Field(default="gpt-35-turbo", alias="model")
File .../.venv/lib/python3.11/site-packages/pydantic/v1/main.py:221, in ModelMetaclass.__new__(mcs, name, bases, namespace, **kwargs)
219 elif is_valid_field(var_name) and var_name not in annotations and can_be_changed:
220 validate_field_name(bases, var_name)
--> 221 inferred = ModelField.infer(
222 name=var_name,
223 value=value,
224 annotation=annotations.get(var_name, Undefined),
225 class_validators=vg.get_validators(var_name),
226 config=config,
227 )
228 if var_name in fields:
229 if lenient_issubclass(inferred.type_, fields[var_name].type_):
File .../.venv/lib/python3.11/site-packages/pydantic/v1/fields.py:506, in ModelField.infer(cls, name, value, annotation, class_validators, config)
503 required = False
504 annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment)
--> 506 return cls(
507 name=name,
508 type_=annotation,
509 alias=field_info.alias,
510 class_validators=class_validators,
511 default=value,
512 default_factory=field_info.default_factory,
513 required=required,
514 model_config=config,
515 field_info=field_info,
516 )
File .../.venv/lib/python3.11/site-packages/pydantic/v1/fields.py:436, in ModelField.__init__(self, name, type_, class_validators, model_config, default, default_factory, required, final, alias, field_info)
434 self.shape: int = SHAPE_SINGLETON
435 self.model_config.prepare_field(self)
--> 436 self.prepare()
File .../.venv/lib/python3.11/site-packages/pydantic/v1/fields.py:546, in ModelField.prepare(self)
539 def prepare(self) -> None:
540 """
541 Prepare the field but inspecting self.default, self.type_ etc.
542
543 Note: this method is **not** idempotent (because _type_analysis is not idempotent),
544 e.g. calling it it multiple times may modify the field and configure it incorrectly.
545 """
--> 546 self._set_default_and_type()
547 if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType:
548 # self.type_ is currently a ForwardRef and there's nothing we can do now,
549 # user will need to call model.update_forward_refs()
550 return
...
--> 161 rv = reductor(4)
162 else:
163 reductor = getattr(x, "__reduce__", None)
TypeError: cannot pickle 'classmethod' object
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install pydantic v2
2. Install langchain
3. Try running following in for example notebook:
```
import logging
from typing import ( Any,ClassVar,)
from langchain.chat_models.base import BaseChatModel
from pydantic import ConfigDict, Extra, Field, field_validator, model_validator
logger = logging.getLogger(__name__)
class MyChat(BaseChatModel):
client: Any #: :meta private:
model_name: str = Field(default="gpt-35-turbo", alias="model")
temperature: float = 0.0
model_kwargs: dict[str, Any] = Field(default_factory=dict)
request_timeout: float | tuple[float, float] | None = None
max_retries: int = 6
max_tokens: int | None = 2024
gpu: bool = False
model_config: ClassVar[ConfigDict] = ConfigDict(
populate_by_name=True, strict=False, extra="ignore"
)
@property
def _llm_type(self) -> str:
return "my-chat"
@field_validator("max_tokens", mode="before")
def check_max_tokens(cls, v: int, values: dict[str, Any]) -> int:
"""Validate max_tokens."""
if v is not None and v < 1:
raise ValueError("max_tokens must be greater than 0.")
return v
```
### Expected behavior
There should be no error.
|
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langserve doc
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[] | null | 1 | 2023-10-26T18:20:58 | 2023-10-26T18:40:58 | 2023-10-26T18:40:57 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,964,102,825 |
PR_kwDOIPDwls5d5c2G
| 12,356 |
Update multi query template README, ntbk
|
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PR_kwDOIPDwls5d5UKk
| 12,355 |
E2B tool - Improve description wuth uploaded files info
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **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),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5d5LoY
| 12,354 |
Improves the description of the installation command
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CONTRIBUTOR
| null |
- **Description:**
Before:
`
To install modules needed for the common LLM providers, run:
`
After:
`
To install modules needed for the common LLM providers, run the following command. Please bear in mind that this command is exclusively compatible with the `bash` shell:
`
> This is required for the user so that the user will know if this command is compatible with `zsh` or not.
|
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PR_kwDOIPDwls5d5CxH
| 12,353 |
Add template for Pinecone + Multi-Query
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PR_kwDOIPDwls5d479g
| 12,352 |
bump 324 and 35
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I_kwDOIPDwls51Dz7E
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InvalidRequestError: This is not a chat model and thus not supported in the v1/chat/completions endpoint. Did you mean to use v1/completions?
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[] | null | 1 | 2023-10-26T16:32:02 | 2023-10-26T16:50:36 | null |
NONE
| null |
### System Info
For this code:
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from decouple import config
oak = openai_secret_key (witheld for sake of security)
openai_api_key = oak
llm = ChatOpenAI(temperature=0, model="babbage-002", openai_api_key=openai_api_key)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
db = SQLDatabase.from_uri("sqlite:///maids.db")
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
tools = [
Tool(
name="Yellowsense_Database",
func=db_chain.run,
description="useful for when you need to answer questions about maids/cooks/nannies."
)
]
agent = initialize_agent(
tools=tools, llm=llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)
user_input = input(
"""You can now chat with your database.
Please enter your question or type 'quit' to exit: """
)
agent.run(user_input) I am getting this error:
InvalidRequestError: This is not a chat model and thus not supported in the v1/chat/completions endpoint. Did you mean to use v1/completions?
What all models can I use for chatmodel?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is the code I am using: from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from decouple import config
a = "sk-wLQRwTr8RS"
b = "Z1ph7bIlovT3B"
c = "lbkFJe49s56n"
d = "KfeWgts8MpMQC"
oak = a + b + c + d
openai_api_key = oak
# create LLM model
llm = ChatOpenAI(temperature=0, model="babbage-002", openai_api_key=openai_api_key)
# create an LLM math tool
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
# connect to our database
db = SQLDatabase.from_uri("sqlite:///maids.db")
# create the database chain
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
tools = [
Tool(
name="Maid_Database",
func=db_chain.run,
description="useful for when you need to answer questions about maids/cooks/nannies."
)
]
# creating the agent
agent = initialize_agent(
tools=tools, llm=llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True)
user_input = input(
"""You can now chat with your database.
Please enter your question or type 'quit' to exit: """
)
# ask the LLM a question
agent.run(user_input)
### Expected behavior
I should be getting the answer using the model I selected
|
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| 1,963,895,510 |
PR_kwDOIPDwls5d4vVg
| 12,349 |
fix self query constructor
|
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I_kwDOIPDwls51DdIf
| 12,348 |
Fix rrror "Could not parse function call data" for JsonKeyOutputFunctionsParser (and other Json parsers) without additional LLM call
|
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[] | null | 9 | 2023-10-26T15:34:55 | 2023-11-01T10:57:22 | null |
NONE
| null |
### Feature request
LangChain v0.0.323
Python 3.10.12
turbo-3.5
I use JsonKeyOutputFunctionsParser to parse JSON output:
```
extraction_functions = [convert_pydantic_to_openai_function(Information)]
extraction_model = llm.bind(functions=extraction_functions, function_call={"name": "Information"})
prompt = ChatPromptTemplate.from_messages([
("system", "Extract the relevant information, if not explicitly provided do not guess. Extract partial info"),
("human", "{input}")
])
extraction_chain = prompt | extraction_model | JsonKeyOutputFunctionsParser(key_name="people")
```
For some text (in my tests it's 1 from 500) we have wrong JSON, because there are wrong placed comma or JSON is wrapped by extra text and that can't be parsed by json.loads.
It seems we can use OutputFixingParser, but in most of cases it's not needed to call LLM - we can just fix it my regex.
I did special code to fix such JSONs:
```
class JsonFixOutputFunctionsParser(JsonOutputFunctionsParser):
"""Parse an output as the Json object."""
def get_fixed_json(self, text : str) -> str:
"""Fix LLM json"""
text = re.sub(r'",\s*}', '"}', text)
text = re.sub(r"},\s*]", "}]", text)
text = re.sub(r"}\s*{", "},{", text)
open_bracket = min(text.find('['), text.find('{'))
if open_bracket == -1:
return text
close_bracket = max(text.rfind(']'), text.rfind('}'))
if close_bracket == -1:
return text
return text[open_bracket:close_bracket+1]
def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
.................
fixed_json = self.get_fixed_json(str(function_call["arguments"]))
return json.loads(fixed_json)
```
Now it works without error:
`extraction_chain_fixed = prompt | extraction_model | JsonFixOutputFunctionsParser()`
Code example with this bug and fix: https://colab.research.google.com/drive/1LiT6-ljO_g7xn4Y8qaXKZaUlcpVdEEGI?usp=sharing
### Motivation
With get_fixed_json we can avoid extra LLM call and easy and fast fix most of wrong Jsons.
I tested it on 9000 runs and it fixed all errors that I got.
### Your contribution
Feel free to use get_fixed_json function where we need to parse JSON, not only for function calls.
|
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| 1,963,827,069 |
PR_kwDOIPDwls5d4gZa
| 12,347 |
Gmail multiple encodings support when getting or searching for messages
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NONE
| null |
**Description:
This PR ensures better support for various email encodings. Encoding Support in search and get message functions: Integrated the decode_payload function to handle multiple email encodings gracefully. This ensures that emails with different charsets are decoded correctly without causing errors.
|
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| 12,346 |
add docs for templates
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| 12,345 |
e2b tool - fix initialization and improve tool description
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PR_kwDOIPDwls5d4cxF
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fix : replace openllm config params with user input rather than defau…
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[] | null | 5 | 2023-10-26T15:18:36 | 2023-11-08T11:30:04 | null |
NONE
| null |
**Description:** Configured to update OpenLLM server configs with user specified values for each parameter.
**Issue:** Currently user specified values for each parameter is overwritten by default values due to the mixed of order of python dictionary updates.
**Dependencies:** No additional dependencies
@baskaryan , @eyurtsev , @hwchase17
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| 1,963,761,221 |
I_kwDOIPDwls51DJ5F
| 12,343 |
Langchain evaluate_strings should allow custom output parsing
|
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[] | null | 1 | 2023-10-26T14:54:17 | 2023-10-26T14:56:36 | null |
CONTRIBUTOR
| null |
### Feature request
Currently, there's only a specific output type for the evaluate_strings function in langchain.
{
reasoning: "blah blah"
score: 0 or 1
value: "Y" or "N"
}
Ideally users should be able to override this with the type of request they would want.
### Motivation
It really ties you down when you have to go with the same type of output parser.
### Your contribution
I can work on a PR for the change.
|
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| 1,963,755,881 |
PR_kwDOIPDwls5d4Q29
| 12,342 |
Fix the exception from langchain.utilities import ArceeWrapper
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[] | null | 1 | 2023-10-26T14:51:39 | 2023-10-26T16:19:43 | 2023-10-26T16:19:43 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** Fix the exception from langchain.utilities import ArceeWrapper
|
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| 1,963,750,813 |
I_kwDOIPDwls51DHWd
| 12,341 |
i am trying to optimize the cost of SQLDatabaseToolkit calls
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[] | null | 3 | 2023-10-26T14:49:05 | 2023-10-26T22:30:34 | null |
NONE
| null |
### System Info
aws
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
here's my PROMPT and code:
from langchain.prompts.chat import ChatPromptTemplate
updated_prompt = ChatPromptTemplate.from_messages(
[
("system",
"""
You are a knowledgeable AI assistant specializing in extracting information from the 'inquiry' table in the MySQL Database.
Your primary task is to perform a single query on the schema of the 'inquiry' table and table and retrieve the data using SQL.
When formulating SQL queries, keep the following context in mind:
- Filter records based on exact column value matches.
- If the user inquires about the Status of the inquiry fetch all these columns: status, name, and time values, and inform the user about these specific values.
- Limit query results to a maximum of 3 unless the user specifies otherwise.
- Only query necessary columns.
- Avoid querying for non-existent columns.
- Place the 'ORDER BY' clause after 'WHERE.'
- Do not add a semicolon at the end of the SQL.
If the query results in an empty set, respond with "information not found"
Use this format:
Question: The user's query
Thought: Your thought process
Action: SQL Query
Action Input: SQL query
Observation: Query results
... (repeat for multiple queries)
Thought: Summarize what you've learned
Final Answer: Provide the final answer
Begin!
"""),
("user", "{question}\n ai: "),
]
)
llm = ChatOpenAI(model=os.getenv("OPENAI_CHAT_MODEL"), temperature=0) # best result
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
sqldb_agent.run(updated_prompt.format(
question="What is the status of inquiry 123?"
))
### Expected behavior
This is my current cost
response: The inquiry 123 is Completed on August 21, 2020, at 12 PM.
Total Tokens: 18566
Prompt Tokens: 18349
Completion Tokens: 217
Total Cost (USD): $0.055915
I want to reduce the cost to less than > $0.01
any suggestions will help.
|
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| 1,963,570,897 |
I_kwDOIPDwls51CbbR
| 12,339 |
Issue with OpenAI tokenizer not functioning as expected in streaming mode
|
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| false | null |
[] | null | 3 | 2023-10-26T13:26:24 | 2024-01-16T22:33:08 | null |
NONE
| null |
### System Info
I am developing an app utilizing the streaming functionality in Python 3.10. I've crafted my own generator and callback for this purpose. However, I've stumbled upon an issue when setting llm.streaming to true and utilizing a callback - the OpenAI tokenizer appears to malfunction, displaying a token count of 0$ which is not the expected behavior.
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
def arun(self, g, chat_history, query:str, config:dict, agent_id:str):
try:
llm = ChatOpenAI(
temperature=0,
verbose=False,
model='gpt-4',
streaming=True,
callbacks=[ChainStreamHandler(g)]
)
with get_openai_callback() as cb:
custom_agent = customAgent.run(g, llm, chat_history, query, config, agent_id)
print(cb)
return custom_agent
finally:
g.close()
Tokens Used: 0
Prompt Tokens: 0
Completion Tokens: 0
Successful Requests: 0
Total Cost (USD): $0.0
### Expected behavior
I aim to have the tokenizer working correctly to gauge the token usage and its corresponding cost. Any guidance or suggestions to rectify this problem would be greatly appreciated
|
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| 1,963,514,428 |
PR_kwDOIPDwls5d3bnQ
| 12,338 |
fix typo
|
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[] | null | 2 | 2023-10-26T13:00:08 | 2023-10-26T16:18:48 | 2023-10-26T16:18:48 |
CONTRIBUTOR
| null |
fixed a typo in docs/docs/integrations/toolkits/github.ipynb
|
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| 1,963,492,115 |
I_kwDOIPDwls51CIMT
| 12,337 |
Issue: Update `anyio` dependency
|
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open
| false | null |
[] | null | 1 | 2023-10-26T12:48:24 | 2023-10-27T18:22:08 | null |
NONE
| null |
### Issue you'd like to raise.
Since version `v0.0.299` `LangChain` depends on `anyio = "<4.0"`.
### Suggestion:
Could you please relax the `anyio` dependency to support a wider range of versions `<5.0` as `4.0.0` has been out since August?
|
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| 1,963,490,327 |
I_kwDOIPDwls51CHwX
| 12,336 |
load_summarize_chain ValidationError, str type expected
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[] | null | 3 | 2023-10-26T12:47:28 | 2023-10-27T11:19:25 | null |
NONE
| null |
### System Info
langchain 0.0.275
Ubuntu 20.04.6 LTS
llama-2-7b-chat.ggmlv3.q4_0.bin
llama-cpp-python 0.1.78
I am getting a validation error when trying to use the document summary chain. It is giving an error "str type expected". I am passing Document objects to the model. I have checked the type of all page contents and they are indeed strings.
**Error**
```bash
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
[/home/waleedalfaris/localGPT/notebooks/doc_summary.ipynb](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/doc_summary.ipynb) Cell 9 line 5
[3](vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a224c4c4d227d/home/waleedalfaris/localGPT/notebooks/doc_summary.ipynb#X34sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2) docs = loader.load_and_split()
[4](vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a224c4c4d227d/home/waleedalfaris/localGPT/notebooks/doc_summary.ipynb#X34sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3) chain = load_summarize_chain(llm, chain_type='map_reduce')
----> [5](vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a224c4c4d227d/home/waleedalfaris/localGPT/notebooks/doc_summary.ipynb#X34sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4) summary = chain.run(docs[:1])
[6](vscode-notebook-cell://ssh-remote%2B7b22686f73744e616d65223a224c4c4d227d/home/waleedalfaris/localGPT/notebooks/doc_summary.ipynb#X34sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5) print(summary)
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py:475](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py:475), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
473 if len(args) != 1:
474 raise ValueError("`run` supports only one positional argument.")
--> 475 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
476 _output_key
477 ]
479 if kwargs and not args:
480 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
481 _output_key
482 ]
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py:282](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py:282), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
280 except (KeyboardInterrupt, Exception) as e:
281 run_manager.on_chain_error(e)
--> 282 raise e
283 run_manager.on_chain_end(outputs)
284 final_outputs: Dict[str, Any] = self.prep_outputs(
285 inputs, outputs, return_only_outputs
286 )
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py:276](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/base.py:276), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
270 run_manager = callback_manager.on_chain_start(
271 dumpd(self),
272 inputs,
273 )
274 try:
275 outputs = (
--> 276 self._call(inputs, run_manager=run_manager)
277 if new_arg_supported
278 else self._call(inputs)
279 )
280 except (KeyboardInterrupt, Exception) as e:
281 run_manager.on_chain_error(e)
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py:105](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py:105), in BaseCombineDocumentsChain._call(self, inputs, run_manager)
103 # Other keys are assumed to be needed for LLM prediction
104 other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
--> 105 output, extra_return_dict = self.combine_docs(
106 docs, callbacks=_run_manager.get_child(), **other_keys
107 )
108 extra_return_dict[self.output_key] = output
109 return extra_return_dict
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/combine_documents/map_reduce.py:209](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/combine_documents/map_reduce.py:209), in MapReduceDocumentsChain.combine_docs(self, docs, token_max, callbacks, **kwargs)
197 def combine_docs(
198 self,
199 docs: List[Document],
(...)
202 **kwargs: Any,
203 ) -> Tuple[str, dict]:
204 """Combine documents in a map reduce manner.
205
206 Combine by mapping first chain over all documents, then reducing the results.
207 This reducing can be done recursively if needed (if there are many documents).
208 """
--> 209 map_results = self.llm_chain.apply(
210 # FYI - this is parallelized and so it is fast.
211 [{self.document_variable_name: d.page_content, **kwargs} for d in docs],
212 callbacks=callbacks,
213 )
214 question_result_key = self.llm_chain.output_key
215 result_docs = [
216 Document(page_content=r[question_result_key], metadata=docs[i].metadata)
217 # This uses metadata from the docs, and the textual results from `results`
218 for i, r in enumerate(map_results)
219 ]
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/llm.py:189](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/llm.py:189), in LLMChain.apply(self, input_list, callbacks)
187 except (KeyboardInterrupt, Exception) as e:
188 run_manager.on_chain_error(e)
--> 189 raise e
190 outputs = self.create_outputs(response)
191 run_manager.on_chain_end({"outputs": outputs})
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/llm.py:186](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/llm.py:186), in LLMChain.apply(self, input_list, callbacks)
181 run_manager = callback_manager.on_chain_start(
182 dumpd(self),
183 {"input_list": input_list},
184 )
185 try:
--> 186 response = self.generate(input_list, run_manager=run_manager)
187 except (KeyboardInterrupt, Exception) as e:
188 run_manager.on_chain_error(e)
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/llm.py:101](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/chains/llm.py:101), in LLMChain.generate(self, input_list, run_manager)
99 """Generate LLM result from inputs."""
100 prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
--> 101 return self.llm.generate_prompt(
102 prompts,
103 stop,
104 callbacks=run_manager.get_child() if run_manager else None,
105 **self.llm_kwargs,
106 )
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:467](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:467), in BaseLLM.generate_prompt(self, prompts, stop, callbacks, **kwargs)
459 def generate_prompt(
460 self,
461 prompts: List[PromptValue],
(...)
464 **kwargs: Any,
465 ) -> LLMResult:
466 prompt_strings = [p.to_string() for p in prompts]
--> 467 return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:602](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:602), in BaseLLM.generate(self, prompts, stop, callbacks, tags, metadata, **kwargs)
593 raise ValueError(
594 "Asked to cache, but no cache found at `langchain.cache`."
595 )
596 run_managers = [
597 callback_manager.on_llm_start(
598 dumpd(self), [prompt], invocation_params=params, options=options
599 )[0]
600 for callback_manager, prompt in zip(callback_managers, prompts)
601 ]
--> 602 output = self._generate_helper(
603 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
604 )
605 return output
606 if len(missing_prompts) > 0:
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:504](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:504), in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
502 for run_manager in run_managers:
503 run_manager.on_llm_error(e)
--> 504 raise e
505 flattened_outputs = output.flatten()
506 for manager, flattened_output in zip(run_managers, flattened_outputs):
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:491](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:491), in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
481 def _generate_helper(
482 self,
483 prompts: List[str],
(...)
487 **kwargs: Any,
488 ) -> LLMResult:
489 try:
490 output = (
--> 491 self._generate(
492 prompts,
493 stop=stop,
494 # TODO: support multiple run managers
495 run_manager=run_managers[0] if run_managers else None,
496 **kwargs,
497 )
498 if new_arg_supported
499 else self._generate(prompts, stop=stop)
500 )
501 except (KeyboardInterrupt, Exception) as e:
502 for run_manager in run_managers:
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:985](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/llms/base.py:985), in LLM._generate(self, prompts, stop, run_manager, **kwargs)
979 for prompt in prompts:
980 text = (
981 self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
982 if new_arg_supported
983 else self._call(prompt, stop=stop, **kwargs)
984 )
--> 985 generations.append([Generation(text=text)])
986 return LLMResult(generations=generations)
File [~/localGPT/venv/lib/python3.10/site-packages/langchain/load/serializable.py:74](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/langchain/load/serializable.py:74), in Serializable.__init__(self, **kwargs)
73 def __init__(self, **kwargs: Any) -> None:
---> 74 super().__init__(**kwargs)
75 self._lc_kwargs = kwargs
File [~/localGPT/venv/lib/python3.10/site-packages/pydantic/main.py:341](https://vscode-remote+ssh-002dremote-002b7b22686f73744e616d65223a224c4c4d227d.vscode-resource.vscode-cdn.net/home/waleedalfaris/localGPT/notebooks/~/localGPT/venv/lib/python3.10/site-packages/pydantic/main.py:341), in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
```
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.chains.summarize import load_summarize_chain
from langchain.prompts import PromptTemplate
from langchain.document_loaders import PDFMinerLoader
from langchain.llms import LlamaCpp
kwargs = {
"model_path": model_path,
"n_ctx": 4096,
"max_tokens": 4096,
"seed": 42,
"n_gpu_layers": -1,
"verbose": True,
}
llm = LlamaCpp(**kwargs)
file = 'test_file.pdf'
loader = PDFMinerLoader(file)
docs = loader.load_and_split()
chain = load_summarize_chain(llm, chain_type='map_reduce')
summary = chain.run(docs)
print(summary)
```
### Expected behavior
The chain should provide a summary of the Documents provided
|
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| 1,963,275,005 |
I_kwDOIPDwls51BTL9
| 12,335 |
Unable to filter results in Neo4j Vectorstore.as_retriever() method using filter paramter in search_kwargs
|
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[] | null | 7 | 2023-10-26T11:08:53 | 2023-12-04T23:41:57 | null |
NONE
| null |
### Issue you'd like to raise.
Hello Team,
I am trying to build a QA system with text files as its knowledge base. While creating embeddings I have provided different metadata tags to documents.
I want to use this metadata to filter documents before sending them to LLM to generate answer.
I am using as_retriever() method to filter the documents based on 'key':'value' pair. Code below
```
vectordb.as_retriever(
search_type="similarity",
search_kwargs = {
'filter' : {'key1' : 'value1'}
}
)
```
This method works great to filter out the documents when I am using ChromaDB as VectorStore, but does not work when I use Neo4j as VectorStore. It returns the same results with or without filter using Neo4j
To figure out the issue, I checked langchain's source code for implementation of ChromaDB and Neo4j Vectorstore.
In ChomaDB the similarity_search() method is implemented as follows -
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/chroma.py
Here the filter parameter is passed to the next function - self.similarity_search_with_score(query, k, filter=filter).
But when I checked the same method for Neo4j
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/neo4j_vector.py
Here the filter parameter or the **kwargs is not passed to the next function -
self.similarity_search_by_vector(
embedding=embedding,
k=k,
query=query,
)
Please check if this is what is causing the issue.
Also let me know if something is wrong in my implementation.
Thanks in Advance
### Suggestion:
_No response_
|
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| 1,963,269,136 |
PR_kwDOIPDwls5d2kok
| 12,334 |
Update README.md
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[] | null | 2 | 2023-10-26T11:05:33 | 2023-10-26T16:16:24 | 2023-10-26T16:16:23 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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| 1,963,239,930 |
PR_kwDOIPDwls5d2eLv
| 12,333 |
Fix typos in quickstart.mdx
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[] | null | 1 | 2023-10-26T10:48:14 | 2023-10-26T16:14:50 | 2023-10-26T16:14:50 |
CONTRIBUTOR
| null |
- **Description:** Fixes a few typos in quickstart.mdx
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| 1,966,025,938 |
I_kwDOIPDwls51LyzS
| 12,441 |
langserve with Llamacpp
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NONE
| null |
I am trying to use langserve with langchain llamacpp like this (chain.py):
```from flask import Flask, jsonify, request
from langchain.chains import RetrievalQA,ConversationalRetrievalChain
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.llms import LlamaCpp
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from prompt_template_utils import get_prompt_template
from langchain.vectorstores import Chroma, FAISS
from werkzeug.utils import secure_filename
from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME
DEVICE_TYPE = "cuda" if torch.cuda.is_available() else "cpu"
SHOW_SOURCES = True
logging.info(f"Running on: {DEVICE_TYPE}")
logging.info(f"Display Source Documents set to: {SHOW_SOURCES}")
MODEL_PATH = '../llama-2-7b-32k-instruct.Q6_K.gguf'
def load_model():
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
n_gpu_layers = 30 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
llm = LlamaCpp(
model_path=MODEL_PATH,
n_ctx = 8192,
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
repeat_penalty=1,
temperature=0.2,
max_tokens=100,
top_p=0.9,
top_k = 50,
rope_freq_scale=0.125,
stop = ["[INST]"],
# callback_manager=callback_manager,
streaming=True,
verbose=True, # Verbose is required to pass to the callback manager
)
return llm
EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME,
model_kwargs={"device": DEVICE_TYPE})
DB = FAISS.load_local(PERSIST_DIRECTORY, EMBEDDINGS)
RETRIEVER = DB.as_retriever(search_kwargs={'k': 16})
template = """\
[INST] <<SYS>>
{context}
<</SYS>>
[/INST]
[INST]{question}[/INST]
"""
LLM = load_model()#load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME)
QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
QA = ConversationalRetrievalChain.from_llm(LLM, retriever=RETRIEVER,
combine_docs_chain_kwargs={"prompt": QA_CHAIN_PROMPT})
```
The app.py file has this:
```
from fastapi import FastAPI
from langserve import add_routes
from chain import QA
app = FastAPI(title="Retrieval App")
add_routes(app, QA)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)`
However when running /stream route, I get this warning:
NFO: Started server process [491117]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO: 127.0.0.1:48024 - "GET /docs HTTP/1.1" 200 OK
INFO: 127.0.0.1:48024 - "GET /openapi.json HTTP/1.1" 200 OK
INFO: 127.0.0.1:53430 - "POST /stream HTTP/1.1" 200 OK
/home/zeeshan/miniconda3/envs/llama/lib/python3.10/site-packages/langchain/llms/llamacpp.py:352: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited
run_manager.on_llm_new_token(
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
```
And the output is like this:
This seems not to be streaming output tokens. Does langserve even support langchain llamacpp or I am doing something wrong?
|
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PR_kwDOIPDwls5d2I5i
| 12,332 |
Fix json key output parser in partial (streaming) mode
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[] | null | 1 | 2023-10-26T09:54:28 | 2023-10-26T16:45:06 | 2023-10-26T16:45:05 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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If you're adding a new integration, please include:
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PR_kwDOIPDwls5d1z32
| 12,331 |
Add `openaichat` to llms class mapping
|
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[] | null | 4 | 2023-10-26T09:03:12 | 2023-10-26T16:36:03 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:**
The `get_type_to_cls_dict` method provides type to class mapping. This PR fixes the missing `openaichat` type for importer for `OpenAIChat`.
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| 1,962,991,552 |
I_kwDOIPDwls51AN_A
| 12,330 |
Issue: Can't get verbose output in a variable in python
|
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[] | null | 3 | 2023-10-26T08:31:31 | 2023-10-26T08:49:06 | null |
NONE
| null |
### Need Route Name : Coming in console but not in output
I am using Router chain to route my chain with MultiPromptChain. It gets routed and shows in console to which route it went but it doesn't come in output variable. It just shows in console. I want that in a variable. I used every available method like return_intermediate_steps=True and return_route_name=True, But it is not working
Below is the code:
`destination_chains = {}
for p_info in prompt_infos:
name = p_info["name"]
prompt_template = p_info["prompt_template"]
prompt = ChatPromptTemplate.from_template(template=prompt_template)
chain = LLMChain(llm=llm, prompt=prompt)
destination_chains[name] = chain
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
default_prompt = ChatPromptTemplate.from_template("{input}")
default_chain = LLMChain(llm=llm, prompt=default_prompt)
MULTI_PROMPT_ROUTER_TEMPLATE = """Given a text input to a \
language model select the model prompt best suited for the input. \
You will be given the names of the available prompts and a \
description of what the prompt is best suited for. \
<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
```json
{{{{
"destination": string \ name of the prompt to use or "DEFAULT"
"next_inputs": string \ a potentially modified version of the original input
}}}}
```
REMEMBER: "destination" MUST be one of the candidate prompt \
names specified below.
REMEMBER: "next_inputs" can just be the original input \
if you don't think any modifications are needed.
<< CANDIDATE PROMPTS >>
{destinations}
<< INPUT >>
{{input}}
Output should be only in below format:
Destination used: Output of it
"""
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
destinations=destinations_str
)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
chain = MultiPromptChain(router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain, verbose=True
)
inputQues="How to turn on TV"
output = chain.run(input=inputQues)`
### Suggestion:
_No response_
|
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| 1,962,983,060 |
I_kwDOIPDwls51AL6U
| 12,329 |
return_source documents does not work
|
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[] | null | 3 | 2023-10-26T08:26:40 | 2023-10-26T15:47:17 | null |
NONE
| null |
### System Info
Mac os sonoma
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just use any chain that supports return_source_documents, try setting it to true.
### Expected behavior
I will say too many output keys and throw the error, please fix this otherwise metadata for items does not make sense, since you cannot return source docs anyway.
|
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| 1,962,961,898 |
PR_kwDOIPDwls5d1hiC
| 12,328 |
add allowed_operators property in QdrantTranslator
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[] | null | 2 | 2023-10-26T08:14:06 | 2023-10-27T03:24:39 | 2023-10-26T16:18:29 |
CONTRIBUTOR
| null |
- **Description:**
This PR adds `allowd_operators` property to `QdrantTranslator` to fix the `TypeError: can only join an iterable` bug. This property is required in `get_query_constructor_prompt` in `query_constructor\base.py`:
```
allowed_operators=" | ".join(allowed_operators),
```
- **Issue:**
#12061
|
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| 1,962,923,456 |
PR_kwDOIPDwls5d1ZQv
| 12,327 |
Feature/implement multi query rerank fusion retrieval
|
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[] | null | 2 | 2023-10-26T07:51:00 | 2023-11-06T13:18:40 | null |
CONTRIBUTOR
| null |
Hi, I added the `MultiQueryRankFusionRetriever`, which inherits from the `MultiQueryRetriever` but uses a different merge strategy, i.e. rerank fusion instead of unique union. For the rerank algorithm itself I reused the implementation in EnsembleRetriever (I moved it in a separate file to reuse it).
To save some code duplication, I adapted the return types of the `retrieve_documents` function to return a `List[List[Document]]`, so that the rerank fusion works out of the box.
Additional changes:
* Optionally append original query to query variations
* Fix docstrings
* Use kwargs in classmethod to allow some freedom in the class creation
Thanks for your reviews @hwchase17, @baskaryan
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I_kwDOIPDwls50_3-Y
| 12,326 |
Issue: Similarity_search on Vector Store does not work.
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[] | null | 11 | 2023-10-26T07:37:18 | 2023-11-02T01:19:25 | null |
NONE
| null |
### Issue you'd like to raise.
I am using Langchain + Chroma to do a Q&A program with a csv document as its knowledge base.
The CSV file looks like below:
[![enter image description here][1]][1]
Here is the CSV file:
https://1drv.ms/u/s!Asflam6BEzhjgbkdegCGfZ7FI4O1Og?e=2X6ior
And code for creating embedding:
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter as RCTS
file_path = "Test.csv"
doc_pages = []
csv_loader = CSVLoader(file_path)
doc_pages = csv_loader.load()
print(f"Extracted {file_path} with {len(doc_pages)} pages...")
splitter = RCTS(chunk_size = 3000, chunk_overlap = 300)
splitted_docs = splitter.split_documents(doc_pages)
embedding = OpenAIEmbeddings()
persist_directory = "docs_t/chroma/"
vectordb = Chroma.from_documents(
documents=splitted_docs,
embedding=embedding,
persist_directory=persist_directory
)
vectordb.persist()
print(vectordb._collection.count())
Here is the Testing code:
result = vectordb.similarity_search("what is the Support Item Name for 01_003_0107_1_1", k=3)
for r in result:
print(r.page_content, end="\n\n")
And I see this testing code returns all other non-relevant information.
Which part leads to this issue?
[1]: https://i.stack.imgur.com/Ev3cn.png
### Suggestion:
_No response_
|
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| 1,962,818,790 |
PR_kwDOIPDwls5d1Cbv
| 12,324 |
Qianfan/arguments pass through
|
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[] | null | 6 | 2023-10-26T06:45:54 | 2023-11-16T20:54:52 | 2023-11-16T20:54:51 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Add `init_kwargs` to qianfan endpoints' parameter list,
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
-->
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| 1,962,808,683 |
I_kwDOIPDwls50_hVr
| 12,323 |
GPT-4 reads the images
|
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[] | null | 2 | 2023-10-26T06:38:05 | 2023-10-26T07:02:19 | null |
NONE
| null |
### System Info
GPT-4 can now read images. How to combine images and text in PDF or WORD when calling the API?
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
document loader?
### Expected behavior
maybe we can have a document loader that loads text and images at the same time.
|
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| 1,962,804,701 |
I_kwDOIPDwls50_gXd
| 12,322 |
How do I define the stop logic for a custom llm in agent
|
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[] | null | 1 | 2023-10-26T06:35:00 | 2023-10-26T06:46:04 | null |
NONE
| null |
I defined a custom LLM and a custom search, and used the agent for testing, but I found some problems in parsing the llm response, which was prone to result parsing errors after multiple rounds of dialogue. I suspect that there is something wrong with the stop logic of the custom llm, please help me analyze it.
```
if __name__ == '__main__':
gpt35_llm = gpt35_llm()
tools = [GoogleSearchTool(), BaiduSearchTool()]
agent = initialize_agent(tools, gpt35_llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True,verbose=True)
print(agent.run('杭州亚运会的奖牌榜前三名是哪些国家？杭州亚运会的第一枚金牌是谁获得的？'))
```
```
class gpt35_llm(LLM):
model: str = "gpt-3.5"
@property
def _llm_type(self) -> str:
return "gpt-3.5"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
response = gpt_35_chat(prompt, None)
if stop is not None:
print(f'stop:{stop}')
content = response['content']
output = content[:content.find(stop[0])]
return output
if response['code'] != 200:
raise ValueError(response['content'])
return response['content']
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"model": self.model}```
|
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| 1,962,749,683 |
PR_kwDOIPDwls5d0zn9
| 12,321 |
Adds linter in templates
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CONTRIBUTOR
| null |
Did not actually run/fix errors yet @efriis
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PR_kwDOIPDwls5d0uEi
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Fix Typo in CONTRIBUTING.md
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[] | null | 1 | 2023-10-26T05:19:23 | 2023-10-26T15:56:28 | 2023-10-26T15:56:28 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
I have corrected the typos, grammar, and formatting issues.
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| 1,962,704,317 |
I_kwDOIPDwls50_H29
| 12,319 |
Conversation Buffer Memory clear histroy
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[] | null | 1 | 2023-10-26T04:56:32 | 2023-10-26T05:06:59 | null |
NONE
| null |
### System Info
langchain version: 0.0320
python version: 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I want to clear history of ConversationBufferMemory. I used ConversationBufferMemory to maintain chat history but right now I don't know how can I delete history of that . If anyone knows these please help me out of this.
### Expected behavior
ConversationBufferMemory history should be clear.
|
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| 1,962,695,255 |
PR_kwDOIPDwls5d0n6I
| 12,318 |
Fix SupbaseVectoreStore write operation timeout
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[] | null | 5 | 2023-10-26T04:46:24 | 2023-11-08T06:14:56 | 2023-10-26T21:19:18 |
CONTRIBUTOR
| null |
**Description**
This small change will make chunk_size a configurable parameter for loading documents into a Supabase database.
**Issue**
https://github.com/langchain-ai/langchain/issues/11422
**Dependencies**
No chanages
**Twitter**
@ j1philli
**Reminder**
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls50-8rL
| 12,317 |
should be @search.rerankerScore, not @search.score for hybrid semantic search (Azure Cognitive Search)
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open
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[] | null | 1 | 2023-10-26T04:00:06 | 2023-10-26T04:07:57 | null |
NONE
| null |
### System Info
latest langchain code AzureSearch at langchain/libs/langchain/langchain/vectorstores/azuresearch.py:
semantic_hybrid_search_with_score function, when returning search result, the score should be @search.rerankerScore, not @search.score for hybrid semantic search, according to the latest Azure Doc here: https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking
### Who can help?
@hwchase17
### Information
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### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
the code is not following the latest Azure Search document: https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking
### Expected behavior
float(result["@search.rerankerScore"]) (instead of float(result["@search.score"]))
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PR_kwDOIPDwls5d0dtW
| 12,316 |
Fix a typo in the summarization use case.
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[] | null | 1 | 2023-10-26T03:49:00 | 2023-10-30T03:10:26 | 2023-10-26T16:35:12 |
CONTRIBUTOR
| null |
- **Description:** Fix a tiny typo in the summarization use case Jupyter notebook.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @hwchase17
- **Twitter handle:** @seasonsaw
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| 1,962,623,183 |
PR_kwDOIPDwls5d0Ya6
| 12,315 |
Allow index name customization via env var in rag-conversation
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[] | null | 2 | 2023-10-26T03:16:19 | 2023-10-26T05:11:14 | 2023-10-26T05:11:13 |
CONTRIBUTOR
| null | null |
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| 1,962,600,617 |
I_kwDOIPDwls50-uip
| 12,314 |
OpenAIEmbeddings vector different from the openai library and changes from time to time
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NONE
| null |
### System Info
I got different embedding results using OpenAIEmbeddings and the original openai library. Besides the embeddings from both OpenAIEmbeddings and openai change from time to time. Sometimes it returns the same results but sometimes it returns differently , especially after I exceeds the time limit.
I am using langchain-0.0.321. I tried dig a little deeper in the OpenAIEmbeddings source code and find that there is a process using the tokenizer. I tried use the same tokenizer in the original openai library and get the same result compared to not using it, so I think this is not the problem. Can someone explain a little bit why the results in different libraries and different requests may change?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
text = 'where are you going'
vec1 = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY).embed_query(text)
vec2 = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY).embed_query(text)
if not vec1 == vec2:
print('vec1:', vec1[:3])
print('vec2:', vec2[:3])
vec3 = openai.Embedding.create(input=[text], model='text-embedding-ada-002')['data'][0]['embedding']
if not (vec1 == vec3 or vec2 == vec3):
print('vec1:', vec1[:3])
print('vec2:', vec2[:3])
print('vec3:', vec3[:3])
```
### Expected behavior
the results are as below
```
vec1: [0.0004729258755920449, -0.011402048647760918, -0.012062848996486476]
vec2: [0.00046853933082822383, -0.01143268296919534, -0.012086534739296315]
vec1: [0.0004729258755920449, -0.011402048647760918, -0.012062848996486476]
vec2: [0.00046853933082822383, -0.01143268296919534, -0.012086534739296315]
vec3: [0.00040566621464677155, -0.011434691026806831, -0.012049459852278233]
```
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| 1,962,572,634 |
PR_kwDOIPDwls5d0N7Z
| 12,313 |
Templates CI
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[] | null | 1 | 2023-10-26T02:09:04 | 2023-10-27T03:29:38 | 2023-10-27T03:29:37 |
COLLABORATOR
| null |
Adds a `langchain-location` param to lint, so we can properly locate it.
Regular langchain and experimental lint steps are passing, so default value seems to be working.
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| 1,962,571,837 |
I_kwDOIPDwls50-ng9
| 12,312 |
Elevenlabs-langchain API should integrate very important parameter 'voice'
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[] | null | 0 | 2023-10-26T02:08:00 | 2023-10-26T02:10:08 | null |
NONE
| null |
### Feature request
The langchain API integration Elevenlabs should accomodate the very important parameter `voice`.
### Motivation
Without the voice parameter, the Elevenlabs langchain integration is unusable because `play()` switches the voice randomly with every execution.
### Your contribution
I tracked down the releveant code in the [Elevenlabs-langchain integration API](https://api.python.langchain.com/en/latest/_modules/langchain/tools/eleven_labs/text2speech.html#ElevenLabsText2SpeechTool.stream_speech) down to this section:
```
def _run(
self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Use the tool."""
elevenlabs = _import_elevenlabs()
try:
speech = elevenlabs.generate(text=query, model=self.model)
with tempfile.NamedTemporaryFile(
mode="bx", suffix=".wav", delete=False
) as f:
f.write(speech)
return f.name
except Exception as e:
raise RuntimeError(f"Error while running ElevenLabsText2SpeechTool: {e}")
[[docs]](https://api.python.langchain.com/en/latest/tools/langchain.tools.eleven_labs.text2speech.ElevenLabsText2SpeechTool.html#langchain.tools.eleven_labs.text2speech.ElevenLabsText2SpeechTool.play) def play(self, speech_file: str) -> None:
"""Play the text as speech."""
elevenlabs = _import_elevenlabs()
with open(speech_file, mode="rb") as f:
speech = f.read()
elevenlabs.play(speech)
[[docs]](https://api.python.langchain.com/en/latest/tools/langchain.tools.eleven_labs.text2speech.ElevenLabsText2SpeechTool.html#langchain.tools.eleven_labs.text2speech.ElevenLabsText2SpeechTool.stream_speech) def stream_speech(self, query: str) -> None:
"""Stream the text as speech as it is generated.
Play the text in your speakers."""
elevenlabs = _import_elevenlabs()
speech_stream = elevenlabs.generate(text=query, model=self.model, stream=True)
elevenlabs.stream(speech_stream)
```
The calls to `elevenlabs.generate` and `elevenlabs.play` must be extended with the `voice` parameter.
After this change, the langchain calls `run`, `play`, and importantly, `stream_speech`, could be made analogue the calls from the ElevenLabs API, e.g. seen [in this repo](https://github.com/langchain-ai/langchain/issues/1076#issuecomment-1552363555):
```
prediction = agent_chain.run(input=user_input.text)
audio = generate(
text=prediction,
voice="Adam",
model="eleven_monolingual_v1"
)
```
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CLI Git Improvements
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[] | null | 1 | 2023-10-26T01:28:38 | 2023-10-26T01:30:03 | 2023-10-26T01:30:02 |
COLLABORATOR
| null |
- delete repo sources like pip
- git dep fixes
- error messaging
|
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| 1,962,517,339 |
PR_kwDOIPDwls5d0Cmi
| 12,310 |
Docs: Add lcel to combine_docs chains
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[] | null | 1 | 2023-10-26T00:50:31 | 2023-10-26T18:05:38 | 2023-10-26T18:05:37 |
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| 1,962,469,330 |
PR_kwDOIPDwls5dz4Mk
| 12,309 |
Update code_understanding.ipynb
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[] | null | 2 | 2023-10-25T23:50:19 | 2023-10-26T16:35:38 | 2023-10-26T16:35:38 |
CONTRIBUTOR
| null |
- **Description:** Super simple fix for colab link on code_understanding.ipynb,
- **Issue:** not applicable
- **Dependencies:** none,
- **Tag maintainer:** ,
- **Twitter handle:** @kengoodridge
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Enhance GoogleDriveLoader to Support Spreadsheets when loading documents from ids (issue #3637)
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NONE
| null |
**Issue**: [#3637]
**Summary**:
The codebase includes a private method `_load_sheet_from_id` that can handle Google Sheets, which is currently only invoked when loading documents from a folder. This PR aims to unify the document loading approach, ensuring both individual IDs and folders consider the `mimeType` to determine the document type.
**Changes**:
1. Introduced `_process_file_by_mimetype` function to handle the processing logic based on file `mimeType`, reducing code repetition.
2. Refactored `_load_documents_from_ids` and `_load_documents_from_folder` to utilize the new `_process_file_by_mimetype` function, ensuring consistent behavior.
Reminder
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5dzckb
| 12,307 |
removed CardLists for LLMs and ChatModels
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[] | null | 1 | 2023-10-25T22:22:54 | 2023-10-26T03:27:05 | 2023-10-26T02:13:44 |
COLLABORATOR
| null |
Problem statement:
In the `integrations/llms` and `integrations/chat` pages, we have a sidebar with ToC, and we also have a ToC at the end of the page.
The ToC at the end of the page is not necessary, and it is confusing when we mix the index page styles; moreover, it requires manual work.
So, I removed ToC at the end of the page (it was discussed with and approved by @baskaryan)
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| 1,962,311,288 |
PR_kwDOIPDwls5dzUDq
| 12,305 |
Rm langchain server
|
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[] | null | 1 | 2023-10-25T22:03:09 | 2023-10-25T22:26:47 | 2023-10-25T22:26:47 |
COLLABORATOR
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I_kwDOIPDwls509kPE
| 12,304 |
AttributeError: 'CustomTool' object has no attribute 'fields_set'
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[] | null | 6 | 2023-10-25T21:55:37 | 2023-11-25T18:37:22 | 2023-10-26T12:20:52 |
NONE
| null |
### System Info
python=`3.11`
langchain=`0.0.314`
pydantic=`2.3.0`
pydantic_core=`2.6.3`
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
From the documentation, I can create a fake tool that works as follows:
```python
# Define the tool
class FakeSearchTool(BaseTool):
name: str | None = "custom_search"
description: str | None = "useful for when you need to answer questions about current events"
def _run(
self, query: str
) -> str:
"""Use the tool."""
print("Query:", query)
return "Used the tool, and got the answer!"
async def _arun(
self, query: str
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
# Test if the tool is working
search_tool = FakeSearchTool()
print(search_tool("What is my answer?"))
```
> Query: What is my answer?
> Used the tool, and got the answer!
My case however requires me to give an initial configuration object to every tool, something like this:
```python
from langchain.tools.base import BaseTool
# Create the config class
class Config:
def __init__(self, init_arg1: str, init_arg2: str):
self.init_arg1 = init_arg1
self.init_arg2 = init_arg2
# Create the tool
class FakeSearchTool(BaseTool):
name: str | None = "custom_search"
description: str | None = "useful for when you need to answer questions about current events"
config: Config
def __init__(self, config: Config):
self.config = config
def _run(
self, query: str
) -> str:
"""Use the tool."""
print("Query:", query)
print("Obtained config:", self.config.init_arg1, self.config.init_arg2)
return "Used the tool, and got the answer using the config!"
async def _arun(
self, query: str
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
# Test if the tool is working
search_tool = FakeSearchTool(
config=Config(init_arg1="arg1", init_arg2="arg2")
)
print(search_tool("What is my answer?"))
```
However, this does not work and return the following error:
> AttributeError: 'FakeSearchTool' object has no attribute '__fields_set__'
<details>
<summary>Stack Trace</summary>
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/ai-service/notebooks/agent-with-elasticsearch-tool.ipynb Cell 18 line 2
20 """Use the tool asynchronously."""
21 raise NotImplementedError("custom_search does not support async")
---> 23 search_tool = FakeSearchTool(
24 config=Config(init_arg1="arg1", init_arg2="arg2")
25 )
26 print(search_tool("What is my answer?"))
/ai-service/notebooks/agent-with-elasticsearch-tool.ipynb Cell 18 line 7
6 def __init__(self, config: Config):
----> 7 self.config = config
File ~/.pyenv/versions/3.11.5/envs/ai-service/lib/python3.11/site-packages/pydantic/v1/main.py:405, in BaseModel.__setattr__(self, name, value)
402 else:
403 self.__dict__[name] = value
--> 405 self.__fields_set__.add(name)
AttributeError: 'FakeSearchTool' object has no attribute '__fields_set__'
```
</details>
### Expected behavior
I expect to get the following output out of the tool:
> Query: What is my answer?
> Obtained config: arg1 arg2
> Used the tool, and got the answer using the config!
|
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PR_kwDOIPDwls5dzLfx
| 12,303 |
Better wrap traceable
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[] | null | 1 | 2023-10-25T21:40:03 | 2023-10-25T23:34:24 | 2023-10-25T23:34:23 |
COLLABORATOR
| null |
If user function is wrapped as a traceable function, this will help hand off the trace between the two.
Also update handling fields to reflect optional values
|
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Error querying DocArrayInMemorySearch
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| false | null |
[] | null | 5 | 2023-10-25T20:49:36 | 2023-12-03T17:16:59 | null |
NONE
| null |
### System Info
I start a jupyter notebook with
```python
file = 'OutdoorClothingCatalog_1000.csv'
loader = CSVLoader(file_path=file)
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator(
vectorstore_cls=DocArrayInMemorySearch
).from_loaders([loader])
query ="Please list all your shirts with sun protection \
in a table in markdown and summarize each one."
esponse = index.query(query)
```
Unfortunately this leads to
```log
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
[/workspaces/langchaintutorial/L4-QnA.ipynb](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/workspaces/langchaintutorial/L4-QnA.ipynb) Zelle 17 line 1
----> [1](vscode-notebook-cell://codespaces%2Bautomatic-funicular-qpwv7xqr7j249j6/workspaces/langchaintutorial/L4-QnA.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0) response = index.query(query)
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/indexes/vectorstore.py:45](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/indexes/vectorstore.py:45), in VectorStoreIndexWrapper.query(self, question, llm, retriever_kwargs, **kwargs)
41 retriever_kwargs = retriever_kwargs or {}
42 chain = RetrievalQA.from_chain_type(
43 llm, retriever=self.vectorstore.as_retriever(**retriever_kwargs), **kwargs
44 )
---> 45 return chain.run(question)
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py:505](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py:505), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
503 if len(args) != 1:
504 raise ValueError("`run` supports only one positional argument.")
--> 505 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
506 _output_key
507 ]
509 if kwargs and not args:
510 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
511 _output_key
512 ]
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py:310](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py:310), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py:304](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py:304), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
297 run_manager = callback_manager.on_chain_start(
298 dumpd(self),
299 inputs,
300 name=run_name,
301 )
302 try:
303 outputs = (
--> 304 self._call(inputs, run_manager=run_manager)
305 if new_arg_supported
306 else self._call(inputs)
307 )
308 except BaseException as e:
309 run_manager.on_chain_error(e)
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py:136](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py:136), in BaseRetrievalQA._call(self, inputs, run_manager)
132 accepts_run_manager = (
133 "run_manager" in inspect.signature(self._get_docs).parameters
134 )
135 if accepts_run_manager:
--> 136 docs = self._get_docs(question, run_manager=_run_manager)
137 else:
138 docs = self._get_docs(question) # type: ignore[call-arg]
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py:216](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/retrieval_qa/base.py:216), in RetrievalQA._get_docs(self, question, run_manager)
209 def _get_docs(
210 self,
211 question: str,
212 *,
213 run_manager: CallbackManagerForChainRun,
214 ) -> List[Document]:
215 """Get docs."""
--> 216 return self.retriever.get_relevant_documents(
217 question, callbacks=run_manager.get_child()
218 )
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/schema/retriever.py:211](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/schema/retriever.py:211), in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)
209 except Exception as e:
210 run_manager.on_retriever_error(e)
--> 211 raise e
212 else:
213 run_manager.on_retriever_end(
214 result,
215 **kwargs,
216 )
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/schema/retriever.py:204](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/schema/retriever.py:204), in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)
202 _kwargs = kwargs if self._expects_other_args else {}
203 if self._new_arg_supported:
--> 204 result = self._get_relevant_documents(
205 query, run_manager=run_manager, **_kwargs
206 )
207 else:
208 result = self._get_relevant_documents(query, **_kwargs)
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/schema/vectorstore.py:585](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/schema/vectorstore.py:585), in VectorStoreRetriever._get_relevant_documents(self, query, run_manager)
581 def _get_relevant_documents(
582 self, query: str, *, run_manager: CallbackManagerForRetrieverRun
583 ) -> List[Document]:
584 if self.search_type == "similarity":
--> 585 docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
586 elif self.search_type == "similarity_score_threshold":
587 docs_and_similarities = (
588 self.vectorstore.similarity_search_with_relevance_scores(
589 query, **self.search_kwargs
590 )
591 )
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/vectorstores/docarray/base.py:127](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/vectorstores/docarray/base.py:127), in DocArrayIndex.similarity_search(self, query, k, **kwargs)
115 def similarity_search(
116 self, query: str, k: int = 4, **kwargs: Any
117 ) -> List[Document]:
118 """Return docs most similar to query.
119
120 Args:
(...)
125 List of Documents most similar to the query.
126 """
--> 127 results = self.similarity_search_with_score(query, k=k, **kwargs)
128 return [doc for doc, _ in results]
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/vectorstores/docarray/base.py:106](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/vectorstores/docarray/base.py:106), in DocArrayIndex.similarity_search_with_score(self, query, k, **kwargs)
94 """Return docs most similar to query.
95
96 Args:
(...)
103 Lower score represents more similarity.
104 """
105 query_embedding = self.embedding.embed_query(query)
--> 106 query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
107 docs, scores = self.doc_index.find(query_doc, search_field="embedding", limit=k)
109 result = [
110 (Document(page_content=doc.text, metadata=doc.metadata), score)
111 for doc, score in zip(docs, scores)
112 ]
File [/usr/local/python/3.10.8/lib/python3.10/site-packages/pydantic/main.py:164](https://vscode-remote+codespaces-002bautomatic-002dfunicular-002dqpwv7xqr7j249j6.vscode-resource.vscode-cdn.net/usr/local/python/3.10.8/lib/python3.10/site-packages/pydantic/main.py:164), in BaseModel.__init__(__pydantic_self__, **data)
162 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
163 __tracebackhide__ = True
--> 164 __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
ValidationError: 2 validation errors for DocArrayDoc
text
Field required [type=missing, input_value={'embedding': [0.00328089... -0.021201754016760964]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.4/v/missing
metadata
Field required [type=missing, input_value={'embedding': [0.00328089... -0.021201754016760964]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.4/v/missing
```
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
file = 'OutdoorClothingCatalog_1000.csv'
loader = CSVLoader(file_path=file)
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator(
vectorstore_cls=DocArrayInMemorySearch
).from_loaders([loader])
query ="Please list all your shirts with sun protection \
in a table in markdown and summarize each one."
esponse = index.query(query)
```
### Expected behavior
List of articles with sun protection. The script is an example from deeplearning.ai
|
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PR_kwDOIPDwls5dy0Qy
| 12,301 |
Update telegram.py
|
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[] | null | 1 | 2023-10-25T20:20:42 | 2023-10-25T20:26:41 | 2023-10-25T20:26:41 |
NONE
| null |
This will add the message URL to the existing data returned by TelegramChatApiLoader without needing to modify much else in the source code.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **This will add the message URL to the existing data returned by TelegramChatApiLoader without needing to modify much else in the source code.:** a description of the change,
- **12246** 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,962,158,685 |
PR_kwDOIPDwls5dyyj0
| 12,300 |
Relax type annotation for custom input/output types
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[] | null | 1 | 2023-10-25T20:14:48 | 2023-10-25T23:00:23 | 2023-10-25T23:00:22 |
COLLABORATOR
| null |
This is needed to be able to do stuff like:
```python
runnable.with_types(input_type=List[str])
```
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PR_kwDOIPDwls5dyuqN
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15 Oct Update telegram.py
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[] | null | 1 | 2023-10-25T20:03:24 | 2023-10-25T20:05:19 | 2023-10-25T20:05:19 |
NONE
| null |
Update Telegram.py This will add the message URL to the existing data returned by TelegramChatApiLoader without needing to modify much else in the source code.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **This will add the message URL to the existing data returned by TelegramChatApiLoader without needing to modify much else in the source code.:** a description of the change,
- **12246:** 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,962,120,048 |
PR_kwDOIPDwls5dyqMG
| 12,298 |
feat: Add Google Speech to Text API Document Loader
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[] | null | 3 | 2023-10-25T19:50:35 | 2023-10-27T17:14:59 | 2023-10-27T16:34:26 |
CONTRIBUTOR
| null |
- Add Document Loader for Google Speech to Text
- Similar Structure to [Assembly AI Document Loader][1]
[1]: https://python.langchain.com/docs/integrations/document_loaders/assemblyai
<!-- Thank you for contributing to LangChain!
Replace this entire 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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| 1,962,099,017 |
PR_kwDOIPDwls5dylm7
| 12,297 |
Strips leading/trailing whitespace before parsing xml
|
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[] | null | 2 | 2023-10-25T19:36:49 | 2023-10-25T20:34:59 | 2023-10-25T20:34:59 |
CONTRIBUTOR
| null |
**Description:** When llms output leading or trailing whitespace for xml (when using XMLOutputParser) the parser would raise a `ValueError: Could not parse output: ...`. However, leading or trailing whitespace are "ignorable" in the sense of XML standard.
**Issue:** I did not find an issue related.
**Dependencies:** None
**Tag maintainer:**
**Twitter handle:** donatoaz
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
Done, updated unit test and ran `make docker_test`.
|
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PR_kwDOIPDwls5dyYYt
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CLI Point to Templates
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[] | null | 1 | 2023-10-25T18:57:39 | 2023-10-25T22:08:10 | 2023-10-25T22:08:09 |
COLLABORATOR
| null |
Fixes
- points to langserve-templates branch by default for pulling templates
|
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I_kwDOIPDwls508ks9
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SQLDatabaseChain Error
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[] | null | 2 | 2023-10-25T18:56:26 | 2023-11-22T04:01:51 | null |
NONE
| null |
### System Info
Langchain version == 0.0.323
python == 3.10
```
I have a simple code to query a postgres DB:
from langchain.llms import Bedrock
from langchain_experimental.sql import SQLDatabaseChain
db = SQLDatabase.from_uri(database_uri="postgresql://xxx:yyy:zzzzz")
llm = Bedrock(
credentials_profile_name="langchain",
model_id="anthropic.claude-v2"
)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("how many lab reports are there?")
```
Get the following error:
```
Entering new SQLDatabaseChain chain...
how many lab reports are there?
SQLQuery:Here is the query and response for your question:
Question: how many lab reports are there?
SQLQuery: SELECT COUNT(*) FROM lab_reportsTraceback (most recent call last):
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1969, in _exec_single_context
self.dialect.do_execute(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/default.py", line 922, in do_execute
cursor.execute(statement, parameters)
psycopg2.errors.SyntaxError: syntax error at or near "Here"
LINE 1: Here is the query and response for your question:
^
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/ec2-user/environment/Langchain/langchain-sql.py", line 12, in <module>
db_chain.run("how many lab reports are there?")
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain/chains/base.py", line 505, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain_experimental/sql/base.py", line 198, in _call
raise exc
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain_experimental/sql/base.py", line 143, in _call
result = self.database.run(sql_cmd)
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain/utilities/sql_database.py", line 429, in run
result = self._execute(command, fetch)
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/langchain/utilities/sql_database.py", line 407, in _execute
cursor = connection.execute(text(command))
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1416, in execute
return meth(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/sql/elements.py", line 516, in _execute_on_connection
return connection._execute_clauseelement(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1639, in _execute_clauseelement
ret = self._execute_context(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1848, in _execute_context
return self._exec_single_context(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1988, in _exec_single_context
self._handle_dbapi_exception(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 2343, in _handle_dbapi_exception
raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/base.py", line 1969, in _exec_single_context
self.dialect.do_execute(
File "/home/ec2-user/miniconda3/envs/langchain3.10/lib/python3.10/site-packages/sqlalchemy/engine/default.py", line 922, in do_execute
cursor.execute(statement, parameters)
sqlalchemy.exc.ProgrammingError: (psycopg2.errors.SyntaxError) syntax error at or near "Here"
LINE 1: Here is the query and response for your question:
^
[SQL: Here is the query and response for your question:
Question: how many lab reports are there?
SQLQuery: SELECT COUNT(*) FROM lab_reports]
(Background on this error at: https://sqlalche.me/e/20/f405)
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Execute the code with a postgres DB
### Expected behavior
Expect the query to execute and get results.
|
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[] | null | 1 | 2023-10-25T18:55:50 | 2023-10-26T01:47:43 | 2023-10-26T01:47:43 |
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| null | null |
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| 1,962,023,731 |
PR_kwDOIPDwls5dyVX4
| 12,293 |
rm .html from local doc links
|
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| 1,962,011,348 |
PR_kwDOIPDwls5dySuf
| 12,292 |
fix some stuff
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| 1,961,978,551 |
PR_kwDOIPDwls5dyLzu
| 12,291 |
dev guide
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire 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 your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,961,978,458 |
I_kwDOIPDwls508Wpa
| 12,290 |
Not able to print verbose result in interface.
|
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[] | null | 3 | 2023-10-25T18:17:54 | 2023-10-25T22:19:51 | null |
NONE
| null |
### Issue you'd like to raise.
Terminal Output:
> Entering new SQLDatabaseChain chain...
how many tracks have pop genre?
SQLQuery:SELECT COUNT(*) FROM tracks WHERE "GenreId" = 3 LIMIT 5;
SQLResult: [(374,)]
Answer:374 tracks have pop genre.
> Finished chain.
How do I access and print the SQLQuery, SQLResult in my ui?
im only able to print the Answer here in response
with st.chat_message("bot"):
st.markdown(response)
### Suggestion:
_No response_
|
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| 1,961,971,418 |
PR_kwDOIPDwls5dyKRY
| 12,289 |
chat loader doc titles
|
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PR_kwDOIPDwls5dyDVA
| 12,288 |
fixes to cli
|
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| 1,961,919,942 |
I_kwDOIPDwls508IXG
| 12,287 |
langchain-experimental csv_agent how to?
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| null |
### Issue with current documentation:
How can I use csv_agent with langchain-experimental being that importing csv_agent from langchain.agents now produces this error:
On 2023-10-27 this module will be be deprecated from langchain, and will be available from the langchain-experimental package.This code is already available in langchain-experimental.See https://github.com/langchain-ai/langchain/discussions/11680.
### Idea or request for content:
_No response_
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CLI
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Remove CLI
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See contribution guidelines for more information on how to write/run tests, lint, etc:
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Pop max concurrency when recursing
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I_kwDOIPDwls507fiB
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DOC: Error in code for splitting and summarizing in a single chain
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NONE
| null |
### Issue with current documentation:
There is an error in the code. [link](https://python.langchain.com/docs/use_cases/summarization#splitting-and-summarizing-in-a-single-chain)
```
from langchain.chains import AnalyzeDocumentChain
summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=chain, text_splitter=text_splitter)
summarize_document_chain.run(docs[0])
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[17], line 4
1 from langchain.chains import AnalyzeDocumentChain
3 summarize_document_chain = AnalyzeDocumentChain(combine_docs_chain=chain, text_splitter=text_splitter)
----> 4 summarize_document_chain.run(docs[0])
File ~/langchain/libs/langchain/langchain/chains/base.py:496, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
459 """Convenience method for executing chain.
460
461 The main difference between this method and `Chain.__call__` is that this
(...)
493 # -> "The temperature in Boise is..."
494 """
495 # Run at start to make sure this is possible/defined
--> 496 _output_key = self._run_output_key
498 if args and not kwargs:
499 if len(args) != 1:
File ~/langchain/libs/langchain/langchain/chains/base.py:445, in Chain._run_output_key(self)
442 @property
443 def _run_output_key(self) -> str:
444 if len(self.output_keys) != 1:
--> 445 raise ValueError(
446 f"`run` not supported when there is not exactly "
447 f"one output key. Got {self.output_keys}."
448 )
449 return self.output_keys[0]
ValueError: `run` not supported when there is not exactly one output key. Got ['output_text', 'intermediate_steps'].
```
### Idea or request for content:
There is nothing missing but the code needs to be accurate so the code returns the expected output.
|
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| 1,961,749,463 |
I_kwDOIPDwls507evX
| 12,279 |
Langchain js - can't get token usage
|
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[] | null | 3 | 2023-10-25T15:54:36 | 2023-10-25T17:45:16 | null |
NONE
| null |
### System Info
Langchain JS 0.0.172 on DEbian Linux 12 using Nodejs 18.15.0
With the following code I don't get the tokenUsage back from the chain/ call. I checked the docs, asked the AI and did a google search. Am I doing anything wrong?
`const chain = RetrievalQAChain.fromLLM(
model,
vectorStore.asRetriever(),
{
verbose: true,
}
)
const res = await chain.call({
context: "You assist a Systems Engineer. Be as concise as possible.",
query: prompt,
})
console.log('usage:', res.tokenUsage);
`
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { JSONLoader } from "langchain/document_loaders/fs/json";
import { CSVLoader } from "langchain/document_loaders/fs/csv";
import { OpenAI } from "langchain/llms/openai";
import { RetrievalQAChain, loadQARefineChain } from "langchain/chains";
import { HNSWLib } from "langchain/vectorstores/hnswlib";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import * as dotenv from 'dotenv';
dotenv.config()
const loader = new DirectoryLoader("./docs", {
".json": (path) => new JSONLoader(path),
".csv": (path) => new CSVLoader(path),
".txt": (path) => new TextLoader(path)
})
const normalizeDocuments = (docs) => {
return docs.map((doc) => {
if (typeof doc.pageContent === "string") {
return doc.pageContent;
} else if (Array.isArray(doc.pageContent)) {
return doc.pageContent.join("\n");
}
});
}
const VECTOR_STORE_PATH = "Documents.index";
export const run = async (params) => {
console.log("Loading docs...")
const docs = await loader.load();
console.log('Processing...')
const model = new OpenAI({
openAIApiKey: process.env.OPENAI_API_KEY,
modelName: "gpt-3.5-turbo",
maxTokens: 2500,
temperature: 0.1,
maxConcurrency: 1,
});
let vectorStore;
try{
console.log("Checking for local vectorStore");
const embeddings = new OpenAIEmbeddings(process.env.OPENAI_API_KEY);
vectorStore = await HNSWLib.load(VECTOR_STORE_PATH, embeddings);
}catch(e){
console.log('Creating new vector store...')
const textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 3000,
});
const normalizedDocs = normalizeDocuments(docs);
const splitDocs = await textSplitter.createDocuments(normalizedDocs);
console.log('splitdos : ',splitDocs)
// 8. Generate the vector store from the documents
vectorStore = await HNSWLib.fromDocuments(
splitDocs,
new OpenAIEmbeddings()
);
await vectorStore.save(VECTOR_STORE_PATH);
console.log("Vector store created.")
}
console.log("Creating retrieval chain...")
const chain = RetrievalQAChain.fromLLM(
model,
vectorStore.asRetriever(),
{
verbose: true,
}
)
//const promptStart = `rewrite all requirements using the following guidelines:`;
const promptStart = ``;
//const reWrite = `Rewrite the Requirement Text so that is does comply.`;
const reWrite = ``;
const prompts = [
`R3: Requirements should be consistent with each other and should not contain contradictions or conflicts. Report the Id\'s for those that do not comply. Rewrite the Requirement Text so that is does comply with R3 `,
];
prompts.forEach(async prompt => {
//console.log("Querying chain...")
const res = await chain.call({
context: "You assist a Systems Engineer. Be as concise as possible.",
query: prompt,
})
console.log('usage:', res.tokenUsage);
console.log(prompt);
console.log('Reply: ',res, '\n')
});
}
run(process.argv.slice(2))
### Expected behavior
I expect the res object to return the token usage
|
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PR_kwDOIPDwls5dxZhp
| 12,278 |
bump 323
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[] | null | 1 | 2023-10-25T15:52:14 | 2023-10-25T16:06:13 | 2023-10-25T16:06:12 |
COLLABORATOR
| null | null |
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| 1,961,684,616 |
PR_kwDOIPDwls5dxMVK
| 12,277 |
feat: Add page metadata on PDFMinerLoader
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CONTRIBUTOR
| null |
- **Description:** #12273 's suggestion PR
Like other PDFLoader, loading pdf per each page and giving page metadata.
- **Issue:** #12273
- **Twitter handle:** @blue0_0hope
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I_kwDOIPDwls507HwN
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LLM (chain) output parsing in MultiRetrievalQAChain: OutputParserException Got invalid JSON object
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[] | null | 1 | 2023-10-25T15:07:03 | 2023-10-25T17:59:09 | null |
NONE
| null |
### System Info
System:
LangChain 0.0.321
Python 3.10
I'm trying to build a MultiRetrievalQAChain using only Llama2 chat models served by vLLM (no OpenAI). For that end I have created a ConversationChain that acts as the default chain for the MultiRetrievalQAChain. I have customized the prompts for both chains to meet LLama 2 Chat format requirements. It looks like the routing chain works properly but I'm getting the following exception:
```
[chain/error] [1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain] [7.00s] Chain run errored with error:
"OutputParserException(\"Parsing text ... raised following error:\\nGot invalid JSON object. Error: Expecting value: line 1 column 1 (char 0)\")"
```
Here the routing and generation trace:
```
[32;1m[1;3m[chain/start][0m [1m[1:chain:MultiRetrievalQAChain] Entering Chain run with input:
[0m{
"input": "What is prompt injection?"
}
[32;1m[1;3m[chain/start][0m [1m[1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain] Entering Chain run with input:
[0m{
"input": "What is prompt injection?"
}
[32;1m[1;3m[chain/start][0m [1m[1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain > 3:chain:LLMChain] Entering Chain run with input:
[0m{
"input": "What is prompt injection?"
}
[32;1m[1;3m[llm/start][0m [1m[1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain > 3:chain:LLMChain > 4:llm:VLLMOpenAI] Entering LLM run with input:
[0m{
"prompts": [
"Given a query to a question answering system select the system best suited for the input. You will be given the names of the available systems and a description of what questions the system is best suited for. You may also revise the original input if you think that revising it will ultimately lead to a better response.\n\n<< FORMATTING >>\nReturn a markdown code snippet with a JSON object formatted to look like:\n```json\n{\n \"destination\": string \\ name of the question answering system to use or \"DEFAULT\"\n \"next_inputs\": string \\ a potentially modified version of the original input\n}\n```\n\nREMEMBER: \"destination\" MUST be one of the candidate prompt names specified below OR it can be \"DEFAULT\" if the input is not well suited for any of the candidate prompts.\nREMEMBER: \"next_inputs\" can just be the original input if you don't think any modifications are needed.\n\n<< CANDIDATE PROMPTS >>\nNIST AI Risk Management Framework: Guidelines provided by the NIST for organizations and people to manage risks associated with the use of AI. \n The NIST risk management framework consists of four cyclical tasks: Govern, Map, Measure and Manage.\nOWASP Top 10 for LLM Applications: Provides practical security guidance to navigate the complex and evolving terrain of LLM security focusing on the top 10 vulnerabilities of LLM applications. These are 1) Prompt Injection, 2) Insecure Output Handling, 3) Training Data Poisoning, 4) Model Denial of Service, 5) Supply Chain Vulnerabilities, 6) Sensitive Information Disclosure, 7) Insecure Plugin Design\n 8) Excessive Agency, 9) Overreliance, and 10) Model Theft\n \nThreat Modeling LLM Applications: A high-level example from Gavin Klondike on how to build a threat model for LLM applications utilizing the STRIDE modeling framework based on trust boundaries.\n\n<< INPUT >>\nWhat is prompt injection?\n\n<< OUTPUT >>"
]
}
/home/vmuser/miniconda3/envs/llm-env2/lib/python3.10/site-packages/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
warnings.warn(
[36;1m[1;3m[llm/end][0m [1m[1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain > 3:chain:LLMChain > 4:llm:VLLMOpenAI] [7.00s] Exiting LLM run with output:
[0m{
"generations": [
[
{
"text": "Prompt injection is a security vulnerability in LLM applications where an attacker can manipulate the input prompts to an LLM model to elicit a specific response from the model. This can be done by exploiting the lack of proper input validation and sanitization in the model's architecture. \n",
"generation_info": {
"finish_reason": "length",
"logprobs": null
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 488,
"completion_tokens": 512,
"total_tokens": 1000
},
"model_name": "meta-llama/Llama-2-7b-chat-hf"
},
"run": null
}
[36;1m[1;3m[chain/end][0m [1m[1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain > 3:chain:LLMChain] [7.00s] Exiting Chain run with output:
[0m{
"text": "Prompt injection is a security vulnerability in LLM applications where an attacker can manipulate the input prompts to an LLM model to elicit a specific response from the model. This can be done by exploiting the lack of proper input validation and sanitization in the model's architecture.\n"
}
[31;1m[1;3m[chain/error][0m [1m[1:chain:MultiRetrievalQAChain > 2:chain:LLMRouterChain] [7.00s] Chain run errored with error:
[0m"OutputParserException(\"Parsing text\\nPrompt injection is a security vulnerability in LLM applications where an attacker can manipulate the input prompts to an LLM model to elicit a specific response from the model. This can be done by exploiting the lack of proper input validation and sanitization in the model's architecture.\\\n\\n raised following error:\\nGot invalid JSON object. Error: Expecting value: line 1 column 1 (char 0)\")"
[0;31m---------------------------------------------------------------------------[0m
[0;31mJSONDecodeError[0m Traceback (most recent call last)
File [0;32m~/miniconda3/envs/llm-env2/lib/python3.10/site-packages/langchain/output_parsers/json.py:163[0m, in [0;36mparse_and_check_json_markdown[0;34m(text, expected_keys)[0m
[1;32m 162[0m [38;5;28;01mtry[39;00m:
[0;32m--> 163[0m json_obj [38;5;241m=[39m [43mparse_json_markdown[49m[43m([49m[43mtext[49m[43m)[49m
[1;32m 164[0m [38;5;28;01mexcept[39;00m json[38;5;241m.[39mJSONDecodeError [38;5;28;01mas[39;00m e:
raised following error:
Got invalid JSON object. Error: Expecting value: line 1 column 1 (char 0)
```
The issue seems to be related to a warning that I'm also getting: `llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.`
Unfortunately it is unclear how one is supposed to implement an output parser for the LLM (ConversationChain) chain that meets expectations from the MultiRetrievalQAChain. The documentation for these chains relies a lot on OpenAI models to do the formatting but there's no much guidance on how to do it with other LLMs.
Any guidance on how to move forward would be appreciated.
Here my code:
```
import torch
import langchain
langchain.debug = True
from langchain.llms import VLLMOpenAI
from langchain.document_loaders import PyPDFLoader
from langchain.prompts import PromptTemplate
# Import for retrieval-augmented generation RAG
from langchain import hub
from langchain.chains import ConversationChain, MultiRetrievalQAChain
from langchain.vectorstores import Chroma
from langchain.text_splitter import SentenceTransformersTokenTextSplitter
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
#%%
# URL for the vLLM service
INFERENCE_SRV_URL = "http://localhost:8000/v1"
def setup_chat_llm(vllm_url, max_tokens=512, temperature=0):
"""
Intializes the vLLM service object.
:param vllm_url: vLLM service URL
:param max_tokens: Max number of tokens to get generated by the LLM
:param temperature: Temperature of the generation process
:return: The vLLM service object
"""
chat = VLLMOpenAI(
model_name="meta-llama/Llama-2-7b-chat-hf",
openai_api_key="EMPTY",
openai_api_base=vllm_url,
temperature=temperature,
max_tokens=max_tokens,
)
return chat
#%%
# Initialize LLM service
llm = setup_chat_llm(vllm_url=INFERENCE_SRV_URL)
#%%
%%time
# Set up the embedding encoder (Sentence Transformers) and vector store
model_name = "all-mpnet-base-v2"
model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = SentenceTransformerEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
# Set up the document splitter
text_splitter = SentenceTransformersTokenTextSplitter(chunk_size=500, chunk_overlap=0)
# Load PDF documents
loader = PyPDFLoader(file_path="../data/AI_RMF_Playbook.pdf")
rmf_splits = loader.load_and_split()
rmf_retriever = Chroma.from_documents(documents=rmf_splits, embedding=embeddings)
loader = PyPDFLoader(file_path="../data/OWASP-Top-10-for-LLM-Applications-v101.pdf")
owasp_splits = loader.load_and_split()
owasp_retriever = Chroma.from_documents(documents=owasp_splits, embedding=embeddings)
loader = PyPDFLoader(file_path="../data/Threat Modeling LLM Applications - AI Village.pdf")
ai_village_splits = loader.load_and_split()
ai_village_retriever = Chroma.from_documents(documents=ai_village_splits, embedding=embeddings)
#%%
retrievers_info = [
{
"name": "NIST AI Risk Management Framework",
"description": """Guidelines provided by the NIST for organizations and people to manage risks associated with the use of AI.
The NIST risk management framework consists of four cyclical tasks: Govern, Map, Measure and Manage.""",
"retriever": rmf_retriever.as_retriever()
},
{
"name": "OWASP Top 10 for LLM Applications",
"description": """Provides practical security guidance to navigate the complex and evolving terrain of LLM security focusing on the top 10 vulnerabilities of LLM applications. These are 1) Prompt Injection, 2) Insecure Output Handling, 3) Training Data Poisoning, 4) Model Denial of Service, 5) Supply Chain Vulnerabilities, 6) Sensitive Information Disclosure, 7) Insecure Plugin Design
8) Excessive Agency, 9) Overreliance, and 10) Model Theft
""",
"retriever": owasp_retriever.as_retriever()
},
{
"name": "Threat Modeling LLM Applications",
"description": "A high-level example from Gavin Klondike on how to build a threat model for LLM applications utilizing the STRIDE modeling framework based on trust boundaries.",
"retriever": ai_village_retriever.as_retriever()
}
]
#%%
prompt_template = (
""" [INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>>
Question: {query}
Context: {history}
Answer: [/INST]
""")
prompt = PromptTemplate(template=prompt_template, input_variables=['history', 'query'])
routing_prompt_template = (
"""
[INST]<<SYS>> Given a query to a question answering system select the system best suited for the input. You will be given the names of the available systems and a description of what questions the system is best suited for. You could also revise the original input if you think that revising it will ultimately lead to a better response.
Return a markdown code snippet with a JSON object formatted as follows:
\```json
\{
"destination": "destination_key_value"
"next_inputs": "revised_or_original_input"
\}
\```
WHERE:
- The "destination" key value MUST be a text string matching one of the candidate prompt names specified below OR it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
- The "next_inputs" key value can just be the original input string if you don't think any modifications are needed.
<</SYS>>
<< CANDIDATE PROMPTS >>
{destinations}
<< INPUT >>
{input}
<< OUTPUT >>
[/INST]
""")
routing_prompt = PromptTemplate(template=routing_prompt_template, input_variables=['destinations', 'input'])
#%%
default_chain = ConversationChain(
llm=llm, # Your own LLM
prompt=prompt, # Your own prompt
input_key="query",
output_key="result",
verbose=True,
)
multi_retriever_chain = MultiRetrievalQAChain.from_retrievers(
llm=llm,
retriever_infos=retrievers_info,
default_chain=default_chain,
default_prompt=routing_prompt,
default_retriever=rmf_retriever.as_retriever(),
verbose=True
)
#%%
question = "What is prompt injection?"
result = multi_retriever_chain.run(question)
#%%
result
#%%
#%%
from langchain.chains.router.multi_retrieval_prompt import (
MULTI_RETRIEVAL_ROUTER_TEMPLATE,
)
MULTI_RETRIEVAL_ROUTER_TEMPLATE
#%%
print(MULTI_RETRIEVAL_ROUTER_TEMPLATE)
#%%
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I included the entire script I'm using
### Expected behavior
Proper query/question routing to the retriever better suited to provide the content required by the LLM to answer a question.
|
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PR_kwDOIPDwls5dxBXc
| 12,275 |
CohereEmbeddings: Add max_retries and request_timeout
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[] | null | 1 | 2023-10-25T14:59:13 | 2023-10-27T17:32:36 | 2023-10-25T17:37:25 |
CONTRIBUTOR
| null |
Add max_retries and request_timeout to CohereEmbeddings, akin to how it works in OpenAIEmbeddings.
Since the Cohere client already implements these parameters, we can simply pass them down.
Uses parameters from these two cohere client objects:
https://github.com/cohere-ai/cohere-python/blob/main/cohere/client.py
https://github.com/cohere-ai/cohere-python/blob/main/cohere/client_async.py
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Add Cube Semantic Layer Agent
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CONTRIBUTOR
| null |
- **Description:** SQL Chains and Agents support building and running SQL queries based on natural language prompts. However, when faced with complex queries, they may not perform well in producing results.
Now, I'm providing a Cube Semantic Layer Agent, which can better support complex queries. A Cube data model is used to transform raw data into meaningful business definitions and pre-aggregate data for optimal results.
- **Twitter handle:** @JohnMai95
- **Tag maintainer:** @baskaryan @hwchase17 @eyurtsev
<img width="1307" alt="ag" src="https://github.com/langchain-ai/langchain/assets/1825679/fbea9996-9eac-4a9c-baab-08ce74bbb3df">
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Issue: `PDFMinerLoader` not gives `page` metadata when loading with `extract_images=False` - is it intended?
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[] | null | 2 | 2023-10-25T14:48:30 | 2023-11-02T05:03:37 | 2023-11-02T05:03:37 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Unlike other PDF loaders, I've found that only the PDFMinerLoader doesn't provide pages in metadata when loading. Digging deeper, the PDFMinerParser only gives the page metadata when extract_images=True. I wonder if this was intentional.
### Suggestion:
#12277
|
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| 1,961,606,028 |
I_kwDOIPDwls5067uM
| 12,272 |
User based memorization with ConversationBufferMemory and CombinedMemory
|
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[] | null | 1 | 2023-10-25T14:45:46 | 2023-10-25T14:56:17 | null |
NONE
| null |
### System Info
langchain version: 0.0320
python version: 3.10
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
User based memorization with ConversationBufferMemory and CombinedMemory. Attaching my code below, Please let me know where I am doing wrong:
session_id= "user2"
vect_memory = VectorStoreRetrieverMemory(retriever=retriever)
_DEFAULT_TEMPLATE = """
You are having a conversation with a human. Please interact like a human being and give an accurate answer.
Relevant pieces of the previous conversation:
{history}
(You do not need to use these pieces of information if not relevant)
Current conversation:
{session_id}
User: {input}
Chatbot:
"""
PROMPT = PromptTemplate(
input_variables=["history","session_id", "input"],
template=_DEFAULT_TEMPLATE
)
print(PROMPT)
message_history = RedisChatMessageHistory(url=redis_url, session_id=session_id)
conv_memory = ConversationBufferMemory(
memory_key="session_id",
chat_memory=message_history,
input_key="input",
prompt=PROMPT,
return_messages=True
)
memory = CombinedMemory(memories=[conv_memory, vect_memory])
llm = OpenAI(temperature=0, model_name='gpt-3.5-turbo') # Can be any valid LLM
conversation_with_summary = ConversationChain(
llm=llm,
prompt=PROMPT,
memory=memory,
verbose=True,
)
conversation_with_summary(input="hELLO")
It is not working on the base of session.
=================I am getting sthis error=======================
Traceback (most recent call last):
File "C:\Users\ankit\PycharmProjects\spendidchatbot\chatbot.py", line 306, in <module>
conversation_with_summary = ConversationChain(
File "C:\Users\ankit\PycharmProjects\spendidchatbot\env_spen\lib\site-packages\langchain\load\serializable.py", line 97, in _init_
super().__init__(**kwargs)
File "C:\Users\ankit\PycharmProjects\spendidchatbot\env_spen\lib\site-packages\pydantic\v1\main.py", line 341, in _init_
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for ConversationChain
_root_
Got unexpected prompt input variables. The prompt expects ['history', 'input', 'session_id'], but got ['user2', 'history'] as inputs from memory, and input as the normal input key. (type=value_error)
### Expected behavior
User based memorization with ConversationBufferMemory and CombinedMemory. Attaching my code below, Please let me know where I am doing wrong:
session_id= "user2"
vect_memory = VectorStoreRetrieverMemory(retriever=retriever)
_DEFAULT_TEMPLATE = """
You are having a conversation with a human. Please interact like a human being and give an accurate answer.
Relevant pieces of the previous conversation:
{history}
(You do not need to use these pieces of information if not relevant)
Current conversation:
{session_id}
User: {input}
Chatbot:
"""
PROMPT = PromptTemplate(
input_variables=["history","session_id", "input"],
template=_DEFAULT_TEMPLATE
)
print(PROMPT)
message_history = RedisChatMessageHistory(url=redis_url, session_id=session_id)
conv_memory = ConversationBufferMemory(
memory_key="session_id",
chat_memory=message_history,
input_key="input",
prompt=PROMPT,
return_messages=True
)
memory = CombinedMemory(memories=[conv_memory, vect_memory])
llm = OpenAI(temperature=0, model_name='gpt-3.5-turbo') # Can be any valid LLM
conversation_with_summary = ConversationChain(
llm=llm,
prompt=PROMPT,
memory=memory,
verbose=True,
)
conversation_with_summary(input="hELLO")
It is not working on the base of session.
=================I am getting sthis error=======================
Traceback (most recent call last):
File "C:\Users\ankit\PycharmProjects\spendidchatbot\chatbot.py", line 306, in <module>
conversation_with_summary = ConversationChain(
File "C:\Users\ankit\PycharmProjects\spendidchatbot\env_spen\lib\site-packages\langchain\load\serializable.py", line 97, in _init_
super().__init__(**kwargs)
File "C:\Users\ankit\PycharmProjects\spendidchatbot\env_spen\lib\site-packages\pydantic\v1\main.py", line 341, in _init_
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for ConversationChain
_root_
Got unexpected prompt input variables. The prompt expects ['history', 'input', 'session_id'], but got ['user2', 'history'] as inputs from memory, and input as the normal input key. (type=value_error)
|
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| 1,961,603,019 |
PR_kwDOIPDwls5dw6Rp
| 12,271 |
Update stuff_prompt.py (Fix the sources field is blank)
|
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open
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[] | null | 2 | 2023-10-25T14:44:22 | 2023-10-30T07:59:44 | null |
NONE
| null |
**Description:** This change aims to resolve the issue by enhancing source extraction from the following link: [LangChain Issue #9913](https://github.com/langchain-ai/langchain/issues/9913). The problem is fixed by replacing "source" in the prompt with "reference."
**Dependencies:** There are no specific dependencies required for this change.
**Twitter handle:** [@davinci137](https://twitter.com/davinci137).
The "SOURCES" field is currently empty due to a split occurring at the word "Source:."
```python
# In the middle of the answer, a split is performed
def _split_sources(self, answer: str) -> Tuple[str, str]:
"""
This function is responsible for splitting sources from the answer.
Args:
answer (str): The answer text.
Returns:
Tuple[str, str]: A tuple containing the modified answer and extracted sources.
"""
if re.search(r"SOURCES?:", answer, re.IGNORECASE):
# It utilizes regular expressions to split the answer and sources based on "SOURCES:" or "QUESTION:" (case-insensitive).
answer, sources = re.split(r"SOURCES?:|QUESTION:\s", answer, flags=re.IGNORECASE)[:2]
# The sources are split based on newline characters, and the first line is extracted, with leading and trailing whitespace removed.
sources are then stripped.
else:
sources = ""
return answer, sources
|
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| 1,961,587,718 |
PR_kwDOIPDwls5dw23L
| 12,270 |
response parser for ArceeRetriever
|
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[] | null | 2 | 2023-10-25T14:38:22 | 2023-10-25T17:55:13 | 2023-10-25T17:55:13 |
CONTRIBUTOR
| null |
- **Description:** Response parser for arcee retriever,
- **Issue:** follow-up pr on #11578 and [discussion](https://github.com/arcee-ai/arcee-python/issues/15#issuecomment-1759874053),
- **Dependencies:** NA
This pr implements a parser for the response from ArceeRetreiver to convert to langchain `Document`. This closes the loop of generation and retrieval for Arcee DALMs in langchain.
The reference for the response parser is [api-docs:retrieve](https://api.arcee.ai/docs#/v2/retrieve_model)
Attaching screenshot of working implementation:
<img width="1984" alt="Screenshot 2023-10-25 at 7 42 34 PM" src="https://github.com/langchain-ai/langchain/assets/65639964/026987b9-34b2-4e4b-b87d-69fcd0c6641a">
\*api key deleted
---
Successful tests, lints, etc.
```shell
Re-run pytest with --snapshot-update to delete unused snapshots.
==================================================================================================================== slowest 5 durations =====================================================================================================================
1.56s call tests/unit_tests/schema/runnable/test_runnable.py::test_retrying
0.63s call tests/unit_tests/schema/runnable/test_runnable.py::test_map_astream
0.33s call tests/unit_tests/schema/runnable/test_runnable.py::test_map_stream_iterator_input
0.30s call tests/unit_tests/schema/runnable/test_runnable.py::test_map_astream_iterator_input
0.20s call tests/unit_tests/indexes/test_indexing.py::test_cleanup_with_different_batchsize
======================================================================================================= 1265 passed, 270 skipped, 32 warnings in 6.55s =======================================================================================================
[ "." = "" ] || poetry run black .
All done! ✨ 🍰 ✨
1871 files left unchanged.
[ "." = "" ] || poetry run ruff --select I --fix .
./scripts/check_pydantic.sh .
./scripts/check_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run black . --check
All done! ✨ 🍰 ✨
1871 files would be left unchanged.
[ "." = "" ] || poetry run mypy .
Success: no issues found in 1868 source files
poetry run codespell --toml pyproject.toml
poetry run codespell --toml pyproject.toml -w
```
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PR_kwDOIPDwls5dwsPz
| 12,268 |
Deep memory support in LangChain
|
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[] | null | 1 | 2023-10-25T14:16:28 | 2023-10-30T19:16:15 | 2023-10-30T19:16:15 |
CONTRIBUTOR
| null |
- Description: adding support to Activeloop's DeepMemory feature that boosts recall up to 25%. Added Jupyter notebook showcasing the feature and also made index params explicit.
- Twitter handle: will really appreciate if we could announce this on twitter.
|
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Finetuned openai azure models cost calculation
|
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[] | null | 3 | 2023-10-25T12:40:39 | 2023-10-26T16:38:10 | 2023-10-26T16:38:10 |
CONTRIBUTOR
| null |
**Description:**
Add cost calculation for fine tuned **Azure** with relevant unit tests.
see https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo&pivots=programming-language-studio for more information.
this PR is the result of this PR: https://github.com/langchain-ai/langchain/pull/12190
Twitter handle: @nirkopler
|
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| 1,961,319,260 |
I_kwDOIPDwls5051tc
| 12,266 |
ConversationalRetrievalChain doesn't work with ConversationEntityMemory + SQLiteEntityStore
|
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[] | null | 3 | 2023-10-25T12:34:48 | 2023-10-25T14:50:34 | 2023-10-25T14:50:33 |
NONE
| null |
### System Info
Langchain == 0.0.320
Following step by step procedures used in actual codebase followed by error message
```
llm = ChatOpenAI(openai_api_key=openai_api_key, model_name=engine)
```
```
db = Chroma(
collection_name=f'collection_1233',
embedding_function=OpenAIEmbeddings(openai_api_key=openai_api_key),
persist_directory=os.path.abspath('storage/vectors/')
)
```
```
chain = ConversationalRetrievalChain.from_llm(
retriever=db.as_retriever(),
llm=self.llm,
memory=SQLiteEntityStore(session_id=f'session_1233', db_file='storage/entities.db')
condense_question_prompt=PromptTemplate.from_template(template=template),
verbose=True
)
```
```
response = chain({"question": prompt})
```
The above step throws error as below
```
CRITICAL django Missing some input keys: {'chat_history'}
```
After providing ```chat_history```, the error is as follwed
```
CRITICAL django One input key expected got ['question', 'chat_history']
```
So, what to do now? I need the following entities to be used strictly:
- ChatOpenAI
- ConversationalRetrievalChain + Chroma
- ConversationEntityMemory + SQLiteEntityStore
Please help asap...
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Following step by step procedures used in actual codebase followed by error message
```
llm = ChatOpenAI(openai_api_key=openai_api_key, model_name=engine)
```
```
db = Chroma(
collection_name=f'collection_1233',
embedding_function=OpenAIEmbeddings(openai_api_key=openai_api_key),
persist_directory=os.path.abspath('storage/vectors/')
)
```
```
chain = ConversationalRetrievalChain.from_llm(
retriever=db.as_retriever(),
llm=self.llm,
memory=SQLiteEntityStore(session_id=f'session_1233', db_file='storage/entities.db')
condense_question_prompt=PromptTemplate.from_template(template=template),
verbose=True
)
```
```
response = chain({"question": prompt})
```
The above step throws error as below
```
CRITICAL django Missing some input keys: {'chat_history'}
```
After providing ```chat_history```, the error is as follwed
```
CRITICAL django One input key expected got ['question', 'chat_history']
```
So, what to do now? I need the following entities to be used strictly:
- ChatOpenAI
- ConversationalRetrievalChain + Chroma
- ConversationEntityMemory + SQLiteEntityStore
### Expected behavior
Conversations should be working with all chat history saved in mysqli db as well as returned to the user in the response.
|
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I_kwDOIPDwls505zHr
| 12,265 |
Upgrade to Pydantic V2
|
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open
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[] | null | 10 | 2023-10-25T12:28:55 | 2023-10-26T19:36:04 | null |
NONE
| null |
### Feature request
Langchain is currently using Pydantic V1, and I believe it would be good to upgrade this to V2
### Motivation
V2 is far more performant than V1 because of the python-rust binding
### Your contribution
I can create a PR here.
|
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| 1,961,177,348 |
I_kwDOIPDwls505TEE
| 12,264 |
Token limitation due to model's maximum context length
|
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[] | null | 1 | 2023-10-25T11:34:43 | 2023-10-25T11:49:21 | null |
NONE
| null |
### System Info
I've been using the conversation chain to retrieve answers from gpt3.5, vertex ai chat-bison. For the user's memory I've been passing the session memory appended to the conversation chain. But once the token limit exceeds I'm getting this following error from the llm
`[chain/error] [1:chain:LLMChain > 2:llm:ChatOpenAI] [1.69s] Llm run errored with error:
"InvalidRequestError(message=\"This model's maximum context length is 4097 tokens. However, your messages resulted in 9961 tokens. Please reduce the length of the messages.\", param='messages', code='context_length_exceeded', http_status=400, request_id=None)"
[chain/error] [1:chain:LLMChain] [1.72s] Chain run errored with error:
"InvalidRequestError(message=\"This model's maximum context length is 4097 tokens. However, your messages resulted in 9961 tokens. Please reduce the length of the messages.\", param='messages', code='context_length_exceeded', http_status=400, request_id=None)"
`
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Once you add history appended to the chain, with more than 4097 tokens this will appear.
### Expected behavior
The chat should work normally without any internal exception. Is there any langchain support for this issue?
|
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I_kwDOIPDwls505PQR
| 12,263 |
ConversationalRetrievalChain returns sources to questions without context
|
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[] | null | 0 | 2023-10-25T11:25:40 | 2023-11-07T19:35:17 | 2023-11-07T19:35:17 |
NONE
| null |
### Issue you'd like to raise.
`ConversationalRetrievalChain` returns sources to questions without relevant documents. The returned sources are not related to the question or the answer.
Example with relevant document:
```
chain = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(),
question_generator=question_generator,
combine_docs_chain=doc_chain,
)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
"""
The president said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who is one of the nation's top legal minds ...
SOURCES: /content/state_of_the_union.txt
```
Example without relevant document:
```
query = "How are you doing?"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
"""
I'm doing well, thank you.
SOURCES: /content/state_of_the_union.txt
"""
```
Another example without relevant document:
```
query = "Who is Elon Musk?"
result = chain({"question": query, "chat_history": chat_history})
result['answer']
"""
I don't know.
SOURCES: /content/state_of_the_union.txt
"""
```
### Suggestion:
Sources should not be returned for questions without relevant documents.
|
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| 12,262 |
Issue: No module named 'langchain.vectorstores.azure_cosmos_db_vector_search'
|
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[] | null | 1 | 2023-10-25T10:51:32 | 2023-10-25T11:00:17 | null |
NONE
| null |
### Issue you'd like to raise.
I am getting below error while trying to use Azure Cosmos DB as vector store.
**_No module named 'langchain.vectorstores.azure_cosmos_db_vector_search'_**
### Suggestion:
_No response_
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InvalidRequestError: Unrecognized request argument supplied: functions
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[] | null | 2 | 2023-10-25T09:45:12 | 2023-10-25T14:30:45 | 2023-10-25T14:30:44 |
NONE
| null |
### System Info
Python 3.11.4
LangChain 0.0.321
Platform info (WSL2):
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=20.04
DISTRIB_CODENAME=focal
DISTRIB_DESCRIPTION="Ubuntu 20.04.6 LTS"
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This is my code (based on https://python.langchain.com/docs/use_cases/question_answering/conversational_retrieval_agents and modified to use AzureOpenAI and OpenSearch):
```pyhton
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models.azure_openai import AzureChatOpenAI
from langchain.vectorstores.opensearch_vector_search import OpenSearchVectorSearch
from langchain.memory import ConversationTokenBufferMemory
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.chains import ConversationalRetrievalChain
# Load environment variables
host = os.environ['HOST']
auth = os.environ['AUTH_PASS']
index_uk = os.environ['INDEX_NAME_UK']
opensearch_url = os.environ['OPENSEARCH_URL']
embedding_model = os.environ['EMBEDDING_MODEL']
model_name = os.environ['MODEL_NAME']
openai_api_base = os.environ['OPENAI_API_BASE']
openai_api_key = os.environ['OPENAI_API_KEY']
openai_api_type = os.environ['OPENAI_API_TYPE']
openai_api_version = os.environ['OPENAI_API_VERSION']
# Define Azure OpenAI component
llm = AzureChatOpenAI(
openai_api_key = openai_api_key,
openai_api_base = openai_api_base,
openai_api_type = openai_api_type,
openai_api_version = openai_api_version,
deployment_name = model_name,
temperature=0
)
# Define Memory component for chat history
memory = ConversationTokenBufferMemory(llm=llm,memory_key="chat_history",return_messages=True, max_token_limit=1000)
# Build a Retriever
embeddings = OpenAIEmbeddings(deployment=embedding_model, chunk_size=1)
docsearch = OpenSearchVectorSearch(index_name=index_uk, embedding_function=embeddings,opensearch_url=opensearch_url, http_auth=('admin', auth))
doc_retriever = docsearch.as_retriever()
# Build a retrieval tool
from langchain.agents.agent_toolkits import create_retriever_tool
tool = create_retriever_tool(
doc_retriever,
"search_hr_documents",
"Searches and returns documents regarding HR questions."
)
tools = [tool]
# Build an Agent Constructor
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True)
result = agent_executor({"input": "hi, im bob"})
```
When I execute it, it generates the log error below:
```
InvalidRequestError Traceback (most recent call last)
[c:\k8s-developer\git\lambda-hr-docQA\conv_retrieval_tool.ipynb](file:///C:/k8s-developer/git/lambda-hr-docQA/conv_retrieval_tool.ipynb) Cell 3 line 6
[58](vscode-notebook-cell:/c%3A/k8s-developer/git/lambda-hr-docQA/conv_retrieval_tool.ipynb#W2sZmlsZQ%3D%3D?line=57) from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
[59](vscode-notebook-cell:/c%3A/k8s-developer/git/lambda-hr-docQA/conv_retrieval_tool.ipynb#W2sZmlsZQ%3D%3D?line=58) agent_executor = create_conversational_retrieval_agent(llm, tools, verbose=True)
---> [61](vscode-notebook-cell:/c%3A/k8s-developer/git/lambda-hr-docQA/conv_retrieval_tool.ipynb#W2sZmlsZQ%3D%3D?line=60) result = agent_executor({"input": "hi, im bob"})
File [c:\Users\fezzef\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\chains\base.py:310](file:///C:/Users/fezzef/AppData/Local/Programs/Python/Python311/Lib/site-packages/langchain/chains/base.py:310), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
308 except BaseException as e:
309 run_manager.on_chain_error(e)
--> 310 raise e
311 run_manager.on_chain_end(outputs)
312 final_outputs: Dict[str, Any] = self.prep_outputs(
313 inputs, outputs, return_only_outputs
314 )
File [c:\Users\fezzef\AppData\Local\Programs\Python\Python311\Lib\site-packages\langchain\agents\openai_functions_agent\base.py:104](file:///C:/Users/fezzef/AppData/Local/Programs/Python/Python311/Lib/site-packages/langchain/agents/openai_functions_agent/base.py:104), in OpenAIFunctionsAgent.plan(self, intermediate_steps, callbacks, with_functions, **kwargs)
102 messages = prompt.to_messages()
103 if with_functions:
--> 104 predicted_message = self.llm.predict_messages(
105 messages,
106 functions=self.functions,
107 callbacks=callbacks,
108 )
109 else:
110 predicted_message = self.llm.predict_messages(
111 messages,
112 callbacks=callbacks,
113 )
InvalidRequestError: Unrecognized request argument supplied: functions
```
I suspect the `OpenAIFunctionsAgent` is not compatible with AzureOpenAI. I'm not sure. Please check and let me know.
### Expected behavior
It should be getting a response but LangChain throws an error
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PR_kwDOIPDwls5dusL7
| 12,259 |
Add quip loader
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[] | null | 7 | 2023-10-25T09:28:02 | 2023-11-01T05:29:13 | 2023-10-31T21:11:24 |
CONTRIBUTOR
| null |
- **Description:** implement [quip](https://quip.com) loader
- **Issue:** https://github.com/langchain-ai/langchain/issues/10352
- **Dependencies:** No
- pass make format, make lint, make test
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| 12,257 |
Returning detailed results of intermediate components
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[] | null | 2 | 2023-10-25T09:11:34 | 2023-10-26T20:54:51 | null |
NONE
| null |
### Issue you'd like to raise.
I am currently using a chain with the following components:
FewshotPrompt ( with SemanticSimilarityExampleSelector) -> LLM -> Outputparser
By default, this will give me the parsed text output of my LLM.
For my purposes, I would need the output of this chain to include all intermediate steps (preferably in a dict).
For this chain that would be:
1. The samples selected by the Example Selector
2. The generated Fewshot Prompt
3. The raw LLM output
4. The parsed LLM output
From the documentation, it is not apparent to me how this could be achieved with the current state of the library.
(The documentation also does not outline details about the usage of Callbacks in the LCEL language)
### Suggestion:
If this is currently implemented then add a simple example to the documentation. If this is not the case then some type of callback that allows collecting this data from intermediate steps should be added.
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Add serialisation props to Fireworks and ChatFireworks
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CONTRIBUTOR
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User based memorization
|
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"node_id": "U_kgDOCBiVUQ",
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"gravatar_id": "",
"url": "https://api.github.com/users/ankitathakur12",
"html_url": "https://github.com/ankitathakur12",
"followers_url": "https://api.github.com/users/ankitathakur12/followers",
"following_url": "https://api.github.com/users/ankitathakur12/following{/other_user}",
"gists_url": "https://api.github.com/users/ankitathakur12/gists{/gist_id}",
"starred_url": "https://api.github.com/users/ankitathakur12/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/ankitathakur12/subscriptions",
"organizations_url": "https://api.github.com/users/ankitathakur12/orgs",
"repos_url": "https://api.github.com/users/ankitathakur12/repos",
"events_url": "https://api.github.com/users/ankitathakur12/events{/privacy}",
"received_events_url": "https://api.github.com/users/ankitathakur12/received_events",
"type": "User",
"site_admin": false
}
|
[
{
"id": 4899126096,
"node_id": "LA_kwDOIPDwls8AAAABJAK7UA",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/area:%20memory",
"name": "area: memory",
"color": "BFDADC",
"default": false,
"description": "Related to memory module"
},
{
"id": 5680700848,
"node_id": "LA_kwDOIPDwls8AAAABUpidsA",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/auto:question",
"name": "auto:question",
"color": "BFD4F2",
"default": false,
"description": "A specific question about the codebase, product, project, or how to use a feature"
}
] |
open
| false | null |
[] | null | 3 | 2023-10-25T07:15:18 | 2023-10-25T09:36:59 | null |
NONE
| null |
### System Info
How i can implement user based memorization please can provide a link for refrence
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
How i can implement user based memorization please can provide a link for refrence
### Expected behavior
How i can implement user based memorization please can provide a link for refrence
|
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https://api.github.com/repos/langchain-ai/langchain/issues/12254/timeline
| null | null | null | null |
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